From 6950d71a17e47d3b2643ceaf468bb42fdb79f8a0 Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Fri, 21 Jun 2024 16:28:20 +0200 Subject: [PATCH 01/21] Preparing test script for tests before master merging --- README.md | 30 ++- .../master_merge_tests/merge_tests.ipynb | 72 ++++++ base/data | 1 + changes/model/Balmorel.gms | 210 ++++++++++++++++++ changes/model/cplex.op4 | 14 ++ 5 files changed, 326 insertions(+), 1 deletion(-) create mode 100644 base/auxils/master_merge_tests/merge_tests.ipynb create mode 160000 base/data create mode 100644 changes/model/Balmorel.gms create mode 100644 changes/model/cplex.op4 diff --git a/README.md b/README.md index 5ca9491c..d54bcc95 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,7 @@ # Balmorel +A scenario that's possible to run with an academic trial license of GAMS can be found in the balmorel-demo branch. + ## What is Balmorel? Balmorel is a partial equilibrium model for analysing the electricity and combined heat and power sectors in an international perspective. It is highly versatile and may be applied for long range planning as well as shorter time operational analysis. Balmorel is implemented as a mainly linear programming optimisation problem. @@ -18,4 +20,30 @@ Balmorel is a modelling tool that can be used by energy system experts, energy c ## How is Balmorel supported and further developed? -The model is developed and distributed under open source ideals. The source code has been provided on its homepage since 2001 and was assigned the [ISC license](https://opensource.org/licenses/ISC) in 2017. Ample documentation is available in the folder [within this repository](base/documentation). Application examples and contact information can be found on the [Balmorel homepage](https://balmorel.com). Presently the model development is mainly project driven, with a users' network around it, supporting the open source development idea. +The model is developed and distributed under open source ideals. The source code has been provided on its homepage since 2001 and was assigned the [ISC license](https://opensource.org/licenses/ISC) in 2017. Ample documentation is available in the folder [within this repository](base/documentation). We also refer to information on [openmod](https://wiki.openmod-initiative.org/wiki/Balmorel). Presently the model development is mainly project driven, with a users' network around it, supporting the open source development idea. + +### Procedure for Merging Changes to Master + +The DTU Balmorel team has decided on the following procedure for merging new changes to the master: +1. A decision to merge should be agreed upon in a Balmorel meeting with the admins +2. Create a new branch from master, which will be used as a temporary branch to do all the changes +3. Make the desired changes in a new scenario: + + a. Add a new scenario called 'changes', which result in the following folder structure: + + Balmorel + ├── base + ├── changes + │ └── model + │ │ ├── cplex.op4 + │ │ └── Balmorel.gms + ├── simex + ├── README.md + ├── .gitignore + └── .gitattributes + + b. Keep the base scenario identical to the current master and apply all changes to the 'changes' scenario + +4. Run the [test script](base/auxils/master_merge_tests/merge_tests.ipynb) and make sure that the model stays feasible and that you verify the changes +5. Make a pull request and report the changes made with the KPI outputs from the tests in 4. in ... (todo: check where it makes sense to put these descriptions, as simple as possible) + diff --git a/base/auxils/master_merge_tests/merge_tests.ipynb b/base/auxils/master_merge_tests/merge_tests.ipynb new file mode 100644 index 00000000..401a5c77 --- /dev/null +++ b/base/auxils/master_merge_tests/merge_tests.ipynb @@ -0,0 +1,72 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test Runs for Merging Changes\n", + "\n", + "This notebook is used for testing changes made to the master branch, by running the models in base/model (which should be identical to the master branch) and change/model (which should be the model run with your changes) \n", + "\n", + "Please follow the procedure described in the [readme](../../../README.md)!\n", + "\n", + "The [pybalmorel](https://pypi.org/project/pybalmorel/) package is required to run this notebook. In a terminal, create a new python environment, activate it and install version 0.2.2 by running the following command:\n", + "```\n", + "pip install pybalmorel==0.2.2\n", + "```\n", + "Documentation on creating and activating virtual environments for standalone python can be found [here](https://docs.python.org/3/library/venv.html) and for a conda installation [here](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pybalmorel import MainResults\n", + "from gams import GamsWorkspace\n", + "import os\n", + "\n", + "### 1.1 Running Balmorel\n", + "scenarios = ['base', 'changes']\n", + "for scenario in scenarios:\n", + " ws = GamsWorkspace(working_directory='../../../%s/model'%scenario)\n", + "\n", + " job = ws.add_job_from_file(os.path.join(os.path.abspath('../../../%s/model'%scenario), 'Balmorel'))\n", + " out = job.run()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### 1.2 Load Results\n", + "mr = MainResults(files=['MainResults.gdx']*2,\n", + " paths=['../../../%s/model'%scenario for scenario in scenarios])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (.BAF-Env)", + "language": "python", + "name": ".baf-env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/base/data b/base/data new file mode 160000 index 00000000..ebe4d2ca --- /dev/null +++ b/base/data @@ -0,0 +1 @@ +Subproject commit ebe4d2ca7a771b8f026ae3d0d3de1eebb7914297 diff --git a/changes/model/Balmorel.gms b/changes/model/Balmorel.gms new file mode 100644 index 00000000..753013d6 --- /dev/null +++ b/changes/model/Balmorel.gms @@ -0,0 +1,210 @@ +* File Balmorel.gms +$TITLE Balmorel version 4.03 (March 2022; latest 20220318) + +SCALAR IBALVERSN 'This version of Balmorel' /403.20220318/; +* Efforts have been made to make a good model. +* However, most probably the model is incomplete and subject to errors. +* It is distributed with the idea that it will be usefull anyway, +* and with the purpose of getting the essential feedback, +* which in turn will permit the development of improved versions +* to the benefit of other user. +* Hopefully it will be applied in that spirit. + +* All GAMS code of the Balmorel model is distributed under ICS license, +* see the license file in the base/model folder. + + +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- + + +* This is a preliminary version of Balmorel 3.03. +* It is intended for review and commenting. +* See a short list of changes since previous version 3.02 in base/documentation/Balmorel303.txt. +* It is scheduled that a final version 3.03 will be available early 2017. + +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- + + +* GAMS options are $included from file balgams.opt. +* In order to make them apply globally, the option $ONGLOBAL will first be set here: +$ONGLOBAL + +$ifi %system.filesys%==UNIX +execute 'chmod -R ug+rw "../.."'; + +* The balgams file holds control settings for GAMS. +* Use local file if it exists, otherwise use the one in folder ../../base/model/. +$ifi exist 'balgams.opt' $include 'balgams.opt'; +$ifi not exist 'balgams.opt' $include '../../base/model/balgams.opt'; + +* The balopt file holds option (control) settings for the Balmorel model. +* Use local file if it exists, otherwise use the one in folder ../../base/model/. + + +$ifi exist 'balopt.opt' $include 'balopt.opt'; +$ifi not exist 'balopt.opt' $include '../../base/model/balopt.opt'; + +$ifi %system.filesys%==UNIX +execute 'chmod -R ug+rw "../.."'; + +* If merging of savepoint files is to be performed, +* make sure that there are no gdx files initially in applied folders: +$ifi %system.filesys%==UNIX +$ifi %MERGESAVEPOINTRESULTS%==yes execute "rm *.gdx"; +$ifi %system.filesys%==MSNT +$ifi %MERGESAVEPOINTRESULTS%==yes execute "del *.gdx"; +$ifi %system.filesys%==UNIX +$ifi %MERGESAVEPOINTRESULTS%==yes execute "rm ../output/temp/*.gdx"; +$ifi %system.filesys%==MSNT +$ifi %MERGESAVEPOINTRESULTS%==yes execute "del ..\output\temp\*.gdx"; + +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- +*---- Some generally applicable stuff: ----------------------------------------- +*------------------------------------------------------------------------------- + + +* The following may be used in display, put or execute_unload statements (the text, not the value, holds relevant information) +SCALAR SystemDateTime "%system.date%, %system.time%" /NA/; + +* Displaying only a text string will not be accessible through the 'table of content' on the .lst file, therefore this +SCALAR INFODISPLAY "See information next:" /NA/; +DISPLAY INFODISPLAY, "Balmorel run stared at", SystemDateTime; + +* Convenient Factors, typically relating Output and Input: +SCALAR IOF100 'Multiplier 100' /100/; +SCALAR IOF1000 'Multiplier 1000' /1000/; +SCALAR IOF1000000 'Multiplier 1000000' /1000000/; +SCALAR IOF0001 'Multiplier 0.001' /0.001/; +SCALAR IOF0000001 'Multiplier 0.000001' /0.000001/; +SCALAR IOF3P6 'Multiplier 3.6' /3.6/; +SCALAR IOF24 'Multiplier 24' /24/; +SCALAR IOF8760 'Multiplier 8760' /8760/; +SCALAR IOF8784 'Multiplier 8784' /8784/; +SCALAR IOF365 'Multiplier 365' /365/; +SCALAR IOF05 'Multiplier 0.5' /0.5/; +SCALAR IOF1_ 'Multiplier 1 (used with QOBJ and derivation of marginal values)' /1/;!! special, possibly to disappear in future versions +* Scalars for occational use, their meaning will be context dependent: +SCALAR ISCALAR1 '(Context dependent)'; +SCALAR ISCALAR2 '(Context dependent)'; +SCALAR ISCALAR3 '(Context dependent)'; +SCALAR ISCALAR4 '(Context dependent)'; +SCALAR ISCALAR5 '(Context dependent)'; + +* Initialisations of printing of log and error files and messages: +$INCLUDE '../../base/logerror/logerinc/error1.inc'; + +* Ensuring existence of needed output folders: +$ifi not dexist "../../simex" execute 'mkdir -p "../../simex"'; +$ifi not dexist "../logerror/logerinc" execute 'mkdir -p "../logerror/logerinc"'; +$ifi not dexist "../output/economic" execute 'mkdir -p "../output/economic"'; +$ifi not dexist "../output/inputout" execute 'mkdir -p "../output/inputout"'; +$ifi not dexist "../output/printout" execute 'mkdir -p "../output/printout"'; +$ifi not dexist "../output/temp" execute 'mkdir -p "../output/temp"'; + + +*------------------------------------------------------------------------------- +*---- End: Some generally applicable stuff ------------------------------------- +*------------------------------------------------------------------------------- + + + +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- +* In case of Model Balbase4 the following included file substitutes the remaining part of the present file Balmorel.gms: +* Use local file if it exists, otherwise use the one in folder ../../base/model/. +$ifi %BB4%==yes $ifi exist 'Balmorelbb4.inc' $include 'Balmorelbb4.inc'; +$ifi %BB4%==yes $ifi not exist 'Balmorelbb4.inc' $include '../../base/model/Balmorelbb4.inc'; +$ifi %BB4%==yes $goto ENDOFMODEL +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- + +*----- End of model:------------------------------------------------------------ +$label ENDOFMODEL +$ifi %all_endofmodelgdx%==yes execute_unload "all_endofmodel.gdx"; +*----- End of model ------------------------------------------------------------ + + +*--- Results collection for this case ------------------------------------------ + +$ifi not %system.filesys%==MSNT $goto endofMSNToutput +*The following section until $label endofMSNToutput is related to Windows output only +*Please use only backslash \ instead of forward slash / in this section until the label + +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "%relpathoutput%temp\*.gdx"'; +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move merged.gdx "%relpathoutput%%CASEID%.gdx"'; + +$ifi %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "..\output\%CASEID%.gdx"'; +$ifi %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move merged.gdx "%relpathoutput%%CASEID%-results.gdx"' + +$ifi %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access "%relpathoutput%%CASEID%-results.gdx"'; +$ifi %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i "%relpathoutput%%CASEID%-results.gdx" -o "%relpathoutput%%CASEID%-results.db"'; + +*--- Results collection and comparison for differents cases -------------------- + +$ifi not %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "%relpathoutput%%CASEID%.gdx" "%relpathModel%..\..\%MERGEWITH%/output\%MERGEWITH%.gdx"'; +$ifi not %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move merged.gdx "%relpathoutput%%CASEID%-resmerged.gdx"'; + +$ifi not %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access "%relpathoutput%%CASEID%-resmerged.gdx"'; +$ifi not %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i "%relpathoutput%%CASEID%-resmerged.gdx" -o "%relpathoutput%%CASEID%-resmerged.db"'; + +$ifi not %DIFFCASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxdiff "%relpathoutput%%CASEID%-results.gdx" "%relpathModel%..\..\%DIFFWITH%/output/%DIFFWITH%-results.gdx"'; +$ifi not %DIFFCASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move diffile.gdx "%relpathoutput%%CASEID%-diff.gdx"'; + +$label endofMSNToutput + +$ifi not %system.filesys%==UNIX $goto endofUNIXoutput +*The following section until $label endofUNIXoutput is related to UNIX output only +*Please use only forward slash / instead of backslash \ in this section until the label + +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "../output/temp/*.gdx"'; +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./merged.gdx ./"%relpathoutput%%CASEID%.gdx"'; + +$ifi %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "../output/%CASEID%.gdx"'; +$ifi %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./merged.gdx ./"%relpathoutput%%CASEID%-results.gdx"' + +$ifi %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access ./"%relpathoutput%%CASEID%-results.gdx"'; +$ifi %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i ./"%relpathoutput%%CASEID%-results.gdx" -o ./"%relpathoutput%%CASEID%-results.db"'; + +*--- Results collection and comparison for differents cases -------------------- + +$ifi not %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge ./"%relpathoutput%%CASEID%.gdx" ./"%relpathModel%../../%MERGEWITH%/output/%MERGEWITH%.gdx"'; +$ifi not %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./merged.gdx ./"%relpathoutput%%CASEID%-resmerged.gdx"'; + +$ifi not %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access ./"%relpathoutput%%CASEID%-resmerged.gdx"'; +$ifi not %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i ./"%relpathoutput%%CASEID%-resmerged.gdx" -o ./"%relpathoutput%%CASEID%-resmerged.db"'; + +$ifi not %DIFFCASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxdiff ./"%relpathoutput%%CASEID%-results.gdx" ./"%relpathModel%../../%DIFFWITH%/output/%DIFFWITH%-results.gdx"'; +$ifi not %DIFFCASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./diffile.gdx ./"%relpathoutput%%CASEID%-diff.gdx"'; + + +$label endofUNIXoutput + +*----- End of file:------------------------------------------------------------ +$label endoffile + + diff --git a/changes/model/cplex.op4 b/changes/model/cplex.op4 new file mode 100644 index 00000000..194d4c61 --- /dev/null +++ b/changes/model/cplex.op4 @@ -0,0 +1,14 @@ +* Option file 4 for CPLEX solver. For possible options, see the GAMS Solver Manual. +* To use with CPLEX, set '$Setglobal USEOPTIONFILE 4' in balgams.opt. +* Do not change this file, it is predefined. +* To use other options with CPLEX, make and/or use some other file, see USEOPTIONFILE in balgams.opt. +* "LPmethod 4": Barrier method +* "Threads -1": Use all available threads except 1. +* If memory tight, set the Threads parameter to 1. +* advind is set to 0 by default since for large problems a better performance has been experienced. If 0, the optimization will not use the previous solution to try to find the next run faster. + +LPmethod 4 +Threads -1 +advind 0 + + From eee84d4f1399cedf26d70e269a9785b053756918 Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Sat, 22 Jun 2024 12:38:14 +0200 Subject: [PATCH 02/21] Ignore GAMS project files and other --- .gitignore | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 6f19ff2b..3d8b6b5a 100644 --- a/.gitignore +++ b/.gitignore @@ -16,5 +16,6 @@ **/model/Balmorel.l* **/output/economic/*.* /base/bui/gamsscr/*.* - - +*.gsp +base/data/.gitignore +base/data/README.md From 193f57d77a7f21f02392b4a9411b212bd6488a95 Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Sat, 22 Jun 2024 12:49:05 +0200 Subject: [PATCH 03/21] New addons --- base/addons/Fjernvarme/Addon vejledening.txt | 25 + base/addons/Fjernvarme/demodata/data_fv.inc | 183 + base/addons/Fjernvarme/model/cost_fv.inc | 7 + base/addons/Fjernvarme/model/eqN_fv.inc | 5 + base/addons/Fjernvarme/model/eq_fv.inc | 36 + .../Fjernvarme/model/heatbalance_fv.inc | 4 + base/addons/Fjernvarme/model/sets_fv.inc | 47 + base/addons/Fjernvarme/model/unload.inc | 6 + base/addons/Fjernvarme/model/var_fv.inc | 13 + .../addons/Fjernvarme/model/yearupdate_fv.inc | 9 + base/addons/REShareE/RESEeqns.inc | 16 + base/addons/REShareE/RESEintrn.inc | 2 + .../REShareE/demodata/RESEEDATA_demodata1.inc | 96 + .../REShareE/demodata/RESEEDATA_demodata2.inc | 59 + base/addons/REShareE/printinc/RESEPrt4.inc | 55 + base/addons/REShareEH/Comments.txt | 4 + base/addons/REShareEH/RESEHeqns.inc | 52 + base/addons/REShareEH/RESEHintrn.inc | 3 + .../REShareEH/demodata/RESEHdata_demodata.inc | 93 + base/addons/REShareEH/printinc/RESEHPrt4.inc | 120 + base/addons/_hooks/acronyms.inc | 15 + .../bb1234compare/bb1234_checkassumptions.inc | 9 + base/addons/bb1234compare/bb1234_hellolog.inc | 32 + .../addons/bb1234compare/bb1234_varassign.inc | 418 + .../bb1234compare/bb1234_vardeclare.inc | 37 + 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.../addons/hydrogen/bb4/hydrogen_acronyms.inc | 2 + base/addons/hyrsbb123/HYRSBB123sim3.inc | 39 + base/addons/hyrsbb123/bb4/HYRSBB123sim3.inc | 39 + .../hyrsbb123/bb4/hyrsbb123_hellolog.inc | 11 + base/addons/hyrsbb123/bb4/hyrsbb123addobj.inc | 13 + .../hyrsbb123/bb4/hyrsbb123balbase3.inc | 6 + .../hyrsbb123/bb4/hyrsbb123documentation.txt | 106 + .../hyrsbb123/bb4/hyrsbb123equations.inc | 54 + .../bb4/hyrsbb123includebb12data.inc | 20 + .../hyrsbb123/bb4/hyrsbb123internals.inc | 26 + base/addons/hyrsbb123/bb4/hyrsbb123sim1.inc | 17 + base/addons/hyrsbb123/bb4/hyrsbb123sim2.inc | 24 + base/addons/hyrsbb123/bb4/hyrsbb123unload.inc | 14 + .../hyrsbb123/bb4/hyrsbb123variables.inc | 7 + base/addons/hyrsbb123/hyrsbb123_hellolog.inc | 11 + base/addons/hyrsbb123/hyrsbb123addobj.inc | 13 + base/addons/hyrsbb123/hyrsbb123balbase3.inc | 6 + .../hyrsbb123/hyrsbb123documentation.txt | 106 + base/addons/hyrsbb123/hyrsbb123equations.inc | 54 + .../hyrsbb123/hyrsbb123includebb12data.inc | 20 + base/addons/hyrsbb123/hyrsbb123internals.inc | 26 + base/addons/hyrsbb123/hyrsbb123sim1.inc | 17 + base/addons/hyrsbb123/hyrsbb123sim2.inc | 24 + base/addons/hyrsbb123/hyrsbb123unload.inc | 14 + base/addons/hyrsbb123/hyrsbb123variables.inc | 7 + base/addons/policies/Addon vejledening.txt | 30 + base/addons/policies/demodata/POLAREA.inc | 11 + base/addons/policies/demodata/POLAREAGEO.inc | 8 + base/addons/policies/demodata/POLICY.inc | 8 + base/addons/policies/demodata/POLICYTYPE.inc | 12 + base/addons/policies/demodata/POLREQ.inc | 44 + base/addons/policies/demodata/POLTECH.inc | 21 + base/addons/policies/demodata/POLTYP.inc | 15 + base/addons/policies/pol_cost.inc | 12 + base/addons/policies/pol_eq.inc | 162 + base/addons/policies/pol_eqN.inc | 7 + base/addons/policies/pol_err2.inc | 62 + base/addons/policies/pol_sets.inc | 77 + base/addons/policies/pol_var.inc | 11 + base/addons/policies/pol_yearupdate.inc | 4 + .../systemcost/SysCost addon instruction.txt | 14 + base/addons/systemcost/cost_syscost.inc | 8 + .../systemcost/demodata/BASELOADSERVICE.inc | 8 + .../systemcost/demodata/sets_syscost.inc | 7 + base/addons/systemcost/eqN_syscost.inc | 5 + base/addons/systemcost/eq_syscost.inc | 15 + base/addons/systemcost/var_syscost.inc | 8 + base/addons/welfare/welfare.opt | 0 base/addons/welfare/welfarecmp/compprint1.inc | 123 + base/addons/welfare/welfarecmp/compprint3.inc | 411 + base/addons/welfare/welfarecmp/mkcompare.inc | 167 + base/addons/welfare/welfarecmp/welfarebb3.inc | 2 + base/auxils/annuity_calculation/ANNUITYCG.inc | 23150 ++++++++++++++++ base/auxils/annuity_calculation/ANNUITYCX.inc | 38 + .../HEATTRANS_ANNUITYCXH.inc | 38 + .../HYDROGEN_ANNUITYCXH2.inc | 38 + .../Transmission_Map-checkpoint.ipynb | 1091 + 134 files changed, 62042 insertions(+) create mode 100644 base/addons/Fjernvarme/Addon vejledening.txt create mode 100644 base/addons/Fjernvarme/demodata/data_fv.inc create mode 100644 base/addons/Fjernvarme/model/cost_fv.inc create mode 100644 base/addons/Fjernvarme/model/eqN_fv.inc create mode 100644 base/addons/Fjernvarme/model/eq_fv.inc create mode 100644 base/addons/Fjernvarme/model/heatbalance_fv.inc create mode 100644 base/addons/Fjernvarme/model/sets_fv.inc create mode 100644 base/addons/Fjernvarme/model/unload.inc create mode 100644 base/addons/Fjernvarme/model/var_fv.inc create mode 100644 base/addons/Fjernvarme/model/yearupdate_fv.inc create mode 100644 base/addons/REShareE/RESEeqns.inc create mode 100644 base/addons/REShareE/RESEintrn.inc create mode 100644 base/addons/REShareE/demodata/RESEEDATA_demodata1.inc create mode 100644 base/addons/REShareE/demodata/RESEEDATA_demodata2.inc create mode 100644 base/addons/REShareE/printinc/RESEPrt4.inc create mode 100644 base/addons/REShareEH/Comments.txt create mode 100644 base/addons/REShareEH/RESEHeqns.inc create mode 100644 base/addons/REShareEH/RESEHintrn.inc create mode 100644 base/addons/REShareEH/demodata/RESEHdata_demodata.inc create mode 100644 base/addons/REShareEH/printinc/RESEHPrt4.inc create mode 100644 base/addons/_hooks/acronyms.inc create mode 100644 base/addons/bb1234compare/bb1234_checkassumptions.inc create mode 100644 base/addons/bb1234compare/bb1234_hellolog.inc create mode 100644 base/addons/bb1234compare/bb1234_varassign.inc create mode 100644 base/addons/bb1234compare/bb1234_vardeclare.inc create mode 100644 base/addons/gas/DH-MR - foer varmebehandling.inc create mode 100644 base/addons/gas/DH-MR.inc create mode 100644 base/addons/gas/Export_GER.INC create mode 100644 base/addons/gas/GDATA-MR-old.inc create mode 100644 base/addons/gas/GDATA-MR.inc create mode 100644 base/addons/gas/GGGMRset.inc create mode 100644 base/addons/gas/GKFX-MR.inc create mode 100644 base/addons/gas/GPFX_VAR_T.INC create mode 100644 base/addons/gas/MRareas.inc create mode 100644 base/addons/gas/MRregions2areas.inc create mode 100644 base/addons/gas/SE_DG_VAR_T.INC create mode 100644 base/addons/gas/andetgasforbrug.inc create mode 100644 base/addons/gas/annualupdate.inc create mode 100644 base/addons/gas/dg-MR.inc create mode 100644 base/addons/gas/dg.inc create mode 100644 base/addons/gas/ensmpol.inc create mode 100644 base/addons/gas/flowsets.inc create mode 100644 base/addons/gas/fuelp.inc create mode 100644 base/addons/gas/gasbal_1.inc create mode 100644 base/addons/gas/gasbb1.inc create mode 100644 base/addons/gas/gasbb2.inc create mode 100644 base/addons/gas/gasbb3.inc create mode 100644 base/addons/gas/gascompprint1.inc create mode 100644 base/addons/gas/gascompprint2.inc create mode 100644 base/addons/gas/gascompprint3.inc create mode 100644 base/addons/gas/gascontracts.inc create mode 100644 base/addons/gas/gascosts.inc create mode 100644 base/addons/gas/gasdata.inc create mode 100644 base/addons/gas/gasdocompare.inc create mode 100644 base/addons/gas/gasequations.inc create mode 100644 base/addons/gas/gasfacilities.inc create mode 100644 base/addons/gas/gasgdx.inc create mode 100644 base/addons/gas/gasgeninput.inc create mode 100644 base/addons/gas/gasheating.inc create mode 100644 base/addons/gas/gasinit.inc create mode 100644 base/addons/gas/gasinternal.inc create mode 100644 base/addons/gas/gasmakecompare.inc create mode 100644 base/addons/gas/gasmkcompare.inc create mode 100644 base/addons/gas/gasopt.opt create mode 100644 base/addons/gas/gasproduction.inc create mode 100644 base/addons/gas/gasregions2areas.inc create mode 100644 base/addons/gas/gasscaling.inc create mode 100644 base/addons/gas/gassets.inc create mode 100644 base/addons/gas/gastech.inc create mode 100644 base/addons/gas/gastransit.inc create mode 100644 base/addons/gas/gastransprices.inc create mode 100644 base/addons/gas/gasvar-MR.inc create mode 100644 base/addons/gas/gasvar.inc create mode 100644 base/addons/gas/gasvariables.inc create mode 100644 base/addons/gas/gentariffs.inc create mode 100644 base/addons/gas/gx3vex.inc create mode 100644 base/addons/gas/network.inc create mode 100644 base/addons/hydrogen/bb4/hydrogen_acronyms.inc create mode 100644 base/addons/hyrsbb123/HYRSBB123sim3.inc create mode 100644 base/addons/hyrsbb123/bb4/HYRSBB123sim3.inc create mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123_hellolog.inc create mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123addobj.inc create mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123balbase3.inc create mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123documentation.txt create mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123equations.inc create mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123includebb12data.inc create mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123internals.inc create mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123sim1.inc create mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123sim2.inc create mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123unload.inc create mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123variables.inc create mode 100644 base/addons/hyrsbb123/hyrsbb123_hellolog.inc create mode 100644 base/addons/hyrsbb123/hyrsbb123addobj.inc create mode 100644 base/addons/hyrsbb123/hyrsbb123balbase3.inc create mode 100644 base/addons/hyrsbb123/hyrsbb123documentation.txt create mode 100644 base/addons/hyrsbb123/hyrsbb123equations.inc create mode 100644 base/addons/hyrsbb123/hyrsbb123includebb12data.inc create mode 100644 base/addons/hyrsbb123/hyrsbb123internals.inc create mode 100644 base/addons/hyrsbb123/hyrsbb123sim1.inc create mode 100644 base/addons/hyrsbb123/hyrsbb123sim2.inc create mode 100644 base/addons/hyrsbb123/hyrsbb123unload.inc create mode 100644 base/addons/hyrsbb123/hyrsbb123variables.inc create mode 100644 base/addons/policies/Addon vejledening.txt create mode 100644 base/addons/policies/demodata/POLAREA.inc create mode 100644 base/addons/policies/demodata/POLAREAGEO.inc create mode 100644 base/addons/policies/demodata/POLICY.inc create mode 100644 base/addons/policies/demodata/POLICYTYPE.inc create mode 100644 base/addons/policies/demodata/POLREQ.inc create mode 100644 base/addons/policies/demodata/POLTECH.inc create mode 100644 base/addons/policies/demodata/POLTYP.inc create mode 100644 base/addons/policies/pol_cost.inc create mode 100644 base/addons/policies/pol_eq.inc create mode 100644 base/addons/policies/pol_eqN.inc create mode 100644 base/addons/policies/pol_err2.inc create mode 100644 base/addons/policies/pol_sets.inc create mode 100644 base/addons/policies/pol_var.inc create mode 100644 base/addons/policies/pol_yearupdate.inc create mode 100644 base/addons/systemcost/SysCost addon instruction.txt create mode 100644 base/addons/systemcost/cost_syscost.inc create mode 100644 base/addons/systemcost/demodata/BASELOADSERVICE.inc create mode 100644 base/addons/systemcost/demodata/sets_syscost.inc create mode 100644 base/addons/systemcost/eqN_syscost.inc create mode 100644 base/addons/systemcost/eq_syscost.inc create mode 100644 base/addons/systemcost/var_syscost.inc create mode 100644 base/addons/welfare/welfare.opt create mode 100644 base/addons/welfare/welfarecmp/compprint1.inc create mode 100644 base/addons/welfare/welfarecmp/compprint3.inc create mode 100644 base/addons/welfare/welfarecmp/mkcompare.inc create mode 100644 base/addons/welfare/welfarecmp/welfarebb3.inc create mode 100644 base/auxils/annuity_calculation/ANNUITYCG.inc create mode 100644 base/auxils/annuity_calculation/ANNUITYCX.inc create mode 100644 base/auxils/annuity_calculation/HEATTRANS_ANNUITYCXH.inc create mode 100644 base/auxils/annuity_calculation/HYDROGEN_ANNUITYCXH2.inc create mode 100644 base/auxils/plotting_tool/.ipynb_checkpoints/Transmission_Map-checkpoint.ipynb diff --git a/base/addons/Fjernvarme/Addon vejledening.txt b/base/addons/Fjernvarme/Addon vejledening.txt new file mode 100644 index 00000000..8bdb9623 --- /dev/null +++ b/base/addons/Fjernvarme/Addon vejledening.txt @@ -0,0 +1,25 @@ + +balmorel.gms: + +Internal sets for true addon +$ifi %FV%==yes $include '../../base/addons/fjernvarme/sets_fv.inc'; + +Parameters for true addon +$ifi %FV%==yes $INCLUDE '../../base/addons/fjernvarme/data_fj.inc'; + +Variables for true addon +$ifi %FV%==yes $include '../../base/addons/fjernavrme/var_fv.inc'; + +Objective function contributions +$ifi %FV%==yes $include '../../base/addons/fjernvarme/cost_fv.inc'; + +QHEQ +$ifi %FV%==yes $include '../../base/addons/fjernvarme/heatbalance_fv.inc'; + +Equations for true addon +$ifi %FV%==yes $include '../../base/addons/fjernvarme/eq_fv.inc'; + +Equations +$ifi %FV%==yes $include '../../base/addons/fjernvarme/eqN_fv.inc'; + + diff --git a/base/addons/Fjernvarme/demodata/data_fv.inc b/base/addons/Fjernvarme/demodata/data_fv.inc new file mode 100644 index 00000000..9e93e215 --- /dev/null +++ b/base/addons/Fjernvarme/demodata/data_fv.inc @@ -0,0 +1,183 @@ +* File data_fv.inc to the addon "District hearting". + +PARAMETER NETINVESTED(AAA,AAA,STEPP) 'Percentatage of possible expansion performed'; + +$ifi %USENETINVESTED%==yes execute_load '../printout/NETINVESTED.gdx',NETINVESTED=NETINVESTED; + +Table DHFV(AAA,STEPFV) 'Heat demand in extention areas GWh' +* Scenario: 20 pct heat savings + STEPFV1 STEPFV2 STEPFV3 STEPFV4 STEPFV5 STEPFV6 STEPFV7 STEPFV8 STEPFV9 +DK_E_1_IND 0.04750 0.01094 0.05543 0.06216 0.03737 0.03104 0.03541 0.04080 0.06990 +DK_E_2_IND 0.06361 0.03578 0.05736 0.07187 0.04657 0.04195 0.04577 0.06043 0.08407 +DK_E_3_IND 0.00654 0.00706 0.00799 0.00972 0.01225 0.00779 0.00512 0.00820 0.00752 +DK_E_4_IND 0.02412 0.02392 0.02904 0.04372 0.02221 0.02288 0.02927 0.03421 0.05038 +DK_E_5_IND 0.02289 0.02222 0.02006 0.02503 0.01249 0.01369 0.01273 0.01234 0.01821 +DK_E_Rural_IND 0.01124 0.00769 0.01028 0.00730 0.00531 0.00344 0.00316 0.00312 0.00460 +DK_E_Urban_IND 0.57664 0.43643 0.50292 0.20579 0.26225 0.29678 0.33728 0.36011 0.50862 +DK_W_1_IND 0.08336 0.09985 0.19439 0.21012 0.20216 0.18581 0.15126 0.15307 0.18914 +DK_W_2_IND 0.03109 0.01998 0.03903 0.01550 0.04920 0.04447 0.02479 0.02246 0.02230 +DK_W_3_IND 0.01407 0.00613 0.00974 0.00871 0.01011 0.00927 0.00723 0.00555 0.00621 +DK_W_4_IND 0.00187 0.00375 0.00015 0.00446 0.00286 0.00320 0.00220 0.00183 0.00162 +DK_W_5_IND 0.00584 0.01138 0.00970 0.000001 0.03597 0.01647 0.02384 0.01699 0.01361 +DK_W_6_IND 0.00561 0.00460 0.00729 0.00478 0.00826 0.00424 0.00409 0.00395 0.00476 +DK_W_ODENSE_IND 0.09835 0.04973 0.02396 0.03022 0.02682 0.03032 0.03135 0.03504 0.05089 +DK_W_Rural_IND 0.09881 0.09114 0.03798 0.09207 0.07854 0.04839 0.03687 0.04222 0.05377 +DK_W_TREFOR_IND 0.11307 0.05749 0.11527 0.06082 0.04306 0.04493 0.04679 0.04355 0.05418 +DK_W_Urban_IND 0.04070 0.00964 0.01595 0.01024 0.00792 0.01250 0.00793 0.01254 0.00498 +DK_W_AALBORG_IND 0.10008 0.04200 0.02073 0.01624 0.01425 0.00973 0.00970 0.01125 0.01415 +DK_W_AARHUS_IND 0.01870 0.09573 0.01133 0.05277 0.04696 0.01962 0.02094 0.01365 0.01984 +DK_E_PUREDH_IND 0.01375 0.01941 0.02157 0.04151 0.03817 0.02578 0.03013 0.01710 0.01976 +DK_W_PUREDH_IND 0.03976 0.05612 0.06236 0.11999 0.11035 0.07454 0.08709 0.04942 0.05714 + + +PARAMETER HEATPEAK(AAA); + +HEATPEAK(AIND) = DH('2010',AIND)*SMAX((S,T), DH_VAR_T(AIND,S,T))/IDH_SUMST(AIND); + +* Table currently in mDKK06/GWh +Table PHFV(AAA,STEPFV) 'Price of head extention areas' +* These price data are given in mDKK06/GWh/year (converted by assignment below). + STEPFV1 STEPFV2 STEPFV3 STEPFV4 STEPFV5 STEPFV6 STEPFV7 STEPFV8 STEPFV9 +DK_E_1_IND 1.021 1.504 1.988 2.471 2.955 3.438 3.922 4.405 4.889 +DK_E_2_IND 0.833 1.266 1.699 2.131 2.564 2.997 3.430 3.862 4.295 +DK_E_3_IND 0.970 1.287 1.604 1.921 2.238 2.555 2.872 3.189 3.506 +DK_E_4_IND 0.856 1.330 1.804 2.278 2.752 3.226 3.700 4.174 4.649 +DK_E_5_IND 1.109 1.525 1.941 2.357 2.773 3.190 3.606 4.022 4.438 +DK_E_Rural_IND 1.420 1.819 2.218 2.618 3.017 3.416 3.815 4.214 4.613 +DK_E_Urban_IND 1.000 1.655 2.310 2.965 3.620 4.275 4.930 5.586 6.241 +DK_W_1_IND 0.469 0.772 1.076 1.379 1.682 1.985 2.288 2.591 2.894 +DK_W_2_IND 0.445 0.667 0.889 1.111 1.333 1.555 1.776 1.998 2.220 +DK_W_3_IND 0.429 0.663 0.896 1.129 1.363 1.596 1.829 2.063 2.296 +DK_W_4_IND 0.754 0.957 1.161 1.365 1.569 1.772 1.976 2.180 2.384 +DK_W_5_IND 0.306 0.476 0.647 0.817 0.987 1.158 1.328 1.498 1.669 +DK_W_6_IND 0.873 1.164 1.456 1.747 2.039 2.330 2.622 2.913 3.204 +DK_W_ODENSE_IND 1.107 1.668 2.229 2.789 3.350 3.911 4.472 5.032 5.593 +DK_W_Rural_IND 0.453 0.694 0.934 1.175 1.415 1.656 1.896 2.136 2.377 +DK_W_TREFOR_IND 0.731 1.107 1.483 1.858 2.234 2.610 2.986 3.361 3.737 +DK_W_Urban_IND 0.841 0.987 1.134 1.280 1.427 1.574 1.720 1.867 2.013 +DK_W_AALBORG_IND 0.866 1.147 1.428 1.710 1.991 2.272 2.554 2.835 3.116 +DK_W_AARHUS_IND 0.964 1.217 1.471 1.724 1.978 2.231 2.485 2.738 2.992 +DK_E_PUREDH_IND 0.616 0.890 1.163 1.437 1.711 1.985 2.259 2.533 2.807 +DK_W_PUREDH_IND 0.616 0.890 1.163 1.437 1.711 1.985 2.259 2.533 2.807 + +* Conversion from mDKK06/GWh/year to EUR90/MWh/year. +PHFV(AIND,STEPFV)=PHFV(AIND,STEPFV)/10.42*1000000/1000; + + +TABLE DHCD(AAA,TECH) + NAT OLIE +DK_E_1_IND 214351 250684 +DK_E_2_IND 335212 313807 +DK_E_3_IND 15533 68172 +DK_E_4_IND 207404 167164 +DK_E_5_IND 53036 127590 +DK_E_Rural_IND 10865 39420 +DK_E_Urban_IND 2377965 2301916 +DK_W_1_IND 727438 1045574 +DK_W_2_IND 110752 214327 +DK_W_3_IND 46598 48468 +DK_W_4_IND 1999 19571 +DK_W_5_IND 26147 114057 +DK_W_6_IND 31083 25226 +DK_W_ODENSE_IND 211753 189870 +DK_W_Rural_IND 309799 367115 +DK_W_TREFOR_IND 340962 337724 +DK_W_Urban_IND 10257 80602 +DK_W_AALBORG_IND 68486 129430 +DK_W_AARHUS_IND 35172 255720 + +DK_E_IND 564 11212 +DK_W_IND 5680 18794 +DK_E_PUREDH_IND 249 1133 +DK_W_PUREDH_IND 291 194 +; + +Parameter HPRATIO(YYY)/ +2010 0 +2010 0 +2011 0.07 +2012 0.14 +2013 0.21 +2014 0.28 +2015 0.35 +2016 0.42 +2017 0.49 +2018 0.56 +2019 0.63 +2020 0.7 +2021 0.73 +2022 0.76 +2023 0.79 +2024 0.82 +2025 0.85 +2026 0.88 +2027 0.91 +2028 0.94 +2029 0.97 +2030 1 +2031 1 +2032 1 +2033 1 +2034 1 +2035 1 +2036 1 +2037 1 +2038 1 +2039 1 +2040 1 +2041 1 +2042 1 +2043 1 +2044 1 +2045 1 +2046 1 +2047 1 +2048 1 +2049 1 +2050 1 +/; + +PARAMETER HEATGROWTH(AAA,YYY); + +HEATGROWTH(AIND,YYY)=DH(YYY,AIND)/DH('2010',AIND); + +PARAMETER DHCD_SUM(AAA); + +DHCD_SUM(AIND) = SUM(TECH, DHCD(AIND,TECH)); + + +GKFX('2010',AIND,'HO-IND1-NG')$DHCD_SUM(AIND) = DHCD(AIND,'NAT')/DHCD_SUM(AIND)*HEATPEAK(AIND); +GKFX('2010',AIND,'HO-IND1-LO')$DHCD_SUM(AIND) = DHCD(AIND,'OLIE')/DHCD_SUM(AIND)*HEATPEAK(AIND); + +*GKFX(YYY,AIND,G)$(YVALUE(YYY) > 2010 and GKFX('2010',AIND,G)) = max(0, GKFX('2010',AIND,G)-(YVALUE(YYY)-YVALUE('2010'))*GKFX('2010',AIND,G)/GDATA(G,'GDLIFETIME') ); +GKFX(YYY,AIND,G)$(YVALUE(YYY) > 2010 and GKFX('2010',AIND,G)) = GKFX('2010',AIND,G)*(1- HPRATIO(YYY))*HEATGROWTH(AIND,YYY); +GKFX(YYY,AIND,'EH-IND1-GV')$(YVALUE(YYY) > 2010 ) = HPRATIO(YYY)*HEATGROWTH(AIND,YYY)*HEATPEAK(AIND); + + +FKPOT(AIND,'NAT_GAS') = GKFX('2008',AIND,'HO-IND1-NG'); + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/base/addons/Fjernvarme/model/cost_fv.inc b/base/addons/Fjernvarme/model/cost_fv.inc new file mode 100644 index 00000000..492cba91 --- /dev/null +++ b/base/addons/Fjernvarme/model/cost_fv.inc @@ -0,0 +1,7 @@ + +* The price of extenstions of heat are added to the objective ++SUM((IA,AIND,STEPP)$AINDN(IA,AIND), PHFV(AIND,STEPP)*VIHFV(IA,AIND,STEPP)*SUM(C$ICA(C,IA), ANNUITYC(C)) ) + +* Penalty are added if constraints are violated ++SUM((IA,IGH,S,T,IPLUSMINUS)$(AIND(IA) and IAGK_Y(IA,IGH)),VQGH_IND(IA,IGH,S,T,IPLUSMINUS))*PENALTYQ/2 ++SUM((IA,IGH,S,T,IPLUSMINUS)$(AIND(IA) and IAGKN(IA,IGH)),VQGHN_IND(IA,IGH,S,T,IPLUSMINUS))*PENALTYQ/2 diff --git a/base/addons/Fjernvarme/model/eqN_fv.inc b/base/addons/Fjernvarme/model/eqN_fv.inc new file mode 100644 index 00000000..f50fed4f --- /dev/null +++ b/base/addons/Fjernvarme/model/eqN_fv.inc @@ -0,0 +1,5 @@ +* Names of equations regarding district heating + +QDHFV +QGH_IND +QGHN_IND diff --git a/base/addons/Fjernvarme/model/eq_fv.inc b/base/addons/Fjernvarme/model/eq_fv.inc new file mode 100644 index 00000000..884f8517 --- /dev/null +++ b/base/addons/Fjernvarme/model/eq_fv.inc @@ -0,0 +1,36 @@ +* Definitions of equations regarding district heating + +Equations + QDHFV(AAA,IAAAI,S,T) 'Transmission into adjacent area limited by invested capacity (MW)' + QGH_IND(AAA,G,S,T) 'Generation on individual technologies must follow demand (MW)' + QGHN_IND(AAA,G,S,T) 'Generation on individual new technologies must follow demand (MW)' +; + +* Setting production by transmissioncapacity and demand variation +* Consider making this an less-than-constraint +QDHFV(IA,IAI,IS3,T)$AINDN(IA,IAI) .. + VDHFV(IA,IAI,IS3,T) + =L= + SUM(STEPP,VIHFV(IA,IAI,STEPP))*DH_VAR_T(IAI,IS3,T)/IDH_SUMST(IAI) +; + +QGH_IND(IA,IGH,IS3,T)$(AIND(IA) and IAGK_Y(IA,IGH) and (GDATA(IGH,'GDCOMB') < 2)) .. + VGH_IND(IA,IGH)*DH_VAR_T(IA,IS3,T)/IDH_SUMST(IA) +* Slack and surplus + +VQGH_IND(IA,IGH,IS3,T,'IPLUS') + -VQGH_IND(IA,IGH,IS3,T,'IMINUS') + =E= + VGH_T(IA,IGH,IS3,T) +$ifi %COMBINATION%==yes + SUM(IGCOMB2$GGCOMB(IGH,IGCOMB2), VGH_T(IA,IGCOMB2,IS3,T)) $ IGCOMB1(IGH) +; + +QGHN_IND(IA,IGH,IS3,T)$(AIND(IA) and IAGKN(IA,IGH) and (GDATA(IGH,'GDCOMB') < 2)) .. + VGHN_IND(IA,IGH)*DH_VAR_T(IA,IS3,T)/IDH_SUMST(IA) +* Slack and surplus + +VQGHN_IND(IA,IGH,IS3,T,'IPLUS') + -VQGHN_IND(IA,IGH,IS3,T,'IMINUS') + =E= + VGHN_T(IA,IGH,IS3,T) +$ifi %COMBINATION%==yes + SUM(IGCOMB2$GGCOMB(IGH,IGCOMB2), VGHN_T(IA,IGCOMB2,IS3,T)) $ IGCOMB1(IGH) +; + diff --git a/base/addons/Fjernvarme/model/heatbalance_fv.inc b/base/addons/Fjernvarme/model/heatbalance_fv.inc new file mode 100644 index 00000000..83b2919a --- /dev/null +++ b/base/addons/Fjernvarme/model/heatbalance_fv.inc @@ -0,0 +1,4 @@ +* Additional heat production from technoligies in these areas are to be added to the heat balance equation + ++(SUM(IAE$AINDN(IA,IAE), VDHFV(IA,IAE,IS3,T))/(1-DISLOSS_H(IA)) )$(not AIND(IA)) +-(SUM(IAE$AINDN(IAE,IA), VDHFV(IAE,IA,IS3,T)) )$AIND(IA) diff --git a/base/addons/Fjernvarme/model/sets_fv.inc b/base/addons/Fjernvarme/model/sets_fv.inc new file mode 100644 index 00000000..ca0ac0fc --- /dev/null +++ b/base/addons/Fjernvarme/model/sets_fv.inc @@ -0,0 +1,47 @@ +Sets regarding district heating + +Set AINDN(AAA,IAAAI) 'Nearest area for areas with possible extenstions on heat' +/ + DK_E_1. DK_E_1_IND + DK_E_2. DK_E_2_IND + DK_E_3. DK_E_3_IND + DK_E_4. DK_E_4_IND + DK_E_5. DK_E_5_IND + + DK_W_1. DK_W_1_IND + DK_W_2. DK_W_2_IND + DK_W_3. DK_W_3_IND + DK_W_4. DK_W_4_IND + DK_W_5. DK_W_5_IND + DK_W_6. DK_W_6_IND + + DK_W_AALBORG. DK_W_AALBORG_IND + DK_W_AARHUS. DK_W_AARHUS_IND + DK_W_TREFOR. DK_W_TREFOR_IND + DK_W_ODENSE. DK_W_ODENSE_IND + + DK_E_Urban. DK_E_Urban_IND + DK_E_Rural. DK_E_Rural_IND + DK_W_Urban. DK_W_Urban_IND + DK_W_Rural. DK_W_Rural_IND + + DK_E_PUREDH. DK_E_PUREDH_IND + DK_W_PUREDH. DK_W_PUREDH_IND + +/; + +$ifi %IND%==yes AINDN(AAA,IAAAI) = no; + +Set STEPFV 'Steps in head exstentions' /STEPFV1*STEPFV10/; + +Set STEPP(STEPFV) 'Steps for price of demand' / +STEPFV1*STEPFV9 +/; + +Set STEPD(STEPFV) 'Steps for demand' / +STEPFV1*STEPFV9 +/; + +set tech / nat,olie/; + +alias(techalias,tech); diff --git a/base/addons/Fjernvarme/model/unload.inc b/base/addons/Fjernvarme/model/unload.inc new file mode 100644 index 00000000..243bceb7 --- /dev/null +++ b/base/addons/Fjernvarme/model/unload.inc @@ -0,0 +1,6 @@ + +$ifi %IND%==yes $goto skip_netinvested +$ifi not %USENETINVESTED%==yes NETINVESTED(IA,IAI,STEPP)$(AINDN(IA,IAI) and VIHFV.up(IA,IAI,STEPP) and (VIHFV.up(IA,IAI,STEPP) < inf)) = VIHFV.l(IA,IAI,STEPP)/VIHFV.up(IA,IAI,STEPP); +$ifi not %USENETINVESTED%==yes DISPLAY NETINVESTED; +$ifi not %USENETINVESTED%==yes execute_unload '../printout/NETINVESTED.gdx', NETINVESTED; +$label skip_netinvested \ No newline at end of file diff --git a/base/addons/Fjernvarme/model/var_fv.inc b/base/addons/Fjernvarme/model/var_fv.inc new file mode 100644 index 00000000..712b1d73 --- /dev/null +++ b/base/addons/Fjernvarme/model/var_fv.inc @@ -0,0 +1,13 @@ +* Variables regarding district heating + +Positive Variables + VIHFV(AAA,IAAAI,STEPFV) 'Extenstion of heat in each step' + VDHFV(AAA,IAAAI,S,T) 'Production of heat in time period' + VGH_IND(AAA,G) 'Level of production on individual technologies' + VQGH_IND(AAA,G,S,T,IPLUSMINUS) 'Penalty on diviation from profile generation' + VGHN_IND(AAA,G) 'Level of production on individual technologies' + VQGHN_IND(AAA,G,S,T,IPLUSMINUS) 'Penalty on diviation from profile generation' +; + +* Initialize. +VIHFV.l(IA,AIND,STEPP)$AINDN(IA,AIND) =0; diff --git a/base/addons/Fjernvarme/model/yearupdate_fv.inc b/base/addons/Fjernvarme/model/yearupdate_fv.inc new file mode 100644 index 00000000..77965cc2 --- /dev/null +++ b/base/addons/Fjernvarme/model/yearupdate_fv.inc @@ -0,0 +1,9 @@ +* Alternative: $ (VIHFV.l(AFV,STEPP) = VIHFV.up(AFV,STEPP)); +* Do something else with: $ (VIHFV.l(AFV,STEPP) > VIHFV.l(AFV,STEPP); +* Perhaps better alternative: VIHFV.u(AFV,STEPP) = VIHFV.u(AFV,STEPP) - VIHFV.l(AFV,STEPP); +* - but still make sure that the extra investment is utilized in balance eqn (could be looking for VIHF's with ub = 0) + +$ifi not %IND%==yes VIHFV.up(IA,IAI,STEPP)$AINDN(IA,IAI) = DHFV(IAI,STEPP); +$ifi not %IND%==yes VIHFV.lo(IA,IAI,STEPP)$AINDN(IA,IAI) = VIHFV.l(IA,IAI,STEPP); + +$ifi %USENETINVESTED%==yes VIHFV.fx(IA,IAI,STEPP)$AINDN(IA,IAI) = max(0,(YVALUE(Y)-2012)/(2025-2012)*VIHFV.up(IA,IAI,STEPP)*NETINVESTED(IA,IAI,STEPP)*DH(Y,IAI)/DH('2025',IAI)); \ No newline at end of file diff --git a/base/addons/REShareE/RESEeqns.inc b/base/addons/REShareE/RESEeqns.inc new file mode 100644 index 00000000..378da747 --- /dev/null +++ b/base/addons/REShareE/RESEeqns.inc @@ -0,0 +1,16 @@ +* File RESEeqns.inc to the addon REShareE. + + +* Equation QRESHAREE is with inspiration from equation QEEQ: +QRESHAREE(CCCRESHAREE).. + +SUM(C$CCCRESHAREEC(CCCRESHAREE,C), + SUM(IR$CCCRRR(C,IR), + SUM((IS3,T), + IHOURSINST(IS3,T)* + (SUM(IAGK_Y(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREE(IGE)), VGE_T(IA,IGE,IS3,T) ) + + SUM(IAGKN(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREE(IGE)), VGEN_T(IA,IGE,IS3,T))) ) + +)) + +=G= RESHAREE(CCCRESHAREE)* SUM(C$CCCRESHAREEC(CCCRESHAREE,C), SUM(IR$CCCRRR(C,IR), DE_Y(IR))); diff --git a/base/addons/REShareE/RESEintrn.inc b/base/addons/REShareE/RESEintrn.inc new file mode 100644 index 00000000..686c06ca --- /dev/null +++ b/base/addons/REShareE/RESEintrn.inc @@ -0,0 +1,2 @@ +* +PARAMETER DE_Y(RRR) 'Nominal electricity demand this year (MWh)'; diff --git a/base/addons/REShareE/demodata/RESEEDATA_demodata1.inc b/base/addons/REShareE/demodata/RESEEDATA_demodata1.inc new file mode 100644 index 00000000..ee254a4d --- /dev/null +++ b/base/addons/REShareE/demodata/RESEEDATA_demodata1.inc @@ -0,0 +1,96 @@ +* File RESEEDATA_demodata1.inc to the Balmorel addon REShareE. + +* The data given here will probably not be valid for your model due to inconsistencies with other data, +* however, this file demonstrates the idea. + +* The data file with real data is supposed to be located in the data folder, like any other input data file. + + +SET GREE(GGG) 'Generation technologies qualified as renewable electricity technologies' +/ + +ST-B9-MW +ST-B9-ST +ST-B9-WO +ST-B9-WW +ST-E0-WOsn +ST-E0-WWsn +ST-E1-WOsn +ST-B1-MWn +ST-B1-STn +ST-B1-WOn +ST-B1-WWn +CC-C1-WOn +CC-E1-WOn + HY-S01 + HY-S02 + HY-S03 + HY-S04 + HY-S05 + HY-S06 + HY-S07 + HY-S08 + HY-S09 + HY-S10 + HY-R +WI-L9 +WI-S9 +WI-L0 +WI-S0 +WI-L1 +WI-S1 +HO-B8-MW +HO-B8-ST +HO-B8-WO +HO-B0-MW +HO-B0-ST +HO-B0-WO +ST-E8-WW +ST-E9-WO +ST-B8-WW +ST-E8-ST +DK_W_CHP3 +DK_W_CHP4 +DK_E_CHP3 +DK_E_CHP4 + +/; + + + +SET GREEH(GGG) 'Generation technologies qualified as renewable electricity and heat technologies' ; + +GREEH(GGG)$IGF(GGG,'FUEL_WIND') = yes; +GREEH(GGG)$IGF(GGG,'FUEL_MUNIWASTE') = yes; +GREEH(GGG)$IGF(GGG,'FUEL_STRAW') = yes; +GREEH(GGG)$IGF(GGG,'FUEL_WOOD') = yes; +GREEH(GGG)$IGF(GGG,'FUEL_WIND') = yes; +GREEH(GGG)$IGF(GGG,'FUEL_WATER)' = yes; +GREEH(GGG)$IGF(GGG,'FUEL_SUN') = yes; +GREEH(GGG)$IGF(GGG,'FUEL_WIND') = yes; + + + + +SET CCCRESHAREE 'Group of countries with a common renewable electricity share goal' +/ + CCCRESHAREE_NORDIC + +/; + + + +SET CCCRESHAREEC(CCCRESHAREE,CCC) 'Countries within group with common renewable electricity share goal' +/ + CCCRESHAREE_NORDIC. DENMARK + CCCRESHAREE_NORDIC. Norway + CCCRESHAREE_NORDIC. Sweden + CCCRESHAREE_NORDIC. FINLAND +/; + + + +PARAMETER RESHAREE(CCCRESHAREE) 'Share of renewable electricity (fraction)' +/ + CCCRESHAREE_NORDIC 0.0 +/; diff --git a/base/addons/REShareE/demodata/RESEEDATA_demodata2.inc b/base/addons/REShareE/demodata/RESEEDATA_demodata2.inc new file mode 100644 index 00000000..9f690251 --- /dev/null +++ b/base/addons/REShareE/demodata/RESEEDATA_demodata2.inc @@ -0,0 +1,59 @@ +* File RESEdata.inc to the addon REShareEH. + +*$ontext +SET GREE(GGG) 'Generation technologies qualified as renewable electricity technologies' +/ + +ST-B9-MW +ST-B9-WW + HY-S01 + HY-S02 + HY-S03 + HY-S04 + HY-S05 + HY-S06 + HY-S07 + HY-S08 + HY-S09 + HY-S10 + +WI-L9 + +/; + + + +SET GREEH(GGG) 'Generation technologies qualified as renewable electricity and heat technologies' ; + +GREEH(GGG)$IGF(GGG,'WIND') = yes; +GREEH(GGG)$IGF(GGG,'STRAW') = yes; +GREEH(GGG)$IGF(GGG,'WOOD') = yes; +GREEH(GGG)$IGF(GGG,'WIND') = yes; +GREEH(GGG)$IGF(GGG,'WATER') = yes; +GREEH(GGG)$IGF(GGG,'SUN') = yes; +*$OFFTEXT + + + +SET CCCRESHAREE 'Group of countries with a common renewable electricity share goal' +/ + CCCRESHAREE_NORDIC + +/; + + + +SET CCCRESHAREEC(CCCRESHAREE,CCC) 'Countries within group with common renewable electricity share goal' +/ + CCCRESHAREE_NORDIC. DENMARK + CCCRESHAREE_NORDIC. Norway + CCCRESHAREE_NORDIC. Sweden + CCCRESHAREE_NORDIC. FINLAND +/; + + + +PARAMETER RESHAREE(CCCRESHAREE) 'Share of renewable electricity (fraction)' +/ + CCCRESHAREE_NORDIC 0.0 +/; diff --git a/base/addons/REShareE/printinc/RESEPrt4.inc b/base/addons/REShareE/printinc/RESEPrt4.inc new file mode 100644 index 00000000..ff865b28 --- /dev/null +++ b/base/addons/REShareE/printinc/RESEPrt4.inc @@ -0,0 +1,55 @@ +* File RESEPrt4.inc to the addon REShareE. +* Hans Ravn 2007/8. + + +* ----------------------------------------------------------------------------- + +PUT REShareEPrt4; + +* First print headings: + +if ((REShareEPrt4.pw LT 32000), +REShareEPrt4.pw = 32000; +PUT "Version: " SYSTEM.TITLE /; +PUT "File name: RESEprt4.out" /; +PUT "File created: " SYSTEM.DATE " " SYSTEM.TIME /; +PUT "Case-identification: " OCASEID.TS //; + +PUT "Output related to Add-on REShareE" / ; +PUT "Control settings:"/; +PUT "$Setglobal bb1 %bb1%" /; +PUT "$Setglobal bb2 %bb2%" /; +PUT "$Setglobal REShareE %REShareE%" /; +put /; + +); + +* End of headings. + +* ----------------------------------------------------------------------------- + +PUT "Year " Y.tl/; + +PUT " Marginal cost (price) of the REShareE equation: "/; +LOOP(CCCRESHAREE, +PUT " Group " CCCRESHAREE.tl:20 ": Required share " RESHAREE(CCCRESHAREE):5:2 ", marginal cost " (QRESHAREE.m(CCCRESHAREE)/IOF0000001) " Money/MWh, Group consists of countries "; +LOOP(C$CCCRESHAREEC(CCCRESHAREE,C), PUT c.TL); PUT /; +ISCALAR1= /* Cf. the expression in the equation QRESHAREE */ +SUM(C$CCCRESHAREEC(CCCRESHAREE,C), + SUM(IR$CCCRRR(C,IR), + SUM((IS3,T), + IHOURSINST(IS3,T)* + (SUM(IAGK_Y(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREE(IGE)), VGE_T.L(IA,IGE,IS3,T) ) + + SUM(IAGKN(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREE(IGE)), VGEN_T.L(IA,IGE,IS3,T))) ) + +)); +ISCALAR2=SUM(C$CCCRESHAREEC(CCCRESHAREE,C), SUM(IR$CCCRRR(C,IR), DE_Y(IR))); /* Cf. the expression in the equation QRESHAREE */ + +PUT " Prod: " (ISCALAR1 * IOF0001):10:0 " GWh, demand " (ISCALAR2 * IOF0001):10:0 " GWh" /; + +); + + +* Tilføjelser, der er eksperimentelle, og nok ender et andet sted: + +* Fuel use for \ No newline at end of file diff --git a/base/addons/REShareEH/Comments.txt b/base/addons/REShareEH/Comments.txt new file mode 100644 index 00000000..81241f9e --- /dev/null +++ b/base/addons/REShareEH/Comments.txt @@ -0,0 +1,4 @@ +Comment to RESEH, Hans Ravn, 20080603 + +1. The factor IOF1_ in file RESEHPrt4.inc may have to be replaced by IOF1000000. +2. \ No newline at end of file diff --git a/base/addons/REShareEH/RESEHeqns.inc b/base/addons/REShareEH/RESEHeqns.inc new file mode 100644 index 00000000..b8fd1d00 --- /dev/null +++ b/base/addons/REShareEH/RESEHeqns.inc @@ -0,0 +1,52 @@ +* File equations.inc to the addon REShareEH. + + +* Equation QRESHAREEH is with inspiration from equations QEEQ and QHEQ: +QRESHAREEH(CCCRESHAREEH).. + +SUM(C$CCCRESHAREEHC(CCCRESHAREEH,C), +* SUM(IR$CCCRRR(C,IR), + SUM((IS3,T), + IHOURSINST(IS3,T)* + ( + SUM(IAGK_Y(IA,IGE)$(ICA(C,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGE_T(IA,IGE,IS3,T) ) + - SUM(IA$ICA(C,IA), VESTOLOADT(IA,IS3,T)) + + SUM(IAGKN(IA,IGE)$(ICA(C,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGEN_T(IA,IGE,IS3,T)) +* NEW? + + SUM(IAGK_Y(IA,IGH)$(ICA(C,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T(IA,IGH,IS3,T) ) + - SUM(IA$ICA(C,IA), VHSTOLOADT(IA,IS3,T)) + + SUM(IAGKN(IA,IGH)$(ICA(C,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T(IA,IGH,IS3,T)) +* NEW? + ) + +/* This (outcommneted) expresses fuel use: + ( + SUM(IAGK_Y(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGE_T(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE') * (1-GEFFDERATE(IA,IGE))) ) + + SUM(IAGKN(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGEN_T(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IA,IGE))) ) + + SUM(IAGK_Y(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T(IA,IGH,IS3,T)/(GDATA(IGH,'GDFE') * (1-GEFFDERATE(IA,IGH))) ) + + SUM(IAGKN(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T(IA,IGH,IS3,T)/(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IA,IGH))) ) + ) +*/ + ) +) + +=G= +* Distribution and transmission loss? +RESHAREEH(CCCRESHAREEH)* SUM(C$CCCRESHAREEHC(CCCRESHAREEH,C), SUM(IR$CCCRRR(C,IR), DE_Y(IR)) + SUM(IA$ICA(C,IA), DH_Y(IA)) ); + +/* +*(271569 )*1000; +271669*1000 +$ifi %NEPcase%==6 + (37*1000000) +$ifi %NEPcase%==12 + (74*1000000) +$ifi %NEPcase%==18 + (111*1000000) + -1000000000 +*/ + +; + +/* +Tillægene 6, 12, 18% giver hhv 37, 74, 111 TWh. +Jeg tror, aftalen var at dette skulle gælde for 2020. +Det kunne så være rimeligt med en lineær interpolation, f.eks. 1/3 i 2010, 1/1 i 2020, 4/3 i 2030? +*/ diff --git a/base/addons/REShareEH/RESEHintrn.inc b/base/addons/REShareEH/RESEHintrn.inc new file mode 100644 index 00000000..d21a4a76 --- /dev/null +++ b/base/addons/REShareEH/RESEHintrn.inc @@ -0,0 +1,3 @@ +* +PARAMETER DE_Y(RRR) 'Nominal electricity demand this year (MWh)'; +PARAMETER DH_Y(AAA) 'Nominal heat demand this year (MWh)'; \ No newline at end of file diff --git a/base/addons/REShareEH/demodata/RESEHdata_demodata.inc b/base/addons/REShareEH/demodata/RESEHdata_demodata.inc new file mode 100644 index 00000000..f89d4020 --- /dev/null +++ b/base/addons/REShareEH/demodata/RESEHdata_demodata.inc @@ -0,0 +1,93 @@ +* File data.inc to the addon REShareEH. + +SET GREE(GGG) 'Generation technologies qualified as renewable electricity technologies' +/ + +ST-B9-MW +ST-B9-ST +ST-B9-WO +ST-B9-WW +ST-E0-WOsn +ST-E0-WWsn +ST-E1-WOsn +ST-B1-MWn +ST-B1-STn +ST-B1-WOn +ST-B1-WWn +CC-C1-WOn +CC-E1-WOn + HY-S01 + HY-S02 + HY-S03 + HY-S04 + HY-S05 + HY-S06 + HY-S07 + HY-S08 + HY-S09 + HY-S10 + HY-R +WI-L9 +WI-S9 +WI-L0 +WI-S0 +WI-L1 +WI-S1 +HO-B8-MW +HO-B8-ST +HO-B8-WO +HO-B0-MW +HO-B0-ST +HO-B0-WO +ST-E8-WW +ST-E9-WO +ST-B8-WW +ST-E8-ST +DK_W_CHP3 +DK_W_CHP4 +DK_E_CHP3 +DK_E_CHP4 + +/; + + + +SET GREEH(GGG) 'Generation technologies qualified as renewable electricity and heat technologies' ; + +GREEH(GGG)$IGF(GGG,'FUEL_WIND') = yes; +GREEH(GGG)$IGF(GGG,'FUEL_MUNIWASTE') = yes; +GREEH(GGG)$IGF(GGG,'FUEL_STRAW') = yes; +GREEH(GGG)$IGF(GGG,'FUEL_WOOD') = yes; +GREEH(GGG)$IGF(GGG,'FUEL_WIND') = yes; +GREEH(GGG)$IGF(GGG,'FUEL_WATER)' = yes; +GREEH(GGG)$IGF(GGG,'FUEL_SUN') = yes; +GREEH(GGG)$IGF(GGG,'FUEL_WIND') = yes; + + + + + + +SET CCCRESHAREEH 'Group of countries with a common renewable electricity and heat share goal' +/ + CCCRESHAREEH_NORDIC +/; + + + + +SET CCCRESHAREEHC(CCCRESHAREEH,CCC) 'Countries within group with common renewable electricity share goal' +/ + CCCRESHAREEH_NORDIC. DENMARK + CCCRESHAREEH_NORDIC. Norway + CCCRESHAREEH_NORDIC. Sweden + CCCRESHAREEH_NORDIC. FINLAND + +/; + + + +PARAMETER RESHAREEH(CCCRESHAREEH) 'Share of renewable electricity (fraction)' +/ + CCCRESHAREEH_NORDIC 0.5 +/; \ No newline at end of file diff --git a/base/addons/REShareEH/printinc/RESEHPrt4.inc b/base/addons/REShareEH/printinc/RESEHPrt4.inc new file mode 100644 index 00000000..cc37014f --- /dev/null +++ b/base/addons/REShareEH/printinc/RESEHPrt4.inc @@ -0,0 +1,120 @@ +* File RESEHPrt4.inc to the addon REShareEH. +* Hans Ravn 2007/8. + + + +* ----------------------------------------------------------------------------- + +PUT REShareEHPrt4; + +* First print headings: + +if ((REShareEHPrt4.pw LT 32000), +REShareEHPrt4.pw = 32000; +PUT "Version: " SYSTEM.TITLE /; +PUT "File name: RESEHprt4.out" /; +PUT "File created: " SYSTEM.DATE " " SYSTEM.TIME /; +PUT "Case-identification: " OCASEID.TS //; + +PUT "Output related to Add-on REShareEH" / ; +PUT "Control settings:"/; +PUT "$Setglobal bb1 %bb1%" /; +PUT "$Setglobal bb2 %bb2%" /; +PUT "$Setglobal REShareEH %REShareEH%" /; +put /; + + + +* Here we also print some info on technologies: +PUT / / "Technologies qualified as producing renewable electricity and heat, and its fuel: "/ /; +LOOP(GREEH, +PUT GREEH.TL:OTLG; +LOOP(FFF$IGF(GREEH,FFF), PUT FFF.TL; + + + + ); PUT /; +); + +put / / ; + +); +* End of headings. + +* ----------------------------------------------------------------------------- + +PUT "Year " Y.tl/; + +PUT " Marginal cost (price) of the REShareEH equation: "/; +LOOP(CCCRESHAREEH, +PUT " Group " CCCRESHAREEH.tl:20 ": Required share: " RESHAREEH(CCCRESHAREEH):5:2 ", marginal cost: " (QRESHAREEH.m(CCCRESHAREEH)/IOF1_) " Money/MWh,"; +PUT " Group consists of countries: "; LOOP(C$CCCRESHAREEHC(CCCRESHAREEH,C), PUT C.TL); PUT /; +ISCALAR1= /* Electricity production */ /* Cf. the expression in the equation QRESHAREEH */ +SUM(C$CCCRESHAREEHC(CCCRESHAREEH,C), +* SUM(IR$CCCRRR(C,IR), + SUM((IS3,T), + IHOURSINST(IS3,T)* + ( + SUM(IAGK_Y(IA,IGE)$(ICA(C,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGE_T.L(IA,IGE,IS3,T) ) + - SUM(IA$ICA(C,IA), VESTOLOADT.L(IA,IS3,T)) + + SUM(IAGKN(IA,IGE)$(ICA(C,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGEN_T.L(IA,IGE,IS3,T)) +* NEW ESTO? +* + SUM(IAGK_Y(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T.L(IA,IGH,IS3,T) ) +* + SUM(IAGKN(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T.L(IA,IGH,IS3,T)) + ) + /* + ( + SUM(IAGK_Y(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGE_T.L(IA,IGE,IS3,T) /(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IA,IGE))) ) + + SUM(IAGKN(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGEN_T.L(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IA,IGE))) ) +* + SUM(IAGK_Y(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T.L(IA,IGH,IS3,T) /(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IA,IGH))) ) +* + SUM(IAGKN(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T.L(IA,IGH,IS3,T)/(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IA,IGH))) ) + ) + */ +)); + +ISCALAR2= /* Heat production */ /* Cf. the expression in the equation QRESHAREEH */ +SUM(C$CCCRESHAREEHC(CCCRESHAREEH,C), + SUM((IS3,T), + IHOURSINST(IS3,T)* + ( +* SUM(IAGK_Y(IA,IGE)$(ICA(C,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGE_T.L(IA,IGE,IS3,T) ) +* + SUM(IAGKN(IA,IGE)$(ICA(C,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGEN_T.L(IA,IGE,IS3,T)) + + SUM(IAGK_Y(IA,IGH)$(ICA(C,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T.L(IA,IGH,IS3,T) ) + - SUM(IA$ICA(C,IA), VHSTOLOADT.L(IA,IS3,T)) + + SUM(IAGKN(IA,IGH)$(ICA(C,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T.L(IA,IGH,IS3,T)) +* NEW? VHSTOLOADT.L ? + + ) + + /* + ( +* SUM(IAGK_Y(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGE_T.L(IA,IGE,IS3,T) /(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IA,IGE))) ) +* + SUM(IAGKN(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGEN_T.L(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IA,IGE))) ) + + SUM(IAGK_Y(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T.L(IA,IGH,IS3,T) /(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IA,IGH))) ) + + SUM(IAGKN(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T.L(IA,IGH,IS3,T)/(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IA,IGH))) ) + ) + */ + + +)); + + +ISCALAR3=SUM(C$CCCRESHAREEHC(CCCRESHAREEH,C), SUM(IR$CCCRRR(C,IR), DE_Y(IR)) ); /* Cf. the expression in the equation QRESHAREEH */ +ISCALAR4=SUM(C$CCCRESHAREEHC(CCCRESHAREEH,C), SUM(IA$ICA(C,IA), DH_Y(IA)) ); /* Cf. the expression in the equation QRESHAREEH */ + + +PUT " Renewable production (GWh): " ((ISCALAR1+ISCALAR2)*IOF0001):10:0 " of which: electricity: "(ISCALAR1*IOF0001):10:0 " and heat: " (ISCALAR2*IOF0001):10:0 /; +PUT " Nominal demand: (GWh): " ((ISCALAR3+ISCALAR4)*IOF0001):10:0 " of which: electricity: "(ISCALAR3*IOF0001):10:0 " and heat: " (ISCALAR4*IOF0001):10:0 /; + +); + + /* +iscalar1= sum(c,sum(ia$ica(c,ia), SUM((IS3,T), + IHOURSINST(IS3,T)* ( + + SUM(IAGK_Y(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T.L(IA,IGH,IS3,T) ) + + SUM(IAGKN(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T.L(IA,IGH,IS3,T)) + + ) +))); +put "varmeialt: " iscalar1; +*/ diff --git a/base/addons/_hooks/acronyms.inc b/base/addons/_hooks/acronyms.inc new file mode 100644 index 00000000..b758abcb --- /dev/null +++ b/base/addons/_hooks/acronyms.inc @@ -0,0 +1,15 @@ +* Location of acronyms used in add-ons +$ifi %BB4%==yes $goto beginbb4 + +$ifi %HYDROGEN%==yes $include "../../base/addons/hydrogen/hydrogen_acronyms.inc"; +$ifi not %BB4%==yes $goto endoffile + +$label beginbb4 + +$ifi not %bb4%==yes $goto endoffile + +$ifi %HYDROGEN%==yes $include "../../base/addons/hydrogen/bb4/hydrogen_acronyms.inc"; + +$label endoffile + + diff --git a/base/addons/bb1234compare/bb1234_checkassumptions.inc b/base/addons/bb1234compare/bb1234_checkassumptions.inc new file mode 100644 index 00000000..1555f6e4 --- /dev/null +++ b/base/addons/bb1234compare/bb1234_checkassumptions.inc @@ -0,0 +1,9 @@ +* File for addon testbb1234 for the Balmorel model + +* Addon testbb1234 compares results from Balbase1 or Balbase2 against results from Balbase4 (without and with investments, respectively). +* It assumes that only a single year is considered. + +IF((CARD(Y) NE 1), +* PUT .. +* ABORT .. +); diff --git a/base/addons/bb1234compare/bb1234_hellolog.inc b/base/addons/bb1234compare/bb1234_hellolog.inc new file mode 100644 index 00000000..75b6badb --- /dev/null +++ b/base/addons/bb1234compare/bb1234_hellolog.inc @@ -0,0 +1,32 @@ +* File for addon testbb1234 for the Balmorel model + +SCALAR IBB1234VERSION "" / 303.20170102 /; + +PUT " - Addon testbb1234 (incomplete, but useful anyway)" /; + +IF((IBB1234VERSION LT IBALVERSN), + PUT " *** Addon testbb1234 is older than Balmorel version. You should verify consistency. (Message from \base\addons\bb1234compare\bb1234_hellolog.inc)" /; +); + + + + +$ontext + +This addon is for comparing output from BB1/BB2/BB3 with simular output from appropriate BB4. +It is intended for use in a transitional phase, where BB4 is developed. + +It is assumed that a single year only is considered. +The input that are similar between the two model to be compared must be identical. +The additional data for BB4 must be set up to be consistent with BB1/BB2/BB3 data. +In order for marginals to be the same in BB1/BB2/BB3 as in BB4, the BB4 discount factor must be 1. + +When activate, the addon will produce a gdx file with model results and place it in simex. File naves are +BB1234_BB1.gdx/BB1234_BB2.gdx/BB1234_BB3.gdx to be compared with BB1234_BB4.gdx. +To compare the two gdx files, use GDXDiff (under Untilities in the the IDE). + +Involved files are, besides this one: +bb1234_checkassumptions, bb12234_vardecl, bb12234_varassign + + +$offtext diff --git a/base/addons/bb1234compare/bb1234_varassign.inc b/base/addons/bb1234compare/bb1234_varassign.inc new file mode 100644 index 00000000..9183b659 --- /dev/null +++ b/base/addons/bb1234compare/bb1234_varassign.inc @@ -0,0 +1,418 @@ +* File for addon testbb1234 for the Balmorel model + + +display infodisplay, "bb1234_varassign !! TODO"; +*todo for bb4: lave unload også for svarende til VGKN, VxKN + +$ifi %BB1%==yes $goto doBB1andBB2 +$ifi %BB2%==yes $goto doBB1andBB2 +$ifi %BB3%==yes display infodisplay, "Error: addon testbb1234 is not implemented for BB3 (message from bb1234_varassign)"; !! Large parts must be identical to BB1? +$ifi %BB3%==yes $goto endoffile +$ifi %BB4%==yes $goto doBB4 + +* BB1 and part of BB2 ========================================================== +$label doBB1andBB2 + +* Variables: +IBB1234_VOBJ.L = VOBJ.L; + +IBB1234_VGE_T.L(Y,IA,G,S,T) = VGE_T.L(ia,g,s,t); +IBB1234_VGE_T.LO(Y,IA,G,S,T) = VGE_T.LO(ia,g,s,t); +IBB1234_VGE_T.UP(Y,IA,G,S,T) = VGE_T.UP(ia,g,s,t); +IBB1234_VGE_T.M(Y,IA,G,S,T) = VGE_T.M(ia,g,s,t); + +IBB1234_VGH_T.L(Y,IA,G,S,T) = VGH_T.L(ia,g,s,t); +IBB1234_VGH_T.LO(Y,IA,G,S,T) = VGH_T.LO(ia,g,s,t); +IBB1234_VGH_T.UP(Y,IA,G,S,T) = VGH_T.UP(ia,g,s,t); +IBB1234_VGH_T.M(Y,IA,G,S,T) = VGH_T.M(ia,g,s,t); + +*IBB1234_VDECOM(YYY,IA,G) ? + +IBB1234_VGF_T.L(Y,IA,G,S,T) = VGF_T.L(IA,G,S,T); +IBB1234_VGF_T.LO(Y,IA,G,S,T) = VGF_T.LO(IA,G,S,T); +IBB1234_VGF_T.UP(Y,IA,G,S,T) = VGF_T.UP(IA,G,S,T); +IBB1234_VGF_T.M(Y,IA,G,S,T) = VGF_T.M(IA,G,S,T); + +IBB1234_VX_T.L(Y,IRE,IRI,S,T) = VX_T.L(IRE,IRI,S,T); +IBB1234_VX_T.LO(Y,IRE,IRI,S,T) = VX_T.LO(IRE,IRI,S,T); +IBB1234_VX_T.UP(Y,IRE,IRI,S,T) = VX_T.UP(IRE,IRI,S,T); +IBB1234_VX_T.M(Y,IRE,IRI,S,T) = VX_T.M(IRE,IRI,S,T); + +$ifi %dflexquant%==yes IBB1234_VDEF_T.L(Y,IR,DEUSER,S,T,DEF) = VDEF_T.L(IR,DEUSER,S,T,DEF); +$ifi %dflexquant%==yes IBB1234_VDEF_T.LO(Y,IR,DEUSER,S,T,DEF) = VDEF_T.LO(IR,DEUSER,S,T,DEF); +$ifi %dflexquant%==yes IBB1234_VDEF_T.UP(Y,IR,DEUSER,S,T,DEF) = VDEF_T.UP(IR,DEUSER,S,T,DEF); +$ifi %dflexquant%==yes IBB1234_VDEF_T.M(Y,IR,DEUSER,S,T,DEF) = VDEF_T.M(IR,DEUSER,S,T,DEF); + +$ifi %dflexquant%==yes IBB1234_VDHF_T.L(Y,IA,DHUSER,S,T,DHF) = VDHF_T.L(IA,DHUSER,S,T,DHF); +$ifi %dflexquant%==yes IBB1234_VDHF_T.LO(Y,IA,DHUSER,S,T,DHF) = VDHF_T.LO(IA,DHUSER,S,T,DHF); +$ifi %dflexquant%==yes IBB1234_VDHF_T.UP(Y,IA,DHUSER,S,T,DHF) = VDHF_T.UP(IA,DHUSER,S,T,DHF); +$ifi %dflexquant%==yes IBB1234_VDHF_T.M(Y,IA,DHUSER,S,T,DHF) = VDHF_T.M(IA,DHUSER,S,T,DHF); + +*IBB1234_VGHYPMS_T.L(Y,IA,S,T) = VGHYPMS_T.L(IA,S,T); (Contents of pumped hydro storage (MWh)) TODO: handle that no values assigned (if VGHYPMS_T is at all to be there) +*IBB1234_VGHYPMS_T.LO(Y,IA,S,T) = VGHYPMS_T.LO(IA,S,T); +*IBB1234_VGHYPMS_T.UP(Y,IA,S,T) = VGHYPMS_T.UP(IA,S,T); +*IBB1234_VGHYPMS_T.M(Y,IA,S,T) = VGHYPMS_T.M(IA,S,T); + +IBB1234_VHYRS_S.L(Y,IA,S) = VHYRS_S.L(IA,S); +IBB1234_VHYRS_S.LO(Y,IA,S) = VHYRS_S.LO(IA,S); +IBB1234_VHYRS_S.UP(Y,IA,S) = VHYRS_S.UP(IA,S); +IBB1234_VHYRS_S.M(Y,IA,S) = VHYRS_S.M(IA,S); + +IBB1234_VESTOLOADT.L(Y,IA,S,T) = VESTOLOADT.L(IA,S,T); +IBB1234_VESTOLOADT.LO(Y,IA,S,T) = VESTOLOADT.LO(IA,S,T); +IBB1234_VESTOLOADT.UP(Y,IA,S,T) = VESTOLOADT.UP(IA,S,T); +IBB1234_VESTOLOADT.M(Y,IA,S,T) = VESTOLOADT.M(IA,S,T); + +IBB1234_VESTOLOADTS.L(Y,IA,S,T) = VESTOLOADTS.L(IA,S,T); +IBB1234_VESTOLOADTS.LO(Y,IA,S,T) = VESTOLOADTS.LO(IA,S,T); +IBB1234_VESTOLOADTS.UP(Y,IA,S,T) = VESTOLOADTS.UP(IA,S,T); +IBB1234_VESTOLOADTS.M(Y,IA,S,T) = VESTOLOADTS.M(IA,S,T); + +IBB1234_VHSTOLOADT.L(Y,IA,S,T) = VHSTOLOADT.L(IA,S,T); +IBB1234_VHSTOLOADT.LO(Y,IA,S,T) = VHSTOLOADT.LO(IA,S,T); +IBB1234_VHSTOLOADT.UP(Y,IA,S,T) = VHSTOLOADT.UP(IA,S,T); +IBB1234_VHSTOLOADT.M(Y,IA,S,T) = VHSTOLOADT.M(IA,S,T); + +IBB1234_VHSTOLOADTS.L(Y,IA,S,T) = VHSTOLOADTS.L(IA,S,T); +IBB1234_VHSTOLOADTS.LO(Y,IA,S,T) = VHSTOLOADTS.LO(IA,S,T); +IBB1234_VHSTOLOADTS.UP(Y,IA,S,T) = VHSTOLOADTS.UP(IA,S,T); +IBB1234_VHSTOLOADTS.M(Y,IA,S,T) = VHSTOLOADTS.M(IA,S,T); + +IBB1234_VESTOVOLT.L(Y,IA,S,T) = VESTOVOLT.L(IA,S,T); +IBB1234_VESTOVOLT.LO(Y,IA,S,T) = VESTOVOLT.LO(IA,S,T); +IBB1234_VESTOVOLT.UP(Y,IA,S,T) = VESTOVOLT.UP(IA,S,T); +IBB1234_VESTOVOLT.M(Y,IA,S,T) = VESTOVOLT.M(IA,S,T); + +IBB1234_VESTOVOLTS.L(Y,IA,S,T) = VESTOVOLTS.L(IA,S,T); +IBB1234_VESTOVOLTS.LO(Y,IA,S,T) = VESTOVOLTS.LO(IA,S,T); +IBB1234_VESTOVOLTS.UP(Y,IA,S,T) = VESTOVOLTS.UP(IA,S,T); +IBB1234_VESTOVOLTS.M(Y,IA,S,T) = VESTOVOLTS.M(IA,S,T); + +IBB1234_VHSTOVOLT.L(Y,IA,S,T) = VHSTOVOLT.L(IA,S,T); +IBB1234_VHSTOVOLT.LO(Y,IA,S,T) = VHSTOVOLT.LO(IA,S,T); +IBB1234_VHSTOVOLT.UP(Y,IA,S,T) = VHSTOVOLT.UP(IA,S,T); +IBB1234_VHSTOVOLT.M(Y,IA,S,T) = VHSTOVOLT.M(IA,S,T); + +IBB1234_VHSTOVOLTS.L(Y,IA,S,T) = VHSTOVOLTS.L(IA,S,T); +IBB1234_VHSTOVOLTS.LO(Y,IA,S,T) = VHSTOVOLTS.LO(IA,S,T); +IBB1234_VHSTOVOLTS.UP(Y,IA,S,T) = VHSTOVOLTS.UP(IA,S,T); +IBB1234_VHSTOVOLTS.M(Y,IA,S,T) = VHSTOVOLTS.M(IA,S,T); + +/* +IBB1234_VGKN.L(Y,IA,G) = VGKN.L(IA,G); +IBB1234_VGKN.LO(Y,IA,G) = VGKN.LO(IA,G); +IBB1234_VGKN.UP(Y,IA,G) = VGKN.UP(IA,G); +IBB1234_VGKN.M(Y,IA,G) = VGKN.M(IA,G); + +IBB1234_VXKN.L(Y,IRE,IRI) = VXKN.L(IRE,IRI); +IBB1234_VXKN.LO(Y,IRE,IRI) = VXKN.LO(IRE,IRI); +IBB1234_VXKN.UP(Y,IRE,IRI) = VXKN.UP(IRE,IRI); +IBB1234_VXKN.M(Y,IRE,IRI) = VXKN.M(IRE,IRI); +*/ + +IBB1234_VGKN.L(Y,IA,G) = 0; !! Kind of dummy, nice for GDXDiff with BB4 +IBB1234_VXKN.L(Y,IRE,IRI) = 0; !! Kind of dummy, nice for GDXDiff with BB4 + + +* Equations slack vars: +BB1234_VQEEQ.L(Y,IR,S,T,IPLUSMINUS) = VQEEQ.L(IR,S,T,IPLUSMINUS); +BB1234_VQHEQ.L(Y,IA,S,T,IPLUSMINUS) = VQHEQ.L(IA,S,T,IPLUSMINUS); + +/* todo more slack vars as above +IBB1234_VQESTOVOLT(Y,IA,S,T,IPLUSMINUS) +IBB1234_VQESTOVOLTS(Y,IA,S,T,IPLUSMINUS)execute_unload "..\output\temp\testbb1234.gdx", IBB1234_VGE_T, IBB1234_VGH_T, VQEEQ, VQHEQ ; +IBB1234_VQHSTOVOLT(Y,IA,S,T,IPLUSMINUS) +IBB1234_VQHSTOVOLTS(Y,IA,S,T,IPLUSMINUS) +*/ + +* TODO: main equations, .L and .M +BB1234_QEEQ.L(Y,IR,S,T) = QEEQ.L(IR,S,T); +BB1234_QEEQ.M(Y,IR,S,T) = QEEQ.M(IR,S,T); +BB1234_QHEQ.L(Y,IA,S,T) = QHEQ.L(IA,S,T); +BB1234_QHEQ.M(Y,IA,S,T) = QHEQ.M(IA,S,T); + !! todo: more eqns + + + + +$ifi not %dflexquant%==yes execute_unload "..\..\simex\BB1234_BB1.gdx" IBB1234_VOBJ IBB1234_VGKN IBB1234_VXKN IBB1234_VGE_T IBB1234_VGH_T IBB1234_VGF_T IBB1234_VX_T IBB1234_VGHYPMS_T IBB1234_VHYRS_S IBB1234_VESTOLOADT IBB1234_VESTOLOADTS IBB1234_VHSTOLOADT IBB1234_VHSTOLOADTS IBB1234_VESTOVOLT IBB1234_VESTOVOLTS IBB1234_VHSTOVOLT IBB1234_VHSTOVOLTS BB1234_VQEEQ BB1234_VQHEQ BB1234_QEEQ BB1234_QHEQ ; +$ifi %dflexquant%==yes execute_unload "..\..\simex\BB1234_BB1.gdx" IBB1234_VOBJ IBB1234_VGKN IBB1234_VXKN IBB1234_VGE_T IBB1234_VGH_T IBB1234_VGF_T IBB1234_VX_T IBB1234_VDEF_T IBB1234_VDHF_T IBB1234_VGHYPMS_T IBB1234_VHYRS_S IBB1234_VESTOLOADT IBB1234_VESTOLOADTS IBB1234_VHSTOLOADT IBB1234_VHSTOLOADTS IBB1234_VESTOVOLT IBB1234_VESTOVOLTS IBB1234_VHSTOVOLT IBB1234_VHSTOVOLTS BB1234_VQEEQ BB1234_VQHEQ BB1234_QEEQ BB1234_QHEQ ; + +$ifi %BB2%==yes $goto domoreBB2 + +$goto endoffile + +* BB2 ========================================================================== +$label domoreBB2 + +IBB1234_VGE_T.L(Y,IA,G,S,T) = VGE_T.L(ia,g,s,t) + VGEN_T.L(ia,g,s,t); +*IBB1234_VGE_T.LO(Y,IA,G,S,T) = VGE_T.LO(ia,g,s,t); !! ?? + VGEN_T.LO(ia,g,s,t); +*IBB1234_VGE_T.UP(Y,IA,G,S,T) = VGE_T.UP(ia,g,s,t); !! ?? + VGEN_T.UP(ia,g,s,t);; +*IBB1234_VGE_T.M(Y,IA,G,S,T) = VGE_T.M(ia,g,s,t); !! ?? + VGEN_T.LO(ia,g,s,t);; + +IBB1234_VGH_T.L(Y,IA,G,S,T) = VGH_T.L(ia,g,s,t) + VGHN_T.L(ia,g,s,t); +*IBB1234_VGH_T.LO(Y,IA,G,S,T) = VGH_T.LO(ia,g,s,t); +*IBB1234_VGH_T.UP(Y,IA,G,S,T) = VGH_T.UP(ia,g,s,t); +*IBB1234_VGH_T.M(Y,IA,G,S,T) = VGH_T.M(ia,g,s,t); + +IBB1234_VGKN.L(Y,IA,G) = VGKN.L(IA,G); +*IBB1234_VGKN.LO(Y,IA,G) = VGKN.LO(IA,G); !! ?? +*IBB1234_VGKN.UP(Y,IA,G) = VGKN.UP(IA,G); +*IBB1234_VGKN.M(Y,IA,G) = VGKN.M(IA,G); + +IBB1234_VXKN.L(Y,IRE,IRI) = VXKN.L(IRE,IRI); +*IBB1234_VXKN.LO(Y,IRE,IRI) = VXKN.LO(IRE,IRI); +*IBB1234_VXKN.UP(Y,IRE,IRI) = VXKN.UP(IRE,IRI); +*IBB1234_VXKN.M(Y,IRE,IRI) = VXKN.M(IRE,IRI); + +IBB1234_VGF_T.L(Y,IA,G,S,T) = VGF_T.L(IA,G,S,T) + VGFN_T.L(IA,G,S,T); +*IBB1234_VGF_T.LO(Y,IA,G,S,T) = VGF_T.LO(IA,G,S,T); +*IBB1234_VGF_T.UP(Y,IA,G,S,T) = VGF_T.UP(IA,G,S,T); +*IBB1234_VGF_T.M(Y,IA,G,S,T) = VGF_T.M(IA,G,S,T); + + +* TODO: main equations, .L and .M + + +execute_unload "..\..\simex\BB1234_BB2.gdx" IBB1234_VOBJ IBB1234_VGKN IBB1234_VXKN IBB1234_VGE_T IBB1234_VGH_T IBB1234_VGF_T IBB1234_VX_T /*IBB1234_VDEF_T IBB1234_VDHF_T */ IBB1234_VGHYPMS_T IBB1234_VHYRS_S IBB1234_VESTOLOADT IBB1234_VESTOLOADTS IBB1234_VHSTOLOADT IBB1234_VHSTOLOADTS IBB1234_VESTOVOLT IBB1234_VESTOVOLTS IBB1234_VHSTOVOLT IBB1234_VHSTOVOLTS + BB1234_VQEEQ BB1234_VQHEQ BB1234_QEEQ BB1234_QHEQ +; + +$goto endoffile + +* BB3 ========================================================================== +$label doBB3 + +Not implemented yet + +$goto endoffile + +* BB4 ========================================================================== +$label doBB4 + +IBB1234_VOBJ.L = VOBJ.L; + +IBB1234_VGE_T.L(Y,IA,G,S,T) = VGE_T.L(Y,ia,g,s,t); +IBB1234_VGE_T.LO(Y,IA,G,S,T) = VGE_T.LO(Y,ia,g,s,t); +IBB1234_VGE_T.UP(Y,IA,G,S,T) = VGE_T.UP(Y,ia,g,s,t); +IBB1234_VGE_T.M(Y,IA,G,S,T) = VGE_T.M(Y,ia,g,s,t); + +IBB1234_VGH_T.L(Y,IA,G,S,T) = VGH_T.L(Y,ia,g,s,t); +IBB1234_VGH_T.LO(Y,IA,G,S,T) = VGH_T.LO(Y,ia,g,s,t); +IBB1234_VGH_T.UP(Y,IA,G,S,T) = VGH_T.UP(Y,ia,g,s,t); +IBB1234_VGH_T.M(Y,IA,G,S,T) = VGH_T.M(Y,ia,g,s,t); + +IBB1234_VGF_T.L(Y,IA,G,S,T) = VGF_T.L(Y,IA,G,S,T); +IBB1234_VGF_T.LO(Y,IA,G,S,T) = VGF_T.LO(Y,IA,G,S,T); +IBB1234_VGF_T.UP(Y,IA,G,S,T) = VGF_T.UP(Y,IA,G,S,T); +IBB1234_VGF_T.M(Y,IA,G,S,T) = VGF_T.M(Y,IA,G,S,T); + +IBB1234_VX_T.L(Y,IRE,IRI,S,T) = VX_T.L(Y,IRE,IRI,S,T); +IBB1234_VX_T.LO(Y,IRE,IRI,S,T) = VX_T.LO(Y,IRE,IRI,S,T); +IBB1234_VX_T.UP(Y,IRE,IRI,S,T) = VX_T.UP(Y,IRE,IRI,S,T); +IBB1234_VX_T.M(Y,IRE,IRI,S,T) = VX_T.M(Y,IRE,IRI,S,T); + +IBB1234_VGKN.L(Y,IA,G) = VGKN.L(Y,IA,G); +IBB1234_VGKN.LO(Y,IA,G) = VGKN.LO(Y,IA,G); +IBB1234_VGKN.UP(Y,IA,G) = VGKN.UP(Y,IA,G); +IBB1234_VGKN.M(Y,IA,G) = VGKN.M(Y,IA,G); + +IBB1234_VXKN.L(Y,IRE,IRI) = VXKN.L(Y,IRE,IRI); +IBB1234_VXKN.LO(Y,IRE,IRI) = VXKN.LO(Y,IRE,IRI); +IBB1234_VXKN.UP(Y,IRE,IRI) = VXKN.UP(Y,IRE,IRI); +IBB1234_VXKN.M(Y,IRE,IRI) = VXKN.M(Y,IRE,IRI); + +/* renamed option to dflexquant +$ifi %dflex%==yes IBB1234_VDEF_T.L(Y,IR,S,T,DEF) = VDEF_T.L(Y,IR,S,T,DEF); +$ifi %dflex%==yes IBB1234_VDEF_T.LO(Y,IR,S,T,DEF) = VDEF_T.LO(Y,IR,S,T,DEF); +$ifi %dflex%==yes IBB1234_VDEF_T.UP(Y,IR,S,T,DEF) = VDEF_T.UP(Y,IR,S,T,DEF); +$ifi %dflex%==yes IBB1234_VDEF_T.M(Y,IR,S,T,DEF) = VDEF_T.M(Y,IR,S,T,DEF); + +$ifi %dflex%==yes IBB1234_VDHF_T.L(Y,IA,S,T,DHF) = VDHF_T.L(Y,IA,S,T,DHF); +$ifi %dflex%==yes IBB1234_VDHF_T.LO(Y,IA,S,T,DHF) = VDHF_T.LO(Y,IA,S,T,DHF); +$ifi %dflex%==yes IBB1234_VDHF_T.UP(Y,IA,S,T,DHF) = VDHF_T.UP(Y,IA,S,T,DHF); +$ifi %dflex%==yes IBB1234_VDHF_T.M(Y,IA,S,T,DHF) = VDHF_T.M(Y,IA,S,T,DHF); +*/ +$ifi %dflexquant%==yes IBB1234_VDEF_T.L(Y,IR,DEUSER,S,T,DEF) = VDEF_T.L(Y,IR,DEUSER,S,T,DEF); +$ifi %dflexquant%==yes IBB1234_VDEF_T.LO(Y,IR,DEUSER,S,T,DEF) = VDEF_T.LO(Y,IR,DEUSER,S,T,DEF); +$ifi %dflexquant%==yes IBB1234_VDEF_T.UP(Y,IR,DEUSER,S,T,DEF) = VDEF_T.UP(Y,IR,DEUSER,S,T,DEF); +$ifi %dflexquant%==yes IBB1234_VDEF_T.M(Y,IR,DEUSER,S,T,DEF) = VDEF_T.M(Y,IR,DEUSER,S,T,DEF); + +$ifi %dflexquant%==yes IBB1234_VDHF_T.L(Y,IA,DHUSER,S,T,DHF) = VDHF_T.L(Y,IA,DHUSER,S,T,DHF); +$ifi %dflexquant%==yes IBB1234_VDHF_T.LO(Y,IA,DHUSER,S,T,DHF) = VDHF_T.LO(Y,IA,DHUSER,S,T,DHF); +$ifi %dflexquant%==yes IBB1234_VDHF_T.UP(Y,IA,DHUSER,S,T,DHF) = VDHF_T.UP(Y,IA,DHUSER,S,T,DHF); +$ifi %dflexquant%==yes IBB1234_VDHF_T.M(Y,IA,DHUSER,S,T,DHF) = VDHF_T.M(Y,IA,DHUSER,S,T,DHF); + + +*IBB1234_VGHYPMS_T.L(Y,IA,S,T) = VGHYPMS_T.L(Y,IA,S,T); TODO: handle that no alues assigned +*IBB1234_VGHYPMS_T.LO(Y,IA,S,T) = VGHYPMS_T.LO(Y,IA,S,T); +*IBB1234_VGHYPMS_T.UP(Y,IA,S,T) = VGHYPMS_T.UP(Y,IA,S,T); +*IBB1234_VGHYPMS_T.M(Y,IA,S,T) = VGHYPMS_T.M(Y,IA,S,T); + +IBB1234_VHYRS_S.L(Y,IA,S) = VHYRS_S.L(Y,IA,S); +IBB1234_VHYRS_S.LO(Y,IA,S) = VHYRS_S.LO(Y,IA,S); +IBB1234_VHYRS_S.UP(Y,IA,S) = VHYRS_S.UP(Y,IA,S); +IBB1234_VHYRS_S.M(Y,IA,S) = VHYRS_S.M(Y,IA,S); + +IBB1234_VESTOLOADT.L(Y,IA,S,T) = VESTOLOADT.L(Y,IA,S,T); +IBB1234_VESTOLOADT.LO(Y,IA,S,T) = VESTOLOADT.LO(Y,IA,S,T); +IBB1234_VESTOLOADT.UP(Y,IA,S,T) = VESTOLOADT.UP(Y,IA,S,T); +IBB1234_VESTOLOADT.M(Y,IA,S,T) = VESTOLOADT.M(Y,IA,S,T); + +IBB1234_VESTOLOADTS.L(Y,IA,S,T) = VESTOLOADTS.L(Y,IA,S,T); +IBB1234_VESTOLOADTS.LO(Y,IA,S,T) = VESTOLOADTS.LO(Y,IA,S,T); +IBB1234_VESTOLOADTS.UP(Y,IA,S,T) = VESTOLOADTS.UP(Y,IA,S,T); +IBB1234_VESTOLOADTS.M(Y,IA,S,T) = VESTOLOADTS.M(Y,IA,S,T); + +IBB1234_VHSTOLOADT.L(Y,IA,S,T) = VHSTOLOADT.L(Y,IA,S,T); +IBB1234_VHSTOLOADT.LO(Y,IA,S,T) = VHSTOLOADT.LO(Y,IA,S,T); +IBB1234_VHSTOLOADT.UP(Y,IA,S,T) = VHSTOLOADT.UP(Y,IA,S,T); +IBB1234_VHSTOLOADT.M(Y,IA,S,T) = VHSTOLOADT.M(Y,IA,S,T); + +IBB1234_VHSTOLOADTS.L(Y,IA,S,T) = VHSTOLOADTS.L(Y,IA,S,T); +IBB1234_VHSTOLOADTS.LO(Y,IA,S,T) = VHSTOLOADTS.LO(Y,IA,S,T); +IBB1234_VHSTOLOADTS.UP(Y,IA,S,T) = VHSTOLOADTS.UP(Y,IA,S,T); +IBB1234_VHSTOLOADTS.M(Y,IA,S,T) = VHSTOLOADTS.M(Y,IA,S,T); + +IBB1234_VESTOVOLT.L(Y,IA,S,T) = VESTOVOLT.L(Y,IA,S,T); +IBB1234_VESTOVOLT.LO(Y,IA,S,T) = VESTOVOLT.LO(Y,IA,S,T); +IBB1234_VESTOVOLT.UP(Y,IA,S,T) = VESTOVOLT.UP(Y,IA,S,T); +IBB1234_VESTOVOLT.M(Y,IA,S,T) = VESTOVOLT.M(Y,IA,S,T); + +IBB1234_VESTOVOLTS.L(Y,IA,S,T) = VESTOVOLTS.L(Y,IA,S,T); +IBB1234_VESTOVOLTS.LO(Y,IA,S,T) = VESTOVOLTS.LO(Y,IA,S,T); +IBB1234_VESTOVOLTS.UP(Y,IA,S,T) = VESTOVOLTS.UP(Y,IA,S,T); +IBB1234_VESTOVOLTS.M(Y,IA,S,T) = VESTOVOLTS.M(Y,IA,S,T); + +IBB1234_VHSTOVOLT.L(Y,IA,S,T) = VHSTOVOLT.L(Y,IA,S,T); +IBB1234_VHSTOVOLT.LO(Y,IA,S,T) = VHSTOVOLT.LO(Y,IA,S,T); +IBB1234_VHSTOVOLT.UP(Y,IA,S,T) = VHSTOVOLT.UP(Y,IA,S,T); +IBB1234_VHSTOVOLT.M(Y,IA,S,T) = VHSTOVOLT.M(Y,IA,S,T); + +IBB1234_VHSTOVOLTS.L(Y,IA,S,T) = VHSTOVOLTS.L(Y,IA,S,T); +IBB1234_VHSTOVOLTS.LO(Y,IA,S,T) = VHSTOVOLTS.LO(Y,IA,S,T); +IBB1234_VHSTOVOLTS.UP(Y,IA,S,T) = VHSTOVOLTS.UP(Y,IA,S,T); +IBB1234_VHSTOVOLTS.M(Y,IA,S,T) = VHSTOVOLTS.M(Y,IA,S,T); + +* todo: VQ variables +BB1234_VQEEQ.L(Y,IR,S,T,IPLUSMINUS) = VQEEQ.L(Y,IR,S,T,IPLUSMINUS); +BB1234_VQHEQ.L(Y,IA,S,T,IPLUSMINUS) = VQHEQ.L(Y,IA,S,T,IPLUSMINUS); + + + +* TODO: main equations, .L and .M: +BB1234_QEEQ.L(Y,IR,S,T) = QEEQ.L(Y,IR,S,T); +BB1234_QEEQ.M(Y,IR,S,T) = QEEQ.M(Y,IR,S,T); +BB1234_QHEQ.L(Y,IA,S,T) = QHEQ.L(Y,IA,S,T); +BB1234_QHEQ.M(Y,IA,S,T) = QHEQ.M(Y,IA,S,T); + + + + !! and more, compare BB1 + + + +$ifi not %dflexquant%==yes execute_unload "..\..\simex\BB1234_BB4.gdx" IBB1234_VOBJ IBB1234_VGE_T IBB1234_VGH_T IBB1234_VGF_T IBB1234_VX_T IBB1234_VGKN IBB1234_VXKN IBB1234_VGHYPMS_T IBB1234_VHYRS_S IBB1234_VESTOLOADT IBB1234_VESTOLOADTS IBB1234_VHSTOLOADT IBB1234_VHSTOLOADTS IBB1234_VESTOVOLT IBB1234_VESTOVOLTS IBB1234_VHSTOVOLT IBB1234_VHSTOVOLTS BB1234_VQEEQ BB1234_VQHEQ BB1234_QEEQ BB1234_QHEQ; +$ifi %dflexquant%==yes execute_unload "..\..\simex\BB1234_BB4.gdx" IBB1234_VOBJ IBB1234_VGE_T IBB1234_VGH_T IBB1234_VGF_T IBB1234_VX_T IBB1234_VGKN IBB1234_VXKN IBB1234_VDEF_T IBB1234_VDHF_T IBB1234_VGHYPMS_T IBB1234_VHYRS_S IBB1234_VESTOLOADT IBB1234_VESTOLOADTS IBB1234_VHSTOLOADT IBB1234_VHSTOLOADTS IBB1234_VESTOVOLT IBB1234_VESTOVOLTS IBB1234_VHSTOVOLT IBB1234_VHSTOVOLTS BB1234_VQEEQ BB1234_VQHEQ BB1234_QEEQ BB1234_QHEQ; + +$goto endoffile + +$label endoffile + + + +$ontext ------------------------------------------------------------------------------- + +testing field etc. + +IBB1234_VGF_T.L(Y,IA,G,S,T) = VGF_T.L(Y,IA,G,S,T); +IBB1234_VGF_T.LO(Y,IA,G,S,T) = VGF_T.LO(Y,IA,G,S,T); +IBB1234_VGF_T.UP(Y,IA,G,S,T) = VGF_T.UP(Y,IA,G,S,T); +IBB1234_VGF_T.M(Y,IA,G,S,T) = VGF_T.M(Y,IA,G,S,T); + +IBB1234_VX_T.L(Y,IRE,IRI,S,T) = VX_T.L(Y,IRE,IRI,S,T); +IBB1234_VX_T.LO(Y,IRE,IRI,S,T) = VX_T.LO(Y,IRE,IRI,S,T); +IBB1234_VX_T.UP(Y,IRE,IRI,S,T) = VX_T.UP(Y,IRE,IRI,S,T); +IBB1234_VX_T.M(Y,IRE,IRI,S,T) = VX_T.M(Y,IRE,IRI,S,T); + +IBB1234_VGKN.L(Y,IA,G) = VGKN.L(Y,IA,G); +IBB1234_VGKN.LO(Y,IA,G) = VGKN.LO(Y,IA,G); +IBB1234_VGKN.UP(Y,IA,G) = VGKN.UP(Y,IA,G); +IBB1234_VGKN.M(Y,IA,G) = VGKN.M(Y,IA,G); + +IBB1234_VXKN.L(Y,IRE,IRI) = VXKN.L(Y,IRE,IRI); +IBB1234_VXKN.LO(Y,IRE,IRI) = VXKN.LO(Y,IRE,IRI); +IBB1234_VXKN.UP(Y,IRE,IRI) = VXKN.UP(Y,IRE,IRI); +IBB1234_VXKN.M(Y,IRE,IRI) = VXKN.M(Y,IRE,IRI); + +IBB1234_VDEF_T.L(Y,IR,S,T,DEF) = VDEF_T.L(Y,IR,S,T,DEF); +IBB1234_VDEF_T.LO(Y,IR,S,T,DEF) = VDEF_T.LO(Y,IR,S,T,DEF); +IBB1234_VDEF_T.UP(Y,IR,S,T,DEF) = VDEF_T.UP(Y,IR,S,T,DEF); +IBB1234_VDEF_T.M(Y,IR,S,T,DEF) = VDEF_T.M(Y,IR,S,T,DEF); + +IBB1234_VDHF_T.L(Y,IA,S,T,DHF) = VDHF_T.L(Y,IA,S,T,DHF); +IBB1234_VDHF_T.LO(Y,IA,S,T,DHF) = VDHF_T.LO(Y,IA,S,T,DHF); +IBB1234_VDHF_T.UP(Y,IA,S,T,DHF) = VDHF_T.UP(Y,IA,S,T,DHF); +IBB1234_VDHF_T.M(Y,IA,S,T,DHF) = VDHF_T.M(Y,IA,S,T,DHF); + +*IBB1234_VGHYPMS_T.L(Y,IA,S,T) = VGHYPMS_T.L(Y,IA,S,T); TODO: handle that no alues assigned +*IBB1234_VGHYPMS_T.LO(Y,IA,S,T) = VGHYPMS_T.LO(Y,IA,S,T); +*IBB1234_VGHYPMS_T.UP(Y,IA,S,T) = VGHYPMS_T.UP(Y,IA,S,T); +*IBB1234_VGHYPMS_T.M(Y,IA,S,T) = VGHYPMS_T.M(Y,IA,S,T); + +IBB1234_VHYRS_S.L(Y,IA,S) = VHYRS_S.L(Y,IA,S); +IBB1234_VHYRS_S.LO(Y,IA,S) = VHYRS_S.LO(Y,IA,S); +IBB1234_VHYRS_S.UP(Y,IA,S) = VHYRS_S.UP(Y,IA,S); +IBB1234_VHYRS_S.M(Y,IA,S) = VHYRS_S.M(Y,IA,S); + +IBB1234_VESTOLOADT.L(Y,IA,S,T) = VESTOLOADT.L(Y,IA,S,T); +IBB1234_VESTOLOADT.LO(Y,IA,S,T) = VESTOLOADT.LO(Y,IA,S,T); +IBB1234_VESTOLOADT.UP(Y,IA,S,T) = VESTOLOADT.UP(Y,IA,S,T); +IBB1234_VESTOLOADT.M(Y,IA,S,T) = VESTOLOADT.M(Y,IA,S,T); + +IBB1234_VESTOLOADTS.L(Y,IA,S,T) = VESTOLOADTS.L(Y,IA,S,T); +IBB1234_VESTOLOADTS.LO(Y,IA,S,T) = VESTOLOADTS.LO(Y,IA,S,T); +IBB1234_VESTOLOADTS.UP(Y,IA,S,T) = VESTOLOADTS.UP(Y,IA,S,T); +IBB1234_VESTOLOADTS.M(Y,IA,S,T) = VESTOLOADTS.M(Y,IA,S,T); + +IBB1234_VHSTOLOADT.L(Y,IA,S,T) = VHSTOLOADT.L(Y,IA,S,T); +IBB1234_VHSTOLOADT.LO(Y,IA,S,T) = VHSTOLOADT.LO(Y,IA,S,T); +IBB1234_VHSTOLOADT.UP(Y,IA,S,T) = VHSTOLOADT.UP(Y,IA,S,T); +IBB1234_VHSTOLOADT.M(Y,IA,S,T) = VHSTOLOADT.M(Y,IA,S,T); + +IBB1234_VHSTOLOADTS.L(Y,IA,S,T) = VHSTOLOADTS.L(Y,IA,S,T); +IBB1234_VHSTOLOADTS.LO(Y,IA,S,T) = VHSTOLOADTS.LO(Y,IA,S,T); +IBB1234_VHSTOLOADTS.UP(Y,IA,S,T) = VHSTOLOADTS.UP(Y,IA,S,T); +IBB1234_VHSTOLOADTS.M(Y,IA,S,T) = VHSTOLOADTS.M(Y,IA,S,T); + +IBB1234_VESTOVOLT.L(Y,IA,S,T) = VESTOVOLT.L(Y,IA,S,T); +IBB1234_VESTOVOLT.LO(Y,IA,S,T) = VESTOVOLT.LO(Y,IA,S,T); +IBB1234_VESTOVOLT.UP(Y,IA,S,T) = VESTOVOLT.UP(Y,IA,S,T); +IBB1234_VESTOVOLT.M(Y,IA,S,T) = VESTOVOLT.M(Y,IA,S,T); + +IBB1234_VESTOVOLTS.L(Y,IA,S,T) = VESTOVOLTS.L(Y,IA,S,T); +IBB1234_VESTOVOLTS.LO(Y,IA,S,T) = VESTOVOLTS.LO(Y,IA,S,T); +IBB1234_VESTOVOLTS.UP(Y,IA,S,T) = VESTOVOLTS.UP(Y,IA,S,T); +IBB1234_VESTOVOLTS.M(Y,IA,S,T) = VESTOVOLTS.M(Y,IA,S,T); + +IBB1234_VHSTOVOLT.L(Y,IA,S,T) = VHSTOVOLT.L(Y,IA,S,T); +IBB1234_VHSTOVOLT.LO(Y,IA,S,T) = VHSTOVOLT.LO(Y,IA,S,T); +IBB1234_VHSTOVOLT.UP(Y,IA,S,T) = VHSTOVOLT.UP(Y,IA,S,T); +IBB1234_VHSTOVOLT.M(Y,IA,S,T) = VHSTOVOLT.M(Y,IA,S,T); + +IBB1234_VHSTOVOLTS.L(Y,IA,S,T) = VHSTOVOLTS.L(Y,IA,S,T); +IBB1234_VHSTOVOLTS.LO(Y,IA,S,T) = VHSTOVOLTS.LO(Y,IA,S,T); +IBB1234_VHSTOVOLTS.UP(Y,IA,S,T) = VHSTOVOLTS.UP(Y,IA,S,T); +IBB1234_VHSTOVOLTS.M(Y,IA,S,T) = VHSTOVOLTS.M(Y,IA,S,T); + + +----------- + +alle bal-var med ny-invest: + + + + + + +$offtext diff --git a/base/addons/bb1234compare/bb1234_vardeclare.inc b/base/addons/bb1234compare/bb1234_vardeclare.inc new file mode 100644 index 00000000..2e763728 --- /dev/null +++ b/base/addons/bb1234compare/bb1234_vardeclare.inc @@ -0,0 +1,37 @@ +* File for addon testbb1234 for the Balmorel model +* Last revised 20180418 (hr) + +$ifi not declared IPLUSMINUS SET IPLUSMINUS "Violation of equation" /IPLUS "Violation of equation upwards", IMINUS "Violation of equation downwards"/; + +FREE VARIABLE IBB1234_VOBJ "Objective function value (MMoney)"; +POSITIVE VARIABLE IBB1234_VGE_T(Y,AAA,G,S,T) "Electricity generation (MW)"; +POSITIVE VARIABLE IBB1234_VGH_T(Y,AAA,G,S,T) "Heat generation (MW)"; +POSITIVE VARIABLE IBB1234_VGF_T(Y,AAA,G,S,T) "Fuel consumption rate (), existing units" +POSITIVE VARIABLE IBB1234_VX_T(Y,IRRRE,IRRRI,S,T) "Electricity export from region IRRRE to IRRRI (MW)"; +POSITIVE VARIABLE IBB1234_VGKN(Y,AAA,G) "New generation capacity (MW)"; !! se kommentarer til YYY i bb4.sim Hans TODO +POSITIVE VARIABLE IBB1234_VXKN(Y,IRRRE,IRRRI) "New electricity transmission capacity (MW)"; +POSITIVE VARIABLE IBB1234_VDECOM(YYY,AAA,G) "Decommissioned capacity(MW). NOT IN USE PRESENTLY"; +$ifi %dflexquant%==yes POSITIVE VARIABLE IBB1234_VDEF_T(Y,RRR,DEUSER,S,T,DEF) "Flexible electricity demands (MW)"; +$ifi %dflexquant%==yes POSITIVE VARIABLE IBB1234_VDHF_T(Y,AAA,DHUSER,S,T,DHF) "Flexible heat demands (MW)"; +POSITIVE VARIABLE IBB1234_VGHYPMS_T(Y,AAA,S,T) "Contents of pumped hydro storage (MWh)"; +POSITIVE VARIABLE IBB1234_VHYRS_S(Y,AAA,S) "Hydro energy equivalent at the start of the season (MWh)"; +POSITIVE VARIABLE IBB1234_VESTOLOADT(Y,AAA,S,T) "Intra-seasonal electricity storage loading (MW)"; +POSITIVE VARIABLE IBB1234_VESTOLOADTS(Y,AAA,S,T) "Inter-seasonal electricity storage loading (MW)"; +POSITIVE VARIABLE IBB1234_VHSTOLOADT(Y,AAA,S,T) "Intra-seasonal heat storage loading (MW)"; +POSITIVE VARIABLE IBB1234_VHSTOLOADTS(Y,AAA,S,T) "Inter-seasonal heat storage loading (MW)"; +POSITIVE VARIABLE IBB1234_VESTOVOLT(Y,AAA,S,T) "Electricity storage contents at beginning of time segment (MWh)"; +POSITIVE VARIABLE IBB1234_VESTOVOLTS(Y,AAA,S,T) "Electricity storage contents at beginning of time segment (MWh)"; +POSITIVE VARIABLE IBB1234_VHSTOVOLT(Y,AAA,S,T) "Heat storage contents at beginning of time segment (MWh)"; +POSITIVE VARIABLE IBB1234_VHSTOVOLTS(Y,AAA,S,T) "Heat storage contents at beginning of time segment (MWh)"; + + +POSITIVE VARIABLE BB1234_VQEEQ(Y,RRR,S,T,IPLUSMINUS) "Feasibility in electricity balance QEEQ (IMINUS is short of el, IPLUS is surplus of el) (MW)"; +POSITIVE VARIABLE BB1234_VQHEQ(Y,AAA,S,T,IPLUSMINUS) "Feasibility in heat balance QHEQ (IMINUS is short of heat, IPLUS is surplus of heat) (MW)"; +POSITIVE VARIABLE IBB1234_VQESTOVOLT(Y,AAA,S,T,IPLUSMINUS) "Feasibility in intra-seasonal electricity storage equation QESTOVOLT (MWh)"; +POSITIVE VARIABLE IBB1234_VQESTOVOLTS(Y,AAA,S,T,IPLUSMINUS) "Feasibility in inter-seasonal electricity storage equation QESTOVOLTS (MWh)"; +POSITIVE VARIABLE IBB1234_VQHSTOVOLT(Y,AAA,S,T,IPLUSMINUS) "Feasibility in intra-seasonal heat storage equation VQHSTOVOLT (MWh)"; +POSITIVE VARIABLE IBB1234_VQHSTOVOLTS(Y,AAA,S,T,IPLUSMINUS) "Feasibility in inter-seasonal heat storage equation VQHSTOVOLTS (MWh)"; + +POSITIVE VARIABLE BB1234_QEEQ(Y,RRR,S,T) "Electricity balance QEEQ (MW)"; +POSITIVE VARIABLE BB1234_QHEQ(Y,AAA,S,T) "Heat balance QHEQ (MW)"; +* More eqns diff --git a/base/addons/gas/DH-MR - foer varmebehandling.inc b/base/addons/gas/DH-MR - foer varmebehandling.inc new file mode 100644 index 00000000..03268ef9 --- /dev/null +++ b/base/addons/gas/DH-MR - foer varmebehandling.inc @@ -0,0 +1,130 @@ +* SOURCE: Energiproducenttælling 2004. + +PARAMETER DH_MR2004(AAA) +/ +amagerf 2224904.91666667 +billesboelle 10818.3333333333 +brande 47014.8888888889 +avedoere 1055380.83611111 +dragoer 7277.27777777778 +egtved 531798.639888889 +ellidshoej 164185.830555556 +froeslev 642938.566666667 +haverslev 556340.64 +herning 6586890.71007009 +hoejby 3261777.32422222 +karlslunde 36660.3777777778 +karup 234517.878888889 +koege 217484.897777778 +koelbjerg 336272.124444444 +liselskaer 301802.232 +litorup 49496.1 +lynge 1237484.6 +middelfart 90500.5288888889 +maaloev 501278.626388889 +noerskov 295146.515777778 +nyborg 172319.452222222 +nybro 58330.5 +DK_E_Rural 835210.904111111 +pottehuse 243976 +ringsted 632562.144444445 +slagelse 302631.638888889 +soroe 833593.757666667 +standst 301744.235777778 +stenlille 6076 +HCOersted 4335603.74522778 +* Ændret manuelt fra Taulov +skaerbaek 945821.5 +terkelsboel 224073.725555556 +torslunde 415368.915555556 +ullerslev 43140 +vallensbaek 1057007.5 +varde 1357966.38188889 +DK_W_Rural 163721.528444445 +viborg 1694392.61202222 +aalborg 2754115.00622222 +/; + +DH(YYY,AAA)$ICA('Denmark',AAA) = 0; +DH(YYY,AAA)$(DH_MR2004(AAA)) = DH_MR2004(AAA); + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/base/addons/gas/DH-MR.inc b/base/addons/gas/DH-MR.inc new file mode 100644 index 00000000..d4d996a7 --- /dev/null +++ b/base/addons/gas/DH-MR.inc @@ -0,0 +1,154 @@ +* SOURCE: Energiproducenttælling 2004. + +PARAMETER DH_MR2004(AAA) +/ +billesboelle 10818.3333333333 +brande 47014.8888888889 +dragoer 7277.27777777778 +egtved 531798.639888889 +ellidshoej 164185.830555556 +froeslev 642938.566666667 +haverslev 556340.64 + +*herning 6586890.71007009 Original fra database +* Frastrukket DK_W_AARHUS +herning 3585684 +DK_W_AARHUS 3001207 + +* hoejby 3261777.32422222 Original fra database +* Fratrukket Odense postnumre +hoejby 327055 +* Kun anlæg med Odense postnummer: +DK_W_ODENSE 2934722 + +karup 234517.878888889 +koege 217484.897777778 +koelbjerg 336272.124444444 +liselskaer 301802.232 +litorup 49496.1 +lynge 1237484.6 +middelfart 90500.5288888889 +maaloev 501278.626388889 +noerskov 295146.515777778 +nyborg 172319.452222222 +nybro 58330.5 +DK_E_Rural 835210.904111111 +pottehuse 243976 +ringsted 632562.144444445 +slagelse 302631.638888889 +soroe 833593.757666667 +standst 301744.235777778 +stenlille 6076 + +* Bør lige undersøges igen fordi DH TREFOR er større end skaerbaek/taulov +* Taulov 945821.5 +DK_W_TREFOR 987722 + +terkelsboel 224073.725555556 +varde 1357966.38188889 + +DK_W_Rural 163721.528444445 +viborg 1694392.61202222 + + + +*aalborg 2754115.00622222 Original fra database +* Fratrukket DK_W_AALBORG: +aalborg 1561429 +DK_W_AALBORG 1192686 + +* Original DK_E_Urban: +*DK_E_Urban 10056899 +* DK_E_Urban dannet udfra sum af følgende MR stationer +*torslunde 415368.9156 +*ullerslev 43140 +*vallensbaek 1057007.5 +*karlslunde 36660.37778 +*avedoere 1055380.836 +*amagerf 2224904.917 +*HCOersted 4335603.745 +DK_E_Urban 9168066.291 +/; + +DH(YYY,AAA)$ICA('Denmark',AAA) = 0; +DH(YYY,AAA)$(DH_MR2004(AAA)) = DH_MR2004(AAA); + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/base/addons/gas/Export_GER.INC b/base/addons/gas/Export_GER.INC new file mode 100644 index 00000000..3597c511 --- /dev/null +++ b/base/addons/gas/Export_GER.INC @@ -0,0 +1,8746 @@ +* ----------------------------------------------------- +* MDB2GMEllund.S VerEllund.Sion 2.6, AuguEllund.St 2005 +* Erwin Kalvelagen, GAMEllund.S Development Corp +* ----------------------------------------------------- +* DAO verEllund.Sion: 3.6 +* Jet verEllund.Sion: 4.0 +* DatabaEllund.Se: D:\LarEllund.SB\1\GaEllund.STranEllund.SmiEllund.SEllund.Sion.mdb +* Query: Ellund.Select * from ellund +* ----------------------------------------------------- +Ellund.S01.T001 211.8 +Ellund.S01.T002 212.2 +Ellund.S01.T003 211.6 +Ellund.S01.T004 211.7 +Ellund.S01.T005 211.9 +Ellund.S01.T006 211.5 +Ellund.S01.T007 216.6 +Ellund.S01.T008 222.4 +Ellund.S01.T009 222.8 +Ellund.S01.T010 222.7 +Ellund.S01.T011 222.7 +Ellund.S01.T012 222.8 +Ellund.S01.T013 223.1 +Ellund.S01.T014 222.6 +Ellund.S01.T015 223.2 +Ellund.S01.T016 222.8 +Ellund.S01.T017 223 +Ellund.S01.T018 222.5 +Ellund.S01.T019 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297.2 +Ellund.S52.T109 297.7 +Ellund.S52.T110 298.2 +Ellund.S52.T111 298.4 +Ellund.S52.T112 298.3 +Ellund.S52.T113 297.5 +Ellund.S52.T114 296.5 +Ellund.S52.T115 296.3 +Ellund.S52.T116 296.1 +Ellund.S52.T117 296.1 +Ellund.S52.T118 296.3 +Ellund.S52.T119 296.5 +Ellund.S52.T120 296.5 +Ellund.S52.T121 226.7 +Ellund.S52.T122 226.7 +Ellund.S52.T123 226.4 +Ellund.S52.T124 220.3 +Ellund.S52.T125 217.9 +Ellund.S52.T126 218.3 +Ellund.S52.T127 222.5 +Ellund.S52.T128 222.6 +Ellund.S52.T129 222.6 +Ellund.S52.T130 222.7 +Ellund.S52.T131 222.6 +Ellund.S52.T132 222.7 +Ellund.S52.T133 223 +Ellund.S52.T134 223.5 +Ellund.S52.T135 223.6 +Ellund.S52.T136 223.7 +Ellund.S52.T137 223.5 +Ellund.S52.T138 223.4 +Ellund.S52.T139 223.5 +Ellund.S52.T140 223.3 +Ellund.S52.T141 222.9 +Ellund.S52.T142 220.4 +Ellund.S52.T143 220.2 +Ellund.S52.T144 220.1 +Ellund.S52.T145 220.1 +Ellund.S52.T146 220.2 +Ellund.S52.T147 220 +Ellund.S52.T148 221 +Ellund.S52.T149 223.2 +Ellund.S52.T150 225.7 +Ellund.S52.T151 222.6 +Ellund.S52.T152 207.1 +Ellund.S52.T153 207 +Ellund.S52.T154 206.9 +Ellund.S52.T155 206.7 +Ellund.S52.T156 206.8 +Ellund.S52.T157 207.3 +Ellund.S52.T158 209 +Ellund.S52.T159 208.9 +Ellund.S52.T160 208.9 +Ellund.S52.T161 208.7 +Ellund.S52.T162 208.8 +Ellund.S52.T163 208.8 +Ellund.S52.T164 208.8 +Ellund.S52.T165 209 +Ellund.S52.T166 211.8 +Ellund.S52.T167 211.8 +Ellund.S52.T168 212.2 +* ----------------------------------------------------- \ No newline at end of file diff --git a/base/addons/gas/GDATA-MR-old.inc b/base/addons/gas/GDATA-MR-old.inc new file mode 100644 index 00000000..77fbc523 --- /dev/null +++ b/base/addons/gas/GDATA-MR-old.inc @@ -0,0 +1,341 @@ +TABLE GDATA_MR(GGG,GDATASET) + GDTYPE GDFUEL GDCV GDCB GDDESO2 GDNOX GDCH4 GDOMFCOST0 GDOMVCOST0 GDFE +'amagerf_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.922418703225379 +'amagerf_ST_B0_MW' 2 10 0.00 0.18 95.90 0.10 0.00 272.00 25.00 0.821780609388745 +'amagerf_ST_E0_CO1' 3 3 0.15 0.71 96.00 0.04 16.00 1.80 0.333763061604313 +'amagerf_ST_E0_CO3' 3 3 0.15 0.75 96.00 0.04 16.00 1.80 0.439429183716792 +'amagerf_ST_E0_ST2' 3 11 0.15 0.52 0.00 0.04 25.00 2.70 0.289521678413297 +'billesboelle_GE_B0_NG' 2 2 0.00 0.58 0.17 0.40 82.00 0.02 0.96151107538341 +'billesboelle_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.93993015663171 +'brande_GE_B0_NG' 2 2 0.00 0.75 0.17 0.40 82.00 0.02 0.852915886478752 +'brande_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.880357142857143 +'brande_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.987967725091219 +'brande_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.999981661134442 +'brande_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'broendby_GE_B0_NG' 2 2 0.00 0.70 0.17 0.40 82.00 0.02 0.871020537161038 +'broendby_GE_C0_LO' 1 6 0.00 0.17 0.40 82.00 0.02 0.371755652295276 +'broendby_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.975232448220041 +'broendby_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.908757550200865 +'broendby_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.758863854556707 +'broendby_SOLE_SOL' 11 16 0.00 0.00 0.00 0.00 0.00 0.00 1 +'broendby_ST_E0_CO1' 3 3 0.15 0.75 96.00 0.04 16.00 1.80 0.428113489887122 +'dragoer_GE_B0_NG' 2 2 0.00 0.71 0.17 0.40 82.00 0.02 0.902494396211146 +'dragoer_GE_C0_NG' 1 2 0.00 0.17 0.40 82.00 0.02 0.42 +'dragoer_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.910037011174666 +'dragoer_SOLE_SOL' 11 16 0.00 0.00 0.00 0.00 0.00 0.00 1 +'egtved_CC_B0_MW' 2 10 0.00 0.62 95.90 0.10 0.00 272.00 25.00 0.788405025998439 +'egtved_GE_B0_NG' 2 2 0.00 0.78 0.17 0.40 82.00 0.02 0.913151733013408 +'egtved_GE_C0_NG' 1 2 0.00 0.17 0.40 82.00 0.02 0.42 +'egtved_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.943585538924134 +'egtved_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.940222142642606 +'egtved_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.978159629745657 +'egtved_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'ellidshoej_GE_B0_NG' 2 2 0.00 0.70 0.17 0.40 82.00 0.02 0.895364238272827 +'ellidshoej_GT_B0_NG' 2 2 0.00 0.49 0.12 1.5e-03 8.00 5.80 0.839999274540564 +'ellidshoej_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.910560869993016 +'ellidshoej_HO_NG' 4 2 0.00 0.01 0.40 0.50 1.78460333159077 +'ellidshoej_HO_ST' 4 11 0.00 0.08 20.00 0.00 1.00035242491076 +'froeslev_CC_B0_NG' 2 2 0.00 0.79 0.05 5.0e-03 10.00 2.80 0.867274248568445 +'froeslev_GE_B0_NG' 2 2 0.00 0.78 0.17 0.40 82.00 0.02 0.897783553522783 +'froeslev_HO_CO4' 4 3 0.00 0.01 0.90 0.75 0.9 +'froeslev_HO_CO5' 4 3 0.00 0.01 0.90 0.75 0.9 +'froeslev_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.882708277062975 +'froeslev_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.790018662784373 +'froeslev_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.986608710187339 +'froeslev_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.993103448275862 +'froeslev_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.879020408163265 +'froeslev_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'froeslev_ST_C0_CO3' 1 3 0.00 96.00 0.04 16.00 1.80 0.345849361910849 +'froeslev_ST_E0_CO2' 3 3 0.15 7.36 96.00 0.04 16.00 1.80 0.405884709466712 +'haverslev_GE_B0_NG' 2 2 0.00 0.68 0.17 0.40 82.00 0.02 0.905686711575149 +'haverslev_GT_B0_NG' 2 2 0.00 0.67 0.08 0.909050128836475 +'haverslev_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.999881090887909 +'haverslev_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.876344362181598 +'haverslev_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.884791276589154 +'haverslev_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.956373454297346 +'haverslev_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.978300271666168 +'haverslev_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.98899022492414 +'haverslev_HO_WW' 4 13 0.00 0.08 20.00 0.00 1.02964335760037 +'haverslev_ST_B0_MW' 2 10 0.00 0.27 95.90 0.10 0.00 272.00 25.00 1.00347563813305 +'herning_CC_B0_NG' 2 2 0.00 1.24 0.05 5.0e-03 10.00 2.80 0.86070885940225 +'herning_GE_B0_LO' 2 6 0.00 0.66 0.17 0.40 82.00 0.02 0.96949942610512 +'herning_GE_B0_NG' 2 2 0.00 0.73 0.17 0.40 82.00 0.02 0.883415027727384 +'herning_GE_B0_WW' 2 13 0.00 0.35 0.06 2.1e-03 82.00 0.02 0.866944 +'herning_GE_C0_LO1' 1 6 0.00 0.17 0.40 82.00 0.02 0.387434482710191 +'herning_GE_C0_LO2' 1 6 0.00 0.17 0.40 82.00 0.02 0.387628199951546 +'herning_GE_C0_LO3' 1 6 0.00 0.17 0.40 82.00 0.02 0.387628199951546 +'herning_GE_C0_LO4' 1 6 0.00 0.17 0.40 82.00 0.02 0.387628199951546 +'herning_GT_B0_NG' 2 2 0.00 0.52 0.09 3.88888888888889e-04 2.07 1.50 0.735095162455347 +'herning_GT_C0_LO5' 1 6 0.00 0.12 1.5e-03 8.00 5.80 0.191373626373626 +'herning_HO_CO6' 4 3 0.00 0.01 0.90 0.75 0.920715875723601 +'herning_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.893354563822181 +'herning_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.91690148202878 +'herning_HO_LO2' 4 6 0.00 0.01 0.90 0.75 0.926286937623666 +'herning_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.846542406045587 +'herning_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.939000710727021 +'herning_HO_NG2' 4 2 0.00 0.01 0.40 0.50 0.9 +'herning_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.927899094971378 +'herning_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.948928513467262 +'herning_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.957569766522318 +'herning_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'herning_ST_B0_CO' 2 3 0.00 0.22 96.00 0.04 16.00 1.80 0.908265717574404 +'herning_ST_B0_FO' 2 5 0.00 0.07 96.00 0.04 16.00 1.80 0.980933253359316 +'herning_ST_B0_MW' 2 10 0.00 0.17 95.90 0.10 0.00 272.00 25.00 0.890662055686172 +'herning_ST_B0_WW' 2 13 0.00 0.06 0.06 2.1e-03 82.00 0.02 0.854774712144254 +'herning_ST_E0_CO1' 3 3 0.15 0.46 96.00 0.04 16.00 1.80 0.332045173364601 +'herning_ST_E0_CO3' 3 3 0.15 0.76 96.00 0.04 16.00 1.80 0.400721277234751 +'herning_ST_E0_CO4' 3 3 0.15 0.76 96.00 0.04 16.00 1.80 0.422312733610805 +'herning_ST_E0_WW1' 3 13 0.15 0.51 0.00 0.04 25.00 2.70 0.386777648704521 +'hoejby_GE_B0_NG' 2 2 0.00 0.83 0.17 0.40 82.00 0.02 0.876279415597549 +'hoejby_GT_B0_NG' 2 2 0.00 0.55 0.10 1.11971830985916e-03 5.97 4.32 0.859990099530198 +'hoejby_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.927061048140357 +'hoejby_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.924166868369476 +'hoejby_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.93376554225127 +'hoejby_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.939378614229185 +'hoejby_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.950824605998983 +'hoejby_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.942698445507369 +'hoejby_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'hoejby_ST_B0_MW' 2 10 0.00 0.29 95.90 0.10 0.00 272.00 25.00 1.03514399687223 +'hoejby_ST_B0_MW1' 2 10 0.00 0.33 95.90 0.10 0.00 272.00 25.00 0.897987478601386 +'hoejby_ST_E0_NG3' 3 2 0.15 0.82 96.00 0.04 16.00 1.80 0.372287505664969 +'hoejby_ST_E0_CO2' 3 3 0.15 0.90 96.00 0.04 16.00 1.80 0.454594134055191 +'karlslunde_GE_B0_NG' 2 2 0.00 0.71 0.17 0.40 82.00 0.02 0.907004665258234 +'karlslunde_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.851535081497559 +'karlslunde_HO_NG' 4 2 0.00 0.01 0.40 0.50 1.00764081624194 +'karup_GE_B0_NG' 2 2 0.00 0.75 0.17 0.40 82.00 0.02 0.90448101396322 +'karup_HO_LO' 4 6 0.00 0.01 0.90 0.75 1.00910677709404 +'karup_HO_NG' 4 2 0.00 0.01 0.40 0.50 1.00325323734429 +'karup_HO_WW' 4 13 0.00 0.08 20.00 0.00 1.01726256220603 +'karup_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'koege_GE_B0_NG' 2 2 0.00 0.79 0.17 0.40 82.00 0.02 0.839400340135045 +'koege_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.94353060491935 +'koege_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.807434383333148 +'koege_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.910517368954099 +'koege_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.853501477832512 +'koege_ST_B0_NG' 2 2 0.00 0.16 96.00 0.04 16.00 1.80 0.984788670984333 +'koege_ST_B0_WW' 2 13 0.00 1.10 0.06 2.1e-03 82.00 0.02 0.444250579815866 +'koelbjerg_GE_B0_NG' 2 2 0.00 0.76 0.17 0.40 82.00 0.02 0.887356298743507 +'koelbjerg_HO_CO' 4 3 0.00 0.01 0.90 0.75 0.931297064049412 +'koelbjerg_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.898986105895607 +'koelbjerg_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.913813498637514 +'koelbjerg_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.939805327555591 +'koelbjerg_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.923351047202251 +'koelbjerg_HO_WW' 4 13 0.00 0.08 20.00 0.00 1.01322292984934 +'koelbjerg_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'koelbjerg_ST_B0_CO' 2 3 0.00 0.14 96.00 0.04 16.00 1.80 0.900006582890721 +'koelbjerg_ST_B0_FO' 2 5 0.00 0.03 96.00 0.04 16.00 1.80 0.919996023856402 +'koelbjerg_ST_B0_NG' 2 2 0.00 0.10 96.00 0.04 16.00 1.80 1.10568836357856 +'koelbjerg_ST_B0_WW' 2 13 0.00 0.35 0.06 2.1e-03 82.00 0.02 0.846078496553864 +'liselskaer_CC_B0_MW' 2 10 0.00 0.37 95.90 0.10 0.00 272.00 25.00 0.731216004218123 +'liselskaer_GE_B0_NG' 2 2 0.00 0.76 0.17 0.40 82.00 0.02 0.899567057921386 +'liselskaer_HO_NG' 4 2 0.00 0.01 0.40 0.50 1.0333544577565 +'liselskaer_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'liselskaer_ST_B0_NG' 2 2 0.00 0.85 96.00 0.04 16.00 1.80 0.879888695352103 +'litorup_GE_B0_NG' 2 2 0.00 0.70 0.17 0.40 82.00 0.02 0.925577913920772 +'litorup_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.971062342202369 +'litorup_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.761854109969479 +'lynge_CC_B0_NG' 2 2 0.00 0.96 0.05 5.0e-03 10.00 2.80 0.892815553843547 +'lynge_GE_B0_NG' 2 2 0.00 0.68 0.17 0.40 82.00 0.02 0.878064107180874 +'lynge_GE_B0_WW' 2 13 0.00 0.32 0.06 2.1e-03 82.00 0.02 0.270810810810811 +'lynge_GT_B0_NG' 2 2 0.00 1.05 0.05 1.49840595111583e-03 7.87 3.86 0.854537734620137 +'lynge_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.810783555749773 +'lynge_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.963350441387805 +'lynge_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.899512848133447 +'lynge_HO_WW' 4 13 0.00 0.08 20.00 0.00 1.10024310823463 +'lynge_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'lynge_ST_B0_MW' 2 10 0.00 0.29 95.90 0.10 0.00 272.00 25.00 0.87180323093409 +'middelfart_GE_B0_NG' 2 2 0.00 0.74 0.17 0.40 82.00 0.02 0.898480141568657 +'middelfart_HO_LO' 4 6 0.00 0.01 0.90 0.75 1.04616397691005 +'middelfart_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.848459101781476 +'middelfart_HO_NG' 4 2 0.00 0.01 0.40 0.50 1.05111968654446 +'middelfart_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'maaloev_GE_B0_NG' 2 2 0.00 0.76 0.17 0.40 82.00 0.02 0.882718591303977 +'maaloev_GE_C0_LO4' 1 6 0.00 0.17 0.40 82.00 0.02 0.42 +'maaloev_GT_B0_NG' 2 2 0.00 0.59 0.08 0.761216418368918 +'maaloev_GT_C0_LO6' 1 6 0.00 0.01 1.5e-03 7.80 2.50 0.178035952586545 +'maaloev_GT_C0_NG5' 1 2 0.00 0.01 1.5e-03 7.80 2.50 0.42 +'maaloev_GT_C0_NG7' 1 2 0.00 0.08 0.3 +'maaloev_HO_FO8' 4 5 0.00 0.01 0.90 0.75 0.920715875723601 +'maaloev_HO_FO9' 4 5 0.00 0.01 0.90 0.75 0.920715875723601 +'maaloev_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.886904746733735 +'maaloev_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.819028928253104 +'maaloev_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.936636643389796 +'maaloev_HO_WO' 4 12 0.00 0.08 20.00 0.00 1.10768269397674 +'maaloev_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.887549543788595 +'maaloev_ST_C0_FO2' 1 5 0.00 96.00 0.04 16.00 1.80 0.278000849451124 +'maaloev_ST_C0_FO3' 1 5 0.00 96.00 0.04 16.00 1.80 0.293051404662283 +'noerskov_GE_B0_NG' 2 2 0.00 0.81 0.17 0.40 82.00 0.02 0.885169436940683 +'noerskov_GT_B0_NG' 2 2 0.00 0.21 0.08 0.927751180623156 +'noerskov_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.826646251626881 +'noerskov_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.690475004093022 +'noerskov_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.921092092030374 +'noerskov_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.837095345104334 +'noerskov_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.852113172572511 +'noerskov_ST_B0_CO' 2 3 0.00 0.02 96.00 0.04 16.00 1.80 0.942519825149872 +'nyborg_CC_B0_MW' 2 10 0.00 0.12 95.90 0.10 0.00 272.00 25.00 0.822512066962087 +'nyborg_GE_B0_NG' 2 2 0.00 0.44 0.17 0.40 82.00 0.02 0.915810576123031 +*'nyborg_GT_B0_NG' 2 2 0.00 0.61 0.08 +'nyborg_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.914965131710414 +'nyborg_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.859102353668779 +'nyborg_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'nybro_GE_B0_NG' 2 2 0.00 0.77 0.17 0.40 82.00 0.02 0.918143080973865 +'nybro_GT_B0_NG' 2 2 0.00 0.47 0.08 0.737923743133244 +'nybro_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.93449374560109 +'nybro_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.962016761774341 +'nybro_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.983487855336756 +'nybro_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'OEST_GE_B0_LO' 2 6 0.00 0.77 0.17 0.40 82.00 0.02 0.762788664090193 +'OEST_GE_B0_NG' 2 2 0.00 0.61 0.17 0.40 82.00 0.02 0.86700599465545 +'OEST_GE_C0_LO2' 1 6 0.00 0.17 0.40 82.00 0.02 0.30806697108067 +'OEST_HO_CO' 4 3 0.00 0.01 0.90 0.75 0.800002387211707 +'OEST_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.877763037006505 +'OEST_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.89144061089545 +'OEST_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.824050280276879 +'OEST_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.882212488651046 +'OEST_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.982385327446476 +'OEST_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.952702684274961 +'OEST_ST_B0_CO4' 2 3 0.00 0.52 96.00 0.04 16.00 1.80 0.731559594344833 +'OEST_ST_B0_FO' 2 5 0.00 0.19 96.00 0.04 16.00 1.80 0.889825019263249 +'OEST_ST_B0_FO1' 2 5 0.00 0.18 96.00 0.04 16.00 1.80 0.874462974401208 +'OEST_ST_B0_MW' 2 10 0.00 0.29 95.90 0.10 0.00 272.00 25.00 0.850499627496659 +'OEST_ST_B0_ST' 2 11 0.00 0.43 0.13 5.0e-04 82.00 0.02 0.879341260500792 +'OEST_ST_C0_FO3' 1 5 0.00 96.00 0.04 16.00 1.80 0.290269529465509 +'pottehuse_GE_B0_NG' 2 2 0.00 0.72 0.17 0.40 82.00 0.02 0.898883129535391 +'pottehuse_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.934937823258441 +'pottehuse_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.910434226677641 +'pottehuse_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.872874245043883 +'pottehuse_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.995380843551156 +'pottehuse_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'pottehuse_SOLE_SOL' 11 16 0.00 0.00 0.00 0.00 0.00 0.00 1 +'pottehuse_ST_B0_MW' 2 10 0.00 0.36 95.90 0.10 0.00 272.00 25.00 0.966851750227934 +'ringsted_CC_B0_MW' 2 10 0.00 0.26 95.90 0.10 0.00 272.00 25.00 0.700725662926452 +'ringsted_GE_B0_NG' 2 2 0.00 0.80 0.17 0.40 82.00 0.02 0.855982938998647 +'ringsted_GT_C0_LO2' 1 6 0.00 0.01 1.5e-03 7.80 2.50 0.16700195165891 +'ringsted_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.872065683896249 +'ringsted_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.986121971534401 +'ringsted_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.833644223568657 +'ringsted_ST_B0_ST' 2 11 0.00 0.31 0.13 5.0e-04 82.00 0.02 0.89194590350278 +'ringsted_ST_B0_ST3' 2 11 0.00 0.38 0.13 5.0e-04 82.00 0.02 0.874841528255722 +'slagelse_GE_B0_NG' 2 2 0.00 0.65 0.17 0.40 82.00 0.02 0.892788238911068 +'slagelse_GE_C0_NG' 1 2 0.00 0.17 0.40 82.00 0.02 0.350279836853913 +'slagelse_GT_B0_NG' 2 2 0.00 0.69 0.08 0.872339878650794 +'slagelse_HO_FO3' 4 5 0.00 0.01 0.90 0.75 0.920715875723601 +'slagelse_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.87969529009311 +'slagelse_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.870875331564987 +'slagelse_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.935588571428571 +'slagelse_HO_WW' 4 13 0.00 0.08 20.00 0.00 1.03476115721014 +'slagelse_ST_B0_MW' 2 10 0.00 0.28 95.90 0.10 0.00 272.00 25.00 0.810973250811368 +'slagelse_ST_C0_CO1' 1 3 0.00 96.00 0.04 16.00 1.80 0.325972210014942 +'slagelse_ST_C0_CO2' 1 3 0.00 96.00 0.04 16.00 1.80 0.389051247558835 +'soroe_GE_B0_NG' 2 2 0.00 0.69 0.17 0.40 82.00 0.02 0.910988160400231 +'soroe_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.880813465556943 +'soroe_HO_FO6' 4 5 0.00 0.01 0.90 0.75 0.920715875723601 +'soroe_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.933746134624042 +'soroe_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.941745629514801 +'soroe_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.910397220241423 +'soroe_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.88827249564705 +'soroe_ST_E0_CO1' 3 3 0.15 4.26 96.00 0.04 16.00 1.80 0.394688418078519 +'soroe_ST_E0_CO2' 3 3 0.15 2.50 96.00 0.04 16.00 1.80 0.374375862341591 +'soroe_ST_E0_CO3' 3 3 0.15 2.50 96.00 0.04 16.00 1.80 0.403812385571048 +'soroe_ST_E0_CO4' 3 3 0.15 1.47 96.00 0.04 16.00 1.80 0.359087616713138 +'standst_GE_B0_NG' 2 2 0.00 0.77 0.17 0.40 82.00 0.02 0.906871114401447 +*'standst_GT_B0_NG' 2 2 0.00 0.30 0.08 +'standst_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.887576754421319 +'standst_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.874446295062132 +'standst_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.903957012132946 +'standst_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'standst_ST_B0_MW' 2 10 0.00 0.31 95.90 0.10 0.00 272.00 25.00 0.737592929555498 +'stenlille_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.899875364105844 +'stenlille_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.969888627065695 +'sydhavnen_CC_B0_NG' 2 2 0.00 0.63 0.05 5.0e-03 10.00 2.80 0.77359038341798 +'sydhavnen_CC_E0_NG1' 3 2 0.15 1.04 0.05 5.0e-03 14.00 1.50 0.462956542730712 +'sydhavnen_GE_B0_NG' 2 2 0.00 0.66 0.17 0.40 82.00 0.02 0.866608695652174 +'sydhavnen_GE_C0_LO3' 1 6 0.00 0.17 0.40 82.00 0.02 0.42 +'sydhavnen_GT_B0_NG2' 2 2 0.00 0.54 0.01 1.5e-03 7.80 2.50 0.848435445455657 +'sydhavnen_GT_B0_NG4' 2 2 0.00 0.53 0.12 1.5e-03 8.00 5.80 0.865962281856528 +'sydhavnen_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.810118957988255 +'sydhavnen_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.922202149179134 +'sydhavnen_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.850341390906256 +'sydhavnen_SOLE_SOL' 11 16 0.00 0.00 0.00 0.00 0.00 0.00 1 +'sydhavnen_ST_E0_NG1' 3 2 0.15 0.24 96.00 0.04 16.00 1.80 0.320067779899708 +'sydhavnen_ST_E0_NG2' 3 2 0.15 0.33 96.00 0.04 16.00 1.80 0.304958563359101 +'taulov_CC_B0_NG' 2 2 0.00 0.55 0.05 5.0e-03 10.00 2.80 0.828906586057675 +'taulov_GE_B0_NG' 2 2 0.00 0.75 0.17 0.40 82.00 0.02 0.853329120385388 +'taulov_GT_B0_NG' 2 2 0.00 0.55 0.12 1.5e-03 8.00 5.80 0.81158558554439 +'taulov_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.884937826782838 +'taulov_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.873275989252909 +'taulov_HO_NG4' 4 2 0.00 0.01 0.40 0.50 0.9 +'taulov_ST_E0_NG1' 3 2 0.15 0.68 96.00 0.04 16.00 1.80 0.403812385571048 +'taulov_ST_E0_NG3' 3 2 0.15 0.88 96.00 0.04 16.00 1.80 0.455385691020657 +'terkelsboel_CC_B0_NG' 2 2 0.00 0.86 0.05 5.0e-03 10.00 2.80 0.906489256198347 +'terkelsboel_GE_B0_NG' 2 2 0.00 0.68 0.17 0.40 82.00 0.02 0.918289619400934 +'terkelsboel_GT_B0_NG' 2 2 0.00 0.48 0.11 1.44755244755245e-03 7.72 5.59 0.912716297913876 +'terkelsboel_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.904381555293812 +'terkelsboel_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.959062437965399 +'terkelsboel_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.9266115327971 +'terkelsboel_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'terkelsboel_ST_B0_WW' 2 13 0.00 0.10 0.06 2.1e-03 82.00 0.02 0.928366129943049 +'torslunde_GE_B0_NG' 2 2 0.00 0.66 0.17 0.40 82.00 0.02 0.874391865617908 +'torslunde_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.901975404638602 +'torslunde_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.647658137473184 +'torslunde_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.921871805564678 +'torslunde_ST_B0_MW' 2 10 0.00 0.29 95.90 0.10 0.00 272.00 25.00 0.835456765768288 +'ullerslev_GE_B0_NG' 2 2 0.00 0.77 0.17 0.40 82.00 0.02 0.875124284289181 +'ullerslev_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.961044091697121 +'ullerslev_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.812902807309753 +'ullerslev_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'vallensbaek_GE_B0_NG' 2 2 0.00 0.66 0.17 0.40 82.00 0.02 0.880186656055624 +'vallensbaek_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.766045384422719 +'vallensbaek_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.756600478784132 +'vallensbaek_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.969389554240465 +'vallensbaek_HO_WO' 4 12 0.00 0.08 20.00 0.00 1 +'vallensbaek_ST_B0_MW' 2 10 0.00 0.18 95.90 0.10 0.00 272.00 25.00 0.851685559087456 +'varde_GE_B0_NG' 2 2 0.00 0.81 0.17 0.40 82.00 0.02 0.866816009531384 +'varde_GT_B0_NG' 2 2 0.00 0.46 0.08 0.852356118467559 +'varde_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.859188544152745 +'varde_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.870830326684186 +'varde_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.901953451182568 +'varde_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.94292949421061 +'varde_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.972627372284606 +'varde_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'varde_ST_B0_MW' 2 10 0.00 0.22 95.90 0.10 0.00 272.00 25.00 0.956341487675551 +'varde_ST_E0_CO1' 3 3 0.15 0.82 96.00 0.04 16.00 1.80 0.460273015274646 +'VEST_GE_B0_NG' 2 2 0.00 0.80 0.17 0.40 82.00 0.02 0.914713790552023 +'VEST_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.942438719176533 +'VEST_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.918336932388455 +'VEST_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.852676053984195 +'VEST_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.784066687218694 +'VEST_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.966738864668541 +'VEST_ST_B0_ST' 2 11 0.00 0.29 0.13 5.0e-04 82.00 0.02 0.846437569765588 +'viborg_CC_B0_NG' 2 2 0.00 0.88 0.05 5.0e-03 10.00 2.80 0.845908213446733 +'viborg_CC_B0_WW' 2 13 0.00 0.26 0.06 2.1e-03 82.00 0.02 0.895702473473154 +'viborg_GE_B0_NG' 2 2 0.00 0.76 0.17 0.40 82.00 0.02 0.876535248347928 +'viborg_GT_B0_NG' 2 2 0.00 0.41 0.11 1.14179104477612e-03 6.08 4.41 0.861980552614891 +'viborg_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.838726700727277 +'viborg_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.863730019824607 +'viborg_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.959715933658655 +'viborg_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.902032609569251 +'viborg_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.861940615511178 +'viborg_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.982081596977569 +'viborg_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'viborg_ST_B0_MW' 2 10 0.00 0.34 95.90 0.10 0.00 272.00 25.00 0.804908714316671 +'aalborg_CC_B0_NG' 2 2 0.00 1.07 0.05 5.0e-03 10.00 2.80 0.86662752545309 +'aalborg_GE_B0_NG' 2 2 0.00 0.73 0.17 0.40 82.00 0.02 0.918028922807716 +'aalborg_GE_C0_NG' 1 2 0.00 0.17 0.40 82.00 0.02 0.42 +'aalborg_GT_B0_NG' 2 2 0.00 0.47 0.11 1.20870870870871e-03 6.44 4.67 0.778406676813188 +'aalborg_GT_C0_LO3' 1 6 0.00 0.12 1.5e-03 8.00 5.80 0.202625204582651 +'aalborg_HO_CO5' 4 3 0.00 0.01 0.90 0.75 0.920715875723601 +'aalborg_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.901540302267283 +'aalborg_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.856159768532087 +'aalborg_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.942594202988504 +'aalborg_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.893428294582883 +'aalborg_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.991141112788141 +'aalborg_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.927417892176339 +'aalborg_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 +'aalborg_ST_B0_MW' 2 10 0.00 0.29 95.90 0.10 0.00 272.00 25.00 0.843988610233222 +'aalborg_ST_B0_NG' 2 2 0.00 0.05 96.00 0.04 16.00 1.80 0.901097662248667 +'aalborg_ST_E0_CO2' 3 3 0.15 6.78 96.00 0.04 16.00 1.80 0.374987610727755 +'aalborg_ST_E0_CO4' 3 3 0.15 0.90 96.00 0.04 16.00 1.80 0.469748583629186 +; + +GDATA(GGGMR,GDATASET)=GDATA_MR(GGGMR,GDATASET); \ No newline at end of file diff --git a/base/addons/gas/GDATA-MR.inc b/base/addons/gas/GDATA-MR.inc new file mode 100644 index 00000000..00f2cb54 --- /dev/null +++ b/base/addons/gas/GDATA-MR.inc @@ -0,0 +1,2207 @@ +TABLE GDATA_MR(GGG,GDATASET) + GDTYPE GDFUEL GDCV GDCB GDFE GDDESO2 GDNOX GDCH4 GDOMFCOST0 GDOMVCOST0 +'amagerf_HO_LO' 4 6 0 0.922418703225379 0.015 0.9 0.75 +'amagerf_ST_B0_MW' 2 10 0 0.183572273666027 0.803354143896783 95.9 0.109 0 272 25 +'amagerf_ST_E0_CO1' 3 3 0.15 0.715789473684211 0.297480733676603 96 0.04 16 1.8 +'amagerf_ST_E0_CO3' 3 3 0.15 0.755287009063444 0.410525757091008 96 0.04 16 1.8 +'amagerf_ST_E0_ST2' 3 11 0.15 0.529069767441861 0.242618614761152 0 0.04 25 2.7 +'billesboelle_GE_B0_NG' 2 2 0 0.580313775579008 0.960226095664813 0.17 0.4 82 0.025 +'billesboelle_HO_NG' 4 2 0 0.93993015663171 0.015 0.4 0.5 +'brande_GE_B0_NG' 2 2 0 0.75786765649876 0.782299419889603 0.17 0.4 82 0.025 +'brande_HO_LO' 4 6 0 0.880357142857143 0.015 0.9 0.75 +'brande_HO_NG' 4 2 0 0.984957378569543 0.015 0.4 0.5 +'brande_HO_WO' 4 12 0 0.980175686332044 0.08 20 0 +'brande_HYRR_HY' 9 15 0 1 0 0 0 0 0 +'broendby_GE_B0_NG' 2 2 0 0.703317168429701 0.732250194246709 0.17 0.4 82 0.025 +'broendby_GE_C0_LO' 1 6 0 0.371755652295276 0.17 0.4 82 0.025 +'broendby_HO_FO' 4 5 0 0.975232448220041 0.015 0.9 0.75 +'broendby_HO_LO' 4 6 0 0.908757550200865 0.015 0.9 0.75 +'broendby_HO_NG' 4 2 0 0.758863854556707 0.015 0.4 0.5 +'broendby_SOLE_SOL' 11 16 0 1 0 0 0 0 0 +'broendby_ST_E0_CO1' 3 3 0.15 0.755287009063444 0.397470505821311 96 0.04 16 1.8 +'dragoer_GE_B0_NG' 2 2 0 0.715649946625824 0.800444567249047 0.17 0.4 82 0.025 +'dragoer_GE_C0_NG' 1 2 0 0.42 0.17 0.4 82 0.025 +'dragoer_HO_LO' 4 6 0 0.910037011174666 0.015 0.9 0.75 +'dragoer_SOLE_SOL' 11 16 0 1 0 0 0 0 0 +'egtved_CC_B0_MW' 2 10 0 0.621030704698669 0.767652005672387 95.9 0.109 0 272 25 +'egtved_GE_B0_NG' 2 2 0 0.781169843326666 0.845184800539746 0.17 0.4 82 0.025 +'egtved_GE_C0_NG' 1 2 0 0.42 0.17 0.4 82 0.025 +'egtved_HO_LO' 4 6 0 0.865697633847704 0.015 0.9 0.75 +'egtved_HO_NG' 4 2 0 0.936230093952891 0.015 0.4 0.5 +'egtved_HO_WO' 4 12 0 0.970519185803239 0.08 20 0 +'egtved_HYRR_HY' 9 15 0 1 0 0 0 0 0 +'ellidshoej_GE_B0_NG' 2 2 0 0.700330951440733 0.890193302036873 0.17 0.4 82 0.025 +'ellidshoej_GT_B0_NG' 2 2 0 0.496943314709736 0.155991999553513 0.12 0.0015 8 5.8 +'ellidshoej_HO_LO' 4 6 0 0.910560869993016 0.015 0.9 0.75 +'ellidshoej_HO_NG' 4 2 0 1.78122727983196 0.015 0.4 0.5 +'ellidshoej_HO_ST' 4 11 0 0.977433437166049 0.08 20 0 +'froeslev_CC_B0_NG' 2 2 0 0.796058029274548 0.840468367343671 0.05 0.005 10 2.8 +'froeslev_GE_B0_NG' 2 2 0 0.781220661066641 0.782009993328424 0.17 0.4 82 0.025 +'froeslev_HO_CO4' 4 3 0 0.9 0.015 0.9 0.75 +'froeslev_HO_CO5' 4 3 0 0.9 0.015 0.9 0.75 +'froeslev_HO_FO' 4 5 0 0.882708277062975 0.015 0.9 0.75 +'froeslev_HO_LO' 4 6 0 0.790018662784373 0.015 0.9 0.75 +'froeslev_HO_NG' 4 2 0 0.986608710187339 0.015 0.4 0.5 +'froeslev_HO_ST' 4 11 0 0.993103448275862 0.08 20 0 +'froeslev_HO_WO' 4 12 0 0.879020408163265 0.08 20 0 +'froeslev_HYRR_HY' 9 15 0 1 0 0 0 0 0 +'froeslev_ST_C0_CO3' 1 3 0 0.305873457225946 96 0.04 16 1.8 +'froeslev_ST_E0_CO2' 3 3 0.15 7.36470588235294 0.387307257755467 96 0.04 16 1.8 +'haverslev_GE_B0_NG' 2 2 0 0.683482410226591 0.859659143115474 0.17 0.4 82 0.025 +'haverslev_GT_B0_NG' 2 2 0 0.67640739149119 0.909050128836475 0.08 +'haverslev_HO_FO' 4 5 0 0.999881090887909 0.015 0.9 0.75 +'haverslev_HO_LO' 4 6 0 0.876344362181598 0.015 0.9 0.75 +'haverslev_HO_MW' 4 10 0 0.767084047665826 95.9 0.109 0 49 5.1 +'haverslev_HO_NG' 4 2 0 0.95273248145122 0.015 0.4 0.5 +'haverslev_HO_ST' 4 11 0 0.975373822548164 0.08 20 0 +'haverslev_HO_WO' 4 12 0 0.98899022492414 0.08 20 0 +'haverslev_ST_B0_MW' 2 10 0 0.27331072899007 0.976680235400847 95.9 0.109 0 272 25 +'herning_CC_B0_NG' 2 2 0 1.24176724701893 0.834525348501305 0.05 0.005 10 2.8 +'herning_GE_B0_LO' 2 6 0 0.66046511627907 0.967347374104886 0.17 0.4 82 0.025 +'herning_GE_B0_NG' 2 2 0 0.737402863683826 0.733785020153837 0.17 0.4 82.0000000000001 0.025 +'herning_GE_B0_WO' 2 12 0 0.3546 0.226944 0.069 0.0021 82 0.025 +'herning_GE_C0_LO1' 1 6 0 0.42 0.17 0.4 82 0.025 +'herning_GE_C0_LO2' 1 6 0 0.42 0.17 0.4 82 0.025 +'herning_GE_C0_LO3' 1 6 0 0.42 0.17 0.4 82 0.025 +'herning_GE_C0_LO4' 1 6 0 0.42 0.17 0.4 82 0.025 +'herning_GT_B0_NG' 2 2 0 0.524083088750361 0.45606527524741 9.03703703703704E-02 3.88888888888889E-04 2.07407407407407 1.5037037037037 +'herning_GT_C0_LO5' 1 6 0 0.191373626373626 0.12 0.0015 8 5.8 +'herning_HO_CO6' 4 3 0 0.90700864029122 0.015 0.9 0.75 +'herning_HO_FO' 4 5 0 0.887583469560023 0.015 0.9 0.75 +'herning_HO_LO' 4 6 0 0.914672558513859 0.015 0.9 0.75 +'herning_HO_LO2' 4 6 0 0.926286937623666 0.015 0.9 0.75 +'herning_HO_MW' 4 10 0 0.842653760501089 95.9 0.109 0 49 5.1 +'herning_HO_NG' 4 2 0 0.938497512123432 0.015 0.4 0.5 +'herning_HO_NG2' 4 2 0 0.9 0.015 0.4 0.5 +'herning_HO_ST' 4 11 0 0.922358685020805 0.08 20 0 +'herning_HO_WO' 4 12 0 0.938961539037037 0.08 20 0 +'herning_HYRR_HY' 9 15 0 1 0 0 0 0 0 +'herning_ST_B0_CO' 2 3 0 0.226220727929744 0.883618960634134 96 0.04 16 1.8 +'herning_ST_B0_FO' 2 5 0 7.24860534124629E-02 0.712520781306027 96 0.04 16 1.8 +'herning_ST_B0_MW' 2 10 0 0.179989733916807 0.878518459120176 95.9 0.109 0 272 25 +'herning_ST_B0_WO' 2 12 0 6.84625871030464E-02 0.712520781306027 0.069 0.0021 82 0.025 +'herning_ST_E0_CO1' 3 3 0.15 0.461318051575931 0.307959473780625 96 0.04 16 1.8 +'herning_ST_E0_CO3' 3 3 0.15 0.769230769230769 0.366574133455059 96 0.04 16 1.8 +'herning_ST_E0_CO4' 3 3 0.15 0.769230769230769 0.388344440059035 96 0.04 16 1.8 +'herning_ST_E0_WO1' 3 12 0.15 0.511494252873563 0.364824761940699 0 0.04 25 2.7 +'hoejby_GE_B0_NG' 2 2 0 0.830178503379087 0.751149300167299 0.17 0.4 82 0.025 +'hoejby_GT_B0_NG' 2 2 0 0.555884620733494 0.235925359322043 0.109859154929577 1.11971830985916E-03 5.97183098591549 4.32957746478873 +'hoejby_HO_FO' 4 5 0 0.927061048140357 0.015 0.9 0.75 +'hoejby_HO_LO' 4 6 0 0.924166868369476 0.015 0.9 0.75 +'hoejby_HO_NG' 4 2 0 0.93297459708766 0.015 0.4 0.5 +'hoejby_HO_ST' 4 11 0 0.939378614229185 0.08 20 0 +'hoejby_HO_WO' 4 12 0 0.950824605998983 0.08 20 0 +'hoejby_HYRR_HY' 9 15 0 1 0 0 0 0 0 +'hoejby_ST_B0_MW' 2 10 0 0.29226694632811 1.02632332444596 95.9 0.109 0 272 25 +'hoejby_ST_B0_MW1' 2 10 0 0.336057651547266 0.870128724435103 95.9 0.109 0 272 25 +'hoejby_ST_E0_NG3' 3 2 0.15 0.825153374233129 0.341178147885138 96 0.04 16 1.8 +'hoejby_ST_E0_CO2' 3 3 0.15 0.904444444444445 0.419018303924098 96 0.04 16 1.8 +'karlslunde_GE_B0_NG' 2 2 0 0.716918859631733 0.788218104058496 0.17 0.4 82 0.025 +'karlslunde_HO_LO' 4 6 0 0.851535081497559 0.015 0.9 0.75 +'karlslunde_HO_NG' 4 2 0 1.00764081624194 0.015 0.4 0.5 +'karup_GE_B0_NG' 2 2 0 0.754275648198038 0.889716241523669 0.17 0.4 82 0.025 +'karup_HO_LO' 4 6 0 0.90700864029122 0.015 0.9 0.75 +'karup_HO_NG' 4 2 0 0.996013175042951 0.015 0.4 0.5 +'karup_HO_WO' 4 12 0 1.01726256220603 0.08 20 0 +'karup_HYRR_HY' 9 15 0 1 0 0 0 0 0 +'koege_GE_B0_NG' 2 2 0 0.792942297645533 0.684684154654352 0.17 0.4 82 0.025 +'koege_HO_FO' 4 5 0 0.94353060491935 0.015 0.9 0.75 +'koege_HO_LO' 4 6 0 0.807434383333148 0.015 0.9 0.75 +'koege_HO_NG' 4 2 0 0.910517368954099 0.015 0.4 0.5 +'koege_HO_ST' 4 11 0 0.853501477832512 0.08 20 0 +'koege_ST_B0_NG' 2 2 0 0.160511111111111 0.712520781306027 96 0.04 16 1.8 +'koege_ST_B0_WO' 2 12 0 1.10218081815537 0.419220511634931 0.069 0.0021 82 0.025 +'koelbjerg_GE_B0_NG' 2 2 0 0.766890799153077 0.757862740924676 0.17 0.4 82 0.025 +'koelbjerg_HO_CO' 4 3 0 0.931297064049412 0.015 0.9 0.75 +'koelbjerg_HO_FO' 4 5 0 0.898986105895607 0.015 0.9 0.75 +'koelbjerg_HO_LO' 4 6 0 0.912738335301215 0.015 0.9 0.75 +'koelbjerg_HO_NG' 4 2 0 0.928641225850213 0.015 0.4 0.5 +'koelbjerg_HO_WO' 4 12 0 0.923351047202251 0.08 20 0 +'koelbjerg_HYRR_HY' 9 15 0 1 0 0 0 0 0 +'koelbjerg_ST_B0_CO' 2 3 0 0.145894226994217 0.712520781306027 96 0.04 16 1.8 +'koelbjerg_ST_B0_FO' 2 5 0 3.65184999627105E-02 0.712520781306027 96 0.04 16 1.8 +'koelbjerg_ST_B0_NG' 2 2 0 0.105687678140379 1.24676697306392E-04 96 0.04 16 1.8 +'koelbjerg_ST_B0_WO' 2 12 0 0.355218879123064 0.81571704009042 0.069 0.0021 82 0.025 +'liselskaer_CC_B0_MW' 2 10 0 0.373172244151216 0.695820086074372 95.9 0.109 0 272 25 +'liselskaer_GE_B0_NG' 2 2 0 0.764755244602224 0.845448620569991 0.17 0.4 82 0.025 +'liselskaer_HO_NG' 4 2 0 1.02550016145121 0.015 0.4 0.5 +'liselskaer_HYRR_HY' 9 15 0 1 0 0 0 0 0 +'liselskaer_ST_B0_NG' 2 2 0 0.854012041884817 0.405302406010856 96 0.04 16 1.8 +'litorup_GE_B0_NG' 2 2 0 0.701791174297813 0.895295257066276 0.17 0.4 82 0.025 +'litorup_HO_LO' 4 6 0 0.922509225092251 0.015 0.9 0.75 +'litorup_HO_NG' 4 2 0 0.761854109969479 0.015 0.4 0.5 +'lynge_CC_B0_NG' 2 2 0 0.96386098930925 0.878171836725561 0.05 0.005 10 2.8 +'lynge_GE_B0_NG' 2 2 0 0.687196734942597 0.81582676813945 0.17 0.4 82 0.025 +'lynge_GE_B0_WO' 2 12 0 0.325396825396825 6.64864864864865E-02 0.069 0.0021 82 0.025 +'lynge_GT_B0_NG' 2 2 0 1.05243530255202 0.838220451276975 0.058586609989373 1.49840595111583E-03 7.87460148777896 3.86503719447396 +'lynge_HO_FO' 4 5 0 0.810783555749773 0.015 0.9 0.75 +'lynge_HO_MW' 4 10 0 0.963350441387805 95.9 0.109 0 49 5.1 +'lynge_HO_NG' 4 2 0 0.888766098931617 0.015 0.4 0.5 +'lynge_HO_WO' 4 12 0 1.10024310823463 0.08 20 0 +'lynge_HYRR_HY' 9 15 0 1 0 0 0 0 0 +'lynge_ST_B0_MW' 2 10 0 0.294855014694859 0.764830118116887 95.9 0.109 0 272 25 +'middelfart_GE_B0_NG' 2 2 0 0.745728859405476 0.856468076069397 0.17 0.4 82 0.025 +'middelfart_HO_LO' 4 6 0 1.04616397691005 0.015 0.9 0.75 +'middelfart_HO_MW' 4 10 0 0.806036146692402 95.9 0.109 0 49 5.1 +'middelfart_HO_NG' 4 2 0 1.05111968654446 0.015 0.4 0.5 +'middelfart_HYRR_HY' 9 15 0 1 0 0 0 0 0 +'maaloev_GE_B0_NG' 2 2 0 0.766980159752875 0.860489610229792 0.17 0.4 82 0.025 +'maaloev_GE_C0_LO4' 1 6 0 0.42 0.17 0.4 82 0.025 +'maaloev_GT_B0_NG' 2 2 0 0.599776567518503 0.28538970992445 0.08 +'maaloev_GT_C0_LO6' 1 6 0 0.172649658528927 0.015 0.0015 7.8 2.5 +'maaloev_GT_C0_NG5' 1 2 0 0.42 0.015 0.0015 7.8 2.5 +'maaloev_GT_C0_NG7' 1 2 0 0.3 0.08 +'maaloev_HO_FO8' 4 5 0 0.90700864029122 0.015 0.9 0.75 +'maaloev_HO_FO9' 4 5 0 0.90700864029122 0.015 0.9 0.75 +'maaloev_HO_LO' 4 6 0 0.886904746733735 0.015 0.9 0.75 +'maaloev_HO_MW' 4 10 0 0.819028928253104 95.9 0.109 0 49 5.1 +'maaloev_HO_NG' 4 2 0 0.931038407660837 0.015 0.4 0.5 +'maaloev_HO_WO' 4 12 0 0.887549543788595 0.08 20 0 +'maaloev_ST_C0_FO2' 1 5 0 0.178975414924203 96 0.04 16 1.8 +'maaloev_ST_C0_FO3' 1 5 0 0.241734148696492 96 0.04 16 1.8 +'noerskov_GE_B0_NG' 2 2 0 0.815192871505796 0.873852672118979 0.17 0.4 82 0.025 +'noerskov_GT_B0_NG' 2 2 0 0.219612840584987 0.927751180623156 0.08 +'noerskov_HO_FO' 4 5 0 0.826646251626881 0.015 0.9 0.75 +'noerskov_HO_LO' 4 6 0 0.690475004093022 0.015 0.9 0.75 +'noerskov_HO_NG' 4 2 0 0.921092092030374 0.015 0.4 0.5 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/base/addons/gas/GGGMRset.inc b/base/addons/gas/GGGMRset.inc new file mode 100644 index 00000000..9d44e73b --- /dev/null +++ b/base/addons/gas/GGGMRset.inc @@ -0,0 +1,324 @@ +'amagerf_HO_LO' +'amagerf_ST_B0_MW' +'amagerf_ST_E0_CO1' +'amagerf_ST_E0_CO3' +'amagerf_ST_E0_ST2' +'billesboelle_GE_B0_NG' +'billesboelle_HO_NG' +'brande_GE_B0_NG' +'brande_HO_LO' +'brande_HO_NG' +'brande_HO_WO' +'brande_HYRR_HY' +'broendby_GE_B0_NG' +'broendby_GE_C0_LO' +'broendby_HO_FO' +'broendby_HO_LO' +'broendby_HO_NG' +'broendby_SOLE_SOL' +'broendby_ST_E0_CO1' +'dragoer_GE_B0_NG' +'dragoer_GE_C0_NG' +'dragoer_HO_LO' +'dragoer_SOLE_SOL' +'egtved_CC_B0_MW' +'egtved_GE_B0_NG' +'egtved_GE_C0_NG' +'egtved_HO_LO' +'egtved_HO_NG' +'egtved_HO_WO' +'egtved_HYRR_HY' +'ellidshoej_GE_B0_NG' +'ellidshoej_GT_B0_NG' +'ellidshoej_HO_LO' +'ellidshoej_HO_NG' +'ellidshoej_HO_ST' +'froeslev_CC_B0_NG' +'froeslev_GE_B0_NG' +'froeslev_HO_CO4' +'froeslev_HO_CO5' +'froeslev_HO_FO' +'froeslev_HO_LO' +'froeslev_HO_NG' +'froeslev_HO_ST' +'froeslev_HO_WO' +'froeslev_HYRR_HY' +'froeslev_ST_C0_CO3' +'froeslev_ST_E0_CO2' +'haverslev_GE_B0_NG' +'haverslev_GT_B0_NG' +'haverslev_HO_FO' +'haverslev_HO_LO' +'haverslev_HO_MW' +'haverslev_HO_NG' +'haverslev_HO_ST' +'haverslev_HO_WO' +'haverslev_ST_B0_MW' +'herning_CC_B0_NG' +'herning_GE_B0_LO' +'herning_GE_B0_NG' +'herning_GE_B0_WO' +'herning_GE_C0_LO1' +'herning_GE_C0_LO2' +'herning_GE_C0_LO3' +'herning_GE_C0_LO4' +'herning_GT_B0_NG' +'herning_GT_C0_LO5' +'herning_HO_CO6' +'herning_HO_FO' +'herning_HO_LO' +'herning_HO_LO2' +'herning_HO_MW' +'herning_HO_NG' +'herning_HO_NG2' +'herning_HO_ST' +'herning_HO_WO' +'herning_HYRR_HY' +'herning_ST_B0_CO' +'herning_ST_B0_FO' +'herning_ST_B0_MW' +'herning_ST_B0_WO' +'herning_ST_E0_CO1' +'herning_ST_E0_CO3' +'herning_ST_E0_CO4' +'herning_ST_E0_WO1' +'hoejby_GE_B0_NG' +'hoejby_GT_B0_NG' +'hoejby_HO_FO' +'hoejby_HO_LO' +'hoejby_HO_NG' +'hoejby_HO_ST' +'hoejby_HO_WO' +'hoejby_HYRR_HY' +'hoejby_ST_B0_MW' +'hoejby_ST_B0_MW1' +'hoejby_ST_E0_NG3' +'hoejby_ST_E0_CO2' +'karlslunde_GE_B0_NG' +'karlslunde_HO_LO' +'karlslunde_HO_NG' +'karup_GE_B0_NG' +'karup_HO_LO' +'karup_HO_NG' +'karup_HO_WO' +'karup_HYRR_HY' +'koege_GE_B0_NG' +'koege_HO_FO' +'koege_HO_LO' +'koege_HO_NG' +'koege_HO_ST' +'koege_ST_B0_NG' +'koege_ST_B0_WO' +'koelbjerg_GE_B0_NG' +'koelbjerg_HO_CO' +'koelbjerg_HO_FO' +'koelbjerg_HO_LO' +'koelbjerg_HO_NG' +'koelbjerg_HO_WO' +'koelbjerg_HYRR_HY' +'koelbjerg_ST_B0_CO' +'koelbjerg_ST_B0_FO' +'koelbjerg_ST_B0_NG' +'koelbjerg_ST_B0_WO' +'liselskaer_CC_B0_MW' +'liselskaer_GE_B0_NG' +'liselskaer_HO_NG' +'liselskaer_HYRR_HY' +'liselskaer_ST_B0_NG' +'litorup_GE_B0_NG' +'litorup_HO_LO' +'litorup_HO_NG' +'lynge_CC_B0_NG' +'lynge_GE_B0_NG' +'lynge_GE_B0_WO' +'lynge_GT_B0_NG' +'lynge_HO_FO' +'lynge_HO_MW' +'lynge_HO_NG' +'lynge_HO_WO' +'lynge_HYRR_HY' +'lynge_ST_B0_MW' +'middelfart_GE_B0_NG' +'middelfart_HO_LO' +'middelfart_HO_MW' +'middelfart_HO_NG' +'middelfart_HYRR_HY' +'maaloev_GE_B0_NG' +'maaloev_GE_C0_LO4' +'maaloev_GT_B0_NG' +'maaloev_GT_C0_LO6' +'maaloev_GT_C0_NG5' +'maaloev_GT_C0_NG7' +'maaloev_HO_FO8' +'maaloev_HO_FO9' +'maaloev_HO_LO' +'maaloev_HO_MW' +'maaloev_HO_NG' +'maaloev_HO_WO' +'maaloev_ST_C0_FO2' +'maaloev_ST_C0_FO3' +'noerskov_GE_B0_NG' +'noerskov_GT_B0_NG' +'noerskov_HO_FO' +'noerskov_HO_LO' +'noerskov_HO_NG' +'noerskov_HO_WO' +'noerskov_ST_B0_CO' +'nyborg_CC_B0_MW' +'nyborg_GE_B0_NG' +*'nyborg_GT_B0_NG' +'nyborg_HO_NG' +'nyborg_HO_WO' +'nyborg_HYRR_HY' +'nybro_GE_B0_NG' +'nybro_GT_B0_NG' +'nybro_HO_LO' +'nybro_HO_NG' +'nybro_HO_WO' +'nybro_HYRR_HY' +'OEST_GE_B0_LO' +'OEST_GE_B0_NG' +'OEST_GE_C0_LO2' +'OEST_HO_CO' +'OEST_HO_FO' +'OEST_HO_LO' +'OEST_HO_MW' +'OEST_HO_ST' +'OEST_HO_WO' +'OEST_ST_B0_CO4' +'OEST_ST_B0_FO' +'OEST_ST_B0_FO1' +'OEST_ST_B0_MW' +'OEST_ST_B0_ST' +'OEST_ST_C0_FO3' +'pottehuse_GE_B0_NG' +'pottehuse_HO_LO' +'pottehuse_HO_MW' +'pottehuse_HO_NG' +'pottehuse_HO_WO' +'pottehuse_HYRR_HY' +'pottehuse_SOLE_SOL' +'pottehuse_ST_B0_MW' +'ringsted_CC_B0_MW' +'ringsted_GE_B0_NG' +'ringsted_GT_C0_LO2' +'ringsted_HO_MW' +'ringsted_HO_NG' +'ringsted_HO_ST' +'ringsted_ST_B0_ST' +'ringsted_ST_B0_ST3' +'slagelse_GE_B0_NG' +'slagelse_GE_C0_NG' +'slagelse_GT_B0_NG' +'slagelse_HO_FO3' +'slagelse_HO_NG' +'slagelse_HO_ST' +'slagelse_HO_WO' +'slagelse_ST_B0_MW' +'slagelse_ST_C0_CO1' +'slagelse_ST_C0_CO2' +'soroe_GE_B0_NG' +'soroe_HO_FO' +'soroe_HO_FO6' +'soroe_HO_LO' +'soroe_HO_NG' +'soroe_HO_ST' +'soroe_HO_WO' +'soroe_ST_E0_CO1' +'soroe_ST_E0_CO2' +'soroe_ST_E0_CO3' +'soroe_ST_E0_CO4' +'standst_GE_B0_NG' +*'standst_GT_B0_NG' +'standst_HO_LO' +'standst_HO_NG' +'standst_HO_WO' +'standst_HYRR_HY' +'standst_ST_B0_MW' +'stenlille_HO_LO' +'stenlille_HO_ST' +'sydhavnen_CC_B0_NG' +'sydhavnen_CC_E0_NG1' +'sydhavnen_GE_B0_NG' +'sydhavnen_GE_C0_LO3' +'sydhavnen_GT_B0_NG2' +'sydhavnen_GT_B0_NG4' +'sydhavnen_HO_FO' +'sydhavnen_HO_LO' +'sydhavnen_HO_NG' +'sydhavnen_SOLE_SOL' +'sydhavnen_ST_E0_NG1' +'sydhavnen_ST_E0_NG2' +'taulov_CC_B0_NG' +'taulov_GE_B0_NG' +'taulov_GT_B0_NG' +'taulov_HO_LO' +'taulov_HO_NG' +'taulov_HO_NG4' +'taulov_ST_E0_NG1' +'taulov_ST_E0_NG3' +'terkelsboel_CC_B0_NG' +'terkelsboel_GE_B0_NG' +'terkelsboel_GT_B0_NG' +'terkelsboel_HO_LO' +'terkelsboel_HO_NG' +'terkelsboel_HO_WO' +'terkelsboel_HYRR_HY' +'terkelsboel_ST_B0_WO' +'torslunde_GE_B0_NG' +'torslunde_HO_LO' +'torslunde_HO_MW' +'torslunde_HO_NG' +'torslunde_ST_B0_MW' +'ullerslev_GE_B0_NG' +'ullerslev_HO_LO' +'ullerslev_HO_NG' +'ullerslev_HYRR_HY' +'vallensbaek_GE_B0_NG' +'vallensbaek_HO_LO' +'vallensbaek_HO_MW' +'vallensbaek_HO_NG' +'vallensbaek_HO_WO' +'vallensbaek_ST_B0_MW' +'varde_GE_B0_NG' +'varde_GT_B0_NG' +'varde_HO_FO' +'varde_HO_LO' +'varde_HO_NG' +'varde_HO_WO' +'varde_HYRR_HY' +'varde_ST_B0_MW' +'varde_ST_E0_CO1' +'VEST_GE_B0_NG' +'VEST_HO_FO' +'VEST_HO_LO' +'VEST_HO_ST' +'VEST_HO_WO' +'VEST_ST_B0_ST' +'viborg_CC_B0_NG' +'viborg_CC_B0_WO' +'viborg_GE_B0_NG' +'viborg_GT_B0_NG' +'viborg_HO_FO' +'viborg_HO_LO' +'viborg_HO_NG' +'viborg_HO_ST' +'viborg_HO_WO' +'viborg_HYRR_HY' +'viborg_ST_B0_MW' +'aalborg_CC_B0_NG' +'aalborg_GE_B0_NG' +'aalborg_GE_C0_NG' +'aalborg_GT_B0_NG' +'aalborg_GT_C0_LO3' +'aalborg_HO_CO5' +'aalborg_HO_LO' +'aalborg_HO_MW' +'aalborg_HO_NG' +'aalborg_HO_ST' +'aalborg_HO_WO' +'aalborg_HYRR_HY' +'aalborg_ST_B0_MW' +'aalborg_ST_B0_NG' +'aalborg_ST_E0_CO2' +'aalborg_ST_E0_CO4' \ No newline at end of file diff --git a/base/addons/gas/GKFX-MR.inc b/base/addons/gas/GKFX-MR.inc new file mode 100644 index 00000000..e55bb02c --- /dev/null +++ b/base/addons/gas/GKFX-MR.inc @@ -0,0 +1,452 @@ + +* All WW canged to WO + +PARAMETER GKFXMR(AAA,GGG)/ +* DK_W_AALBORG +DK_W_AALBORG.DK_W_AALBORG_G-HSTORE 3500 +DK_W_AALBORG.DK_W_AALBORG_HO-B8-FO 800 +DK_W_AALBORG.DK_W_Aalborg_ST-E7-CO 285 +DK_W_AALBORG.DK_W_Aalborg_ST-E9-Cosn 372 + +DK_W_Rural.DK_W_Rural_G-HSTORE 5000 +DK_W_Rural.DK_W_Rural_HO-B8-FO 100 +DK_W_Rural.DK_W_Rural_ST-B8-CO 54.8000001907349 +*DK_W_Rural.DK_W_Rural_ST-B8-NG 44.5 +DK_W_Rural.DK_W_Rural_ST-B9-MW 110 + +*DK_E_Rural.DK_E_Rural_WI 618 + +* TEST! +DK_W_Rural.DK_W_Urban_ST-E9-Cosn 980 + +* DK_E_RURAL +DK_E_Rural.DK_E_Rural_ST-B8-CO 37 +DK_E_Rural.DK_E_Rural_ST-E7-CO 415 +DK_E_Rural.DK_E_Rural_ST-B8-FO 45 +DK_E_Rural.DK_E_Rural_HO-B8-FO 30 + +* Kyndby +DK_E_Rural.DK_E_Rural_ST-C7-FO 550 +DK_E_Rural.DK_E_Rural_GT-C8-LO 196 + +* Asnæs +DK_E_Rural.DK_E_Rural_ST-C8-COs 270 +DK_E_Rural.DK_E_Rural_ST-E8-ORs 640 +* Stigsnæs +DK_E_Rural.DK_E_Rural_ST-C7-CO 143 + +*slagelse.'slagelse_ST_C0_CO1' 143.00 +*slagelse.'slagelse_ST_C0_CO2' 266.00 + +*soroe.'soroe_ST_E0_CO1' 640.00 +*soroe.'soroe_ST_E0_CO2' 270.00 +* Ikke i drift. Ingen produktion i 2004 +* *soroe.'soroe_ST_E0_CO3' 270.00 +*soroe.'soroe_ST_E0_CO4' 147.00 + +* ---- DK_E_Urban +DK_E_Urban.DK_E_Rural_ST-E8-ST 70 + +*DK_E_Urban.DK_E_Urban_CC-B8-NG 155 +*DK_E_Urban.DK_E_Urban_CC-B8-NG 0 + +DK_E_Urban.DK_E_Urban_G-HSTORE=10000 +DK_E_Urban.DK_E_Urban_HO-B8-FO=1500 + +*DK_E_Urban.DK_E_Urban_ST-B8-NG=229 +*DK_E_Urban.DK_E_Urban_ST-B8-NG 154 + +DK_E_Urban.DK_E_Urban_ST-B9-ST=35 +DK_E_Urban.DK_E_Urban_ST-E0-NGsn=267.5 +DK_E_Urban.DK_E_Urban_ST-E0-Wosn=267.5 +DK_E_Urban.DK_E_Urban_ST-E9-Cosn=500 + +* Amager værket er omfattet af DK_E_Urban +*amagerf.'amagerf_HO_LO' 102.00 +*amagerf.'amagerf_ST_B0_MW' 28.50 +* Lukket: +** amagerf.'amagerf_ST_E0_CO1' 136.00 +*amagerf.'amagerf_ST_E0_CO3' 250.00 +*amagerf.'amagerf_ST_E0_ST2' 91.00 + +billesboelle.'billesboelle_GE_B0_NG' 1.11 +billesboelle.'billesboelle_HO_NG' 3.50 +brande.'brande_GE_B0_NG' 6.27 +brande.'brande_HO_LO' 1.00 +brande.'brande_HO_NG' 5.70 +brande.'brande_HO_WO' 1.20 +brande.'brande_HYRR_HY' 0.25 +* Nogle af disse er måske på Avedøre... men det er tvivlsomt +broendby.'broendby_GE_B0_NG' 6.20 +broendby.'broendby_GE_C0_LO' 1.68 +broendby.'broendby_HO_FO' 90.00 +broendby.'broendby_HO_LO' 227.60 +broendby.'broendby_HO_NG' 111.80 +broendby.'broendby_SOLE_SOL' 0.01 +* Avedoere er omfattet af DK_E_Urban +* avedoere.'broendby_ST_E0_CO1' 250.00 +* Avedøre blok 2 længere nede. +* avedoere.DK_E_Urban_ST-E0-NGsn 380 +* avedoere.DK_E_Urban_ST-E0-WOsn 190 +dragoer.'dragoer_GE_B0_NG' 1.44 +dragoer.'dragoer_GE_C0_NG' 0.20 +dragoer.'dragoer_HO_LO' 45.00 +dragoer.'dragoer_SOLE_SOL' 0.02 +egtved.'egtved_CC_B0_MW' 33.00 +egtved.'egtved_GE_B0_NG' 46.42 +egtved.'egtved_GE_C0_NG' 0.40 +egtved.'egtved_HO_LO' 37.05 +egtved.'egtved_HO_NG' 342.36 +egtved.'egtved_HO_WO' 12.30 +egtved.'egtved_HYRR_HY' 1.19 +ellidshoej.'ellidshoej_GE_B0_NG' 19.25 +ellidshoej.'ellidshoej_GT_B0_NG' 5.20 +ellidshoej.'ellidshoej_HO_LO' 28.30 +ellidshoej.'ellidshoej_HO_NG' 34.25 +ellidshoej.'ellidshoej_HO_ST' 1.60 +froeslev.'froeslev_CC_B0_NG' 58.00 +froeslev.'froeslev_GE_B0_NG' 23.42 +*froeslev.'froeslev_HO_CO4' 0.00 +*froeslev.'froeslev_HO_CO5' 0.00 +froeslev.'froeslev_HO_FO' 115.20 +froeslev.'froeslev_HO_LO' 26.00 +froeslev.'froeslev_HO_NG' 153.97 +froeslev.'froeslev_HO_ST' 3.15 +froeslev.'froeslev_HO_WO' 1.75 +froeslev.'froeslev_HYRR_HY' 7.0e-03 +* Findes i DK_W_Rural +* Ikke i drift: +** froeslev.'froeslev_ST_C0_CO3' 327.00 +*froeslev.'froeslev_ST_E0_CO2' 626.00 +haverslev.'haverslev_GE_B0_NG' 41.25 +haverslev.'haverslev_GT_B0_NG' 0.92 +haverslev.'haverslev_HO_FO' 19.00 +haverslev.'haverslev_HO_LO' 40.00 +haverslev.'haverslev_HO_MW' 24.10 +haverslev.'haverslev_HO_NG' 72.60 +haverslev.'haverslev_HO_ST' 11.15 +haverslev.'haverslev_HO_WO' 38.60 +haverslev.'haverslev_ST_B0_MW' 2.89 +herning.'herning_CC_B0_NG' 107.00 +herning.'herning_GE_B0_LO' 0.94 +herning.'herning_GE_B0_NG' 92.65 +herning.'herning_GE_B0_WO' 0.05 +* Del af DK_W_Rural +*herning.'herning_GE_C0_LO1' 0.21 +*herning.'herning_GE_C0_LO2' 0.33 +*herning.'herning_GE_C0_LO3' 0.27 +*herning.'herning_GE_C0_LO4' 0.38 +herning.'herning_GT_B0_NG' 24.30 +*herning.'herning_GT_C0_LO5' 12.00 +*herning.'herning_HO_CO6' 0.00 +herning.'herning_HO_FO' 221.85 +*herning.'herning_HO_LO' 842.20 +*herning.'herning_HO_LO2' 69.60 +herning.'herning_HO_MW' 46.10 +herning.'herning_HO_NG' 436.00 +* No information: +* herning.'herning_HO_NG2' 0.00 +herning.'herning_HO_ST' 74.00 +herning.'herning_HO_WO' 144.64 +herning.'herning_HYRR_HY' 0.51 +*herning.'herning_ST_B0_CO' 19.60 +*herning.'herning_ST_B0_FO' 5.00 +*herning.'herning_ST_B0_MW' 27.15 +herning.'herning_ST_B0_WO' 5.00 +*herning.'herning_ST_E0_CO1' 48.30 +* Findes i DK_W_AAHURS: +*herning.'herning_ST_E0_CO3' 350.00 +*herning.'herning_ST_E0_CO4' 350.00 +*herning.'herning_ST_E0_WO1' 89.00 +* DK_W_AARHUS +DK_W_AARHUS.DK_W_AARHUS_G-HSTORE 3500 +DK_W_AARHUS.DK_W_AARHUS_HO-B8-FO 600 +DK_W_AARHUS.DK_W_Aarhus_ST-B9-MW 60 +DK_W_AARHUS.DK_W_Aarhus_ST-E8-Cos 665 +DK_W_AARHUS.DK_W_Aarhus_ST-E8-ST 35 + + + +hoejby.'hoejby_GE_B0_NG' 58.15 +hoejby.'hoejby_GT_B0_NG' 7.10 +hoejby.'hoejby_HO_FO' 159.90 +hoejby.'hoejby_HO_LO' 223.59 +hoejby.'hoejby_HO_NG' 134.05 +hoejby.'hoejby_HO_ST' 3.78 +hoejby.'hoejby_HO_WO' 187.51 +hoejby.'hoejby_HYRR_HY' 6.0e-03 +hoejby.'hoejby_ST_B0_MW' 4.50 +* Fjernet da de eksisterer i DK_W_ODENSE: +*hoejby.'hoejby_ST_B0_MW1' 24.00 +*hoejby.'hoejby_ST_E0_NG3' 269.00 +*hoejby.'hoejby_ST_E0_CO2' 407.00 +* DK_W_ODENSE +DK_W_ODENSE.DK_W_ODENSE_G-HSTORE 3500 +DK_W_ODENSE.DK_W_ODENSE_HO-B8-FO 600 +DK_W_ODENSE.DK_W_Odense_ST-B9-MW 24 +DK_W_ODENSE.DK_W_Odense_ST-E7-NG 217 +DK_W_ODENSE.DK_W_Odense_ST-E9-Cosn 339 +karlslunde.'karlslunde_GE_B0_NG' 4.22 +karlslunde.'karlslunde_HO_LO' 75.50 +karlslunde.'karlslunde_HO_NG' 11.00 +karup.'karup_GE_B0_NG' 30.23 +karup.'karup_HO_LO' 0.87 +karup.'karup_HO_NG' 120.42 +karup.'karup_HO_WO' 10.00 +karup.'karup_HYRR_HY' 3.92 +koege.'koege_GE_B0_NG' 14.05 +koege.'koege_HO_FO' 10.00 +koege.'koege_HO_LO' 12.00 +koege.'koege_HO_NG' 6.14 +koege.'koege_HO_ST' 4.00 +koege.'koege_ST_B0_NG' 5.80 +koege.'koege_ST_B0_WO' 26.00 +koelbjerg.'koelbjerg_GE_B0_NG' 59.00 +koelbjerg.'koelbjerg_HO_CO' 10.00 +koelbjerg.'koelbjerg_HO_FO' 3.60 +koelbjerg.'koelbjerg_HO_LO' 62.47 +koelbjerg.'koelbjerg_HO_NG' 77.85 +koelbjerg.'koelbjerg_HO_WO' 24.90 +koelbjerg.'koelbjerg_HYRR_HY' 0.11 +koelbjerg.'koelbjerg_ST_B0_CO' 5.30 +koelbjerg.'koelbjerg_ST_B0_FO' 4.00 +koelbjerg.'koelbjerg_ST_B0_NG' 11.00 +koelbjerg.'koelbjerg_ST_B0_WO' 4.68 +liselskaer.'liselskaer_CC_B0_MW' 5.40 +liselskaer.'liselskaer_GE_B0_NG' 40.26 +liselskaer.'liselskaer_HO_NG' 107.32 +liselskaer.'liselskaer_HYRR_HY' 0.40 +liselskaer.'liselskaer_ST_B0_NG' 3.20 +litorup.'litorup_GE_B0_NG' 8.59 +litorup.'litorup_HO_LO' 7.00 +litorup.'litorup_HO_NG' 12.90 +lynge.'lynge_CC_B0_NG' 77.00 +lynge.'lynge_GE_B0_NG' 33.69 +lynge.'lynge_GE_B0_WO' 0.09 +lynge.'lynge_GT_B0_NG' 94.10 +lynge.'lynge_HO_FO' 3.15 +lynge.'lynge_HO_MW' 21.00 +lynge.'lynge_HO_NG' 221.38 +lynge.'lynge_HO_WO' 15.00 +lynge.'lynge_HYRR_HY' 0.02 +lynge.'lynge_ST_B0_MW' 7.40 +middelfart.'middelfart_GE_B0_NG' 6.18 +middelfart.'middelfart_HO_LO' 19.30 +middelfart.'middelfart_HO_MW' 7.00 +middelfart.'middelfart_HO_NG' 16.34 +middelfart.'middelfart_HYRR_HY' 0.01 +maaloev.'maaloev_GE_B0_NG' 51.88 +*maaloev.'maaloev_GE_C0_LO4' 20.00 +maaloev.'maaloev_GT_B0_NG' 4.50 +*maaloev.'maaloev_GT_C0_LO6' 63.00 +*maaloev.'maaloev_GT_C0_NG5' 63.00 +*maaloev.'maaloev_GT_C0_NG7' 2.00 +*maaloev.'maaloev_HO_FO8' 50.00 +*maaloev.'maaloev_HO_FO9' 0.00 +maaloev.'maaloev_HO_LO' 30.40 +maaloev.'maaloev_HO_MW' 16.00 +maaloev.'maaloev_HO_NG' 68.50 +maaloev.'maaloev_HO_WO' 40.70 +*maaloev.'maaloev_ST_C0_FO2' 260.00 +*maaloev.'maaloev_ST_C0_FO3' 260.00 +noerskov.'noerskov_GE_B0_NG' 42.85 +noerskov.'noerskov_GT_B0_NG' 0.80 +noerskov.'noerskov_HO_FO' 9.90 +noerskov.'noerskov_HO_LO' 7.50 +noerskov.'noerskov_HO_NG' 59.20 +noerskov.'noerskov_HO_WO' 25.28 +noerskov.'noerskov_ST_B0_CO' 6.70 +nyborg.'nyborg_CC_B0_MW' 21.00 +nyborg.'nyborg_GE_B0_NG' 0.04 +*nyborg.'nyborg_GT_B0_NG' 4.90 +nyborg.'nyborg_HO_NG' 12.04 +nyborg.'nyborg_HO_WO' 71.90 +nyborg.'nyborg_HYRR_HY' 0.04 +nybro.'nybro_GE_B0_NG' 7.87 +nybro.'nybro_GT_B0_NG' 4.00 +nybro.'nybro_HO_LO' 4.65 +nybro.'nybro_HO_NG' 9.16 +nybro.'nybro_HO_WO' 1.90 +nybro.'nybro_HYRR_HY' 0.02 +DK_E_Rural.'OEST_GE_B0_LO' 0.35 +DK_E_Rural.'OEST_GE_B0_NG' 1.24 +*DK_E_Rural.'OEST_GE_C0_LO2' 19.60 +DK_E_Rural.'OEST_HO_CO' 26.00 +DK_E_Rural.'OEST_HO_FO' 54.40 +DK_E_Rural.'OEST_HO_LO' 108.30 +DK_E_Rural.'OEST_HO_MW' 22.00 +DK_E_Rural.'OEST_HO_ST' 87.65 +DK_E_Rural.'OEST_HO_WO' 129.00 +*DK_E_Rural.'OEST_ST_B0_CO4' 37.00 +*DK_E_Rural.'OEST_ST_B0_FO' 34.00 +*DK_E_Rural.'OEST_ST_B0_FO1' 4.00 +DK_E_Rural.'OEST_ST_B0_MW' 6.70 +DK_E_Rural.'OEST_ST_B0_ST' 11.30 +*DK_E_Rural.'OEST_ST_C0_FO3' 27.50 +pottehuse.'pottehuse_GE_B0_NG' 3.17 +pottehuse.'pottehuse_HO_LO' 43.30 +pottehuse.'pottehuse_HO_MW' 12.40 +pottehuse.'pottehuse_HO_NG' 230.10 +pottehuse.'pottehuse_HO_WO' 8.20 +pottehuse.'pottehuse_HYRR_HY' 0.95 +pottehuse.'pottehuse_SOLE_SOL' 0.10 +pottehuse.'pottehuse_ST_B0_MW' 6.26 +ringsted.'ringsted_CC_B0_MW' 38.00 +ringsted.'ringsted_GE_B0_NG' 63.86 +ringsted.'ringsted_GT_C0_LO2' 70.00 +ringsted.'ringsted_HO_MW' 10.00 +ringsted.'ringsted_HO_NG' 232.66 +ringsted.'ringsted_HO_ST' 8.00 +ringsted.'ringsted_ST_B0_ST' 5.00 +ringsted.'ringsted_ST_B0_ST3' 9.00 +slagelse.'slagelse_GE_B0_NG' 13.41 +slagelse.'slagelse_GE_C0_NG' 0.31 +slagelse.'slagelse_GT_B0_NG' 0.97 +*slagelse.'slagelse_HO_FO3' 2.00 +slagelse.'slagelse_HO_NG' 139.80 +slagelse.'slagelse_HO_ST' 4.50 +slagelse.'slagelse_HO_WO' 7.80 +slagelse.'slagelse_ST_B0_MW' 12.00 +* Sigsnæsværket fjernet fordi det er omfattet af DK_E_Rural +*slagelse.'slagelse_ST_C0_CO1' 143.00 +*slagelse.'slagelse_ST_C0_CO2' 266.00 +soroe.'soroe_GE_B0_NG' 31.83 +soroe.'soroe_HO_FO' 16.70 +*soroe.'soroe_HO_FO6' 42.00 +soroe.'soroe_HO_LO' 19.40 +soroe.'soroe_HO_NG' 62.00 +soroe.'soroe_HO_ST' 11.50 +soroe.'soroe_HO_WO' 11.10 +* Asnæsværket fjernet fordi det er omfattet af DK_E_Rural +*soroe.'soroe_ST_E0_CO1' 640.00 +*soroe.'soroe_ST_E0_CO2' 270.00 +* Ikke i drift. Ingen produktion i 2004 +* soroe.'soroe_ST_E0_CO3' 270.00 +*soroe.'soroe_ST_E0_CO4' 147.00 +standst.'standst_GE_B0_NG' 33.98 +*standst.'standst_GT_B0_NG' 0.60 +standst.'standst_HO_LO' 11.43 +standst.'standst_HO_NG' 94.50 +standst.'standst_HO_WO' 32.26 +standst.'standst_HYRR_HY' 0.10 +standst.'standst_ST_B0_MW' 2.60 +stenlille.'stenlille_HO_LO' 3.30 +stenlille.'stenlille_HO_ST' 2.50 +DK_E_Urban.'sydhavnen_CC_B0_NG' 13.30 +*Avedoere.'sydhavnen_CC_E0_NG1' 591.00 +* Effekt fordelt på naturgas og træ i forholdet 2-1 +*Avedoere.'sydhavnen_CC_E0_NG1' 380.00 +DK_E_Urban.'sydhavnen_GE_B0_NG' 0.55 +* Ses på museet Dieselhouse +*HCOersted.'sydhavnen_GE_C0_LO3' 12.00 +* Svanemølle blok 7: +*HCOersted.'sydhavnen_GT_B0_NG2' 60.00 +*HCO Blok 8: +*HCOersted.'sydhavnen_GT_B0_NG4' 24.30 +DK_E_Urban.'sydhavnen_HO_FO' 171.00 +DK_E_Urban.'sydhavnen_HO_LO' 331.90 +DK_E_Urban.'sydhavnen_HO_NG' 47.80 +DK_E_Urban.'sydhavnen_SOLE_SOL' 0.04 +* Svanemøllen blok 1 + HCØ blok 1-4 med 70 MW Skrottet i 2005: +*HCOersted.'sydhavnen_ST_E0_NG1' 155 +* HCØ Blok 7: +*HCOersted.'sydhavnen_ST_E0_NG2' 88.00 +skaerbaek.'taulov_CC_B0_NG' 6.25 +skaerbaek.'taulov_GE_B0_NG' 1.17 +skaerbaek.'taulov_GT_B0_NG' 33.40 +skaerbaek.'taulov_HO_LO' 139.41 +skaerbaek.'taulov_HO_NG' 42.00 +skaerbaek.'taulov_HO_NG4' 0.00 +*Seperat område til Skærbæk værketet +* Mangler i systemplan 2006. Er skrottet. +** skaerbaek.'taulov_ST_E0_NG1' 93.00 +*skaerbaek.'taulov_ST_E0_NG3' 392.00 +DK_W_TREFOR.DK_W_TREFOR_G-HSTORE=3500 +DK_W_TREFOR.DK_W_TREFOR_HO-B8-FO=800 +DK_W_TREFOR.DK_W_Trefor_ST-E9-NGn=391 +terkelsboel.'terkelsboel_CC_B0_NG' 6.00 +terkelsboel.'terkelsboel_GE_B0_NG' 14.60 +terkelsboel.'terkelsboel_GT_B0_NG' 14.30 +terkelsboel.'terkelsboel_HO_LO' 28.45 +terkelsboel.'terkelsboel_HO_NG' 107.81 +terkelsboel.'terkelsboel_HO_WO' 5.80 +terkelsboel.'terkelsboel_HYRR_HY' 0.03 +terkelsboel.'terkelsboel_ST_B0_WO' 0.60 +torslunde.'torslunde_GE_B0_NG' 3.33 +torslunde.'torslunde_HO_LO' 77.70 +torslunde.'torslunde_HO_MW' 28.00 +torslunde.'torslunde_HO_NG' 101.90 +torslunde.'torslunde_ST_B0_MW' 13.70 +ullerslev.'ullerslev_GE_B0_NG' 13.39 +ullerslev.'ullerslev_HO_LO' 66.82 +ullerslev.'ullerslev_HO_NG' 19.60 +ullerslev.'ullerslev_HYRR_HY' 0.02 +vallensbaek.'vallensbaek_GE_B0_NG' 4.32 +vallensbaek.'vallensbaek_HO_LO' 38.99 +vallensbaek.'vallensbaek_HO_MW' 104.00 +vallensbaek.'vallensbaek_HO_NG' 42.50 +vallensbaek.'vallensbaek_HO_WO' 32.00 +vallensbaek.'vallensbaek_ST_B0_MW' 39.00 +varde.'varde_GE_B0_NG' 16.83 +varde.'varde_GT_B0_NG' 3.74 +varde.'varde_HO_FO' 7.00 +varde.'varde_HO_LO' 202.46 +varde.'varde_HO_NG' 67.54 +varde.'varde_HO_WO' 51.45 +varde.'varde_HYRR_HY' 1.60 +*varde.'varde_ST_B0_MW' 18.00 +*varde.'varde_ST_E0_CO1' 378.00 +DK_W_Rural.'VEST_GE_B0_NG' 0.45 +DK_W_Rural.'VEST_HO_FO' 24.60 +DK_W_Rural.'VEST_HO_LO' 5.10 +DK_W_Rural.'VEST_HO_ST' 22.05 +DK_W_Rural.'VEST_HO_WO' 25.53 +DK_W_Rural.'VEST_ST_B0_ST' 2.30 +viborg.'viborg_CC_B0_NG' 87.00 +viborg.'viborg_CC_B0_WO' 1.40 +viborg.'viborg_GE_B0_NG' 90.15 +viborg.'viborg_GT_B0_NG' 20.10 +viborg.'viborg_HO_FO' 97.45 +viborg.'viborg_HO_LO' 60.90 +viborg.'viborg_HO_NG' 445.13 +viborg.'viborg_HO_ST' 8.80 +viborg.'viborg_HO_WO' 157.13 +viborg.'viborg_HYRR_HY' 0.02 +viborg.'viborg_ST_B0_MW' 32.92 +aalborg.'aalborg_CC_B0_NG' 56.15 +aalborg.'aalborg_GE_B0_NG' 145.28 +aalborg.'aalborg_GE_C0_NG' 0.05 +aalborg.'aalborg_GT_B0_NG' 39.96 +aalborg.'aalborg_GT_C0_LO3' 25.00 +aalborg.'aalborg_HO_CO5' 0.00 +aalborg.'aalborg_HO_LO' 260.15 +aalborg.'aalborg_HO_MW' 48.60 +aalborg.'aalborg_HO_NG' 582.13 +aalborg.'aalborg_HO_ST' 5.00 +aalborg.'aalborg_HO_WO' 65.86 +aalborg.'aalborg_HYRR_HY' 0.21 +aalborg.'aalborg_ST_B0_MW' 14.10 +aalborg.'aalborg_ST_B0_NG' 0.73 +*Findes i DK_W_AALBORG +*aalborg.'aalborg_ST_E0_CO2' 285.00 +*aalborg.'aalborg_ST_E0_CO4' 380.00 +/; + +GKFX(YYY,AAA,GGG)$(ICA('Denmark',AAA) and GDATA(GGG,'GDTYPE')<> 10)=0; +GKFX(YYY,MRAREAS,GGG)$(GDATA(GGG,'GDTYPE')<> 10)=GKFXMR(MRAREAS,GGG); + +GKFX(YYY,MRAREAS(AAA),'DK_E_1_G-HSTORE')$(not SUM(IGHSTO, GKFX('2006',AAA,IGHSTO))) +=8*(SUM(IGKHNOSTO, GKFX(YYY,AAA,IGKHNOSTO)) + SUM(G$(IGKE(G) and not IGH(G) and GDATA(G,'GDCB')and GKFX(YYY,AAA,G)>0), GKFX(YYY,AAA,G)/GDATA(G,'GDCB'))); + + + +SETS MR2CENTRAL(AAA,AAA,GGG) +/ +Hoejby.DK_W_Odense.DK_W_Odense_ST-E7-NG +Avedoere.DK_E_Urban.DK_E_Urban_ST-E0-NGsn +HCOersted.DK_E_Urban.DK_E_Urban_ST-B8-NG +HCOersted.DK_E_Urban.'sydhavnen_CC_B0_NG' +HCOersted.DK_E_Urban.'sydhavnen_HO_NG' +HCOersted.DK_E_Urban.'sydhavnen_GE_B0_NG' +SKAERBAEK.DK_W_TREFOR.DK_W_Trefor_ST-E9-NGn +/; \ No newline at end of file diff --git a/base/addons/gas/GPFX_VAR_T.INC b/base/addons/gas/GPFX_VAR_T.INC new file mode 100644 index 00000000..6b7e50c2 --- /dev/null +++ b/base/addons/gas/GPFX_VAR_T.INC @@ -0,0 +1,8746 @@ +* ----------------------------------------------------- +* MDB2GMNYBRO.S VerNYBRO.Sion 2.6, AuguNYBRO.St 2005 +* Erwin Kalvelagen, GAMNYBRO.S Development Corp +* ----------------------------------------------------- +* DAO verNYBRO.Sion: 3.6 +* Jet verNYBRO.Sion: 4.0 +* DatabaNYBRO.Se: D:\LarNYBRO.SB\1\GaNYBRO.STranNYBRO.SmiNYBRO.SNYBRO.Sion.mdb +* Query: NYBRO.Select * from nybro +* ----------------------------------------------------- +NYBRO.S01.T001 1007.69 +NYBRO.S01.T002 979.64 +NYBRO.S01.T003 965.476 +NYBRO.S01.T004 957.064 +NYBRO.S01.T005 949.957 +NYBRO.S01.T006 955.704 +NYBRO.S01.T007 969.582 +NYBRO.S01.T008 995.973 +NYBRO.S01.T009 1031.864 +NYBRO.S01.T010 1034.847 +NYBRO.S01.T011 1064.654 +NYBRO.S01.T012 1088.146 +NYBRO.S01.T013 1076.203 +NYBRO.S01.T014 1048.409 +NYBRO.S01.T015 1041.322 +NYBRO.S01.T016 1048.434 +NYBRO.S01.T017 1054.539 +NYBRO.S01.T018 1063.716 +NYBRO.S01.T019 1064.568 +NYBRO.S01.T020 1066.26 +NYBRO.S01.T021 1074.772 +NYBRO.S01.T022 1087.013 +NYBRO.S01.T023 1091.482 +NYBRO.S01.T024 1085.088 +NYBRO.S01.T025 1079.2 +NYBRO.S01.T026 1083.575 +NYBRO.S01.T027 1064.059 +NYBRO.S01.T028 1079.009 +NYBRO.S01.T029 1068.721 +NYBRO.S01.T030 1057.204 +NYBRO.S01.T031 1051.208 +NYBRO.S01.T032 1051.698 +NYBRO.S01.T033 1058.948 +NYBRO.S01.T034 1055.819 +NYBRO.S01.T035 1059.871 +NYBRO.S01.T036 1059.866 +NYBRO.S01.T037 1057.906 +NYBRO.S01.T038 1071.62 +NYBRO.S01.T039 1071.729 +NYBRO.S01.T040 1066.39 +NYBRO.S01.T041 1054.871 +NYBRO.S01.T042 1055.575 +NYBRO.S01.T043 1054.212 +NYBRO.S01.T044 1052.897 +NYBRO.S01.T045 1047.398 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1025.743 +NYBRO.S52.T145 1023.553 +NYBRO.S52.T146 1023.719 +NYBRO.S52.T147 1019.282 +NYBRO.S52.T148 1008.046 +NYBRO.S52.T149 988.962 +NYBRO.S52.T150 991.965 +NYBRO.S52.T151 988.904 +NYBRO.S52.T152 993.973 +NYBRO.S52.T153 1003.004 +NYBRO.S52.T154 1001.056 +NYBRO.S52.T155 1007.4 +NYBRO.S52.T156 1007.803 +NYBRO.S52.T157 1008.204 +NYBRO.S52.T158 1013.624 +NYBRO.S52.T159 1011.812 +NYBRO.S52.T160 1015.582 +NYBRO.S52.T161 1008.571 +NYBRO.S52.T162 1022.306 +NYBRO.S52.T163 1023.505 +NYBRO.S52.T164 1031.016 +NYBRO.S52.T165 1031.24 +NYBRO.S52.T166 1035.202 +NYBRO.S52.T167 1035.593 +NYBRO.S52.T168 1030.31 +* ----------------------------------------------------- \ No newline at end of file diff --git a/base/addons/gas/MRareas.inc b/base/addons/gas/MRareas.inc new file mode 100644 index 00000000..c4097aac --- /dev/null +++ b/base/addons/gas/MRareas.inc @@ -0,0 +1,52 @@ +BROENDBY +DRAGOER +EGTVED +ELLIDSHOEJ +HERNING +HOEJBY +HVIDOVRE +POTTEHUSE +SKAERBAEK +SYDHAVNEN +TAULOV +TORSLUNDE +AVEDOERE +AMAGERF +AALBORG +VESTAMAGER +VARDE +VALLENSBAEK +STENLILLE +HCOERSTED +ELLUND +HAVERSLEV +HELLE +SLAGELSE +SOROE +STANDST +ULLERSLEV +BILLESBOELLE +BRANDE +VIBORG +TERKELSBOEL +RINGSTED +NOERSKOV +NYBORG +NYBRO +LYNGE +MIDDELFART +MAALOEV +KARLSLUNDE +KARUP +KOEGE +KOELBJERG +LISELSKAER +LITORUP +OERESUND +FROESLEV +LILBALLE +LYNGSODDE +KONGSMARK +$ifi %HEATING%==yes VEKS_V, CTR_V, VFORBRANDING_V, NFORBRANDING_V, AALBORG_V, AARHUS_V, ODENSE_V, HERNING_V, TVIS_V + + diff --git a/base/addons/gas/MRregions2areas.inc b/base/addons/gas/MRregions2areas.inc new file mode 100644 index 00000000..4cc37cf4 --- /dev/null +++ b/base/addons/gas/MRregions2areas.inc @@ -0,0 +1,7 @@ +DK_E.(AMAGERF, AVEDOERE, BROENDBY,DRAGOER,HCOERSTED,KARLSLUNDE,KOEGE,MAALOEV, + SYDHAVNEN,TORSLUNDE,VALLENSBAEK,VESTAMAGER,LYNGE,RINGSTED,SLAGELSE, + SOROE, HVIDOVRE, KONGSMARK) +DK_W.(AALBORG, BILLESBOELLE, BRANDE, KARUP, LISELSKAER, HAVERSLEV, + HELLE, NOERSKOV, NYBORG, STANDST,TERKELSBOEL,ULLERSLEV,VIBORG, + ELLIDSHOEJ, HOEJBY, KOELBJERG,MIDDELFART,HERNING,SKAERBAEK,TAULOV,VARDE, + POTTEHUSE,EGTVED,LILBALLE,LYNGSODDE, FROESLEV) \ No newline at end of file diff --git a/base/addons/gas/SE_DG_VAR_T.INC b/base/addons/gas/SE_DG_VAR_T.INC new file mode 100644 index 00000000..0ffdcb03 --- /dev/null +++ b/base/addons/gas/SE_DG_VAR_T.INC @@ -0,0 +1,8746 @@ +* ----------------------------------------------------- +* MDB2GMSE_S_Rural.S VerSE_S_Rural.Sion 2.6, AuguSE_S_Rural.St 2005 +* Erwin Kalvelagen, GAMSE_S_Rural.S Development Corp +* ----------------------------------------------------- +* DAO verSE_S_Rural.Sion: 3.6 +* Jet verSE_S_Rural.Sion: 4.0 +* DatabaSE_S_Rural.Se: D:\LarSE_S_Rural.SB\1\GaSE_S_Rural.STranSE_S_Rural.SmiSE_S_Rural.SSE_S_Rural.Sion.mdb +* Query: SE_S_Rural.Select * from dragoer +* ----------------------------------------------------- +SE_S_Rural.S01.T001 150.125 +SE_S_Rural.S01.T002 149.898 +SE_S_Rural.S01.T003 150.066 +SE_S_Rural.S01.T004 150.176 +SE_S_Rural.S01.T005 149.676 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+SE_S_Rural.S51.T110 99.034 +SE_S_Rural.S51.T111 101.632 +SE_S_Rural.S51.T112 103.933 +SE_S_Rural.S51.T113 107.021 +SE_S_Rural.S51.T114 108.686 +SE_S_Rural.S51.T115 110.315 +SE_S_Rural.S51.T116 110.2 +SE_S_Rural.S51.T117 110.445 +SE_S_Rural.S51.T118 113.965 +SE_S_Rural.S51.T119 118.21 +SE_S_Rural.S51.T120 122.365 +SE_S_Rural.S51.T121 126.204 +SE_S_Rural.S51.T122 129.233 +SE_S_Rural.S51.T123 130.431 +SE_S_Rural.S51.T124 130.195 +SE_S_Rural.S51.T125 129.96 +SE_S_Rural.S51.T126 128.639 +SE_S_Rural.S51.T127 114.538 +SE_S_Rural.S51.T128 109.771 +SE_S_Rural.S51.T129 109.657 +SE_S_Rural.S51.T130 109.826 +SE_S_Rural.S51.T131 109.134 +SE_S_Rural.S51.T132 108.307 +SE_S_Rural.S51.T133 108.407 +SE_S_Rural.S51.T134 109.489 +SE_S_Rural.S51.T135 109.429 +SE_S_Rural.S51.T136 109.619 +SE_S_Rural.S51.T137 109.402 +SE_S_Rural.S51.T138 130.252 +SE_S_Rural.S51.T139 124.656 +SE_S_Rural.S51.T140 118.834 +SE_S_Rural.S51.T141 114.949 +SE_S_Rural.S51.T142 113.101 +SE_S_Rural.S51.T143 113.763 +SE_S_Rural.S51.T144 115.284 +SE_S_Rural.S51.T145 118.041 +SE_S_Rural.S51.T146 120.693 +SE_S_Rural.S51.T147 121.369 +SE_S_Rural.S51.T148 119.497 +SE_S_Rural.S51.T149 117.872 +SE_S_Rural.S51.T150 115.864 +SE_S_Rural.S51.T151 113.034 +SE_S_Rural.S51.T152 110.861 +SE_S_Rural.S51.T153 111.038 +SE_S_Rural.S51.T154 109.471 +SE_S_Rural.S51.T155 107.258 +SE_S_Rural.S51.T156 104.343 +SE_S_Rural.S51.T157 100.918 +SE_S_Rural.S51.T158 101.869 +SE_S_Rural.S51.T159 103.881 +SE_S_Rural.S51.T160 104.184 +SE_S_Rural.S51.T161 106.757 +SE_S_Rural.S51.T162 111.586 +SE_S_Rural.S51.T163 114.143 +SE_S_Rural.S51.T164 117.902 +SE_S_Rural.S51.T165 120.076 +SE_S_Rural.S51.T166 122.297 +SE_S_Rural.S51.T167 120.812 +SE_S_Rural.S51.T168 125.856 +SE_S_Rural.S52.T001 129.289 +SE_S_Rural.S52.T002 130.761 +SE_S_Rural.S52.T003 130.478 +SE_S_Rural.S52.T004 129.856 +SE_S_Rural.S52.T005 129.362 +SE_S_Rural.S52.T006 128.359 +SE_S_Rural.S52.T007 132.86 +SE_S_Rural.S52.T008 137.696 +SE_S_Rural.S52.T009 134.652 +SE_S_Rural.S52.T010 132.533 +SE_S_Rural.S52.T011 132.382 +SE_S_Rural.S52.T012 134.063 +SE_S_Rural.S52.T013 135.282 +SE_S_Rural.S52.T014 135.087 +SE_S_Rural.S52.T015 134.896 +SE_S_Rural.S52.T016 134.318 +SE_S_Rural.S52.T017 149.39 +SE_S_Rural.S52.T018 153.358 +SE_S_Rural.S52.T019 152.798 +SE_S_Rural.S52.T020 152.84 +SE_S_Rural.S52.T021 152.364 +SE_S_Rural.S52.T022 152.293 +SE_S_Rural.S52.T023 155.392 +SE_S_Rural.S52.T024 159.88 +SE_S_Rural.S52.T025 164.137 +SE_S_Rural.S52.T026 167.574 +SE_S_Rural.S52.T027 170.308 +SE_S_Rural.S52.T028 172.21 +SE_S_Rural.S52.T029 172.646 +SE_S_Rural.S52.T030 170.939 +SE_S_Rural.S52.T031 165.476 +SE_S_Rural.S52.T032 161.129 +SE_S_Rural.S52.T033 156.09 +SE_S_Rural.S52.T034 151.183 +SE_S_Rural.S52.T035 148.542 +SE_S_Rural.S52.T036 147.566 +SE_S_Rural.S52.T037 147.161 +SE_S_Rural.S52.T038 144.599 +SE_S_Rural.S52.T039 152.506 +SE_S_Rural.S52.T040 149.201 +SE_S_Rural.S52.T041 148.957 +SE_S_Rural.S52.T042 150.207 +SE_S_Rural.S52.T043 152.706 +SE_S_Rural.S52.T044 154.499 +SE_S_Rural.S52.T045 156.42 +SE_S_Rural.S52.T046 158.676 +SE_S_Rural.S52.T047 163.398 +SE_S_Rural.S52.T048 167.485 +SE_S_Rural.S52.T049 169.791 +SE_S_Rural.S52.T050 168.801 +SE_S_Rural.S52.T051 167.391 +SE_S_Rural.S52.T052 165.219 +SE_S_Rural.S52.T053 161.757 +SE_S_Rural.S52.T054 157.151 +SE_S_Rural.S52.T055 149.597 +SE_S_Rural.S52.T056 143.358 +SE_S_Rural.S52.T057 135.397 +SE_S_Rural.S52.T058 131.28 +SE_S_Rural.S52.T059 129.136 +SE_S_Rural.S52.T060 123.842 +SE_S_Rural.S52.T061 116.648 +SE_S_Rural.S52.T062 117.752 +SE_S_Rural.S52.T063 118.118 +SE_S_Rural.S52.T064 120.352 +SE_S_Rural.S52.T065 123.137 +SE_S_Rural.S52.T066 124.078 +SE_S_Rural.S52.T067 132.584 +SE_S_Rural.S52.T068 142.392 +SE_S_Rural.S52.T069 145.179 +SE_S_Rural.S52.T070 146.618 +SE_S_Rural.S52.T071 150.829 +SE_S_Rural.S52.T072 154.807 +SE_S_Rural.S52.T073 158.477 +SE_S_Rural.S52.T074 159.07 +SE_S_Rural.S52.T075 158.025 +SE_S_Rural.S52.T076 157.731 +SE_S_Rural.S52.T077 154.448 +SE_S_Rural.S52.T078 146.144 +SE_S_Rural.S52.T079 135.55 +SE_S_Rural.S52.T080 113.956 +SE_S_Rural.S52.T081 111.806 +SE_S_Rural.S52.T082 114.169 +SE_S_Rural.S52.T083 116.722 +SE_S_Rural.S52.T084 110.286 +SE_S_Rural.S52.T085 106.195 +SE_S_Rural.S52.T086 105.246 +SE_S_Rural.S52.T087 104.793 +SE_S_Rural.S52.T088 100.824 +SE_S_Rural.S52.T089 101.988 +SE_S_Rural.S52.T090 105.416 +SE_S_Rural.S52.T091 105.537 +SE_S_Rural.S52.T092 105.117 +SE_S_Rural.S52.T093 107.827 +SE_S_Rural.S52.T094 113.876 +SE_S_Rural.S52.T095 125.881 +SE_S_Rural.S52.T096 113.056 +SE_S_Rural.S52.T097 112.185 +SE_S_Rural.S52.T098 111.649 +SE_S_Rural.S52.T099 111.371 +SE_S_Rural.S52.T100 113.831 +SE_S_Rural.S52.T101 112.198 +SE_S_Rural.S52.T102 106.83 +SE_S_Rural.S52.T103 98.558 +SE_S_Rural.S52.T104 92.281 +SE_S_Rural.S52.T105 91.717 +SE_S_Rural.S52.T106 93.923 +SE_S_Rural.S52.T107 91.934 +SE_S_Rural.S52.T108 87.305 +SE_S_Rural.S52.T109 90.766 +SE_S_Rural.S52.T110 90.934 +SE_S_Rural.S52.T111 90.61 +SE_S_Rural.S52.T112 95.319 +SE_S_Rural.S52.T113 111.255 +SE_S_Rural.S52.T114 108.726 +SE_S_Rural.S52.T115 108.697 +SE_S_Rural.S52.T116 111.774 +SE_S_Rural.S52.T117 118.011 +SE_S_Rural.S52.T118 123.141 +SE_S_Rural.S52.T119 127.099 +SE_S_Rural.S52.T120 132.525 +SE_S_Rural.S52.T121 218.587 +SE_S_Rural.S52.T122 217.594 +SE_S_Rural.S52.T123 217.311 +SE_S_Rural.S52.T124 216.966 +SE_S_Rural.S52.T125 220.892 +SE_S_Rural.S52.T126 233.516 +SE_S_Rural.S52.T127 163.286 +SE_S_Rural.S52.T128 155.497 +SE_S_Rural.S52.T129 155.935 +SE_S_Rural.S52.T130 156.296 +SE_S_Rural.S52.T131 155.897 +SE_S_Rural.S52.T132 155.727 +SE_S_Rural.S52.T133 155.84 +SE_S_Rural.S52.T134 156.519 +SE_S_Rural.S52.T135 155.728 +SE_S_Rural.S52.T136 155.602 +SE_S_Rural.S52.T137 156.309 +SE_S_Rural.S52.T138 155.587 +SE_S_Rural.S52.T139 156.305 +SE_S_Rural.S52.T140 155.591 +SE_S_Rural.S52.T141 160.572 +SE_S_Rural.S52.T142 185.808 +SE_S_Rural.S52.T143 188.6 +SE_S_Rural.S52.T144 187.786 +SE_S_Rural.S52.T145 187.203 +SE_S_Rural.S52.T146 186.949 +SE_S_Rural.S52.T147 191.885 +SE_S_Rural.S52.T148 192.997 +SE_S_Rural.S52.T149 193.822 +SE_S_Rural.S52.T150 192.488 +SE_S_Rural.S52.T151 192.482 +SE_S_Rural.S52.T152 176.143 +SE_S_Rural.S52.T153 163.704 +SE_S_Rural.S52.T154 163.3 +SE_S_Rural.S52.T155 163.161 +SE_S_Rural.S52.T156 164.209 +SE_S_Rural.S52.T157 162.617 +SE_S_Rural.S52.T158 163.88 +SE_S_Rural.S52.T159 163.582 +SE_S_Rural.S52.T160 164.85 +SE_S_Rural.S52.T161 165.185 +SE_S_Rural.S52.T162 165.441 +SE_S_Rural.S52.T163 163.441 +SE_S_Rural.S52.T164 164.412 +SE_S_Rural.S52.T165 164.91 +SE_S_Rural.S52.T166 162.019 +SE_S_Rural.S52.T167 149.96 +SE_S_Rural.S52.T168 150.244 +* ----------------------------------------------------- \ No newline at end of file diff --git a/base/addons/gas/andetgasforbrug.inc b/base/addons/gas/andetgasforbrug.inc new file mode 100644 index 00000000..d7da68da --- /dev/null +++ b/base/addons/gas/andetgasforbrug.inc @@ -0,0 +1,26 @@ +SET gasanvendelser "FROM MDB2GMS" / +'Centralt kraftværk' +'Andre timeaflæste' +'Decentral kraftvarme' +'Decentral ren varmeproduktion' +Fjernvarmekedeler +'Industriel andet formål' +'Industriel kraftvarme' +residual +/; + +PARAMETER DG_HNG_VAR(gasanvendelser,SSS,TTT), + DG_MidtNord_VAR(gasanvendelser,SSS,TTT), + DG_DongOest_VAR(gasanvendelser,SSS,TTT), + DG_DongVest_VAR(gasanvendelser,SSS,TTT), + DG_NGF_VAR(gasanvendelser,SSS,TTT); + +execute_load '../../base/addons/gas/gasforbrug.gdx', DG_HNG_VAR,DG_MidtNord_VAR, DG_DongOest_VAR, DG_DongVest_VAR, DG_NGF_VAR; + + +PARAMETER DG_VAR(gasanvendelser,AAA,SSS,TTT) +/ +$ondelim +$include '../../base/addons/gas/gasforbrug.csv'; +$offdelim +/; \ No newline at end of file diff --git a/base/addons/gas/annualupdate.inc b/base/addons/gas/annualupdate.inc new file mode 100644 index 00000000..cb96096f --- /dev/null +++ b/base/addons/gas/annualupdate.inc @@ -0,0 +1,58 @@ +* Transfer investments from previous year by raising the lower bound on investment +* quantity. +$ifi %bb2%==yes VGINVSTORE.lo(STORE,STORETYPE) = VGINVSTORE.l(STORE,STORETYPE); +$ifi %bb2%==yes VGINVINJ.lo(STORE,STORETYPE) = VGINVINJ.l(STORE,STORETYPE); +$ifi %bb2%==yes VGINVEXT.lo(STORE,STORETYPE) = VGINVEXT.l(STORE,STORETYPE); +$ifi %bb2%==yes VGINFINV.lo(NGINV) = VGINFINV.l(NGINV); +$ifi %bb2%==yes VGNETINV.lo(C,GNETSTEPS) = VGNETINV.l(C,GNETSTEPS); + +* Load capacity reservations (BB3 only) reservations are generated in BB1 or BB2 +$ifi %bb3%==yes ITC_EX_Y(SYSEXIT) = TC_EX_Y(SYSEXIT,Y); +$ifi %bb3%==yes ITC_EX_M(SYSEXIT,S,T) = TC_EX_M(SYSEXIT,Y,S,T); +$ifi %bb3%==yes ITC_EN_Y(SYSENTRY) = TC_EN_Y(SYSENTRY,Y); +$ifi %bb3%==yes ITC_EN_M(SYSENTRY,S,T) = TC_EN_M(SYSENTRY,Y,S,T); + +$ifi %bb3%==yes IGSFXW(STORE,STORETYPE,S) = GSFXW(Y,STORE,STORETYPE,S); +$ifi %bb3%==yes ISC(SCON) = SC_Y(Y,SCON); +$ifi %bb3%==yes IHYFXRW(S,IR) = HYFXRW_GAS(Y,S,IR); +$ifi %bb3%==yes IWATERVAL(IA,S) = WATERVAL(Y,S,IA); + +* Gas consumption not instigated by generation of electricity and/or district +* heating: +IDGRES_Y(IA,S,T)= DGRES(Y,IA) * (DGRES_VAR(IA,S,T) / IDGRES_SUMVAR(IA))$(IDGRES_SUMVAR(IA) >0); +IDGOTH_Y(IA,S,T)= DGOTH(Y,IA) * (DGOTH_VAR(IA,S,T) / IDGOTH_SUMVAR(IA))$(IDGOTH_SUMVAR(IA) >0); + +* Use simple bounds for capacity of supply, export, and storage operations when +* not simulating with endogenous investments. +$ifi not %bb2%==yes VSTOCK.UP(STORE,STORETYPE,S,T)= STORAGE_MAX(Y,STORE,STORETYPE)-TSOSTORAGE(Y,STORE,STORETYPE); +$ifi not %bb2%==yes VINJ.UP(STORE,STORETYPE,S,T) = INJ_MAX(Y,STORE,STORETYPE) ; +$ifi not %bb2%==yes VEXT.UP(STORE,STORETYPE,S,T) = EXT_MAX(Y,STORE,STORETYPE) ; +$ifi not %bb2%==yes VEXP.UP(SOURCE,S) = EXP_MAX(Y,SOURCE) ; +$ifi not %BB2%==yes VTRANSIT.up(TRANSIT(AAA),S,T) = SUM(SOURCE(AAA), EXP_MAX(Y,SOURCE)); +$ifi not %bb2%==yes VSUP.UP(SOURCE,S,T) = SUPPLY_MAX(Y,SOURCE); + +* Delivery maximum is never a simple bound. +IDEV_MAX(SYSEXIT) = DEV_MAX(Y,SYSEXIT); + +* Add investments in gas infrastructure to bb1 or bb3 simulation +$ifi %ADDINVEST%==yes VSTOCK.UP(STORE,STORETYPE,S,T)= STOREACC(Y,STORE,STORETYPE)+ VSTOCK.UP(STORE,STORETYPE,S,T) ; +$ifi %ADDINVEST%==yes VINJ.UP(STORE,STORETYPE,S,T) = INJACC(Y,STORE,STORETYPE)+ VINJ.UP(STORE,STORETYPE,S,T) ; +$ifi %ADDINVEST%==yes VEXT.UP(STORE,STORETYPE,S,T) = EXTACC(Y,STORE,STORETYPE)+ VEXT.UP(STORE,STORETYPE,S,T) ; +$ifi %ADDINVEST%==yes VEXP.UP(SOURCE,S) = EXPACC(Y,SOURCE)+ VEXP.UP(SOURCE,S) ; +$ifi %ADDINVEST%==yes VTRANSIT.UP(TRANSIT(AAA),S,T) = SUM(SOURCE(AAA), EXPACC(Y,SOURCE))+ VTRANSIT.UP(TRANSIT,S,T); +$ifi %ADDINVEST%==yes VSUP.UP(SOURCE,S,T) = SUPACC(Y,SOURCE)+ VSUP.UP(SOURCE,S,T) ; +$ifi %ADDINVEST%==yes IDEV_MAX(SYSEXIT) = DEV_MAX(Y,SYSEXIT)+DEVACC(Y,SYSEXIT); + +* Parameters used for define capacity bounds of existing infrastructure. Total +* capacity also includes capacity effects of investments. +$ifi %bb2%==yes ISUPPLY_MAX(SOURCE) = SUPPLY_MAX(Y,SOURCE); +$ifi %bb2%==yes IEXP_MAX(SOURCE) = EXP_MAX(Y,SOURCE) ; +$ifi %bb2%==yes IINJ_MAX(STORE,STORETYPE) = INJ_MAX(Y,STORE,STORETYPE) ; +$ifi %bb2%==yes IEXT_MAX(STORE,STORETYPE) = EXT_MAX(Y,STORE,STORETYPE) ; +$ifi %bb2%==yes ISTORE_MAX(STORE,STORETYPE) = STORAGE_MAX(Y,STORE,STORETYPE)-TSOSTORAGE(Y,STORE,STORETYPE) ; + +* Define border price at adjacent systems. +IGEX_PRICE(SOURCE,S) = GEX_PRICE(Y,SOURCE,S); + +* Assign wellhead price. +IGEXT_COST(SOURCE,S) = GEXT_COST(Y,SOURCE,S); \ No newline at end of file diff --git a/base/addons/gas/dg-MR.inc b/base/addons/gas/dg-MR.inc new file mode 100644 index 00000000..a527918c --- /dev/null +++ b/base/addons/gas/dg-MR.inc @@ -0,0 +1,13 @@ + + +* No seperate tables for gas consumption not used for electricity and district heating, +* therefore the annual values are obtained by summing the variation profiles. Hereby, consumption +* by this group of consumers is expected to remain unchanged. + +DGRES(YYY,AAA)$ICA('Denmark',AAA) = sum((SSS,TTT),365/364*DG_VAR('residual',AAA,SSS,TTT)); +DGOTH(YYY,AAA)$ICA('Denmark',AAA) = sum((SSS,TTT),365/364*DG_VAR('Andre timeaflæste',AAA,SSS,TTT)) + +sum((SSS,TTT),365/364*DG_VAR('Industriel andet formål',AAA,SSS,TTT)); + +DGRES(YYY,'hoejby')= sum((SSS,TTT),365/364*DG_NGF_VAR('residual',SSS,TTT)); +DGOTH(YYY,'hoejby')= sum((SSS,TTT),365/364*(DG_NGF_VAR('Andre timeaflæste',SSS,TTT) + +DG_NGF_VAR('Industriel andet formål',SSS,TTT))); \ No newline at end of file diff --git a/base/addons/gas/dg.inc b/base/addons/gas/dg.inc new file mode 100644 index 00000000..b43b7dbe --- /dev/null +++ b/base/addons/gas/dg.inc @@ -0,0 +1,23 @@ + + +* No seperate tables for gas consumption not used for electricity and district heating, +* therefore the annual values are obtained by summing the variation profiles. Hereby, consumption +* by this group of consumers is expected to remain unchanged. + +DGRES(YYY,'DK_E_Urban')= sum((SSS,TTT),365/364*DG_HNG_VAR('residual',SSS,TTT)); +DGOTH(YYY,'DK_E_Urban')= sum((SSS,TTT),365/364*(DG_HNG_VAR('Andre timeaflæste',SSS,TTT) + +DG_HNG_VAR('Industriel andet formål',SSS,TTT))); +DGRES(YYY,'DK_E_Rural')= sum((SSS,TTT),365/364*DG_DongOest_VAR('residual',SSS,TTT)); +DGOTH(YYY,'DK_E_Rural')= sum((SSS,TTT),365/364*(DG_DongOest_VAR('Andre timeaflæste',SSS,TTT) + +DG_DongOest_VAR('Industriel andet formål',SSS,TTT))); +DGRES(YYY,'DK_W_AALBORG')= sum((SSS,TTT),365/364*DG_MidtNord_VAR('residual',SSS,TTT)); +DGOTH(YYY,'DK_W_AALBORG')= sum((SSS,TTT),365/364*(DG_MidtNord_VAR('Andre timeaflæste',SSS,TTT) + +DG_MidtNord_VAR('Industriel andet formål',SSS,TTT))); +DGRES(YYY,'DK_W_TREFOR')= sum((SSS,TTT),365/364*DG_DongVest_VAR('residual',SSS,TTT)); +DGOTH(YYY,'DK_W_TREFOR')= sum((SSS,TTT),365/364*(DG_DongVest_VAR('Andre timeaflæste',SSS,TTT) + +DG_DongVest_VAR('Industriel andet formål',SSS,TTT))); + +DGRES(YYY,'DK_W_ODENSE')= sum((SSS,TTT),365/364*DG_NGF_VAR('residual',SSS,TTT)); +DGOTH(YYY,'DK_W_ODENSE')= sum((SSS,TTT),365/364*(DG_NGF_VAR('Andre timeaflæste',SSS,TTT) + +DG_NGF_VAR('Industriel andet formål',SSS,TTT))); + diff --git a/base/addons/gas/ensmpol.inc b/base/addons/gas/ensmpol.inc new file mode 100644 index 00000000..0acbe45a --- /dev/null +++ b/base/addons/gas/ensmpol.inc @@ -0,0 +1,350 @@ + +TABLE M_POL(YYY,MPOLSET,CCC) 'Emission policy data' + + LIM_CO2.DENMARK LIM_SO2.DENMARK LIM_NOX.DENMARK +* Units: t CO2/year t SO2/year t NOx/year +1995 INF INF INF +1996 INF INF INF +1997 INF INF INF +1998 INF INF INF +1999 INF INF INF +2000 INF INF INF +2001 INF INF INF +2002 INF INF INF +2003 INF INF INF +2004 INF INF INF +2005 INF INF INF +2006 INF INF INF +2007 INF INF INF +2008 INF INF INF +2009 INF INF INF +2010 INF INF INF +2011 INF INF INF +2012 INF INF INF +2013 INF INF INF +2014 INF INF INF +2015 INF INF INF +2016 INF INF INF +2017 INF INF INF +2018 INF INF INF +2019 INF INF INF +2020 INF INF INF +2021 INF INF INF +2022 INF INF INF +2023 INF INF INF +2024 INF INF INF +2025 INF INF INF +2026 INF INF INF +2027 INF INF INF +2028 INF INF INF +2029 INF INF INF +2030 INF INF INF + ++ TAX_CO2.DENMARK TAX_SO2.DENMARK TAX_NOX.DENMARK +* Units: EUR90/t CO2 EUR90/t SO2 EUR90/t NOx +1995 0 988 +1996 0 988 +1997 0 988 +1998 0 988 +1999 0 988 +2000 0 988 +2001 0 988 +2002 0 988 +2003 0 988 +2004 0 988 +2005 4.94071 988 +2006 4.94071 988 +2007 4.94071 988 +2008 4.94071 988 +2009 4.94071 988 +2010 4.94071 988 +2011 4.94071 988 +2012 4.94071 988 +2013 9.881423 988 +2014 9.881423 988 +2015 9.881423 988 +2016 9.881423 988 +2017 9.881423 988 +2018 9.881423 988 +2019 9.881423 988 +2020 9.881423 988 +2021 9.881423 988 +2022 9.881423 988 +2023 9.881423 988 +2024 9.881423 988 +2025 9.881423 988 +2026 9.881423 988 +2027 9.881423 988 +2028 9.881423 988 +2029 9.881423 988 +2030 9.881423 988 + ++ LIM_CO2.FINLAND LIM_SO2.FINLAND LIM_NOX.FINLAND +* Units: t CO2/year t SO2/year t NOx/year +1995 INF INF INF +1996 INF INF INF +1997 INF INF INF +1998 INF INF INF +1999 INF INF INF +2000 INF INF INF +2001 INF INF INF +2002 INF INF INF +2003 INF INF INF +2004 INF INF INF +2005 INF INF INF +2006 INF INF INF +2007 INF INF INF +2008 INF INF INF +2009 INF INF INF +2010 INF INF INF +2011 INF INF INF +2012 INF INF INF +2013 INF INF INF +2014 INF INF INF +2015 INF INF INF +2016 INF INF INF +2017 INF INF INF +2018 INF INF INF +2019 INF INF INF +2020 INF INF INF +2021 INF INF INF +2022 INF INF INF +2023 INF INF INF +2024 INF INF INF +2025 INF INF INF +2026 INF INF INF +2027 INF INF INF +2028 INF INF INF +2029 INF INF INF +2030 INF INF INF + ++ TAX_CO2.FINLAND TAX_SO2.FINLAND TAX_NOX.FINLAND +* Units: EUR90/t CO2 EUR90/t SO2 EUR90/t NOx +1995 0 0 0 +1996 0 0 0 +1997 0 0 0 +1998 0 0 0 +1999 0 0 0 +2000 0 0 0 +2001 0 0 0 +2002 0 0 0 +2003 0 0 0 +2004 0 0 0 +2005 2.96443 0 0 +2006 2.96443 0 0 +2007 2.96443 0 0 +2008 4.94071 0 0 +2009 4.94071 0 0 +2010 4.94071 0 0 +2011 4.94071 0 0 +2012 4.94071 0 0 +2013 9.881423 0 0 +2014 9.881423 0 0 +2015 9.881423 0 0 +2016 9.881423 0 0 +2017 9.881423 0 0 +2018 9.881423 0 0 +2019 9.881423 0 0 +2020 9.881423 0 0 +2021 9.881423 0 0 +2022 9.881423 0 0 +2023 9.881423 0 0 +2024 9.881423 0 0 +2025 9.881423 0 0 +2026 9.881423 0 0 +2027 9.881423 0 0 +2028 9.881423 0 0 +2029 9.881423 0 0 +2030 9.881423 0 0 + ++ LIM_CO2.NORWAY LIM_SO2.NORWAY LIM_NOX.NORWAY +* Units: t CO2/year t SO2/year t NOx/year +1995 INF INF INF +1996 INF INF INF +1997 INF INF INF +1998 INF INF INF +1999 INF INF INF +2000 INF INF INF +2001 INF INF INF +2002 INF INF INF +2003 INF INF INF +2004 INF INF INF +2005 INF INF INF +2006 INF INF INF +2007 INF INF INF +2008 INF INF INF +2009 INF INF INF +2010 INF INF INF +2011 INF INF INF +2012 INF INF INF +2013 INF INF INF +2014 INF INF INF +2015 INF INF INF +2016 INF INF INF +2017 INF INF INF +2018 INF INF INF +2019 INF INF INF +2020 INF INF INF +2021 INF INF INF +2022 INF INF INF +2023 INF INF INF +2024 INF INF INF +2025 INF INF INF +2026 INF INF INF +2027 INF INF INF +2028 INF INF INF +2029 INF INF INF +2030 INF INF INF + ++ TAX_CO2.NORWAY TAX_SO2.NORWAY TAX_NOX.NORWAY +* Units: EUR90/t CO2 EUR90/t SO2 EUR90/t NOx +1995 0 1482 0 +1996 0 1482 0 +1997 0 1482 0 +1998 0 1482 0 +1999 0 1482 0 +2000 0 1482 0 +2001 0 1482 0 +2002 0 1482 0 +2003 0 1482 0 +2004 0 1482 0 +2005 2.96443 1482 0 +2006 2.96443 1482 0 +2007 2.96443 1482 0 +2008 4.94071 1482 0 +2009 4.94071 1482 0 +2010 4.94071 1482 0 +2011 4.94071 1482 0 +2012 4.94071 1482 0 +2013 9.881423 1482 0 +2014 9.881423 1482 0 +2015 9.881423 1482 0 +2016 9.881423 1482 0 +2017 9.881423 1482 0 +2018 9.881423 1482 0 +2019 9.881423 1482 0 +2020 9.881423 1482 0 +2021 9.881423 1482 0 +2022 9.881423 1482 0 +2023 9.881423 1482 0 +2024 9.881423 1482 0 +2025 9.881423 1482 0 +2026 9.881423 1482 0 +2027 9.881423 1482 0 +2028 9.881423 1482 0 +2029 9.881423 1482 0 +2030 9.881423 1482 0 + ++ LIM_CO2.SWEDEN LIM_SO2.SWEDEN LIM_NOX.SWEDEN +* Units: t CO2/year t SO2/year t NOx/year +1995 INF INF INF +1996 INF INF INF +1997 INF INF INF +1998 INF INF INF +1999 INF INF INF +2000 INF INF INF +2001 INF INF INF +2002 INF INF INF +2003 INF INF INF +2004 INF INF INF +2005 INF INF INF +2006 INF INF INF +2007 INF INF INF +2008 INF INF INF +2009 INF INF INF +2010 INF INF INF +2011 INF INF INF +2012 INF INF INF +2013 INF INF INF +2014 INF INF INF +2015 INF INF INF +2016 INF INF INF +2017 INF INF INF +2018 INF INF INF +2019 INF INF INF +2020 INF INF INF +2021 INF INF INF +2022 INF INF INF +2023 INF INF INF +2024 INF INF INF +2025 INF INF INF +2026 INF INF INF +2027 INF INF INF +2028 INF INF INF +2029 INF INF INF +2030 INF INF INF + ++ TAX_CO2.SWEDEN TAX_SO2.SWEDEN TAX_NOX.SWEDEN +* Units: EUR90/t CO2 EUR90/t SO2 EUR90/t NOx +1995 0 1215 0 +1996 0 1215 0 +1997 0 1215 0 +1998 0 1215 0 +1999 0 1215 0 +2000 0 1215 0 +2001 0 1215 0 +2002 0 1215 0 +2003 0 1215 0 +2004 0 1215 0 +2005 2.96443 1215 0 +2006 2.96443 1215 0 +2007 2.96443 1215 0 +2008 4.94071 1215 0 +2009 4.94071 1215 0 +2010 4.94071 1215 0 +2011 4.94071 1215 0 +2012 4.94071 1215 0 +2013 9.881423 1215 0 +2014 9.881423 1215 0 +2015 9.881423 1215 0 +2016 9.881423 1215 0 +2017 9.881423 1215 0 +2018 9.881423 1215 0 +2019 9.881423 1215 0 +2020 9.881423 1215 0 +2021 9.881423 1215 0 +2022 9.881423 1215 0 +2023 9.881423 1215 0 +2024 9.881423 1215 0 +2025 9.881423 1215 0 +2026 9.881423 1215 0 +2027 9.881423 1215 0 +2028 9.881423 1215 0 +2029 9.881423 1215 0 +2030 9.881423 1215 0 +; + + +* Emission limits values not assigned a positive level in the table above. +* are here given the default value INF: + +LOOP(YYY, LOOP(CCC, M_POL(YYY,'LIM_CO2',CCC) + $(M_POL(YYY,'LIM_CO2',CCC) EQ 0) = INF )); +LOOP(YYY, LOOP(CCC, M_POL(YYY,'LIM_SO2',CCC) + $(M_POL(YYY,'LIM_SO2',CCC) EQ 0) = INF )); +LOOP(YYY, LOOP(CCC, M_POL(YYY,'LIM_NOX',CCC) + $(M_POL(YYY,'LIM_NOX',CCC) EQ 0) = INF )); + + + +PARAMETER CO2_PRICE(YYY) +/ +2005 50 +2006 50 +2007 50 +2008 50 +2009 50 +2010 50 +2011 50 +2012 50 +2013 70 +2014 90 +2015 110 +2016 130 +2017 150 +/; + +CO2_PRICE(YYY)$(YVALUE(YYY)>2017) = CO2_PRICE(YYY); +M_POL(YYY,'TAX_CO2',CCC)=CO2_PRICE(YYY)/7.42/1.3; + +$ifi %quickfix%==yes M_POL(YYY,'TAX_CO2',CCC)=150/7.42/1.3; \ No newline at end of file diff --git a/base/addons/gas/flowsets.inc b/base/addons/gas/flowsets.inc new file mode 100644 index 00000000..83bd9ab2 --- /dev/null +++ b/base/addons/gas/flowsets.inc @@ -0,0 +1,76 @@ +SET EDGE +/ +NYBRO_VARDE +VARDE_HELLE +HELLE_EGTVED +NYBRO_VARDE2 +VARDE_EGTVED2 +EGTVED_STANDST +STANDST_POTTEHUSE +POTTEHUSE_LISELSKAER +LISELSKAER_TERKELSBOEL +TERKELSBOEL_ELLUND +EGTVED_NOERSKOV +NOERSKOV_BRANDE +BRANDE_HERNING +HERNING_KARUP +KARUP_VIBORG +VIBORG_LITORUP +LITORUP_HAVERSLEV +HAVERSLEV_ELLIDSHOEJ +ELLIDSHOEJ_AALBORG +EGTVED_LILBALLE +LILBALLE_TAULOV +TAULOV_LYNGSODDE +LILLEBAELT_SYD +LILLEBAELT_NORD +MIDDELFART_BILLESBOELLE +BILLESBOELLE_KOELBJERG +KOELBJERG_HOEJBY +HOEJBY_ULLERSLEV +ULLERSLEV_NYBORG +TAULOV_SKAERBAEK +STOREBAELTSYD +STORBAELTNORD +KONGSMARK_SLAGELSE +SLAGELSE_SOROE +SOROE_RINGSTED +RINGSTED_KOEGE +KOEGE_KARLSLUNDE +KARLSLUNDE_TORSLUNDE +TORSLUNDE_VALLENSBAEK +VALLENSBAEK_BROENDBY +BROENDBY_HVIDOVRE +HVIDOVRE_VESTAMAGER +TORSLUNDE_MAALOEV +MAALOEV_LYNGE +TORSLUNDE_STENLILLE +VESTAMAGER_DRAGOER +HVIDOVRE_AVEDOERE +VESTAMAGER_AMAGERF +AMAGERF_SYDHAVNEN +SYDHAVNEN_HCOERSTED +DRAGOER_OERESUND +DUMMY1 +DUMMY2 +DUMMY3 +/; + +ALIAS(EDGE,EDGES,e); + +SETS DUMMY(e) /DUMMY1, DUMMY2, DUMMY3/; + + +SETS EDGEDATASET "Pipe specific datatypes" + / + L + WT + ID + KNOT + ROUGH + PRESSURE + LOSSFACT + /; + +SET l_plus1 'Steps in linearization' / l1* l3, lM/, + l(l_plus1) 'Steps in linearization' / l1*l3 /; \ No newline at end of file diff --git a/base/addons/gas/fuelp.inc b/base/addons/gas/fuelp.inc new file mode 100644 index 00000000..d9d52c12 --- /dev/null +++ b/base/addons/gas/fuelp.inc @@ -0,0 +1,221 @@ +* File Fuelp.inc + +* This file is part of the Balmorel model and should be used with the gas addon, +* Fuel prices are updated according to World Energy Outlook 2004 +* A transportation element to gas has be subtracted from DK and Swedish gas prices +* since this is repressented seperately in the gas addon +*------------------------------------------------------------------------------ + +* PARAMETER FUELPRICE contains fuel prices. +* Data source: +* IEA World Energy Outlook +* Energistyrrelsen: Forudsætninger for samfundsøkonomiske analyser + + +$ontext +* Units: EUR90/GJ +TABLE FUELPRICE(YYY,AAA,FFF) 'Fuel prices (Money/GJ)' + + DK_E_Urban.NUCLEAR DK_E_Urban.ORIMULSION DK_E_Urban.PEAT DK_E_Urban.WOOD_WASTE DK_E_Urban.COAL DK_E_Urban.FUELOIl DK_E_Urban.LIGHTOIl DK_E_Urban.STRAW DK_E_Urban.WOOD DK_E_Urban.NAT_GAS DK_E_Urban.WIND DK_E_Urban.WATER DK_E_Urban.SUN DK_E_Urban.ELECTRIC DK_E_Urban.MUNI_WASTE +2002 0.52 1.46 1.09 0.57 1.21 2.83 4.90 3.56 4.55 2.6621634204 0 0 0 0 0 +2003 0.52 1.46 1.09 0.57 1.20 2.67 4.62 3.56 4.55 2.6846252354 0 0 0 0 0 +2004 0.51 1.43 1.24 0.57 1.56 3.12 5.43 3.56 4.55 3.1675542560 0 0 0 0 0 +2005 0.60 1.59 1.47 0.57 1.72 4.35 7.64 3.56 4.55 4.1895668346 0 0 0 0 0 +2006 0.56 1.47 1.37 0.57 1.60 3.89 6.81 3.56 4.55 3.6617141841 0 0 0 0 0 +2007 0.50 1.32 1.21 0.57 1.43 3.14 5.46 3.56 4.55 3.1450924411 0 0 0 0 0 +2008 0.44 1.16 1.06 0.57 1.26 2.41 4.17 3.56 4.55 2.650932513 0 0 0 0 0 +2009 0.40 1.06 0.96 0.57 1.15 2.02 3.45 3.56 4.55 2.1792343998 0 0 0 0 0 +2010 0.40 1.06 0.96 0.57 1.15 2.02 3.45 3.56 4.55 2.1792343998 0 0 0 0 0 +2011 0.40 1.06 0.97 0.57 1.15 2.04 3.51 3.56 4.55 2.2129271221 0 0 0 0 0 +2012 0.40 1.07 0.98 0.57 1.16 2.07 3.57 3.56 4.55 2.2466198445 0 0 0 0 0 +2013 0.40 1.07 0.99 0.57 1.16 2.11 3.61 3.56 4.55 2.2803125669 0 0 0 0 0 +2014 0.40 1.08 1.00 0.57 1.17 2.13 3.67 3.56 4.55 2.3140052893 0 0 0 0 0 +2015 0.40 1.08 1.01 0.57 1.17 2.16 3.72 3.56 4.55 2.3476980116 0 0 0 0 0 +2016 0.41 1.09 1.02 0.57 1.19 2.19 3.77 3.56 4.55 2.3813907340 0 0 0 0 0 +2017 0.40 1.09 1.03 0.57 1.19 2.22 3.82 3.56 4.55 2.4150834564 0 0 0 0 0 +2018 0.40 1.10 1.04 0.57 1.20 2.25 3.88 3.56 4.55 2.4487761787 0 0 0 0 0 +2019 0.40 1.10 1.05 0.57 1.20 2.27 3.93 3.56 4.55 2.4824689011 0 0 0 0 0 +2020 0.40 1.11 1.06 0.57 1.21 2.31 3.98 3.56 4.55 2.5161616235 0 0 0 0 0 +2021 0.40 1.11 1.06 0.57 1.21 2.33 4.04 3.56 4.55 2.5498543459 0 0 0 0 0 +2022 0.41 1.12 1.10 0.57 1.22 2.36 4.08 3.56 4.55 2.5723161608 0 0 0 0 0 +2023 0.40 1.12 1.07 0.57 1.22 2.40 4.14 3.56 4.55 2.6060088831 0 0 0 0 0 +2024 0.40 1.13 1.08 0.57 1.23 2.42 4.18 3.56 4.55 2.6397016055 0 0 0 0 0 +2025 0.40 1.13 1.08 0.57 1.23 2.45 4.24 3.56 4.55 2.6733943279 0 0 0 0 0 +2026 0.40 1.14 1.09 0.57 1.24 2.48 4.30 3.56 4.55 2.7070870503 0 0 0 0 0 +2027 0.40 1.14 1.08 0.57 1.24 2.51 4.34 3.56 4.55 2.7407797726 0 0 0 0 0 +2028 0.41 1.15 1.09 0.57 1.25 2.54 4.40 3.56 4.55 2.7744724950 0 0 0 0 0 +2029 0.40 1.15 1.09 0.57 1.25 2.57 4.45 3.56 4.55 2.8081652714 0 0 0 0 0 +2030 0.40 1.16 1.10 0.57 1.26 2.60 4.50 3.56 4.55 2.8418579398 0 0 0 0 0 + ++ FI_R_Urban.NAT_GAS FI_R_Rural.NAT_GAS +2002 2.9874213836478 2.9874213836478 +2003 3.00988319856244 3.00988319856244 +2004 3.49281221922731 3.49281221922731 +2005 4.51482479784367 4.51482479784367 +2006 3.98697214734951 3.98697214734951 +2007 3.47035040431267 3.47035040431267 +2008 2.97619047619048 2.97619047619048 +2009 2.50449236298293 2.50449236298293 +2010 2.50449236298293 2.50449236298293 +2011 2.5381850853549 2.5381850853549 +2012 2.57187780772686 2.57187780772686 +2013 2.60557053009883 2.60557053009883 +2014 2.6392632524708 2.6392632524708 +2015 2.67295597484277 2.67295597484277 +2016 2.70664869721474 2.70664869721474 +2017 2.7403414195867 2.7403414195867 +2018 2.77403414195867 2.77403414195867 +2019 2.80772686433064 2.80772686433064 +2020 2.84141958670261 2.84141958670261 +2021 2.87511230907457 2.87511230907457 +2022 2.89757412398922 2.89757412398922 +2023 2.93126684636119 2.93126684636119 +2024 2.96495956873315 2.96495956873315 +2025 2.99865229110512 2.99865229110512 +2026 3.03234501347709 3.03234501347709 +2027 3.06603773584906 3.06603773584906 +2028 3.09973045822102 3.09973045822102 +2029 3.13342318059299 3.13342318059299 +2030 3.16711590296496 3.16711590296496 + ++ NO_N_Rural.NAT_GAS NO_S_Rural.NAT_GAS NO_M_Rural.NAT_GAS +2002 2.68913991164904 2.68913991164904 2.68913991164904 +2003 2.70926298509004 2.70926298509004 2.70926298509004 +2004 3.14353099730458 3.14353099730458 3.14353099730458 +2005 4.06433458724564 4.06433458724564 4.06433458724564 +2006 3.58843498769875 3.58843498769875 3.58843498769875 +2007 3.12303751356881 3.12303751356881 3.12303751356881 +2008 2.67833314270464 2.67833314270464 2.67833314270464 +2009 2.25384260687975 2.25384260687975 2.25384260687975 +2010 2.25384260687975 2.25384260687975 2.25384260687975 +2011 2.28396623216872 2.28396623216872 2.28396623216872 +2012 2.31409052163769 2.31409052163769 2.31409052163769 +2013 2.34521298478651 2.34521298478651 2.34521298478651 +2014 2.37523729478686 2.37523729478686 2.37523729478686 +2015 2.40536194535535 2.40536194535535 2.40536194535535 +2016 2.43548719470478 2.43548719470478 2.43548719470478 +2017 2.466604817413 2.466604817413 2.466604817413 +2018 2.49672976504027 2.49672976504027 2.49672976504027 +2019 2.52675631131094 2.52675631131094 2.52675631131094 +2020 2.55787041375975 2.55787041375975 2.55787041375975 +2021 2.58799574011757 2.58799574011757 2.58799574011757 +2022 2.60801302420032 2.60801302420032 2.60801302420032 +2023 2.63902277803879 2.63902277803879 2.63902277803879 +2024 2.66826777569281 2.66826777569281 2.66826777569281 +2025 2.69839584838846 2.69839584838846 2.69839584838846 +2026 2.7285243629644 2.7285243629644 2.7285243629644 +2027 2.75962912698317 2.75962912698317 2.75962912698317 +2028 2.78965990593594 2.78965990593594 2.78965990593594 +2029 2.8197886564553 2.8197886564553 2.8197886564553 +2030 2.84991781252976 2.84991781252976 2.8499178125297 + ++ SE_N_Rural.NAT_GAS SE_M_Rural.NAT_GAS SE_S_Rural.NAT_GAS SE_M_Urban.NAT_GAS +2002 2.95932331325086 2.95932331325086 2.95932331325086 2.95932331325086 +2003 2.98412386963915 2.98412386963915 2.98412386963915 2.98412386963915 +2004 3.51571389875435 3.51571389875435 3.51571389875435 3.51571389875435 +2005 4.64092000441868 4.64092000441868 4.64092000441868 4.64092000441868 +2006 4.06073031544654 4.06073031544654 4.06073031544654 4.06073031544654 +2007 3.48989450109786 3.48989450109786 3.48989450109786 3.48989450109786 +2008 2.94647683794668 2.94647683794668 2.94647683794668 2.94647683794668 +2009 2.427759977666 2.427759977666 2.427759977666 2.427759977666 +2010 2.427759977666 2.427759977666 2.427759977666 2.427759977666 +2011 2.46502353451866 2.46502353451866 2.46502353451866 2.46502353451866 +2012 2.50228637589287 2.50228637589287 2.50228637589287 2.50228637589287 +2013 2.53954853301547 2.53954853301547 2.53954853301547 2.53954853301547 +2014 2.57690966775905 2.57690966775905 2.57690966775905 2.57690966775905 +2015 2.6141704619345 2.6141704619345 2.6141704619345 2.6141704619345 +2016 2.65043739175204 2.65043739175204 2.65043739175204 2.65043739175204 +2017 2.68769847936471 2.68769847936471 2.68769847936471 2.68769847936471 +2018 2.72495897648137 2.72495897648137 2.72495897648137 2.72495897648137 +2019 2.76231787495464 2.76231787495464 2.76231787495464 2.76231787495464 +2020 2.79957719346211 2.79957719346211 2.79957719346211 2.79957719346211 +2021 2.83683599051202 2.83683599051202 2.83683599051202 2.83683599051202 +2022 2.86173724443255 2.86173724443255 2.86173724443255 2.86173724443255 +2023 2.89811205708091 2.89811205708091 2.89811205708091 2.89811205708091 +2024 2.93429263854268 2.93429263854268 2.93429263854268 2.93429263854268 +2025 2.97155115741072 2.97155115741072 2.97155115741072 2.97155115741072 +2026 3.00880920631912 3.00880920631912 3.00880920631912 3.00880920631912 +2027 3.04606680231925 3.04606680231925 3.04606680231925 3.04606680231925 +2028 3.08342146811042 3.08342146811042 3.08342146811042 3.08342146811042 +2029 3.12067816233499 3.12067816233499 3.12067816233499 3.12067816233499 +2030 3.15696145072706 3.15696145072706 3.15696145072706 3.15696145072706 +; + + +* Unless other values are assigned in the TABLE FUELPRICE above, +* the values for 'DK_E_Urban' are used for all A: + +FUELPRICE(YYY,IA,FFF)$(FUELPRICE(YYY,IA,FFF) EQ 0)=FUELPRICE(YYY,'DK_E_Urban',FFF); +$offtext + +$ifi %GAS%==yes FUELPRICE(Y,IA,'NAT_GAS')$(ICA('Denmark',IA)) = FUELPRICE(Y,IA,'NAT_GAS')-2.91/(1.398*7.43); + + +$ifi %FREEGAS%==yes FUELPRICE(YYY,IA,'NAT_GAS')$((ICA('Denmark',IA) or ICA('Sweden',IA)) +$ifi %FREEGAS%==yes and (not sameas('Nybro',IA)) +$ifi %FREEGAS%==yes and (not sameas('Ellund',IA)) +$ifi %FREEGAS%==yes and (not sameas('Oeresund',IA))) +$ifi %FREEGAS%==yes =0; + +PARAMETER GEXT_COST(YYY,SOURCE) 'Average Extraction Cost EURO/MWh', + GEX_PRICE(YYY,SOURCE,S) 'Export Price Money/MWh'; + +GEXT_COST(YYY,'Nybro')=FUELPRICE(YYY,'Nybro','NAT_GAS')*%NG_PROD_FRAC%; +GEXT_COST(YYY,'Oeresund')=FUELPRICE(YYY,'Oeresund','NAT_GAS')*%NG_PROD_FRAC%; + +GEX_PRICE(YYY,SOURCE,S)(IFUELP_Y('Nybro','NAT_GAS')*(1+WSF(S))+%NG_EXP_ADD%; + +$ontext +TABLE GEXT_COST(YYY,SOURCE) 'Average Extraction Cost EURO97/MWh' + + Nybro Oeresund +2003 3.00 3.00 +2004 3.00 3.00 +2005 3.00 3.00 +2006 3.00 3.00 +2007 3.00 3.00 +2008 3.00 3.00 +2009 3.00 3.00 +2010 3.00 3.00 +2011 3.00 3.00 +2012 3.00 3.00 +2013 3.00 3.00 +2014 3.00 3.00 +2015 3.00 3.00 +2016 3.00 3.00 +2017 3.00 3.00 +2018 3.00 3.00 +2019 3.00 3.00 +2020 3.00 3.00 +2021 3.00 3.00 +2022 3.00 3.00 +2023 3.00 3.00 +2024 3.00 3.00 +2025 3.00 3.00 + + Nybro Oeresund +2003 0.65 0.65 +2004 0.65 0.65 +2005 0.65 0.65 +2006 0.65 0.65 +2007 0.65 0.65 +2008 0.65 0.65 +2009 0.65 0.65 +2010 0.65 0.65 +2011 0.65 0.65 +2012 0.65 0.65 +2013 0.65 0.65 +2014 0.65 0.65 +2015 0.65 0.65 +2016 0.65 0.65 +2017 0.65 0.65 +2018 0.65 0.65 +2019 0.65 0.65 +2020 0.65 0.65 +2021 0.65 0.65 +2022 0.65 0.65 +2023 0.65 0.65 +2024 0.65 0.65 +2025 0.65 0.65 +; +$offtext \ No newline at end of file diff --git a/base/addons/gas/gasbal_1.inc b/base/addons/gas/gasbal_1.inc new file mode 100644 index 00000000..de6ecc5d --- /dev/null +++ b/base/addons/gas/gasbal_1.inc @@ -0,0 +1,252 @@ +* File: bal_1.out + +* Modified last time: 29-05-2002 by (rus), 2003.08.07 (hrv), 16-02-2004 (ld), 20060328(hr) + +* This file will print a table containing a energy balance for each country + + + + +* ----------------------------------------------------------------------------- + +PUT bal_1; + +* First print file headings: + +if ((bal_1.pw LT 32000), +bal_1.pw = 32000; +PUT "Version: " SYSTEM.TITLE /; +PUT "File name: bal_1.out" /; +PUT "Time-stamp: '" SYSTEM.DATE " " SYSTEM.TIME "'" /; +PUT "Case-identification: '" OCASEID.TS "'" //; + + +PUT "Energy balance" //; +PUT "Units: GWh" //; + +); + +* End of file headings. + +* ------------------------------------------------------------------------------------ +* If scenarios (i.e., more than base case) print scenario heading: +IF((CARD(SCENARIO) GT 1), +PUT /"*==============================================================================="/; +PUT "* " SCENARIO.TL " " SCENARIO.TE(SCENARIO) /; +PUT "*==============================================================================="//; +); +* End of scenario heading +* ------------------------------------------------------------------------------------ + +bal_1.LJ = 1; +PUT "Electricity balance for "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; +bal_1.LJ = 2; + +PUT "Total electricity generation (excl. heat pumps) ;" ; LOOP(C, PUT ( EZGqCY(C)*IOF0001):12:0 ; PUT ";";); PUT /; +PUT " - nuclear ;" ; LOOP(C, PUT ( SUM((IA,IGNUC,S,T)$((IAGK_Y(IA,IGNUC) or AGKN(IA,IGNUC)) and ICA(C,IA)),(VGE_T.l(IA,IGNUC,S,T)+VGEN_T.l(IA,IGNUC,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; +PUT " - natural gas ;" ; LOOP(C, PUT ( SUM((IA,IGE,S,T)$((IAGK_Y(IA,IGE) or AGKN(IA,IGE)) and ICA(C,IA) and GDATA(IGE,'GDFUEL')=2),(VGE_T.l(IA,IGE,S,T)+VGEN_T.l(IA,IGE,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; +PUT " - other thermal ;" ; LOOP(C, PUT ( SUM((IA,IGE,S,T)$((IAGK_Y(IA,IGE) or AGKN(IA,IGE)) and ICA(C,IA) and not (GDATA(IGE,'GDTYPE')=5) and not (GDATA(IGE,'GDFUEL')=2) and not IGWND(IGE) and not IGHYRR(IGE) and not IGHYRS(IGE) and not IGNUC(IGE)),(VGE_T.l(IA,IGE,S,T)+VGEN_T.l(IA,IGE,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; +PUT " - hydro ;" ; LOOP(C, PUT ( SUM((IA,G,S,T)$((IAGK_Y(IA,G) or AGKN(IA,G)) and (IGHYRR(G) or IGHYRS(G)) and ICA(C,IA)),(VGE_T.l(IA,G,S,T)+VGEN_T.l(IA,G,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; +PUT " - wind ;" ; LOOP(C, PUT ( SUM((IA,IGWND,S,T)$((IAGK_Y(IA,IGWND) or AGKN(IA,IGWND)) and ICA(C,IA)),(VGE_T.l(IA,IGWND,S,T)+VGEN_T.l(IA,IGWND,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; +PUT " (hereof CHP) ;" ; LOOP(C, PUT ( SUM((IA,G,S,T)$((IAGK_Y(IA,G) or AGKN(IA,G)) and (IGEXT(G) or IGBPR(G)) and ICA(C,IA)),(VGE_T.l(IA,G,S,T)+VGEN_T.l(IA,G,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; +PUT "Consumption for heat pumps ;" ; LOOP(C, PUT (-EZG13qCY(C)*IOF0001):12:0 ; PUT ";";)PUT /; /*sarah*/ +PUT "Net import from other Nordic countries ;" ; LOOP(C, PUT ( SUM((IRE,S,T)$CCCRRR(C,IRE), SUM(IRI$(not CCCRRR(C,IRI)), (VX_T.l(IRI,IRE,S,T)-VX_T.l(IRE,IRI,S,T))*IHOURSINST(S,T)))*IOF0001 ):12:0 ; PUT ";";)PUT /; +PUT "Net import from other countries ;" ; LOOP(C, PUT ( EX3qCY(C)*IOF0001):12:0 ; PUT ";";)PUT /; +PUT "Transmission loss ;" ; LOOP(C, PUT ( EXLqCY(C)*IOF0001):12:0 ; PUT ";";)PUT /; +PUT "Distribution loss ;" ; LOOP(C, PUT ( ELqCY(C)*IOF0001):12:0 ; PUT ";";)PUT /; +PUT "Electricity demand ;" ; LOOP(C, PUT ( EDqCY(C)*IOF0001):12:0 ; PUT ";";); PUT //; + + +bal_1.LJ = 1; +PUT "Natural gas balance for "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; +bal_1.LJ = 2; + +PUT "Total endogenous gas production ;" ; LOOP(C, PUT ( 0+IOF0001*SUM((S,T), VSUP.l('Nybro',s,t)*IHOURSINST(S,T))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; +PUT "Consumption of natural gas ;" ; LOOP(C, PUT ( IOF0001*SUM((IA,S,T), VGUD.l(IA,s,t)*IHOURSINST(S,T))$SAMEAS(C,'Denmark')+IOF0001*SUM((TRANSIT,S,T), VTRANSIT.l(TRANSIT,s,t)*IHOURSINST(S,T))$SAMEAS(C,'Sweden') ):12:0 ; PUT ";";); PUT /; +PUT " - for electricity and district heating ;" ; LOOP(C, PUT ( IOF0001*SUM((IA,S,T)$ICA(C,IA), + IHOURSINST(S,T)*sum(IGE$(IAGKN(IA,IGE) and GDATA(IGE,'GDFUEL') EQ 2), VGEN_T.l(IA,IGE,S,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IGE,IA))) ) + +IHOURSINST(S,T)*sum(IGH$(IAGKN(IA,IGH) and GDATA(IGH,'GDFUEL') EQ 2), GDATA(IGH,'GDCV')*VGHN_T.l(IA,IGH,S,T)/(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IGH,IA))) ) + +IHOURSINST(S,T)*sum(IGE$(IAGK_Y(IA,IGE) and GDATA(IGE,'GDFUEL') EQ 2),VGE_T.l(IA,IGE,S,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IGE,IA))) ) + +IHOURSINST(S,T)*sum(IGH$(IAGK_Y(IA,IGH) and GDATA(IGH,'GDFUEL') EQ 2),GDATA(IGH,'GDCV')*VGH_T.l(IA,IGH,S,T) /(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IGH,IA))) ) ) ):12:0 ; PUT ";";); PUT /; +PUT " - other consumption ;" ; LOOP(C, PUT ( IOF0001*SUM((IA,S,T)$ICA(C,IA), (IDGRES_Y(IA,S,T)+IDGOTH_Y(IA,S,T))*IHOURSINST(S,T)) ):12:0 ; PUT ";";); PUT /; +PUT "Exports of natural gas ;" ; LOOP(C, PUT ( 0+IOF0001*SUM((SOURCE,S,T), VEXP.l(SOURCE,S)*IHOURSINST(S,T))$SAMEAS(C,'Denmark')+IOF0001*SUM((TRANSIT,S,T), VTRANSIT.l(TRANSIT,S,T)*IHOURSINST(S,T))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; +PUT "Imports of natural gas ;" ; LOOP(C, PUT ( 0+IOF0001*SUM((SOURCE,S,T)$(not SAMEAS(SOURCE,'Nybro')), VSUP.l(SOURCE,S,T)*IHOURSINST(S,T))$SAMEAS(C,'Denmark')+IOF0001*SUM((TRANSIT,S,T), VTRANSIT.l(TRANSIT,S,T)*IHOURSINST(S,T))$SAMEAS(C,'Sweden') ):12:0 ; PUT ";";); PUT /; + +PUT /; + +bal_1.LJ = 1; +PUT "District heating balance for "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; +bal_1.LJ = 2; + +PUT "Total heat generation ;" ; LOOP(C, PUT ( HZGqCY(C)*IOF0001):12:0 ; PUT ";";); PUT /; +PUT " - natural gas ;" ; LOOP(C, PUT ( SUM((IA,IGH,S,T)$((IAGK_Y(IA,IGH) or AGKN(IA,IGH)) and ICA(C,IA) and (GDATA(IGH,'GDFUEL')=2)),(VGH_T.l(IA,IGH,S,T)+VGHN_T.l(IA,IGH,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; +PUT " - other ;" ; LOOP(C, PUT ( SUM((IA,IGH,S,T)$((IAGK_Y(IA,IGH) or AGKN(IA,IGH)) and ICA(C,IA) and not (GDATA(IGH,'GDFUEL')=2) and not (GDATA(IGH,'GDTYPE')=6)),(VGH_T.l(IA,IGH,S,T)+VGHN_T.l(IA,IGH,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; +PUT "Distribution loss ;" ; LOOP(C, PUT ( HLqCY(C)*IOF0001):12:0 ; PUT ";";)PUT /; +PUT "Heat demand ;" ; LOOP(C, PUT ( HDqCY(C)*IOF0001):12:0 ; PUT ";";); PUT //; + + + +*----------------------------------- +* Investments: +*----------------------------------- + +$ifi not %BB2%==yes $goto BAL1_INVEST_END + + +PUT "Investments" //; +PUT "Units: Mio EURO6" //; + + + +* End of file headings. + +* ------------------------------------------------------------------------------------ +* If scenarios (i.e., more than base case) print scenario heading: +IF((CARD(SCENARIO) GT 1), +PUT /"*==============================================================================="/; +PUT "* " SCENARIO.TL " " SCENARIO.TE(SCENARIO) /; +PUT "*==============================================================================="//; +); +* End of scenario heading +* ------------------------------------------------------------------------------------ + +bal_1.LJ = 1; +PUT "Electricity generation and infrastructure MW el "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; +bal_1.LJ = 2; + +PUT "Total electricity generation (excl. heat pumps) ;" ; LOOP(C, PUT ( SUM((IA,IGE)$((not IGETOH(IGE)) and IAGKN(IA,IGE) and ICA(C,IA)), VGKN.l(IA,IGE))):12:0 ;PUT ";"; ); PUT /; +PUT " - nuclear ;" ; LOOP(C, PUT ( SUM((IA,IGNUC)$(IAGKN(IA,IGNUC) and ICA(C,IA)),VGKN.l(IA,IGNUC))):12:0 ; PUT ";";); PUT /; +PUT " - natural gas ;" ; LOOP(C, PUT ( SUM((IA,IGE)$(IAGKN(IA,IGE) and ICA(C,IA) and GDATA(IGE,'GDFUEL')=2),VGKN.l(IA,IGE))):12:0 ; PUT ";";); PUT /; +PUT " - other thermal ;" ; LOOP(C, PUT ( SUM((IA,IGE)$(IAGKN(IA,IGE) and ICA(C,IA) and not (GDATA(IGE,'GDTYPE')=5) and not (GDATA(IGE,'GDFUEL')=2) and not IGWND(IGE) and not IGHYRR(IGE) and not IGHYRS(IGE) and not IGNUC(IGE)),VGKN.l(IA,IGE))):12:0 ; PUT ";";); PUT /; +PUT " - hydro ;" ; LOOP(C, PUT ( SUM((IA,G)$(IAGKN(IA,G) and (IGHYRR(G) or IGHYRS(G)) and ICA(C,IA)),VGKN.l(IA,G))):12:0 ; PUT ";";); PUT /; +PUT " - wind ;" ; LOOP(C, PUT ( SUM((IA,IGWND)$(IAGKN(IA,IGWND) and ICA(C,IA)),VGKN.l(IA,IGWND))):12:0 ; PUT ";";); PUT /; +PUT " (hereof CHP) ;" ; LOOP(C, PUT ( SUM((IA,G)$(IAGKN(IA,G) and (IGEXT(G) or IGBPR(G)) and ICA(C,IA)),VGKN.l(IA,G))):12:0 ; PUT ";";); PUT /; +PUT "Electric heating ;" ; LOOP(C, PUT ( SUM((IA,IGETOH)$(IAGKN(IA,IGETOH) and ICA(C,IA)), VGKN.l(IA,IGETOH))):12:0 ; PUT ";";); PUT /; +*PUT "Transmission capacity ;" ; LOOP(C, PUT ( SUM(IRE$CCCRRR(C,IRE), SUM(VXKN.l(IRE,IRE) ))):12:0 ; PUT ";";)PUT /; + +PUT //; + +bal_1.LJ = 1; +PUT "Natural gas capacity gained from investments "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; +bal_1.LJ = 2; + +PUT "Total new supply capacity (MWh/h) ;" ; LOOP(C, PUT ( sum((NGINV,SOURCE),SUPEFFECT(NGINV,SOURCE)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; +PUT "Total new delivery capacity (MWh/h) ;" ; LOOP(C, PUT ( sum((GNETSTEPS,SYSEXIT), GNET_DEV_EFFECT(C,SYSEXIT)*VGNETINV.l(C,GNETSTEPS))+ sum((NGINV,SYSEXIT),DEVEFFECT(NGINV,SYSEXIT)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; +PUT " - onshore gas transmission network ;" ; LOOP(C, PUT ( sum((GNETSTEPS,SYSEXIT), GNET_DEV_EFFECT(C,SYSEXIT)*VGNETINV.l(C,GNETSTEPS)) ):12:0 ; PUT ";";); PUT /; +PUT " - compressors and interconnectors ;" ; LOOP(C, PUT ( sum((NGINV,SYSEXIT),DEVEFFECT(NGINV,SYSEXIT)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; +PUT "Total new export/transit capacity (MWh/h) ;" ; LOOP(C, PUT ( sum((GNETSTEPS,SOURCE), GNET_EXP_EFFECT(C,SOURCE)*VGNETINV.l(C,GNETSTEPS)) + sum((NGINV,SOURCE),EXPEFFECT(NGINV,SOURCE)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ):12:0 ;PUT ";"; ); PUT /; +PUT " - onshore gas transmission network ;" ; LOOP(C, PUT ( sum((GNETSTEPS,SOURCE), GNET_EXP_EFFECT(C,SOURCE)*VGNETINV.l(C,GNETSTEPS)) ):12:0 ; PUT ";";); PUT /; +PUT " - compressors and interconnectors ;" ; LOOP(C, PUT ( sum((NGINV,SOURCE),EXPEFFECT(NGINV,SOURCE)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; +PUT "Gas storage capacity ;" ; LOOP(C, PUT "-" ; ); PUT /; +PUT " - volumetric capacity (MWh) ;" ; LOOP(C, PUT ( sum((STORE,STORETYPE)$ICA(C,STORE),VGINVSTORE.l(STORE,STORETYPE)) ):12:0 ; PUT ";";); PUT /; +PUT " - injection capacity (MWh/h) ;" ; LOOP(C, PUT ( sum((STORE,STORETYPE)$ICA(C,STORE),GSTOREDATA(STORETYPE,'Injeffect')*VGINVSTORE.l(STORE,STORETYPE)+VGINVINJ.l(STORE,STORETYPE)) ):12:0 ; PUT ";";); PUT /; +PUT " - extraction capacity (MWh/h) ;" ; LOOP(C, PUT ( sum((STORE,STORETYPE)$ICA(C,STORE),GSTOREDATA(STORETYPE,'Exteffect')*VGINVSTORE.l(STORE,STORETYPE)+VGINVEXT.l(STORE,STORETYPE)) ):12:0 ; PUT ";";); PUT /; +PUT "Total gas consumption capacity (MWh/h) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,IGCND)$(ICA(C,IA) and GDATA(IGCND,'GDFUEL')=2), VGKN.l(IA,IGCND)/GDATA(IGCND,'GDFE') ) + SUM(IAGKN(IA,IGE)$(ICA(C,IA) and (IGBPR(IGE) or IGEXT(IGE)) and GDATA(IGE,'GDFUEL')=2), ( (VGKN.l(IA,IGE)+VGKN.l(IA,IGE)/GDATA(IGE,'GDCB'))$IGBPR(IGE) + VGKN.l(IA,IGE)$IGEXT(IGE) )/GDATA(IGE,'GDFE') ) + SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and GDATA(IGHH,'GDFUEL')=2), VGKN.l(IA,IGHH)/GDATA(IGHH,'GDFE') ) ):12:0 ; PUT ";";); PUT /; +PUT " - condensing gas-fired electricity generation ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,IGCND)$(ICA(C,IA) and GDATA(IGCND,'GDFUEL')=2), VGKN.l(IA,IGCND)/GDATA(IGCND,'GDFE') ) ):12:0 ; PUT ";";); PUT /; +PUT " - combined heat and power (gas-fired) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,IGE)$(ICA(C,IA) and (IGBPR(IGE) or IGEXT(IGE)) and GDATA(IGE,'GDFUEL')=2), ( (VGKN.l(IA,IGE)+VGKN.l(IA,IGE)/GDATA(IGE,'GDCB'))$IGBPR(IGE) + VGKN.l(IA,IGE)$IGEXT(IGE) )/GDATA(IGE,'GDFE') ) ):12:0 ; PUT ";";); PUT /; +PUT " - heat boilers (gas-fired) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and GDATA(IGHH,'GDFUEL')=2), VGKN.l(IA,IGHH)/GDATA(IGHH,'GDFE') ) ):12:0 ; PUT ";";); PUT /; + +PUT //; + +bal_1.LJ = 1; +PUT "District heating capacity gained from investments "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; +bal_1.LJ = 2; + +PUT "Total heat generation capacity from investments ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,G)$(ICA(C,IA) and (IGEXT(G) or IGBPR(G))), VGKN.l(IA,G)/GDATA(G,'GDCB') ) + SUM(IAGKN(IA,IGHH), VGKN.l(IA,IGHH)) ):12:0 ; PUT ";";); PUT /; +PUT " - CHP (natural gas-fired) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,G)$(ICA(C,IA) and (IGEXT(G) or IGBPR(G)) and (GDATA(G,'GDFUEL') =2)), VGKN.l(IA,G)/GDATA(G,'GDCB') ) ):12:0 ; PUT ";";); PUT /; +PUT " - CHP (other fuels) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,G)$(ICA(C,IA) and (IGEXT(G) or IGBPR(G)) and (not GDATA(G,'GDFUEL') =2) ), VGKN.l(IA,G)/GDATA(G,'GDCB')) ):12:0 ; PUT ";";); PUT /; +PUT " - heat only (natural gas-fired) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and GDATA(IGHH,'GDFUEL') =2), VGKN.l(IA,IGHH) ) ):12:0 ; PUT ";";); PUT /; +PUT " - heat only (other fuels) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and not GDATA(IGHH,'GDFUEL') =2), VGKN.l(IA,IGHH) ) ):12:0 ; PUT ";";); PUT /; +PUT //; + +PUT "*==============================================================================="//; + + +bal_1.LJ = 1; +PUT "Electricity generation and infrastructure Mio EURO06 "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; +bal_1.LJ = 2; + +PUT "Total electricity generation (excl. heat pumps) ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,IGE)$((not IGETOH(IGE)) and IAGKN(IA,IGE) and ICA(C,IA)), GDATA(IGE,'GDINVCOST0')*VGKN.l(IA,IGE))):12:0 ; PUT ";";); PUT /; +PUT " - nuclear ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,IGNUC)$(IAGKN(IA,IGNUC) and ICA(C,IA)),GDATA(IGNUC,'GDINVCOST0')*VGKN.l(IA,IGNUC))):12:0 ; PUT ";";); PUT /; +PUT " - natural gas ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,IGE)$(IAGKN(IA,IGE) and ICA(C,IA) and GDATA(IGE,'GDFUEL')=2),GDATA(IGE,'GDINVCOST0')*VGKN.l(IA,IGE))):12:0 ; PUT ";";); PUT /; +PUT " - other thermal ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,IGE)$(IAGKN(IA,IGE) and ICA(C,IA) and not (GDATA(IGE,'GDTYPE')=5) and not (GDATA(IGE,'GDFUEL')=2) and not IGWND(IGE) and not IGHYRR(IGE) and not IGHYRS(IGE) and not IGNUC(IGE)),GDATA(IGE,'GDINVCOST0')*VGKN.l(IA,IGE))):12:0 ; PUT ";";); PUT /; +PUT " - hydro ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,G)$(IAGKN(IA,G) and (IGHYRR(G) or IGHYRS(G)) and ICA(C,IA)),GDATA(G,'GDINVCOST0')*VGKN.l(IA,G))):12:0 ; PUT ";";); PUT /; +PUT " - wind ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,IGWND)$(IAGKN(IA,IGWND) and ICA(C,IA)),GDATA(IGWND,'GDINVCOST0')*VGKN.l(IA,IGWND))):12:0 ; PUT ";";); PUT /; +PUT " (hereof CHP) ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,G)$(IAGKN(IA,G) and (IGEXT(G) or IGBPR(G)) and ICA(C,IA)),GDATA(G,'GDINVCOST0')*VGKN.l(IA,G))):12:0 ; PUT ";";); PUT /; +PUT "Electric heating ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,IGETOH)$(IAGKN(IA,IGETOH) and ICA(C,IA)), GDATA(IGETOH,'GDINVCOST0')*VGKN.l(IA,IGETOH))):12:0 ; PUT ";";); PUT /; +*PUT "Transmission capacity ;" ; LOOP(C, PUT ( OMONEY*SUM(IRE$CCCRRR(C,IRE), SUM(VXKN.l(IRE,IRE) ))):12:0 ; PUT ";";)PUT /; MANGLER INVCOST + +PUT //; + +bal_1.LJ = 1; +PUT "Natural gas infrastructure Mio EURO06 "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; +bal_1.LJ = 2; + +PUT "Total investments in transmision infrastructure ;" ; LOOP(C, PUT ( OMONEY*IOF0000001*( sum(GNETSTEPS, GNET_COST(C,GNETSTEPS)*VGNETINV.l(C,GNETSTEPS)) + sum(NGINV,COST_NGINV(NGINV)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ) ):12:0 ; PUT ";";); PUT /; +PUT " - onshore gas transmission network ;" ; LOOP(C, PUT ( OMONEY*IOF0000001* sum(GNETSTEPS, GNET_COST(C,GNETSTEPS)*VGNETINV.l(C,GNETSTEPS)) ):12:0 ; PUT ";";); PUT /; +PUT " - compressors and interconnectors ;" ; LOOP(C, PUT ( OMONEY*IOF0000001* sum(NGINV,COST_NGINV(NGINV)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; +PUT "Total investments in natural gas storage ;" ; LOOP(C, PUT ( OMONEY*IOF0000001* sum((STORE,STORETYPE)$ICA(C,STORE),GSTOREDATA(STORETYPE,'MargExtInvCost')*VGINVEXT.l(STORE,STORETYPE)+GSTOREDATA(STORETYPE,'MargInjInvCost')*VGINVINJ.l(STORE,STORETYPE)+GSTOREDATA(STORETYPE,'INVCOST')*VGINVSTORE.l(STORE,STORETYPE))/GASANNUITY ):12:0 ; PUT ";";); PUT /; +PUT " - gas storage facilities ;" ; LOOP(C, PUT ( OMONEY*IOF0000001* sum((STORE,STORETYPE)$ICA(C,STORE),GSTOREDATA(STORETYPE,'INVCOST')*VGINVSTORE.l(STORE,STORETYPE))/GASANNUITY ):12:0 ; PUT ";";); PUT /; +PUT " - injection capacity upgrades ;" ; LOOP(C, PUT ( OMONEY*IOF0000001* sum((STORE,STORETYPE)$ICA(C,STORE),GSTOREDATA(STORETYPE,'MargInjInvCost')*VGINVINJ.l(STORE,STORETYPE))/GASANNUITY ):12:0 ; PUT ";";); PUT /; +PUT " - extraction capacity expansion ;" ; LOOP(C, PUT ( OMONEY*IOF0000001* sum((STORE,STORETYPE)$ICA(C,STORE),GSTOREDATA(STORETYPE,'MargExtInvCost')*VGINVEXT.l(STORE,STORETYPE))/GASANNUITY ):12:0 ; PUT ";";); PUT /; +PUT "Total gas consumption capacity ;" ; LOOP(C, PUT ( OMONEY* SUM(IAGKN(IA,G)$(ICA(C,IA) and GDATA(G,'GDFUEL')=2), GDATA(G,'GDINVCOST0')*VGKN.l(IA,G)) ):12:0 ; PUT ";";); PUT /; +PUT " - condensing gas-fired electricity generation ;" ; LOOP(C, PUT ( OMONEY* SUM(IAGKN(IA,IGCND)$(ICA(C,IA) and GDATA(IGCND,'GDFUEL')=2), GDATA(IGCND,'GDINVCOST0')*VGKN.l(IA,IGCND) ) ):12:0 ; PUT ";";); PUT /; +PUT " - combined heat and power (gas-fired) ;" ; LOOP(C, PUT ( OMONEY* SUM(IAGKN(IA,IGE)$(ICA(C,IA) and (IGBPR(IGE) or IGEXT(IGE)) and GDATA(IGE,'GDFUEL')=2), GDATA(IGE,'GDINVCOST0')*VGKN.l(IA,IGE) ) ):12:0 ; PUT ";";); PUT /; +PUT " - heat boilers (gas-fired) ;" ; LOOP(C, PUT ( OMONEY* SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and GDATA(IGHH,'GDFUEL')=2), GDATA(IGHH,'GDINVCOST0')*VGKN.l(IA,IGHH) ) ):12:0 ; PUT ";";); PUT /; + +PUT //; + +bal_1.LJ = 1; +PUT "Investments in capacity for district heating "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; +bal_1.LJ = 2; + +PUT "Total heat generation capacity from investments ;" ; LOOP(C, PUT ( OMONEY*SUM(IAGKN(IA,G)$(ICA(C,IA) and (IGEXT(G) or IGBPR(G))), GDATA(G,'GDINVCOST0')*VGKN.l(IA,G) ) + SUM(IAGKN(IA,IGHH), GDATA(IGHH,'GDINVCOST0')*VGKN.l(IA,IGHH)) ):12:0 ; PUT ";";); PUT /; +PUT " - CHP (natural gas-fired) ;" ; LOOP(C, PUT ( OMONEY*SUM(IAGKN(IA,G)$(ICA(C,IA) and (IGEXT(G) or IGBPR(G)) and (GDATA(G,'GDFUEL') =2)), GDATA(G,'GDINVCOST0')*VGKN.l(IA,G) ) ):12:0 ; PUT ";";); PUT /; +PUT " - CHP (other fuels) ;" ; LOOP(C, PUT ( OMONEY*SUM(IAGKN(IA,G)$(ICA(C,IA) and (IGEXT(G) or IGBPR(G)) and (not GDATA(G,'GDFUEL') =2) ), GDATA(G,'GDINVCOST0')*VGKN.l(IA,G) ) ):12:0 ; PUT ";";); PUT /; +PUT " - heat only (natural gas-fired) ;" ; LOOP(C, PUT ( OMONEY*SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and GDATA(IGHH,'GDFUEL') =2), GDATA(IGHH,'GDINVCOST0')*VGKN.l(IA,IGHH) ) ):12:0 ; PUT ";";); PUT /; +PUT " - heat only (other fuels) ;" ; LOOP(C, PUT ( OMONEY*SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and not GDATA(IGHH,'GDFUEL') =2), GDATA(IGHH,'GDINVCOST0')*VGKN.l(IA,IGHH) ) ):12:0 ; PUT ";";); PUT /; +PUT //; + +$label BAL1_INVEST_END + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/base/addons/gas/gasbb1.inc b/base/addons/gas/gasbb1.inc new file mode 100644 index 00000000..495ca538 --- /dev/null +++ b/base/addons/gas/gasbb1.inc @@ -0,0 +1,10 @@ +QGEQ +QCONSUMPTION +QStoreVol +QStoreInj +QStoreExt +QTransEntryCap +QTransExitCap +QGSTOREBAL +QGSVERIGE +QGDEV_MAX \ No newline at end of file diff --git a/base/addons/gas/gasbb2.inc b/base/addons/gas/gasbb2.inc new file mode 100644 index 00000000..e3163a65 --- /dev/null +++ b/base/addons/gas/gasbb2.inc @@ -0,0 +1,16 @@ +QGEQ +QCONSUMPTION +$ifi %BB2KAPPROD%==yes QStoreVol +$ifi %BB2KAPPROD%==yes QStoreInj +$ifi %BB2KAPPROD%==yes QStoreExt +$ifi %BB2KAPPROD%==yes QTransEntryCap +$ifi %BB2KAPPROD%==yes QTransExitCap +QGSTOREBAL +QGSVERIGE +QGSUPPLY_MAX +QGINJ_MAX +QGEXT_MAX +QGSTORE_MAX +QGEXP_MAX +QGTRANSIT_MAX +QGDEV_MAX \ No newline at end of file diff --git a/base/addons/gas/gasbb3.inc b/base/addons/gas/gasbb3.inc new file mode 100644 index 00000000..48d9120a --- /dev/null +++ b/base/addons/gas/gasbb3.inc @@ -0,0 +1,9 @@ +QGEQ +QCONSUMPTION +QStoreInj +QStoreExt +QTransEntryCap +QTransExitCap +QGSFXW +QGSVERIGE +QGDEV_MAX \ No newline at end of file diff --git a/base/addons/gas/gascompprint1.inc b/base/addons/gas/gascompprint1.inc new file mode 100644 index 00000000..b9a9ea28 --- /dev/null +++ b/base/addons/gas/gascompprint1.inc @@ -0,0 +1,12 @@ +* File gascompprint1.inc. +* This file is an extension to the welfare calculation module for the Balmorel model. +* Created by Jens Hauch and Thomas E. Pedersen, COWI A/S (DK). +* Adapted for Balmorel version 2.10, 2.13 by Hans Ravn. +* Extended to for the gas add-on by Lars Bregnbæk, X-mas 2006. + + +* These files are used for comparison between scenarios +File gascompfile /..\compare\GAS%CASEID%.MED/; + + + diff --git a/base/addons/gas/gascompprint2.inc b/base/addons/gas/gascompprint2.inc new file mode 100644 index 00000000..2b062826 --- /dev/null +++ b/base/addons/gas/gascompprint2.inc @@ -0,0 +1,122 @@ +* File gascompprint2.inc. +* This file is an extension to the welfare calculation module for the Balmorel model. +* Created by Jens Hauch and Thomas E. Pedersen, COWI A/S (DK). +* Adapted for Balmorel version 2.10, 2.13 by Hans Ravn. +* Extended to for the gas add-on by Lars Bregnbæk, X-mas 2006. + + + +*=============================================================================== +* Overview +*=============================================================================== + +* This file contains +* data +* declarations of internal parameters. + +* Data: +* scalar irt Interest rate; +* parameter tlifetime Life time for transmission capacity - years; + + +* Comments on terminology: +* p: prices +* e: electricity (pe: electricity price) +* h: heat +* psur: producers surplus +* earnings: producers surplus (at national level - so the terminology seems slightly inconsistent) +* cw: consumers welfare (cwe: electricity, cwh: heat) +* dc: discounted +* dlt, delta: difference + +* Note that internal parameters do not obey to the conventions that their name should start with 'I'. + + + +*=============================================================================== +* Overview end +*=============================================================================== + +PARAMETERS + residualcosts(Y,C) 'Gas costs for residual consumers (Input currency)' + residualcosts00(Y,C) 'Gas costs for residual consumers (Input currency)' + dresidualbenifit(C) 'Discounted welfare change for residual consumers (Input currency)' + + othercosts(Y,C) 'Gas costs for other consumers (Input currency)' + othercosts00(Y,C) 'Gas costs for other consumers (Input currency)' + dotherbenifit(C) 'Discounted welfare change for residual consumers (Input currency)' + + DSOrevenue(Y,C) 'Revenue for distribution companies' + DSOcosts(Y,C) + DSOprofit(Y,C) 'Profits for distribution companies (Input currency)' + DSOprofit00(Y,C) 'Profits for distribution companies (Input currency)' + dcdltDSOprofit(C) 'Discounted welfare change for distribution companies (Input currency)' + + TSOCAPrev(Y,C) 'TSO Revenue from capacity products' + TSOCAPrev00(Y,C) 'TSO Revenue from capacity products' + dcdltTSOCAPrevbenefit(C) 'Discounted welfare change in TSO from Capacity products (Input currency)' + + TSOVOLrev(Y,C) 'TSO volumetric revenue' + TSOVOLrev00(Y,C) 'TSO volumetric revenue' + dcdltTSOVOLrevbenefit(C) 'Discounted welfare change in TSO from Volumetic tariffs (Input currency)' + + TSOCAPRent(Y,C) 'TSO Capacity rent' + TSOCAPRent00(Y,C) 'TSO Capacity rent' + dcdltTSOCAPRentbenefit(C) 'Discounted welfare change in TSO Capacity rent (Input currency)' + + TSOfixedcosts(Y,C) + TSOfixedcosts00(Y,C) + TSOvariablecosts(Y,C) + TSOvariablecosts00(Y,C) + TSOprofit(Y,C) + TSOprofit00(Y,C) + + dcdltTSOProfit(C) 'Discounted welfare change in TSO profit (Input currency)' + + SSOCAPrev(Y,C) 'SSO Capacity revenue' + SSOCAPrev00(Y,C) 'SSO Capacity revenue' + dcdltSSOCAPrevbenefit(C) 'Discounted welfare change in SSO Capacity revenue (Input currency)' + + SSOVOLrev(Y,C) 'SSO volumetric revenue' + SSOVOLrev00(Y,C) 'SSO volumetric revenue' + dcdltSSOVOLrevbenefit(C) 'Discounted welfare change in SSO volumetric revenue (Input currency)' + + SSOCAPrent(Y,C) 'SSO Capacity rent' + SSOCAPrent00(Y,C) 'SSO Capacity rent' + dcdltSSOCAPrentbenefit(C) 'Discountred welfare change in SSO Capacity rent (Input currency)' + + SSOfixedcosts(Y,C) + SSOvariablecosts(Y,C) + + SSOprofit(Y,C) + SSOprofit00(Y,C) + dcdltSSOProfit(C) 'Discounted welfare change in SSO profits (Input currency)' + + GasSupplierCosts(Y,C) + GasSupplierRevenue(Y,C) + GasSupplierProfit(Y,C) + GasSupplierProfit00(Y,C) + dcdltGasSupplierProfit(C) 'Discounted welfare change for gas suppliers (Input currency)' + + GasShipperCosts(Y,C) + GasShipperRevenue(Y,C) + GasShipperProfit(Y,C) + GasShipperProfit00(Y,C) + dcdltGasShipperProfit(C) 'Discounted welfare change for gas shippers (Input currency)' + + GasProducerCosts(Y,C) + GasProducerRevenue(Y,C) + GasProducerProfit(Y,C) + GasProducerProfit00(Y,C) + dcdltGasProducerProfit(C) 'Discounted welfare change for gas shippers (Input currency)' + + GasStorageUserCost(Y,C) + GasStorageUserRevenue(Y,C) + GasStorageUserProfit(Y,C) + GasStorageUserProfit00(Y,C) + dcdltGasStorageUserProfit(C) + + ShipperCapRent(Y,C) + ShipperCapRent00(Y,C) + dcdltShipperCapRent(C) +; \ No newline at end of file diff --git a/base/addons/gas/gascompprint3.inc b/base/addons/gas/gascompprint3.inc new file mode 100644 index 00000000..fab34af9 --- /dev/null +++ b/base/addons/gas/gascompprint3.inc @@ -0,0 +1,221 @@ +* File gascompprint2.inc. +* This file is an extension to the welfare calculation module for the Balmorel model. +* Created by Jens Hauch and Thomas E. Pedersen, COWI A/S (DK). +* Adapted for Balmorel version 2.10, 2.13 by Hans Ravn. +* Extended to for the gas add-on by Lars Bregnbæk, X-mas 2006. + + +*=============================================================================== +* Overview +*=============================================================================== + +* This file contains calculation of intermediate parameter values. + +* There are 6 parts: gas producers, gasconsumers (except heat and power generators), +* TSOs, Distributors, Suppliers, Storageoperators, Exporers. + +* Much of the code in this file may seem superfluous. The development phylosophy +* has been to prepare the code for any forseeable additions with regard to agent +* detail. For instance many agentgroups have associated costs or profits which are +* set to zero as there is at present no available data, nor an accurate repressentation +* of these factors in the model. + +*=============================================================================== +* End: Overview +*=============================================================================== + +*=============================================================================== +* Gas Producers +*=============================================================================== + +* Only Danish natural gas production considered. Should be modified for new investments. +GasProducerRevenue(Y,'Denmark')= Sum((S,T),(IGEXT_COST('Nybro',S)*IOF3P6 + IOF1000000*(-1*VSUP.m('Nybro',S,T)/IHOURSINST(S,T)))*VSUP.l('Nybro',S,T)*IHOURSINST(S,T)); + +* Artificially set to zero +GasProducerCosts(Y,C) = 0; + +GasProducerProfit(Y,C) = GasProducerRevenue(Y,C)-GasProducerCosts(Y,C); + + + +*=============================================================================== +* Gas Consumers +*=============================================================================== + + +* Gasconsumers (except heat and power generators) + +* Costs to residual consumers (e.g. private homes and small businesses) (Input currency) +residualcosts(Y,C) = SUM((IA,S,T)$ICA(C,IA), IDGRES_Y(IA,S,T)*IOF1000000 *(QCONSUMPTION.m(IA,S,T)$ICA('Denmark',IA) + +QGSVERIGE.m(S,T)$ICA('Sweden',IA))); + +* Costs to other consumers (e.g. Industry) (Input currency) +othercosts(Y,C) = SUM((IA,S,T)$ICA(C,IA), IDGOTH_Y(IA,S,T)*IOF1000000 *(QCONSUMPTION.m(IA,S,T)$ICA('Denmark',IA) + +QGSVERIGE.m(S,T)$ICA('Sweden',IA))); + +*=============================================================================== +* Gas Distributors +*=============================================================================== + +* Distributioners revenue. +DSOrevenue(Y,C) = 1/7.43/1.389*SUM((DDD,IA,S,T)$(DDDNODE(DDD,IA) and ICA(C,IA)), TAU_DIST(DDD)*VGUD.l(IA,S,T)*IHOURSINST(S,T))$SAMEAS(C,'Denmark') + +1/7.43/1.389*SUM((TRANSIT,S,T), TAU_DIST('SE_S_DIST')*VTRANSIT.l(TRANSIT,S,T)*IHOURSINST(S,T))$SAMEAS(C,'Sweden'); + +* Distributor costs artificially set to zero. +DSOcosts(Y,C) = 0; + +DSOprofit(Y,C) = DSOrevenue(Y,C)-DSOcosts(Y,C); + +*=============================================================================== +* Gas TSOs +*=============================================================================== + +* TSO capacity revenue (Input currency) +TSOCAPrev(Y,'Denmark') = +1/7.43/1.389*( +$ifi %bb1%==yes + SUM(SYSENTRY, TAU_EN_Y*VTC_EN_Y.l(SYSENTRY) ) +$ifi %bb1%==yes + SUM(SYSEXIT, TAU_EX_Y*VTC_EX_Y.l(SYSEXIT) ) +$ifi %bb1%==yes + SUM(SYSENTRY, SUM(IMONTH, TAU_EN_M(IMONTH)*VTC_EN_M.l(SYSENTRY,IMONTH)*MONTHSCALE(IMONTH)) ) +$ifi %bb1%==yes + SUM(SYSEXIT, SUM(IMONTH, TAU_EX_M(IMONTH)*VTC_EX_M.l(SYSEXIT,IMONTH)*MONTHSCALE(IMONTH)) ) +$ifi %bb2%==yes + SUM(SYSENTRY, TAU_EN_Y*VTC_EN_Y.l(SYSENTRY) ) +$ifi %bb2%==yes + SUM(SYSEXIT, TAU_EX_Y*VTC_EX_Y.l(SYSEXIT) ) +$ifi %bb2%==yes + SUM(SYSENTRY, SUM(IMONTH, TAU_EN_M(IMONTH)*VTC_EN_M.l(SYSENTRY,IMONTH)*MONTHSCALE(IMONTH)) ) +$ifi %bb2%==yes + SUM(SYSEXIT, SUM(IMONTH, TAU_EX_M(IMONTH)*VTC_EX_M.l(SYSEXIT,IMONTH)*MONTHSCALE(IMONTH)) ) ++ SUM(SYSENTRY, sum(IS3,IDAYSIN_S(IS3)/7*(TAU_EN_W(IS3)*VTC_EN_W.l(SYSENTRY,IS3))) ) ++ SUM(SYSEXIT, sum(IS3,IDAYSIN_S(IS3)/7*(TAU_EX_W(IS3)*VTC_EX_W.l(SYSEXIT,IS3))) ) +$ifi %bb3%==yes + SUM(SYSENTRY, TAU_EN_Y*ITC_EN_Y(SYSENTRY) ) +$ifi %bb3%==yes + SUM(SYSEXIT, TAU_EX_Y*ITC_EX_Y(SYSEXIT) ) +$ifi %bb3%==yes + SUM(SYSENTRY, SUM(IMONTH, TAU_EN_M(IMONTH)*ITC_EN_M(SYSENTRY,IMONTH)*MONTHSCALE(IMONTH)) ) +$ifi %bb3%==yes + SUM(SYSEXIT, SUM(IMONTH, TAU_EX_M(IMONTH)*ITC_EX_M(SYSEXIT,IMONTH)*MONTHSCALE(IMONTH)) ) +$ifi %bb3%==yes + SUM(SYSENTRY, sum((IS3,DAY), TAU_EN_D(IS3,DAY)*VTC_EN_D.l(SYSENTRY,IS3,DAY)) ) +$ifi %bb3%==yes + SUM(SYSEXIT, sum((IS3,DAY), TAU_EX_D(IS3,DAY)*VTC_EX_D.l(SYSEXIT,IS3,DAY)) ) +); + +* TSO volumetric revenue +TSOVOLrev(Y,'Denmark') = TAU_VOL/7.43/1.389*sum((S,T), Sum(SOURCE, VSUP.l(SOURCE,S,T)*IHOURSINST(S,T))-VEXP.l('Nybro',S)*IHOURSINST(S,T)); + +* TSO capacity rent (i.e. additional congestion charges that could be applied without changing production, transmission or consumption). +TSOCAPRent(Y,'Denmark') = +$ifi %BB1%==yes +SUM((SOURCE,S,T)$(not SAMEAS(SOURCE,'Nybro')), VSUP.l(SOURCE,S,T)*(-1)*VSUP.m(SOURCE,S,T)*IOF1000000) +$ifi %BB2%==yes +SUM((SOURCE,S,T)$(not SAMEAS(SOURCE,'Nybro')), VSUP.l(SOURCE,S,T)*QSUPPLYMAX.m(SOURCE,S,T)*IOF1000000) +$ifi %BB3%==yes +SUM((SOURCE,S,T)$(not SAMEAS(SOURCE,'Nybro')), VSUP.l(SOURCE,S,T)*(-1)*VSUP.m(SOURCE,S,T)*IOF1000000) +$ifi %BB1%==yes +SUM((SOURCE,S)$(not SAMEAS(SOURCE,'Nybro')), VEXP.l(SOURCE,S)*(-1)*VEXP.m(SOURCE,S)*IOF1000000) +$ifi %BB2%==yes +SUM((SOURCE,S)$(not SAMEAS(SOURCE,'Nybro')), VEXP.l(SOURCE,S)*QEXPMAX.m(SOURCE,S)*IOF1000000) +$ifi %BB3%==yes +SUM((SOURCE,S)$(not SAMEAS(SOURCE,'Nybro')), VEXP.l(SOURCE,S)*(-1)*VEXP.m(SOURCE,S)*IOF1000000) +$ifi %BB1%==yes +SUM((TRANSIT,S,T), VTRANSIT.l(TRANSIT,S,T)*(-1)*VTRANSIT.m(TRANSIT,S,T)*IOF1000000) +$ifi %BB2%==yes +SUM((TRANSIT,S,T), VTRANSIT.l(TRANSIT,S,T)*QGTRANSIT_MAX.m(TRANSIT,S,T)*IOF1000000) +$ifi %BB3%==yes +SUM((TRANSIT,S,T), VTRANSIT.l(TRANSIT,S,T)*(-1)*VTRANSIT.m(TRANSIT,S,T)*IOF1000000) +; + +* Artificially set to zero baring investments (investments pending). +TSOfixedcosts(Y,C)=0; + +* Artificially set to zero. +TSOvariablecosts(Y,C)=0; + +TSOprofit(Y,C) = TSOCAPrev(Y,C) + TSOVOLrev(Y,C) -TSOfixedcosts(Y,C) - TSOvariablecosts(Y,C); + +*=============================================================================== +* Gas SSOs +*=============================================================================== + +* Storage costs +*NOTICE ERROR HERE. STORAGE CAPACITY MAY EXIST IN OTHER COURTRIES i.e. SWEDEN) +SSOCAPrev(Y,'Denmark')= +$ifi %bb1%==yes +1/7.43/1.389*sum(SCON, VSC.l(SCON)*SCONDATA(SCON,'TARRIF')) +$ifi %bb2%==yes +1/7.43/1.389*sum(SCON, VSC.l(SCON)*SCONDATA(SCON,'TARRIF')) +$ifi %bb3%==yes +1/7.43/1.389*sum(SCON, ISC_Y(SCON)*SCONDATA(SCON,'TARRIF')) +; + +SSOVOLrev(Y,C) = SUM((STORE(IA),S,T)$ICA(C,IA), + +SUM((IR,STORETYPE)$RRRAAA(IR,STORE),VINJ.l(STORE,STORETYPE,S,T)*GSTOREDATA(STORETYPE,'ELECT')*QEEQ.m(IR,S,T)) + +SUM(STORETYPE, VEXT.l(STORE,STORETYPE,S,T)*GSTOREDATA(STORETYPE,'GAS')*QGEQ.m(S,T)) + )*IOF1000000; + +SSOCAPrent(Y,C) = SUM((STORE(IA),S,T)$ICA(C,IA), +$ifi %bb1%==yes SUM(STORETYPE, VEXT.l(STORE,STORETYPE,S,T)*VEXT.m(STORE,STORETYPE,S,T)) +$ifi %bb2%==yes SUM(STORETYPE, VEXT.l(STORE,STORETYPE,S,T)*QGEXT_MAX.m(STORE,STORETYPE,S,T)) +$ifi %bb3%==yes SUM(STORETYPE, VEXT.l(STORE,STORETYPE,S,T)*VEXT.m(STORE,STORETYPE,S,T)) +$ifi %bb1%==yes +SUM(STORETYPE, VINJ.l(STORE,STORETYPE,S,T)*(-1*VINJ.m(STORE,STORETYPE,S,T))) +$ifi %bb2%==yes +SUM(STORETYPE, VINJ.l(STORE,STORETYPE,S,T)*QGINJ_MAX.m(STORE,STORETYPE,S,T))) +$ifi %bb3%==yes +SUM(STORETYPE, VINJ.l(STORE,STORETYPE,S,T)*(-1*VINJ.m(STORE,STORETYPE,S,T)))) +$ifi %bb1%==yes +SUM(STORETYPE, VSTOCK.l(STORE,STORETYPE,S,T)*(-1*VSTOCK.m(STORE,STORETYPE,S,T))) +$ifi %bb2%==yes +SUM(STORETYPE, VSTOCK.l(STORE,STORETYPE,S,T)*QGSTORE_MAX.m(STORE,STORETYPE,S,T)) +$ifi %bb3%==yes +SUM(STORETYPE, VSTOCK.l(STORE,STORETYPE,S,T)*(-1*VSTOCK.m(STORE,STORETYPE,S,T))) + )*IOF1000000; + +* Artificially set to zero unless investments are made (pending) +SSOfixedcosts(Y,C)= 0; + +* Set electricity costs = volumetric revenue +SSOvariablecosts(Y,C)=SSOVOLrev(Y,C); + +SSOprofit(Y,C) = SSOCAPrev(Y,C)+SSOVOLrev(Y,C)-(SSOfixedcosts(Y,C)+SSOvariablecosts(Y,C)); + +*=============================================================================== +* Gas Shippers +*=============================================================================== + +GasShipperCosts(Y,C) = +* Køb fra producenter + GasProducerRevenue(Y,C)$SAMEAS(C,'Denmark') +* Import + +Sum((SOURCE,S,T)$(ICA(C,SOURCE) and not SAMEAS(SOURCE,'Nybro')),(IOF3P6*IGEX_PRICE(SOURCE,S))*VSUP.l(SOURCE,S,T)*IHOURSINST(S,T))$SAMEAS(C,'Denmark') +* Gas transport + +TSOCAPrev(Y,C) + TSOVOLrev(Y,C) +* Køb på det danske marked: + +IOF1000000*Sum((STORE,STORETYPE,S,T)$ICA(C,STORE), VEXT.l(STORE,STORETYPE,S,T)*QGEQ.m(S,T)) +; + +GasShipperRevenue(Y,C) = IOF1000000*Sum((IA,S,T)$ICA('Denmark',IA),QCONSUMPTION.m(IA,S,T)*VGUD.l(IA,S,T))$SAMEAS(C,'Denmark')-DSOrevenue(Y,'Denmark')$SAMEAS(C,'Denmark') + +IOF1000000*Sum((STORE,STORETYPE,S,T)$ICA(C,STORE), VINJ.l(STORE,STORETYPE,S,T)*QGEQ.m(S,T))$SAMEAS(C,'Denmark') + +Sum((SOURCE,S,T),IHOURSINST(S,T)*(IGEX_PRICE(SOURCE,S)*IOF3P6)*VEXP.l(SOURCE,S))$SAMEAS(C,'Denmark') + +IOF1000000*Sum((TRANSIT,S,T)$ICA(C,TRANSIT),QGSVERIGE.m(S,T)*VTRANSIT.l(TRANSIT,S,T))$SAMEAS(C,'Denmark')-DSOrevenue(Y,'Sweden')$SAMEAS(C,'Denmark'); + +GasShipperProfit(Y,C) = GasShipperRevenue(Y,C) - GasShipperCosts(Y,C) ; + +$ifi %BB1%==yes ShipperCapRent(Y,'Denmark') = +SUM(S, VEXP.l('Nybro',S)*(-1)*VEXP.m('Nybro',S)*IOF1000000); +$ifi %BB2%==yes ShipperCapRent(Y,'Denmark') = +SUM(S, VEXP.l('Nybro',S)*QEXPMAX.m('Nybro',S)*IOF1000000); +$ifi %BB3%==yes ShipperCapRent(Y,'Denmark') = +SUM(S, VEXP.l('Nybro',S)*(-1)*VEXP.m('Nybro',S)*IOF1000000); + +*=============================================================================== +* Gas Suppliers +*=============================================================================== +* This agent's economics appear rather artificial. The supplier is considered to be +* an arbitrageur, purchasing gas from the shipper upon exit from the transmission +* system and upon entry to the distribution system, and reselling to the enduser +* thereby making a profit. However, the models fundamentals ensure that there are +* no excess profits and that the markets are arbitrag-free. Thereby, the supplier +* should never make a profit, and is only included here for the sake of generality. + +* Suppliercosts are defined as retail less distribution costs. The distribution +* costs are then added to the costs. +GasSupplierCosts(Y,C)= IOF1000000*Sum((IA,S,T)$ICA(C,IA),QCONSUMPTION.m(IA,S,T)*VGUD.l(IA,S,T)) + +IOF1000000*Sum((TRANSIT,S,T)$ICA(C,TRANSIT),QGSVERIGE.m(S,T)*VTRANSIT.l(TRANSIT,S,T))$SAMEAS(C,'Sweden') + -DSOrevenue(Y,C)+DSOrevenue(Y,C); + +* Supplier revenue is the retail value of gas. +GasSupplierRevenue(Y,C) = IOF1000000*Sum((IA,S,T)$ICA(C,IA),QCONSUMPTION.m(IA,S,T)*VGUD.l(IA,S,T)) + +IOF1000000*Sum((TRANSIT,S,T)$ICA(C,TRANSIT),QGSVERIGE.m(S,T)*VTRANSIT.l(TRANSIT,S,T))$SAMEAS(C,'Sweden'); + + +GasSupplierProfit(Y,C) = GasSupplierRevenue(Y,C) - GasSupplierCosts(Y,C); + +*=============================================================================== +* Gas Storage User +*=============================================================================== + + +GasStorageUserCost(Y,C) = SSOCAPrev(Y,C) + SSOVOLrev(Y,C) + +SUM((STORE,STORETYPE,S,T)$ICA(C,STORE), + QGEQ.m(S,T)*VINJ.l(STORE,STORETYPE,S,T) +* +QGSTOREBAL.m(STORE,STORETYPE,S,T)*VEXT.l(STORE,STORETYPE,S,T)*IHOURSINST(S,T) + )*IOF1000000 +; + +GasStorageUserRevenue(Y,C) = +SUM((STORE,STORETYPE,S,T)$ICA(C,STORE), + VEXT.l(STORE,STORETYPE,S,T)*QGEQ.m(S,T) +* +VINJ.l(STORE,STORETYPE,S,T)*QGSTOREBAL.m(STORE,STORETYPE,S,T)*IHOURSINST(S,T) + )*IOF1000000 +; + +GasStorageUserProfit(Y,C) = GasStorageUserRevenue(Y,C)-GasStorageUserCost(Y,C); \ No newline at end of file diff --git a/base/addons/gas/gascontracts.inc b/base/addons/gas/gascontracts.inc new file mode 100644 index 00000000..8c986ca2 --- /dev/null +++ b/base/addons/gas/gascontracts.inc @@ -0,0 +1,40 @@ +*-----------------------------------------------------------------------------------------------* +* File : gascontracts.inc +* Author : +* Version : 1.0 +* Remarks : # +* Date : 06/10/2005 10:39:54 +* Changed : 27-06-2006 13:47:04 +* Changed by: +*-----------------------------------------------------------------------------------------------* +* Volumen- Udtrækskapacitet Injektionskapacitet Tarif uden +* kapacitet pr. time pr. time fyldningskrav +* kWh % af volumen % af volumen kr/MWh + +TABLE SCONDATA(SCON,SCONDATASET) + VOLCAP EXTCAP INJCAP TARRIF +STORE1 1 0.00042 0.00021 35.4 +STORE2 1 0.00209 0.00104 71.7 +; + + + + +$ontext +TABLE SCONDATA(SCON,SCONDATASET) + VOLCAP EXTCAP INJCAP TARRIF +STORE1 1 0.00042 0.00021 0.0303 +STORE2 1 0.00209 0.00104 0.0655 +; + + +Gamle tariffer: +TABLE SCONDATA(SCON,SCONDATASET) + VOLCAP INJCAP EXTCAP TARRIF +* kWh of vol of vol kr./MWh +STORE1 0.001 0.00042 0.00021 29.5 +STORE2 0.001 0.00209 0.00104 65.5; + +$offtext +$ifi not %FREESTORAGE%==yes TAU_INJ = 0.123*1000/100; +$ifi %FREESTORAGE%==yes TAU_INJ = 0; diff --git a/base/addons/gas/gascosts.inc b/base/addons/gas/gascosts.inc new file mode 100644 index 00000000..19757297 --- /dev/null +++ b/base/addons/gas/gascosts.inc @@ -0,0 +1,71 @@ +* -------- GAS COSTS -------- * +* Gas expenses in kr. exchange rate 7.43 used +$ontext +$ifi %bb1%==yes + CTC_EN_Y *VTC_EN_Y +$ifi %bb1%==yes + CTC_EX_Y *VTC_EX_Y +$ifi %bb1%==yes + SUM(IMONTH, CTC_EN_M(IMONTH) *VTC_EN_M(IMONTH)) +$ifi %bb1%==yes + SUM(IMONTH, CTC_EX_M(IMONTH) *VTC_EX_M(IMONTH)) +$ifi %bb2%==yes + CTC_EN_Y *VTC_EN_Y +$ifi %bb2%==yes + CTC_EX_Y *VTC_EX_Y +$ifi %bb2%==yes + SUM(IMONTH, CTC_EN_M(IMONTH) *VTC_EN_M(IMONTH)) +$ifi %bb2%==yes + SUM(IMONTH, CTC_EX_M(IMONTH) *VTC_EX_M(IMONTH)) + + SUM(IS3, CTC_EN_W(IS3) *VTC_EN_W(IS3)) + + SUM(IS3, CTC_EX_W(IS3) *VTC_EX_W(IS3)) +$ifi %bb3%==yes + SUM((IS3,DAY), CTC_EN_D(IS3,DAY) *VTC_EN_D(IS3,DAY)) +$ifi %bb3%==yes + SUM((IS3,DAY), CTC_EX_D(IS3,DAY) *VTC_EX_D(IS3,DAY)) + + SUM((SOURCE,IS3,T), CSUP(SOURCE,IS3,T) *VSUP(SOURCE,IS3,T)) +$ifi %bb1%==yes + SUM(SCON, CSC(SCON) *VSC(SCON)) +$ifi %bb2%==yes + SUM(SCON, CSC(SCON) *VSC(SCON)) + + SUM((STORE,IS3,T), CINJ(STORE, IS3,T) *VINJ(STORE, IS3,T)) + + SUM((SOURCE,IS3,T), CEXP(SOURCE,IS3,T) *VEXP(SOURCE,IS3,T)) +$offtext + ++1/7.43/1.389*( +* Transmission costs +$ifi %bb1%==yes + SUM(SYSENTRY, TAU_EN_Y*VTC_EN_Y(SYSENTRY) ) +$ifi %bb1%==yes + SUM(SYSEXIT, TAU_EX_Y*VTC_EX_Y(SYSEXIT) ) +$ifi %bb1%==yes + SUM(SYSENTRY, SUM(IMONTH, TAU_EN_M(IMONTH)*VTC_EN_M(SYSENTRY,IMONTH)*MONTHSCALE(IMONTH)) ) +$ifi %bb1%==yes + SUM(SYSEXIT, SUM(IMONTH, TAU_EX_M(IMONTH)*VTC_EX_M(SYSEXIT,IMONTH)*MONTHSCALE(IMONTH)) ) +$ifi %bb2%==yes + SUM(SYSENTRY, TAU_EN_Y*VTC_EN_Y(SYSENTRY) ) +$ifi %bb2%==yes + SUM(SYSEXIT, TAU_EX_Y*VTC_EX_Y(SYSEXIT) ) +$ifi %bb2%==yes + SUM(SYSENTRY, SUM(IMONTH, TAU_EN_M(IMONTH)*VTC_EN_M(SYSENTRY,IMONTH)*MONTHSCALE(IMONTH)) ) +$ifi %bb2%==yes + SUM(SYSEXIT, SUM(IMONTH, TAU_EX_M(IMONTH)*VTC_EX_M(SYSEXIT,IMONTH)*MONTHSCALE(IMONTH)) ) ++ SUM(SYSENTRY, sum(IS3,IDAYSIN_S(IS3)/7*(TAU_EN_W(IS3)*VTC_EN_W(SYSENTRY,IS3))) ) ++ SUM(SYSEXIT, sum(IS3,IDAYSIN_S(IS3)/7*(TAU_EX_W(IS3)*VTC_EX_W(SYSEXIT,IS3))) ) ++ SUM((SYSENTRY,IS3,T), VTC_EN_FLEX(SYSENTRY,IS3,T)*IHOURSINST(IS3,T)*TAU_EN_FLEX(SYSENTRY) ) ++ SUM((SYSEXIT,IS3,T), VTC_EX_FLEX(SYSEXIT,IS3,T) *IHOURSINST(IS3,T)*TAU_EX_FLEX(SYSEXIT) ) +$ifi %bb3%==yes + SUM(SYSENTRY, sum((IS3,DAY), TAU_EN_D(IS3,DAY)*VTC_EN_D(SYSENTRY,IS3,DAY)) ) +$ifi %bb3%==yes + SUM(SYSEXIT, sum((IS3,DAY), TAU_EX_D(IS3,DAY)*VTC_EX_D(SYSEXIT,IS3,DAY)) ) ++ SUM((IA,IS3,T)$ICA('Denmark',IA), TAU_VOL*VGUD(IA,IS3,T)) ++ SUM((SOURCE,IS3,T)$(not sameas(SOURCE,'Nybro')), IHOURSINST(IS3,T)*VEXP(SOURCE,IS3)) ++ SUM((TRANSIT,IS3,T),IHOURSINST(IS3,T)*VTRANSIT(TRANSIT,IS3,T)) +* Storage costs +$ifi %bb1%==yes +sum(SCON, VSC(SCON)*SCONDATA(SCON,'TARRIF')) +$ifi %bb2%==yes +sum(SCON, VSC(SCON)*SCONDATA(SCON,'TARRIF')) +* Udkommenteret fordi variable omkostninger kobles til el og gas priser +* +TAU_INJ*sum((STORE,IS3,T), IHOURSINST(IS3,T)*VINJ(STORE, IS3,T)) + +* Distribution costs ++ sum((DDD,AAA,IS3,T)$(DDDNODE(DDD,AAA) and IA(AAA)) , IHOURSINST(IS3,T)*TAU_DIST(DDD)*VGUD(AAA,IS3,T)) ++ sum((DDD,TRANSIT,IS3,T)$DDDNODE(DDD,TRANSIT), IHOURSINST(IS3,T)*TAU_DIST(DDD)*VTRANSIT(TRANSIT,IS3,T)) +) +* Consumer utility + +* Value of exports +- sum((SOURCE,IS3,T),IHOURSINST(IS3,T)*(IGEX_PRICE(SOURCE,IS3)*IOF3P6)*VEXP(SOURCE,IS3)) + +* Cost of imports ++ sum((SOURCE,IS3,T)$(not sameas(SOURCE,'Nybro')),IHOURSINST(IS3,T)*(IGEX_PRICE(SOURCE,IS3)*IOF3P6)*VSUP(SOURCE,IS3,T)) + +* Cost of extraction ++ sum((SOURCE,IS3,T),IHOURSINST(IS3,T)*IGEXT_COST(SOURCE,IS3)*IOF3P6*VSUP(SOURCE,IS3,T)) + +* Investment costs +$ifi %bb2%==yes + sum(NGINV,COST_NGINV(NGINV)*VGINFINV(NGINV))*GASANNUITY + +$ifi %bb2%==yes + sum((C,GNETSTEPS), GNET_COST(C,GNETSTEPS)*VGNETINV(C,GNETSTEPS))*GASANNUITY + +$ifi %bb2%==yes + sum((STORE,STORETYPE),GSTOREDATA(STORETYPE,'INVCOST')*VGINVSTORE(STORE,STORETYPE)) +$ifi %bb2%==yes + sum((STORE,STORETYPE),GSTOREDATA(STORETYPE,'FOMK')*VGINVSTORE(STORE,STORETYPE)) +$ifi %bb2%==yes + sum((STORE,STORETYPE),GSTOREDATA(STORETYPE,'MargExtInvCost')*VGINVEXT(STORE,STORETYPE)) +$ifi %bb2%==yes + sum((STORE,STORETYPE),GSTOREDATA(STORETYPE,'MargInjInvCost')*VGINVINJ(STORE,STORETYPE)) diff --git a/base/addons/gas/gasdata.inc b/base/addons/gas/gasdata.inc new file mode 100644 index 00000000..b91d205d --- /dev/null +++ b/base/addons/gas/gasdata.inc @@ -0,0 +1,234 @@ +* This file contains and references data required for running the Balmorel model +* with the GAS add-on. + + +SCALAR kNm3ToMWh /11/; + +* If simulating with high geografical detail alternative values for district +* heating demand must be used. + +*------------------------------------------------------------------------------- +* Include demand for district heating at MR geografical level. +*------------------------------------------------------------------------------- + +$ifi %MR%==yes $INCLUDE '../../base/addons/gas/DH-MR.inc'; + +* Heat demand variation profile used: +$ifi %MR%==yes DH_VAR_T(MRAREAS(AAA),SSS,TTT)$DDDNODE('HNG',AAA) = DH_VAR_T('DK_E_Urban',SSS,TTT); +$ifi %MR%==yes DH_VAR_T(MRAREAS(AAA),SSS,TTT)$DDDNODE('DONG_OEST',AAA) = DH_VAR_T('DK_E_Rural',SSS,TTT); +$ifi %MR%==yes DH_VAR_T(MRAREAS(AAA),SSS,TTT)$DDDNODE('DONG_VEST',AAA) = DH_VAR_T('DK_W_Rural',SSS,TTT); +$ifi %MR%==yes DH_VAR_T(MRAREAS(AAA),SSS,TTT)$DDDNODE('NGF',AAA) = DH_VAR_T('DK_E_Urban',SSS,TTT); +$ifi %MR%==yes DH_VAR_T(MRAREAS(AAA),SSS,TTT)$DDDNODE('Midt_Nord',AAA) = DH_VAR_T('DK_W_Urban',SSS,TTT); + +*------------------------------------------------------------------------------- +* Swedish gas consumption which is not instigated by generation of district heating +* or electricity. +*------------------------------------------------------------------------------- + +PARAMETER SE_DGRES(YYY,AAA,IMONTH)/ +2004.SE_S_Rural.JAN 104764 +2004.SE_S_Rural.FEB 123812 +2004.SE_S_Rural.MAR 85716 +2004.SE_S_Rural.APR 47620 +2004.SE_S_Rural.MAY 28572 +2004.SE_S_Rural.JUN 28572 +2004.SE_S_Rural.JUL 19048 +2004.SE_S_Rural.AUG 28572 +2004.SE_S_Rural.SEP 28572 +2004.SE_S_Rural.OCT 47620 +2004.SE_S_Rural.NOV 76192 +2004.SE_S_Rural.DEC 104764 +/; + +PARAMETER SE_DGOTH(YYY,AAA,IMONTH)/ +2004.SE_S_Rural.JAN 514285 +2004.SE_S_Rural.FEB 485713 +2004.SE_S_Rural.MAR 495237 +2004.SE_S_Rural.APR 409523 +2004.SE_S_Rural.MAY 380952 +2004.SE_S_Rural.JUN 371428 +2004.SE_S_Rural.JUL 323809 +2004.SE_S_Rural.AUG 371428 +2004.SE_S_Rural.SEP 380952 +2004.SE_S_Rural.OCT 409523 +2004.SE_S_Rural.NOV 466666 +2004.SE_S_Rural.DEC 523809 +/; + +SE_DGRES(Y,AAA,IMONTH)=SE_DGRES('2004',AAA,IMONTH) ; +SE_DGOTH(Y,AAA,IMONTH)=SE_DGOTH('2004',AAA,IMONTH) ; + + + + +PARAMETER + DGRES(YYY,AAA) 'Residual gasforbrug, årligt per område', + DGOTH(YYY,AAA) 'Gasforbrug andre timeaflæste, årligt per område' + DGRES_VAR(AAA,SSS,TTT) 'Residual gasforbrug, timevariation', + DGOTH_VAR(AAA,SSS,TTT) 'Gasforbrug andre timeaflæste, timevariation' + DGRESF_STEPS(AAA,SSS,TTT,DF_QP,DGRESF) 'Elastic residual gas demands' + DGOTHF_STEPS(AAA,SSS,TTT,DF_QP,DGOTHF) 'Elastic gas demands from "other" large consumers' + SE_DGRES_VAR_T(AAA,SSS,TTT) 'Variation in Swedish residual consumption', + SE_DGOTH_VAR_T(AAA,SSS,TTT) 'Variation in Swedish "other" large consumers demand' + ISE_DGRES_SUMVAR(AAA,IMONTH) 'Residual gas consumption, weightsum over the variation' + ISE_DGOTH_SUMVAR(AAA,IMONTH) 'Other large gas consumers, weightsum over the variation' +; + + + + + + +DGRES(YYY,'SE_S_Rural')= sum(IMONTH, SE_DGRES(YYY,'SE_S_Rural',IMONTH)); +DGOTH(YYY,'SE_S_Rural')= sum(IMONTH, SE_DGOTH(YYY,'SE_S_Rural',IMONTH)); + +* Load variation profiles for gas consumption which is not instigated by generation of +* electricity or district heating. +$if %MR%==yes $INCLUDE '../../base/addons/gas/gasvar-MR.inc'; +$if not %MR%==yes $INCLUDE '../../base/addons/gas/gasvar.inc'; + +* To help make the variation profiles for Swedish gas consumption we use the profiles from NGF since these are based on Danish national averages. +$if %MR%==yes ISE_DGRES_SUMVAR('SE_S_Rural',IMONTH) = sum((SSS,TTT)$MONTHSSS(IMONTH,SSS), DGRES_VAR('hoejby',SSS,TTT)); +$if %MR%==yes ISE_DGOTH_SUMVAR('SE_S_Rural',IMONTH) = sum((SSS,TTT)$MONTHSSS(IMONTH,SSS), DGOTH_VAR('hoejby',SSS,TTT)); +$if not %MR%==yes ISE_DGRES_SUMVAR('SE_S_Rural',IMONTH) = sum((SSS,TTT)$MONTHSSS(IMONTH,SSS), DGRES_VAR('DK_W_ODENSE',SSS,TTT)); +$if not %MR%==yes ISE_DGOTH_SUMVAR('SE_S_Rural',IMONTH) = sum((SSS,TTT)$MONTHSSS(IMONTH,SSS), DGOTH_VAR('DK_W_ODENSE',SSS,TTT)); + +$if %MR%==yes DGRES_VAR('SE_S_Rural',SSS,TTT) = DGRES_VAR('hoejby',SSS,TTT)/SUM(IMONTH$MONTHSSS(IMONTH,SSS),ISE_DGRES_SUMVAR('SE_S_Rural',IMONTH))*SUM(IMONTH$MONTHSSS(IMONTH,SSS), SE_DGRES('2004','SE_S_Rural',IMONTH)/DGRES('2004','SE_S_Rural')); +$if %MR%==yes DGOTH_VAR('SE_S_Rural',SSS,TTT) = DGOTH_VAR('hoejby',SSS,TTT)/SUM(IMONTH$MONTHSSS(IMONTH,SSS),ISE_DGOTH_SUMVAR('SE_S_Rural',IMONTH))*SUM(IMONTH$MONTHSSS(IMONTH,SSS), SE_DGOTH('2004','SE_S_Rural',IMONTH)/DGOTH('2004','SE_S_Rural')); +$if not %MR%==yes DGRES_VAR('SE_S_Rural',SSS,TTT) = DGRES_VAR('DK_W_ODENSE',SSS,TTT)/SUM(IMONTH$MONTHSSS(IMONTH,SSS),ISE_DGRES_SUMVAR('SE_S_Rural',IMONTH))*SUM(IMONTH$MONTHSSS(IMONTH,SSS), SE_DGRES('2004','SE_S_Rural',IMONTH)/DGRES('2004','SE_S_Rural')); +$if not %MR%==yes DGOTH_VAR('SE_S_Rural',SSS,TTT) = DGOTH_VAR('DK_W_ODENSE',SSS,TTT)/SUM(IMONTH$MONTHSSS(IMONTH,SSS),ISE_DGOTH_SUMVAR('SE_S_Rural',IMONTH))*SUM(IMONTH$MONTHSSS(IMONTH,SSS), SE_DGOTH('2004','SE_S_Rural',IMONTH)/DGOTH('2004','SE_S_Rural')); + + +* Load annual values for gas consumption which is not instigated by generation of +* electricity or district heating. +$if not %MR%==yes $INCLUDE '../../base/addons/gas/dg.inc'; +$if %MR%==yes $INCLUDE '../../base/addons/gas/dg-MR.inc'; + +PARAMETER + SCONDATA(SCON,SCONDATASET) 'Storage contracts', + TAU_INJ 'Injection tarrif', + TAU_EN_Y 'Annual entry tarrif', + TAU_EX_Y 'Annual exit tarrif', + TAU_EN_M(IMONTH) 'Monthly entry tarrif', + TAU_EX_M(IMONTH) 'Monthly exit tarrif', + TAU_EN_W(SSS) 'Weekly entry tarrif', + TAU_EX_W(SSS) 'Weekly exit tarrif', + TAU_EN_D(SSS,DAY) 'Daily entry tarrif', + TAU_EX_D(SSS,DAY) 'Daily exit tarrif', + TAU_VOL 'Tranmission volume tarrif', + TAU_DIST(DDD) 'Distribution tariff', + TC_EN_Y(SYSENTRY,YYY) 'Yearly transmissions capacity reserved exogenously' + TC_EX_Y(SYSEXIT,YYY) 'Yearly transmissions capacity reserved exogenously' + TC_EN_M(SYSENTRY,YYY,SSS,TTT) 'Monthly transmissions capacity reserved exogenously' + TC_EX_M(SYSEXIT,YYY,SSS,TTT) 'Monthly transmissions capacity reserved exogenously' +; +$ifi not EXIST 'gascontracts.inc' $INCLUDE '../../base/addons/gas/gascontracts.inc'; +$ifi EXIST 'gascontracts.inc' $INCLUDE 'gascontracts.inc'; +$ifi not EXIST 'gastransprices.inc' $INCLUDE '../../base/addons/gas/gastransprices.inc'; +$ifi EXIST 'gastransprices.inc' $INCLUDE 'gastransprices.inc'; +$ifi not EXIST 'gx3vex.inc' $INCLUDE '../../base/addons/gas/gx3vex.inc'; +$ifi EXIST 'gx3vex.inc' $INCLUDE 'gx3vex.inc'; + + +* In the base scenario, gasprices are adjusted to reflect price difference with continent, and +* cleaned of transportation expenses. +FUELPRICE(YYY,IA,'NAT_GAS')$((ICA('Denmark',IA) or ICA('Sweden',IA)) + and (not sameas('Nybro',IA)) + and (not sameas('Ellund',IA)) + and (not sameas('Oeresund',IA))) + =0; + + +* Priser justeres i overenstemmelse med forudsætninger i gas-el rapport. +$ifi %LOWGAS%==yes FUELPRICE(Y,'Nybro','NAT_GAS') = 1.5/0.25*0.343/1.398; +$ifi %LOWGAS%==yes FUELPRICE(Y,'Oeresund','NAT_GAS') = 1.5/0.25*0.343/1.398; + +$ifi %MEDGAS%==yes FUELPRICE(Y,'Nybro','NAT_GAS') = FUELPRICE(Y,'Nybro','NAT_GAS') -1.026/1.398; +$ifi %MEDGAS%==yes FUELPRICE(Y,'Oeresund','NAT_GAS') = FUELPRICE(Y,'Oeresund','NAT_GAS') -1.026/1.398; + +$ifi %HIGHGAS%==yes FUELPRICE(Y,'Nybro','NAT_GAS') = FUELPRICE(Y,'Nybro','NAT_GAS') -0.418/1.398; +$ifi %HIGHGAS%==yes FUELPRICE(Y,'Oeresund','NAT_GAS') = FUELPRICE(Y,'Oeresund','NAT_GAS') -0.418/1.398; + + +PARAMETER GEXT_COST(YYY,SOURCE,S) 'Average Extraction Cost EURO/MWh', + GEX_PRICE(YYY,SOURCE,S) 'Export Price Money/MWh'; + +GEXT_COST(YYY,'Nybro',S) =FUELPRICE(YYY,'Nybro','NAT_GAS'); +GEXT_COST(YYY,'Oeresund',S) =FUELPRICE(YYY,'Oeresund','NAT_GAS'); +GEX_PRICE(YYY,SOURCE,S)$(not SAMEAS(SOURCE,'Oeresund')) =FUELPRICE(YYY,'Ellund','NAT_GAS')*WSF(S); + + + + +PARAMETER + SUPPLY_MAX(YYY,SOURCE) 'Hourly exogenous natural gas supply capacity' + EXP_MAX(YYY,SOURCE) 'Hourly exogenous natural gas export capacity' + COST_NGINV(NGINV0) 'Investment costs' + SUPEFFECT(NGINV0,SOURCE) 'Effect on supply capacity from investment' + EXPEFFECT(NGINV0,SOURCE) 'Effect on export capacity from investment' + INJ_MAX(YYY,STORE,STORETYPE) 'Injection capacity, exogenous' + EXT_MAX(YYY,STORE,STORETYPE) 'Extraction capacity, exogenous' + STORAGE_MAX(YYY,STORE,STORETYPE) 'Storage volumetric capacity, exogenous' + GSTOREDATA(STORETYPE,*) 'Storage investment data' + MAXINVEST(STORE,STORETYPE) 'Maximum possible capacity at storage location' + TSOSTORAGE(YYY,STORE,STORETYPE) 'Storage reserved for emergencies by the TSO' + ; + +$ifi EXIST 'gasfacilities.inc' $INCLUDE 'gasfacilities.inc'; +$ifi not EXIST 'gasfacilities.inc' $INCLUDE '../../base/addons/gas/gasfacilities.inc'; + +PARAMETER + GSFXW(YYY,STORE,STORETYPE,SSS) 'Allokeret netto gasudtræk fra lager per uge' + SC_Y(YYY,SCON) 'Bestilte gaslagerprodukter' + GPFXW(YYY,SOURCE,SSS) 'Allokering af gasproduktion' + HYFXRW_GAS(YYY,SSS,RRR) 'Allokering af vandkraftmængder til uger' + STOREACC(YYY,STORE,STORETYPE) 'Storage volumetric investment from bb2' + INJACC(YYY,STORE,STORETYPE) 'Storage injection investment from bb2' + EXTACC(YYY,STORE,STORETYPE) 'Storage extraction investment from bb2' + EXPACC(YYY,SOURCE) 'Gas export capacity investment from bb2' + SUPACC(YYY,SOURCE) 'Gas supply capacity investment from bb2' + DEVACC(YYY,SYSEXIT) 'Gas delivery capacity investment from bb2' +; + +* Load bb1/bb2 allocations to bb3 model +$ifi %bb3%==yes execute_load '../../base/addons/gas/GSFXW.gdx', GSFXW; +$ifi %bb3%==yes execute_load '../../base/addons/gas/reserved_capacity.gdx', TC_EN_Y,TC_EX_Y,TC_EN_M,TC_EX_M; +$ifi %bb3%==yes execute_load '../../base/addons/gas/SC_Y.gdx', SC_Y; + +* Load investments undertaken in BB2 to BB1 or BB3 simulations. +$ifi %ADDINVEST%==yes execute_load '../../base/addons/gas/gasinvest.gdx',STOREACC,INJACC,EXTACC,EXPACC,SUPACC,DEVACC; + +PARAMETER + GASANNUITY / 0.117 / + ; + +* The following are currently unused functional representations of the cost +* elements of the objective function using gams-f. These are especially applicable +* in a MCP framework. Note that elements may not match that of the objective function +* in this model and therefore should be verified before use. + +$ontext +* FUNCTIONS using gams-f +CTC_EN_YEAR == IOF0000001/7.43*TAU_EN_Y; +CTC_EX_Y == IOF0000001/7.43*TAU_EX_Y; +CTC_EN_M(IMONTH) == IOF0000001/7.43*TAU_EN_M(IMONTH)*MONTHSCALE(IMONTH); +CTC_EX_M(IMONTH) == IOF0000001/7.43*TAU_EX_M(IMONTH)*MONTHSCALE(IMONTH); +CTC_EN_W(IS3) == IOF0000001/7.43*TAU_EN_W(IS3); +CTC_EX_W(IS3) == IOF0000001/7.43*TAU_EX_W(IS3); +CTC_EN_D(IS3,DAY) == IOF0000001/7.43*TAU_EN_D(IS3,DAY); +CTC_EX_D(IS3,DAY) == IOF0000001/7.43*TAU_EX_D(IS3,DAY); +CSUP(SOURCE,IS3,T) == IOF0000001/7.43*IHOURSINST(IS3,T)*TAU_VOL; +CSC(SCON) == IOF0000001/7.43*SCONDATA(SCON,'TARRIF'); +CINJ(STORE, IS3,T) == IOF0000001/7.43*IHOURSINST(IS3,T)*TAU_INJ; +CGUD(AAA,IS3,T) == IOF0000001/7.43*TAU_DIST(DDD); +CEXP(SOURCE,IS3,T) == -IOF0000001/7.43*IHOURSINST(IS3,T)*(IGEX_PRICE(SOURCE,IS3)-IFUELP_Y('Nybro','NAT_GAS')); +$offtext + + +* The following concerns a possible future endogenous implementation of gas +* transmissions constrains based on modelling physical flows. + +$ifi %PIPELINE%==yes $INCLUDE '../../base/addons/gasprops.inc'; +$ifi %PIPELINE%==yes $INCLUDE '../../base/addons/gastech.inc'; +$ifi %PIPELINE%==yes $INCLUDE '../../base/addons/network.inc'; +$ifi %PIPELINE%==yes $INCLUDE '../../base/addons/linearize.inc'; \ No newline at end of file diff --git a/base/addons/gas/gasdocompare.inc b/base/addons/gas/gasdocompare.inc new file mode 100644 index 00000000..afc0ec33 --- /dev/null +++ b/base/addons/gas/gasdocompare.inc @@ -0,0 +1,96 @@ +* File gasdocompare.INC. +* This file is part of the welfare calculation module for the Balmorel model. +* Created by Jens Hauch and Thomas E. Pedersen, COWI A/S (DK). +* Adapted for Balmorel version 2.10, 2.13 by Hans Ravn. +* Extended to for the gas add-on by Lars Bregnbæk, X-mas 2006. + +* mangler: Base er næsten hårdkodet (men som det ses, er der mulighed for at vælge et andet base !) +* i welfare.inc ses, at der ikke er mellemvariabler for trans - ... + +*=============================================================================== +* Overview +*=============================================================================== + +* This file contains: + +* calculation of differences between cases +* discounting of welfare changes for groups + + + +*=============================================================================== +* Overview end +*=============================================================================== + +*=============================================================================== +* Gas Consumers +*=============================================================================== + +* Residual consumers (discounted welfare gains for consumers) +dresidualbenifit(C) = Sum(Y, -(residualcosts(Y,C)-residualcosts00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + +* Other consumers (discounted welfare gains for consumers) +dotherbenifit(C) = Sum(Y, -(othercosts(Y,C)-othercosts00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + + +*=============================================================================== +* Distribution Companies +*=============================================================================== + + +dcdltDSOprofit(C) = Sum(Y, (DSOprofit(Y,C)-DSOprofit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + + +*=============================================================================== +* TSO +*=============================================================================== + +dcdltTSOVOLrevbenefit(C) = Sum(Y, (TSOVOLrev(Y,C)-TSOVOLrev00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + +dcdltTSOCAPrevbenefit(C) = Sum(Y, (TSOCAPrev(Y,C)-TSOCAPrev00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + +dcdltTSOCAPRentbenefit(C) = Sum(Y, (TSOCAPRent(Y,C)-TSOCAPRent00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + +dcdltTSOProfit(C) = Sum(Y, (TSOProfit(Y,C) - TSOProfit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + +*=============================================================================== +* SSO +*=============================================================================== + +dcdltSSOCAPrevbenefit(C) = Sum(Y, (SSOCAPrev(Y,C)-SSOCAPrev00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + + +dcdltSSOVOLrevbenefit(C) = Sum(Y, (SSOVOLrev(Y,C)-SSOVOLrev00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + + +dcdltSSOCAPrentbenefit(C) = Sum(Y, (SSOCAPrent(Y,C)-SSOCAPrent00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + + +dcdltSSOProfit(C) = Sum(Y, (SSOProfit(Y,C) - SSOProfit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + + +*=============================================================================== +* Producers +*=============================================================================== + +dcdltGasProducerProfit(C) = Sum(Y,(GasProducerProfit(Y,C) - GasProducerProfit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + +*=============================================================================== +* Suppliers +*=============================================================================== + +dcdltGasSupplierProfit(C) = Sum(Y, (GasSupplierProfit(Y,C)-GasSupplierProfit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + +*=============================================================================== +* Shippers +*=============================================================================== + +dcdltGasShipperProfit(C) = Sum(Y, (GasShipperProfit(Y,C) - GasShipperProfit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + +dcdltShipperCapRent(C) = Sum(Y, (ShipperCapRent(Y,C) - ShipperCapRent00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; + +*=============================================================================== +* Gas Storage User +*=============================================================================== + +dcdltGasStorageUserProfit(C) = Sum(Y, (GasStorageUserProfit(Y,C) - GasStorageUserProfit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; diff --git a/base/addons/gas/gasequations.inc b/base/addons/gas/gasequations.inc new file mode 100644 index 00000000..a50bcf3b --- /dev/null +++ b/base/addons/gas/gasequations.inc @@ -0,0 +1,270 @@ +Equations + QGEQ(s,t) 'Gas balance at Gas Pool' + QCONSUMPTION(i,s,t) 'Gas balance at consumption node' + QTransEntryCap(SYSENTRY,s,t) 'Entry capacity reservation constraint' + QTransExitCap(SYSEXIT,s,t) 'Exit capacity reservation constraint' + QStoreVol(s,T) 'Volumetric storage capacity reservation constraint' + QStoreInj(s,t) 'Injection capacity reservation constraint' + QStoreExt(s,t) 'Extraction capacity reservation constraint' + QGSTOREBAL(STORE,STORETYPE,S,T) 'Gas storage balance' + QGSFXW(STORE,STORETYPE,S) 'Net gas storage for week' + QGSVERIGE(S,T) 'Gas balance in Sweden' +; +ALIAS (IA, IAALIAS); + + +QGSVERIGE(IS3,T) .. + VTRANSIT('OERESUND',IS3,T) + +sum((STORE,STORETYPE)$ICA('Sweden',STORE), + VEXT(STORE,STORETYPE,IS3,T) + ) + + =G= + +sum(IA$ICA('Sweden',IA), + +sum(IGE$(IAGKN(IA,IGE) and GDATA(IGE,'GDFUEL') EQ 2), + VGEN_T(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IGE,IA))) + ) + +sum(IGH$(IAGKN(IA,IGH) and GDATA(IGH,'GDFUEL') EQ 2), + GDATA(IGH,'GDCV')*VGHN_T(IA,IGH,IS3,T)/(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IGH,IA))) + ) + +sum(IGE$(IAGK_Y(IA,IGE) and GDATA(IGE,'GDFUEL') EQ 2), + VGE_T(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IGE,IA))) + ) + +sum(IGH$(IAGK_Y(IA,IGH) and GDATA(IGH,'GDFUEL') EQ 2), + GDATA(IGH,'GDCV')*VGH_T(IA,IGH,IS3,T) /(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IGH,IA))) + ) + +* +sum(IA$ICA('Sweden',IA),ISE_DGRES_Y('SE_S_Rural',IS3,T)) +* +sum(IA$ICA('Sweden',IA),ISE_DGOTH_Y('SE_S_Rural',IS3,T)) + +IDGRES_Y(IA,IS3,T) + +IDGOTH_Y(IA,IS3,T) + + ) + +sum((STORE,STORETYPE)$ICA('Sweden',STORE), + VINJ(STORE,STORETYPE,IS3,T) + ) + +sum((STORE,STORETYPE)$ICA('Sweden',STORE), + + VEXT(STORE,STORETYPE,IS3,T)*GSTOREDATA(STORETYPE,'GAS') + ) +; + +QGEQ(IS3,T) .. + sum(SOURCE, + VSUP(SOURCE,IS3,T) + ) + +sum((STORE,STORETYPE)$ICA('Denmark',STORE), + + VEXT(STORE,STORETYPE,IS3,T) + ) + -sum((STORE,STORETYPE)$ICA('Denmark',STORE), + + VEXT(STORE,STORETYPE,IS3,T)*GSTOREDATA(STORETYPE,'GAS') + ) + =G= + sum(IA$ICA('Denmark',IA), + VGUD(IA,IS3,T) + ) + +sum((STORE,STORETYPE)$ICA('Denmark',STORE), + VINJ(STORE,STORETYPE,IS3,T) + ) + +sum(SOURCE, + VEXP(SOURCE,IS3) + ) + +sum(TRANSIT, + VTRANSIT(TRANSIT,IS3,T) + ) + ; + + +$ifi not %MR%==yes QCONSUMPTION(IA,IS3,T)$ICA('Denmark',IA) .. +$ifi %MR%==yes QCONSUMPTION(IA,IS3,T)$(ICA('Denmark',IA) and not NOTMR(IA)) .. + + VGUD(IA,IS3,T) + =G= + IDGRES_Y(IA,IS3,T) + + IDGOTH_Y(IA,IS3,T) + +sum(IGE$(IAGKN(IA,IGE) and GDATA(IGE,'GDFUEL') EQ 2), + VGEN_T(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IGE,IA))) + ) + +sum(IGH$(IAGKN(IA,IGH) and GDATA(IGH,'GDFUEL') EQ 2), + GDATA(IGH,'GDCV')*VGHN_T(IA,IGH,IS3,T)/(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IGH,IA))) + ) + +sum(IGE$(IAGK_Y(IA,IGE) and GDATA(IGE,'GDFUEL') EQ 2), + VGE_T(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IGE,IA))) + ) + +sum(IGH$(IAGK_Y(IA,IGH) and GDATA(IGH,'GDFUEL') EQ 2), + GDATA(IGH,'GDCV')*VGH_T(IA,IGH,IS3,T) /(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IGH,IA))) + ) +$ifi %MR%==yes +SUM((IAALIAS,G)$MR2CENTRAL(IA,IAALIAS,G), +$ifi %MR%==yes +( +$ifi %MR%==yes VGEN_T(IAALIAS,G,IS3,T)/(GDATA(G,'GDFE')* (1-GEFFDERATE(G,IA))) +$ifi %MR%==yes )$(IGE(G) and IAGKN(IAALIAS,G) and GDATA(G,'GDFUEL') EQ 2) +$ifi %MR%==yes +( +$ifi %MR%==yes GDATA(G,'GDCV')*VGHN_T(IAALIAS,G,IS3,T)/(GDATA(G,'GDFE')* (1-GEFFDERATE(G,IAALIAS))) +$ifi %MR%==yes )$(IGH(G) and IAGKN(IAALIAS,G) and GDATA(G,'GDFUEL') EQ 2) +$ifi %MR%==yes +( +$ifi %MR%==yes VGE_T(IAALIAS,G,IS3,T)/(GDATA(G,'GDFE')* (1-GEFFDERATE(G,IAALIAS))) +$ifi %MR%==yes )$(IGE(G) and IAGK_Y(IAALIAS,G) and GDATA(G,'GDFUEL') EQ 2) +$ifi %MR%==yes +( +$ifi %MR%==yes GDATA(G,'GDCV')*VGH_T(IAALIAS,G,IS3,T) /(GDATA(G,'GDFE')* (1-GEFFDERATE(G,IAALIAS))) +$ifi %MR%==yes )$(IGH(G) and IAGK_Y(IAALIAS,G) and GDATA(G,'GDFUEL') EQ 2) +$ifi %MR%==yes ) + +$ifi %HEATING%==yes + sum(HEAT$HEATMR(HEAT,IA), VGX(IA,HEAT,IS3,T)) + ; + +QTransEntryCap(SYSENTRY,IS3,T) .. +$ifi %bb3%== yes ITC_EN_Y(SYSENTRY) +$ifi %bb3%== yes + ITC_EN_M(SYSENTRY,IS3,T) +$ifi %bb3%== yes + sum(DAY$DAYT(DAY,T), VTC_EN_D(SYSENTRY,IS3,DAY)) +$ifi %bb1%== yes VTC_EN_Y(SYSENTRY) +$ifi %bb1%== yes + sum(IMONTH$MONTHSSS(IMONTH,IS3), VTC_EN_M(SYSENTRY,IMONTH)) +$ifi %bb2%== yes VTC_EN_Y(SYSENTRY) +$ifi %bb2%== yes + sum(IMONTH$MONTHSSS(IMONTH,IS3), VTC_EN_M(SYSENTRY,IMONTH)) + + VTC_EN_W(SYSENTRY,IS3) + + VTC_EN_FLEX(SYSENTRY,IS3,T) + =G= + (VSUP('Nybro',IS3,T)-VEXP('Nybro',IS3))$(SAMEAS(SYSENTRY,'Nybro')) + +VSUP('Ellund',IS3,T)$(SAMEAS(SYSENTRY,'Ellund')) ; + +QTransExitCap(SYSEXIT,IS3,T) .. +$ifi %bb3%== yes ITC_EX_Y(SYSEXIT) +$ifi %bb3%== yes + ITC_EX_M(SYSEXIT,IS3,T) +$ifi %bb3%== yes + sum(DAY$DAYT(DAY,T), VTC_EX_D(SYSEXIT,IS3,DAY)) +$ifi %bb1%== yes VTC_EX_Y(SYSEXIT) +$ifi %bb1%== yes + sum(IMONTH$MONTHSSS(IMONTH,IS3), VTC_EX_M(SYSEXIT,IMONTH)) +$ifi %bb2%== yes VTC_EX_Y(SYSEXIT) +$ifi %bb2%== yes + sum(IMONTH$MONTHSSS(IMONTH,IS3), VTC_EX_M(SYSEXIT,IMONTH)) + + VTC_EX_W(SYSEXIT,IS3) + + VTC_EX_FLEX(SYSEXIT,IS3,T) + =G= + + sum(IA$ICA('Denmark',IA), + VGUD(IA,IS3,T) + )$(SAMEAS(SYSEXIT,'DK-EXIT')) + + + VEXP('Ellund',IS3)$(SAMEAS(SYSEXIT,'Ellund')) +* + VEXP('Oeresund',IS3)$(SAMEAS(SYSEXIT,'Oeresund')) + + VTRANSIT('Oeresund',IS3,T)$(SAMEAS(SYSEXIT,'Oeresund')) +; + +QStoreVol(IS3,T) .. + sum(SCON, + VSC(SCON)*SCONDATA(SCON,'VOLCAP') + ) +*$ifi %bb2%==yes +SUM((STORE,STORETYPE),VGINVSTORE(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) +*$ifi %bb2%==yes -VGINVSTORE.l(STORE,STORETYPE)$STOREINV(STORE, STORETYPE)) + =G= sum((STORE,STORETYPE), + VSTOCK(STORE,STORETYPE,IS3,T) + ); + +QStoreInj(IS3,T) .. + sum(SCON, +$ifi %bb1%== yes VSC(SCON) +$ifi %bb2%== yes VSC(SCON) +$ifi %bb3%== yes ISC(SCON) + *SCONDATA(SCON,'INJCAP')*SCONDATA(SCON,'VOLCAP') + ) +*$ifi %bb2%==yes +SUM((STORE,STORETYPE),VGINVINJ(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) +*$ifi %bb2%==yes +(GSTOREDATA(STORETYPE,'INJEFFECT')*VGINVSTORE(STORE,STORETYPE))$STOREINV(STORE, STORETYPE) +*$ifi %bb2%==yes -VGINVINJ.l(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) +*$ifi %bb2%==yes -(GSTOREDATA(STORETYPE,'INJEFFECT')*VGINVSTORE.l(STORE,STORETYPE))$STOREINV(STORE, STORETYPE)) + =G= + sum((STORE,STORETYPE), + VINJ(STORE,STORETYPE,IS3,T) + ); + +QStoreExt(IS3,T) .. + sum(SCON, +$ifi %bb1%== yes VSC(SCON) +$ifi %bb2%== yes VSC(SCON) +$ifi %bb3%== yes ISC(SCON) + *SCONDATA(SCON,'EXTCAP')*SCONDATA(SCON,'VOLCAP') + ) +*$ifi %bb2%==yes +SUM((STORE,STORETYPE),VGINVEXT(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) +*$ifi %bb2%==yes +(GSTOREDATA(STORETYPE,'EXTEFFECT')*VGINVSTORE(STORE,STORETYPE))$STOREINV(STORE, STORETYPE) +*$ifi %bb2%==yes -VGINVEXT.l(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) +*$ifi %bb2%==yes -(GSTOREDATA(STORETYPE,'EXTEFFECT')*VGINVSTORE.l(STORE,STORETYPE))$STOREINV(STORE, STORETYPE)) + =G= + sum((STORE,STORETYPE), + VEXT(STORE,STORETYPE,IS3,T) + ); + +QGSTOREBAL(STORE,STORETYPE,S,T).. + VSTOCK(STORE,STORETYPE,S,T) + +IHOURSINST(S,T)*(VINJ(STORE,STORETYPE,S,T)-VEXT(STORE,STORETYPE,S,T)) +=G= VSTOCK(STORE,STORETYPE,S,T+1)$(ord(T) 0) .. + IDEV_MAX(SYSEXIT) +$ifi %BB2%==yes +sum(NGINV,DEVEFFECT(NGINV,SYSEXIT)*VGINFINV(NGINV)) + +sum((C,GNETSTEPS), GNET_DEV_EFFECT(C,SYSEXIT)*VGNETINV(C,GNETSTEPS)) + =G= + 1.21*sum(IA$ICA('Denmark',IA), + VGUD(IA,IS3,T) + )$(SAMEAS(SYSEXIT,'DK-EXIT')) + ; + + +QGINJ_MAX(STORE,STORETYPE,IS3,T).. + IINJ_MAX(STORE,STORETYPE) + +VGINVINJ(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) + +(GSTOREDATA(STORETYPE,'INJEFFECT')*VGINVSTORE(STORE,STORETYPE))$STOREINV(STORE, STORETYPE) + =G= + VINJ(STORE,STORETYPE,IS3,T) + ; + +QGEXT_MAX(STORE,STORETYPE,IS3,T).. + IEXT_MAX(STORE,STORETYPE) + +VGINVEXT(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) + +(GSTOREDATA(STORETYPE,'EXTEFFECT')*VGINVSTORE(STORE,STORETYPE))$STOREINV(STORE, STORETYPE) + =G= + VEXT(STORE,STORETYPE,IS3,T) + ; + +QGSTORE_MAX(STORE,STORETYPE,IS3,T).. + ISTORE_MAX(STORE,STORETYPE) + +VGINVSTORE(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) + =G= + VSTOCK(STORE,STORETYPE,IS3,T) + ; diff --git a/base/addons/gas/gasfacilities.inc b/base/addons/gas/gasfacilities.inc new file mode 100644 index 00000000..a1aa6ee3 --- /dev/null +++ b/base/addons/gas/gasfacilities.inc @@ -0,0 +1,327 @@ +*------------------------------------------------------------------------------* +* Transmission posibilities +*------------------------------------------------------------------------------* + +TABLE SUPPLY_MAX(YYY,SOURCE) 'Hourly exogenous natural gas supply capacity' + Nybro Ellund Oeresund +2003 1002.029427 344 eps +2004 1059.107052 344 eps +2005 1116.184678 344 eps +2006 1116.184678 344 eps +2007 1116.184678 344 eps +2008 1116.184678 344 eps +2009 1116.184678 344 eps +2010 1116.184678 344 eps +2011 1045.154744 344 eps +2012 974.1248097 344 eps +2013 903.0948757 344 eps +2014 832.0649417 344 eps +2015 761.0350076 344 eps +2016 731.2278031 344 eps +2017 701.4205987 344 eps +2018 671.6133942 344 eps +2019 641.8061898 344 eps +2020 611.9989853 344 eps +2021 582.1917808 344 eps +2022 552.3845764 344 eps +2023 522.5773719 344 eps +2024 492.7701674 344 eps +2025 462.962963 344 eps +2026 462.962963 344 eps +2027 462.962963 344 eps +2028 462.962963 344 eps +2029 462.962963 344 eps +2030 462.962963 344 eps +* 2005.ELLUND 344, +* 2005.OERESUND 0, +* 2005.NYBRO 1040 +; + +TABLE EXP_MAX(YYY,SOURCE) 'Hourly exogenous natural gas export capacity' + OERESUND NYBRO ELLUND +2005 286 303 344 +2006 358 303 344 +2007 358 303 344 +2008 358 303 344 +2009 358 303 344 +2010 358 303 344 +2011 358 303 344 +2012 358 303 344 +2013 358 303 344 +2014 358 303 344 +2015 358 303 344 +2016 358 303 344 +2017 358 303 344 +2018 358 303 344 +2019 358 303 344 +2020 358 303 344 +2021 358 303 344 +2022 358 303 344 +2023 358 303 344 +2024 358 303 344 +2025 358 303 344 +2026 358 303 344 +2027 358 303 344 +2028 358 303 344 +2029 358 303 344 +2030 358 303 344 +; + +TABLE DEV_MAX(YYY,SYSEXIT) 'Hourly exogenous natural gas delivery capacity' + DK-EXIT +2005 1191 +2006 1191 +2007 1191 +2008 1191 +2009 1191 +2010 1191 +2011 1191 +2012 1191 +2013 1191 +2014 1191 +2015 1191 +2016 1191 +2017 1191 +2018 1191 +2019 1191 +2020 1191 +2021 1191 +2022 1191 +2023 1191 +2024 1191 +2025 1191 +2026 1191 +2027 1191 +2028 1191 +2029 1191 +2030 1191 +; + +DEV_MAX(YYY,SYSEXIT) = DEV_MAX(YYY,SYSEXIT) * kNm3ToMWh; +SUPPLY_MAX(YYY,SOURCE) = SUPPLY_MAX(YYY,SOURCE) * kNm3ToMWh; +EXP_MAX(YYY,SOURCE) = EXP_MAX(YYY,SOURCE) * kNm3ToMWh; + + +* TAL FOR ELLUND FORSTÆRKELSE OG BGI KAPACITET ER TAGET UD AF LUFTEN + +PARAMETER COST_NGINV(NGINV0) 'Investment costs' +/ +* Investment cost in Bill. DKkr 2006 +BGI 2.0 +NO_SYDARNE 0.8 +NO_NYBRO 1.2 +NO_TYRA 0.8 +DK_COMPR 0.255 +NEGP_ELLUND 1.0 +/; +* Omregning til EURO90 +COST_NGINV(NGINV0)=1/(7.43)/(1.389)*1000*1000*1000*COST_NGINV(NGINV0); + +PARAMETER GNET_COST(C,GNETSTEPS0) 'Marginal cost of gas transmissionnetwork expansion EURO90/(Unit expansion)' +* Data source: Kapacitetsudvidelse i transmissionssystemet - Simulering med Pipeline studio. +* Den anvendte pris er en gennemsnitsværdi, da omkostningsstrukturen i notat er konkav kapacitetsforøgelsen. +* Døgnbelastningsfaktor på 1.07 er anvendt til at skalere døgnmængder til timemængder. +* COST +* EURO90/MWh/h +/ +Denmark.GNETSTEP1 28923 +Denmark.GNETSTEP2 28923 +Denmark.GNETSTEP3 28923 +/; + +PARAMETER GNET_MAX(C,GNETSTEPS0) 'Steplength for marginal expansion cost curve (Unit expansion)' +* Unit cost must be increasing with step number +* Data source: Kapacitetsudvidelse i transmissionssystemet - Simulering med Pipeline studio. (Energinet.dk) +* Max_Effect +* kNm3/d +/ +Denmark.GNETSTEP1 4757 +Denmark.GNETSTEP2 0 +Denmark.GNETSTEP3 0 +/; + +TABLE GNET_DEV_EFFECT(C,SYSEXIT) 'Increased delivery capacity from network expansion (MWh/h)/(Unit expansion)' + DK-EXIT +Denmark 1 +; + +TABLE GNET_EXP_EFFECT(C,SOURCE) 'Increased export capacity from network expansion (MWh/h)/(Unit expansion)' + OERESUND +Denmark 1 +; + +TABLE DEVEFFECT(NGINV0,SYSEXIT) 'Increased delivery capacity from infrastrukture investments (MWh/h)' + DK-EXIT +BGI eps +NO_SYDARNE eps +NO_NYBRO eps +NO_TYRA eps +DK_COMPR 1 +NEGP_ELLUND eps +; + + +TABLE SUPEFFECT(NGINV0,SOURCE) +* Additional hourly supply capacity gained from investment + NYBRO ELLUND OERESUND +BGI eps eps 542 +NO_SYDARNE 542 eps eps +NO_NYBRO 1125 eps eps +NO_TYRA 420 eps eps +DK_COMPR eps eps eps +NEGP_ELLUND eps 378.84 eps +; +SUPEFFECT(NGINV0,SOURCE) = SUPEFFECT(NGINV0,SOURCE)*kNm3ToMWh; + +TABLE EXPEFFECT(NGINV0,SOURCE) +* Additional hourly export capacity gained from investment MNm3/h + NYBRO ELLUND OERESUND +BGI eps eps 542 +NO_SYDARNE eps eps eps +NO_NYBRO eps eps eps +NO_TYRA eps eps eps +DK_COMPR eps eps 200 +NEGP_ELLUND eps eps 378.84 +; +EXPEFFECT(NGINV0,SOURCE) = EXPEFFECT(NGINV0,SOURCE)*kNm3ToMWh; + +*------------------------------------------------------------------------------* +* STORAGE FACILITIES +*------------------------------------------------------------------------------* + +TABLE INJ_MAX(YYY,STORE,STORETYPE) +* kNm3/h + LITORUP.Cavern STENLILLE.Aquifer +2005 165 110 +2006 165 110 +2007 165 110 +2008 165 110 +2009 165 110 +2010 165 110 +2011 165 110 +2012 165 110 +2013 165 110 +2014 165 110 +2015 165 110 +2016 165 110 +2017 165 110 +2018 165 110 +2019 165 110 +2020 165 110 +2021 165 110 +2022 165 110 +2023 165 110 +2024 165 110 +2025 165 110 +2026 165 110 +2027 165 110 +2028 165 110 +2029 165 110 +2030 165 110 +; + +TABLE EXT_MAX(YYY,STORE,STORETYPE) +* kNm3/h + LITORUP.Cavern STENLILLE.Aquifer +2005 600 600 +2006 600 600 +2007 600 600 +2008 600 600 +2009 600 600 +2010 600 600 +2011 600 600 +2012 600 600 +2013 600 600 +2014 600 600 +2015 600 600 +2016 600 600 +2017 600 600 +2018 600 600 +2019 600 600 +2020 600 600 +2021 600 600 +2022 600 600 +2023 600 600 +2024 600 600 +2025 600 600 +2026 600 600 +2027 600 600 +2028 600 600 +2029 600 600 +2030 600 600 +; + +TABLE STORAGE_MAX(YYY,STORE,STORETYPE) +* kNm3 + LITORUP.Cavern STENLILLE.Aquifer +2005 400000 300000 +2006 400000 300000 +2007 400000 300000 +2008 400000 300000 +2009 400000 300000 +2010 400000 300000 +2011 400000 300000 +2012 400000 300000 +2013 400000 300000 +2014 400000 300000 +2015 400000 300000 +2016 400000 300000 +2017 400000 300000 +2018 400000 300000 +2019 400000 300000 +2020 400000 300000 +2021 400000 300000 +2022 400000 300000 +2023 400000 300000 +2024 400000 300000 +2025 400000 300000 +2026 400000 300000 +2027 400000 300000 +2028 400000 300000 +2029 400000 300000 +2030 400000 300000 +; + + +TABLE GSTOREDATA(STORETYPE,*) 'Storage investment data' + INVCOST FOMK INJEFFECT EXTEFFECT ELECT GAS MargInjInvCost MargExtInvCost +* EURO02/kNm3 EURO02/kN3 -Ratio to volume capacity- fraction fraction Marginal cost Marginal cost +* of INJ of EXT of inj capacity of ext capacity +* EURO02/kNm3/h EURO02/kNm3/h +Dep_Field 42.40 13 0.0002 0.0006 0.00341 0.002 10320 10320 +Aquifer 49.10 15 0.0002 0.000458 0.00341 0.002 10560 10560 +Cavern 83.90 11 0.00041 0.00198 0.0033 0.0009 10080 10080 +LNG 150.60 45 0.0004557 0.00651 0.00526 0.00319 7104 7104 +; + +* INVCOST is the yearly annuity payment. +* DATASOURCES: The 2002 European Gas Storage Study, Lille Torup Naturgaslager - Grønt regnskab 2004, +* Stenlille Naturgaslager - Grønt regnskab 2004 + +* Faktor 1.302272195 brug til omregning af EURO02 til EURO90 +* Translate costs to EURO90/MWh +GSTOREDATA(STORETYPE,'INVCOST') = GSTOREDATA(STORETYPE,'INVCOST')/1.302272195/kNm3ToMWh; +GSTOREDATA(STORETYPE,'MargInjInvCost') = GSTOREDATA(STORETYPE,'MargInjInvCost')/1.302272195/kNm3ToMWh; +GSTOREDATA(STORETYPE,'MargExtInvCost') = GSTOREDATA(STORETYPE,'MargExtInvCost')/1.302272195/kNm3ToMWh; +GSTOREDATA(STORETYPE,'FOMK') = GSTOREDATA(STORETYPE,'FOMK')/1.302272195/kNm3ToMWh; + +PARAMETER MAXINVEST(STORE,STORETYPE) 'Maximum possible capacity at storage location' +/ +LITORUP.Cavern 300000 +STENLILLE.Aquifer 100000 +STENLILLE.Cavern 1000000 +STENLILLE.LNG 1000000 +SE_S_RURAL.LNG 1000000 +/; +* Location of LNG investment in Denmark is irrelevant for the model. +* STENLILLE.Cavern repressents investment in a storage facility in Tønder. Location is irrelevant for model so no individual location node is defined. + +PARAMETER TSOSTORAGE(YYY,STORE,STORETYPE) 'Storage reserved for emergencies by the TSO'; + +TSOSTORAGE(Y,'Stenlille','Aquifer') = STORAGE_MAX(Y,'Stenlille','Aquifer')/sum((STORE,STORETYPE), STORAGE_MAX(Y,STORE,STORETYPE))*(sum((STORE,STORETYPE), STORAGE_MAX(Y,STORE,STORETYPE))-605000); +TSOSTORAGE(Y,'LiTorup','Cavern') = STORAGE_MAX(Y,'LiTorup','Cavern')/sum((STORE,STORETYPE), STORAGE_MAX(Y,STORE,STORETYPE))*(sum((STORE,STORETYPE), STORAGE_MAX(Y,STORE,STORETYPE))-605000); + +TSOSTORAGE(YYY,STORE,STORETYPE) = TSOSTORAGE(YYY,STORE,STORETYPE) * kNm3ToMWh; +INJ_MAX(YYY,STORE,STORETYPE) = INJ_MAX(YYY,STORE,STORETYPE) * kNm3ToMWh; +EXT_MAX(YYY,STORE,STORETYPE) = EXT_MAX(YYY,STORE,STORETYPE) * kNm3ToMWh; +STORAGE_MAX(YYY,STORE,STORETYPE) = STORAGE_MAX(YYY,STORE,STORETYPE) * kNm3ToMWh; \ No newline at end of file diff --git a/base/addons/gas/gasgdx.inc b/base/addons/gas/gasgdx.inc new file mode 100644 index 00000000..b3eed4f3 --- /dev/null +++ b/base/addons/gas/gasgdx.inc @@ -0,0 +1,14 @@ + + +$ifi %bb3%==yes $goto outbb3 +$ifi %STORAGEALLOCATIONS%==yes execute_unload '../../base/addons/gas/GSFXW.gdx', GSFXW; +$ifi %STORAGEALLOCATIONS%==yes execute_unload '../../base/addons/gas/SC_Y.gdx',SC_Y; +$ifi %CAPACITYALLOCATIONS%==yes execute_unload '../../base/addons/gas/reserved_capacity.gdx', TC_EX_Y, TC_EX_M, TC_EN_Y, TC_EN_M; + +$ifi %MAKEINVEST%==yes execute_unload '../../base/addons/gas/gasinvest.gdx',SUPACC,EXPACC ,INJACC ,EXTACC ,STOREACC, DEVACC; + +$goto end_out + +$label outbb3 + +$label end_out \ No newline at end of file diff --git a/base/addons/gas/gasgeninput.inc b/base/addons/gas/gasgeninput.inc new file mode 100644 index 00000000..80d00bdf --- /dev/null +++ b/base/addons/gas/gasgeninput.inc @@ -0,0 +1,26 @@ + + +* RUN only in BB1 or BB2 +$ifi %BB3%==yes $goto not_bb1orbb2 + +$ifi %STORAGEALLOCATIONS%==yes GSFXW(Y,STORE,STORETYPE,S) = SUM(T,IHOURSINST(S,T)*(VINJ.l(STORE,STORETYPE,S,T)-VEXT.l(STORE,STORETYPE,S,T))); +$ifi %STORAGEALLOCATIONS%==yes SC_Y(Y,SCON) = VSC.l(SCON); + + +$ifi %CAPACITYALLOCATIONS%==yes TC_EX_Y(SYSEXIT,Y) = VTC_EX_Y.l(SYSEXIT); +$ifi %CAPACITYALLOCATIONS%==yes TC_EX_M(SYSEXIT,Y,SSS,TTT) = SUM((IMONTH,DAY)$(DAYT(DAY,TTT) and IWHEN(IMONTH,SSS,DAY)), VTC_EX_M.l(SYSEXIT,IMONTH)); + +$ifi %CAPACITYALLOCATIONS%==yes TC_EN_Y(SYSENTRY,Y) = VTC_EN_Y.l(SYSENTRY); +$ifi %CAPACITYALLOCATIONS%==yes TC_EN_M(SYSENTRY,Y,SSS,TTT) = SUM((IMONTH,DAY)$(DAYT(DAY,TTT) and IWHEN(IMONTH,SSS,DAY)), VTC_EN_M.l(SYSENTRY,IMONTH)); + + +$ifi %MAKEWATER%==yes HYFXRW_GAS(Y,S,IR) = SUM(IA$RRRAAA(IR,IA), SUM(IGHYRR$IAGK_Y(IA,IGHYRR),SUM(T,IHOURSINST(S,T)*(VGE_T.l(IA,IGHYRR,S,T))))+SUM(IGHYRS$IAGK_Y(IA,IGHYRS),SUM(T,IHOURSINST(S,T)*(VGE_T.l(IA,IGHYRS,S,T))))); + +$ifi %MAKEINVEST%==yes SUPACC(Y,SOURCE) = SUM(NGINV,SUPEFFECT(NGINV,SOURCE)*VGINFINV.l(NGINV)); +$ifi %MAKEINVEST%==yes EXPACC(Y,SOURCE) = SUM(NGINV,EXPEFFECT(NGINV,SOURCE)*VGINFINV.l(NGINV)); +$ifi %MAKEINVEST%==yes INJACC(Y,STORE,STORETYPE) = VGINVINJ.l(STORE,STORETYPE)+VGINVSTORE.l(STORE,STORETYPE)*GSTOREDATA(STORETYPE,'INJEFFECT'); +$ifi %MAKEINVEST%==yes EXTACC(Y,STORE,STORETYPE) = VGINVEXT.l(STORE,STORETYPE)+VGINVSTORE.l(STORE,STORETYPE)*GSTOREDATA(STORETYPE,'EXTEFFECT'); +$ifi %MAKEINVEST%==yes STOREACC(Y,STORE,STORETYPE) = VGINVSTORE.l(STORE,STORETYPE); +$ifi %MAKEINVEST%==yes DEVACC(Y,SYSEXIT) = SUM((C,GNETSTEPS), GNET_DEV_EFFECT(C,SYSEXIT)*VGNETINV.l(C,GNETSTEPS)) + SUM(NGINV,DEVEFFECT(NGINV,SYSEXIT)*VGINFINV.l(NGINV)); + +$label not_bb1orbb2 diff --git a/base/addons/gas/gasheating.inc b/base/addons/gas/gasheating.inc new file mode 100644 index 00000000..5c09a052 --- /dev/null +++ b/base/addons/gas/gasheating.inc @@ -0,0 +1,2 @@ +$ifi %HEATING%==yes + sum(HEAT$IHX(HEAT,AAA), VHX(HEAT,IAURBH,IS3,T) )$HEAT(AAA) +$ifi %HEATING%==yes - sum(HEAT$IHX(AAA,HEAT), VHX(IAURBH,IAURBH,IS3,T) )$HEAT(AAA) \ No newline at end of file diff --git a/base/addons/gas/gasinit.inc b/base/addons/gas/gasinit.inc new file mode 100644 index 00000000..71e1c0bb --- /dev/null +++ b/base/addons/gas/gasinit.inc @@ -0,0 +1,8 @@ +$ifi %bb2%==yes VGINFINV.l(NGINV) = 0; +$ifi %bb2%==yes VGINVSTORE.l(STORE,STORETYPE) = 0; +$ifi %bb2%==yes VGINVINJ.l(STORE,STORETYPE) = 0; +$ifi %bb2%==yes VGINVEXT.l(STORE,STORETYPE) = 0; +$ifi %bb2%==yes VGINVSTORE.up(STORE,STORETYPE) = MAXINVEST(STORE,STORETYPE); +$ifi %bb2%==yes STOREINV(STORE, STORETYPE) = YES$MAXINVEST(STORE,STORETYPE); +$ifi %bb2%==yes VGNETINV.up(C,GNETSTEPS) = 0; +$ifi %bb2%==yes VGNETINV.up(C,GNETSTEPS) = GNET_MAX(C,GNETSTEPS); \ No newline at end of file diff --git a/base/addons/gas/gasinternal.inc b/base/addons/gas/gasinternal.inc new file mode 100644 index 00000000..0f5350ea --- /dev/null +++ b/base/addons/gas/gasinternal.inc @@ -0,0 +1,42 @@ +$ifi %MR%==yes IA(AAA)$ICA('Denmark',AAA)=yes$MRAREAS(AAA); +$ifi %MR%==yes IA('DK_E_Rural') = yes; +$ifi %MR%==yes IA('DK_W_Rural') = yes; +$ifi %MR%==yes IA('DK_E_Offshore') = yes; +$ifi %MR%==yes IA('DK_W_Offshore') = yes; + +PARAMETERS + ITC_EX_Y(SYSEXIT) 'Reserved yearly exit capacity' + ITC_EX_M(SYSEXIT,s,t) 'Reserved monthly exit capacity' + ITC_EN_Y(SYSENTRY) 'Reserved yearly entry capacity' + ITC_EN_M(SYSENTRY,s,t) 'Reserved monthly entry capacity' + IDGRES_Y(AAA,s,t) 'Residual gas demand' + IDGOTH_Y(AAA,s,t) 'Gas demand from other large consumers' + IDGRES_SUMVAR(AAA) 'Residual gasforbrug, weightsum over the variation' + IDGOTH_SUMVAR(AAA) 'Gasforbrug andre timeaflæste, vægtsum over variation' + IGSFXW(STORE,STORETYPE,SSS) 'Net quantity gas stored in present week (MWh)' + ISC(SCON) 'Storage allowances procured' + IGEX_PRICE(SOURCE,SSS) 'Export value of natural gas' + + ISE_DGRES_Y(AAA,S,T) + ISE_DGOTH_Y(AAA,S,T) + + + IGEXT_COST(SOURCE,S) 'Gas extraction variable costs' + + + ISUPPLY_MAX(SOURCE) 'Exogenous maximum supply capacity' + IEXP_MAX(SOURCE) 'Exogenous maximum supply capacity' + IDEV_MAX(SYSEXIT) 'Exogenous maximum delivery capacity' + ISTORE_MAX(STORE,STORETYPE) 'Exogenous maximum storage capacity' + IINJ_MAX(STORE,STORETYPE) 'Exogenous maximum injection capacity' + IEXT_MAX(STORE,STORETYPE) 'Exogenous maximum extraction capacity' + ; + +$ifi %bb1%==yes IDGRES_SUMVAR(IA) = sum((S,T), IHOURSINST(S,T)*DGRES_VAR(IA,S,T)); +$ifi %bb1%==yes IDGOTH_SUMVAR(IA) = sum((S,T), IHOURSINST(S,T)*DGOTH_VAR(IA,S,T)); + +$ifi %bb2%==yes IDGRES_SUMVAR(IA) = sum((S,T), IHOURSINST(S,T)*DGRES_VAR(IA,S,T)); +$ifi %bb2%==yes IDGOTH_SUMVAR(IA) = sum((S,T), IHOURSINST(S,T)*DGOTH_VAR(IA,S,T)); + +$ifi %bb3%==yes IDGRES_SUMVAR(IA) = sum((SSS,T), IHOURSINST(SSS,T)*DGRES_VAR(IA,SSS,T)); +$ifi %bb3%==yes IDGOTH_SUMVAR(IA) = sum((SSS,T), IHOURSINST(SSS,T)*DGOTH_VAR(IA,SSS,T)); \ No newline at end of file diff --git a/base/addons/gas/gasmakecompare.inc b/base/addons/gas/gasmakecompare.inc new file mode 100644 index 00000000..34e276e8 --- /dev/null +++ b/base/addons/gas/gasmakecompare.inc @@ -0,0 +1,22 @@ +* File mkcompare.inc. +* This file is an extension to the welfare calculation module for the Balmorel model. +* Created by Jens Hauch and Thomas E. Pedersen, COWI A/S (DK). +* Adapted for Balmorel version 2.10, 2.13 by Hans Ravn. +* Extended to for the gas add-on by Lars Bregnbæk, X-mas 2006. + + +*=============================================================================== +* Overview +*=============================================================================== + +* Intermediate parameter values are printed to files for later use. +* The parameter values were calculated in gascompprint3.inc, as ..,..,.., etc. +* They are here saved under names ...00, ...00, ...00, etc. + +*=============================================================================== +* Overview end +*=============================================================================== + + +$libinclude gams2prm residualcosts residualcosts00 +$libinclude gams2prm othercosts othercosts00 diff --git a/base/addons/gas/gasmkcompare.inc b/base/addons/gas/gasmkcompare.inc new file mode 100644 index 00000000..0b219aa7 --- /dev/null +++ b/base/addons/gas/gasmkcompare.inc @@ -0,0 +1,76 @@ +* File gasmkcompare.inc. +* This file is an extension to the welfare calculation module for the Balmorel model. +* Created by Jens Hauch and Thomas E. Pedersen, COWI A/S (DK). +* Adapted for Balmorel version 2.10, 2.13 by Hans Ravn. +* Extended to for the gas add-on by Lars Bregnbæk, X-mas 2006. + + +*=============================================================================== +* Overview +*=============================================================================== + +* Intermediate parameter values are printed to files for later use. +* The parameter values were calculated in gascompprint3.inc, as ..,..,.., etc. +* They are here saved under names ...00, ...00, ...00, etc. + +*=============================================================================== +* Overview end +*=============================================================================== + +*=============================================================================== +* Consumers +*=============================================================================== + +$libinclude gams2prm residualcosts residualcosts00 +$libinclude gams2prm othercosts othercosts00 + +*=============================================================================== +* Distribution companies +*=============================================================================== + +$libinclude gams2prm DSOprofit DSOprofit00 + +*=============================================================================== +* TSO +*=============================================================================== + +$libinclude gams2prm TSOCAPrev TSOCAPrev00 +$libinclude gams2prm TSOVOLrev TSOVOLrev00 +$libinclude gams2prm TSOCAPRent TSOCAPRent00 +$libinclude gams2prm TSOprofit TSOprofit00 + + +*=============================================================================== +* SSO +*=============================================================================== + +$libinclude gams2prm SSOCAPrev SSOCAPrev00 +$libinclude gams2prm SSOVOLrev SSOVOLrev00 +$libinclude gams2prm SSOCAPrent SSOCAPrent00 +$libinclude gams2prm SSOProfit SSOProfit00 + +*=============================================================================== +* Producers +*=============================================================================== + +$libinclude gams2prm GasProducerProfit GasProducerProfit00 + +*=============================================================================== +* Suppliers +*=============================================================================== + +$libinclude gams2prm GasSupplierProfit GasSupplierProfit00 + +*=============================================================================== +* Shippers +*=============================================================================== + +$libinclude gams2prm GasShipperProfit GasShipperProfit00 +$libinclude gams2prm ShipperCapRent ShipperCapRent00 + + +*=============================================================================== +* Gas Storage Customers +*=============================================================================== + +$libinclude gams2prm GasStorageUserProfit GasStorageUserProfit00 \ No newline at end of file diff --git a/base/addons/gas/gasopt.opt b/base/addons/gas/gasopt.opt new file mode 100644 index 00000000..6a87470f --- /dev/null +++ b/base/addons/gas/gasopt.opt @@ -0,0 +1,46 @@ +* The following variables define options pertaining to the gas add-on module. + +* Geographical resolution at MR level +$Setglobal MR +* Employ special nodes for larger district heating areas +$Setglobal HEATING +* Run pipeline simulation +$Setglobal PIPELINE +* (bb1 only) Create an input file for gas made available in specific weeks for +* use in BB3 +$Setglobal STORAGEALLOCATIONS yes +* (bb1 only) Create an input file for yearly and monthly capacity reservations +* for use in BB3 +$Setglobal CAPACITYALLOCATIONS yes +* (bb1 only) Create an input file for gas to be produced in specific weeks for +* use in BB3 + +* Include microCHP in the model +$Setglobal micro +* Include CAES in the model +$Setglobal CAES + +* Fjernvarme områder med gasforsyning er tvungen til at fyre med gas? +$Setglobal MUSTGAS yes + +* Fjernvarme områder uden gasforsyning kan udbygges med gas? +$Setglobal EXPANDGAS + +* Allow electricity for Danish district heating +$Setglobal ALLOWEH yes + + +* Gasprice level at production nodes. Defaults to MEDGAS. +$Setglobal LOWGAS +$Setglobal MEDGAS yes +$Setglobal HIGHGAS + +* No exogenous capacity charge on gas storage. However kapacitet tariffs kan be interpretted from the +* marginal of the storage level value VSTOCK. +$Setglobal FREESTORAGE yes +$Setglobal FREEGAS yes + +$Setglobal NOCAPACITY + +* Set to yes if using descrete investments. +$Setglobal SOLVEMIP yes \ No newline at end of file diff --git a/base/addons/gas/gasproduction.inc b/base/addons/gas/gasproduction.inc new file mode 100644 index 00000000..57b03c69 --- /dev/null +++ b/base/addons/gas/gasproduction.inc @@ -0,0 +1,13 @@ + +TABLE GP(YYY,SOURCE) +* PJ/år + NYBRO +2005 376 +2006 377 +2007 380 +2008 387 +2009 392 +; +GP(YYY,SOURCE)=GP(YYY,SOURCE)/3.6*1000000; + + diff --git a/base/addons/gas/gasregions2areas.inc b/base/addons/gas/gasregions2areas.inc new file mode 100644 index 00000000..c0e79094 --- /dev/null +++ b/base/addons/gas/gasregions2areas.inc @@ -0,0 +1,6 @@ +DK_E.(STENLILLE,OERESUND) +DK_W.(LITORUP, ELLUND, NYBRO) + +$ifi %HEATING%==yes DK_E.(VEKS_V,CTR_V,VFORBRANDING_V,NFORBRANDING_V) +$ifi %HEATING%==yes DK_W.(AALBORG_V,AARHUS_V,ODENSE_V,HERNING_V,TVIS_V) +$ifi %MR%==yes $INCLUDE '../../base/addons/gas/MRregions2areas.inc'; \ No newline at end of file diff --git a/base/addons/gas/gasscaling.inc b/base/addons/gas/gasscaling.inc new file mode 100644 index 00000000..bf7ab437 --- /dev/null +++ b/base/addons/gas/gasscaling.inc @@ -0,0 +1,44 @@ +$ifi %bb1%==yes balbase1.scaleopt=1; +$ifi %bb1%==yes balbase1.scaleopt=1; +$ifi %bb1%==yes balbase1.scaleopt=1; + + +VGUD.scale(IA,S,T)$ICA('Denmark',IA) = 1/(1+2*(IDGRES_Y(IA,S,T)+IDGOTH_Y(IA,S,T))); +VSTOCK.scale(STORE,STORETYPE,S,T) = 1/(1+ISTORE_MAX(STORE,STORETYPE)); +VINJ.scale(STORE,STORETYPE,S,T) = 1/(1+IINJ_MAX(STORE,STORETYPE)); +VEXT.scale(STORE,STORETYPE,S,T) = 1/(1+IEXT_MAX(STORE,STORETYPE)); +VEXP.scale(SOURCE,S,T) = 1/(1+IEXP_MAX(SOURCE)); +VSUP.scale(SOURCE,S,T) = 1/(1+ISUPPLY_MAX(SOURCE)); +VTC_EN_Y.scale = 1/10000; +VTC_EN_M.scale(IMONTH) = 1/1000; +VTC_EN_W.scale(S) = 1/1000; +VTC_EN_D.scale(S,DAY) = 1/1000; +VTC_EX_Y.scale = 1/10000; +VTC_EX_M.scale(IMONTH) = 1/1000; +VTC_EX_W.scale(S) = 1/1000; +VTC_EX_D.scale(S,DAY) = 1/1000; +VSC.scale(SCON) = 1/(1+SUM((STORE,STORETYPE),ISTORE_MAX(STORE,STORETYPE))); +* VGINFINV(NGINV) - Well scaled +VGINVSTORE.scale(STORE,STORETYPE) = 1/10000; +VGINVEXT.scale(STORE,STORETYPE) = 1/1000; +VGINVINJ.scale(STORE,STORETYPE) = 1/1000; + + + VOBJ.scale =1/1000; + VGE_T.scale(IAGK_Y(IA,G),S,T) =1/(1+VGE_T.up(IA,G,S,T)$(VGE_T.up(IA,G,S,T) LE INF)); + VGH_T.scale(IAGK_Y(IA,G),S,T) =1/(1+VGH_T.up(IA,G,S,T)$(VGH_T.up(IA,G,S,T) LE INF)); + VX_T.scale(IRE,IRI,S,T) =1/(1+IXKINI_Y(IRE,IRI)); + VGEN_T.scale(IAGKN(IA,G),S,T) =1/1000; + VGHN_T.scale(IAGKN(IA,G),S,T) =1/1000; + VGKN.scale(IAGKN(IA,G)) =1/1000; + VXKN.scale(IRE,IRI) =1/(1+IXKINI_Y(IRE,IRI)); + VHYRS_S.scale(IA,S) =1/1000000; + VGE2LEVEL.scale(IAGK_Y(IA,IG2LEVEL),S,DAYTYPE) =1/(1+Sum(t,VGE_T.up(IA,IG2LEVEL,S,T)$(VGE_T.up(IA,IG2LEVEL,S,T) LE INF))); + VESTOLOADT.scale(IA,S,T) =1/(1+Sum(IAGK_Y(IA,IGESTO),VGE_T.up(IA,IGESTO,S,T)$(VGE_T.up(IA,IGESTO,S,T) LE INF))); + VHSTOLOADT.scale(IA,S,T) =1/(1+Sum(IAGK_Y(IA,IGHSTO),VGH_T.up(IA,IGHSTO,S,T)$(VGH_T.up(IA,IGHSTO,S,T) LE INF))); + VESTOVOLT.scale(IA,S,T) =1/(1+Sum(IAGK_Y(IA,IGESTO),VGE_T.up(IA,IGESTO,S,T)$(VGE_T.up(IA,IGESTO,S,T) LE INF))); + VHSTOVOLT.scale(IA,S,T) =1/(1+Sum(IAGK_Y(IA,IGHSTO),VGH_T.up(IA,IGHSTO,S,T)$(VGH_T.up(IA,IGHSTO,S,T) LE INF))); +$ifi %X3V%==yes VX3VIM_T.scale(X3VX(IR,X3VPLACE),X3VSTEP,S,T) = 1/(1+VX3VIM_T.up(IR,X3VPLACE,X3VSTEP,S,T)); +$ifi %X3V%==yes VX3VEX_T.scale(X3VX(IR,X3VPLACE),X3VSTEP,S,T) = 1/(1+VX3VEX_T.up(IR,X3VPLACE,X3VSTEP,S,T)); + +balbase2.optfile=1; \ No newline at end of file diff --git a/base/addons/gas/gassets.inc b/base/addons/gas/gassets.inc new file mode 100644 index 00000000..20992037 --- /dev/null +++ b/base/addons/gas/gassets.inc @@ -0,0 +1,347 @@ + + + +$ifi %MR%==yes SET MRAREAS(AAA) / +$ifi %MR%==yes $include '../../base/addons/gas/MRareas.inc'; +$ifi %MR%==yes DK_W_Urban +$ifi %MR%==yes DK_W_Rural +$ifi %MR%==yes DK_W_Odense +$ifi %MR%==yes DK_W_Trefor +$ifi %MR%==yes DK_W_Aarhus +$ifi %MR%==yes DK_W_Aalborg +$ifi %MR%==yes DK_E_Urban +$ifi %MR%==yes DK_E_Rural +$ifi %MR%==yes /; + + + + +ALIAS(AAA,NODE,i,j,NODES); + +SETS DDD "Regional NAT_GAS Distribution", + NODEDATASET "MR Station specific datatypes" +; + +SETS DDDAAA(DDD,AAA) "MR Stations in distribution area", + RRRDDD(RRR, DDD) "NAT_GAS Distribution areas in region"; + +SETS DDD "Regional NAT_GAS Distribution" + / DONG_VEST, + MIDT_NORD, + NGF, + DONG_OEST, + HNG, + SE_S_DIST + /; + +SETS RRRDDD(RRR,DDD) "NAT_GAS Distribution areas in region" + / DK_E.(DONG_OEST,HNG), + DK_W.(DONG_VEST,MIDT_NORD,NGF) + /; + +* Depending on geografical resolution different areas (MR) much be contained in +* the distribition areas. +$ifi %MR%==yes $goto MRDist +SETS DDDNODE(DDD,AAA) "MR Stations in distribution area" +/ + MIDT_NORD.(DK_W_Urban, DK_W_AALBORG,DK_W_AARHUS,DK_W_1, DK_W_Rural), + DONG_VEST.(DK_W_TREFOR,DK_W_2), + NGF.(DK_W_ODENSE), + DONG_OEST.(DK_E_2,DK_E_1,DK_E_Rural), + HNG.(DK_E_Urban ) + SE_S_DIST.SE_S_Rural +/; +$goto end_dist +$Label MRDist +SETS DDDNODE(DDD,AAA) "MR Stations in distribution area" +/ + MIDT_NORD.(BRANDE, ELLIDSHOEJ, HAVERSLEV, HERNING, KARUP, LITORUP, VIBORG, AALBORG), + DONG_VEST.(EGTVED,ELLUND,HELLE,LILBALLE,LISELSKAER,NYBRO,NOERSKOV,POTTEHUSE,STANDST,TAULOV,TERKELSBOEL,FROESLEV), + NGF.(MIDDELFART,BILLESBOELLE,KOELBJERG,HOEJBY,ULLERSLEV,NYBORG), + DONG_OEST.(SLAGELSE,SOROE,RINGSTED,STENLILLE), + HNG.(BROENDBY,DRAGOER,KARLSLUNDE,KOEGE,LYNGE,MAALOEV,TORSLUNDE,VALLENSBAEK,SYDHAVNEN,AMAGERF) + SE_S_Dist.SE_S_Rural +/; +$label end_dist + +SETS NODEDATASET "MR Station specific datatypes" + / + X + Y + Capacity + PRESSURE + /; + + +* The following will probably not be implemented. + +$ifi %MR%==yes SET NOTMR(AAA)/ DK_E_Urban, DK_W_Odense, DK_W_TREFOR/; + + +$ifi NOT %HEATING%==yes $goto end_heat +SET HEAT(AAA) / +VEKS_V +CTR_V +VFORBRANDING_V +NFORBRANDING_V +AALBORG_V +AARHUS_V +ODENSE_V +HERNING_V +TVIS_V +/; +ALIAS(HEAT,HEAT2); + +SET HX(i,j) / +VEKS_V.CTR_V +VEKS_V.VFORBRANDING_V +CTR_V.VEKS_V +CTR_V.VFORBRANDING_V +VFORBRANDING_V.CTR_V +VFORBRANDING_V.VEKS_V +/; + +SET HEATMR(HEAT,AAA)/ +VEKS_V.(TORSLUNDE), +CTR_V.(HCOERSTED), +VFORBRANDING_V.(VALLENSBAEK) +NFORBRANDING_V.(LYNGE) +AALBORG_V.(AALBORG, ELLIDSHOEJ) +AARHUS_V.(HERNING) +ODENSE_V.(HOEJBY) +HERNING_V.(HERNING) +TVIS_V.(TAULOV, SKAERBAEK) +/; + +SETS + IHX(i,j) + ; + +IHX(i,j)=YES$(HEAT(i)*HEAT(j)*HX(i,j)); +$label end_heat + + + +SET SOURCE(AAA) +/ +NYBRO +OERESUND +ELLUND +/; + +SET TRANSIT(AAA) +/ +OERESUND +/; + + +SET STORE(AAA) +/ +STENLILLE +LITORUP +SE_S_RURAL +/; + +$ifi %MR%==yes SET GGGMR(GGG) / +$ifi %MR%==yes $include '../../base/addons/gas/GGGMRset.inc'; +$ifi %MR%==yes /; + +SET SYSENTRY +/ +Nybro +Ellund +Oresund +/; + +SET SYSEXIT +/ +Oeresund +Ellund +DK-EXIT +/; + + + + + +* ----- Storage ------* +SET SCON / STORE1, + STORE2/; + +SET SCONDATASET +/ +VOLCAP 'Volume capacity in the storage facilities' +INJCAP 'Injection capcity' +EXTCAP 'Withdrawal capacity' +TARRIF 'Package tarrif' +/; + + +SET DAY 'Days of the week' +/ +MONDAY +TUESDAY +WEDNESDAY +THURSDAY +FRIDAY +SATURDAY +SUNDAY +/; + +SET DAYT 'Hours to days relationship' +/ + MONDAY.(T1*T24), + TUESDAY.(T25*T48), + WEDNESDAY.(T49*T72), + THURSDAY.(T73*T96), + FRIDAY.(T97*T130), + SATURDAY.(T131*T154), + SUNDAY.(T155*T168) +/; + + +* Definition of individual steps in the residual gas demand function: + +SET DGRESF 'Steps in elastic residual gas demand' +/ DGRESF_D1_9,DGRESF_D1_8,DGRESF_D1_7,DGRESF_D1_6,DGRESF_D1_5,DGRESF_D1_4,DGRESF_D1_3,DGRESF_D1_2,DGRESF_D1_1, + DGRESF_U1_1,DGRESF_U1_2,DGRESF_U1_3,DGRESF_U1_4,DGRESF_U1_5,DGRESF_U1_6,DGRESF_U1_7,DGRESF_U1_8,DGRESF_U1_9, + DGRESF_D2_9,DGRESF_D2_8,DGRESF_D2_7,DGRESF_D2_6,DGRESF_D2_5,DGRESF_D2_4,DGRESF_D2_3,DGRESF_D2_2,DGRESF_D2_1, + DGRESF_U2_1,DGRESF_U2_2,DGRESF_U2_3,DGRESF_U2_4,DGRESF_U2_5,DGRESF_U2_6,DGRESF_U2_7,DGRESF_U2_8,DGRESF_U2_9 +/; + + + + +* Definition of subsets of steps for regulation downwards (decreased demand) +* and upwards (increased demands) of electricity demand: + +SET DGRESF_D1(DGRESF) 'Downwards steps in elastic residual gas demand, relative data format' +/ +*DGRESF_D1_9,DGRESF_D1_8,DGRESF_D1_7,DGRESF_D1_6,DGRESF_D1_5,DGRESF_D1_4,DGRESF_D1_3,DGRESF_D1_2,DGRESF_D1_1 +/; + +SET DGRESF_U1(DGRESF) 'Upwards steps in elastic residual gas demand, relative data format' +/ +*DGRESF_U1_1,DGRESF_U1_2,DGRESF_U1_3,DGRESF_U1_4,DGRESF_U1_5,DGRESF_U1_6,DGRESF_U1_7,DGRESF_U1_8,DGRESF_U1_9 +/; + +SET DGRESF_D2(DGRESF) 'Downwards steps in elastic residual gas demand, absolute and MW-incremental data format' +/ +* DGRESF_D2_9,DGRESF_D2_8,DGRESF_D2_7,DGRESF_D2_6,DGRESF_D2_5,DGRESF_D2_4,DGRESF_D2_3,DGRESF_D2_2,DGRESF_D2_1 +/; + +SET DGRESF_U2(DGRESF) 'Upwards steps in elastic residual gas demand, absolute and MW-incremental data format' +/ +* DGRESF_U2_1,DGRESF_U2_2,DGRESF_U2_3,DGRESF_U2_4,DGRESF_U2_5,DGRESF_U2_6,DGRESF_U2_7,DGRESF_U2_8,DGRESF_U2_9 +/; + + +* Definition of individual steps in the other large consumers gas demand function: + +SET DGOTHF 'Steps in elastic other large consumers gas demand' +/ DGOTHF_D1_9,DGOTHF_D1_8,DGOTHF_D1_7,DGOTHF_D1_6,DGOTHF_D1_5,DGOTHF_D1_4,DGOTHF_D1_3,DGOTHF_D1_2,DGOTHF_D1_1, + DGOTHF_U1_1,DGOTHF_U1_2,DGOTHF_U1_3,DGOTHF_U1_4,DGOTHF_U1_5,DGOTHF_U1_6,DGOTHF_U1_7,DGOTHF_U1_8,DGOTHF_U1_9, + DGOTHF_D2_9,DGOTHF_D2_8,DGOTHF_D2_7,DGOTHF_D2_6,DGOTHF_D2_5,DGOTHF_D2_4,DGOTHF_D2_3,DGOTHF_D2_2,DGOTHF_D2_1, + DGOTHF_U2_1,DGOTHF_U2_2,DGOTHF_U2_3,DGOTHF_U2_4,DGOTHF_U2_5,DGOTHF_U2_6,DGOTHF_U2_7,DGOTHF_U2_8,DGOTHF_U2_9 +/; + + + + +* Definition of subsets of steps for regulation downwards (decreased demand) +* and upwards (increased demands) of electricity demand: + +SET DGOTHF_D1(DGOTHF) 'Downwards steps in elastic other large consumers gas demand, relative data format' +/ +*DGOTHF_D1_9,DGOTHF_D1_8,DGOTHF_D1_7,DGOTHF_D1_6,DGOTHF_D1_5,DGOTHF_D1_4,DGOTHF_D1_3,DGOTHF_D1_2,DGOTHF_D1_1 +/; + +SET DGOTHF_U1(DGOTHF) 'Upwards steps in elastic other large consumers gas demand, relative data format' +/ +*DGOTHF_U1_1,DGOTHF_U1_2,DGOTHF_U1_3,DGOTHF_U1_4,DGOTHF_U1_5,DGOTHF_U1_6,DGOTHF_U1_7,DGOTHF_U1_8,DGOTHF_U1_9 +/; + +SET DGOTHF_D2(DGOTHF) 'Downwards steps in elastic other large consumers gas demand, absolute and MW-incremental data format' +/ +* DGOTHF_D2_9,DGOTHF_D2_8,DGOTHF_D2_7,DGOTHF_D2_6,DGOTHF_D2_5,DGOTHF_D2_4,DGOTHF_D2_3,DGOTHF_D2_2,DGOTHF_D2_1 +/; + +SET DGOTHF_U2(DGOTHF) 'Upwards steps in elastic other large consumers gas demand, absolute and MW-incremental data format' +/ +* DGOTHF_U2_1,DGOTHF_U2_2,DGOTHF_U2_3,DGOTHF_U2_4,DGOTHF_U2_5,DGOTHF_U2_6,DGOTHF_U2_7,DGOTHF_U2_8,DGOTHF_U2_9 +/; + +$ifi %PIPELINE%==yes $include '../../base/addons/gas/flowsets.inc'; + + +SET + IMONTH /JAN, FEB, MAR, APR, MAY, JUN, JUL, AUG, SEP, OCT, NOV, DEC/; + +PARAMETER IMONTH_W(IMONTH) +/ +JAN 31, +FEB 28, +MAR 31, +APR 30, +MAY 31, +JUN 30, +JUL 31, +AUG 31, +SEP 30, +OCT 31, +NOV 30, +DEC 31 +/; +ALIAS(IMONTH,IMONTHALIAS) +PARAMETER IFROMDAY(IMONTH), + ITODAY(IMONTH); + +IFROMDAY(IMONTH)=sum(IMONTHALIAS$(ord(IMONTH)0); + + +SOS1 VARIABLE MN_TURN(s,t,MNLEG); + +SOS1 VARIABLE S_TURN(s,t,SLEG); + +SOS1 VARIABLE FYN_TURN(s,t,FYNLEG); + +PARAMETER BigM(e), + P_PIPES(e,s,t) "Pressure in pipes", + P_NODES(i,s,t) "Pressure at nodes"; + +BigM(e) = 4375; +* ---------------------------------------- * +* BOUNDS +* ---------------------------------------- * + +* --- SUPPLY LIMITS --- * +VSUP.UP(SOURCE,IST) = SUPPLY_MAX(SOURCE)*MWHperkNM3; +VEXP.UP(SOURCE,IST) = EXP_MAX(SOURCE)*MWHperkNM3; +VSUP.fx(i,IST)$(not SOURCE(i)) = 0; +VEXP.fx(i,IST)$(not SOURCE(i)) = 0; + +* --- d LIMITS --- * +VGUD.UP(IA,IST) = NODEDATA(IA,'Capacity')*MWHperkNM3; + +* ---PRESSURE BOUNDS AND REGULATORS--- * +VPs_NODES.LO(IA,IST) = power(NODEDATA(IA,'Pressure'),2); +VPs_NODES.UP(IA,IST) = 80*80; +VPs_PIPES.UP(e,IST)$(not dummy(e)) = power(EDGEDATA(e,'PRESSURE'),2); + +* --- STORAGE BOUNDS --- * +VINJ.UP(STORE,IST) = INJ_MAX(STORE)*MWHperkNM3; +VEXT.UP(STORE,IST) = EXT_MAX(STORE)*MWHperkNM3; +VSTOCK.UP(STORE,IST) = STORAGE_MAX(STORE)*MWHperkNM3; + +* --- FINGER BOUNDS --- * +*uu.lo(i,j,IST)$ARC(i,j) = 0; +uu.lo(i,j,IST)$ARC(i,j) = -sqrt((80*80-45*45)/sum(e$SEMIARC(e,i,j), cc(e))); +uu.up(i,j,IST)$ARC(i,j) = sqrt((80*80-45*45)/sum(e$SEMIARC(e,i,j), cc(e))); + + +u.lo(i,e,IST)$N(i,e) = -sqrt((80*80-45*45)/cc(e)); +u.up(i,e,IST)$N(i,e) = sqrt((80*80-45*45)/cc(e)); + +u.lo(i,fingers(e),IST)$(ARC_NODE_INC(e,i)=1) = 0; +u.up(i,fingers(e),IST)$(ARC_NODE_INC(e,i)=-1)= 0; + + +* ---------------------------------------- * +* EQUATIONS +* ---------------------------------------- * + +Equations + massbalance(i,s,t) "Node continuity", + ssmassbalance(i,s,t) "SS-Node contunuity", + linepack(e,s,t) "Linepack", + storagebalance(i,s,t) "Storage balances", + pipepressure(e,s,t,l) "Mean pressure level in the pipes", + flowdir_pos_MN(i,MNLEG,s,t) + flowdir_neg_MN(i,MNLEG,s,t) + flowdir_pos_S(i,SLEG,s,t) + flowdir_neg_S(i,SLEG,s,t) + flowdir_pos_FYN(i,FYNLEG,s,t) + flowdir_neg_FYN(i,FYNLEG,s,t) + + ssflowdir_pos_MN(i,j,MNLEG,s,t) + ssflowdir_neg_MN(i,j,MNLEG,s,t) + ssflowdir_pos_S(i,j,SLEG,s,t) + ssflowdir_neg_S(i,j,SLEG,s,t) + ssflowdir_pos_FYN(i,j,FYNLEG,s,t) + ssflowdir_neg_FYN(i,j,FYNLEG,s,t) + + linepackss(e,s,t) + seasonstock(i,s), + seasonlinepack(e,s) ; + +ssmassbalance(IA,IST) .. + VSUP(IA,IST)$SOURCE(IA) + - VEXP(IA,IST)$SOURCE(IA) + + VEXT(IA,IST)$STORE(IA) + - VINJ(IA,IST)$STORE(IA) + =E= sum(j$(ARC(IA,j)), + uu(IA,j,IST) + ) + -sum(j$(ARC(j,IA)), + uu(j,IA,IST) + ) + + VGUD(IA,IST); + + + +massbalance(IA,IST) .. + VSUP(IA,IST)$SOURCE(IA) + - VEXP(IA,IST)$SOURCE(IA) + + VEXT(IA,IST)$STORE(IA) + - VINJ(IA,IST)$STORE(IA) + =E= sum(e$(N(IA,e)), + u(IA,e,IST) + ) + + VGUD(IA,IST); + +linepack(e,IST(s,t)) .. + VO(e,s,t++1) + =E= VO(e,s,t) + +sum(i$(N(i,e)), + u(i,e,s,t) + ); + +linepackss(e,IST(s,t)) .. + 0 + =E= + +sum(i$(N(i,e)), + u(i,e,s,t) + ); + +pipepressure(e,IST,l)$(not DUMMY(e)) .. + 1000*VO(e,IST)/(K(IST)*VOL(e)) + =G= aa(l)*VPs_PIPES(e,IST)+bb(l); + +storagebalance(STORE,IST(s,t))$(ord(t)0); +NN(i,j) = YES$(ARC(i,j) or ARC(j,i)); \ No newline at end of file diff --git a/base/addons/hydrogen/bb4/hydrogen_acronyms.inc b/base/addons/hydrogen/bb4/hydrogen_acronyms.inc new file mode 100644 index 00000000..c7d8eecb --- /dev/null +++ b/base/addons/hydrogen/bb4/hydrogen_acronyms.inc @@ -0,0 +1,2 @@ +ACRONYM HYDROGEN_GH2STO,H2_STORAGE, HYDROGEN_GETOH2, HYDROGEN_GETOHH2, HYDROGEN_GEHTOH2, HYDROGEN_GCH4TOH2, HYDROGEN_GH2FUEL, HYDROGEN_GH2TOE, HYDROGEN_GH2TOEH, HYDROGEN_GH2TOBIOMETH,FUELCELL, BIOMETHDAC, ELECTROLYZER,STEAMREFORMING,HYDROGEN_GIMPORTH2 +; diff --git a/base/addons/hyrsbb123/HYRSBB123sim3.inc b/base/addons/hyrsbb123/HYRSBB123sim3.inc new file mode 100644 index 00000000..5f3233a8 --- /dev/null +++ b/base/addons/hyrsbb123/HYRSBB123sim3.inc @@ -0,0 +1,39 @@ +* File HYRSBB123sim3.inc for the Balmorel model. +* For use with addon HYRSBB123. +* Included into bb123.sim after SOLVE BALBASE3 statement, inside the loop over seasons. + + +* Update accumulated hydro amounts used: + +IHYRSBB123BB3GACCUM(IA) = IHYRSBB123BB3GACCUM(IA) + SUM(IGHYRS, SUM(T, (IHOURSINST(IS3LOOPSET,T) * VGE_T.L(IA,IGHYRS,IS3LOOPSET,T))$IAGK_Y(IA,IGHYRS) )); +IHYRSBB123BB12GACCUM(IA) = IHYRSBB123BB12GACCUM(IA) + HYRSG(Y,IA,IS3LOOPSET); + + +*hydroinflow(ia,IS3LOOPSET) = IHYINF_S(IA,IS3LOOPSET) * SUM(IGHYRS$IAGK_Y(IA,IGHYRS), (IGKVACCTOY(IA,IGHYRS)+IGKFX_Y(IA,IGHYRS))); +*hydroinflow(ia,s) = IHYINF_S(IA,S) * SUM(IGHYRS$IAGK_Y(IA,IGHYRS), (IGKVACCTOY(IA,IGHYRS)+IGKFX_Y(IA,IGHYRS))); + + +* Set volume deviation relative to BB1/BB2for the next season: +* The following adjustment distributes the hydro energy deviation evenly over the remaining seasons. +* It may need adaptation to specific circumstancs, and may also be generally improved. But it is simple. +* Todo: if S and SSS are different then a correction might be needed (not implemented). So for now: make sure that S and SSS are identical, see file hyrsbb123internals.inc. +IHYRSBB123DELTAVOL(IA) = ((IHYRSBB123BB12GACCUM(IA) - IHYRSBB123BB3GACCUM(IA))/(CARD(IS3LOOPSET)+1 - ORD(IS3LOOPSET)))$IHYRSBB123G_Y(IA,IS3LOOPSET); + +DISPLAY "FØR UPDATE I SIM3", IHYRSBB123VOLINI ; +* IHYRSBB123VOLINI: next season initial volume level. +* With equation QWATERVOLINI it will make VHYRS_S.L = IHYRSBB123VOLINI, which will be part of the information generated through positive savepoint value. +IHYRSBB123VOLINI(IA,IS3LOOPSET+1) = ( IHYRSBB123VOLINI(IA,IS3LOOPSET) + + IHYINF_S(IA,IS3LOOPSET)*SUM(IGHYRS, IGKFX_Y(IA,IGHYRS)) + - SUM(IGHYRS, SUM(T, (IHOURSINST(IS3LOOPSET,T) * VGE_T.L(IA,IGHYRS,IS3LOOPSET,T))$IAGK_Y(IA,IGHYRS) )) )$IHYRSBB123G_Y(IA,IS3LOOPSET); +* Since variable VHYRS_S is declared as positive it will create infeasibilities if IHYRSBB123VOLINI were negative, therefore correct if negative. +ISCALAR1 = SMIN(IA,IHYRSBB123VOLINI(IA,IS3LOOPSET+1)); +IF((ISCALAR1 < 0), + DISPLAY INFODISPLAY, "Warning: SMIN(IA,IS3LOOPSET+1) is negative " ; + PUT ERRORFILE "Warning from addon hyrsbb123, file hyrsbb123sim3.inc:"/; + PUT " BB3 simulation would like to have negative hydro storage volume after simulation of Season ", IS3LOOPSET.TL:4, ", most negative value would be ", ISCALAR1:6:0 /; + PUT " Hydro starage levels were artificially increased by that amount."//; +); + + +IHYRSBB123VOLINI(IA,IS3LOOPSET+1) = MAX(0,IHYRSBB123VOLINI(IA,IS3LOOPSET+1)); + diff --git a/base/addons/hyrsbb123/bb4/HYRSBB123sim3.inc b/base/addons/hyrsbb123/bb4/HYRSBB123sim3.inc new file mode 100644 index 00000000..5f3233a8 --- /dev/null +++ b/base/addons/hyrsbb123/bb4/HYRSBB123sim3.inc @@ -0,0 +1,39 @@ +* File HYRSBB123sim3.inc for the Balmorel model. +* For use with addon HYRSBB123. +* Included into bb123.sim after SOLVE BALBASE3 statement, inside the loop over seasons. + + +* Update accumulated hydro amounts used: + +IHYRSBB123BB3GACCUM(IA) = IHYRSBB123BB3GACCUM(IA) + SUM(IGHYRS, SUM(T, (IHOURSINST(IS3LOOPSET,T) * VGE_T.L(IA,IGHYRS,IS3LOOPSET,T))$IAGK_Y(IA,IGHYRS) )); +IHYRSBB123BB12GACCUM(IA) = IHYRSBB123BB12GACCUM(IA) + HYRSG(Y,IA,IS3LOOPSET); + + +*hydroinflow(ia,IS3LOOPSET) = IHYINF_S(IA,IS3LOOPSET) * SUM(IGHYRS$IAGK_Y(IA,IGHYRS), (IGKVACCTOY(IA,IGHYRS)+IGKFX_Y(IA,IGHYRS))); +*hydroinflow(ia,s) = IHYINF_S(IA,S) * SUM(IGHYRS$IAGK_Y(IA,IGHYRS), (IGKVACCTOY(IA,IGHYRS)+IGKFX_Y(IA,IGHYRS))); + + +* Set volume deviation relative to BB1/BB2for the next season: +* The following adjustment distributes the hydro energy deviation evenly over the remaining seasons. +* It may need adaptation to specific circumstancs, and may also be generally improved. But it is simple. +* Todo: if S and SSS are different then a correction might be needed (not implemented). So for now: make sure that S and SSS are identical, see file hyrsbb123internals.inc. +IHYRSBB123DELTAVOL(IA) = ((IHYRSBB123BB12GACCUM(IA) - IHYRSBB123BB3GACCUM(IA))/(CARD(IS3LOOPSET)+1 - ORD(IS3LOOPSET)))$IHYRSBB123G_Y(IA,IS3LOOPSET); + +DISPLAY "FØR UPDATE I SIM3", IHYRSBB123VOLINI ; +* IHYRSBB123VOLINI: next season initial volume level. +* With equation QWATERVOLINI it will make VHYRS_S.L = IHYRSBB123VOLINI, which will be part of the information generated through positive savepoint value. +IHYRSBB123VOLINI(IA,IS3LOOPSET+1) = ( IHYRSBB123VOLINI(IA,IS3LOOPSET) + + IHYINF_S(IA,IS3LOOPSET)*SUM(IGHYRS, IGKFX_Y(IA,IGHYRS)) + - SUM(IGHYRS, SUM(T, (IHOURSINST(IS3LOOPSET,T) * VGE_T.L(IA,IGHYRS,IS3LOOPSET,T))$IAGK_Y(IA,IGHYRS) )) )$IHYRSBB123G_Y(IA,IS3LOOPSET); +* Since variable VHYRS_S is declared as positive it will create infeasibilities if IHYRSBB123VOLINI were negative, therefore correct if negative. +ISCALAR1 = SMIN(IA,IHYRSBB123VOLINI(IA,IS3LOOPSET+1)); +IF((ISCALAR1 < 0), + DISPLAY INFODISPLAY, "Warning: SMIN(IA,IS3LOOPSET+1) is negative " ; + PUT ERRORFILE "Warning from addon hyrsbb123, file hyrsbb123sim3.inc:"/; + PUT " BB3 simulation would like to have negative hydro storage volume after simulation of Season ", IS3LOOPSET.TL:4, ", most negative value would be ", ISCALAR1:6:0 /; + PUT " Hydro starage levels were artificially increased by that amount."//; +); + + +IHYRSBB123VOLINI(IA,IS3LOOPSET+1) = MAX(0,IHYRSBB123VOLINI(IA,IS3LOOPSET+1)); + diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123_hellolog.inc b/base/addons/hyrsbb123/bb4/hyrsbb123_hellolog.inc new file mode 100644 index 00000000..d465f122 --- /dev/null +++ b/base/addons/hyrsbb123/bb4/hyrsbb123_hellolog.inc @@ -0,0 +1,11 @@ +* File HYRSBB123_hellolog.inc for the Balmorel model. +* For use with addon HYRSBB123. + +PUT " - Addon hyrsbb123" /; + + +$ontext + +Explanation - todo. + +$offtext \ No newline at end of file diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123addobj.inc b/base/addons/hyrsbb123/bb4/hyrsbb123addobj.inc new file mode 100644 index 00000000..e25947f8 --- /dev/null +++ b/base/addons/hyrsbb123/bb4/hyrsbb123addobj.inc @@ -0,0 +1,13 @@ +* File HYRSBB123addobj.inc for the Balmorel model. +* For use with addon HYRSBB123. +* Included into the objective function QOBJ in Balmorel.gms. + +$ifi not %BB3%==yes $goto label_HYRSBB123addobj + +$ifi %BB3%==yes $ifi %HYRSBB123%==price + SUM(IAGK_Y(IA,IGHYRS),SUM((IS3,T), IWATERVAL_Y(IA,IS3)* IHOURSINST(IS3,T) * VGE_T(IA,IGHYRS,IS3,T))) !! no investments in BB3 + +$ifi %BB3%==yes $ifi %HYRSBB123%==quantprice + SUM(IAGK_Y(IA,IGHYRS),SUM((IS3,T), IWATERVAL_Y(IA,IS3)* IHOURSINST(IS3,T) * VGE_T(IA,IGHYRS,IS3,T))) !! no investments in BB3 +$ifi %BB3%==yes $ifi %HYRSBB123%==quantprice + SUM((IA,IS3)$IHYRSBB123G_Y(IA,IS3), (SUM(T,IHOURSINST(IS3,T)))* SUM(IHYRSBB12DELTASET, VIHYRSBB12DELTAMHW(IA,IHYRSBB12DELTASET) *IHYRSBB123EXTRAVAL(IA,IS3,IHYRSBB12DELTASET))) + +$label label_HYRSBB123addobj; +$ifi not %BB3%==yes !! print some error message - todo. diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123balbase3.inc b/base/addons/hyrsbb123/bb4/hyrsbb123balbase3.inc new file mode 100644 index 00000000..0f141dcd --- /dev/null +++ b/base/addons/hyrsbb123/bb4/hyrsbb123balbase3.inc @@ -0,0 +1,6 @@ +* Equations for addon hyrsbb123 used in Balbase3 model definition +* Latest revision 20170419 (hr) + +$ifi %HYRSBB123%==quant QHYRSG +$ifi %HYRSBB123%==quantprice QHYRSG +$ifi %HYRSBB123%==quantprice QWATERVOLINI diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123documentation.txt b/base/addons/hyrsbb123/bb4/hyrsbb123documentation.txt new file mode 100644 index 00000000..e71de7d2 --- /dev/null +++ b/base/addons/hyrsbb123/bb4/hyrsbb123documentation.txt @@ -0,0 +1,106 @@ +$ontext +Hans Ravn +Preliminary. Revised 20170412. + +Addon HYDROBB123 + + +* For model BB3 it is possible to use values found from BB1 or BB2 solutions to achieve use of hydro power +* that corresponds to that found in a BB1 or BB2 solution. +$setglobal HYDROBB123 quantprice +*!option quant "Use seasonal quantities of hydro generation (from HYRSG.gdx) in BB3 ('primal decomposition')" +*!mayif %BB3%==yes +*!option price "Use seasonal prices for hydro generation (from WATERVAL.gdx) in BB3 ('dual decomposition')" +*!mayif %BB3%==yes +*!option quantprice "Use seasonal quantities and prices for hydro generation (from HYRSG.gdx?? and WATERVAL.gdx) in BB3" +*!mayif %BB3%==yes +*!option none "Do not not use this hydro addon" +*!mustif not %BB3%==yes +$ifi not %BB3%==yes $setglobal HYDROBB123 none + + +The option replaces controls: MAKEWATER and WATERVAL. + +This option variable has influence on hydro with storage and no influence on hydro run of river. + +For any of the options it makes sense to use it only if the seasons in S (SET S) are the same for BB3 and BB1/BB2, and have the same relative weights (WEIGHT_S). +(might be permitted in later versions) + +Allocates a fixed hydro quantity WATERVOL for use in each season, this volume was generated by a BB1 or BB2 solution and saved as simex\WATERVOL.gdx. +That amount may be exceeded at a marginal cost. That cost depends on the water values WATERVAL found in the BB1 or BB2 solution and saved as simex\WATERVAL.gdx. +The cost will be somewhat higher that the cost given in WATERVAL and will increase by increasing hydro amounts used. +General: + +After successful solution from BB1 or BB2 the water values, hydro production and beginning-of-season storage volumes will be saved to files WATERVAL.gdx, HYRSG.gdx and WATERVOLINI.gdx. +These parameters are declared in Balmorel and saved irrespective of the chosen option for HYDROBB123. +Values saved (unconditionally) to folder simex as gdx files: + HYRSG.gdx', HYRSG; + VHYRS_SL.gdx', VHYRS_S.L; + WATERVAL.gdx', WATERVAL; + +When BB3 is to be solved these values are loaded into Balmorel. What happens next depends in the option chosen. +Option specific: + +For %HYDROBB123%==quant: +Equation QWATERVOLINI is part of model BALBASE3. This limits the hydro energy generation to the same as found in the BB1/BB2 solution, known from VHYRS_SL.gdx. +Values saved (conditionally) to folder simex af gdx files with option quant : + HYRSG.gdx',HYRSG; + WATERVAL.gdx',WATERVAL; + +For %HYDROBB123%==price: +Equation QWATERVOLINI is not part of model BALBASE3. In the objective function QOBJ the water values found in BB1/BB2, known from VHYRS_SL.gdx, +are used as marginal cost for hydro power generation. +Values saved to folder simex af gdx files with option price: + HYRSG.gdx',HYRSG; + WATERVAL.gdx',WATERVAL; + +For %HYDROBB123%==quantprice: + +Before entering the loop over seasons the hydro volumes for the beginning of the first season are fixed to that of the BB1/BB2 solution, +which is known from VHYRS_SL.gdx (containing values VHYRS_S.L). + +When BB3 is solved for a season the equation QWATERVOLINI attempts to limit the use of hydro power each season +to not exceed what was found in BB1/BB2. QWATERVOLINI is located in Balmorel.gms. todo-noget inkonsistent... +The limit may be exceeded at marginal costs which for smaller amounts are not much higher than the water values found in BB1/BB2, +known from WATERVAL.gdx, but if more is used the marginal costs increase. +In the objective function QOBJ the cost of hydro power generation is the same as given as variable costs XXX . +If the hydro amounts used exceed what was found in BB1/BB2 an additional variable cost component is added in QOBJ. + +In the loop over seasons a bookkeeping mechanism tracks the amounts of water used in current season and accumulated over the simulated seasons. +Similarly the amounts of water use for BB1/BB2 by season (known from VHYRS_SL.gdx) and accumulated over the simulated seasons are kept track of. + +If after the solution for a particular season it is found that there is a deviation between the accumulated amounts for BB3 and BB1/BB2 +then data is manipulated with the aim to restore accumulated amounts for the remaining seasons. +The available main mechanisms are tightening of equation QWATERVOLINI and increase of the marginal cost for exceeding that level. + +If variable VHYRS_S (hydro energy equivalent (storage level) at the start of the season (MWh)) +were declared as a positive variable an error would be issued in case of negative values of IHYDROBB123VOLINI, +cf. equation QWATERVOLINI which essentially states that VHYRS_S =E= IHYDROBB123VOLINI. +To avoid this, the variable VHYRS_S is declared as a free variable for model BB3 (while positive in models BB1 and BB2). +The sole intention of equation IHYDROBB123VOLINI is to permit convenient comparison with the BB1/BB2 solution through VHYRS_S. + +Values saved to folder simex af gdx files with option quantprice: + HYRSG_bb3.gdx',HYRSG; + VHYRS_SL_bb3.gdx',VHYRS_S.L; + WATERVAL_bb3.gdx',WATERVAL; ? + + +Some details: +PARAMETER IHYRSG_Y(AAA,SSS) "Water (hydro) generation quantity of the seasons transferred from model BB1/BB2 for use in model Balbase3 (MWh)" ; +PARAMETER IWATERVOL_Y(AAA,SSS) "Water quantity (storage level at beginning of season) transferred from model BB1/BB2 for use in model Balbase3 (MWh)" ; +PARAMETER IWATERVAL_Y(AAA,SSS) "Water value (in input Money) transferred from model BB1/BB2 for use in model Balbase3 (MWh)" ; +SET IHYDROBB12DELTASET "Increase steps of hydro generation above BB1 level" / IBB123HYG1*IBB123HYG5 /; +PARAMETER IHYDROBB123BB3GACCUM(AAA) "The amount of water used so far during present year during BB3 solutions (MWh)"; +PARAMETER IHYDROBB123BB12GACCUM(AAA) "The amount of water used so far during present year according to BB1/BB2 solution (MWh)"; +PARAMETER IHYDROBB123DELTAVOL(AAA) "Amount of additional hydro energy (relative to BB1/BB2) pre-allocated this season (may be negative) in model BB3 (MWh)"; +PARAMETER IHYDROBB123VOLINI(AAA,S) "Initial storage level (MWh)"; +PARAMETER VHYRS_SL(Y,AAA,S) "To be saved for comparison with BB1/BB2 solution value for VHYRS_S.L (declared as a parameter) (MWh)"; +PARAMETER IHYDROBB123EXTRAVAL(AAA,S,IHYDROBB12DELTASET) ""; + +POSITIVE VARIABLE VIHYRSBB12DELTAMHW(AAA,IHYDROBB12DELTASET) "Amount of additional hydro energy allocated this season during optimization of model Balbase3 (MWh)"; + +EQUATION QWATERVOLINI(AAA,S) "Hydro energy (volume) in storage at the beginning of season in model Balbase3 (MWh)" ; +EQUATION QHYRSG(AAA,S) "Hydro energy available for this region and season in model Balbase3 (MWh)"; + + +$offtext diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123equations.inc b/base/addons/hyrsbb123/bb4/hyrsbb123equations.inc new file mode 100644 index 00000000..00ec70ee --- /dev/null +++ b/base/addons/hyrsbb123/bb4/hyrsbb123equations.inc @@ -0,0 +1,54 @@ +* File HYRSBB123equations.inc for the Balmorel model. +* For use with addon HYRSBB123. +* Latest revision 20170419 (hr) + +$ifi not %BB3%==yes $goto label_hydrobb123equations +$ifi %HYRSBB123%==price $goto label_hydrobb123equations + +$ifi %BB4%==yes $goto BB4begin + +EQUATION QWATERVOLINI(AAA,S) 'Hydro energy (volume) in storage at the beginning of season in model Balbase3 (MWh)' ; +EQUATION QHYRSG(AAA,S) 'Hydro energy available for this region and season in model Balbase3 (MWh)'; + +* The equation QWATERVOLINI atttempts to achieve start-of-season values (MWh) for VHYRS_S.L that are comparable for those from BB1/BB2. +* Note that VHYRS_S is declared as a positive variable, therefore it is elsewhere artificially assured that IHYRSBB123VOLINI is non-negative. + +QWATERVOLINI(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. VHYRS_S(IA,IS3) =E= IHYRSBB123VOLINI(IA,IS3); + +* The equation QHYRSG tries to limit the HYRS generation to same level as in BB1/BB2. +* The equation will not be infeasible since VIHYRSBB12DELTAMHW.UP is INF for the last element wrt. IHYRSBB12DELTASET. +QHYRSG(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. + IHYRSBB123G_Y(IA,IS3) !! Unit: (MWh) +$ifi %HYRSBB123%==quantprice + IHYRSBB123DELTAVOL(IA) + SUM(IHYRSBB12DELTASET, VIHYRSBB12DELTAMHW(IA,IHYRSBB12DELTASET)) + =G= + SUM(IGHYRS$IAGK_Y(IA,IGHYRS), SUM(T,IHOURSINST(IS3,T)*VGE_T(IA,IGHYRS,IS3,T))) +; + +$goto label_hydrobb123equations + +$label BB4begin + +* Equations not verified for BB4! + +EQUATION QWATERVOLINI(Y,AAA,S) 'Hydro energy (volume) in storage at the beginning of season in model Balbase3 (MWh)' ; +EQUATION QHYRSG(Y,AAA,S) 'Hydro energy available for this region and season in model Balbase3 (MWh)'; + +* The equation QWATERVOLINI atttempts to achieve start-of-season values (MWh) for VHYRS_S.L that are comparable for those from BB1/BB2. +* Note that VHYRS_S is declared as a positive variable, therefore it is elsewhere artificially assured that IHYRSBB123VOLINI is non-negative. + +TODO: + + +QWATERVOLINI(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. VHYRS_S(IA,IS3) =E= IHYRSBB123VOLINI(IA,IS3); + +* The equation QHYRSG tries to limit the HYRS generation to same level as in BB1/BB2. +* The equation will not be infeasible since VIHYRSBB12DELTAMHW.UP is INF for the last element wrt. IHYRSBB12DELTASET. +QHYRSG(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. + IHYRSBB123G_Y(IA,IS3) !! Unit: (MWh) +$ifi %HYRSBB123%==quantprice + IHYRSBB123DELTAVOL(IA) + SUM(IHYRSBB12DELTASET, VIHYRSBB12DELTAMHW(IA,IHYRSBB12DELTASET)) + =G= + SUM(IGHYRS$IAGK_Y(IA,IGHYRS), SUM(T,IHOURSINST(IS3,T)*VGE_T(IA,IGHYRS,IS3,T))) +; + + +$label label_hydrobb123equations diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123includebb12data.inc b/base/addons/hyrsbb123/bb4/hyrsbb123includebb12data.inc new file mode 100644 index 00000000..2866dd70 --- /dev/null +++ b/base/addons/hyrsbb123/bb4/hyrsbb123includebb12data.inc @@ -0,0 +1,20 @@ +* File HYRSBB123includebb12data.inc for the Balmorel model. +* For use with addon HYRSBB123. + +* Data for handling of annual hydro constraints in BB3. +* Supposed to be saved by a previous execution of model BALBASE1 or BALBASE2 (BB1 or BB2). + +$ifi not %BB3%==yes $goto label_enofhyrsbb123includebb12data +$ifi %HYRSBB123%==none $goto label_enofhyrsbb123includebb12data + +execute_load '../../simex/HYRSG.gdx', HYRSG; +execute_load '../../simex/WATERVAL.gdx', WATERVAL; +execute_load '../../simex/VHYRS_SL.gdx', VHYRS_SL; + +* Trim with respect to S for the case that S in BB3 is a proper subset of the S used in BB1\BB2, for nicety of output only: +HYRSG(Y,AAA,SSS)$(not S(SSS)) = 0; +WATERVAL(Y,AAA,SSS)$(not S(SSS)) = 0; +VHYRS_SL(Y,AAA,SSS)$(not S(SSS)) = 0; +* The indexes Y and IA(AAA) are simply assumed to be the same. + +$label label_enofhyrsbb123includebb12data diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123internals.inc b/base/addons/hyrsbb123/bb4/hyrsbb123internals.inc new file mode 100644 index 00000000..4ad0c3a8 --- /dev/null +++ b/base/addons/hyrsbb123/bb4/hyrsbb123internals.inc @@ -0,0 +1,26 @@ +* The three parameters WATERVOLINI, WATERVAL and VHYRS_SLare declared as standard in the Balmorel model. +* Additional declaration and definitions for HYRSBB123 only are given here. + +* In BB3 the following set and parameters are used to control the use of hydro with storage, conditioned by HYRSBB123: + +SET IHYRSBB12DELTASET "Increase steps of hydro generation above BB1 level" / IHYRSBB123G1*IHYRSBB123G5 /; + +PARAMETER IHYRSBB123G_Y(AAA,SSS) "Water (hydro) generation quantity of the seasons transferred from model BB1/BB2 for use in model Balbase3 (MWh)"; +PARAMETER IWATERVAL_Y(AAA,SSS) "Water value (in input Money) transferred from model BB1/BB2 for use in model Balbase3 (MWh)"; +PARAMETER IHYRSBB123BB3GACCUM(AAA) "The amount of water used so far during present year during BB3 solutions (MWh)"; +PARAMETER IHYRSBB123BB12GACCUM(AAA) "The amount of water used so far during present year according to BB1/BB2 solution (MWh)"; +PARAMETER IHYRSBB123DELTAVOL(AAA) "Amount of additional hydro energy (relative to BB1/BB2) pre-allocated this season (may be negative) in model BB3 (MWh)"; +PARAMETER IHYRSBB123VOLINI(AAA,S) "Initial storage level (MWh)"; +PARAMETER IHYRSBB123EXTRAVAL(AAA,S,IHYRSBB12DELTASET); + + +* Test that Card(S) is equal to Card(SSS): +IF((CARD(S) NE CARD(SSS)), +* PUT ERRORFILE "Warning from addon hyrsbb123, file hyrsbb123sim3.inc:"/; +* PUT "Card(S) is not equal to Card(SSS), it must be." /; + DISPLAY "Warning from addon hyrsbb123, file hyrsbb123sim3.inc:"; + DISPLAY "Card(S) is not equal to Card(SSS), it must be." ; +); + + +$label label_hydrobb123internals \ No newline at end of file diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123sim1.inc b/base/addons/hyrsbb123/bb4/hyrsbb123sim1.inc new file mode 100644 index 00000000..489cc976 --- /dev/null +++ b/base/addons/hyrsbb123/bb4/hyrsbb123sim1.inc @@ -0,0 +1,17 @@ +* File HYRSBB123sim1.inc for the Balmorel model. +* For use with addon HYRSBB123. +* Included into bb123.sim before loop over seasons, inside the loop over years. + +IHYRSBB123BB3GACCUM(IA) = 0; +IHYRSBB123BB12GACCUM(IA) = 0; +IHYRSBB123DELTAVOL(IA) = 0; +IHYRSBB123VOLINI(IA,S)$(ORD(S) EQ 1) = VHYRS_SL(Y,IA,S); + +* Todo: if S and SSS are different then a rescaling might be useful for comparison of results (HYRSG). +* So for now: make sure that S and SSS are identical. + + + + + + diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123sim2.inc b/base/addons/hyrsbb123/bb4/hyrsbb123sim2.inc new file mode 100644 index 00000000..b3f05ba4 --- /dev/null +++ b/base/addons/hyrsbb123/bb4/hyrsbb123sim2.inc @@ -0,0 +1,24 @@ +* File HYRSBB123sim2.inc for the Balmorel model. +* For use with addon HYRSBB123. +* Included into bb123.sim before SOLVE BALBASE3 statement, inside the loop over seasons. + +* Set values relative to the possible hydro use reletive to BB1/BB2: + + +ISCALAR1 = 0.1; !! Positive. Defines the largest individual increment, as share of the hydro power used in the BB1/BB2 solution. +VIHYRSBB12DELTAMHW.UP(IA,IHYRSBB12DELTASET) = IHYRSBB123G_Y(IA,IS3LOOPSET)*ISCALAR1*ORD(IHYRSBB12DELTASET)/CARD(IHYRSBB12DELTASET); !! Increments get larger with increased hydro use +VIHYRSBB12DELTAMHW.UP(IA,IHYRSBB12DELTASET)$(ORD(IHYRSBB12DELTASET) EQ CARD(IHYRSBB12DELTASET)) = INF; !! No limit, to ensure feasibility in equation QWATERVOL + +* Water Values: +ISCALAR1 = 2.0; !! Positive. With e.g. 2.0 the increments (above the BB1 values) on the Water Values will be doubled for each step, to reach a maximum of 2 times the BB1 values. +ISCALAR2=POWER(1/ISCALAR1,CARD(IHYRSBB12DELTASET)); +ISCALAR3=1.0; !! Additional increase may be attained by increasing ISCALAR3 above 1.0. +LOOP(IHYRSBB12DELTASET, + ISCALAR2=ISCALAR2*ISCALAR1; + IHYRSBB123EXTRAVAL(IA,IS3LOOPSET,IHYRSBB12DELTASET) = IWATERVAL_Y(IA,IS3LOOPSET)*ISCALAR3*(1+ISCALAR2); +); + + + + + diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123unload.inc b/base/addons/hyrsbb123/bb4/hyrsbb123unload.inc new file mode 100644 index 00000000..3cdd118a --- /dev/null +++ b/base/addons/hyrsbb123/bb4/hyrsbb123unload.inc @@ -0,0 +1,14 @@ +* File HYRSBB123unlaod.inc for the Balmorel model. +* For use with addon HYRSBB123. + +$ifi not %BB3%==yes $goto label_hydrobb123unload + +$ifi %HYRSBB123%==quant execute_unload '../../simex/HYRSG_BB3.gdx',HYRSG; +$ifi %HYRSBB123%==quant execute_unload '../../simex/WATERVAL_BB3.gdx',WATERVAL; +$ifi %HYRSBB123%==price execute_unload '../../simex/HYRSG_BB3.gdx',HYRSG; +$ifi %HYRSBB123%==price execute_unload '../../simex/WATERVAL_BB3.gdx',WATERVAL; +$ifi %HYRSBB123%==quantprice execute_unload '../../simex/HYRSG_BB3.gdx',HYRSG; +$ifi %HYRSBB123%==quantprice execute_unload '../../simex/VHYRS_SL_BB3.gdx',VHYRS_SL; +$ifi %HYRSBB123%==quantprice execute_unload '../../simex/WATERVAL_BB3.gdx',WATERVAL; + +$label label_hydrobb123unload \ No newline at end of file diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123variables.inc b/base/addons/hyrsbb123/bb4/hyrsbb123variables.inc new file mode 100644 index 00000000..3603e249 --- /dev/null +++ b/base/addons/hyrsbb123/bb4/hyrsbb123variables.inc @@ -0,0 +1,7 @@ +* File HYRSBB123internals.inc for the Balmorel model. +* For use with addon HYRSBB123. +* Latest revision 20170419 (hr) + +$ifi not %BB4%==yes POSITIVE VARIABLE VIHYRSBB12DELTAMHW(AAA,IHYRSBB12DELTASET) "Amount of additional hydro energy allocated this season during optimization of model Balbase3 (MWh)"; + +$ifi %BB4%==yes POSITIVE VARIABLE VIHYRSBB12DELTAMHW(Y,AAA,IHYRSBB12DELTASET) "Amount of additional hydro energy allocated this season during optimization of model Balbase3 (MWh)"; diff --git a/base/addons/hyrsbb123/hyrsbb123_hellolog.inc b/base/addons/hyrsbb123/hyrsbb123_hellolog.inc new file mode 100644 index 00000000..d465f122 --- /dev/null +++ b/base/addons/hyrsbb123/hyrsbb123_hellolog.inc @@ -0,0 +1,11 @@ +* File HYRSBB123_hellolog.inc for the Balmorel model. +* For use with addon HYRSBB123. + +PUT " - Addon hyrsbb123" /; + + +$ontext + +Explanation - todo. + +$offtext \ No newline at end of file diff --git a/base/addons/hyrsbb123/hyrsbb123addobj.inc b/base/addons/hyrsbb123/hyrsbb123addobj.inc new file mode 100644 index 00000000..e25947f8 --- /dev/null +++ b/base/addons/hyrsbb123/hyrsbb123addobj.inc @@ -0,0 +1,13 @@ +* File HYRSBB123addobj.inc for the Balmorel model. +* For use with addon HYRSBB123. +* Included into the objective function QOBJ in Balmorel.gms. + +$ifi not %BB3%==yes $goto label_HYRSBB123addobj + +$ifi %BB3%==yes $ifi %HYRSBB123%==price + SUM(IAGK_Y(IA,IGHYRS),SUM((IS3,T), IWATERVAL_Y(IA,IS3)* IHOURSINST(IS3,T) * VGE_T(IA,IGHYRS,IS3,T))) !! no investments in BB3 + +$ifi %BB3%==yes $ifi %HYRSBB123%==quantprice + SUM(IAGK_Y(IA,IGHYRS),SUM((IS3,T), IWATERVAL_Y(IA,IS3)* IHOURSINST(IS3,T) * VGE_T(IA,IGHYRS,IS3,T))) !! no investments in BB3 +$ifi %BB3%==yes $ifi %HYRSBB123%==quantprice + SUM((IA,IS3)$IHYRSBB123G_Y(IA,IS3), (SUM(T,IHOURSINST(IS3,T)))* SUM(IHYRSBB12DELTASET, VIHYRSBB12DELTAMHW(IA,IHYRSBB12DELTASET) *IHYRSBB123EXTRAVAL(IA,IS3,IHYRSBB12DELTASET))) + +$label label_HYRSBB123addobj; +$ifi not %BB3%==yes !! print some error message - todo. diff --git a/base/addons/hyrsbb123/hyrsbb123balbase3.inc b/base/addons/hyrsbb123/hyrsbb123balbase3.inc new file mode 100644 index 00000000..0f141dcd --- /dev/null +++ b/base/addons/hyrsbb123/hyrsbb123balbase3.inc @@ -0,0 +1,6 @@ +* Equations for addon hyrsbb123 used in Balbase3 model definition +* Latest revision 20170419 (hr) + +$ifi %HYRSBB123%==quant QHYRSG +$ifi %HYRSBB123%==quantprice QHYRSG +$ifi %HYRSBB123%==quantprice QWATERVOLINI diff --git a/base/addons/hyrsbb123/hyrsbb123documentation.txt b/base/addons/hyrsbb123/hyrsbb123documentation.txt new file mode 100644 index 00000000..e71de7d2 --- /dev/null +++ b/base/addons/hyrsbb123/hyrsbb123documentation.txt @@ -0,0 +1,106 @@ +$ontext +Hans Ravn +Preliminary. Revised 20170412. + +Addon HYDROBB123 + + +* For model BB3 it is possible to use values found from BB1 or BB2 solutions to achieve use of hydro power +* that corresponds to that found in a BB1 or BB2 solution. +$setglobal HYDROBB123 quantprice +*!option quant "Use seasonal quantities of hydro generation (from HYRSG.gdx) in BB3 ('primal decomposition')" +*!mayif %BB3%==yes +*!option price "Use seasonal prices for hydro generation (from WATERVAL.gdx) in BB3 ('dual decomposition')" +*!mayif %BB3%==yes +*!option quantprice "Use seasonal quantities and prices for hydro generation (from HYRSG.gdx?? and WATERVAL.gdx) in BB3" +*!mayif %BB3%==yes +*!option none "Do not not use this hydro addon" +*!mustif not %BB3%==yes +$ifi not %BB3%==yes $setglobal HYDROBB123 none + + +The option replaces controls: MAKEWATER and WATERVAL. + +This option variable has influence on hydro with storage and no influence on hydro run of river. + +For any of the options it makes sense to use it only if the seasons in S (SET S) are the same for BB3 and BB1/BB2, and have the same relative weights (WEIGHT_S). +(might be permitted in later versions) + +Allocates a fixed hydro quantity WATERVOL for use in each season, this volume was generated by a BB1 or BB2 solution and saved as simex\WATERVOL.gdx. +That amount may be exceeded at a marginal cost. That cost depends on the water values WATERVAL found in the BB1 or BB2 solution and saved as simex\WATERVAL.gdx. +The cost will be somewhat higher that the cost given in WATERVAL and will increase by increasing hydro amounts used. +General: + +After successful solution from BB1 or BB2 the water values, hydro production and beginning-of-season storage volumes will be saved to files WATERVAL.gdx, HYRSG.gdx and WATERVOLINI.gdx. +These parameters are declared in Balmorel and saved irrespective of the chosen option for HYDROBB123. +Values saved (unconditionally) to folder simex as gdx files: + HYRSG.gdx', HYRSG; + VHYRS_SL.gdx', VHYRS_S.L; + WATERVAL.gdx', WATERVAL; + +When BB3 is to be solved these values are loaded into Balmorel. What happens next depends in the option chosen. +Option specific: + +For %HYDROBB123%==quant: +Equation QWATERVOLINI is part of model BALBASE3. This limits the hydro energy generation to the same as found in the BB1/BB2 solution, known from VHYRS_SL.gdx. +Values saved (conditionally) to folder simex af gdx files with option quant : + HYRSG.gdx',HYRSG; + WATERVAL.gdx',WATERVAL; + +For %HYDROBB123%==price: +Equation QWATERVOLINI is not part of model BALBASE3. In the objective function QOBJ the water values found in BB1/BB2, known from VHYRS_SL.gdx, +are used as marginal cost for hydro power generation. +Values saved to folder simex af gdx files with option price: + HYRSG.gdx',HYRSG; + WATERVAL.gdx',WATERVAL; + +For %HYDROBB123%==quantprice: + +Before entering the loop over seasons the hydro volumes for the beginning of the first season are fixed to that of the BB1/BB2 solution, +which is known from VHYRS_SL.gdx (containing values VHYRS_S.L). + +When BB3 is solved for a season the equation QWATERVOLINI attempts to limit the use of hydro power each season +to not exceed what was found in BB1/BB2. QWATERVOLINI is located in Balmorel.gms. todo-noget inkonsistent... +The limit may be exceeded at marginal costs which for smaller amounts are not much higher than the water values found in BB1/BB2, +known from WATERVAL.gdx, but if more is used the marginal costs increase. +In the objective function QOBJ the cost of hydro power generation is the same as given as variable costs XXX . +If the hydro amounts used exceed what was found in BB1/BB2 an additional variable cost component is added in QOBJ. + +In the loop over seasons a bookkeeping mechanism tracks the amounts of water used in current season and accumulated over the simulated seasons. +Similarly the amounts of water use for BB1/BB2 by season (known from VHYRS_SL.gdx) and accumulated over the simulated seasons are kept track of. + +If after the solution for a particular season it is found that there is a deviation between the accumulated amounts for BB3 and BB1/BB2 +then data is manipulated with the aim to restore accumulated amounts for the remaining seasons. +The available main mechanisms are tightening of equation QWATERVOLINI and increase of the marginal cost for exceeding that level. + +If variable VHYRS_S (hydro energy equivalent (storage level) at the start of the season (MWh)) +were declared as a positive variable an error would be issued in case of negative values of IHYDROBB123VOLINI, +cf. equation QWATERVOLINI which essentially states that VHYRS_S =E= IHYDROBB123VOLINI. +To avoid this, the variable VHYRS_S is declared as a free variable for model BB3 (while positive in models BB1 and BB2). +The sole intention of equation IHYDROBB123VOLINI is to permit convenient comparison with the BB1/BB2 solution through VHYRS_S. + +Values saved to folder simex af gdx files with option quantprice: + HYRSG_bb3.gdx',HYRSG; + VHYRS_SL_bb3.gdx',VHYRS_S.L; + WATERVAL_bb3.gdx',WATERVAL; ? + + +Some details: +PARAMETER IHYRSG_Y(AAA,SSS) "Water (hydro) generation quantity of the seasons transferred from model BB1/BB2 for use in model Balbase3 (MWh)" ; +PARAMETER IWATERVOL_Y(AAA,SSS) "Water quantity (storage level at beginning of season) transferred from model BB1/BB2 for use in model Balbase3 (MWh)" ; +PARAMETER IWATERVAL_Y(AAA,SSS) "Water value (in input Money) transferred from model BB1/BB2 for use in model Balbase3 (MWh)" ; +SET IHYDROBB12DELTASET "Increase steps of hydro generation above BB1 level" / IBB123HYG1*IBB123HYG5 /; +PARAMETER IHYDROBB123BB3GACCUM(AAA) "The amount of water used so far during present year during BB3 solutions (MWh)"; +PARAMETER IHYDROBB123BB12GACCUM(AAA) "The amount of water used so far during present year according to BB1/BB2 solution (MWh)"; +PARAMETER IHYDROBB123DELTAVOL(AAA) "Amount of additional hydro energy (relative to BB1/BB2) pre-allocated this season (may be negative) in model BB3 (MWh)"; +PARAMETER IHYDROBB123VOLINI(AAA,S) "Initial storage level (MWh)"; +PARAMETER VHYRS_SL(Y,AAA,S) "To be saved for comparison with BB1/BB2 solution value for VHYRS_S.L (declared as a parameter) (MWh)"; +PARAMETER IHYDROBB123EXTRAVAL(AAA,S,IHYDROBB12DELTASET) ""; + +POSITIVE VARIABLE VIHYRSBB12DELTAMHW(AAA,IHYDROBB12DELTASET) "Amount of additional hydro energy allocated this season during optimization of model Balbase3 (MWh)"; + +EQUATION QWATERVOLINI(AAA,S) "Hydro energy (volume) in storage at the beginning of season in model Balbase3 (MWh)" ; +EQUATION QHYRSG(AAA,S) "Hydro energy available for this region and season in model Balbase3 (MWh)"; + + +$offtext diff --git a/base/addons/hyrsbb123/hyrsbb123equations.inc b/base/addons/hyrsbb123/hyrsbb123equations.inc new file mode 100644 index 00000000..00ec70ee --- /dev/null +++ b/base/addons/hyrsbb123/hyrsbb123equations.inc @@ -0,0 +1,54 @@ +* File HYRSBB123equations.inc for the Balmorel model. +* For use with addon HYRSBB123. +* Latest revision 20170419 (hr) + +$ifi not %BB3%==yes $goto label_hydrobb123equations +$ifi %HYRSBB123%==price $goto label_hydrobb123equations + +$ifi %BB4%==yes $goto BB4begin + +EQUATION QWATERVOLINI(AAA,S) 'Hydro energy (volume) in storage at the beginning of season in model Balbase3 (MWh)' ; +EQUATION QHYRSG(AAA,S) 'Hydro energy available for this region and season in model Balbase3 (MWh)'; + +* The equation QWATERVOLINI atttempts to achieve start-of-season values (MWh) for VHYRS_S.L that are comparable for those from BB1/BB2. +* Note that VHYRS_S is declared as a positive variable, therefore it is elsewhere artificially assured that IHYRSBB123VOLINI is non-negative. + +QWATERVOLINI(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. VHYRS_S(IA,IS3) =E= IHYRSBB123VOLINI(IA,IS3); + +* The equation QHYRSG tries to limit the HYRS generation to same level as in BB1/BB2. +* The equation will not be infeasible since VIHYRSBB12DELTAMHW.UP is INF for the last element wrt. IHYRSBB12DELTASET. +QHYRSG(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. + IHYRSBB123G_Y(IA,IS3) !! Unit: (MWh) +$ifi %HYRSBB123%==quantprice + IHYRSBB123DELTAVOL(IA) + SUM(IHYRSBB12DELTASET, VIHYRSBB12DELTAMHW(IA,IHYRSBB12DELTASET)) + =G= + SUM(IGHYRS$IAGK_Y(IA,IGHYRS), SUM(T,IHOURSINST(IS3,T)*VGE_T(IA,IGHYRS,IS3,T))) +; + +$goto label_hydrobb123equations + +$label BB4begin + +* Equations not verified for BB4! + +EQUATION QWATERVOLINI(Y,AAA,S) 'Hydro energy (volume) in storage at the beginning of season in model Balbase3 (MWh)' ; +EQUATION QHYRSG(Y,AAA,S) 'Hydro energy available for this region and season in model Balbase3 (MWh)'; + +* The equation QWATERVOLINI atttempts to achieve start-of-season values (MWh) for VHYRS_S.L that are comparable for those from BB1/BB2. +* Note that VHYRS_S is declared as a positive variable, therefore it is elsewhere artificially assured that IHYRSBB123VOLINI is non-negative. + +TODO: + + +QWATERVOLINI(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. VHYRS_S(IA,IS3) =E= IHYRSBB123VOLINI(IA,IS3); + +* The equation QHYRSG tries to limit the HYRS generation to same level as in BB1/BB2. +* The equation will not be infeasible since VIHYRSBB12DELTAMHW.UP is INF for the last element wrt. IHYRSBB12DELTASET. +QHYRSG(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. + IHYRSBB123G_Y(IA,IS3) !! Unit: (MWh) +$ifi %HYRSBB123%==quantprice + IHYRSBB123DELTAVOL(IA) + SUM(IHYRSBB12DELTASET, VIHYRSBB12DELTAMHW(IA,IHYRSBB12DELTASET)) + =G= + SUM(IGHYRS$IAGK_Y(IA,IGHYRS), SUM(T,IHOURSINST(IS3,T)*VGE_T(IA,IGHYRS,IS3,T))) +; + + +$label label_hydrobb123equations diff --git a/base/addons/hyrsbb123/hyrsbb123includebb12data.inc b/base/addons/hyrsbb123/hyrsbb123includebb12data.inc new file mode 100644 index 00000000..2866dd70 --- /dev/null +++ b/base/addons/hyrsbb123/hyrsbb123includebb12data.inc @@ -0,0 +1,20 @@ +* File HYRSBB123includebb12data.inc for the Balmorel model. +* For use with addon HYRSBB123. + +* Data for handling of annual hydro constraints in BB3. +* Supposed to be saved by a previous execution of model BALBASE1 or BALBASE2 (BB1 or BB2). + +$ifi not %BB3%==yes $goto label_enofhyrsbb123includebb12data +$ifi %HYRSBB123%==none $goto label_enofhyrsbb123includebb12data + +execute_load '../../simex/HYRSG.gdx', HYRSG; +execute_load '../../simex/WATERVAL.gdx', WATERVAL; +execute_load '../../simex/VHYRS_SL.gdx', VHYRS_SL; + +* Trim with respect to S for the case that S in BB3 is a proper subset of the S used in BB1\BB2, for nicety of output only: +HYRSG(Y,AAA,SSS)$(not S(SSS)) = 0; +WATERVAL(Y,AAA,SSS)$(not S(SSS)) = 0; +VHYRS_SL(Y,AAA,SSS)$(not S(SSS)) = 0; +* The indexes Y and IA(AAA) are simply assumed to be the same. + +$label label_enofhyrsbb123includebb12data diff --git a/base/addons/hyrsbb123/hyrsbb123internals.inc b/base/addons/hyrsbb123/hyrsbb123internals.inc new file mode 100644 index 00000000..4ad0c3a8 --- /dev/null +++ b/base/addons/hyrsbb123/hyrsbb123internals.inc @@ -0,0 +1,26 @@ +* The three parameters WATERVOLINI, WATERVAL and VHYRS_SLare declared as standard in the Balmorel model. +* Additional declaration and definitions for HYRSBB123 only are given here. + +* In BB3 the following set and parameters are used to control the use of hydro with storage, conditioned by HYRSBB123: + +SET IHYRSBB12DELTASET "Increase steps of hydro generation above BB1 level" / IHYRSBB123G1*IHYRSBB123G5 /; + +PARAMETER IHYRSBB123G_Y(AAA,SSS) "Water (hydro) generation quantity of the seasons transferred from model BB1/BB2 for use in model Balbase3 (MWh)"; +PARAMETER IWATERVAL_Y(AAA,SSS) "Water value (in input Money) transferred from model BB1/BB2 for use in model Balbase3 (MWh)"; +PARAMETER IHYRSBB123BB3GACCUM(AAA) "The amount of water used so far during present year during BB3 solutions (MWh)"; +PARAMETER IHYRSBB123BB12GACCUM(AAA) "The amount of water used so far during present year according to BB1/BB2 solution (MWh)"; +PARAMETER IHYRSBB123DELTAVOL(AAA) "Amount of additional hydro energy (relative to BB1/BB2) pre-allocated this season (may be negative) in model BB3 (MWh)"; +PARAMETER IHYRSBB123VOLINI(AAA,S) "Initial storage level (MWh)"; +PARAMETER IHYRSBB123EXTRAVAL(AAA,S,IHYRSBB12DELTASET); + + +* Test that Card(S) is equal to Card(SSS): +IF((CARD(S) NE CARD(SSS)), +* PUT ERRORFILE "Warning from addon hyrsbb123, file hyrsbb123sim3.inc:"/; +* PUT "Card(S) is not equal to Card(SSS), it must be." /; + DISPLAY "Warning from addon hyrsbb123, file hyrsbb123sim3.inc:"; + DISPLAY "Card(S) is not equal to Card(SSS), it must be." ; +); + + +$label label_hydrobb123internals \ No newline at end of file diff --git a/base/addons/hyrsbb123/hyrsbb123sim1.inc b/base/addons/hyrsbb123/hyrsbb123sim1.inc new file mode 100644 index 00000000..489cc976 --- /dev/null +++ b/base/addons/hyrsbb123/hyrsbb123sim1.inc @@ -0,0 +1,17 @@ +* File HYRSBB123sim1.inc for the Balmorel model. +* For use with addon HYRSBB123. +* Included into bb123.sim before loop over seasons, inside the loop over years. + +IHYRSBB123BB3GACCUM(IA) = 0; +IHYRSBB123BB12GACCUM(IA) = 0; +IHYRSBB123DELTAVOL(IA) = 0; +IHYRSBB123VOLINI(IA,S)$(ORD(S) EQ 1) = VHYRS_SL(Y,IA,S); + +* Todo: if S and SSS are different then a rescaling might be useful for comparison of results (HYRSG). +* So for now: make sure that S and SSS are identical. + + + + + + diff --git a/base/addons/hyrsbb123/hyrsbb123sim2.inc b/base/addons/hyrsbb123/hyrsbb123sim2.inc new file mode 100644 index 00000000..b3f05ba4 --- /dev/null +++ b/base/addons/hyrsbb123/hyrsbb123sim2.inc @@ -0,0 +1,24 @@ +* File HYRSBB123sim2.inc for the Balmorel model. +* For use with addon HYRSBB123. +* Included into bb123.sim before SOLVE BALBASE3 statement, inside the loop over seasons. + +* Set values relative to the possible hydro use reletive to BB1/BB2: + + +ISCALAR1 = 0.1; !! Positive. Defines the largest individual increment, as share of the hydro power used in the BB1/BB2 solution. +VIHYRSBB12DELTAMHW.UP(IA,IHYRSBB12DELTASET) = IHYRSBB123G_Y(IA,IS3LOOPSET)*ISCALAR1*ORD(IHYRSBB12DELTASET)/CARD(IHYRSBB12DELTASET); !! Increments get larger with increased hydro use +VIHYRSBB12DELTAMHW.UP(IA,IHYRSBB12DELTASET)$(ORD(IHYRSBB12DELTASET) EQ CARD(IHYRSBB12DELTASET)) = INF; !! No limit, to ensure feasibility in equation QWATERVOL + +* Water Values: +ISCALAR1 = 2.0; !! Positive. With e.g. 2.0 the increments (above the BB1 values) on the Water Values will be doubled for each step, to reach a maximum of 2 times the BB1 values. +ISCALAR2=POWER(1/ISCALAR1,CARD(IHYRSBB12DELTASET)); +ISCALAR3=1.0; !! Additional increase may be attained by increasing ISCALAR3 above 1.0. +LOOP(IHYRSBB12DELTASET, + ISCALAR2=ISCALAR2*ISCALAR1; + IHYRSBB123EXTRAVAL(IA,IS3LOOPSET,IHYRSBB12DELTASET) = IWATERVAL_Y(IA,IS3LOOPSET)*ISCALAR3*(1+ISCALAR2); +); + + + + + diff --git a/base/addons/hyrsbb123/hyrsbb123unload.inc b/base/addons/hyrsbb123/hyrsbb123unload.inc new file mode 100644 index 00000000..3cdd118a --- /dev/null +++ b/base/addons/hyrsbb123/hyrsbb123unload.inc @@ -0,0 +1,14 @@ +* File HYRSBB123unlaod.inc for the Balmorel model. +* For use with addon HYRSBB123. + +$ifi not %BB3%==yes $goto label_hydrobb123unload + +$ifi %HYRSBB123%==quant execute_unload '../../simex/HYRSG_BB3.gdx',HYRSG; +$ifi %HYRSBB123%==quant execute_unload '../../simex/WATERVAL_BB3.gdx',WATERVAL; +$ifi %HYRSBB123%==price execute_unload '../../simex/HYRSG_BB3.gdx',HYRSG; +$ifi %HYRSBB123%==price execute_unload '../../simex/WATERVAL_BB3.gdx',WATERVAL; +$ifi %HYRSBB123%==quantprice execute_unload '../../simex/HYRSG_BB3.gdx',HYRSG; +$ifi %HYRSBB123%==quantprice execute_unload '../../simex/VHYRS_SL_BB3.gdx',VHYRS_SL; +$ifi %HYRSBB123%==quantprice execute_unload '../../simex/WATERVAL_BB3.gdx',WATERVAL; + +$label label_hydrobb123unload \ No newline at end of file diff --git a/base/addons/hyrsbb123/hyrsbb123variables.inc b/base/addons/hyrsbb123/hyrsbb123variables.inc new file mode 100644 index 00000000..3603e249 --- /dev/null +++ b/base/addons/hyrsbb123/hyrsbb123variables.inc @@ -0,0 +1,7 @@ +* File HYRSBB123internals.inc for the Balmorel model. +* For use with addon HYRSBB123. +* Latest revision 20170419 (hr) + +$ifi not %BB4%==yes POSITIVE VARIABLE VIHYRSBB12DELTAMHW(AAA,IHYRSBB12DELTASET) "Amount of additional hydro energy allocated this season during optimization of model Balbase3 (MWh)"; + +$ifi %BB4%==yes POSITIVE VARIABLE VIHYRSBB12DELTAMHW(Y,AAA,IHYRSBB12DELTASET) "Amount of additional hydro energy allocated this season during optimization of model Balbase3 (MWh)"; diff --git a/base/addons/policies/Addon vejledening.txt b/base/addons/policies/Addon vejledening.txt new file mode 100644 index 00000000..41cfa601 --- /dev/null +++ b/base/addons/policies/Addon vejledening.txt @@ -0,0 +1,30 @@ +Policies addon enable predefined constraints on a userdefined geografy and regarding specific technologies or technology groups. +These flexible constraints can be used to model a lot of different policies to constrain the model. + +User guide: + +Each policy consist of a Policy Area + a Polycy Type + a group of Technologies (if nessesary) + +Files for defining Policies: + +In POLICY.inc the names of the different Policies should be stated +In POLAREA.inc the names of the different Policy Area should be stated +In POLAREAGEO.inc the Policy Areas should be defined by a subset of CCCRRRAAA +In POLTYP.inc one of the predefined Policy Type should be assigned to each Policy +In pol_data.inc the yearly data for the different Policies should be stated + +If a Policy Type is choosen which requre specification of technology +In POLTECH.inc the group of technologies concerning the Policy should be stated + + + + + + + + + + + diff --git a/base/addons/policies/demodata/POLAREA.inc b/base/addons/policies/demodata/POLAREA.inc new file mode 100644 index 00000000..e32c5de5 --- /dev/null +++ b/base/addons/policies/demodata/POLAREA.inc @@ -0,0 +1,11 @@ +* All Policy areas +* The names of each Policy Area + +SET POLAREA 'Group of Regimes that share some common requerments' +/ +POLA1 +POLA2 +POLA3 +POLA4 +/; + diff --git a/base/addons/policies/demodata/POLAREAGEO.inc b/base/addons/policies/demodata/POLAREAGEO.inc new file mode 100644 index 00000000..e9eb876f --- /dev/null +++ b/base/addons/policies/demodata/POLAREAGEO.inc @@ -0,0 +1,8 @@ +* Specification of geografi in Policy Areas +* Any combination of Areas, Regions and Contries can be choosen. +* Double reprecentation of a geografy will be identifyed and removed automaticly (leads to error message in printout) + +SET POLAREAGEO(POLAREA,CCCRRRAAA) 'Defining the geografi for each Policy Area' +/ +POLA1.(DK_E) +/; diff --git a/base/addons/policies/demodata/POLICY.inc b/base/addons/policies/demodata/POLICY.inc new file mode 100644 index 00000000..61547a8b --- /dev/null +++ b/base/addons/policies/demodata/POLICY.inc @@ -0,0 +1,8 @@ +* All Policies + +SET POLICY 'Group of policies' +/ +POL1 +POL2 +/; + diff --git a/base/addons/policies/demodata/POLICYTYPE.inc b/base/addons/policies/demodata/POLICYTYPE.inc new file mode 100644 index 00000000..50ca7de5 --- /dev/null +++ b/base/addons/policies/demodata/POLICYTYPE.inc @@ -0,0 +1,12 @@ +* All Policy types +* These types should correspond to the equations in pol_eq.inc + +SET POLICYTYPE 'Set of different policy types' +/ +SHAREGROSS +SHARENETTO +MAXCO2 +MINPROD +CAPTARGET +/; + diff --git a/base/addons/policies/demodata/POLREQ.inc b/base/addons/policies/demodata/POLREQ.inc new file mode 100644 index 00000000..cef9951a --- /dev/null +++ b/base/addons/policies/demodata/POLREQ.inc @@ -0,0 +1,44 @@ +* Addon for securing that Policy requrements are forfilled +* Policy requrements are given for each Policy and userdefined Policy Areas + + +* Policy requrements +* Construct the wished requrement by choosing a policy area 'POLAREA', a 'POLICY' and then state the data in the table. +* A Policy can be one of the following Policy Types. DO mind the units and specify technologies in POLTECH if requered +* SHAREGROSS implies Minimal share of electricity generation from specified technologies compared to gross electricity demand. NOTE: technologies have to be specified. Unit: Proportion +* SHARENETTO implies Minimal share of electricity generation from specified technologies compared to netto electricity demand. NOTE: technologies have to be specified. Unit: Proportion +* MAXCO2 implies Maximal CO2 emission. NO technologies need to be defined. Unit: Ton CO2 +* MINPROD implies Minimal electricity generation in from specified technologies in policy area. Unit: MWh +* CAPTARGET implies Minimal electricity generating capacity in policy area of specified technologies. NOTE: technologies have to be specified. Unit: MW +* ... implies ... +* ... implies ... + + +TABLE POLREQ(YYY,POLAREA,POLICY) + + POLA1.POL1 POLA1.POL2 +2010 +2011 +2012 +2013 +2014 +2015 +2016 +2017 +2018 +2019 +2020 0.5 0.7 +2021 +2022 +2023 +2024 +2025 +2030 +2035 +2040 +2045 +2050 + +; + +PARAMETER IPOLREQ_Y(POLAREA,POLICY) ''; diff --git a/base/addons/policies/demodata/POLTECH.inc b/base/addons/policies/demodata/POLTECH.inc new file mode 100644 index 00000000..0009a2f7 --- /dev/null +++ b/base/addons/policies/demodata/POLTECH.inc @@ -0,0 +1,21 @@ +* Specify which tecnologies the policies cover + +* Defined by individual tecnologi +SET POLTECH(POLICY,G) +/ + POL1.(CC-C1-NGn) + POL2.(CC-C1-NGn) + +/; + +* OR by assigning from group of technologies. +* Choose between: +* Predefined: IGCND, IGBPR, IGEXT, IGHOB, IGETOH, IGHSTO, IGHSTO_S, IGHSTOALL, IGESTO, IGHYRS, IGHYRR, IGWND, IGSOLE, IGSOLH, IGWAVE, IGHH, +* IGHHNOSTO, IGNOTETOH, IGKH, IGKHNOSTO, IGKE, IGKENOSTO, IGDISPATCH, IGEE, IGEENOSTO, IGKKNOWN, IGKFIND, IGEH, IGE or IGH. +* Addon defined: VETECH,.... +* Userdefined: USERTECH (is defined in pol_set.inc) + +*POLTECH('POL1',G) = YES$( IGWND(G) ); + + + diff --git a/base/addons/policies/demodata/POLTYP.inc b/base/addons/policies/demodata/POLTYP.inc new file mode 100644 index 00000000..eced7c8b --- /dev/null +++ b/base/addons/policies/demodata/POLTYP.inc @@ -0,0 +1,15 @@ +* Assignment of Policy Type to Policies +* Assign one Policy Type to each Policy +* Choose between: +* SHAREGROSS implies Minimal share of electricity generation from specified technologies compared to total electricity generation. NOTE: technologies have to be specified. Unit: Proportion +* SHARENETTO implies Minimal share of electricity generation from specified technologies compared to electricity demand. NOTE: technologies have to be specified. Unit: Proportion +* MAXCO2 implies Maximal CO2 emission. NO technologies need to be defined. Unit: Ton CO2 +* MINPROD implies Minimal electricity generation in from specified technologies in policy area. NOTE: technologies have to be specified. Unit: MWh. +* CAPTARGET implies Minimal electricity generating capacity in policy area of specified technologies. NOTE: technologies have to be specified. Unit: MW + + +SET POLTYP(POLICY,POLICYTYPE) 'Define policy type for each policy' +/ +POL1.SHAREGROSS +POL2.SHARENETTO +/; diff --git a/base/addons/policies/pol_cost.inc b/base/addons/policies/pol_cost.inc new file mode 100644 index 00000000..9abb6ee7 --- /dev/null +++ b/base/addons/policies/pol_cost.inc @@ -0,0 +1,12 @@ +* Addon for securing that policy requrements are forfilled +* Slack variables are penaliced to insure that the model works and minimize errors +* in case the requrement for specific policy area and policy can not be forfilled + ++ SUM((POLAREA,POLICY), VQPOLMAXCO2(POLAREA,POLICY)+VQPOLMINPROD(POLAREA,POLICY)+VQPOLCAPTARGET(POLAREA,POLICY) )*PENALTYQ/2 ++ SUM((POLAREA,POLICY), VQPOLSHAREGROSS(POLAREA,POLICY)+VQPOLSHARENET(POLAREA,POLICY) )*PENALTYQ*1000 + + + + + + diff --git a/base/addons/policies/pol_eq.inc b/base/addons/policies/pol_eq.inc new file mode 100644 index 00000000..680e7789 --- /dev/null +++ b/base/addons/policies/pol_eq.inc @@ -0,0 +1,162 @@ +* Addon for securing that Policy requrements are forfilled + +EQUATIONS + +QPOLSHAREGROSS (POLAREA,POLICY) 'Inshures requrements on share of electricity generation for policy area and tecnology group, compared to electricity demand in the area' + +QPOLSHARENET (POLAREA,POLICY) 'Inshures requrements on share of electricity generation for policy area and tecnology group, compared to total electricity generation in the area' + +QPOLMAXCO2 (POLAREA,POLICY) 'Inshures requrements on maximal CO2 emmision for policy area' + +QPOLMINPROD (POLAREA,POLICY) 'Inshures requrements on minimal production for policy ares and tecnology group' + +QPOLCAPTARGET (POLAREA,POLICY) 'Inshures requrements on electricity generation capacity in policy area and technology group' + +; + + +QPOLSHAREGROSS(POLAREA,POLICY)$(IPOLREQ_Y(POLAREA,POLICY) and POLTYP(POLICY,'SHAREGROSS')) .. + + + (SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), + SUM(G$(POLTECH(POLICY,G)), + SUM(C$(sameas(C,CCCRRRAAA)), + SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) + + SUM((IA,IS3,T)$(IAGKN(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) + ) + + SUM(IR$(sameas(IR,CCCRRRAAA)), + SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) + + SUM((IA,IS3,T)$(IAGKN(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) + ) + + SUM(IA$(sameas(IA,CCCRRRAAA)), + SUM((IS3,T)$(IAGK_Y(IA,G)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) + + SUM((IS3,T)$(IAGKN(IA,G)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) + ) + ) + )) + / + (SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), + SUM(C$(sameas(C,CCCRRRAAA)), + SUM((IR,IS3,T)$( CCCRRR(C,IR) ), IHOURSINST(IS3,T)* IDE_T_Y(IR,IS3,T)/(1-DISLOSS_E(IR)) ) + ) + + SUM(IR$(sameas(IR,CCCRRRAAA)), + SUM((IS3,T), IHOURSINST(IS3,T)* IDE_T_Y(IR,IS3,T)/(1-DISLOSS_E(IR)) ) + ) + )) + + VQPOLSHAREGROSS(POLAREA,POLICY) + =G= + IPOLREQ_Y(POLAREA,POLICY) +; + + + +QPOLSHARENET(POLAREA,POLICY)$(IPOLREQ_Y(POLAREA,POLICY) and POLTYP(POLICY,'SHARENETTO')) .. + + + (SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), + SUM(G$(POLTECH(POLICY,G)), + SUM(C$(sameas(C,CCCRRRAAA)), + SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) + + SUM((IA,IS3,T)$(IAGKN(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) + ) + + SUM(IR$(sameas(IR,CCCRRRAAA)), + SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) + + SUM((IA,IS3,T)$(IAGKN(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) + ) + + SUM(IA$(sameas(IA,CCCRRRAAA)), + SUM((IS3,T)$(IAGK_Y(IA,G)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) + + SUM((IS3,T)$(IAGKN(IA,G)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) + ) + ) + )) + / + (SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), + SUM(C$(sameas(C,CCCRRRAAA)), + SUM((IR,IS3,T)$( CCCRRR(C,IR) ), IHOURSINST(IS3,T)* IDE_T_Y(IR,IS3,T) ) + ) + + SUM(IR$(sameas(IR,CCCRRRAAA)), + SUM((IS3,T), IHOURSINST(IS3,T)* IDE_T_Y(IR,IS3,T)) + ) + )) + + VQPOLSHARENET(POLAREA,POLICY) + =G= + IPOLREQ_Y(POLAREA,POLICY) +; + + + + +QPOLMAXCO2(POLAREA,POLICY)$(IPOLREQ_Y(POLAREA,POLICY) and POLTYP(POLICY,'MAXCO2')) .. + + SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), + SUM(G$(IGE(G) and IM_CO2(G)), + SUM(C$(sameas(C,CCCRRRAAA)), + SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T) * (IM_CO2(G)/IOF1000) * IOF3P6 * VGF_T(IA,G,IS3,T)) + + SUM((IA,IS3,T)$(IAGKN(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T) * (IM_CO2(G)/IOF1000) * IOF3P6 * VGFN_T(IA,G,IS3,T)) + ) + + SUM(IR$(sameas(IR,CCCRRRAAA)), + SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T) * (IM_CO2(G)/IOF1000) * IOF3P6 * VGF_T(IA,G,IS3,T)) + + SUM((IA,IS3,T)$(IAGKN(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T) * (IM_CO2(G)/IOF1000) * IOF3P6 * VGFN_T(IA,G,IS3,T)) + ) + + SUM(IA$(sameas(IA,CCCRRRAAA)), + SUM((IS3,T)$(IAGK_Y(IA,G)), IHOURSINST(IS3,T) * (IM_CO2(G)/IOF1000) * IOF3P6 * VGF_T(IA,G,IS3,T)) + + SUM((IS3,T)$(IAGKN(IA,G)), IHOURSINST(IS3,T) * (IM_CO2(G)/IOF1000) * IOF3P6 * VGFN_T(IA,G,IS3,T)) + ) + ) + ) + + =L= + IPOLREQ_Y(POLAREA,POLICY) + + VQPOLMAXCO2(POLAREA,POLICY) +; + + + +QPOLMINPROD (POLAREA,POLICY)$(IPOLREQ_Y(POLAREA,POLICY) and POLTYP(POLICY,'MINPROD')) .. + + SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), + SUM(G$(POLTECH(POLICY,G)), + SUM(C$(sameas(C,CCCRRRAAA)), + SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) + + SUM((IA,IS3,T)$(IAGKN(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) + ) + + SUM(IR$(sameas(IR,CCCRRRAAA)), + SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) + + SUM((IA,IS3,T)$(IAGKN(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) + ) + + SUM(IA$(sameas(IA,CCCRRRAAA)), + SUM((IS3,T)$(IAGK_Y(IA,G)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) + + SUM((IS3,T)$(IAGKN(IA,G)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) + ) + ) + ) + + VQPOLMINPROD(POLAREA,POLICY) + =G= + IPOLREQ_Y(POLAREA,POLICY) +; + + +QPOLCAPTARGET (POLAREA,POLICY)$(IPOLREQ_Y(POLAREA,POLICY) and POLTYP(POLICY,'CAPTARGET')) .. + + + SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), + SUM(G$(POLTECH(POLICY,G)), + SUM(C$(sameas(C,CCCRRRAAA)), + SUM((IA)$(IAGK_Y(IA,G) AND ICA(C,IA)), IGKFX_Y(IA,G) + IGKVACCTOY(IA,G)) + + SUM((IA)$(IAGKN(IA,G) AND ICA(C,IA)), VGKN(IA,G) ) + ) + + SUM(IR$(sameas(IR,CCCRRRAAA)), + SUM((IA)$(IAGK_Y(IA,G) AND RRRAAA(IR,IA)), IGKFX_Y(IA,G) + IGKVACCTOY(IA,G)) + + SUM((IA)$(IAGKN(IA,G) AND RRRAAA(IR,IA)), VGKN(IA,G) ) + ) + + SUM(IA$(sameas(IA,CCCRRRAAA)), + (IGKFX_Y(IA,G) + IGKVACCTOY(IA,G))$(IAGK_Y(IA,G)) + +VGKN(IA,G)$(IAGKN(IA,G)) + ) + ) + ) + + VQPOLCAPTARGET(POLAREA,POLICY) + =G= + IPOLREQ_Y(POLAREA,POLICY) +; + diff --git a/base/addons/policies/pol_eqN.inc b/base/addons/policies/pol_eqN.inc new file mode 100644 index 00000000..924ae9ee --- /dev/null +++ b/base/addons/policies/pol_eqN.inc @@ -0,0 +1,7 @@ +* Addon for securing policy requirements are forfilled + +QPOLSHAREGROSS +QPOLSHARENET +QPOLMAXCO2 +QPOLMINPROD +QPOLCAPTARGET diff --git a/base/addons/policies/pol_err2.inc b/base/addons/policies/pol_err2.inc new file mode 100644 index 00000000..d8e19b47 --- /dev/null +++ b/base/addons/policies/pol_err2.inc @@ -0,0 +1,62 @@ +* File pol_err2.inc +* This file is part of the policies addon + +* Geografy defining Policy Area is checked for dobbelt reprecentation of areas and regionss +* Writing to ERRORFILE. + +PUT ERRORFILE / "Now checking input data for addon Policies." /; + +*------------------------------------------------------------------------------ +*--- DATA IN THE FILE POLAREAGEO.inc:------------------------------------------------- +* Check Area and Regions in Countries +LOOP( POLAREA, + LOOP(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), + LOOP(C$(sameas(C,CCCRRRAAA)), + LOOP(CCCRRRAAAALIAS$(POLAREAGEO(POLAREA,CCCRRRAAAALIAS)), + LOOP(IA$(sameas(IA,CCCRRRAAAALIAS)), + If (ICA(C,IA), + PUT "Invalid geografy for Policy Area: " POLAREA.TL " Area: " IA.TL " is already countained in " C.TL; + PUT "Error is automaticly corrected"; + POLAREAGEO(POLAREA,IA) = NO; + PUT //; +ERRCOUNT2=ERRCOUNT2+1; + ) + ) + ) + LOOP(CCCRRRAAAALIAS$(POLAREAGEO(POLAREA,CCCRRRAAAALIAS)), + LOOP(IR$(sameas(IR,CCCRRRAAAALIAS)), + If (CCCRRR(C,IR), + PUT "Invalid geografy for Policy Area: " POLAREA.TL " Region: " IR.TL " is already countained in " C.TL; + PUT "Error is automaticly corrected"; + POLAREAGEO(POLAREA,IR) = NO; + PUT //; +ERRCOUNT2=ERRCOUNT2+1; + ) + ) + ) + + ) + ) +); + + + +* Check Area in Region +LOOP( POLAREA, + LOOP(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), + LOOP(IR$(sameas(IR,CCCRRRAAA)), + LOOP(CCCRRRAAAALIAS$(POLAREAGEO(POLAREA,CCCRRRAAAALIAS)), + LOOP(IA$(sameas(IA,CCCRRRAAAALIAS)), + If (RRRAAA(IR,IA), + PUT "Invalid geografy for Policy Area: " POLAREA.TL " Area: " IA.TL " is already countained in Region " IR.TL; + PUT "Error is automaticly corrected"; + POLAREAGEO(POLAREA,IA) = NO; +ERRCOUNT2=ERRCOUNT2+1; + ) + ) + ) + ) + ) +); +PUT //; + diff --git a/base/addons/policies/pol_sets.inc b/base/addons/policies/pol_sets.inc new file mode 100644 index 00000000..492bca1a --- /dev/null +++ b/base/addons/policies/pol_sets.inc @@ -0,0 +1,77 @@ +* Addon for securing that policy requirements are forfilled +* Policy requirements are given for userdefined policy area and policy type + +SET POLAREA 'Group of policy areas'; +SET POLAREAGEO(POLAREA,CCCRRRAAA) 'Defining the geografy of each policy area by CCC, RRR and AAA' ; +SET POLICY 'Group of policies'; +SET POLICYTYPE 'Different policy types'; +SET POLTYP(POLICY,POLICYTYPE) 'Policy type assigned to each policy'; +SET POLTECH(POLICY,G) 'Technologies related to each policy'; + +* Addon defined technology sets +SET VETECH(G) '' +/ +WI-L15 +WI-L20 +WI-L25 +WI-S15 +WI-S20 +WI-S25 + +/ +* Userdefined technology set +SET USERTECH(G) '' +/ + +ST-B8-NG + + +/ + + +SET POLAREA 'Group of policy areas' %semislash% +$if EXIST '../data/POLAREA.inc' $INCLUDE '../data/POLAREA.inc'; +$if not EXIST '../data/POLAREA.inc' $INCLUDE '../../base/addons/policies/data/POLAREA.inc'; +%semislash%; + +SET POLAREAGEO(POLAREA,CCCRRRAAA) 'Defining the geografi for each regime' %semislash% +$if EXIST '../data/POLAREAGEO.inc' $INCLUDE '../data/POLAREAGEO.inc'; +$if not EXIST '../data/POLAREAGEO.inc' $INCLUDE '../../base/addons/policies/data/POLAREAGEO.inc'; +%semislash%; + + + +SET POLICY 'Group of policies' %semislash% +$if EXIST '../data/POLICY.inc' $INCLUDE '../data/POLICY.inc'; +$if not EXIST '../data/POLICY.inc' $INCLUDE '../../base/addons/policies/data/POLICY.inc'; +%semislash%; + +SET POLICYTYPE 'Different policy types' %semislash% +$if EXIST '../data/POLICYTYPE.inc' $INCLUDE '../data/POLICYTYPE.inc'; +$if not EXIST '../data/POLICYTYPE.inc' $INCLUDE '../../base/addons/policies/data/POLICYTYPE.inc'; +%semislash%; + +SET POLTYP(POLICY,POLICYTYPE) 'Policy type assigned to each policy' %semislash% +$if EXIST '../data/POLTYP.inc' $INCLUDE '../data/POLTYP.inc'; +$if not EXIST '../data/POLTYP.inc' $INCLUDE '../../base/addons/policies/data/POLTYP.inc'; +%semislash%; + +SET POLTECH(POLICY,G) 'Technologies related to each policy' %semislash% +$if EXIST '../data/POLTECH.inc' $INCLUDE '../data/POLTECH.inc'; +$if not EXIST '../data/POLTECH.inc' $INCLUDE '../../base/addons/policies/data/POLTECH.inc'; +%semislash%; + + + + + + + + + + + + + + + diff --git a/base/addons/policies/pol_var.inc b/base/addons/policies/pol_var.inc new file mode 100644 index 00000000..a0c138fd --- /dev/null +++ b/base/addons/policies/pol_var.inc @@ -0,0 +1,11 @@ +* Addon for securing policy requrements are forfilled +* Slack variables are created to insure that the model works +* even if the policy requirements is not forfilled + +Positive Variables + VQPOLSHAREGROSS(POLAREA,POLICY) 'Penalty on not forefilling the gross share requirement for policy area' + VQPOLSHARENET (POLAREA,POLICY) 'Penalty on not forefilling the net share requirement for policy area' + VQPOLMAXCO2 (POLAREA,POLICY) 'Penalty on not forefilling the maximum CO2 requirement for policy area' + VQPOLMINPROD (POLAREA,POLICY) 'Penalty on not forefilling the minimum production requirement for policy area' + VQPOLCAPTARGET (POLAREA,POLICY) 'Penalty on not forefilling the minimum capacity requirement for policy area' +; diff --git a/base/addons/policies/pol_yearupdate.inc b/base/addons/policies/pol_yearupdate.inc new file mode 100644 index 00000000..064b0200 --- /dev/null +++ b/base/addons/policies/pol_yearupdate.inc @@ -0,0 +1,4 @@ +* Addon for securing policy requrements are forfilled +* Requerements are updated on a yearly basis + +IPOLREQ_Y(POLAREA,POLICY)=POLREQ(Y,POLAREA,POLICY); diff --git a/base/addons/systemcost/SysCost addon instruction.txt b/base/addons/systemcost/SysCost addon instruction.txt new file mode 100644 index 00000000..f55f0e6e --- /dev/null +++ b/base/addons/systemcost/SysCost addon instruction.txt @@ -0,0 +1,14 @@ + + +User guide: + + + + + + + + + + + diff --git a/base/addons/systemcost/cost_syscost.inc b/base/addons/systemcost/cost_syscost.inc new file mode 100644 index 00000000..8b2b5361 --- /dev/null +++ b/base/addons/systemcost/cost_syscost.inc @@ -0,0 +1,8 @@ +* Addon for securing that System Constraints are forfilled +* Slack variables are created to insure that the model works +* in case the System Constraints can not be forfilled + ++ SUM((C,IS3,T), VQBASELOAD(C,IS3,T))*PENALTYQ/2 + + + diff --git a/base/addons/systemcost/demodata/BASELOADSERVICE.inc b/base/addons/systemcost/demodata/BASELOADSERVICE.inc new file mode 100644 index 00000000..c9ed46e4 --- /dev/null +++ b/base/addons/systemcost/demodata/BASELOADSERVICE.inc @@ -0,0 +1,8 @@ +* Addon for securing that System Constraints are forfilled + + +PARAMETER BASELOADSERVICE(CCC) 'Minimum production (MW) on certain generation facilities providing baseload services' +/ + Denmark 5000 +/; + diff --git a/base/addons/systemcost/demodata/sets_syscost.inc b/base/addons/systemcost/demodata/sets_syscost.inc new file mode 100644 index 00000000..088661b7 --- /dev/null +++ b/base/addons/systemcost/demodata/sets_syscost.inc @@ -0,0 +1,7 @@ +* Addon for securing that System Constraints are forfilled + + +SET IGEBASE(G) 'Power plants capable of supplying base load'; + + +IGEBASE(IGE)=YES$(IGEXT(IGE) or IGCND(IGE)); diff --git a/base/addons/systemcost/eqN_syscost.inc b/base/addons/systemcost/eqN_syscost.inc new file mode 100644 index 00000000..f3f6353f --- /dev/null +++ b/base/addons/systemcost/eqN_syscost.inc @@ -0,0 +1,5 @@ +* Addon for securing that System Constraints are forfilled + +QBASELOAD + + diff --git a/base/addons/systemcost/eq_syscost.inc b/base/addons/systemcost/eq_syscost.inc new file mode 100644 index 00000000..a5f3a24a --- /dev/null +++ b/base/addons/systemcost/eq_syscost.inc @@ -0,0 +1,15 @@ +* Addon for securing that System Constraints are forfilled + +Equations + +QBASELOAD(C,S,T) 'Minimum production for baseload service providers (MW)' +; + +QBASELOAD(C,IS3,T)$(BASELOADSERVICE(C) > 0) .. + SUM(IAGK_Y(IA,IGE)$(ICA(C,IA) AND IGEBASE(IGE)), VGE_T(IA,IGE,IS3,T)) + + SUM(IAGKN(IA,IGE)$(ICA(C,IA) AND IGEBASE(IGE)), VGEN_T(IA,IGE,IS3,T)) + + +VQBASELOAD(C,IS3,T) + =G= + BASELOADSERVICE(C) +; \ No newline at end of file diff --git a/base/addons/systemcost/var_syscost.inc b/base/addons/systemcost/var_syscost.inc new file mode 100644 index 00000000..f8d9aaf9 --- /dev/null +++ b/base/addons/systemcost/var_syscost.inc @@ -0,0 +1,8 @@ +* Addon for securing that System Constraints are forfilled +* Slack variables are created to insure that the model works +* even if the System Constraints are not forfilled + +Positive Variables + + VQBASELOAD(C,S,T) 'Penalty on not forefilling the baseload' +; diff --git a/base/addons/welfare/welfare.opt b/base/addons/welfare/welfare.opt new file mode 100644 index 00000000..e69de29b diff --git a/base/addons/welfare/welfarecmp/compprint1.inc b/base/addons/welfare/welfarecmp/compprint1.inc new file mode 100644 index 00000000..fab5eecd --- /dev/null +++ b/base/addons/welfare/welfarecmp/compprint1.inc @@ -0,0 +1,123 @@ +* File compprint1.inc. +* This file is part of the redesigned welfare module for Balmorel. +* Author: Lars Bregnbæk +* Revision: 1.0 LB + + +* These files are used for comparison between scenarios +File compfile /..\output\economic\%CASEID%.MED/; +File compfile1 /..\output\economic\%CASEID%lr.MED/; + +File file_e_rev /..\output\economic\%CASEID% e_rev.med/; +File file_h_rev /..\output\economic\%CASEID% h_rev.med/; +File file_fxcost /..\output\economic\%CASEID% fxcost.med/; +File file_fuelcost /..\output\economic\%CASEID% fuelcost.med/; +File file_vcost /..\output\economic\%CASEID% vcost.med/; +*Erkki +File file_UCstartcost /..\output\economic\%CASEID% UCstartcost.med/; +File file_UCshutcost /..\output\economic\%CASEID% UCshutcost.med/; +File file_UCfixedcost /..\output\economic\%CASEID% UCfixedcost.med/; +*Erkki +File file_capcost_ex /..\output\economic\%CASEID% capcost_ex.med/; +File file_capcost_en /..\output\economic\%CASEID% capcost_en.med/; +File file_capcost_trans_ex /..\output\economic\%CASEID% capcost_trans_ex.med/; +File file_capcost_trans_en /..\output\economic\%CASEID% capcost_trans_en.med/; +File file_co2cost /..\output\economic\%CASEID% co2cost.med/; +File file_so2cost /..\output\economic\%CASEID% so2cost.med/; +File file_noxcost /..\output\economic\%CASEID% noxcost.med/; +File file_fueltax /..\output\economic\%CASEID% fueltax.med/; +File file_heattaxGH /..\output\economic\%CASEID% heattaxGH.med/; +File file_vindSubsidy /..\output\economic\%CASEID% vindSubsidy.med/; + +File file_e_cost /..\output\economic\%CASEID% e_cost.med/; +File file_h_cost /..\output\economic\%CASEID% h_cost.med/; +File file_e_util /..\output\economic\%CASEID% e_util.med/; +File file_h_util /..\output\economic\%CASEID% h_util.med/; + +File file_bottleneck /..\output\economic\%CASEID% bottleneck.med/; +File file_tradesurp /..\output\economic\%CASEID% tradesurp.med/; +File file_transcost /..\output\economic\%CASEID% transcost.med/; +File file_watervaluevar /..\output\economic\%CASEID% watervaluevar.med/; +File file_watervalue /..\output\economic\%CASEID% watervalue.med/; + +File file_NOxemission /..\output\economic\%CASEID% NOxemission.med/; +File file_SO2emission /..\output\economic\%CASEID% SO2emission.med/; +File file_CO2shadowval /..\output\economic\%CASEID% CO2shadowval.med/; +File file_VEShadowval /..\output\economic\%CASEID% VEShadowval.med/; +File file_Fosshadowval /..\output\economic\%CASEID% Fosshadowval.med/; + +File file_e_tax /..\output\economic\%CASEID% e_tax.med/; +File file_h_tax /..\output\economic\%CASEID% h_tax.med/; + +File file_dist_e /..\output\economic\%CASEID% dist_e.med/; +File file_dist_h /..\output\economic\%CASEID% dist_h.med/; + +File file_reacEf /..\output\economic\%CASEID% reacEf.med/; + +put compfile; put "Dummy"; + +Parameters + e_rev(Y,C) + h_rev(Y,C) + fxcost(Y,C) + fuelcost(Y,C) + vcost(Y,C) +*Erkki + UCstartcost(Y,C) + UCshutcost(Y,C) + UCfixedcost(Y,C) +*Erkki + capcost_ex(Y,C) + capcost_en(Y,C) + capcost_trans_ex(Y,C) + capcost_trans_en(Y,C) + + co2cost(Y,C) + so2cost(Y,C) + noxcost(Y,C) + fueltax(Y,C) + heattax_GH(Y,C) + vindSubsidy(Y,C) + + e_cost(Y,C) + h_cost(Y,C) + e_util(Y,C) + h_util(Y,C) + + bottleneck(Y,C) + tradesurp(Y,C) + transcost(Y,C) + + watervaluevar(Y,C) + watervalue(Y,C) + + NOxemission(Y,C) + SO2emission(Y,C) + CO2shadowval(Y,C) + VEShadowval(Y,C) + Fosshadowval(Y,C) + + e_tax(Y,C) + h_tax(Y,C) + + dist_e(Y,C) + dist_h(Y,C) + + reacEf(Y,C) + + +*Erkki- hourly welfare + hourlygenerationcost(Y,C,S,T) + hourlye_rev(Y,C,S,T) + hourlye_cost(Y,C,S,T) + hourlybottleneck(Y,C,S,T) + hourlyX3V(Y,C,S,T) +*Erkki +; + +*------------------------------------------------------------------------------- +* Add output for addon modules. It is best if addon code is in include files. +*------------------------------------------------------------------------------- + +$ifi %H2%==yes $include '../../base/addons/Hydrogen/compare/hycompprint1.inc'; +$ifi %transport%==yes $include '../../base/addons/transport/compare/trcompprint1.inc'; diff --git a/base/addons/welfare/welfarecmp/compprint3.inc b/base/addons/welfare/welfarecmp/compprint3.inc new file mode 100644 index 00000000..1e9b73a8 --- /dev/null +++ b/base/addons/welfare/welfarecmp/compprint3.inc @@ -0,0 +1,411 @@ +* File compprint3.INC. +* This file is part of the new welfare calculation module for the Balmorel model. +* Created by Lars Bregnbæk + + +* This file contains routines for handling of intermediate information that may +* be usefull output. + + +*=============================================================================== +* Overview +*=============================================================================== + + + +*=============================================================================== +* Overview end +*=============================================================================== + + e_rev(Y,C) =eps+ OMONEY*SUM((IR,S,T)$CCCRRR(C,IR), + IOF1000000*QEEQ.m(IR,S,T)*( + SUM(IAGK_Y(IA,IGE)$((RRRAAA(IR,IA)) AND IGNOTETOH(IGE)), VGE_T.l(IA,IGE,S,T)) + + SUM(IAGKN(IA,IGE)$((RRRAAA(IR,IA)) AND IGNOTETOH(IGE)), VGEN_T.l(IA,IGE,S,T)) + - SUM(IA$(RRRAAA(IR,IA) AND sum(IGESTO,IAGK_Y(IA,IGESTO)+IAGKN(IA,IGESTO))),VESTOLOADT.l(IA,S,T)) + ) + )/IOF1000000; + + h_rev(Y,C) =eps+ OMONEY*Sum((IA,S,T)$(IDH_SUMST(IA) NE 0 and ICA(C,IA)), + IOF1000000*QHEQ.m(IA,S,T)*( + SUM(IGBPR$IAGK_Y(IA,IGBPR),VGH_T.l(IA,IGBPR,S,T)) + + SUM(IGBPR$IAGKN(IA,IGBPR),VGHN_T.l(IA,IGBPR,S,T)) + + SUM(IGEXT$IAGK_Y(IA,IGEXT),VGH_T.l(IA,IGEXT,S,T)) + + SUM(IGEXT$IAGKN(IA,IGEXT),VGHN_T.l(IA,IGEXT,S,T)) + + SUM(IGHH$IAGK_Y(IA,IGHH),VGH_T.l(IA,IGHH,S,T)) + + SUM(IGHH$IAGKN(IA,IGHH),VGHN_T.l(IA,IGHH,S,T)) + + SUM(IGETOH$IAGK_Y(IA,IGETOH),VGH_T.l(IA,IGETOH,S,T)) + + SUM(IGETOH$IAGKN(IA,IGETOH),VGHN_T.l(IA,IGETOH,S,T)) + + SUM(IGESTO$(IAGK_Y(IA,IGESTO) and GDATA(IGESTO,'GDCB')),VGE_T.l(IA,IGESTO,S,T)/GDATA(IGESTO,'GDCB')) + - SUM(IGHSTO$(IAGK_Y(IA,IGHSTO) or IAGKN(IA,IGHSTO)),VHSTOLOADT.l(IA,S,T)) + ) + )/IOF1000000; + + fxcost(Y,C) =eps+ OMONEY*IOF1000*(SUM(IAGK_Y(IA,G)$ICA(C,IA),(IGKVACCTOY(IA,G) + IGKFX_Y(IA,G) +$ifi %DECOMPEFF%==yes -VDECOM.l(IA,G) + )*GOMFCOST(IA,G)) + + SUM(IAGKN(IA,G)$ICA(C,IA),VGKN.l(IA,G)*GOMFCOST(IA,G)))/IOF1000000 ; + + fuelcost(Y,C) =eps+ OMONEY*( + + SUM((IAGK_Y(IA,G),FFF)$(GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB') and ICA(C,IA) and IGNOTETOH(G)), + IFUELP_Y(IA,FFF) * IOF3P6 * SUM((S,T), IHOURSINST(S,T)*VGF_T.l(IA,G,S,T)) ) + +SUM((IAGKN(IA,G),FFF)$(GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB') and ICA(C,IA) and IGNOTETOH(G)), + IFUELP_Y(IA,FFF) * IOF3P6 * SUM((S,T), IHOURSINST(S,T)*VGFN_T.l(IA,G,S,T)) ) + + +SUM((IR,S,T)$CCCRRR(C,IR), + IOF1000000*QEEQ.m(IR,S,T)*( + SUM(IAGK_Y(IA,IGE)$((RRRAAA(IR,IA)) AND IGETOH(IGE) and ICA(C,IA)), VGE_T.l(IA,IGE,S,T)) + + SUM(IAGKN(IA,IGE)$((RRRAAA(IR,IA)) AND IGETOH(IGE) and ICA(C,IA)), VGEN_T.l(IA,IGE,S,T)) + ) + ) + )/IOF1000000; + + + + vcost(Y,C) =eps+ OMONEY*( + SUM(IAGK_Y(IA,IGE)$ICA(C,IA), GOMVCOST(IA,IGE) * SUM((S,T), IHOURSINST(S,T)*VGE_T.l(IA,IGE,S,T))) + + SUM(IAGK_Y(IA,IGNOTETOH(IGH))$ICA(C,IA), GOMVCOST(IA,IGNOTETOH) * SUM((S,T), IHOURSINST(S,T)*GDATA(IGNOTETOH,'GDCV')*(VGH_T.l(IA,IGNOTETOH,S,T)))) + + SUM(IAGKN(IA,IGE)$ICA(C,IA), GOMVCOST(IA,IGE) * SUM((S,T), IHOURSINST(S,T)*VGEN_T.l(IA,IGE,S,T))) + + SUM(IAGKN(IA,IGNOTETOH(IGH))$ICA(C,IA), GOMVCOST(IA,IGNOTETOH) * SUM((S,T), IHOURSINST(S,T)*GDATA(IGNOTETOH,'GDCV')*(VGHN_T.l(IA,IGNOTETOH,S,T)))) + )/IOF1000000; + +*Erkki UC welfare + + UCstartcost(Y,C) =eps +$ifi %UnitComm%==yes + OMONEY*( +$ifi %UnitComm%==yes SUM(IAGK_Y(IA,IGUC)$ICA(C,IA),GDATA(IGUC,'GDUCUCOST')*GDATA(IGUC,'GDUCUNITSIZE')*SUM((S,T),VUCU.L(IA,IGUC,S,T) +$ifi %UnitComm%==yes + VUCUN.L(IA,IGUC,S,T)))) +$ifi %UnitComm%==yes /IOF1000000 + ; + + UCshutcost(Y,C) =eps +$ifi %UnitComm%==yes + OMONEY*( +$ifi %UnitComm%==yes SUM(IAGK_Y(IA,IGUC)$ICA(C,IA),GDATA(IGUC,'GDUCDCOST')*GDATA(IGUC,'GDUCUNITSIZE')*SUM((S,T),VUCD.L(IA,IGUC,S,T) +$ifi %UnitComm%==yes + VUCDN.L(IA,IGUC,S,T)))) +$ifi %UnitComm%==yes /IOF1000000 + ; + + + UCfixedcost(Y,C) =eps +$ifi %UnitComm%==yes + OMONEY*( +$ifi %UnitComm%==yes SUM(IAGK_Y(IA,IGUC)$ICA(C,IA),GDATA(IGUC,'GDUCCOST0')*GDATA(IGUC,'GDUCUNITSIZE')*SUM((S,T),VUCON.L(IA,IGUC,S,T) +$ifi %UnitComm%==yes + VUCONN.L(IA,IGUC,S,T)))) +$ifi %UnitComm%==yes /IOF1000000 + ; +*Erkki + + + capcost_ex(Y,C) =eps+ OMONEY*IOF1000000*(SUM(IAGK_Y(IA,G)$ICA(C,IA),(IGKFX_Y(IA,G))*GINVCOST(IA,G)))/IOF1000000; + capcost_en(Y,C) =eps+ ( OMONEY*IOF1000000*(SUM(IAGKN(IA,G)$ICA(C,IA), VGKN.l(IA,G)*GINVCOST(IA,G))) + +OMONEY*IOF1000000*(SUM(IAGK_Y(IA,G)$ICA(C,IA),(IGKVACCTOY(IA,G))*GINVCOST(IA,G))) + )/IOF1000000; +* LARS20070829: Still no capital costs on exogenous transmission capacity. + capcost_trans_ex(Y,C) =eps+ OMONEY*( 0 )/IOF1000000; + capcost_trans_en(Y,C) =eps+ OMONEY*( SUM(IRE$CCCRRR(C,IRE), SUM(IRI$(IXKN(IRI,IRE) OR IXKN(IRE,IRI)), VXKN.l(IRE,IRI)*XINVCOST(IRE,IRI)*IOF05)) + +SUM(IRI$CCCRRR(C,IRI), SUM(IRE$(IXKN(IRI,IRE) OR IXKN(IRE,IRI)), VXKN.l(IRE,IRI)*XINVCOST(IRE,IRI)*IOF05)) + )/IOF1000000; + + co2cost(Y,C) =eps+ OMONEY*ITAX_CO2_Y(C)*SUM((IA,G,S,T)$(ICA(C,IA) and (IAGKN(IA,G) or IAGK_Y(IA,G))), IHOURSINST(S,T) * (IM_CO2(G)/1000) * IOF3P6 * + (VGF_T.l(IA,G,S,T)$IAGK_Y(IA,G)+VGFN_T.l(IA,G,S,T)$IAGKN(IA,G)))/IOF1000000 ; + + so2cost(Y,C) =eps+ OMONEY*ITAX_SO2_Y(C)*SUM((IA,G,S,T)$(ICA(C,IA) and (IAGKN(IA,G) or IAGK_Y(IA,G))), IHOURSINST(S,T) * (IM_SO2(G)/1000) * IOF3P6 * + (VGF_T.l(IA,G,S,T)$IAGK_Y(IA,G)+VGFN_T.l(IA,G,S,T)$IAGKN(IA,G)))/IOF1000000 ; + + noxcost(Y,C) =eps+ OMONEY*ITAX_NOX_Y(C)*SUM((IA,G,S,T)$(ICA(C,IA) and (IAGKN(IA,G) or IAGK_Y(IA,G))), IHOURSINST(S,T) * (GDATA(G,'GDNOX')/1000000)*IOF3P6* + (VGF_T.l(IA,G,S,T)$IAGK_Y(IA,G)+VGFN_T.l(IA,G,S,T)$IAGKN(IA,G)))/IOF1000000 ; + + fueltax(Y,C) =eps+ OMONEY*( + SUM((FFF,S,T), SUM(IAGK_Y(IA,G)$((GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB')) AND ICA(C,IA)), + IHOURSINST(S,T) * (TAX_F(FFF,C)+ITAX_GF(IA,G)) * IOF3P6 *VGF_T.l(IA,G,S,T))) + + SUM((FFF,S,T), SUM(IAGKN(IA,G)$((GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB')) AND ICA(C,IA)), + IHOURSINST(S,T) * (TAX_F(FFF,C)+ITAX_GF(IA,G)) * IOF3P6 *VGFN_T.l(IA,G,S,T))) + )/IOF1000000; + + heattax_GH(Y,C) =eps+ OMONEY*( + SUM((IA,IGH,S,T)$(IAGK_Y(IA,IGH) and ITAX_GH(IA,IGH) AND ICA(C,IA)), + ITAX_GH(IA,IGH)*IOF3P6*VGH_T.l(IA,IGH,S,T)*IHOURSINST(S,T)) + + SUM((IA,IGH,S,T)$(IAGKN(IA,IGH) and ITAX_GH(IA,IGH) AND ICA(C,IA)), + ITAX_GH(IA,IGH)*IOF3P6*VGHN_T.l(IA,IGH,S,T)*IHOURSINST(S,T)) + )/IOF1000000; + + + vindSubsidy(Y,C) =eps +$ifi %WINDTARGET%==yes + OMONEY*QWNDTARGET.m(C)*IOF1000000*SUM((IA,IGWND,S,T)$ICA(C,IA), +$ifi %WINDTARGET%==yes ( VGE_T.l(IA,IGWND,S,T)$IAGK_Y(IA,IGWND) +$ifi %WINDTARGET%==yes +VGEN_T.l(IA,IGWND,S,T)$IAGKN(IA,IGWND))*IHOURSINST(S,T) +$ifi %WINDTARGET%==yes )/IOF1000000 + ; + + e_cost(Y,C) =eps+ OMONEY*SUM((IR,S,T)$CCCRRR(C,IR), IOF1000000*QEEQ.m(IR,S,T)*( (IDE_T_Y(IR,S,T) + + SUM(DEF_U1,VDEF_T.l(IR,S,T,DEF_U1) ) + - SUM(DEF_D1,VDEF_T.l(IR,S,T,DEF_D1) ) + + SUM(DEF_U2,VDEF_T.l(IR,S,T,DEF_U2) ) + - SUM(DEF_D2,VDEF_T.l(IR,S,T,DEF_D2) ) + + SUM(DEF_U3,VDEF_T.l(IR,S,T,DEF_U3) ) + - SUM(DEF_D3,VDEF_T.l(IR,S,T,DEF_D3) ) + )/(1-DISLOSS_E(IR))) + )/IOF1000000; + + h_cost(Y,C) =eps+ OMONEY*Sum((IA,S,T)$(IDH_SUMST(IA) NE 0 and ICA(C,IA)), + IOF1000000*QHEQ.m(IA,S,T)*( + (IDH_T_Y(IA,S,T) + + SUM(DHF_U1,VDHF_T.l(IA,S,T,DHF_U1) ) + - SUM(DHF_D1,VDHF_T.l(IA,S,T,DHF_D1) ) + + SUM(DHF_U2,VDHF_T.l(IA,S,T,DHF_U2) ) + - SUM(DHF_D2,VDHF_T.l(IA,S,T,DHF_D2) ) + + SUM(DHF_U3,VDHF_T.l(IA,S,T,DHF_U3) ) + - SUM(DHF_D3,VDHF_T.l(IA,S,T,DHF_D3) ) + )/(1-DISLOSS_H(IA)) + ) + )/IOF1000000; + + + e_util(Y,C) =eps+OMONEY*( + - SUM(IR$CCCRRR(C,IR), + SUM((S,T), IHOURSINST(S,T)* + (SUM(DEF_D1, VDEF_T.l(IR,S,T,DEF_D1)* (IDEFP_T(IR,S,T,DEF_D1)+TAX_DE(C)+DISCOST_E(IR)) ) + + SUM(DEF_D2, VDEF_T.l(IR,S,T,DEF_D2)* (IDEFP_T(IR,S,T,DEF_D2)+TAX_DE(C)+DISCOST_E(IR)) ) + + SUM(DEF_D3, VDEF_T.l(IR,S,T,DEF_D3)* (IDEFP_T(IR,S,T,DEF_D3)+TAX_DE(C)+DISCOST_E(IR)) ) + ) + ) + + +SUM((S,T), IHOURSINST(S,T)* + (SUM(DEF_U1, VDEF_T.l(IR,S,T,DEF_U1)* (IDEFP_T(IR,S,T,DEF_U1)+TAX_DE(C)+DISCOST_E(IR)) ) + + SUM(DEF_U2, VDEF_T.l(IR,S,T,DEF_U2)* (IDEFP_T(IR,S,T,DEF_U2)+TAX_DE(C)+DISCOST_E(IR)) ) + + SUM(DEF_U3, VDEF_T.l(IR,S,T,DEF_U3)* (IDEFP_T(IR,S,T,DEF_U3)+TAX_DE(C)+DISCOST_E(IR)) ) + ) + ) + ))/IOF1000000; + + h_util(Y,C) =eps+OMONEY*( + - SUM(IA$ICA(C,IA), + SUM((S,T), IHOURSINST(S,T) + * SUM(DHF_D1, VDHF_T.l(IA,S,T,DHF_D1)* (IDHFP_T(IA,S,T,DHF_D1)+TAX_DH(C) ) ))) + + + SUM(IA$ICA(C,IA), + SUM((S,T), IHOURSINST(S,T) + * SUM(DHF_U1, VDHF_T.l(IA,S,T,DHF_U1)* (IDHFP_T(IA,S,T,DHF_U1)+TAX_DH(C) ) ))) + )/IOF1000000; + + + bottleneck(Y,C) =eps+ OMONEY*( + Sum((S,T), +* Udgående strøm + Sum(irrre$(CCCRRR(C,IRRRe) and ire(irrre)), + Sum(irrri$(iri(irrri) and (not sameas(irrre,irrri)) and ((VXKN.l(IRRRE,IRRRI) > 0) or (IXKINI_Y(IRRRE,IRRRI) > 0 ))), + IOF1000000*(qeeq.m(IRRRi,S,T)*vx_t.l(irRRe,irRRi,S,t)*(1-XLOSS(IRRRE,IRRRI)) - qeeq.m(IRRRe,S,T)*vx_t.l(irRRe,irRRi,S,t))))/2 +* Indgående strøm + +Sum(irrri$(CCCRRR(C,IRRRi) and iri(irrri)), + Sum(irrre$(ire(irrre) and (not sameas(irrre,irrri)) and ((VXKN.l(IRRRE,IRRRI) > 0) or (IXKINI_Y(IRRRE,IRRRI) > 0 ))), + IOF1000000*(qeeq.m(IRRRi,S,T)*vx_t.l(irRRe,irRRi,S,t)*(1-XLOSS(IRRRE,IRRRI)) - qeeq.m(IRRRe,S,T)*vx_t.l(irRRe,irRRi,S,t))))/2 + ) + )/IOF1000000; + + tradesurp(Y,C) =eps+ OMONEY*SUM((IR,S,T)$CCCRRR(C,IR), IOF1000000*QEEQ.m(IR,S,T)*(-IX3FX_T_Y(IR,S,T)))/IOF1000000; + + transcost(Y,C) =eps+ OMONEY*( SUM((IRE,IRI)$((IXKINI_Y(IRE,IRI) OR IXKN(IRE,IRI) OR IXKN(IRI,IRE)) and (CCCRRR(C,IRI) or CCCRRR(C,IRE))), + SUM((S,T), IHOURSINST(S,T) * (VX_T.l(IRE,IRI,S,T) * XCOST(IRE,IRI)))))/2/IOF1000000; + + + + watervaluevar(Y,C) =eps+ OMONEY*( + SUM(IAGK_Y(IA,IGHYRS)$ICA(C,IA), SUM((S,T), HYPPROFILS(IA,S)* IHOURSINST(S,T)*VGE_T.l(IA,IGHYRS,S,T))) + + SUM(IAGKN(IA,IGHYRS)$ICA(C,IA), SUM((S,T), HYPPROFILS(IA,S)* IHOURSINST(S,T)*VGEN_T.l(IA,IGHYRS,S,T))) + )/IOF1000000; + + watervalue(Y,C) =eps +$ifi not %bb3%==yes $goto not_waterval_CP +$ifi not %WATERVAL%==yes $goto not_waterval_CP + +OMONEY*( + SUM(IAGK_Y(IA,IGHYRS)$ICA(C,IA),SUM((S,T), IWATERVAL(IA,S)* IHOURSINST(S,T)*VGE_T.l(IA,IGHYRS,S,T))) + + SUM(IAGKN(IA,IGHYRS)$ICA(C,IA), SUM((S,T), IWATERVAL(IA,S)* IHOURSINST(S,T)*VGEN_T.l(IA,IGHYRS,S,T))) + )/IOF1000000 +$label not_waterval_CP + ; + + NOxemission(Y,C) =eps+ SUM((IA,G,S,T)$(ICA(C,IA) and (IAGKN(IA,G) or IAGK_Y(IA,G))), IHOURSINST(S,T) * (GDATA(G,'GDNOX')/1000000)*IOF3P6* + (VGF_T.l(IA,G,S,T)$IAGK_Y(IA,G)+VGFN_T.l(IA,G,S,T)$IAGKN(IA,G)))/IOF1000000 ; + SO2emission(Y,C) =eps+ SUM((IA,G,S,T)$(ICA(C,IA) and (IAGKN(IA,G) or IAGK_Y(IA,G))), IHOURSINST(S,T) * (IM_SO2(G)/1000) * IOF3P6 * + (VGF_T.l(IA,G,S,T)$IAGK_Y(IA,G)+VGFN_T.l(IA,G,S,T)$IAGKN(IA,G)))/IOF1000000 ; + +$ifi %BB3%==yes CO2shadowval(Y,C) =eps +$ifi not %BB3%==yes CO2shadowval(Y,C)$QLIMCO2.M(C) =eps +$ifi not %BB3%==yes + OMONEY*IOF1000000* +$ifi not %BB3%==yes (-QLIMCO2.M(C))* +$ifi not %BB3%==yes SUM((G,S,T), IHOURSINST(S,T) * (IM_CO2(G)/1000) * IOF3P6 * +$ifi not %BB3%==yes SUM(IA$ICA(C,IA),(VGE_T.L(IA,G,S,T) + VGEN_T.L(IA,G,S,T) +$ifi not %BB3%==yes + GDATA(G,'GDCV')*(VGH_T.L(IA,G,S,T) + VGHN_T.L(IA,G,S,T))) / +$ifi not %BB3%==yes (GDATA(G,'GDFE')* GEFFDERATE(IA,G))))/IOF1000000 + ; + + Fosshadowval(Y,C)=eps +$ifi %FOSSILTARGET%==yes +OMONEY*IOF1000000*SUM(CCCFOSSIL$CCCFOSSILEC(CCCFOSSIL,C), +$ifi %FOSSILTARGET%==yes QFOSSILTARGET.m(CCCFOSSIL) +$ifi %FOSSILTARGET%==yes )* +$ifi %FOSSILTARGET%==yes ( +$ifi %FOSSILTARGET%==yes + SUM((S,T), SUM(IAGK_Y(IA,IGE)$(IGFOSSIL(IGE) AND ICA(C,IA)), +$ifi %FOSSILTARGET%==yes IHOURSINST(S,T) * IOF3P6 * +$ifi %FOSSILTARGET%==yes (VGE_T.l(IA,IGE,S,T) / (GDATA(IGE,'GDFE') * GEFFDERATE(IA,IGE))))) +$ifi %FOSSILTARGET%==yes + SUM((S,T), SUM(IAGK_Y(IA,IGH)$(IGFOSSIL(IGH) AND ICA(C,IA)), +$ifi %FOSSILTARGET%==yes IHOURSINST(S,T) * IOF3P6 * +$ifi %FOSSILTARGET%==yes GDATA(IGH,'GDCV')* VGH_T.l(IA,IGH,S,T)/(GDATA(IGH,'GDFE') * GEFFDERATE(IA,IGH)))) +$ifi %FOSSILTARGET%==yes + SUM((S,T), SUM(IAGKN(IA,IGE)$(IGFOSSIL(IGE) AND ICA(C,IA)), +$ifi %FOSSILTARGET%==yes IHOURSINST(S,T) * IOF3P6 * +$ifi %FOSSILTARGET%==yes (VGEN_T.l(IA,IGE,S,T) / (GDATA(IGE,'GDFE') * GEFFDERATE(IA,IGE))))) +$ifi %FOSSILTARGET%==yes + SUM((S,T), SUM(IAGKN(IA,IGH)$(IGFOSSIL(IGH) AND ICA(C,IA)), +$ifi %FOSSILTARGET%==yes IHOURSINST(S,T) * IOF3P6 * +$ifi %FOSSILTARGET%==yes GDATA(IGH,'GDCV')*VGHN_T.l(IA,IGH,S,T)/(GDATA(IGH,'GDFE')*GEFFDERATE(IA,IGH)))) +$ifi %FOSSILTARGET%==yes )/IOF1000000 + ; + + VEShadowval(Y,C) =eps +$ifi %RENEWTARGET%==yes +OMONEY*IOF1000000*SUM(CCCRESHAREE$CCCRESHAREEC(CCCRESHAREE,C), +$ifi %RENEWTARGET%==yes QRENEW.m(CCCRESHAREE) +$ifi %RENEWTARGET%==yes )*SUM((S,T), IHOURSINST(S,T) * +$ifi %RENEWTARGET%==yes (SUM(IAGK_Y(IA,IGRENEW(IGE))$ICA(C,IA), +$ifi %RENEWTARGET%==yes VGE_T.l(IA,IGE,S,T) +$ifi %RENEWTARGET%==yes ) +$ifi %RENEWTARGET%==yes +SUM(IAGKN(IA,IGRENEW(IGE))$ICA(C,IA), +$ifi %RENEWTARGET%==yes VGEN_T.l(IA,IGE,S,T) +$ifi %RENEWTARGET%==yes )) +$ifi %RENEWTARGET%==yes )/IOF1000000 + ; + + +* e_tax(Y,C) =eps+ OMONEY*TAX_DE(C)*SUM((IR,S,T)$CCCRRR(C,IR), IHOURSINST(S,T)* + e_tax(Y,C) =eps+ OMONEY*SUM((IR,S,T)$CCCRRR(C,IR), TAX_DE(C) * IHOURSINST(S,T)* + (IDE_T_Y(IR,S,T) + + SUM(DEF_U1,VDEF_T.l(IR,S,T,DEF_U1) ) + - SUM(DEF_D1,VDEF_T.l(IR,S,T,DEF_D1) ) + + SUM(DEF_U2,VDEF_T.l(IR,S,T,DEF_U2) ) + - SUM(DEF_D2,VDEF_T.l(IR,S,T,DEF_D2) ) + + SUM(DEF_U3,VDEF_T.l(IR,S,T,DEF_U3) ) + - SUM(DEF_D3,VDEF_T.l(IR,S,T,DEF_D3) ) + ) + )/IOF1000000; + + h_tax(Y,C) =eps+ OMONEY*TAX_DH(C)*SUM((IA,S,T)$ICA(C,IA), IHOURSINST(S,T)* + (IDH_T_Y(IA,S,T) + + SUM(DHF_U1,VDHF_T.l(IA,S,T,DHF_U1) ) + - SUM(DHF_D1,VDHF_T.l(IA,S,T,DHF_D1) ) + ) + )/IOF1000000; + + + dist_e(Y,C) =eps+OMONEY*( + - SUM(IR$CCCRRR(C,IR), DISCOST_E(IR)* + SUM((S,T), IHOURSINST(S,T) + * (SUM(DEF_D1, VDEF_T.l(IR,S,T,DEF_D1) ) + + SUM(DEF_D2, VDEF_T.l(IR,S,T,DEF_D2) ) + + SUM(DEF_D3, VDEF_T.l(IR,S,T,DEF_D3) ) + ) + ) + ) + + SUM(IR$CCCRRR(C,IR),DISCOST_E(IR)* + SUM((S,T), IHOURSINST(S,T)* + ( IDE_T_Y(IR,S,T) + + SUM(DEF_U1, VDEF_T.l(IR,S,T,DEF_U1) ) + + SUM(DEF_U2, VDEF_T.l(IR,S,T,DEF_U2) ) + + SUM(DEF_U3, VDEF_T.l(IR,S,T,DEF_U3) ) + ) + ) + ) + )/IOF1000000; + + dist_h(Y,C) =eps+OMONEY*( + - SUM(IA$ICA(C,IA), DISCOST_H(IA)* + SUM((S,T), IHOURSINST(S,T) + * SUM(DHF_D1, VDHF_T.l(IA,S,T,DHF_D1) )))) + + + SUM(IA$ICA(C,IA), DISCOST_H(IA)* + SUM((S,T), IHOURSINST(S,T) + * (IDH_T_Y(IA,S,T)+SUM(DHF_U1, VDHF_T.l(IA,S,T,DHF_U1) ))) + )/IOF1000000; +$ontext + reacEf(Y,C)$BASELOADSERVICE(C) =eps + +OMONEY*IOF1000000*Sum((S,T), QBASELOAD.m(C,S,T)*IHOURSINST(S,T)*( + SUM(IAGK_Y(IA,IGE)$(ICA(C,IA) AND IGEBASE(IGE)), VGE_T.l(IA,IGE,S,T)) + + SUM(IAGKN(IA,IGE)$(ICA(C,IA) AND IGEBASE(IGE)), VGEN_T.l(IA,IGE,S,T))) + )/IOF1000000 + ; +$offtext +*------------------------------------------------------------------------------- +* Add output for addon modules. It is best if addon code is in include files. +*------------------------------------------------------------------------------- + +$ifi %H2%==yes $include '../../base/addons/Hydrogen/compare/hycompprint3.inc'; +$ifi %transport%==yes $include '../../base/addons/transport/compare/trcompprint3.inc'; + +*Erkki- Calculation of welfare results in hourly resolution. Allows for analysis of when exactly the bulk of welfare is won/lost +$ifi not %hourlywelfare%==yes $goto nothourlywelfare + +hourlygenerationcost(Y,C,S,T) =eps+ OMONEY*( + + SUM((IAGK_Y(IA,G),FFF)$(GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB') and ICA(C,IA) and IGNOTETOH(G)), + IFUELP_Y(IA,FFF) * IOF3P6 * IHOURSINST(S,T)*VGF_T.l(IA,G,S,T) ) + +SUM((IAGKN(IA,G),FFF)$(GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB') and ICA(C,IA) and IGNOTETOH(G)), + IFUELP_Y(IA,FFF) * IOF3P6 * IHOURSINST(S,T)*VGFN_T.l(IA,G,S,T) ) + + +SUM((IR)$CCCRRR(C,IR), + IOF1000000*QEEQ.m(IR,S,T)*( + SUM(IAGK_Y(IA,IGE)$((RRRAAA(IR,IA)) AND IGETOH(IGE) and ICA(C,IA)), VGE_T.l(IA,IGE,S,T)) + + SUM(IAGKN(IA,IGE)$((RRRAAA(IR,IA)) AND IGETOH(IGE) and ICA(C,IA)), VGEN_T.l(IA,IGE,S,T)) + ) + ) + )/IOF1000000 + + + OMONEY*( + SUM(IAGK_Y(IA,IGE)$ICA(C,IA), GOMVCOST(IA,IGE) * IHOURSINST(S,T)*VGE_T.l(IA,IGE,S,T)) + + SUM(IAGK_Y(IA,IGNOTETOH(IGH))$ICA(C,IA), GOMVCOST(IA,IGNOTETOH) * IHOURSINST(S,T)*GDATA(IGNOTETOH,'GDCV')*(VGH_T.l(IA,IGNOTETOH,S,T))) + + SUM(IAGKN(IA,IGE)$ICA(C,IA), GOMVCOST(IA,IGE) * IHOURSINST(S,T)*VGEN_T.l(IA,IGE,S,T)) + + SUM(IAGKN(IA,IGNOTETOH(IGH))$ICA(C,IA), GOMVCOST(IA,IGNOTETOH) * IHOURSINST(S,T)*GDATA(IGNOTETOH,'GDCV')*(VGHN_T.l(IA,IGNOTETOH,S,T))) + )/IOF1000000 + + + OMONEY*ITAX_CO2_Y(C)*SUM((IA,G)$(ICA(C,IA) and (IAGKN(IA,G) or IAGK_Y(IA,G))), IHOURSINST(S,T) * (IM_CO2(G)/1000) * IOF3P6 * + (VGF_T.l(IA,G,S,T)$IAGK_Y(IA,G)+VGFN_T.l(IA,G,S,T)$IAGKN(IA,G)))/IOF1000000 + + + OMONEY*( + SUM((FFF), SUM(IAGK_Y(IA,G)$((GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB')) AND ICA(C,IA)), + IHOURSINST(S,T) * (TAX_F(FFF,C)+ITAX_GF(IA,G)) * IOF3P6 *VGF_T.l(IA,G,S,T))) + + SUM((FFF), SUM(IAGKN(IA,G)$((GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB')) AND ICA(C,IA)), + IHOURSINST(S,T) * (TAX_F(FFF,C)+ITAX_GF(IA,G)) * IOF3P6 *VGFN_T.l(IA,G,S,T))) + )/IOF1000000; + +hourlye_rev(Y,C,S,T) =eps+ OMONEY*SUM((IR)$CCCRRR(C,IR), + IOF1000000*QEEQ.m(IR,S,T)*( + SUM(IAGK_Y(IA,IGE)$((RRRAAA(IR,IA)) AND IGNOTETOH(IGE)), VGE_T.l(IA,IGE,S,T)) + + SUM(IAGKN(IA,IGE)$((RRRAAA(IR,IA)) AND IGNOTETOH(IGE)), VGEN_T.l(IA,IGE,S,T)) + - SUM(IA$(RRRAAA(IR,IA) AND sum(IGESTO,IAGK_Y(IA,IGESTO)+IAGKN(IA,IGESTO))),VESTOLOADT.l(IA,S,T)) + ) + )/IOF1000000; + +hourlye_cost(Y,C,S,T) =eps+ OMONEY*SUM((IR)$CCCRRR(C,IR), IOF1000000*QEEQ.m(IR,S,T)*( (IDE_T_Y(IR,S,T) + + SUM(DEF_U1,VDEF_T.l(IR,S,T,DEF_U1) ) + - SUM(DEF_D1,VDEF_T.l(IR,S,T,DEF_D1) ) + + SUM(DEF_U2,VDEF_T.l(IR,S,T,DEF_U2) ) + - SUM(DEF_D2,VDEF_T.l(IR,S,T,DEF_D2) ) + + SUM(DEF_U3,VDEF_T.l(IR,S,T,DEF_U3) ) + - SUM(DEF_D3,VDEF_T.l(IR,S,T,DEF_D3) ) + )/(1-DISLOSS_E(IR))) + )/IOF1000000; + +hourlybottleneck(Y,C,S,T) =eps+ OMONEY*( + +* Udgående strøm + Sum(irrre$(CCCRRR(C,IRRRe) and ire(irrre)), + Sum(irrri$(iri(irrri) and (not sameas(irrre,irrri)) and ((VXKN.l(IRRRE,IRRRI) > 0) or (IXKINI_Y(IRRRE,IRRRI) > 0 ))), + IOF1000000*(qeeq.m(IRRRi,S,T)*vx_t.l(irRRe,irRRi,S,t)*(1-XLOSS(IRRRE,IRRRI)) - qeeq.m(IRRRe,S,T)*vx_t.l(irRRe,irRRi,S,t))))/2 +* Indgående strøm + +Sum(irrri$(CCCRRR(C,IRRRi) and iri(irrri)), + Sum(irrre$(ire(irrre) and (not sameas(irrre,irrri)) and ((VXKN.l(IRRRE,IRRRI) > 0) or (IXKINI_Y(IRRRE,IRRRI) > 0 ))), + IOF1000000*(qeeq.m(IRRRi,S,T)*vx_t.l(irRRe,irRRi,S,t)*(1-XLOSS(IRRRE,IRRRI)) - qeeq.m(IRRRe,S,T)*vx_t.l(irRRe,irRRi,S,t))))/2 + + )/IOF1000000; + + +$ifi %x3v%==yes hourlyX3V(Y,C,S,T) =eps + OMONEY*sum((IR)$CCCRRR(C,IR),sum((X3VPLACE0,X3VSTEP0), IOF1000000*QEEQ.m(IR,S,T)*VX3VIM_T.l(IR,X3VPLACE0,X3VSTEP0,S,T))) + +$ifi %x3v%==yes /IOF1000000; + + +$label nothourlywelfare +*Erkki \ No newline at end of file diff --git a/base/addons/welfare/welfarecmp/mkcompare.inc b/base/addons/welfare/welfarecmp/mkcompare.inc new file mode 100644 index 00000000..5a429b54 --- /dev/null +++ b/base/addons/welfare/welfarecmp/mkcompare.inc @@ -0,0 +1,167 @@ +$SETGLOBAL format prn + + + +put file_e_rev ; +Put "Generator revenue from electricity generation Mio OMONEY"/; +$libinclude gams2tbl e_rev + +put file_h_rev ; +Put "Generator revenue from heat generation"/; +$libinclude gams2tbl h_rev + +put file_fxcost ; +Put "Generator fixed costs"/; +$libinclude gams2tbl fxcost + +put file_fuelcost ; +Put "Generator fuel costs"/; +$libinclude gams2tbl fuelcost + +put file_vcost ; +Put "Generator variable costs"/; +$libinclude gams2tbl vcost +*Erkki +put file_UCstartcost ; +Put "Generator start-up costs"/; +$libinclude gams2tbl UCstartcost + +put file_UCshutcost ; +Put "Generator shut-down costs"/; +$libinclude gams2tbl UCshutcost + +put file_UCfixedcost ; +Put "Generator fixed UC costs"/; +$libinclude gams2tbl UCfixedcost +*Erkki +put file_capcost_ex ; +Put "Generator total capital costs - exogenous units"/; +$libinclude gams2tbl capcost_ex + +put file_capcost_en ; +Put "Generator total capital costs - endogenous units"/; +$libinclude gams2tbl capcost_en + +put file_capcost_trans_ex ; +Put "Transmission total capital costs - exogenous units"/; +$libinclude gams2tbl capcost_trans_ex + +put file_capcost_trans_en ; +Put "Transmission total capital costs - endogenous units"/; +$libinclude gams2tbl capcost_trans_en + +put file_co2cost ; +Put "Value of CO2 emissions"/; +$libinclude gams2tbl co2cost + +put file_so2cost ; +Put "Value of SO2 emissions"/; +$libinclude gams2tbl so2cost + +put file_noxcost ; +Put "Value of NOx emissions"/; +$libinclude gams2tbl noxcost + +put file_fueltax ; +Put "Value of fuel taxes"/; +$libinclude gams2tbl fueltax + +put file_heattaxGH ; +Put "Value of heat taxes on units"/; +$libinclude gams2tbl heattax_GH + +put file_vindSubsidy ; +Put "Value of vind subsidies"/; +$libinclude gams2tbl vindSubsidy + +put file_e_cost ; +Put "Consumer electricity costs"/; +$libinclude gams2tbl e_cost + +put file_h_cost ; +Put "Consumer heating costs"/; +$libinclude gams2tbl h_cost + +put file_e_util ; +Put "Consumer utility of electricity consumption"/; +$libinclude gams2tbl e_util + +put file_h_util ; +Put "Consumer utility heat consumption"/; +$libinclude gams2tbl h_util + +put file_bottleneck ; +Put "TSO bottleneck income net of transmission losses"/; +$libinclude gams2tbl bottleneck + +put file_tradesurp ; +Put "Trade value of exogenous imports from 3rd countries"/; +$libinclude gams2tbl tradesurp + +put file_transcost ; +Put "Transmission variable costs"/; +$libinclude gams2tbl transcost + +put file_watervaluevar ; +Put "Seasonal calibration value of water"/; +$libinclude gams2tbl watervaluevar + + +put file_watervalue ; +Put "Water value transfered and used"/; +$libinclude gams2tbl watervalue + +put file_NOxemission ; +Put "NOx emissions (mio tonnes)"/; +$libinclude gams2tbl NOxemission + +put file_SO2emission ; +Put "SO2 emissions (mio tonnes)"/; +$libinclude gams2tbl SO2emission + +put file_CO2shadowval ; +Put "Shadow value of CO2 constraint"/; +$libinclude gams2tbl CO2shadowval + +put file_VEShadowval ; +Put "Shadow value of VE constraint"/; +$libinclude gams2tbl VEShadowval + +put file_Fosshadowval ; +Put "Shadow value of fossil fuel constraint"/; +$libinclude gams2tbl Fosshadowval + +put file_e_tax ; +Put "Consumer tax on electricity"/; +$libinclude gams2tbl e_tax + +put file_h_tax ; +Put "Consumer tax on heat"/; +$libinclude gams2tbl h_tax + +put file_dist_e ; +Put "Electricity distribution costs"/; +$libinclude gams2tbl dist_e + +put file_dist_h ; +Put "Heat distribution costs"/; +$libinclude gams2tbl dist_h + +*Erkki +$ontext +put file_reacEf ; +Put "Base load services"/; +$libinclude gams2tbl reacEf +$offtext +*Erkki + + + +*------------------------------------------------------------------------------- +* Add output for addon modules. It is best if addon code is in include files. +*------------------------------------------------------------------------------- + +$ifi %H2%==yes $include '../../base/addons/Hydrogen/compare/hymkcompare.inc'; +$ifi %transport%==yes $include '../../base/addons/transport/compare/trmkcompare.inc'; + + diff --git a/base/addons/welfare/welfarecmp/welfarebb3.inc b/base/addons/welfare/welfarecmp/welfarebb3.inc new file mode 100644 index 00000000..de8a8c4b --- /dev/null +++ b/base/addons/welfare/welfarecmp/welfarebb3.inc @@ -0,0 +1,2 @@ +* LB: Disse er fjernet, da de bruger elementer fra det gamle velfærdsmodul. Bør nok opdateres. Dette skal der tænkes lidt over. +$include "..\..\base\addons\welfare\welfarecmp\mkcompare.inc"; \ No newline at end of file diff --git a/base/auxils/annuity_calculation/ANNUITYCG.inc b/base/auxils/annuity_calculation/ANNUITYCG.inc new file mode 100644 index 00000000..6628d991 --- /dev/null +++ b/base/auxils/annuity_calculation/ANNUITYCG.inc @@ -0,0 +1,23150 @@ +*PARAMETER ANNUITYCG(CCC,GGG) CALCULATED WITH AUXILS + +ANNUITYCG('NORWAY','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('NORWAY','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('NORWAY','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('NORWAY','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('NORWAY','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('NORWAY','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('NORWAY','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('NORWAY','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('NORWAY','EE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('NORWAY','EE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('NORWAY','EE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('NORWAY','EE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; 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+ANNUITYCG('LATVIA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_NGAS_E-105_MS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_NGAS_E-106_MS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_NGAS_E-106_MS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_NGAS_E-106_MS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_NGAS_E-95_SS-10-KW_Y-2020')=0.073581750329; +ANNUITYCG('LATVIA','GNR_BO_NGAS_E-96_SS-10-KW_Y-2030')=0.073581750329; +ANNUITYCG('LATVIA','GNR_BO_NGAS_E-96_SS-10-KW_Y-2040')=0.073581750329; +ANNUITYCG('LATVIA','GNR_BO_NGAS_E-97_SS-10-KW_Y-2050')=0.073581750329; +ANNUITYCG('LATVIA','GNR_BO_NGASCCS_E-105_MS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_STRW_E-102_LS-6-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_STRW_E-102_LS-6-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_STRW_E-102_LS-6-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_STRW_E-102_LS-6-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-78_SS-10-KW-FEED_Y-2020')=0.073581750329; +ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2030')=0.073581750329; +ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2040')=0.073581750329; +ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-85_SS-8-KW-FEED_Y-2050')=0.073581750329; +ANNUITYCG('LATVIA','GNR_CC_NGAS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGAS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_CC_NGASCCS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_BGAS_BP_E-43_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_BGAS_BP_E-45_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_BGAS_BP_E-46_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_BGAS_BP_E-47_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_BGAS_CND_E-43_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_BGAS_CND_E-45_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_BGAS_CND_E-46_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_BGAS_CND_E-47_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGAS_BP_E-47_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGAS_BP_E-48_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGAS_BP_E-49_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGAS_BP_E-50_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGAS_CND_E-47_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGAS_CND_E-48_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGAS_CND_E-49_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGAS_CND_E-50_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_BP_E-47_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_BP_E-48_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_BP_E-49_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_BP_E-50_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_CND_E-47_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_CND_E-48_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_CND_E-49_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_CND_E-50_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LEAD_E-82_Y-2020')=0.134492992698; +ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LEAD_E-85_Y-2030')=0.100143727814; +ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2020')=0.073581750329; +ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2030')=0.073581750329; +ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2040')=0.073581750329; +ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2050')=0.073581750329; +ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2020')=0.073581750329; +ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2030')=0.073581750329; +ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2040')=0.073581750329; +ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2050')=0.073581750329; +ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-NAS_E-80_Y-2020')=0.082198522089; +ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-NAS_E-85_Y-2030')=0.065586831340; +ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-VRFB_E-78_Y-2020')=0.076138618402; +ANNUITYCG('LATVIA','GNR_ES_ELEC_CAES_E-71_Y-2020')=0.046258849740; +ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-AIR_COP-340_SS-4-KW_Y-2030')=0.106552172686; +ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-AIR_COP-350_SS-4-KW_Y-2040')=0.106552172686; +ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-AIR_COP-360_SS-4-KW_Y-2050')=0.106552172686; +ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-AIR_COP-490_SS-3-KW_Y-2020')=0.106552172686; +ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-310_LS_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-330_LS_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-335_SS-4-KW_Y-2020')=0.078993328144; +ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-343_LS_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2030')=0.078993328144; +ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-360_LS_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-365_SS-4-KW_Y-2050')=0.078993328144; +ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-1200_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-1800_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-400_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-480_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-500_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-600_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-600_Y-2050')=0.064011962786; 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+ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT_WIND_ONS_SS-25-KW_Y-2020')=0.073581750329; +ANNUITYCG('LATVIA','GNR_WT_WIND_ONS_SS-25-KW_Y-2030')=0.073581750329; +ANNUITYCG('LATVIA','GNR_WT_WIND_ONS_SS-25-KW_Y-2040')=0.073581750329; +ANNUITYCG('LATVIA','GNR_WT_WIND_ONS_SS-25-KW_Y-2050')=0.073581750329; +ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','SE3-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','SE3-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','SE3-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','SE3-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','SE4-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','SE4-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','SE4-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','SE4-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','UK-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_Y-2040')=0.055948589741; +ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_Y-2050')=0.053577322368; +ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_DH_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_DH_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_DH_Y-2040')=0.055948589741; +ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_DH_Y-2050')=0.053577322368; +ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_Y-2020_HUB')=0.064011962786; +ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_Y-2030_HUB')=0.057830099134; +ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_Y-2040_HUB')=0.055948589741; +ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_Y-2050_HUB')=0.053577322368; +ANNUITYCG('LATVIA','GNR_FC_H2_SOFCC_Y-2020')=0.089941100371; +ANNUITYCG('LATVIA','GNR_FC_H2_SOFCC_Y-2030')=0.073581750329; +ANNUITYCG('LATVIA','GNR_FC_H2_SOFCC_Y-2040')=0.073581750329; +ANNUITYCG('LATVIA','GNR_FC_H2_SOFCC_Y-2050')=0.073581750329; +ANNUITYCG('LATVIA','GNR_H2S_H2-TNKC_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_H2S_H2-TNKC_Y-2030')=0.057830099134; +ANNUITYCG('LATVIA','GNR_H2S_H2-TNKC_Y-2040')=0.057830099134; +ANNUITYCG('LATVIA','GNR_H2S_H2-TNKC_Y-2050')=0.057830099134; +ANNUITYCG('LATVIA','GNR_H2S_H2-CAVERN_Y-2020')=0.040808000038; +ANNUITYCG('LATVIA','GNR_H2S_H2-CAVERN_Y-2030')=0.040808000038; +ANNUITYCG('LATVIA','GNR_H2S_H2-CAVERN_Y-2040')=0.040808000038; +ANNUITYCG('LATVIA','GNR_H2S_H2-CAVERN_Y-2050')=0.040808000038; +ANNUITYCG('LATVIA','GNR_METHANATION-DAC_E-60_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_STEAM-REFORMING_E-70_Y-2020')=0.064011962786; +ANNUITYCG('LATVIA','GNR_STEAM-REFORMING-CCS_E-70_Y-2020')=0.064011962786; +ANNUITYCG('LITHUANIA','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LITHUANIA','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LITHUANIA','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LITHUANIA','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LITHUANIA','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('LITHUANIA','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('LITHUANIA','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('LITHUANIA','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('LITHUANIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('LITHUANIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('LITHUANIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('LITHUANIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; 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+ANNUITYCG('LUXEMBOURG','GNR_SH_SUN_SS-4-KW_Y-2050')=0.057830099134; +ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_CND_E-49_LS-400-MW_Y-2020')=0.050523489324; +ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_CND_E-52_LS-400-MW_Y-2030')=0.050523489324; +ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_CND_E-53_LS-400-MW_Y-2040')=0.050523489324; +ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_CND_E-54_LS-400-MW_Y-2050')=0.050523489324; +ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_EXT_E-49_LS-400-MW_Y-2020')=0.050523489324; +ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_EXT_E-52_LS-400-MW_Y-2030')=0.050523489324; +ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_EXT_E-53_LS-400-MW_Y-2040')=0.050523489324; +ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_EXT_E-54_LS-400-MW_Y-2050')=0.050523489324; +ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; 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+ANNUITYCG('MONTENEGRO','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_STRW_E-102_LS-6-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_STRW_E-102_LS-6-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_STRW_E-102_LS-6-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_STRW_E-102_LS-6-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-78_SS-10-KW-FEED_Y-2020')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2030')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2040')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-85_SS-8-KW-FEED_Y-2050')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_BP_E-43_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_BP_E-45_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_BP_E-46_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_BP_E-47_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_CND_E-43_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_CND_E-45_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_CND_E-46_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_CND_E-47_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_BP_E-47_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_BP_E-48_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_BP_E-49_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_BP_E-50_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_CND_E-47_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_CND_E-48_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_CND_E-49_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_CND_E-50_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_BP_E-47_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_BP_E-48_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_BP_E-49_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_BP_E-50_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_CND_E-47_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_CND_E-48_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_CND_E-49_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_CND_E-50_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LEAD_E-82_Y-2020')=0.134492992698; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LEAD_E-85_Y-2030')=0.100143727814; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2020')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2030')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2040')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2050')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2020')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2030')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2040')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2050')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-NAS_E-80_Y-2020')=0.082198522089; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-NAS_E-85_Y-2030')=0.065586831340; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-VRFB_E-78_Y-2020')=0.076138618402; +ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_CAES_E-71_Y-2020')=0.046258849740; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-AIR_COP-340_SS-4-KW_Y-2030')=0.106552172686; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-AIR_COP-350_SS-4-KW_Y-2040')=0.106552172686; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-AIR_COP-360_SS-4-KW_Y-2050')=0.106552172686; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-AIR_COP-490_SS-3-KW_Y-2020')=0.106552172686; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-310_LS_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-330_LS_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-335_SS-4-KW_Y-2020')=0.078993328144; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-343_LS_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2030')=0.078993328144; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-360_LS_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-365_SS-4-KW_Y-2050')=0.078993328144; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-1200_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-1800_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-400_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-480_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-500_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-600_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-600_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-740_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-900_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-360_LS-4-MW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-370_SS-10-KW_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-380_LS-4-MW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-380_SS-10-KW_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-388_SS-10-KW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-395_LS-4-MW_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-400_SS-10-KW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-410_LS-4-MW_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2020')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2030')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2040')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2050')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2020')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2030')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2040')=0.073581750329; 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+ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG2_Y-2020')=0.053577322368; +ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG2_Y-2030')=0.050523489324; +ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG2_Y-2040')=0.050523489324; +ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG2_Y-2050')=0.050523489324; +ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG3_Y-2020')=0.053577322368; +ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG3_Y-2030')=0.050523489324; +ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG3_Y-2040')=0.050523489324; +ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG3_Y-2050')=0.050523489324; +ANNUITYCG('MONTENEGRO','GNR_PV_SUN_MS-100-KW_Y-2020')=0.053577322368; +ANNUITYCG('MONTENEGRO','GNR_PV_SUN_MS-100-KW_Y-2030')=0.050523489324; +ANNUITYCG('MONTENEGRO','GNR_PV_SUN_MS-100-KW_Y-2040')=0.050523489324; +ANNUITYCG('MONTENEGRO','GNR_PV_SUN_MS-100-KW_Y-2050')=0.050523489324; +ANNUITYCG('MONTENEGRO','GNR_PV_SUN_SS-6-KW_Y-2020')=0.053577322368; +ANNUITYCG('MONTENEGRO','GNR_PV_SUN_SS-6-KW_Y-2030')=0.050523489324; 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+ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_NGAS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_ST_NGAS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_ST_NGAS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_ST_NGASCCS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_ST_NGASCCS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_ST_NGASCCS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_ONS_SS-25-KW_Y-2020')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_ONS_SS-25-KW_Y-2030')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_ONS_SS-25-KW_Y-2040')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_WT_WIND_ONS_SS-25-KW_Y-2050')=0.073581750329; +ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; 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+ANNUITYCG('MONTENEGRO','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; 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+ANNUITYCG('MONTENEGRO','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('MONTENEGRO','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('MONTENEGRO','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('MONTENEGRO','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('MONTENEGRO','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; 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+ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_CAES_E-71_Y-2020')=0.046258849740; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-AIR_COP-340_SS-4-KW_Y-2030')=0.106552172686; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-AIR_COP-350_SS-4-KW_Y-2040')=0.106552172686; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-AIR_COP-360_SS-4-KW_Y-2050')=0.106552172686; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-AIR_COP-490_SS-3-KW_Y-2020')=0.106552172686; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-310_LS_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-330_LS_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-335_SS-4-KW_Y-2020')=0.078993328144; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-343_LS_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2030')=0.078993328144; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-360_LS_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-365_SS-4-KW_Y-2050')=0.078993328144; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-1200_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-1800_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-400_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-480_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-500_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-600_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-600_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-740_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-900_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-360_LS-4-MW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-370_SS-10-KW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-380_LS-4-MW_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-380_SS-10-KW_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-388_SS-10-KW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-395_LS-4-MW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-400_SS-10-KW_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-410_LS-4-MW_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2020')=0.073581750329; +ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2030')=0.073581750329; +ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2040')=0.073581750329; +ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2050')=0.073581750329; +ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2020')=0.073581750329; +ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2030')=0.073581750329; +ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2040')=0.073581750329; +ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2050')=0.073581750329; +ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_WTR-TANK_LS_E-95_Y-2010')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_WTR-TANK_SS_E-95_Y-2010')=0.057830099134; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGAS_E-93_MS-20-MW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGAS_E-94_MS-20-MW_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGAS_E-95_MS-20-MW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGAS_E-96_MS-20-MW_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGASCCS_E-93_MS-20-MW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGASCCS_E-94_MS-20-MW_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGASCCS_E-95_MS-20-MW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGASCCS_E-96_MS-20-MW_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-DF_ELEC_E-100_MS-5-MW_Y-2020')=0.103217228610; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-DF_NGAS_E-100_MS-3-MW_Y-2020')=0.089941100371; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-DF_NGASCCS_E-100_MS-3-MW_Y-2020')=0.089941100371; +ANNUITYCG('NORTH_MACEDONIA','GNR_IND-DF_WOODCHI_E-100_MS-10-MW_Y-2020')=0.089941100371; +ANNUITYCG('NORTH_MACEDONIA','GNR_OFF_HUB_2GW_Y-2020')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_OFF_HUB_2GW_Y-2030')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_OFF_HUB_2GW_Y-2040')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_OFF_HUB_2GW_Y-2050')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2020')=0.053577322368; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2030')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2040')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2050')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2020')=0.053577322368; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2030')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2040')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2050')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2020')=0.053577322368; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2030')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2040')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2050')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_MS-100-KW_Y-2020')=0.053577322368; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_MS-100-KW_Y-2030')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_MS-100-KW_Y-2040')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_MS-100-KW_Y-2050')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_SS-6-KW_Y-2020')=0.053577322368; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_SS-6-KW_Y-2030')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_SS-6-KW_Y-2040')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_SS-6-KW_Y-2050')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_RES_WTR_PMP_E-80_Y-2020')=0.044201845123; +ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_LS_Y-2020')=0.057830099134; +ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_LS_Y-2030')=0.057830099134; +ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_LS_Y-2040')=0.057830099134; +ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_LS_Y-2050')=0.057830099134; +ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_SS-4-KW_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_SS-4-KW_Y-2030')=0.057830099134; +ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_SS-4-KW_Y-2040')=0.057830099134; +ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_SS-4-KW_Y-2050')=0.057830099134; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_CND_E-49_LS-400-MW_Y-2020')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_CND_E-52_LS-400-MW_Y-2030')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_CND_E-53_LS-400-MW_Y-2040')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_CND_E-54_LS-400-MW_Y-2050')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_EXT_E-49_LS-400-MW_Y-2020')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_EXT_E-52_LS-400-MW_Y-2030')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_EXT_E-53_LS-400-MW_Y-2040')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_EXT_E-54_LS-400-MW_Y-2050')=0.050523489324; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; 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+ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2020')=0.061238540616; +ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2030')=0.057830099134; 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+ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2020')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2030')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2040')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2050')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2020')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2030')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2040')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2050')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-105_MS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-106_MS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-106_MS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-106_MS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-95_SS-10-KW_Y-2020')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-96_SS-10-KW_Y-2030')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-96_SS-10-KW_Y-2040')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-97_SS-10-KW_Y-2050')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGASCCS_E-105_MS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_STRW_E-102_LS-6-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_STRW_E-102_LS-6-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_STRW_E-102_LS-6-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_STRW_E-102_LS-6-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-78_SS-10-KW-FEED_Y-2020')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2030')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2040')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-85_SS-8-KW-FEED_Y-2050')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_BP_E-43_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_BP_E-45_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_BP_E-46_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_BP_E-47_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_CND_E-43_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_CND_E-45_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_CND_E-46_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_CND_E-47_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_BP_E-47_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_BP_E-48_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_BP_E-49_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_BP_E-50_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_CND_E-47_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_CND_E-48_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_CND_E-49_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_CND_E-50_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_BP_E-47_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_BP_E-48_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_BP_E-49_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_BP_E-50_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_CND_E-47_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_CND_E-48_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_CND_E-49_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_CND_E-50_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LEAD_E-82_Y-2020')=0.134492992698; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LEAD_E-85_Y-2030')=0.100143727814; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2020')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2030')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2040')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2050')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2020')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2030')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2040')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2050')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-NAS_E-80_Y-2020')=0.082198522089; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-NAS_E-85_Y-2030')=0.065586831340; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-VRFB_E-78_Y-2020')=0.076138618402; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_CAES_E-71_Y-2020')=0.046258849740; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-AIR_COP-340_SS-4-KW_Y-2030')=0.106552172686; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-AIR_COP-350_SS-4-KW_Y-2040')=0.106552172686; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-AIR_COP-360_SS-4-KW_Y-2050')=0.106552172686; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-AIR_COP-490_SS-3-KW_Y-2020')=0.106552172686; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-310_LS_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-330_LS_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-335_SS-4-KW_Y-2020')=0.078993328144; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-343_LS_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2030')=0.078993328144; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-360_LS_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-365_SS-4-KW_Y-2050')=0.078993328144; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-1200_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-1800_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-400_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-480_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-500_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-600_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-600_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-740_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-900_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-360_LS-4-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-370_SS-10-KW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-380_LS-4-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-380_SS-10-KW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-388_SS-10-KW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-395_LS-4-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-400_SS-10-KW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-410_LS-4-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2020')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2030')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2040')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2050')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2020')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2030')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2040')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2050')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_WTR-TANK_LS_E-95_Y-2010')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_WTR-TANK_SS_E-95_Y-2010')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGAS_E-93_MS-20-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGAS_E-94_MS-20-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGAS_E-95_MS-20-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGAS_E-96_MS-20-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGASCCS_E-93_MS-20-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGASCCS_E-94_MS-20-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGASCCS_E-95_MS-20-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGASCCS_E-96_MS-20-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-DF_ELEC_E-100_MS-5-MW_Y-2020')=0.103217228610; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-DF_NGAS_E-100_MS-3-MW_Y-2020')=0.089941100371; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-DF_NGASCCS_E-100_MS-3-MW_Y-2020')=0.089941100371; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-DF_WOODCHI_E-100_MS-10-MW_Y-2020')=0.089941100371; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_OFF_HUB_2GW_Y-2020')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_OFF_HUB_2GW_Y-2030')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_OFF_HUB_2GW_Y-2040')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_OFF_HUB_2GW_Y-2050')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG1_Y-2020')=0.053577322368; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG1_Y-2030')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG1_Y-2040')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG1_Y-2050')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG2_Y-2020')=0.053577322368; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG2_Y-2030')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG2_Y-2040')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG2_Y-2050')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG3_Y-2020')=0.053577322368; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG3_Y-2030')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG3_Y-2040')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG3_Y-2050')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_MS-100-KW_Y-2020')=0.053577322368; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_MS-100-KW_Y-2030')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_MS-100-KW_Y-2040')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_MS-100-KW_Y-2050')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_SS-6-KW_Y-2020')=0.053577322368; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_SS-6-KW_Y-2030')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_SS-6-KW_Y-2040')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_SS-6-KW_Y-2050')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_RES_WTR_PMP_E-80_Y-2020')=0.044201845123; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_LS_Y-2020')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_LS_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_LS_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_LS_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_SS-4-KW_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_SS-4-KW_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_SS-4-KW_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_SS-4-KW_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_CND_E-49_LS-400-MW_Y-2020')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_CND_E-52_LS-400-MW_Y-2030')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_CND_E-53_LS-400-MW_Y-2040')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_CND_E-54_LS-400-MW_Y-2050')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_EXT_E-49_LS-400-MW_Y-2020')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_EXT_E-52_LS-400-MW_Y-2030')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_EXT_E-53_LS-400-MW_Y-2040')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_EXT_E-54_LS-400-MW_Y-2050')=0.050523489324; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_NGAS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_NGAS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_NGAS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_NGASCCS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_NGASCCS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_NGASCCS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_ONS_SS-25-KW_Y-2020')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_ONS_SS-25-KW_Y-2030')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_ONS_SS-25-KW_Y-2040')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_ONS_SS-25-KW_Y-2050')=0.073581750329; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; 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+ANNUITYCG('CYPRUS','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('CYPRUS','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('CYPRUS','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('CYPRUS','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('CYPRUS','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; +ANNUITYCG('CYPRUS','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; +ANNUITYCG('CYPRUS','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; +ANNUITYCG('CYPRUS','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; +ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_Y-2020')=0.064011962786; +ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_Y-2030')=0.057830099134; +ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_Y-2040')=0.055948589741; +ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_Y-2050')=0.053577322368; +ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_DH_Y-2020')=0.064011962786; +ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_DH_Y-2030')=0.057830099134; +ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_DH_Y-2040')=0.055948589741; +ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_DH_Y-2050')=0.053577322368; +ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_Y-2020_HUB')=0.064011962786; +ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_Y-2030_HUB')=0.057830099134; +ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_Y-2040_HUB')=0.055948589741; +ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_Y-2050_HUB')=0.053577322368; +ANNUITYCG('CYPRUS','GNR_FC_H2_SOFCC_Y-2020')=0.089941100371; +ANNUITYCG('CYPRUS','GNR_FC_H2_SOFCC_Y-2030')=0.073581750329; +ANNUITYCG('CYPRUS','GNR_FC_H2_SOFCC_Y-2040')=0.073581750329; +ANNUITYCG('CYPRUS','GNR_FC_H2_SOFCC_Y-2050')=0.073581750329; +ANNUITYCG('CYPRUS','GNR_H2S_H2-TNKC_Y-2020')=0.064011962786; +ANNUITYCG('CYPRUS','GNR_H2S_H2-TNKC_Y-2030')=0.057830099134; +ANNUITYCG('CYPRUS','GNR_H2S_H2-TNKC_Y-2040')=0.057830099134; +ANNUITYCG('CYPRUS','GNR_H2S_H2-TNKC_Y-2050')=0.057830099134; +ANNUITYCG('CYPRUS','GNR_H2S_H2-CAVERN_Y-2020')=0.040808000038; +ANNUITYCG('CYPRUS','GNR_H2S_H2-CAVERN_Y-2030')=0.040808000038; +ANNUITYCG('CYPRUS','GNR_H2S_H2-CAVERN_Y-2040')=0.040808000038; +ANNUITYCG('CYPRUS','GNR_H2S_H2-CAVERN_Y-2050')=0.040808000038; +ANNUITYCG('CYPRUS','GNR_METHANATION-DAC_E-60_Y-2020')=0.064011962786; +ANNUITYCG('CYPRUS','GNR_STEAM-REFORMING_E-70_Y-2020')=0.064011962786; +ANNUITYCG('CYPRUS','GNR_STEAM-REFORMING-CCS_E-70_Y-2020')=0.064011962786; diff --git a/base/auxils/annuity_calculation/ANNUITYCX.inc b/base/auxils/annuity_calculation/ANNUITYCX.inc new file mode 100644 index 00000000..6127c2c3 --- /dev/null +++ b/base/auxils/annuity_calculation/ANNUITYCX.inc @@ -0,0 +1,38 @@ +*PARAMETER ANNUITYCX(CCC) CALCULATED WITH AUXILS + +ANNUITYCX('NORWAY')=0.050523489324; +ANNUITYCX('DENMARK')=0.050523489324; +ANNUITYCX('FINLAND')=0.050523489324; +ANNUITYCX('GERMANY')=0.050523489324; +ANNUITYCX('NETHERLANDS')=0.050523489324; +ANNUITYCX('SWEDEN')=0.050523489324; +ANNUITYCX('UNITED_KINGDOM')=0.050523489324; +ANNUITYCX('ESTONIA')=0.050523489324; +ANNUITYCX('LATVIA')=0.050523489324; +ANNUITYCX('LITHUANIA')=0.050523489324; +ANNUITYCX('POLAND')=0.050523489324; +ANNUITYCX('BELGIUM')=0.050523489324; +ANNUITYCX('FRANCE')=0.050523489324; +ANNUITYCX('ITALY')=0.050523489324; +ANNUITYCX('SWITZERLAND')=0.050523489324; +ANNUITYCX('AUSTRIA')=0.050523489324; +ANNUITYCX('CZECH_REPUBLIC')=0.050523489324; +ANNUITYCX('SPAIN')=0.050523489324; +ANNUITYCX('PORTUGAL')=0.050523489324; +ANNUITYCX('SLOVAKIA')=0.050523489324; +ANNUITYCX('HUNGARY')=0.050523489324; +ANNUITYCX('SLOVENIA')=0.050523489324; +ANNUITYCX('CROATIA')=0.050523489324; +ANNUITYCX('ROMANIA')=0.050523489324; +ANNUITYCX('BULGARIA')=0.050523489324; +ANNUITYCX('GREECE')=0.050523489324; +ANNUITYCX('IRELAND')=0.050523489324; +ANNUITYCX('LUXEMBOURG')=0.050523489324; +ANNUITYCX('ALBANIA')=0.050523489324; +ANNUITYCX('MONTENEGRO')=0.050523489324; +ANNUITYCX('NORTH_MACEDONIA')=0.050523489324; +ANNUITYCX('BOSNIA_AND_HERZEGOVINA')=0.050523489324; +ANNUITYCX('SERBIA')=0.050523489324; +ANNUITYCX('TURKEY')=0.050523489324; +ANNUITYCX('MALTA')=0.050523489324; +ANNUITYCX('CYPRUS')=0.050523489324; diff --git a/base/auxils/annuity_calculation/HEATTRANS_ANNUITYCXH.inc b/base/auxils/annuity_calculation/HEATTRANS_ANNUITYCXH.inc new file mode 100644 index 00000000..ce0e978c --- /dev/null +++ b/base/auxils/annuity_calculation/HEATTRANS_ANNUITYCXH.inc @@ -0,0 +1,38 @@ +*PARAMETER ANNUITYCXH(CCC) CALCULATED WITH AUXILS + +ANNUITYCXH('NORWAY')=0.050523489324; +ANNUITYCXH('DENMARK')=0.050523489324; +ANNUITYCXH('FINLAND')=0.050523489324; +ANNUITYCXH('GERMANY')=0.050523489324; +ANNUITYCXH('NETHERLANDS')=0.050523489324; +ANNUITYCXH('SWEDEN')=0.050523489324; +ANNUITYCXH('UNITED_KINGDOM')=0.050523489324; +ANNUITYCXH('ESTONIA')=0.050523489324; +ANNUITYCXH('LATVIA')=0.050523489324; +ANNUITYCXH('LITHUANIA')=0.050523489324; +ANNUITYCXH('POLAND')=0.050523489324; +ANNUITYCXH('BELGIUM')=0.050523489324; +ANNUITYCXH('FRANCE')=0.050523489324; +ANNUITYCXH('ITALY')=0.050523489324; +ANNUITYCXH('SWITZERLAND')=0.050523489324; +ANNUITYCXH('AUSTRIA')=0.050523489324; +ANNUITYCXH('CZECH_REPUBLIC')=0.050523489324; +ANNUITYCXH('SPAIN')=0.050523489324; +ANNUITYCXH('PORTUGAL')=0.050523489324; +ANNUITYCXH('SLOVAKIA')=0.050523489324; +ANNUITYCXH('HUNGARY')=0.050523489324; +ANNUITYCXH('SLOVENIA')=0.050523489324; +ANNUITYCXH('CROATIA')=0.050523489324; +ANNUITYCXH('ROMANIA')=0.050523489324; +ANNUITYCXH('BULGARIA')=0.050523489324; +ANNUITYCXH('GREECE')=0.050523489324; +ANNUITYCXH('IRELAND')=0.050523489324; +ANNUITYCXH('LUXEMBOURG')=0.050523489324; +ANNUITYCXH('ALBANIA')=0.050523489324; +ANNUITYCXH('MONTENEGRO')=0.050523489324; +ANNUITYCXH('NORTH_MACEDONIA')=0.050523489324; +ANNUITYCXH('BOSNIA_AND_HERZEGOVINA')=0.050523489324; +ANNUITYCXH('SERBIA')=0.050523489324; +ANNUITYCXH('TURKEY')=0.050523489324; +ANNUITYCXH('MALTA')=0.050523489324; +ANNUITYCXH('CYPRUS')=0.050523489324; diff --git a/base/auxils/annuity_calculation/HYDROGEN_ANNUITYCXH2.inc b/base/auxils/annuity_calculation/HYDROGEN_ANNUITYCXH2.inc new file mode 100644 index 00000000..f986dd26 --- /dev/null +++ b/base/auxils/annuity_calculation/HYDROGEN_ANNUITYCXH2.inc @@ -0,0 +1,38 @@ +*PARAMETER ANNUITYCXH2(CCC) CALCULATED WITH AUXILS + +ANNUITYCXH2('NORWAY')=0.046550200450; +ANNUITYCXH2('DENMARK')=0.046550200450; +ANNUITYCXH2('FINLAND')=0.046550200450; +ANNUITYCXH2('GERMANY')=0.046550200450; +ANNUITYCXH2('NETHERLANDS')=0.046550200450; +ANNUITYCXH2('SWEDEN')=0.046550200450; +ANNUITYCXH2('UNITED_KINGDOM')=0.046550200450; +ANNUITYCXH2('ESTONIA')=0.046550200450; +ANNUITYCXH2('LATVIA')=0.046550200450; +ANNUITYCXH2('LITHUANIA')=0.046550200450; +ANNUITYCXH2('POLAND')=0.046550200450; +ANNUITYCXH2('BELGIUM')=0.046550200450; +ANNUITYCXH2('FRANCE')=0.046550200450; +ANNUITYCXH2('ITALY')=0.046550200450; +ANNUITYCXH2('SWITZERLAND')=0.046550200450; +ANNUITYCXH2('AUSTRIA')=0.046550200450; +ANNUITYCXH2('CZECH_REPUBLIC')=0.046550200450; +ANNUITYCXH2('SPAIN')=0.046550200450; +ANNUITYCXH2('PORTUGAL')=0.046550200450; +ANNUITYCXH2('SLOVAKIA')=0.046550200450; +ANNUITYCXH2('HUNGARY')=0.046550200450; +ANNUITYCXH2('SLOVENIA')=0.046550200450; +ANNUITYCXH2('CROATIA')=0.046550200450; +ANNUITYCXH2('ROMANIA')=0.046550200450; +ANNUITYCXH2('BULGARIA')=0.046550200450; +ANNUITYCXH2('GREECE')=0.046550200450; +ANNUITYCXH2('IRELAND')=0.046550200450; +ANNUITYCXH2('LUXEMBOURG')=0.046550200450; +ANNUITYCXH2('ALBANIA')=0.046550200450; +ANNUITYCXH2('MONTENEGRO')=0.046550200450; +ANNUITYCXH2('NORTH_MACEDONIA')=0.046550200450; +ANNUITYCXH2('BOSNIA_AND_HERZEGOVINA')=0.046550200450; +ANNUITYCXH2('SERBIA')=0.046550200450; +ANNUITYCXH2('TURKEY')=0.046550200450; +ANNUITYCXH2('MALTA')=0.046550200450; +ANNUITYCXH2('CYPRUS')=0.046550200450; diff --git a/base/auxils/plotting_tool/.ipynb_checkpoints/Transmission_Map-checkpoint.ipynb b/base/auxils/plotting_tool/.ipynb_checkpoints/Transmission_Map-checkpoint.ipynb new file mode 100644 index 00000000..a8cb2e4c --- /dev/null +++ b/base/auxils/plotting_tool/.ipynb_checkpoints/Transmission_Map-checkpoint.ipynb @@ -0,0 +1,1091 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1 Preparations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Set Options" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "### Set options here.\n", + "#Structural options\n", + "filetype_input = 'csv' #Choose input file type: 'gdx' or 'csv' \n", + "gams_dir = 'C:\\GAMS\\36' #Only required if filetype_input == 'gdx'\n", + "market = 'DayAhead' #Choose from ['Balancing', 'DayAhead', 'FullYear', 'Investment']\n", + "COMMODITY = 'Electricity' #Choose from: ['Electricity', 'H2', 'Other']. Add to csv-files name (only relevant if filetype_input == 'csv'). If 'Other': go to cell 1.4.0.\n", + "SCENARIO = 'hubs' #Add scenario to read file name\n", + "YEAR = 'all' #Add year to read file name (e.g. '2025', '2035', 'full')\n", + "SUBSET = 'all' #Add subset to read file name (e.g. 'full')\n", + "year = 2035 #Year to be displayed\n", + "LINES = 'Capacity' #Choose from: ['Capacity', 'Flow', 'CongestionFlow']. For 'CongestionFlow', exo_end automatically switches to 'Total'.\n", + "exo_end = 'Total' # Choose from ['Endogenous', 'Exogenous', 'Total']. For 'CongestionFlow', exo_end automatically switches to 'Total'.\n", + "S = 'S02' #Season \n", + "T = 'T073' #Hour\n", + "\n", + "# hubs\n", + "hub_display = True\n", + "hub_size = 10\n", + "hub_decimals = 0 #Number of decimals shown for hub capacities\n", + "background_hubsize = True #Displaying the true size of the hub as a circle on the map.\n", + "hub_area = 2.8 #MW / km^2, background hub size on map. \n", + "hub_area_opacity = 0.3 #Opacity of background hub size. \n", + "\n", + "#Visual options\n", + "label_min = 4 #Minimum transmission capacity (GW) shown on map in text\n", + "font_line = 10 #Font size of transmission line labels\n", + "font_hub = 10 #Font size of hub labels\n", + "font_region = 12 #Font size of region labels\n", + "line_decimals = 1 #Number of decimals shown for line capacities\n", + "line_width_constant = 5 #Constant related to thickness of lines: the higher the number, the narrower the lines will be\n", + "flowline_breaks = [0, 40, 94.999, 100] #Breaks for different congestion categories\n", + "legend_values = ['Fully congested', '40-95% congested', '< 50% congested'] #Values displayed in legend\n", + "\n", + "#colors\n", + "background_color = 'white'\n", + "regions_ext_color = 'lightgrey'\n", + "regions_model_color = 'grey'\n", + "region_text = 'black'\n", + "capline_color = 'orange'\n", + "flowline_color = ['#3D9200', '#feb24c','#960028']\n", + "line_text = 'black'\n", + "hub_color = 'lightblue'\n", + "hub_background_color = 'lightblue'\n", + "hub_text = 'black'\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Import Packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import sys\n", + "import os\n", + "import glob\n", + "import gdxpds\n", + "import pandas as pd\n", + "import numpy as np\n", + "import geopandas as gpd\n", + "import folium\n", + "from folium import plugins\n", + "from IPython.display import HTML, display\n", + "import json\n", + "from folium.features import DivIcon #For text labels on hubs\n", + "from IPython.display import display, HTML\n", + "from csv import reader\n", + "display(HTML(data=\"\"\"\n", + "\n", + "\"\"\"))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 Read geographic files" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'path' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mproject_dir\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.\\input'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m#Load coordinates files\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mdf_unique\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproject_dir\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;34m'geo_files/coordinates_RRR.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mdf_region\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_unique\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf_unique\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Type'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'region'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'path' is not defined" + ] + } + ], + "source": [ + "project_dir = Path('.\\input')\n", + "\n", + "#Load coordinates files \n", + "df_unique = pd.read_csv(project_dir/'geo_files/coordinates_RRR.csv')\n", + "df_region = df_unique.loc[df_unique['Type'] == 'region', ]\n", + "df_bypass = pd.read_csv(project_dir/'geo_files/bypass_lines.csv') # coordinates of 'hooks' in indirect lines, to avoid going trespassing third regions\n", + "\n", + "\n", + "#Define names of geojson and shapefile layers\n", + "r_in = list(df_unique.loc[(df_unique['Display'] == 1) & (df_unique['Type'] == 'region'), 'RRR'])\n", + "r_out = list(df_unique.loc[(df_unique['Display'] == 0) & (df_unique['Type'] == 'region'), 'RRR'])\n", + "\n", + "layers_in = {region: '' for region in r_in}\n", + "layers_out = {region: '' for region in r_out}\n", + "\n", + "#Create dictionaries with layer names for each region; if both a shapefile and geojson file are available for one region, the geojson file is used. \n", + "for region in r_in:\n", + " layers_in[region] = glob.glob(f'{project_dir}/geo_files/geojson_files/'+ region + '.geojson')\n", + " if bool(layers_in[region]) == False:\n", + " layers_in[region] = glob.glob(f'{project_dir}/geo_files/shapefiles/'+ region + '.shp')\n", + "for region in r_out:\n", + " layers_out[region] = glob.glob(f'{project_dir}/geo_files/geojson_files/'+ region + '.geojson')\n", + " if bool(layers_out[region]) == False:\n", + " layers_out[region] = glob.glob(f'{project_dir}/geo_files/shapefiles/'+ region + '.shp')\n", + "\n", + "for region in layers_in:\n", + " layers_in[region] = str(layers_in[region])[2:-2] #Remove brackets from file names\n", + "for region in layers_out:\n", + " layers_out[region] = str(layers_out[region])[2:-2] #Remove brackets from file names\n", + "\n", + " \n", + "#Convert shapefiles to geojson files \n", + "for region in layers_out:\n", + " if layers_out[region][-4:] == '.shp':\n", + " gpd.read_file(layers_out[region]).to_file(f'{project_dir}/geo_files/geojson_files/'+ region + '.geojson', driver='GeoJSON')\n", + " layers_out[region] = layers_out[region].replace('shapefiles', 'geojson_files').replace('.shp', '.geojson')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1.4 Read run-specific files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 1.4.0 If COMMODITY == 'Other': define variables or file names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if COMMODITY == 'Other':\n", + " if filetype_input == 'gdx':\n", + " var_list = ['G_CAP_YCRAF', 'XH2_CAP_YCR', 'XH2_FLOW_YCRST', 'PRO_YCRAGFST'] #Fill in variables to read, e.g. ['G_CAP_YCRAF', 'X{COMMODITY}_CAP_YCR', 'X{COMMODITY}_FLOW_YCRST', 'PRO_YCRAGST']\n", + " if filetype_input == 'csv':\n", + " flow_file = 'FlowH2Hourly_'+ SCENARIO + '_' + YEAR + '_' + SUBSET + '.csv' #Fill in flow file name if applicable, e.g. 'Flow{COMMODITY}Hourly_'+ SCENARIO + '_' + YEAR + '_' + SUBSET + '.csv'\n", + " transcap_file = 'CapacityH2Transmission_' + SCENARIO + '_' + YEAR + '_'+ SUBSET + '.csv' #Fill in transmission capacity file name, e.g. 'Capacity{COMMODITY}Transmission_'+ SCENARIO + '_' + YEAR + '_'+ SUBSET + '.csv' " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4A - GDX Inputs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 1.4A.1 Function: reading gdx-files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if filetype_input == 'gdx':\n", + " def df_creation(gdx_file, varname):\n", + " df = pd.DataFrame()\n", + " if '_' in gdx_file:\n", + " # if yes: extract scenario name from gdx filename\n", + " scenario = gdx_file.split('_', 3)[-3]\n", + " year = gdx_file.split('_', 3)[-2]\n", + " subset = gdx_file.split('_', 3)[-1][:-4]\n", + " market = gdx_file.split('\\\\', 1)[0].split('/',3)[-1]\n", + " else:\n", + " # if no: use nan instead\n", + " scenario = 'nan'\n", + "\n", + " # create empty temporary dataframe and load the gdx data into it\n", + " temp = pd.DataFrame()\n", + " temp = gdxpds.to_dataframe(gdx_file, varname, gams_dir=gams_dir,\n", + " old_interface=False)\n", + "\n", + " # add a scenario column with the scenario name of the current iteration\n", + " temp['Scenario'] = scenario\n", + " temp['Market'] = market\n", + " temp['run'] = scenario + '_' + year + '_' + subset\n", + "\n", + " # rearrange the columns' order\n", + " cols = list(temp.columns)\n", + " cols = [cols[-1]] + cols[:-1]\n", + " temp = temp[cols]\n", + "\n", + " # concatenate the temporary dataframe to the preceeding data\n", + " df = pd.concat([df, temp], sort=False)\n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 1.4A.2 - Define var_list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if filetype_input == 'gdx':\n", + " if COMMODITY == 'Electricity':\n", + " var_list = []\n", + " if LINES == 'Capacity' or LINES == 'CongestionFlow': \n", + " var_list = var_list + ['G_CAP_YCRAF', 'X_CAP_YCR']\n", + " if LINES == 'Flow' or LINES == 'CongestionFlow':\n", + " var_list = var_list + ['X_FLOW_YCRST']\n", + " if hub_display == True:\n", + " var_list = var_list + ['PRO_YCRAGFST']\n", + " if COMMODITY == 'H2':\n", + " var_list = []\n", + " if LINES == 'Capacity' or LINES == 'CongestionFlow': \n", + " var_list = var_list + ['G_CAP_YCRAF', 'XH2_CAP_YCR']\n", + " if LINES == 'Flow' or LINES == 'CongestionFlow':\n", + " var_list = var_list + ['XH2_FLOW_YCRST']\n", + " if hub_display == True:\n", + " var_list = var_list + ['PRO_YCRAGFST']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 1.4A.3 - Use function to read inputs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if filetype_input == 'gdx':\n", + " runs = list()\n", + " gdx_file_list = list()\n", + "\n", + " # directory to the input gdx file(s)\n", + " #gdx_file_list = gdx_file_list + glob.glob('./input/results/'+ market + '/*.gdx')\n", + " \n", + " gdx_file = glob.glob('./input/results/'+ market + '\\\\MainResults_' + SCENARIO + '_' + YEAR + '_' + SUBSET + '.gdx')\n", + " gdx_file = gdx_file[0]\n", + "\n", + " all_df = {varname: df for varname, df in zip(var_list,var_list)}\n", + "\n", + "\n", + " for varname, df in zip(var_list, var_list):\n", + " all_df[varname] = df_creation(gdx_file, varname)\n", + " if all_df[varname]['run'][0] not in runs:\n", + " runs.append(all_df[varname]['run'][0])\n", + "\n", + " #run_dict = dict(zip(gdx_file_list, runs) )\n", + " #all_df = dict((run_dict[key], value) for (key, value) in all_df.items())\n", + " \n", + " #Transmission capacity data\n", + " if LINES == 'Capacity' or LINES == 'CongestionFlow':\n", + " if COMMODITY == 'Electricity':\n", + " df_capacity = all_df['X_CAP_YCR']\n", + " if COMMODITY == 'H2':\n", + " df_capacity = all_df['XH2_CAP_YCR']\n", + " if COMMODITY == 'Other':\n", + " df_capacity = all_df[var_list[1]]\n", + "\n", + " #Transmission flow data\n", + " if LINES == 'Flow' or LINES == 'CongestionFlow': \n", + " if COMMODITY == 'Electricity':\n", + " df_flow = all_df['X_FLOW_YCRST']\n", + " if COMMODITY == 'H2':\n", + " df_flow = all_df['XH2_FLOW_YCRST']\n", + " if COMMODITY == 'Other':\n", + " if LINES == 'Flow':\n", + " df_flow = all_df[var_list[1]]\n", + " if LINES == 'CongestionFlow':\n", + " df_flow = all_df[var_list[2]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 1.4A.4 - Hub data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if filetype_input == 'gdx' and hub_display == True:\n", + " hub_windgen = (pd.read_csv(project_dir/'geo_files/hub_technologies.csv', sep = ',', quotechar = '\"').hub_name) \n", + " df_capgen = all_df['G_CAP_YCRAF']\n", + " if LINES == 'Flow' or LINES == 'CongestionFlow':\n", + " df_hubprod = all_df['PRO_YCRAGFST']\n", + " df_hubprod['Y'] = df_hubprod['Y'].astype(int)\n", + " df_hubprod = df_hubprod.loc[(df_hubprod['G'].isin(hub_windgen)) & (df_hubprod['TECH_TYPE'] == 'WIND-OFF') & \\\n", + " (df_hubprod['Y']==year) & (df_hubprod['SSS'] == S) & (df_hubprod['TTT']==T), ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4B1 - Read CSV files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "map_name = 'Transmission' + COMMODITY + '_' + LINES + '_' + str(year) + '_Map.html'\n", + "if filetype_input == 'csv':\n", + " generation_file = 'CapacityGeneration_'+ SCENARIO + '_' + YEAR + '_' + SUBSET + '.csv'\n", + " if COMMODITY == 'Electricity':\n", + " flow_file = 'FlowElectricityHourly_'+ SCENARIO + '_' + YEAR + '_' + SUBSET + '.csv'\n", + " transcap_file = 'CapacityElectricityTransmission_'+ SCENARIO + '_' + YEAR + '_'+ SUBSET + '.csv'\n", + " if COMMODITY == 'H2':\n", + " flow_file = 'FlowH2Hourly_'+ SCENARIO + '_' + YEAR + '_' + SUBSET + '.csv'\n", + " transcap_file = 'CapacityH2Transmission_'+ SCENARIO + '_' + YEAR + '_'+ SUBSET + '.csv'\n", + " \n", + " #Transmission capacity data\n", + " df_capacity = pd.read_csv(str(project_dir) + '/results/' + str(market) + '/' + str(transcap_file), sep = ',', quotechar = '\"') \n", + " #Transmission flow data\n", + " if LINES == 'Flow' or LINES == 'CongestionFlow':\n", + " df_flow = pd.read_csv(str(project_dir) + '/results/' + str(market) + '/' + str(flow_file), sep = ',', quotechar = '\"')\n", + "\n", + " if hub_display == True:\n", + " prod_file = 'ProductionHourly_'+ SCENARIO + '_' + YEAR + '_' + SUBSET + '.csv'\n", + " hub_windgen = (pd.read_csv(project_dir/'geo_files/hub_technologies.csv', sep = ',', quotechar = '\"').hub_name) \n", + " #Generation capacity data\n", + " df_capgen = pd.read_csv(str(project_dir) + '/results/' + str(market) + '/' + str(generation_file), sep = ',', quotechar = '\"') \n", + " if LINES == 'Flow' or LINES == 'CongestionFlow':\n", + " #Hub production data\n", + " df_hubprod = pd.read_csv(str(project_dir) + '/results/' + str(market) + '/' + str(prod_file), sep = ',', quotechar = '\"') \n", + " df_hubprod = df_hubprod.loc[(df_hubprod['G'].isin(hub_windgen)) & (df_hubprod['TECH_TYPE'] == 'WIND-OFF') & \\\n", + " (df_hubprod['Y']==year) & (df_hubprod['SSS'] == S) & (df_hubprod['TTT']==T), ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4B2 - Calibrate column names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "column_dict = {'Val':'Value', 'Y':'Year', 'C':'Country'}\n", + "if LINES == 'Capacity' or LINES == 'CongestionFlow':\n", + " df_capacity = df_capacity.rename(columns = column_dict)\n", + "if LINES == 'Flow' or LINES == 'CongestionFlow':\n", + " df_flow = df_flow.rename(columns = column_dict)\n", + "if hub_display == True:\n", + " df_capgen = df_capgen.rename(columns = column_dict)\n", + " if LINES == 'Flow' or LINES == 'CongestionFlow': \n", + " df_hubprod = df_hubprod.rename(columns = column_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2 Processing of dataframes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Replace \"EPS\" with 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Replace possible \"Eps\" with 0\n", + "df_capacity.Value=df_capacity.Value.replace('Eps', 0)\n", + "df_capacity.Value=pd.to_numeric(df_capacity.Value)\n", + "if LINES == 'Flow' or LINES == 'CongestionFlow': #Skip this cell in case LINES == 'Capacity'\n", + " df_flow.Value=df_flow.Value.replace('Eps', 0)\n", + " df_flow.Value=pd.to_numeric(df_flow.Value)\n", + "if hub_display == True:\n", + " df_capgen.Value=df_capgen.Value.replace('Eps', 0)\n", + " df_capgen.Value=pd.to_numeric(df_capgen.Value)\n", + " if LINES == 'Flow' or LINES == 'CongestionFlow':\n", + " df_hubprod.Value=df_hubprod.Value.replace('Eps', 0)\n", + " df_hubprod.Value=pd.to_numeric(df_hubprod.Value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Add Coordinates + Select Time + Convert Units" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Flows\n", + "if LINES == 'Flow' or LINES == 'CongestionFlow': #Skip this cell in case LINES == 'Capacity'\n", + " df_flow['Year'] = df_flow['Year'].astype(int)\n", + " #Keep only data from moment of interest\n", + " df_flow = df_flow.loc[df_flow['Year'] == year] \n", + " df_flow = df_flow.loc[df_flow['SSS'] == S,]\n", + " df_flow = df_flow.loc[df_flow['TTT'] == T, ]\n", + " for i,row in df_flow.iterrows():\n", + " for j in range(0,len(df_unique)):\n", + " if df_flow.loc[i,'IRRRE'] == df_unique.loc[j, 'RRR']:\n", + " df_flow.loc[i,'LatExp'] = df_unique.loc[j, 'Lat']\n", + " df_flow.loc[i,'LonExp'] = df_unique.loc[j, 'Lon']\n", + " if df_flow.loc[i,'IRRRI'] == df_unique.loc[j, 'RRR']:\n", + " df_flow.loc[i,'LatImp'] = df_unique.loc[j, 'Lat']\n", + " df_flow.loc[i,'LonImp'] = df_unique.loc[j, 'Lon']\n", + "\n", + " #Convert flow from MWh to GWh\n", + " df_flow['Value'] = df_flow['Value'] / 1000\n", + " df_flow = df_flow.reset_index(drop = True)\n", + " if len(df_flow) == 0:\n", + " print(\"Error: Timestep not in data; check year, S and T.\")\n", + " sys.exit()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Group hub data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Generation Capacities\n", + "if hub_display == True:\n", + " df_capgen['Year'] = df_capgen['Year'].astype(int)\n", + " df_capgen = df_capgen.merge(df_unique, on = 'RRR', how = 'left', left_index = True).reset_index(drop = True) #Add coordinates of each region\n", + " df_capgen = df_capgen.loc[df_capgen['Year'] == year] #Keep only data from year of interest\n", + " df_hubcap = df_capgen.loc[df_capgen['G'].isin(hub_windgen),] #Keep only hub data \n", + " df_hubcap_agg = pd.DataFrame(df_hubcap.groupby(['Year', 'Country', 'RRR', 'Lat', 'Lon'])['Value'].sum().reset_index()) #Sum all capacities (of different wind turbines) at each location\n", + " df_hubcap_agg['Radius'] = np.sqrt(df_hubcap_agg['Value'] * 1000 / hub_area / np.pi) # Create column of hub radius (in kilometres)\n", + "\n", + " if LINES == 'Flow' or LINES == 'CongestionFlow':\n", + " #Merge all relevant hub info into one dataframe\n", + " df_hubprod = pd.DataFrame(df_hubprod.groupby(['Year', 'Country', 'RRR'])['Value'].sum().reset_index()) #Sum all production (of different wind turbines) at each location\n", + " df_hubprod.Value = df_hubprod.Value/1000\n", + " df_hubprod.rename(columns = {'Value': 'prod_GWh'}, inplace = True)\n", + " df_hub = pd.merge(df_hubcap_agg, df_hubprod[['RRR', 'prod_GWh']], on = 'RRR', how = 'left', left_index = True).reset_index(drop = True) \n", + " #Display a zero instead of NaN values (i.e. if there is no production in that hour, so df_hubprod row does not exist)\n", + " df_hub.loc[df_hub.prod_GWh.isna() == True, 'prod_GWh'] = 0\n", + " else: \n", + " df_hub = df_hubcap_agg.copy()\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Prepare capacity dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Transmission Capacities\n", + "if LINES == 'Capacity' or LINES == 'CongestionFlow': #Skip this cell in case LINES == 'Flow'\n", + " df_capacity['Year'] = df_capacity['Year'].astype(int)\n", + " df_capacity = df_capacity.loc[df_capacity['Year'] == year, ].reset_index(drop = True) #Keep only data from year of interest\n", + " if exo_end == 'Total' or LINES == 'CongestionFlow':\n", + " col_keep = list(np.delete(np.array(df_capacity.columns),np.where((df_capacity.columns == 'VARIABLE_CATEGORY') | \\\n", + " (df_capacity.columns == 'Value')) )) #Create list with all columns except 'Variable_Category' and 'Value'\n", + " df_capacity = pd.DataFrame(df_capacity.groupby(col_keep)['Value'].sum().reset_index() )#Sum exogenous and endogenous capacity for each region\n", + " if exo_end == 'Endogenous' and LINES != 'CongestionFlow':\n", + " df_capacity = df_capacity.loc[df_capacity['VARIABLE_CATEGORY'] == 'ENDOGENOUS', ]\n", + " if exo_end == 'Exogenous' and LINES != 'CongestionFlow':\n", + " df_capacity = df_capacity.loc[df_capacity['VARIABLE_CATEGORY'] == 'EXOGENOUS', ]\n", + "\n", + " for i,row in df_capacity.iterrows():\n", + " for j in range(0,len(df_unique)):\n", + " if df_capacity.loc[i,'IRRRE'] == df_unique.loc[j, 'RRR']:\n", + " df_capacity.loc[i,'LatExp'] = df_unique.loc[j, 'Lat']\n", + " df_capacity.loc[i,'LonExp'] = df_unique.loc[j, 'Lon']\n", + " if df_capacity.loc[i,'IRRRI'] == df_unique.loc[j, 'RRR']:\n", + " df_capacity.loc[i,'LatImp'] = df_unique.loc[j, 'Lat']\n", + " df_capacity.loc[i,'LonImp'] = df_unique.loc[j, 'Lon']\n", + " if len(df_capacity) == 0:\n", + " print(\"Error: No capacity found. Check year and exo_end.\")\n", + " sys.exit()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.5 Add bypass coordinates for indirect lines" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if LINES == 'Capacity':\n", + " df_bypass = pd.merge(df_bypass, df_capacity[['Year', 'Country', 'IRRRE', 'IRRRI', 'UNITS', 'Value']], on = ['IRRRE', 'IRRRI'], how = 'left')\n", + " #Replace existing row by 2 bypass rows\n", + " keys = list(df_bypass.columns.values)[0:2]\n", + " i1 = df_capacity.set_index(keys).index\n", + " i2 = df_bypass.set_index(keys).index\n", + " df_capacity = df_capacity[~i1.isin(i2)] #Delete existing rows that need bypass\n", + " df_capacity = df_capacity.append(df_bypass, ignore_index = True, sort = True) #Append bypass rows\n", + " \n", + "if LINES == 'Flow' or LINES == 'CongestionFlow': #Skip this cell in case LINES == 'Capacity'\n", + " df_bypass = pd.merge(df_bypass, df_flow[['Year', 'Country', 'IRRRE', 'IRRRI', 'SSS', 'TTT', 'UNITS', 'Value']], on = ['IRRRE', 'IRRRI'], how = 'left').dropna()\n", + " #Replace existing row by 2 bypass rows\n", + " keys = list(df_bypass.columns.values)[0:2]\n", + " i1 = df_flow.set_index(keys).index\n", + " i2 = df_bypass.set_index(keys).index\n", + " df_flow = df_flow[~i1.isin(i2)]#Delete existing rows that need bypass\n", + " df_flow = df_flow.append(df_bypass, ignore_index = True, sort = True)#Append bypass rows" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6 Calculate Congestion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if LINES == 'CongestionFlow': #Skip this cell in case LINES != 'CongestionFlow'\n", + " df_flow = pd.merge(df_flow, df_capacity[['Year', 'Country', 'IRRRE', 'IRRRI', 'Value']], on = ['Year', 'Country', 'IRRRE', 'IRRRI'], how = 'left')\n", + " df_flow.rename(columns={'Value_x': 'Value', 'Value_y' : 'Capacity'}, inplace = True)\n", + " df_flow['Congestion'] = df_flow['Value'] / df_flow['Capacity'] * 100\n", + "\n", + " #Create color codes for congestion of lines\n", + " df_flow['color'] = pd.cut(df_flow['Congestion'], bins = flowline_breaks, labels = flowline_color )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.7 One direction capacity lines" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#When capacity is not the same in both directions, display one:\n", + "for i,row in df_capacity.iterrows():\n", + " for k,row in df_capacity.iterrows():\n", + " if (df_capacity.loc[k,'IRRRE'] == df_capacity.loc[i,'IRRRI']) & (df_capacity.loc[k,'IRRRI'] == df_capacity.loc[i,'IRRRE']) & (df_capacity.loc[k,'Value'] != df_capacity.loc[i,'Value']):\n", + " df_capacity.loc[i,'Value'] = df_capacity.loc[k,'Value']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.8 Define line centers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Define centre of each transmission line\n", + "if LINES == 'Flow' or LINES == 'CongestionFlow': #Skip this cell in case LINES == 'Capacity'\n", + " df_flow['LatMid'] = (df_flow['LatImp'] + df_flow['LatExp']) /2\n", + " df_flow['LonMid'] = (df_flow['LonImp'] + df_flow['LonExp']) /2\n", + "if LINES == 'Capacity' or LINES == 'CongestionFlow': #Skip this cell in case LINES == 'Flow'\n", + " df_capacity['LatMid'] = (df_capacity['LatImp'] + df_capacity['LatExp']) /2\n", + " df_capacity['LonMid'] = (df_capacity['LonImp'] + df_capacity['LonExp']) /2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Create Map Features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Create map" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Create map \n", + "map_center = [55.220228, 10.419778]\n", + "m = folium.Map(location= map_center, zoom_start=5, tiles='')\n", + "#Add background layers (sea, regions in model, countries outside of model)\n", + "folium.Polygon(locations = [[-90,-180], [90,-180], [90,180], [-90,180]], color = background_color, fill_color = background_color, opacity = 1, fill_opacity = 1 ).add_to(m) #Background\n", + "\n", + "for region in layers_in: \n", + " folium.GeoJson(data = layers_in[region], name = 'regions_in', \\\n", + " style_function = lambda x:{'fillColor': regions_model_color, 'fillOpacity': 0.5, 'color': regions_model_color, 'weight':1}).add_to(m) #Regions within model\n", + "for region in layers_out: \n", + " folium.GeoJson(data = layers_out[region], name = 'regions_out', \\\n", + " style_function = lambda x:{'fillColor': regions_ext_color, 'fillOpacity': 0.5, 'color': regions_ext_color, 'weight':1}).add_to(m) #Neighbouring countries\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Create background hub size" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if hub_display == True:\n", + " if background_hubsize == True:\n", + " for i,row in df_hub.iterrows():\n", + " folium.Circle(\n", + " location=[df_hub.loc[i,'Lat'], df_hub.loc[i,'Lon']],\n", + " popup=df_hub.loc[i,'RRR'],\n", + " radius = df_hub.loc[i,'Radius']*1000,\n", + " color = hub_background_color,\n", + " opacity = 0,\n", + " fill=True,\n", + " fill_color = hub_background_color,\n", + " fill_opacity = hub_area_opacity\n", + " ).add_to(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 Add lines" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Add capacity lines\n", + "if LINES == 'Capacity':\n", + " for i,row in df_capacity.iterrows():\n", + " folium.PolyLine(([df_capacity.loc[i,'LatExp'], df_capacity.loc[i,'LonExp']], \\\n", + " [df_capacity.loc[i,'LatImp'],df_capacity.loc[i,'LonImp']]), \\\n", + " color=capline_color, line_cap = 'butt', weight=df_capacity.loc[i,'Value']/line_width_constant, opacity=1).add_to(m) \n", + " if df_capacity.loc[i,'Value'] > label_min:\n", + " if line_decimals == 0:\n", + " folium.Marker(location=[df_capacity.loc[i,'LatMid'], df_capacity.loc[i,'LonMid']],\n", + " icon=DivIcon(\n", + " icon_size=(150,36), \n", + " icon_anchor=(11,7),\n", + " html='
{}
'.format(font_line, line_text, \\\n", + " df_capacity.loc[i,'Value'].round(line_decimals).astype(int)))).add_to(m)\n", + " else: \n", + " folium.Marker(location=[df_capacity.loc[i,'LatMid'], df_capacity.loc[i,'LonMid']],\n", + " icon=DivIcon(\n", + " icon_size=(150,36), \n", + " icon_anchor=(11,7),\n", + " html='
{}
'.format(font_line, line_text, \\\n", + " round(df_capacity.loc[i,'Value'],line_decimals)))).add_to(m)\n", + "#Add flows (single color) \n", + "if LINES == 'Flow':\n", + " attr = {'font-weight': 'bold', 'font-size': '24'}\n", + " for i,row in df_flow.iterrows():\n", + " flow = folium.PolyLine(([df_flow.loc[i,'LatExp'], df_flow.loc[i,'LonExp']], \\\n", + " [df_flow.loc[i,'LatImp'],df_flow.loc[i,'LonImp']]), \\\n", + " color=capline_color, line_cap = 'butt', weight=df_flow.loc[i,'Value']/line_width_constant, opacity=1).add_to(m) \n", + " plugins.PolyLineTextPath(flow, '\\u2192', repeat=False ,center = True, offset=6, orientation = -90, \\\n", + " attributes=attr).add_to(m) #Arrow\n", + " if df_flow.loc[i,'Value'] > label_min:\n", + " if line_decimals == 0:\n", + " folium.Marker(location=[df_flow.loc[i,'LatMid'], df_flow.loc[i,'LonMid']],\n", + " icon=DivIcon(\n", + " icon_size=(150,36), \n", + " icon_anchor=(11,7),\n", + " html='
{}
'.format(font_line, line_text, \\\n", + " df_flow.loc[i,'Value'].round(line_decimals).astype(int)))).add_to(m)\n", + " else: \n", + " folium.Marker(location=[df_flow.loc[i,'LatMid'], df_flow.loc[i,'LonMid']],\n", + " icon=DivIcon(\n", + " icon_size=(150,36), \n", + " icon_anchor=(11,7),\n", + " html='
{}
'.format(font_line, line_text, \\\n", + " round(df_flow.loc[i,'Value'],line_decimals)))).add_to(m) \n", + "#Add flows (color based on congestion)\n", + "if LINES == 'CongestionFlow':\n", + " attr = {'font-weight': 'bold', 'font-size': '24'}\n", + " for i,row in df_flow.iterrows():\n", + " flow = folium.PolyLine(([df_flow.loc[i,'LatExp'], df_flow.loc[i,'LonExp']], \\\n", + " [df_flow.loc[i,'LatImp'],df_flow.loc[i,'LonImp']]), \\\n", + " color=df_flow.loc[i,'color'], line_cap = 'butt', weight=df_flow.loc[i,'Value']/line_width_constant, opacity=1).add_to(m) \n", + " plugins.PolyLineTextPath(flow, '\\u2192', repeat=False ,center = True, offset=6, orientation = -90, \\\n", + " attributes=attr).add_to(m) #Arrow\n", + " if df_flow.loc[i,'Value'] > label_min:\n", + " if line_decimals == 0:\n", + " folium.Marker(location=[df_flow.loc[i,'LatMid'], df_flow.loc[i,'LonMid']],\n", + " icon=DivIcon(\n", + " icon_size=(150,36), \n", + " icon_anchor=(11,7),\n", + " html='
{}
'.format(font_line, line_text, \\\n", + " df_flow.loc[i,'Value'].round(line_decimals).astype(int)))).add_to(m)\n", + " else: \n", + " folium.Marker(location=[df_flow.loc[i,'LatMid'], df_flow.loc[i,'LonMid']],\n", + " icon=DivIcon(\n", + " icon_size=(150,36), \n", + " icon_anchor=(11,7),\n", + " html='
{}
'.format(font_line, line_text, \\\n", + " round(df_flow.loc[i,'Value'],line_decimals)))).add_to(m) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4 Add region names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Add region names\n", + "for i,row in df_region.loc[df_region['Display']==1, ].iterrows():\n", + " folium.Marker(location=[df_region.loc[i,'Lat'], df_region.loc[i,'Lon']],\n", + " icon=DivIcon(\n", + " icon_size=(150,36), \n", + " icon_anchor=(7,7),\n", + " html='
{}
'.format(font_region, region_text, df_region.loc[i,'RRR']))).add_to(m) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.5 Add hubs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Add hub capacities as bubbles\n", + "if hub_display == True:\n", + " if LINES == 'Capacity': \n", + " for i,row in df_hub.iterrows():\n", + " folium.CircleMarker(\n", + " location=[df_hub.loc[i,'Lat'], df_hub.loc[i,'Lon']],\n", + " popup=df_hub.loc[i,'RRR'],\n", + " radius = hub_size,\n", + " color= hub_color,\n", + " opacity = 0,\n", + " fill=True,\n", + " fill_color= hub_color,\n", + " fill_opacity = 1\n", + " ).add_to(m)\n", + "\n", + " if hub_decimals == 0:\n", + " folium.Marker(location=[df_hub.loc[i,'Lat'], df_hub.loc[i,'Lon']],\n", + " icon=DivIcon(\n", + " icon_size=(150,36), \n", + " icon_anchor=(7,9),\n", + " html='
{}
'.format(font_hub, hub_text, df_hub.loc[i,'Value'].round(hub_decimals).astype(int)))).add_to(m)\n", + " else:\n", + " folium.Marker(location=[df_hub.loc[i,'Lat'], df_hub.loc[i,'Lon']],\n", + " icon=DivIcon(\n", + " icon_size=(150,36), \n", + " icon_anchor=(7,9),\n", + " html='
{}
'.format(font_hub, hub_text, round(df_hub.loc[i,'Value'], hub_decimals)))).add_to(m) \n", + "\n", + " if LINES == 'Flow' or LINES == 'CongestionFlow':\n", + " for i,row in df_hub.iterrows():\n", + " folium.CircleMarker(\n", + " location=[df_hub.loc[i,'Lat'], df_hub.loc[i,'Lon']],\n", + " popup=df_hub.loc[i,'RRR'],\n", + " radius = hub_size,\n", + " color= hub_color,\n", + " opacity = 0,\n", + " fill=True,\n", + " fill_color= hub_color,\n", + " fill_opacity = 1\n", + " ).add_to(m)\n", + "\n", + " if hub_decimals == 0:\n", + " folium.Marker(location=[df_hub.loc[i,'Lat'], df_hub.loc[i,'Lon']],\n", + " icon=DivIcon(\n", + " icon_size=(150,36), \n", + " icon_anchor=(7,9),\n", + " html='
{}
'.format(font_hub, hub_text, df_hub.loc[i,'prod_GWh'].round(hub_decimals).astype(int)))).add_to(m)\n", + " else:\n", + " folium.Marker(location=[df_hub.loc[i,'Lat'], df_hub.loc[i,'Lon']],\n", + " icon=DivIcon(\n", + " icon_size=(150,36), \n", + " icon_anchor=(7,9),\n", + " html='
{}
'.format(font_hub, hub_text, round(df_hub.loc[i,'prod_GWh'], hub_decimals)))).add_to(m) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.6 Add Legend" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "color_keys = ['color1', 'color2', 'color3']\n", + "color_dict = dict(zip(color_keys, flowline_color))\n", + "legend_keys = ['item1', 'item2', 'item3']\n", + "legend_dict = dict(zip(legend_keys, legend_values))\n", + "\n", + "if LINES == 'CongestionFlow':\n", + " from branca.element import Template, MacroElement\n", + "\n", + " template = \"\"\"\n", + " {% macro html(this, kwargs) %}\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " jQuery UI Draggable - Default functionality\n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "
\n", + "\n", + "
Congestion rate
\n", + "
\n", + "
    \n", + "
  • item1
    • \n", + "
  • item2
    • \n", + "
  • item3
    • \n", + "\n", + "
\n", + "
\n", + "
\n", + "\n", + " \n", + " \n", + "\n", + " \n", + " {% endmacro %}\"\"\"\n", + " for key in color_dict.keys():\n", + " template = template.replace(key, color_dict[key])\n", + " for key in legend_dict.keys():\n", + " template = template.replace(key, legend_dict[key])\n", + " \n", + " macro = MacroElement()\n", + " macro._template = Template(template)\n", + "\n", + " m.get_root().add_child(macro)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4 Save Output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make Transmission_Map output folder\n", + "if not os.path.isdir('output/Transmission_Map/' + LINES + '/' + SCENARIO + '/' + market):\n", + " os.makedirs('output/Transmission_Map/' + LINES + '/' + SCENARIO + '/' + market)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output_dir = 'output/Transmission_Map/' + LINES + '/' + SCENARIO + '/' + market\n", + "m.save(output_dir + '/' + map_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5 Display Map" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "m" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 4c1efe4bd7ed05418d56f7c049f61f06151b468a Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Sat, 22 Jun 2024 18:22:50 +0200 Subject: [PATCH 04/21] Created test notebook --- .../master_merge_tests/merge_tests.ipynb | 354 +++++++++++++++++- 1 file changed, 342 insertions(+), 12 deletions(-) diff --git a/base/auxils/master_merge_tests/merge_tests.ipynb b/base/auxils/master_merge_tests/merge_tests.ipynb index 401a5c77..9dabec9c 100644 --- a/base/auxils/master_merge_tests/merge_tests.ipynb +++ b/base/auxils/master_merge_tests/merge_tests.ipynb @@ -6,9 +6,13 @@ "source": [ "# Test Runs for Merging Changes\n", "\n", - "This notebook is used for testing changes made to the master branch, by running the models in base/model (which should be identical to the master branch) and change/model (which should be the model run with your changes) \n", + "This notebook is used for testing changes made to the master branch, by running the models in base/model (which should be identical to the master branch) and changes/model (which should be the model run with your changes) \n", "\n", - "Please follow the procedure described in the [readme](../../../README.md)!\n", + "Please follow the procedure described in the [readme](../../../README.md), and be critical if the tests performed here is enough to verify and validate the changes made. Furthermore, note the following:\n", + "- Make sure that the choice of addons is comparable, unless the change is a new addon\n", + "- If the changes are in files that by default are loaded in the base scenario (such as addons) you will need to apply the changes through options. Or, more simply, run the four tests manually in other folders and copy+paste the relevant MainResults to the paths expected by this script and append _lowtemphighspace and _hightemplowspace to the respective files. Skip to the analysis section below in that case.\n", + "- If a GamsExecutionError occur in the cell running Balmorel, check the _gams_py_gjo0.lst file, or maybe try to run the scenario in GAMS studio for easier debugging\n", + "- All tests may take around 20 minutes.\n", "\n", "The [pybalmorel](https://pypi.org/project/pybalmorel/) package is required to run this notebook. In a terminal, create a new python environment, activate it and install version 0.2.2 by running the following command:\n", "```\n", @@ -19,21 +23,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ - "from pybalmorel import MainResults\n", + "from pybalmorel import MainResults, IncFile\n", "from gams import GamsWorkspace\n", + "import pandas as pd\n", + "import numpy as np\n", "import os\n", "\n", - "### 1.1 Running Balmorel\n", + "## Scenario and test case names\n", "scenarios = ['base', 'changes']\n", - "for scenario in scenarios:\n", - " ws = GamsWorkspace(working_directory='../../../%s/model'%scenario)\n", + "test_cases = ['lowtemphighspace', 'hightemplowspace']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Balmorel\n", "\n", - " job = ws.add_job_from_file(os.path.join(os.path.abspath('../../../%s/model'%scenario), 'Balmorel'))\n", - " out = job.run()" + "The following section runs the base and changes scenario at different resolutions:\n", + "1. Low temporal resolution (Two seasons with four hours each) and high spatial resolution (the existing resolution in each C file)\n", + "2. High temporal resolution (Four seasons with all hours) and low spatial resolution (only Denmark and Norway) " ] }, { @@ -42,9 +55,326 @@ "metadata": {}, "outputs": [], "source": [ - "### 1.2 Load Results\n", - "mr = MainResults(files=['MainResults.gdx']*2,\n", - " paths=['../../../%s/model'%scenario for scenario in scenarios])" + "### 1.0 Prepare .inc-file formats for test cases\n", + "C = IncFile(name='C',\n", + " prefix=\"SET C(CCC) 'Countries in the simulation'\\n/\\n\",\n", + " suffix=\"\\n/;\",\n", + " )\n", + "Y = IncFile(name='Y',\n", + " prefix=\"SET Y(YYY) 'Years in the simulation'\\n/\\n\",\n", + " suffix=\"\\n/;\",\n", + " )\n", + "S = IncFile(name='S',\n", + " prefix=\"SET S(SSS) 'Seasons in the simulation'\\n/\\n\",\n", + " suffix=\"\\n/;\",\n", + " )\n", + "T = IncFile(name='T',\n", + " prefix=\"SET T(TTT) 'Time periods within a season in the simulation'\\n/\\n\",\n", + " suffix=\"\\n/;\",\n", + " )\n", + "\n", + "### 1.1 Running Balmorel Test Cases\n", + "for scenario in scenarios:\n", + " for test_case in test_cases:\n", + " \n", + " # Set resolutions\n", + " C.path ='../../../%s/data'%scenario\n", + " Y.path ='../../../%s/data'%scenario\n", + " S.path ='../../../%s/data'%scenario\n", + " T.path ='../../../%s/data'%scenario\n", + " if test_case == 'lowtemphighspace':\n", + " # keep the country resolution of the base and change in this case\n", + " Y.body = \"2030, 2050\"\n", + " S.body = \"S01, S26\"\n", + " T.body = \"T073, T079, T085, T091\"\n", + " \n", + " # rename original temporal .inc-files so they're not lost\n", + " try:\n", + " os.rename('../../../%s/data/Y.inc'%scenario,\n", + " '../../../%s/data/Y_old.inc'%scenario)\n", + " os.rename('../../../%s/data/S.inc'%scenario,\n", + " '../../../%s/data/S_old.inc'%scenario)\n", + " os.rename('../../../%s/data/T.inc'%scenario,\n", + " '../../../%s/data/T_old.inc'%scenario)\n", + " except (FileNotFoundError, FileExistsError):\n", + " print('Possibly already renamed old resolution .inc-files')\n", + " \n", + " elif test_case == 'hightemplowspace':\n", + " C.body = \"DENMARK, NORWAY\"\n", + " Y.body = \"2030, 2050\"\n", + " S.body = \"S01, S14, S27, S40\"\n", + " T.body = \"T001*T168\"\n", + " \n", + " # rename original C .inc-file so it's not lost\n", + " try:\n", + " os.rename('../../../%s/data/C.inc'%scenario,\n", + " '../../../%s/data/C_old.inc'%scenario)\n", + " except (FileNotFoundError, FileExistsError):\n", + " print('Possibly already renamed old resolution .inc-files')\n", + " \n", + " C.save()\n", + " Y.save()\n", + " S.save()\n", + " T.save()\n", + " \n", + " # Run Balmorel\n", + " ws = GamsWorkspace(working_directory='../../../%s/model'%scenario)\n", + "\n", + " job = ws.add_job_from_file(os.path.join(os.path.abspath('../../../%s/model'%scenario), 'Balmorel'))\n", + " out = job.run()\n", + " \n", + " # Check feasibility\n", + " with open('../../../%s/model/_gams_py_gjo0.lst'%scenario, 'r') as f:\n", + " output = pd.Series(f.readlines())\n", + "\n", + " output = output[output.str.find('LP status') != -1] # Find all status\n", + " all_feasible = output[output.str.find('infeasible') != -1].empty # Check if none are infeasible\n", + " if not(all_feasible):\n", + " raise Exception('Model run infeasible!')\n", + " \n", + " # Rename MainResults\n", + " try:\n", + " os.rename('../../../%s/model/MainResults.gdx'%scenario,\n", + " '../../../%s/model/MainResults_%s.gdx'%(scenario, test_case))\n", + " except FileExistsError:\n", + " print('Previous run existed, overwriting')\n", + " os.replace('../../../%s/model/MainResults.gdx'%scenario,\n", + " '../../../%s/model/MainResults_%s.gdx'%(scenario, test_case))\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis\n", + "\n", + "General results are compared between scenarios for each test case and KPI's are calculated on production of commodities and capacities. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Warning: The GAMS version (45.5.0) differs from the API version (45.7.0).\n" + ] + } + ], + "source": [ + "scenarios = ['base', 'base_changes']\n", + "\n", + "### 2.0 Load Results\n", + "mr = MainResults(files=['MainResults_%s.gdx'%test_case for test_case in test_cases]*2,\n", + " paths=['../../../%s/model'%scenario for scenario in scenarios for i in range(2) ],\n", + " scenario_names=['%s_%s'%(scenario, test_case) for scenario in scenarios for test_case in test_cases])" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All production [TWh]:\n", + "---------------------\n", + " Scenario base_changes_hightemplowspace base_changes_lowtemphighspace base_hightemplowspace base_lowtemphighspace \n", + "Commodity ELECTRICITY HEAT HYDROGEN ELECTRICITY HEAT HYDROGEN ELECTRICITY HEAT HYDROGEN ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 NaN NaN NaN 204.885725 141.036449 31.666812 NaN NaN NaN 146.877826 97.535908 7.353390\n", + "2050 251.124885 140.016999 53.919089 246.528420 143.795472 52.446507 180.074473 95.697868 30.731138 179.786263 97.253493 30.801129 \n", + "\n", + "\n", + "Difference in production in TWh between base and base_changes in for lowtemphighspace \n", + "-------------------------------------------------------------\n", + "\n", + "Commodity ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 58.007899 43.500540 24.313422\n", + "2050 66.742156 46.541979 21.645378\n", + "\n", + "\n", + "Difference in production in TWh between base and base_changes in for hightemplowspace \n", + "-------------------------------------------------------------\n", + "\n", + "Commodity ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 NaN NaN NaN\n", + "2050 71.050412 44.319131 23.187951\n", + "\n" + ] + } + ], + "source": [ + "### 2.1 Calculate Difference in capduction \n", + "pro = mr.get_result('PRO_YCRAGF').pivot_table(index='Year',\n", + " columns=['Scenario', 'Commodity'],\n", + " values='Value',\n", + " aggfunc='sum')\n", + "\n", + "print('All production [TWh]:\\n---------------------\\n', pro.to_string(), '\\n')\n", + "for test_case in test_cases:\n", + " print('\\nDifference in production in TWh between %s and %s in for %s'%tuple(scenarios + [test_case]),\n", + " '\\n-------------------------------------------------------------\\n')\n", + " print((pro['%s_%s'%(scenarios[1], test_case)] - pro['%s_%s'%(scenarios[0], test_case)]).to_string() + '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All generation capacities [GW]:\n", + "-------------------------------\n", + " Scenario base_changes_hightemplowspace base_changes_lowtemphighspace base_hightemplowspace base_lowtemphighspace \n", + "Commodity ELECTRICITY HEAT HYDROGEN ELECTRICITY HEAT HYDROGEN BIOMETHANE ELECTRICITY HEAT HYDROGEN BIOMETHANE ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 0.000000 0.000000 0.000000 62.22878 27.296902 4.084225 0.0 0.000000 0.000000 0.000000 0.0 35.418515 17.154851 0.877514\n", + "2050 92.052875 30.571459 7.951106 84.53370 25.208215 7.378618 0.0 54.292423 20.573951 4.317686 0.0 52.441727 16.601460 4.571381 \n", + "\n", + "\n", + "Difference in generation capacities in GW between base and base_changes in for lowtemphighspace: \n", + "-------------------------------------------------------------\n", + "Commodity BIOMETHANE ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 NaN 26.810265 10.142051 3.206710\n", + "2050 NaN 32.091973 8.606754 2.807238\n", + "\n", + "\n", + "Difference in generation capacities in GW between base and base_changes in for hightemplowspace: \n", + "-------------------------------------------------------------\n", + "Commodity BIOMETHANE ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 NaN 0.000000 0.000000 0.00000\n", + "2050 NaN 37.760452 9.997508 3.63342\n", + "\n" + ] + } + ], + "source": [ + "### 2.2 Calculate Difference in Generation Capacities\n", + "cap = mr.get_result('G_CAP_YCRAF')\n", + "cap = cap[(cap.Technology != 'INTRASEASONAL-ELECT-STORAGE') &\\\n", + " (cap.Technology != 'INTRASEASONAL-HEAT-STORAGE') &\\\n", + " (cap.Technology != 'INTERSEASONAL-HEAT-STORAGE') &\\\n", + " (cap.Technology != 'H2-STORAGE')].pivot_table(index='Year',\n", + " columns=['Scenario', 'Commodity'],\n", + " values='Value',\n", + " aggfunc='sum').fillna(0)\n", + "\n", + "print('All generation capacities [GW]:\\n-------------------------------\\n', cap.to_string(), '\\n')\n", + "for test_case in test_cases:\n", + " print('\\nDifference in generation capacities in GW between %s and %s in for %s:'%tuple(scenarios + [test_case]),\n", + " '\\n-------------------------------------------------------------')\n", + " print((cap['%s_%s'%(scenarios[1], test_case)] - cap['%s_%s'%(scenarios[0], test_case)]).to_string() + '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All storage capacities [GWh]:\n", + "-----------------------------\n", + " Scenario base_changes_hightemplowspace base_changes_lowtemphighspace base_hightemplowspace base_lowtemphighspace \n", + "Commodity ELECTRICITY HEAT HYDROGEN ELECTRICITY HEAT HYDROGEN ELECTRICITY HEAT ELECTRICITY HEAT\n", + "Year \n", + "2030 0.0 0.000000 0.000000 82224.0 395.604526 238.621993 0.0 0.000000 82224.0 671.524689\n", + "2050 82224.0 500.063685 342.978697 82224.0 550.614625 238.621993 82224.0 263.948093 82224.0 692.441894 \n", + "\n", + "\n", + "Difference in storage capacities in GWh between base and base_changes in for lowtemphighspace: \n", + "-------------------------------------------------------------\n", + "Commodity ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 0.0 -275.920163 NaN\n", + "2050 0.0 -141.827269 NaN\n", + "\n", + "\n", + "Difference in storage capacities in GWh between base and base_changes in for hightemplowspace: \n", + "-------------------------------------------------------------\n", + "Commodity ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 0.0 0.000000 NaN\n", + "2050 0.0 236.115593 NaN\n", + "\n" + ] + } + ], + "source": [ + "### 2.3 Calculate Difference in Storage Capacities\n", + "cap = mr.get_result('G_STO_YCRAF').pivot_table(index='Year',\n", + " columns=['Scenario', 'Commodity'],\n", + " values='Value',\n", + " aggfunc='sum').fillna(0)\n", + "\n", + "print('All storage capacities [GWh]:\\n-----------------------------\\n', cap.to_string(), '\\n')\n", + "for test_case in test_cases:\n", + " print('\\nDifference in storage capacities in GWh between %s and %s in for %s:'%tuple(scenarios + [test_case]),\n", + " '\\n-------------------------------------------------------------')\n", + " print((cap['%s_%s'%(scenarios[1], test_case)] - cap['%s_%s'%(scenarios[0], test_case)]).to_string() + '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All electricity transmission capacities [GW]:\n", + "---------------------------------------------\n", + " Scenario base_changes_hightemplowspace base_changes_lowtemphighspace base_hightemplowspace base_lowtemphighspace\n", + "Year \n", + "2030 0.00000 11.077000 0.000000 15.069722\n", + "2050 18.98553 14.256002 15.986844 15.069722 \n", + "\n", + "\n", + "Difference in electricity transmission capacities in GW between base and base_changes in for lowtemphighspace: \n", + "-------------------------------------------------------------\n", + "Year\n", + "2030 -3.992722\n", + "2050 -0.813720\n", + "\n", + "\n", + "Difference in electricity transmission capacities in GW between base and base_changes in for hightemplowspace: \n", + "-------------------------------------------------------------\n", + "Year\n", + "2030 0.000000\n", + "2050 2.998686\n", + "\n" + ] + } + ], + "source": [ + "### 2.4 Calculate Difference in Transmission Capacities\n", + "cap = mr.get_result('X_CAP_YCR').pivot_table(index='Year',\n", + " columns=['Scenario'],\n", + " values='Value',\n", + " aggfunc=lambda x: np.sum(x)/2).fillna(0) # Remember to divide by two\n", + "\n", + "print('All electricity transmission capacities [GW]:\\n---------------------------------------------\\n', cap.to_string(), '\\n')\n", + "for test_case in test_cases:\n", + " print('\\nDifference in electricity transmission capacities in GW between %s and %s in for %s:'%tuple(scenarios + [test_case]),\n", + " '\\n-------------------------------------------------------------')\n", + " print((cap['%s_%s'%(scenarios[1], test_case)] - cap['%s_%s'%(scenarios[0], test_case)]).to_string() + '\\n')" ] } ], From 74712f05640f75de80775795faea320392e6ac15 Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Wed, 26 Jun 2024 18:30:03 +0200 Subject: [PATCH 05/21] Merged Ioannis changes while considering Juan's improvements past Ioannis' version --- .gitignore | 5 + README.md | 12 +- base/addons/_hooks/acronyms.inc | 15 - .../addons/hydrogen/bb4/hydrogen_balbase4.inc | 11 + .../hydrogen/bb4/hydrogen_eqndecdef.inc | 579 +-- .../hydrogen/bb4/hydrogen_ipardecdef.inc | 23 + .../hydrogen/bb4/hydrogen_isetdecdef.inc | 1 - .../hydrogen/bb4/hydrogen_pardeclare.inc | 5 +- .../hydrogen/bb4/hydrogen_pardefine.inc | 6 + base/addons/hydrogen/bb4/hydrogen_qeeq.inc | 2 +- .../hydrogen/bb4/hydrogen_vardeclare.inc | 7 + base/addons/hydrogen/hydrogen_acronyms.inc | 2 +- base/addons/hydrogen/hydrogen_eqndecdef.inc | 2 - base/addons/hydrogen/hydrogen_isetdecdef.inc | 2 - base/addons/hydrogen/hydrogen_pardeclare.inc | 2 - base/addons/hydrogen/hydrogen_pardefine.inc | 5 - .../annuity_calculation.gms | 4 + base/model/Balmorel.gms | 420 +-- base/model/Balmorelbb4.inc | 11 +- base/model/balcomline.opt | 2 +- base/model/balopt.opt | 58 +- base/model/cplex.op4 | 2 + base/output/OUTPUT_SUMMARY.inc | 3356 +++++++++-------- 23 files changed, 2326 insertions(+), 2206 deletions(-) delete mode 100644 base/addons/_hooks/acronyms.inc diff --git a/.gitignore b/.gitignore index 3d8b6b5a..c0ae047f 100644 --- a/.gitignore +++ b/.gitignore @@ -19,3 +19,8 @@ *.gsp base/data/.gitignore base/data/README.md +*.lst +*.log +*.lxi +*.pf +%batfileMERGEWITHBASE% \ No newline at end of file diff --git a/README.md b/README.md index d54bcc95..19f87b12 100644 --- a/README.md +++ b/README.md @@ -29,14 +29,15 @@ The DTU Balmorel team has decided on the following procedure for merging new cha 2. Create a new branch from master, which will be used as a temporary branch to do all the changes 3. Make the desired changes in a new scenario: - a. Add a new scenario called 'changes', which result in the following folder structure: + a. Add a new scenario called 'changes', which the following files and folder structure (copy Balmorel.gms and cplex.op4 from base/model if nothing is to be changed in these files): Balmorel ├── base ├── changes + │ ├── data │ └── model - │ │ ├── cplex.op4 - │ │ └── Balmorel.gms + │ ├── cplex.op4 + │ └── Balmorel.gms ├── simex ├── README.md ├── .gitignore @@ -44,6 +45,7 @@ The DTU Balmorel team has decided on the following procedure for merging new cha b. Keep the base scenario identical to the current master and apply all changes to the 'changes' scenario -4. Run the [test script](base/auxils/master_merge_tests/merge_tests.ipynb) and make sure that the model stays feasible and that you verify the changes -5. Make a pull request and report the changes made with the KPI outputs from the tests in 4. in ... (todo: check where it makes sense to put these descriptions, as simple as possible) +4. Run the [test script](base/auxils/master_merge_tests/merge_tests.ipynb), make sure that the model stays feasible and that the results make sense +5. Consider critically if the tests performed in 4. is enough to verify and validate the changes made +6. Make a pull request with the changes and report them in general terms, including the KPI outputs from the tests in 4. in ... (todo: check where it makes sense to put these descriptions, as simple as possible) diff --git a/base/addons/_hooks/acronyms.inc b/base/addons/_hooks/acronyms.inc deleted file mode 100644 index b758abcb..00000000 --- a/base/addons/_hooks/acronyms.inc +++ /dev/null @@ -1,15 +0,0 @@ -* Location of acronyms used in add-ons -$ifi %BB4%==yes $goto beginbb4 - -$ifi %HYDROGEN%==yes $include "../../base/addons/hydrogen/hydrogen_acronyms.inc"; -$ifi not %BB4%==yes $goto endoffile - -$label beginbb4 - -$ifi not %bb4%==yes $goto endoffile - -$ifi %HYDROGEN%==yes $include "../../base/addons/hydrogen/bb4/hydrogen_acronyms.inc"; - -$label endoffile - - diff --git a/base/addons/hydrogen/bb4/hydrogen_balbase4.inc b/base/addons/hydrogen/bb4/hydrogen_balbase4.inc index 358fa619..f1e44269 100644 --- a/base/addons/hydrogen/bb4/hydrogen_balbase4.inc +++ b/base/addons/hydrogen/bb4/hydrogen_balbase4.inc @@ -10,6 +10,7 @@ QHYDROGEN_STOMAXUNLD QHYDROGEN_STOVOL QHYDROGEN_GCH4KT_UP +$ifi %COMBTECH%==yes QHYDROGENGKE_UP_ADD QBIOMETHANE_EQ QBIOMETHANE_STOVOLTS QGKBIOMETHANE_UP @@ -17,6 +18,7 @@ QGKBIOGASMETHANATION_UP + QXH2K_UP $ifi %H2TransInvest%==yes QXH2KNACCUMNET $ifi %H2TransInvest%==yes QXH2KNACCUMNET_NGTOH2 @@ -26,3 +28,12 @@ $ifi %H2TransInvest%==yes QXH2KMAX_NGTOH2 $ifi %H2TransInvest%==yes QVXH2KN_ES $ifi %H2TransInvest%==yes QXH2LAMBDA_ES +* Flexible hydrogen demand in space by Ioannis +* If-statements here for flexible demand in space option +QSYNTHETIC_EQ_PRODUCTION +QSYNTHETIC_EQ_SHIFT +QSYNTHETIC_EQ_SHIFT_UP +QSYNTHETIC_EQ_SHIFT_DOWN +QSYNTHETIC_EQ_SHIFT_BALANCE_MOVE +QSYNTHETIC_EQ_SHIFT_BALANCE_OVERALL + diff --git a/base/addons/hydrogen/bb4/hydrogen_eqndecdef.inc b/base/addons/hydrogen/bb4/hydrogen_eqndecdef.inc index 260a6afb..a7405e60 100644 --- a/base/addons/hydrogen/bb4/hydrogen_eqndecdef.inc +++ b/base/addons/hydrogen/bb4/hydrogen_eqndecdef.inc @@ -1,257 +1,322 @@ -Equations - QHYDROGEN_HGEHTOH2(Y,AAA,G,S,T) 'Link heat and electricity demand of technologies consuming heat and electricity to produce H2' - QHYDROGEN_HGETOHH2(Y,AAA,G,S,T) 'Link electricity demand of technologies consuming electricity to produce H2 and heat' - QHYDROGEN_H2TOEH(Y,AAA,G,S,T) 'Link heat and electricity in technologies consuming hydrogen and producing heat and electricity' - QHYDROGEN_GCH4KT_UP(Y,AAA,G,S,T) 'Hydrogen production of steam reforming plants restricted by capacity' - QHYDROGEN_STOVOL(Y,AAA,G,S,T) 'Dynamic equation hydrogen storage' - QHYDROGEN_STOMAXCON(Y,AAA,G,S,T) 'Maximum hydrogen storage capacity' - QHYDROGEN_STOMAXLOAD(Y,AAA,G,S,T) 'Maximum loading capacity of hydrogen storage' - QHYDROGEN_STOMAXUNLD(Y,AAA,G,S,T) 'Maximum unloading capacity of hydrogen storage' - QHYDROGEN_EQ(Y,RRR,S,T) 'Hydrogen production equal demand' - QHYDROGEN_EQ_AREA(Y,AAA,S,T) 'Hydrogen production equals demand at an areal level' - QHYDROGENGKE_UP_ADD(Y,AAA,G,S,T) 'Capacity adjustment constraint on electricity generation from biogas upgraded plants (MW)' - QBIOMETHANE_EQ(Y,S,T) 'Biomethane production equals demand on international level in each year' - QBIOMETHANE_STOVOLTS(Y,S,T) 'Dynamic equation biomethane storage' - QGKBIOGASUPGRADING_UP(Y,AAA,G,S,T) 'Maximum generation of biomethane from biogas upgrading restricted by capacity (MW)' - QGKBIOGASMETHANATION_UP(Y,AAA,G,S,T) 'Maximum generation of biomethane via biogas methanation restricted by capacity (MW)' - QGKBIOMETHANE_UP(Y,AAA,G,S,T) 'Maximum generation of SNG restricted by capacity (MW)' -; - - - -QHYDROGEN_HGEHTOH2(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GEHTOH2),IS3,T)$GDATA(IHYDROGEN_GEHTOH2,'GDCB').. - - VGE_T(IY411,IA,IHYDROGEN_GEHTOH2,IS3,T) =E= VGH_T(IY411,IA,IHYDROGEN_GEHTOH2,IS3,T)*GDATA(IHYDROGEN_GEHTOH2,'GDCB'); - -QHYDROGEN_HGETOHH2(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GETOHH2),IS3,T)$GDATA(IHYDROGEN_GETOHH2,'GDCB').. - - VGE_T(IY411,IA,IHYDROGEN_GETOHH2,IS3,T) =E= VGH_T(IY411,IA,IHYDROGEN_GETOHH2,IS3,T)*GDATA(IHYDROGEN_GETOHH2,'GDCB'); - - -QHYDROGEN_H2TOEH(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2TOEH),IS3,T)$GDATA(IHYDROGEN_GH2TOEH,'GDCB').. - - VGE_T(IY411,IA,IHYDROGEN_GH2TOEH,IS3,T) =E= VGH_T(IY411,IA,IHYDROGEN_GH2TOEH,IS3,T)*GDATA(IHYDROGEN_GH2TOEH,'GDCB'); - -* Hydrogen gas storage, dynamic balance for weekly time horizon: -* Start and end filling degree in each week fixed to values obtained -* from yearly simulation - - -* Hydrogen production equal demand on region level: -* Prod + OutputStorage - InputStorage = DemandTransport + DemandPower+DemandBioRefinery + other - -QHYDROGEN_EQ(IY411,IR,IS3,T).. - SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GETOH2)$(RRRAAA(IR,IA)),VGE_T(IY411,IA,IHYDROGEN_GETOH2,IS3,T)*GDATA(IHYDROGEN_GETOH2,'GDFE')) + - SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GETOHH2)$(RRRAAA(IR,IA)),VGE_T(IY411,IA,IHYDROGEN_GETOHH2,IS3,T)*GDATA(IHYDROGEN_GETOHH2,'GDFE'))+ - SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GEHTOH2)$(RRRAAA(IR,IA)),VGE_T(IY411,IA,IHYDROGEN_GEHTOH2,IS3,T)*GDATA(IHYDROGEN_GEHTOH2,'GDFE'))+ - SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GCH4TOH2)$(RRRAAA(IR,IA)),VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GCH4TOH2,IS3,T)) + - SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO)$(RRRAAA(IR,IA)),VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GH2STO,IS3,T)) - - SUM((IA,IHYDROGEN_GH2STO)$(RRRAAA(IR,IA) AND (IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO))),VHYDROGEN_STOLOADT(IY411,IA,IHYDROGEN_GH2STO,IS3,T)) - =E= - IHYDROGEN_DH2_REGION_T_Y(IY411,IR,IS3,T)/(1-DISLOSS_H2(IR))+ - SUM(IA$RRRAAA(IR,IA),IHYDROGEN_DH2_AREA_T_Y(IY411,IA,IS3,T)/(1-DISLOSS_H2(IA))) - + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2FUEL)$(RRRAAA(IR,IA)),VGE_T(IY411,IA,IHYDROGEN_GH2FUEL,IS3,T)/GDATA(IHYDROGEN_GH2FUEL,'GDFE')) - + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2TOBIOMETH)$(RRRAAA(IR,IA)),VGBIOMETH_T(IY411,IA,IHYDROGEN_GH2TOBIOMETH,IS3,T)/GDATA(IHYDROGEN_GH2TOBIOMETH,'GDFE')) - + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GBIOGASMETHANATION)$(RRRAAA(IR,IA)),VGBIOGASMETHANATION_T(IY411,IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T)*METHANATION_DH2(IHYDROGEN_GBIOGASMETHANATION)) - -- (SUM(IRE$IXH2K_HASORPOT(IY411,IRE,IR), VXH2_T(IY411,IRE,IR,IS3,T)*(1-XH2LOSS(IRE,IR)))) !! Import to IR -+ (SUM(IRI$IXH2K_HASORPOT(IY411,IR,IRI), VXH2_T(IY411,IR,IRI,IS3,T))) !! Export from IR - - -$include "../../base/addons/_hooks/qhydrogen_eq.inc" - -; - -* Hydrogen production equal demand on area level: -* Prod + OutputStorage - InputStorage = DemandTransport + DemandPower + DemandBioRefinery + other - -QHYDROGEN_EQ_AREA(IY411,IA,IS3,T)$HYDROGEN_DH2(IY411,IA).. - SUM(IHYDROGEN_GETOH2$IAGK_HASORPOT(IY411,IA,IHYDROGEN_GETOH2),VGE_T(IY411,IA,IHYDROGEN_GETOH2,IS3,T)*GDATA(IHYDROGEN_GETOH2,'GDFE')) + - SUM(IHYDROGEN_GETOHH2$IAGK_HASORPOT(IY411,IA,IHYDROGEN_GETOHH2),VGE_T(IY411,IA,IHYDROGEN_GETOHH2,IS3,T)*GDATA(IHYDROGEN_GETOHH2,'GDFE'))+ - SUM(IHYDROGEN_GEHTOH2$IAGK_HASORPOT(IY411,IA,IHYDROGEN_GEHTOH2),VGE_T(IY411,IA,IHYDROGEN_GEHTOH2,IS3,T)*GDATA(IHYDROGEN_GEHTOH2,'GDFE')) + - SUM(IHYDROGEN_GCH4TOH2$IAGK_HASORPOT(IY411,IA,IHYDROGEN_GCH4TOH2),VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GCH4TOH2,IS3,T)) + - SUM(IHYDROGEN_GH2STO$IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO), VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GH2STO,IS3,T)) - - SUM(IHYDROGEN_GH2STO$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO)),VHYDROGEN_STOLOADT(IY411,IA,IHYDROGEN_GH2STO,IS3,T)) - =E= - IHYDROGEN_DH2_AREA_T_Y(IY411,IA,IS3,T)/(1-DISLOSS_H2(IA)) - + SUM(IHYDROGEN_GH2FUEL$IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2FUEL),VGE_T(IY411,IA,IHYDROGEN_GH2FUEL,IS3,T)/GDATA(IHYDROGEN_GH2FUEL,'GDFE')) - + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2TOBIOMETH),VGBIOMETH_T(IY411,IA,IHYDROGEN_GH2TOBIOMETH,IS3,T)/GDATA(IHYDROGEN_GH2TOBIOMETH,'GDFE')) - + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GBIOGASMETHANATION),VGBIOGASMETHANATION_T(IY411,IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T)*METHANATION_DH2(IHYDROGEN_GBIOGASMETHANATION)) - -$include "../../base/addons/_hooks/qhydrogen_eq_area.inc" -; - -* Hydrogen production from steam reforming restricted by capacity -QHYDROGEN_GCH4KT_UP(IY411,IA,IHYDROGEN_GCH4TOH2,IS3,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GCH4TOH2) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GCH4TOH2)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GCH4TOH2))).. - VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GCH4TOH2,IS3,T) =L= - (GKFX(IY411,IA,IHYDROGEN_GCH4TOH2) -$ifi %DECOM%==yes -VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GCH4TOH2)$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GCH4TOH2) - +VGKNACCUMNET(IY411,IA,IHYDROGEN_GCH4TOH2)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GCH4TOH2)))*(1$(NOT IGKRATE(IA,IHYDROGEN_GCH4TOH2,IS3,T)) + IGKRATE(IA,IHYDROGEN_GCH4TOH2,IS3,T)) -; - - -QHYDROGEN_STOVOL(IY411,IA,IHYDROGEN_GH2STO,S,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO) AND IS3(S)).. - VHYDROGEN_STOVOL_T(IY411,IA,IHYDROGEN_GH2STO,S,T) - + CHRONOHOUR(S,T)*(VHYDROGEN_STOLOADT(IY411,IA,IHYDROGEN_GH2STO,S,T) - VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GH2STO,S,T)/GDATA(IHYDROGEN_GH2STO,'GDFE')) - + ((VHYDROGEN_STOVOL_T(IY411,IA,IHYDROGEN_GH2STO,S,T) + CHRONOHOUR(S,T)*(VHYDROGEN_STOLOADT(IY411,IA,IHYDROGEN_GH2STO,S,T) - VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GH2STO,S,T)/GDATA(IHYDROGEN_GH2STO,'GDFE')) - - SUM(ITALIAS$(ORD(ITALIAS) EQ 1),VHYDROGEN_STOVOL_T(IY411,IA,IHYDROGEN_GH2STO,S,ITALIAS)) - )*(SSIZE(S)-1))$(ORD(T) EQ CARD(T)) - =E= - VHYDROGEN_STOVOL_T(IY411,IA,IHYDROGEN_GH2STO,S,T+1) +SUM(ITALIAS$(ORD(ITALIAS) EQ 1),VHYDROGEN_STOVOL_T(IY411,IA,IHYDROGEN_GH2STO,S++1,ITALIAS))$(CARD(T) EQ ORD(T)) - -$include "../../base/addons/_hooks/qhydrogen_stovol.inc" -; - -*Hydrogen storage is modelled as a inter-seasonal storage that can be used with a T resolutions -QHYDROGEN_STOMAXCON(IY411,IA,IHYDROGEN_GH2STO,S,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2STO)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2STO)) AND (IS3(S) OR (IS3(S-1) AND (ORD(T) EQ 1)))).. - GKFX(IY411,IA,IHYDROGEN_GH2STO) - + VGKNACCUMNET(IY411,IA,IHYDROGEN_GH2STO)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2STO)) -$ifi %DECOM%==yes -VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GH2STO)$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2STO) - =G= - VHYDROGEN_STOVOL_T(IY411,IA,IHYDROGEN_GH2STO,S,T) -; - -* Maximum loading, hydrogen gas storage: -QHYDROGEN_STOMAXLOAD(IY411,IA,IHYDROGEN_GH2STO,IS3,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2STO)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2STO))).. - ( GKFX(IY411,IA,IHYDROGEN_GH2STO)/GDATA(IHYDROGEN_GH2STO,'GDSTOHLOAD') - + (VGKNACCUMNET(IY411,IA,IHYDROGEN_GH2STO)/GDATA(IHYDROGEN_GH2STO,'GDSTOHLOAD'))$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2STO)) -$ifi %DECOM%==yes -(VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GH2STO)/GDATA(IHYDROGEN_GH2STO,'GDSTOHLOAD'))$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2STO) - ) *(1$(NOT IGKRATE(IA,IHYDROGEN_GH2STO,IS3,T)) + IGKRATE(IA,IHYDROGEN_GH2STO,IS3,T)) - =G= VHYDROGEN_STOLOADT(IY411,IA,IHYDROGEN_GH2STO,IS3,T) -; - -* Maximum unloading, hydrogen gas storage: -QHYDROGEN_STOMAXUNLD(IY411,IA,IHYDROGEN_GH2STO,IS3,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2STO)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2STO))).. - ( GKFX(IY411,IA,IHYDROGEN_GH2STO)/GDATA(IHYDROGEN_GH2STO,'GDSTOHUNLD') - + (VGKNACCUMNET(IY411,IA,IHYDROGEN_GH2STO)/GDATA(IHYDROGEN_GH2STO,'GDSTOHUNLD'))$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2STO)) -$ifi %DECOM%==yes -(VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GH2STO)/GDATA(IHYDROGEN_GH2STO,'GDSTOHUNLD'))$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2STO) - ) *(1$(NOT IGKRATE(IA,IHYDROGEN_GH2STO,IS3,T)) + IGKRATE(IA,IHYDROGEN_GH2STO,IS3,T)) - =G= - VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GH2STO,IS3,T) -; - -QBIOMETHANE_EQ(IY411,IS3,T)$SUM((IA,IGBIOMETHANE),IAGK_HASORPOT(IY411,IA,IGBIOMETHANE)).. - -*Production SYNTHETIC NATURAL GAS - + SUM((C,IA,IHYDROGEN_GH2TOBIOMETH)$(ICA(C,IA) AND IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2TOBIOMETH)),VGBIOMETH_T(IY411,IA,IHYDROGEN_GH2TOBIOMETH,IS3,T)) -*Production BIOGAS METHANATION - + SUM((C,IA,IHYDROGEN_GBIOGASMETHANATION)$(ICA(C,IA) AND IAGK_HASORPOT(IY411,IA,IHYDROGEN_GBIOGASMETHANATION)),VGBIOGASMETHANATION_T(IY411,IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T)) -*Production BIOGAS UPGRADING - + SUM((C,IA,IHYDROGEN_GBIOGASUPGRADING)$(ICA(C,IA) AND IAGK_HASORPOT(IY411,IA,IHYDROGEN_GBIOGASUPGRADING)),VGBIOGASUPGRADING_T(IY411,IA,IHYDROGEN_GBIOGASUPGRADING,IS3,T)) - - + VBIOMETH_STOUNLOADT(IY411,IS3,T) - =E= - -*Demand biomethane - SUM((C,IA,IGBIOMETHANE)$(ICA(C,IA) AND IAGK_HASORPOT(IY411,IA,IGBIOMETHANE)),VGF_T(IY411,IA,IGBIOMETHANE,IS3,T)) - + VBIOMETH_STOLOADT(IY411,IS3,T) -*$include "../../base/addons/_hooks/qbiomethane_eq.inc" -; - - -QBIOMETHANE_STOVOLTS(IY411,S,T)$(SUM((IA,IGBIOMETHANE),IAGK_HASORPOT(IY411,IA,IGBIOMETHANE)) AND IS3(S)).. - VBIOMETHSTOVOLTS(IY411,S,T) - + CHRONOHOUR(S,T)*(VBIOMETH_STOLOADT(IY411,S,T) - VBIOMETH_STOUNLOADT(IY411,S,T)) - -+ ( - ( VBIOMETHSTOVOLTS(IY411,S,T) - + CHRONOHOUR(S,T)*(VBIOMETH_STOLOADT(IY411,S,T) - VBIOMETH_STOUNLOADT(IY411,S,T)) - - SUM(ITALIAS$(ORD(ITALIAS) EQ 1),VBIOMETHSTOVOLTS(IY411,S,ITALIAS)) - )*(SSIZE(S)-1) - )$(ORD(T) EQ CARD(T)) - - =E= - - VBIOMETHSTOVOLTS(IY411,S,T+1) +SUM(ITALIAS$(ORD(ITALIAS) EQ 1),VBIOMETHSTOVOLTS(IY411,S++1,ITALIAS))$(CARD(T) EQ ORD(T)) - -$include "../../base/addons/_hooks/qbiomethane_stovolts.inc" - -; - -QGKBIOGASUPGRADING_UP(IY411,IA,IHYDROGEN_GBIOGASUPGRADING,IS3,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GBIOGASUPGRADING) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GBIOGASUPGRADING)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GBIOGASUPGRADING))).. - VGBIOGASUPGRADING_T(IY411,IA,IHYDROGEN_GBIOGASUPGRADING,IS3,T) =L= - (GKFX(IY411,IA,IHYDROGEN_GBIOGASUPGRADING) -$ifi %DECOM%==yes -VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GBIOGASUPGRADING)$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GBIOGASUPGRADING) - +VGKNACCUMNET(IY411,IA,IHYDROGEN_GBIOGASUPGRADING)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GBIOGASUPGRADING)))*(1$(NOT IGKRATE(IA,IHYDROGEN_GBIOGASUPGRADING,IS3,T)) + IGKRATE(IA,IHYDROGEN_GBIOGASUPGRADING,IS3,T)) -; - -QGKBIOGASMETHANATION_UP(IY411,IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GBIOGASMETHANATION) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GBIOGASMETHANATION)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GBIOGASMETHANATION))).. - VGBIOGASMETHANATION_T(IY411,IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T) =L= - (GKFX(IY411,IA,IHYDROGEN_GBIOGASMETHANATION) -$ifi %DECOM%==yes -VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GBIOGASMETHANATION)$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GBIOGASMETHANATION) - +VGKNACCUMNET(IY411,IA,IHYDROGEN_GBIOGASMETHANATION)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GBIOGASMETHANATION)))*(1$(NOT IGKRATE(IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T)) + IGKRATE(IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T)) -; - -QGKBIOMETHANE_UP(IY411,IA,IHYDROGEN_GH2TOBIOMETH,IS3,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2TOBIOMETH) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2TOBIOMETH)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2TOBIOMETH))).. - VGBIOMETH_T(IY411,IA,IHYDROGEN_GH2TOBIOMETH,IS3,T) =L= - (GKFX(IY411,IA,IHYDROGEN_GH2TOBIOMETH) -$ifi %DECOM%==yes -VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GH2TOBIOMETH)$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2TOBIOMETH) - +VGKNACCUMNET(IY411,IA,IHYDROGEN_GH2TOBIOMETH)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2TOBIOMETH)))*(1$(NOT IGKRATE(IA,IHYDROGEN_GH2TOBIOMETH,IS3,T)) + IGKRATE(IA,IHYDROGEN_GH2TOBIOMETH,IS3,T)) -; - -*Hydrogen pipe transmission - -EQUATIONS -QXH2K_UP(Y,IRRRE,IRRRI,S,T) "H2 transmission capacity constraint (MW)" -QXH2KNACCUMNET(Y,IRRRE,IRRRI) "NOT FINISHED, TODO, Accumulated new H2 transmission investments available at beginning of next year (MW)" -QXH2KNACCUMNET_NGTOH2(Y,IRRRE,IRRRI) "NOT FINISHED, TODO, Accumulated new investments in conversion of existing natural gas pipe to H2 transmission available at beginning of next year (MW)" -QXH2INVSYMMETRY(Y,IRRRE,IRRRI) "H2 Transmission investments are set symetric(MW)" -QXH2INVSYMMETRY_NGTOH2(Y,IRRRE,IRRRI) "Conversion of existing natural gas pipe to H2 Transmission investments are set symetric(MW)" -QXH2KMAX_NGTOH2(Y,IRRRE,IRRRI) "Maximum conversion of existing natural gas pipe to H2 transmission capacity between regions" -*Equations for modelling economy of scale -QVXH2KN_ES(YYY,IRRRE,IRRRI) "Equation to ensure the proper H2 capacity when using SOS2 variables" -QXH2LAMBDA_ES(YYY,IRRRE,IRRRI) "Equation to ensure all the Lambda variables are equal to 1 for the same H2 transmission line" -; - -*"H2 transmission capacity constraint (MW)" -QXH2K_UP(IY411,IRE,IRI,IS3,T)$(SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN(Y,IRE,IRI)) OR SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN_NGTOH2(Y,IRE,IRI))).. - ( XH2KFX(IY411,IRE,IRI) - + VXH2KNACCUMNET(IY411,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN(Y,IRE,IRI)) - + VXH2KNACCUMNET_NGTOH2(IY411,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN_NGTOH2(Y,IRE,IRI)) - )*(1$(NOT IXH2KRATE(IRE,IRI,IS3,T))+IXH2KRATE(IRE,IRI,IS3,T)) - =G= VXH2_T(IY411,IRE,IRI,IS3,T) -; - -*"NOT FINISHED, TODO, Accumulated new heat transmission investments available at beginning of next year (MW)" -QXH2KNACCUMNET(IY411,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN(Y,IRE,IRI)).. - VXH2KNACCUMNET(IY411,IRE,IRI) !! End of this year's VXH2KNACCUMNET (available at beginning of next year)' - =E= - SUM(IY402$IY411PREVY(IY411,IY402), VXH2KNACCUMNET(IY402,IRE,IRI)$SUM(Y$(YVALUE(Y) LT YVALUE(IY411)),IXH2KN(Y,IRE,IRI))) + VXH2KN(IY411,IRE,IRI)$IXH2KN(IY411,IRE,IRI) !! VXH2KNVACCUMNET available at end of previous(i.e., start of this) year PLUS investment during this year - -; - -*"NOT FINISHED, TODO, Accumulated new heat transmission investments available at beginning of next year (MW)" -QXH2KNACCUMNET_NGTOH2(IY411,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN_NGTOH2(Y,IRE,IRI)).. - VXH2KNACCUMNET_NGTOH2(IY411,IRE,IRI) !! End of this year's VXH2KNACCUMNET (available at beginning of next year)' - =E= - SUM(IY402$IY411PREVY(IY411,IY402), VXH2KNACCUMNET_NGTOH2(IY402,IRE,IRI)$SUM(Y$(YVALUE(Y) LT YVALUE(IY411)),IXH2KN(Y,IRE,IRI))) + VXH2KN_NGTOH2(IY411,IRE,IRI)$IXH2KN_NGTOH2(IY411,IRE,IRI) !! VXH2KNVACCUMNET_NGTOH2 available at end of previous(i.e., start of this) year PLUS investment during this year - -; - -* "Transmission investments are set symetric(MW)" -QXH2INVSYMMETRY(IY411,IRE,IRI)$IXH2KN(IY411,IRE,IRI).. - VXH2KN(IY411,IRE,IRI) - =E= - VXH2KN(IY411,IRI,IRE) -; - -* "Transmission investments are set symetric(MW)" -QXH2INVSYMMETRY_NGTOH2(IY411,IRE,IRI)$IXH2KN_NGTOH2(IY411,IRE,IRI).. - VXH2KN_NGTOH2(IY411,IRE,IRI) - =E= - VXH2KN_NGTOH2(IY411,IRI,IRE) -; - -*"Maximum H2 transmission capacity between regions" -QXH2KMAX_NGTOH2(IY411,IRE,IRI)$XH2MAXK_NGTOH2(IY411,IRE,IRI).. - XH2MAXK_NGTOH2(IY411,IRE,IRI) - =G= - VXH2KNACCUMNET_NGTOH2(IY411,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN_NGTOH2(Y,IRE,IRI)) -; - - -QVXH2KN_ES(IY411,IRE,IRI)$(IXH2KN(IY411,IRI,IRE) AND (IXH2KN_ES(IRE,IRI))).. - VXH2KN(IY411,IRE,IRI) =E= SUM(XES,VXH2LAMBDA(IY411,IRE,IRI,XES)*XH2KN_ES(IRE,IRI,XES)); - -QXH2LAMBDA_ES(IY411,IRE,IRI)$(IXH2KN(IY411,IRI,IRE) AND (IXH2KN_ES(IRE,IRI))).. - SUM(XES,VXH2LAMBDA(IY411,IRE,IRI,XES)) =E= 1; +Equations + QHYDROGEN_HGEHTOH2(Y,AAA,G,S,T) 'Link heat and electricity demand in hydrogen production of technologies consuming heat and electricity to produce H2' + QHYDROGEN_HGETOHH2(Y,AAA,G,S,T) 'Link heat and electricity demand in hydrogen production of technologies consuming electricity to produce H2 and heat' + QHYDROGEN_H2TOEH(Y,AAA,G,S,T) 'Link heat and electricity in technologies consuming hydrogen and producing heat and electricity' + QHYDROGEN_GCH4KT_UP(Y,AAA,G,S,T) 'Hydrogen production of steam reforming plants restricted by capacity' + QHYDROGEN_STOVOL(Y,AAA,G,S,T) 'Dynamic equation hydrogen storage' + QHYDROGEN_STOMAXCON(Y,AAA,G,S,T) 'Maximum hydrogen storage capacity' + QHYDROGEN_STOMAXLOAD(Y,AAA,G,S,T) 'Maximum loading capacity of hydrogen storage' + QHYDROGEN_STOMAXUNLD(Y,AAA,G,S,T) 'Maximum unloading capacity of hydrogen storage' + QHYDROGEN_EQ(Y,RRR,S,T) 'Hydrogen production equal demand' + QHYDROGEN_EQ_AREA(Y,AAA,S,T) 'Hydrogen production equals demand at an areal level' + QHYDROGENGKE_UP_ADD(Y,AAA,G,S,T) 'Capacity adjustment constraint on electricity generation from biogas upgraded plants (MW)' + QBIOMETHANE_EQ(Y,S,T) 'Biomethane production equals demand on international level in each year' + QBIOMETHANE_STOVOLTS(Y,S,T) 'Dynamic equation biomethane storage' + QGKBIOGASUPGRADING_UP(Y,AAA,G,S,T) 'Maximum generation of biomethane from biogas upgrading restricted by capacity (MW)' + QGKBIOGASMETHANATION_UP(Y,AAA,G,S,T) 'Maximum generation of biomethane via biogas methanation restricted by capacity (MW)' + QGKBIOMETHANE_UP(Y,AAA,G,S,T) 'Maximum generation of SNG restricted by capacity (MW)' +; + +*Demand Shift equations +Equations +QSYNTHETIC_EQ_PRODUCTION(Y,RRR,S,T) +QSYNTHETIC_EQ_SHIFT(Y,RRR) +QSYNTHETIC_EQ_SHIFT_UP(Y,RRR) +QSYNTHETIC_EQ_SHIFT_DOWN(Y,RRR) +QSYNTHETIC_EQ_SHIFT_BALANCE_MOVE(Y) +QSYNTHETIC_EQ_SHIFT_BALANCE_OVERALL(Y) +; + + + +QHYDROGEN_HGEHTOH2(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GEHTOH2),IS3,T)$GDATA(IHYDROGEN_GEHTOH2,'GDCB').. + + VGE_T(IY411,IA,IHYDROGEN_GEHTOH2,IS3,T) =E= VGH_T(IY411,IA,IHYDROGEN_GEHTOH2,IS3,T)*GDATA(IHYDROGEN_GEHTOH2,'GDCB'); + +QHYDROGEN_HGETOHH2(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GETOHH2),IS3,T)$GDATA(IHYDROGEN_GETOHH2,'GDCB').. + + VGE_T(IY411,IA,IHYDROGEN_GETOHH2,IS3,T) =E= VGH_T(IY411,IA,IHYDROGEN_GETOHH2,IS3,T)*GDATA(IHYDROGEN_GETOHH2,'GDCB'); + + +QHYDROGEN_H2TOEH(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2TOEH),IS3,T)$GDATA(IHYDROGEN_GH2TOEH,'GDCB').. + + VGE_T(IY411,IA,IHYDROGEN_GH2TOEH,IS3,T) =E= VGH_T(IY411,IA,IHYDROGEN_GH2TOEH,IS3,T)*GDATA(IHYDROGEN_GH2TOEH,'GDCB'); + +* Hydrogen gas storage, dynamic balance for weekly time horizon: +* Start and end filling degree in each week fixed to values obtained +* from yearly simulation + + +* Hydrogen production equal demand on region level: +* Prod + OutputStorage - InputStorage = DemandTransport + DemandPower+DemandBioRefinery + other + + +QHYDROGEN_EQ(IY411,IR,IS3,T).. + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GETOH2)$(RRRAAA(IR,IA)),VGE_T(IY411,IA,IHYDROGEN_GETOH2,IS3,T)*GDATA(IHYDROGEN_GETOH2,'GDFE')) + + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GETOHH2)$(RRRAAA(IR,IA)),VGE_T(IY411,IA,IHYDROGEN_GETOHH2,IS3,T)*GDATA(IHYDROGEN_GETOHH2,'GDFE'))+ + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GEHTOH2)$(RRRAAA(IR,IA)),VGE_T(IY411,IA,IHYDROGEN_GEHTOH2,IS3,T)*GDATA(IHYDROGEN_GEHTOH2,'GDFE'))+ + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GCH4TOH2)$(RRRAAA(IR,IA)),VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GCH4TOH2,IS3,T)) + + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO)$(RRRAAA(IR,IA)),VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GH2STO,IS3,T)) + - SUM((IA,IHYDROGEN_GH2STO)$(RRRAAA(IR,IA) AND (IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO))),VHYDROGEN_STOLOADT(IY411,IA,IHYDROGEN_GH2STO,IS3,T)) + =E= + IHYDROGEN_DH2_REGION_T_Y(IY411,IR,IS3,T)/(1-DISLOSS_H2(IR))+ +*IK changes for synthetic fuel + VGE_T_SYNFUEL_ST(IY411,IR,IS3,T)/(1-DISLOSS_H2(IR)) + + SUM(IA$RRRAAA(IR,IA),IHYDROGEN_DH2_AREA_T_Y(IY411,IA,IS3,T)/(1-DISLOSS_H2(IA))) + + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2FUEL)$(RRRAAA(IR,IA)),VGE_T(IY411,IA,IHYDROGEN_GH2FUEL,IS3,T)/GDATA(IHYDROGEN_GH2FUEL,'GDFE')) + + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2TOBIOMETH)$(RRRAAA(IR,IA)),VGBIOMETH_T(IY411,IA,IHYDROGEN_GH2TOBIOMETH,IS3,T)/GDATA(IHYDROGEN_GH2TOBIOMETH,'GDFE')) + + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GBIOGASMETHANATION)$(RRRAAA(IR,IA)),VGBIOGASMETHANATION_T(IY411,IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T)*METHANATION_DH2(IHYDROGEN_GBIOGASMETHANATION)) + +- (SUM(IRE$IXH2K_HASORPOT(IY411,IRE,IR), VXH2_T(IY411,IRE,IR,IS3,T)*(1-XH2LOSS(IRE,IR)))) !! Import to IR ++ (SUM(IRI$IXH2K_HASORPOT(IY411,IR,IRI), VXH2_T(IY411,IR,IRI,IS3,T))) !! Export from IR + + +$include "../../base/addons/_hooks/qhydrogen_eq.inc" + +; + +$ONTEXT +*Parameter initiation +*IHYDROGEN_DH2_REGION_T_Y(Y,IR,S,T) = (HYDROGEN_DH2(Y,IR)*HYDROGEN_DH2_VAR_T(IR,S,T)/IHYDROGEN_DH2_REGION_SUMST(IR))$(IHYDROGEN_DH2_REGION_SUMST(IR) GT 0); +*give it a try give the same demand + +*Initiate the total +IHYDROGEN_DH2SYNFUEL_REGION_Y_TOT(YYY,RRR) = HYDROGEN_DH2(YYY,RRR); +*Initiate the first value +IHYDROGEN_DH2SYNFUEL_REGION_Y(YYY,RRR)$((ORD(YYY) EQ 1)) = IHYDROGEN_DH2SYNFUEL_REGION_Y_TOT(YYY,RRR); +*The difference +LOOP(YYY$(NOT(ORD(YYY) EQ 1)), +IHYDROGEN_DH2SYNFUEL_REGION_Y(YYY,RRR) = IHYDROGEN_DH2SYNFUEL_REGION_Y_TOT(YYY,RRR) - IHYDROGEN_DH2SYNFUEL_REGION_Y_TOT(YYY-1,RRR); +); + +$OFFTEXT + +*Additional EQs for synthetic fuel demand shift + +QSYNTHETIC_EQ_PRODUCTION(IY411,IR,IS3,T).. +VGE_T_SYNFUEL_ST(IY411,IR,IS3,T) =E= (VGE_T_SYNFUEL(IY411,IR)*HYDROGEN_DH2_VAR_T(IR,IS3,T)/IHYDROGEN_DH2_REGION_SUMST(IR))$(IHYDROGEN_DH2_REGION_SUMST(IR) GT 0) + +; + +QSYNTHETIC_EQ_SHIFT(IY411,IR).. +VGE_T_SYNFUEL(IY411,IR) =G= VSYN_FUEL_SHIFT(IY411,IR) + +; + +QSYNTHETIC_EQ_SHIFT_UP(IY411,IR).. +VSYN_FUEL_SHIFT(IY411,IR) =G= (1-THETA_FLEX_SYNFUEL)*IHYDROGEN_DH2SYNFUEL_REGION_Y(IY411,IR) + +; + +QSYNTHETIC_EQ_SHIFT_DOWN(IY411,IR).. +(1+THETA_FLEX_SYNFUEL)*IHYDROGEN_DH2SYNFUEL_REGION_Y(IY411,IR) =G= VSYN_FUEL_SHIFT(IY411,IR) + +; + +QSYNTHETIC_EQ_SHIFT_BALANCE_MOVE(IY411).. +sum(IR,VSYN_FUEL_SHIFT(IY411,IR)) =E= sum(IR,IHYDROGEN_DH2SYNFUEL_REGION_Y(IY411,IR)) + +; + +QSYNTHETIC_EQ_SHIFT_BALANCE_OVERALL(IY411).. +sum(IR,VGE_T_SYNFUEL(IY411,IR)) =E= sum(IR,IHYDROGEN_DH2SYNFUEL_REGION_Y(IY411,IR)) + +; +*Stops here + +* Hydrogen production equal demand on area level: +* Prod + OutputStorage - InputStorage = DemandTransport + DemandPower + DemandBioRefinery + other + +QHYDROGEN_EQ_AREA(IY411,IA,IS3,T)$HYDROGEN_DH2(IY411,IA).. + SUM(IHYDROGEN_GETOH2$IAGK_HASORPOT(IY411,IA,IHYDROGEN_GETOH2),VGE_T(IY411,IA,IHYDROGEN_GETOH2,IS3,T)*GDATA(IHYDROGEN_GETOH2,'GDFE')) + + SUM(IHYDROGEN_GETOHH2$IAGK_HASORPOT(IY411,IA,IHYDROGEN_GETOHH2),VGE_T(IY411,IA,IHYDROGEN_GETOHH2,IS3,T)*GDATA(IHYDROGEN_GETOHH2,'GDFE'))+ + SUM(IHYDROGEN_GEHTOH2$IAGK_HASORPOT(IY411,IA,IHYDROGEN_GEHTOH2),VGE_T(IY411,IA,IHYDROGEN_GEHTOH2,IS3,T)*GDATA(IHYDROGEN_GEHTOH2,'GDFE')) + + SUM(IHYDROGEN_GCH4TOH2$IAGK_HASORPOT(IY411,IA,IHYDROGEN_GCH4TOH2),VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GCH4TOH2,IS3,T)) + + SUM(IHYDROGEN_GH2STO$IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO), VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GH2STO,IS3,T)) + - SUM(IHYDROGEN_GH2STO$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO)),VHYDROGEN_STOLOADT(IY411,IA,IHYDROGEN_GH2STO,IS3,T)) + =E= + IHYDROGEN_DH2_AREA_T_Y(IY411,IA,IS3,T)/(1-DISLOSS_H2(IA)) + + SUM(IHYDROGEN_GH2FUEL$IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2FUEL),VGE_T(IY411,IA,IHYDROGEN_GH2FUEL,IS3,T)/GDATA(IHYDROGEN_GH2FUEL,'GDFE')) + + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2TOBIOMETH),VGBIOMETH_T(IY411,IA,IHYDROGEN_GH2TOBIOMETH,IS3,T)/GDATA(IHYDROGEN_GH2TOBIOMETH,'GDFE')) + + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GBIOGASMETHANATION),VGBIOGASMETHANATION_T(IY411,IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T)*METHANATION_DH2(IHYDROGEN_GBIOGASMETHANATION)) + +$include "../../base/addons/_hooks/qhydrogen_eq_area.inc" +; + +* Hydrogen production from steam reforming restricted by capacity +QHYDROGEN_GCH4KT_UP(IY411,IA,IHYDROGEN_GCH4TOH2,IS3,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GCH4TOH2) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GCH4TOH2)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GCH4TOH2))).. + VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GCH4TOH2,IS3,T) =L= + (GKFX(IY411,IA,IHYDROGEN_GCH4TOH2) +$ifi %DECOM%==yes -VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GCH4TOH2)$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GCH4TOH2) + +VGKNACCUMNET(IY411,IA,IHYDROGEN_GCH4TOH2)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GCH4TOH2)))*(1$(NOT IGKRATE(IA,IHYDROGEN_GCH4TOH2,IS3,T)) + IGKRATE(IA,IHYDROGEN_GCH4TOH2,IS3,T)) +; + + +QHYDROGEN_STOVOL(IY411,IA,IHYDROGEN_GH2STO,S,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO) AND IS3(S)).. + VHYDROGEN_STOVOL_T(IY411,IA,IHYDROGEN_GH2STO,S,T) + + CHRONOHOUR(S,T)*(VHYDROGEN_STOLOADT(IY411,IA,IHYDROGEN_GH2STO,S,T) - VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GH2STO,S,T)/GDATA(IHYDROGEN_GH2STO,'GDFE')) + + ((VHYDROGEN_STOVOL_T(IY411,IA,IHYDROGEN_GH2STO,S,T) + CHRONOHOUR(S,T)*(VHYDROGEN_STOLOADT(IY411,IA,IHYDROGEN_GH2STO,S,T) - VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GH2STO,S,T)/GDATA(IHYDROGEN_GH2STO,'GDFE')) + - SUM(ITALIAS$(ORD(ITALIAS) EQ 1),VHYDROGEN_STOVOL_T(IY411,IA,IHYDROGEN_GH2STO,S,ITALIAS)) + )*(SSIZE(S)-1))$(ORD(T) EQ CARD(T)) + =E= + VHYDROGEN_STOVOL_T(IY411,IA,IHYDROGEN_GH2STO,S,T+1) +SUM(ITALIAS$(ORD(ITALIAS) EQ 1),VHYDROGEN_STOVOL_T(IY411,IA,IHYDROGEN_GH2STO,S++1,ITALIAS))$(CARD(T) EQ ORD(T)) + +$include "../../base/addons/_hooks/qhydrogen_stovol.inc" +; + +*Hydrogen storage is modelled as a inter-seasonal storage that can be used with a T resolutions +QHYDROGEN_STOMAXCON(IY411,IA,IHYDROGEN_GH2STO,S,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2STO)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2STO)) AND (IS3(S) OR (IS3(S-1) AND (ORD(T) EQ 1)))).. + GKFX(IY411,IA,IHYDROGEN_GH2STO) + + VGKNACCUMNET(IY411,IA,IHYDROGEN_GH2STO)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2STO)) +$ifi %DECOM%==yes -VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GH2STO)$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2STO) + =G= + VHYDROGEN_STOVOL_T(IY411,IA,IHYDROGEN_GH2STO,S,T) +; + +* Maximum loading, hydrogen gas storage: +QHYDROGEN_STOMAXLOAD(IY411,IA,IHYDROGEN_GH2STO,IS3,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2STO)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2STO))).. + ( GKFX(IY411,IA,IHYDROGEN_GH2STO)/GDATA(IHYDROGEN_GH2STO,'GDSTOHLOAD') + + (VGKNACCUMNET(IY411,IA,IHYDROGEN_GH2STO)/GDATA(IHYDROGEN_GH2STO,'GDSTOHLOAD'))$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2STO)) +$ifi %DECOM%==yes -(VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GH2STO)/GDATA(IHYDROGEN_GH2STO,'GDSTOHLOAD'))$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2STO) + ) *(1$(NOT IGKRATE(IA,IHYDROGEN_GH2STO,IS3,T)) + IGKRATE(IA,IHYDROGEN_GH2STO,IS3,T)) + =G= VHYDROGEN_STOLOADT(IY411,IA,IHYDROGEN_GH2STO,IS3,T) +; + +* Maximum unloading, hydrogen gas storage: +QHYDROGEN_STOMAXUNLD(IY411,IA,IHYDROGEN_GH2STO,IS3,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2STO)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2STO))).. + ( GKFX(IY411,IA,IHYDROGEN_GH2STO)/GDATA(IHYDROGEN_GH2STO,'GDSTOHUNLD') + + (VGKNACCUMNET(IY411,IA,IHYDROGEN_GH2STO)/GDATA(IHYDROGEN_GH2STO,'GDSTOHUNLD'))$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2STO)) +$ifi %DECOM%==yes -(VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GH2STO)/GDATA(IHYDROGEN_GH2STO,'GDSTOHUNLD'))$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2STO) + ) *(1$(NOT IGKRATE(IA,IHYDROGEN_GH2STO,IS3,T)) + IGKRATE(IA,IHYDROGEN_GH2STO,IS3,T)) + =G= + VHYDROGEN_GH2_T(IY411,IA,IHYDROGEN_GH2STO,IS3,T) +; + + + +QBIOMETHANE_EQ(IY411,IS3,T)$SUM((IA,IGBIOMETHANE),IAGK_HASORPOT(IY411,IA,IGBIOMETHANE)).. + +*Production SYNTHETIC NATURAL GAS + + SUM((C,IA,IHYDROGEN_GH2TOBIOMETH)$(ICA(C,IA) AND IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2TOBIOMETH)),VGBIOMETH_T(IY411,IA,IHYDROGEN_GH2TOBIOMETH,IS3,T)) +*Production BIOGAS METHANATION + + SUM((C,IA,IHYDROGEN_GBIOGASMETHANATION)$(ICA(C,IA) AND IAGK_HASORPOT(IY411,IA,IHYDROGEN_GBIOGASMETHANATION)),VGBIOGASMETHANATION_T(IY411,IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T)) +*Production BIOGAS UPGRADING + + SUM((C,IA,IHYDROGEN_GBIOGASUPGRADING)$(ICA(C,IA) AND IAGK_HASORPOT(IY411,IA,IHYDROGEN_GBIOGASUPGRADING)),VGBIOGASUPGRADING_T(IY411,IA,IHYDROGEN_GBIOGASUPGRADING,IS3,T)) + + + VBIOMETH_STOUNLOADT(IY411,IS3,T) + =E= + +*Demand biomethane + SUM((C,IA,IGBIOMETHANE)$(ICA(C,IA) AND IAGK_HASORPOT(IY411,IA,IGBIOMETHANE)),VGF_T(IY411,IA,IGBIOMETHANE,IS3,T)) + + VBIOMETH_STOLOADT(IY411,IS3,T) +*$include "../../base/addons/_hooks/qbiomethane_eq.inc" +; + + +QBIOMETHANE_STOVOLTS(IY411,S,T)$(SUM((IA,IGBIOMETHANE),IAGK_HASORPOT(IY411,IA,IGBIOMETHANE)) AND IS3(S)).. + VBIOMETHSTOVOLTS(IY411,S,T) + + CHRONOHOUR(S,T)*(VBIOMETH_STOLOADT(IY411,S,T) - VBIOMETH_STOUNLOADT(IY411,S,T)) + ++ ( + ( VBIOMETHSTOVOLTS(IY411,S,T) + + CHRONOHOUR(S,T)*(VBIOMETH_STOLOADT(IY411,S,T) - VBIOMETH_STOUNLOADT(IY411,S,T)) + - SUM(ITALIAS$(ORD(ITALIAS) EQ 1),VBIOMETHSTOVOLTS(IY411,S,ITALIAS)) + )*(SSIZE(S)-1) + )$(ORD(T) EQ CARD(T)) + + =E= + + VBIOMETHSTOVOLTS(IY411,S,T+1) +SUM(ITALIAS$(ORD(ITALIAS) EQ 1),VBIOMETHSTOVOLTS(IY411,S++1,ITALIAS))$(CARD(T) EQ ORD(T)) + +$include "../../base/addons/_hooks/qbiomethane_stovolts.inc" + +; + +QGKBIOGASUPGRADING_UP(IY411,IA,IHYDROGEN_GBIOGASUPGRADING,IS3,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GBIOGASUPGRADING) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GBIOGASUPGRADING)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GBIOGASUPGRADING))).. + VGBIOGASUPGRADING_T(IY411,IA,IHYDROGEN_GBIOGASUPGRADING,IS3,T) =L= + (GKFX(IY411,IA,IHYDROGEN_GBIOGASUPGRADING) +$ifi %DECOM%==yes -VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GBIOGASUPGRADING)$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GBIOGASUPGRADING) + +VGKNACCUMNET(IY411,IA,IHYDROGEN_GBIOGASUPGRADING)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GBIOGASUPGRADING)))*(1$(NOT IGKRATE(IA,IHYDROGEN_GBIOGASUPGRADING,IS3,T)) + IGKRATE(IA,IHYDROGEN_GBIOGASUPGRADING,IS3,T)) +; + +QGKBIOGASMETHANATION_UP(IY411,IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GBIOGASMETHANATION) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GBIOGASMETHANATION)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GBIOGASMETHANATION))).. + VGBIOGASMETHANATION_T(IY411,IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T) =L= + (GKFX(IY411,IA,IHYDROGEN_GBIOGASMETHANATION) +$ifi %DECOM%==yes -VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GBIOGASMETHANATION)$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GBIOGASMETHANATION) + +VGKNACCUMNET(IY411,IA,IHYDROGEN_GBIOGASMETHANATION)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GBIOGASMETHANATION)))*(1$(NOT IGKRATE(IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T)) + IGKRATE(IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T)) +; + +QGKBIOMETHANE_UP(IY411,IA,IHYDROGEN_GH2TOBIOMETH,IS3,T)$(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2TOBIOMETH) AND (SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2TOBIOMETH)) OR IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2TOBIOMETH))).. + VGBIOMETH_T(IY411,IA,IHYDROGEN_GH2TOBIOMETH,IS3,T) =L= + (GKFX(IY411,IA,IHYDROGEN_GH2TOBIOMETH) +$ifi %DECOM%==yes -VDECOM_EXO_ACCUM(IY411,IA,IHYDROGEN_GH2TOBIOMETH)$IGDECOMEXOPOT(IY411,IA,IHYDROGEN_GH2TOBIOMETH) + +VGKNACCUMNET(IY411,IA,IHYDROGEN_GH2TOBIOMETH)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IAGKNY(Y,IA,IHYDROGEN_GH2TOBIOMETH)))*(1$(NOT IGKRATE(IA,IHYDROGEN_GH2TOBIOMETH,IS3,T)) + IGKRATE(IA,IHYDROGEN_GH2TOBIOMETH,IS3,T)) +; + + +*Hydrogen pipe transmission + +EQUATIONS +QXH2K_UP(Y,IRRRE,IRRRI,S,T) "H2 transmission capacity constraint (MW)" +QXH2KNACCUMNET(Y,IRRRE,IRRRI) "NOT FINISHED, TODO, Accumulated new H2 transmission investments available at beginning of next year (MW)" +QXH2KNACCUMNET_NGTOH2(Y,IRRRE,IRRRI) "NOT FINISHED, TODO, Accumulated new investments in conversion of existing natural gas pipe to H2 transmission available at beginning of next year (MW)" +QXH2INVSYMMETRY(Y,IRRRE,IRRRI) "H2 Transmission investments are set symetric(MW)" +QXH2INVSYMMETRY_NGTOH2(Y,IRRRE,IRRRI) "Conversion of existing natural gas pipe to H2 Transmission investments are set symetric(MW)" +QXH2KMAX_NGTOH2(Y,IRRRE,IRRRI) "Maximum conversion of existing natural gas pipe to H2 transmission capacity between regions" +*Equations for modelling economy of scale +QVXH2KN_ES(YYY,IRRRE,IRRRI) "Equation to ensure the proper H2 capacity when using SOS2 variables" +QXH2LAMBDA_ES(YYY,IRRRE,IRRRI) "Equation to ensure all the Lambda variables are equal to 1 for the same H2 transmission line" +; + +*"H2 transmission capacity constraint (MW)" +QXH2K_UP(IY411,IRE,IRI,IS3,T)$(SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN(Y,IRE,IRI)) OR SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN_NGTOH2(Y,IRE,IRI))).. + ( XH2KFX(IY411,IRE,IRI) + + VXH2KNACCUMNET(IY411,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN(Y,IRE,IRI)) + + VXH2KNACCUMNET_NGTOH2(IY411,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN_NGTOH2(Y,IRE,IRI)) + )*(1$(NOT IXH2KRATE(IRE,IRI,IS3,T))+IXH2KRATE(IRE,IRI,IS3,T)) + =G= VXH2_T(IY411,IRE,IRI,IS3,T) +; + +*"NOT FINISHED, TODO, Accumulated new heat transmission investments available at beginning of next year (MW)" +QXH2KNACCUMNET(IY411,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN(Y,IRE,IRI)).. + VXH2KNACCUMNET(IY411,IRE,IRI) !! End of this year's VXH2KNACCUMNET (available at beginning of next year)' + =E= + SUM(IY402$IY411PREVY(IY411,IY402), VXH2KNACCUMNET(IY402,IRE,IRI)$SUM(Y$(YVALUE(Y) LT YVALUE(IY411)),IXH2KN(Y,IRE,IRI))) + VXH2KN(IY411,IRE,IRI)$IXH2KN(IY411,IRE,IRI) !! VXH2KNVACCUMNET available at end of previous(i.e., start of this) year PLUS investment during this year + +; + +*"NOT FINISHED, TODO, Accumulated new heat transmission investments available at beginning of next year (MW)" +QXH2KNACCUMNET_NGTOH2(IY411,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN_NGTOH2(Y,IRE,IRI)).. + VXH2KNACCUMNET_NGTOH2(IY411,IRE,IRI) !! End of this year's VXH2KNACCUMNET (available at beginning of next year)' + =E= + SUM(IY402$IY411PREVY(IY411,IY402), VXH2KNACCUMNET_NGTOH2(IY402,IRE,IRI)$SUM(Y$(YVALUE(Y) LT YVALUE(IY411)),IXH2KN(Y,IRE,IRI))) + VXH2KN_NGTOH2(IY411,IRE,IRI)$IXH2KN_NGTOH2(IY411,IRE,IRI) !! VXH2KNVACCUMNET_NGTOH2 available at end of previous(i.e., start of this) year PLUS investment during this year + +; + +* "Transmission investments are set symetric(MW)" +QXH2INVSYMMETRY(IY411,IRE,IRI)$IXH2KN(IY411,IRE,IRI).. + VXH2KN(IY411,IRE,IRI) + =E= + VXH2KN(IY411,IRI,IRE) +; + +* "Transmission investments are set symetric(MW)" +QXH2INVSYMMETRY_NGTOH2(IY411,IRE,IRI)$IXH2KN_NGTOH2(IY411,IRE,IRI).. + VXH2KN_NGTOH2(IY411,IRE,IRI) + =E= + VXH2KN_NGTOH2(IY411,IRI,IRE) +; + +*"Maximum H2 transmission capacity between regions" +QXH2KMAX_NGTOH2(IY411,IRE,IRI)$XH2MAXK_NGTOH2(IY411,IRE,IRI).. + XH2MAXK_NGTOH2(IY411,IRE,IRI) + =G= + VXH2KNACCUMNET_NGTOH2(IY411,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXH2KN_NGTOH2(Y,IRE,IRI)) +; + + +QVXH2KN_ES(IY411,IRE,IRI)$(IXH2KN(IY411,IRI,IRE) AND (IXH2KN_ES(IRE,IRI))).. + VXH2KN(IY411,IRE,IRI) =E= SUM(XES,VXH2LAMBDA(IY411,IRE,IRI,XES)*XH2KN_ES(IRE,IRI,XES)); + +QXH2LAMBDA_ES(IY411,IRE,IRI)$(IXH2KN(IY411,IRI,IRE) AND (IXH2KN_ES(IRE,IRI))).. + SUM(XES,VXH2LAMBDA(IY411,IRE,IRI,XES)) =E= 1; diff --git a/base/addons/hydrogen/bb4/hydrogen_ipardecdef.inc b/base/addons/hydrogen/bb4/hydrogen_ipardecdef.inc index 4ad1e5bd..79526d03 100644 --- a/base/addons/hydrogen/bb4/hydrogen_ipardecdef.inc +++ b/base/addons/hydrogen/bb4/hydrogen_ipardecdef.inc @@ -6,6 +6,29 @@ PARAMETERS PARAMETER IHYDROGEN_DH2_REGION_T_Y(YYY,RRR,SSS,TTT) 'Variation of H2 demand in each region along the year'; PARAMETER IHYDROGEN_DH2_AREA_T_Y(YYY,AAA,SSS,TTT) 'Variation of H2 demand in each region along the year'; + +*IK addd +PARAMETERS +*IHYDROGEN_DH2SYNFUEL_REGION_Y_TOT(YYY,RRR) +IHYDROGEN_DH2SYNFUEL_REGION_Y(YYY,RRR) 'demand for synthetic fuel to shift per year' +; + +* If this is zero, no hydrogen demand related to synthetic fuels can be relocated endogenously +* Make two options here, that: +* 1. Activates synthetic fuel data +* 2. Allocate the fraction of demand that can be allocated (THETA_FLEX_SYNFUEL) +Scalar +THETA_FLEX_SYNFUEL /0/ +; + +*Assing Demand for synthetic fuels +IHYDROGEN_DH2SYNFUEL_REGION_Y(Y,IR) = HYDROGEN_DH2_SYN(Y,IR); + +*Deduct the synthetic fuels, from overall demand +HYDROGEN_DH2(Y,IR) = HYDROGEN_DH2(Y,IR) - HYDROGEN_DH2_SYN(Y,IR); + +*IK finish here + IHYDROGEN_DH2_REGION_T_Y(Y,IR,S,T) = (HYDROGEN_DH2(Y,IR)*HYDROGEN_DH2_VAR_T(IR,S,T)/IHYDROGEN_DH2_REGION_SUMST(IR))$(IHYDROGEN_DH2_REGION_SUMST(IR) GT 0); IHYDROGEN_DH2_AREA_T_Y(Y,IA,S,T) = (HYDROGEN_DH2(Y,IA)*HYDROGEN_DH2_VAR_T(IA,S,T)/IHYDROGEN_DH2_AREA_SUMST(IA))$(IHYDROGEN_DH2_AREA_SUMST(IA) GT 0); diff --git a/base/addons/hydrogen/bb4/hydrogen_isetdecdef.inc b/base/addons/hydrogen/bb4/hydrogen_isetdecdef.inc index e087d7cb..d61fa457 100644 --- a/base/addons/hydrogen/bb4/hydrogen_isetdecdef.inc +++ b/base/addons/hydrogen/bb4/hydrogen_isetdecdef.inc @@ -6,7 +6,6 @@ Set IHYDROGEN_GCH4TOH2(G) 'Steam reforming technologies producing hydrog Set IHYDROGEN_GH2FUEL(G) 'Power plants using hydrogen as fuel (excluding H2 storage and biomethane technologies)'; SET IHYDROGEN_GH2TOE(G) 'Technology producing only electricity consuming hydrogen' SET IHYDROGEN_GH2TOEH(G) 'Technology producing electricity and consuming hydrogen' -Set IHYDROGEN_GH2UP(G) 'Fuels needing boost of hydrogen (not used)' Set IHYDROGEN_GH2TOBIOMETH(G) 'Hydrogen + DAC to synthetic natural gas technologies' SET IHYDROGEN_GBIOGASMETHANATION(G) 'Technologies consuming biogas, electricity, and H2 to produce biomethane' SET IHYDROGEN_GBIOGASUPGRADING(G) 'Technologies consuming biogas and electricity to remove CO2 and produce biomethane' diff --git a/base/addons/hydrogen/bb4/hydrogen_pardeclare.inc b/base/addons/hydrogen/bb4/hydrogen_pardeclare.inc index 00297369..dfbb63b1 100644 --- a/base/addons/hydrogen/bb4/hydrogen_pardeclare.inc +++ b/base/addons/hydrogen/bb4/hydrogen_pardeclare.inc @@ -1,5 +1,8 @@ PARAMETERS HYDROGEN_DH2 (YYY,CCCRRRAAA) 'Yearly demand for hydrogen from the transport sector' +*IK add +HYDROGEN_DH2_SYN (YYY,CCCRRRAAA) 'Yearly demand for hydrogen but synthetic fuels' + HYDROGEN_DH2_VAR_T (CCCRRRAAA,SSS,TTT) 'Variation profile of H2 demand per region' HYDROGEN_STOVOL_START (Y,AAA,G,SSS) 'Start level of hydrogen storage' DAC_DE(GGG) 'Power demand needed to produce biomethane through DAC (Mwe/MWth biomethane)' @@ -55,4 +58,4 @@ PARAMETER XH2KACCUMNET(YYY,IRRRE,IRRRI) "Resulting H2 transmission capacity dev PARAMETER XH2_T(Y,IRRRE,IRRRI,S,T) "H2 export from region IRRRE to IRRRI to be transferred to future runs (MW) to be transferred to future runs"; PARAMETER XH2INVCOST_ES(YYY,IRRRE,IRRRI,XES) 'Investment cost in H2 transmission lines at the beginning of each XES (money/XKN_ES )'; -PARAMETER XH2KN_ES(IRRRE,IRRRI,XES) 'Installed capacity in H2 transmission lines at the beginning of each XES (MW)'; +PARAMETER XH2KN_ES(IRRRE,IRRRI,XES) 'Installed capacity in H2 transmission lines at the beginning of each XES (MW)'; \ No newline at end of file diff --git a/base/addons/hydrogen/bb4/hydrogen_pardefine.inc b/base/addons/hydrogen/bb4/hydrogen_pardefine.inc index 048cb5c3..6e8fbb84 100644 --- a/base/addons/hydrogen/bb4/hydrogen_pardefine.inc +++ b/base/addons/hydrogen/bb4/hydrogen_pardefine.inc @@ -3,6 +3,12 @@ $if EXIST '../data/HYDROGEN_DH2.inc' $INCLUDE '../data/HYDROGEN_DH2. $if not EXIST '../data/HYDROGEN_DH2.inc' $INCLUDE '../../base/data/HYDROGEN_DH2.inc'; %semislash%; +*IK add +PARAMETER HYDROGEN_DH2_SYN(YYY,CCCRRRAAA) 'Yearly demand for hydrogen' %semislash% +$if EXIST '../data/HYDROGEN_DH2_SYN.inc' $INCLUDE '../data/HYDROGEN_DH2_SYN.inc'; +$if not EXIST '../data/HYDROGEN_DH2_SYN.inc' $INCLUDE '../../base/data/HYDROGEN_DH2_SYN.inc'; +%semislash%; + PARAMETER HYDROGEN_DH2_VAR_T(CCCRRRAAA,SSS,TTT) 'Variation profile of H2 demand per region' %semislash% $if EXIST '../data/HYDROGEN_DH2_VAR_T.inc' $INCLUDE '../data/HYDROGEN_DH2_VAR_T.inc'; $if not EXIST '../data/HYDROGEN_DH2_VAR_T.inc' $INCLUDE '../../base/data/HYDROGEN_DH2_VAR_T.inc'; diff --git a/base/addons/hydrogen/bb4/hydrogen_qeeq.inc b/base/addons/hydrogen/bb4/hydrogen_qeeq.inc index da34db6b..a7041b0c 100644 --- a/base/addons/hydrogen/bb4/hydrogen_qeeq.inc +++ b/base/addons/hydrogen/bb4/hydrogen_qeeq.inc @@ -1,5 +1,5 @@ +SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GETOH2)$(RRRAAA(IR,IA)), VGE_T(IY411,IA,IHYDROGEN_GETOH2,IS3,T)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GETOH2))) -+SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GETOHH2)$(RRRAAA(IR,IA)), VGE_T(IY411,IA,IHYDROGEN_GETOHH2,IS3,T)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GETOHH2))) ++SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GETOHH2)$(RRRAAA(IR,IA)), VGE_T(IY411,IA,IHYDROGEN_GETOHH2,IS3,T)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GETOHH2))) +SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GEHTOH2)$(RRRAAA(IR,IA)), VGE_T(IY411,IA,IHYDROGEN_GEHTOH2,IS3,T)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GEHTOH2))) *Adding electricity consumption from BIOMETHANE-DAC generation diff --git a/base/addons/hydrogen/bb4/hydrogen_vardeclare.inc b/base/addons/hydrogen/bb4/hydrogen_vardeclare.inc index e7225cb3..d280f15a 100644 --- a/base/addons/hydrogen/bb4/hydrogen_vardeclare.inc +++ b/base/addons/hydrogen/bb4/hydrogen_vardeclare.inc @@ -17,5 +17,12 @@ POSITIVE VARIABLES ; +*Demand shift IK +Positive variable +VGE_T_SYNFUEL_ST(Y,RRR,S,T) 'Hourly hydrogen for synthetic fuel' +VGE_T_SYNFUEL(Y,RRR) 'Total hydrogen for synthetic fuel' +VSYN_FUEL_SHIFT(Y,RRR) 'how much to shift from one area to area' +; + SOS2 VARIABLE VXH2LAMBDA(Y,IRRRE,IRRRI,XES) 'SOS2 Variable for modelling economy of scale'; diff --git a/base/addons/hydrogen/hydrogen_acronyms.inc b/base/addons/hydrogen/hydrogen_acronyms.inc index e3876d2f..dc9661f5 100644 --- a/base/addons/hydrogen/hydrogen_acronyms.inc +++ b/base/addons/hydrogen/hydrogen_acronyms.inc @@ -1 +1 @@ -ACRONYM HYDROGEN_GH2STO, HYDROGEN_GETOH2, HYDROGEN_GEHTOH2, HYDROGEN_GCH4TOH2, HYDROGEN_GH2FUEL, HYDROGEN_GH2TOE; +ACRONYM HYDROGEN_GH2STO, HYDROGEN_GETOH2, HYDROGEN_GEHTOH2, HYDROGEN_GCH4TOH2, HYDROGEN_GH2FUEL, HYDROGEN_GH2TOE,HYDROGEN_GIMPORTH2; diff --git a/base/addons/hydrogen/hydrogen_eqndecdef.inc b/base/addons/hydrogen/hydrogen_eqndecdef.inc index 77ece89c..21a64bc3 100644 --- a/base/addons/hydrogen/hydrogen_eqndecdef.inc +++ b/base/addons/hydrogen/hydrogen_eqndecdef.inc @@ -151,8 +151,6 @@ QHYDROGEN_EQ(IR,IS3,T).. IHYDROGEN_DH2_REGION_T_Y(IR,IS3,T)+ SUM(IA$RRRAAA(IR,IA),IHYDROGEN_DH2_AREA_T_Y(IA,IS3,T)) + SUM(IAGK_Y(IA,IHYDROGEN_GH2FUEL) $(RRRAAA(IR,IA)),VGE_T(IA,IHYDROGEN_GH2FUEL,IS3,T)/GDATA(IHYDROGEN_GH2FUEL,'GDFE')) + SUM(IAGKN(IA,IHYDROGEN_GH2FUEL) $(RRRAAA(IR,IA)),VGEN_T(IA,IHYDROGEN_GH2FUEL,IS3,T)/GDATA(IHYDROGEN_GH2FUEL,'GDFE')) - + SUM(IAGK_Y(IA,IHYDROGEN_GH2UP) $(RRRAAA(IR,IA)),HYDROGEN_H2TOF(IHYDROGEN_GH2UP)*VGF_T(IA,IHYDROGEN_GH2UP,IS3,T)) - + SUM(IAGKN(IA,IHYDROGEN_GH2UP) $(RRRAAA(IR,IA)),HYDROGEN_H2TOF(IHYDROGEN_GH2UP)*VGFN_T(IA,IHYDROGEN_GH2UP,IS3,T)) * + SUM(HYDROGEN_DH2F_U,VHYDROGEN_DH2F_T(IR,IS3,T,HYDROGEN_DH2F_U) ) * - SUM(HYDROGEN_DH2F_D,VHYDROGEN_DH2F_T(IR,IS3,T,HYDROGEN_DH2F_D) ) - VHYDROGEN_QH2EQ(IR,IS3,T,'IMINUS') + VHYDROGEN_QH2EQ(IR,IS3,T,'IPLUS') diff --git a/base/addons/hydrogen/hydrogen_isetdecdef.inc b/base/addons/hydrogen/hydrogen_isetdecdef.inc index 5d4e634c..fcd63d78 100644 --- a/base/addons/hydrogen/hydrogen_isetdecdef.inc +++ b/base/addons/hydrogen/hydrogen_isetdecdef.inc @@ -5,7 +5,6 @@ Set IHYDROGEN_GCH4TOH2(G) 'Steam reforming technologies producing hydrog Set IHYDROGEN_GH2FUEL(G) 'Power plants using hydrogen as fuel'; *IDA new additions SET IHYDROGEN_GH2TOE(G) 'Solid oxide fuel cells' -Set IHYDROGEN_GH2UP(G) 'Fuels needing boost of hydrogen' ; * Based on 'GDTYPE' @@ -14,7 +13,6 @@ IHYDROGEN_GETOH2(G) = YES$(GDATA(G,'GDTYPE') EQ HYDROGEN_GETOH2); IHYDROGEN_GCH4TOH2(G) = YES$(GDATA(G,'GDTYPE') EQ HYDROGEN_GCH4TOH2); *IDA new additions -IHYDROGEN_GH2UP(G)= YES$(GDATA(G,'GDFUEL') EQ THERMGAS_H2 OR GDATA(G,'GDFUEL') EQ BIOGAS_H2); IHYDROGEN_GH2TOE(G)= YES$(GDATA(G,'GDTYPE') EQ HYDROGEN_GH2TOE); IHYDROGEN_GEHTOH2(G) = YES$(GDATA(G,'GDTYPE') EQ HYDROGEN_GEHTOH2); diff --git a/base/addons/hydrogen/hydrogen_pardeclare.inc b/base/addons/hydrogen/hydrogen_pardeclare.inc index 76079ab7..c635e8a5 100644 --- a/base/addons/hydrogen/hydrogen_pardeclare.inc +++ b/base/addons/hydrogen/hydrogen_pardeclare.inc @@ -2,6 +2,4 @@ PARAMETERS HYDROGEN_DH2 (YYY,CCCRRRAAA) 'Yearly demand for hydrogen from the transport sector' HYDROGEN_DH2_VAR_T (CCCRRRAAA,SSS,TTT) 'Variation profile of H2 demand per region' HYDROGEN_STOVOL_START (Y,AAA,G,SSS) 'Start level of hydrogen storage' -*IDAS new additions -HYDROGEN_H2TOF (GGG) 'Factor for need of hydrogen for fuel production' ; diff --git a/base/addons/hydrogen/hydrogen_pardefine.inc b/base/addons/hydrogen/hydrogen_pardefine.inc index ef739310..bf8c3c1b 100644 --- a/base/addons/hydrogen/hydrogen_pardefine.inc +++ b/base/addons/hydrogen/hydrogen_pardefine.inc @@ -12,11 +12,6 @@ PARAMETER HYDROGEN_STOVOL_START(Y,AAA,G,SSS) 'Start level of hydrogen storage * Include start filling level of hydrogen storage obtained from yearly simulation $ifi %bb3%==yes execute_load '../../simex/HYDROGEN_STOVOL_START.gdx', HYDROGEN_STOVOL_START; -PARAMETER HYDROGEN_H2TOF(GGG) 'Hydrogen to fuel factor' %semislash% -$if EXIST '../data/HYDROGEN_H2TOF.inc' $INCLUDE '../data/HYDROGEN_H2TOF.inc'; -$if not EXIST '../data/HYDROGEN_H2TOF.inc' $INCLUDE '../../base/data/HYDROGEN_H2TOF.inc'; -%semislash% - $onmulti $if EXIST '../data/HYDROGEN_AGKN.inc' $INCLUDE '../data/HYDROGEN_AGKN.inc'; $if not EXIST '../data/HYDROGEN_AGKN.inc' $INCLUDE '../../base/data/HYDROGEN_AGKN.inc'; diff --git a/base/auxils/annuity_calculation/annuity_calculation.gms b/base/auxils/annuity_calculation/annuity_calculation.gms index e92a06f9..290b7558 100644 --- a/base/auxils/annuity_calculation/annuity_calculation.gms +++ b/base/auxils/annuity_calculation/annuity_calculation.gms @@ -30,10 +30,12 @@ SET CCCRRRAAA "All geographical entities (CCC + RRR + AAA)" $if EXIST '../data/CCCRRRAAA.inc' $INCLUDE '../data/CCCRRRAAA.inc'; $if not EXIST '../data/CCCRRRAAA.inc' $INCLUDE '../../base/data/CCCRRRAAA.inc'; + SET CCC(CCCRRRAAA) "All Countries" $if EXIST '../data/CCC.inc' $INCLUDE '../data/CCC.inc'; $if not EXIST '../data/CCC.inc' $INCLUDE '../../base/data/CCC.inc'; + SET C(CCC) "Countries in the simulation" $if EXIST '../data/C.inc' $INCLUDE '../data/C.inc'; $if not EXIST '../data/C.inc' $INCLUDE '../../base/data/C.inc'; @@ -41,6 +43,7 @@ $if not EXIST '../data/C.inc' $INCLUDE '../../base/data/C.inc'; SET FFF "Fuels" $if EXIST '../data/FFF.inc' $INCLUDE '../data/FFF.inc'; $if not EXIST '../data/FFF.inc' $INCLUDE '../../base/data/FFF.inc'; + *INCLUDE FFF FROM OTHER ADDONS $include "../../base/addons/hydrogen/bb4/hydrogen_fffadditions.inc"; @@ -48,6 +51,7 @@ SET FDATASET "Characteristics of fuels " $if EXIST '../data/FDATASET.inc' $INCLUDE '../data/FDATASET.inc'; $if not EXIST '../data/FDATASET.inc' $INCLUDE '../../base/data/FDATASET.inc'; + PARAMETER FDATA(FFF,FDATASET) "Fuel specific values" $if EXIST '../data/FDATA.inc' $INCLUDE '../data/FDATA.inc'; $if not EXIST '../data/FDATA.inc' $INCLUDE '../../base/data/FDATA.inc'; diff --git a/base/model/Balmorel.gms b/base/model/Balmorel.gms index 753013d6..98866a98 100644 --- a/base/model/Balmorel.gms +++ b/base/model/Balmorel.gms @@ -1,210 +1,210 @@ -* File Balmorel.gms -$TITLE Balmorel version 4.03 (March 2022; latest 20220318) - -SCALAR IBALVERSN 'This version of Balmorel' /403.20220318/; -* Efforts have been made to make a good model. -* However, most probably the model is incomplete and subject to errors. -* It is distributed with the idea that it will be usefull anyway, -* and with the purpose of getting the essential feedback, -* which in turn will permit the development of improved versions -* to the benefit of other user. -* Hopefully it will be applied in that spirit. - -* All GAMS code of the Balmorel model is distributed under ICS license, -* see the license file in the base/model folder. - - -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- - - -* This is a preliminary version of Balmorel 3.03. -* It is intended for review and commenting. -* See a short list of changes since previous version 3.02 in base/documentation/Balmorel303.txt. -* It is scheduled that a final version 3.03 will be available early 2017. - -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- - - -* GAMS options are $included from file balgams.opt. -* In order to make them apply globally, the option $ONGLOBAL will first be set here: -$ONGLOBAL - -$ifi %system.filesys%==UNIX -execute 'chmod -R ug+rw "../.."'; - -* The balgams file holds control settings for GAMS. -* Use local file if it exists, otherwise use the one in folder ../../base/model/. -$ifi exist 'balgams.opt' $include 'balgams.opt'; -$ifi not exist 'balgams.opt' $include '../../base/model/balgams.opt'; - -* The balopt file holds option (control) settings for the Balmorel model. -* Use local file if it exists, otherwise use the one in folder ../../base/model/. - - -$ifi exist 'balopt.opt' $include 'balopt.opt'; -$ifi not exist 'balopt.opt' $include '../../base/model/balopt.opt'; - -$ifi %system.filesys%==UNIX -execute 'chmod -R ug+rw "../.."'; - -* If merging of savepoint files is to be performed, -* make sure that there are no gdx files initially in applied folders: -$ifi %system.filesys%==UNIX -$ifi %MERGESAVEPOINTRESULTS%==yes execute "rm *.gdx"; -$ifi %system.filesys%==MSNT -$ifi %MERGESAVEPOINTRESULTS%==yes execute "del *.gdx"; -$ifi %system.filesys%==UNIX -$ifi %MERGESAVEPOINTRESULTS%==yes execute "rm ../output/temp/*.gdx"; -$ifi %system.filesys%==MSNT -$ifi %MERGESAVEPOINTRESULTS%==yes execute "del ..\output\temp\*.gdx"; - -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- -*---- Some generally applicable stuff: ----------------------------------------- -*------------------------------------------------------------------------------- - - -* The following may be used in display, put or execute_unload statements (the text, not the value, holds relevant information) -SCALAR SystemDateTime "%system.date%, %system.time%" /NA/; - -* Displaying only a text string will not be accessible through the 'table of content' on the .lst file, therefore this -SCALAR INFODISPLAY "See information next:" /NA/; -DISPLAY INFODISPLAY, "Balmorel run stared at", SystemDateTime; - -* Convenient Factors, typically relating Output and Input: -SCALAR IOF100 'Multiplier 100' /100/; -SCALAR IOF1000 'Multiplier 1000' /1000/; -SCALAR IOF1000000 'Multiplier 1000000' /1000000/; -SCALAR IOF0001 'Multiplier 0.001' /0.001/; -SCALAR IOF0000001 'Multiplier 0.000001' /0.000001/; -SCALAR IOF3P6 'Multiplier 3.6' /3.6/; -SCALAR IOF24 'Multiplier 24' /24/; -SCALAR IOF8760 'Multiplier 8760' /8760/; -SCALAR IOF8784 'Multiplier 8784' /8784/; -SCALAR IOF365 'Multiplier 365' /365/; -SCALAR IOF05 'Multiplier 0.5' /0.5/; -SCALAR IOF1_ 'Multiplier 1 (used with QOBJ and derivation of marginal values)' /1/;!! special, possibly to disappear in future versions -* Scalars for occational use, their meaning will be context dependent: -SCALAR ISCALAR1 '(Context dependent)'; -SCALAR ISCALAR2 '(Context dependent)'; -SCALAR ISCALAR3 '(Context dependent)'; -SCALAR ISCALAR4 '(Context dependent)'; -SCALAR ISCALAR5 '(Context dependent)'; - -* Initialisations of printing of log and error files and messages: -$INCLUDE '../../base/logerror/logerinc/error1.inc'; - -* Ensuring existence of needed output folders: -$ifi not dexist "../../simex" execute 'mkdir -p "../../simex"'; -$ifi not dexist "../logerror/logerinc" execute 'mkdir -p "../logerror/logerinc"'; -$ifi not dexist "../output/economic" execute 'mkdir -p "../output/economic"'; -$ifi not dexist "../output/inputout" execute 'mkdir -p "../output/inputout"'; -$ifi not dexist "../output/printout" execute 'mkdir -p "../output/printout"'; -$ifi not dexist "../output/temp" execute 'mkdir -p "../output/temp"'; - - -*------------------------------------------------------------------------------- -*---- End: Some generally applicable stuff ------------------------------------- -*------------------------------------------------------------------------------- - - - -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- -* In case of Model Balbase4 the following included file substitutes the remaining part of the present file Balmorel.gms: -* Use local file if it exists, otherwise use the one in folder ../../base/model/. -$ifi %BB4%==yes $ifi exist 'Balmorelbb4.inc' $include 'Balmorelbb4.inc'; -$ifi %BB4%==yes $ifi not exist 'Balmorelbb4.inc' $include '../../base/model/Balmorelbb4.inc'; -$ifi %BB4%==yes $goto ENDOFMODEL -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- - -*----- End of model:------------------------------------------------------------ -$label ENDOFMODEL -$ifi %all_endofmodelgdx%==yes execute_unload "all_endofmodel.gdx"; -*----- End of model ------------------------------------------------------------ - - -*--- Results collection for this case ------------------------------------------ - -$ifi not %system.filesys%==MSNT $goto endofMSNToutput -*The following section until $label endofMSNToutput is related to Windows output only -*Please use only backslash \ instead of forward slash / in this section until the label - -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "%relpathoutput%temp\*.gdx"'; -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move merged.gdx "%relpathoutput%%CASEID%.gdx"'; - -$ifi %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "..\output\%CASEID%.gdx"'; -$ifi %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move merged.gdx "%relpathoutput%%CASEID%-results.gdx"' - -$ifi %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access "%relpathoutput%%CASEID%-results.gdx"'; -$ifi %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i "%relpathoutput%%CASEID%-results.gdx" -o "%relpathoutput%%CASEID%-results.db"'; - -*--- Results collection and comparison for differents cases -------------------- - -$ifi not %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "%relpathoutput%%CASEID%.gdx" "%relpathModel%..\..\%MERGEWITH%/output\%MERGEWITH%.gdx"'; -$ifi not %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move merged.gdx "%relpathoutput%%CASEID%-resmerged.gdx"'; - -$ifi not %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access "%relpathoutput%%CASEID%-resmerged.gdx"'; -$ifi not %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i "%relpathoutput%%CASEID%-resmerged.gdx" -o "%relpathoutput%%CASEID%-resmerged.db"'; - -$ifi not %DIFFCASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxdiff "%relpathoutput%%CASEID%-results.gdx" "%relpathModel%..\..\%DIFFWITH%/output/%DIFFWITH%-results.gdx"'; -$ifi not %DIFFCASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move diffile.gdx "%relpathoutput%%CASEID%-diff.gdx"'; - -$label endofMSNToutput - -$ifi not %system.filesys%==UNIX $goto endofUNIXoutput -*The following section until $label endofUNIXoutput is related to UNIX output only -*Please use only forward slash / instead of backslash \ in this section until the label - -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "../output/temp/*.gdx"'; -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./merged.gdx ./"%relpathoutput%%CASEID%.gdx"'; - -$ifi %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "../output/%CASEID%.gdx"'; -$ifi %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./merged.gdx ./"%relpathoutput%%CASEID%-results.gdx"' - -$ifi %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access ./"%relpathoutput%%CASEID%-results.gdx"'; -$ifi %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i ./"%relpathoutput%%CASEID%-results.gdx" -o ./"%relpathoutput%%CASEID%-results.db"'; - -*--- Results collection and comparison for differents cases -------------------- - -$ifi not %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge ./"%relpathoutput%%CASEID%.gdx" ./"%relpathModel%../../%MERGEWITH%/output/%MERGEWITH%.gdx"'; -$ifi not %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./merged.gdx ./"%relpathoutput%%CASEID%-resmerged.gdx"'; - -$ifi not %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access ./"%relpathoutput%%CASEID%-resmerged.gdx"'; -$ifi not %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i ./"%relpathoutput%%CASEID%-resmerged.gdx" -o ./"%relpathoutput%%CASEID%-resmerged.db"'; - -$ifi not %DIFFCASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxdiff ./"%relpathoutput%%CASEID%-results.gdx" ./"%relpathModel%../../%DIFFWITH%/output/%DIFFWITH%-results.gdx"'; -$ifi not %DIFFCASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./diffile.gdx ./"%relpathoutput%%CASEID%-diff.gdx"'; - - -$label endofUNIXoutput - -*----- End of file:------------------------------------------------------------ -$label endoffile - - +*File Balmorel.gms +$TITLE Balmorel version 5.03 (June 2024; latest 20240622) + +SCALAR IBALVERSN 'This version of Balmorel' /503.20240622/; +* Efforts have been made to make a good model. +* However, most probably the model is incomplete and subject to errors. +* It is distributed with the idea that it will be usefull anyway, +* and with the purpose of getting the essential feedback, +* which in turn will permit the development of improved versions +* to the benefit of other user. +* Hopefully it will be applied in that spirit. + +* All GAMS code of the Balmorel model is distributed under ICS license, +* see the license file in the base/model folder. + + +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- + + +* This is a preliminary version of Balmorel 3.03. +* It is intended for review and commenting. +* See a short list of changes since previous version 3.02 in base/documentation/Balmorel303.txt. +* It is scheduled that a final version 3.03 will be available early 2017. + +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- + + +* GAMS options are $included from file balgams.opt. +* In order to make them apply globally, the option $ONGLOBAL will first be set here: +$ONGLOBAL + +$ifi %system.filesys%==UNIX +execute 'chmod -R ug+rw "../.."'; + +* The balgams file holds control settings for GAMS. +* Use local file if it exists, otherwise use the one in folder ../../base/model/. +$ifi exist 'balgams.opt' $include 'balgams.opt'; +$ifi not exist 'balgams.opt' $include '../../base/model/balgams.opt'; + +* The balopt file holds option (control) settings for the Balmorel model. +* Use local file if it exists, otherwise use the one in folder ../../base/model/. + + +$ifi exist 'balopt.opt' $include 'balopt.opt'; +$ifi not exist 'balopt.opt' $include '../../base/model/balopt.opt'; + +$ifi %system.filesys%==UNIX +execute 'chmod -R ug+rw "../.."'; + +* If merging of savepoint files is to be performed, +* make sure that there are no gdx files initially in applied folders: +$ifi %system.filesys%==UNIX +$ifi %MERGESAVEPOINTRESULTS%==yes execute "rm *.gdx"; +$ifi %system.filesys%==MSNT +$ifi %MERGESAVEPOINTRESULTS%==yes execute "del *.gdx"; +$ifi %system.filesys%==UNIX +$ifi %MERGESAVEPOINTRESULTS%==yes execute "rm ../output/temp/*.gdx"; +$ifi %system.filesys%==MSNT +$ifi %MERGESAVEPOINTRESULTS%==yes execute "del ..\output\temp\*.gdx"; + +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- +*---- Some generally applicable stuff: ----------------------------------------- +*------------------------------------------------------------------------------- + + +* The following may be used in display, put or execute_unload statements (the text, not the value, holds relevant information) +SCALAR SystemDateTime "%system.date%, %system.time%" /NA/; + +* Displaying only a text string will not be accessible through the 'table of content' on the .lst file, therefore this +SCALAR INFODISPLAY "See information next:" /NA/; +DISPLAY INFODISPLAY, "Balmorel run stared at", SystemDateTime; + +* Convenient Factors, typically relating Output and Input: +SCALAR IOF100 'Multiplier 100' /100/; +SCALAR IOF1000 'Multiplier 1000' /1000/; +SCALAR IOF1000000 'Multiplier 1000000' /1000000/; +SCALAR IOF0001 'Multiplier 0.001' /0.001/; +SCALAR IOF0000001 'Multiplier 0.000001' /0.000001/; +SCALAR IOF3P6 'Multiplier 3.6' /3.6/; +SCALAR IOF24 'Multiplier 24' /24/; +SCALAR IOF8760 'Multiplier 8760' /8760/; +SCALAR IOF8784 'Multiplier 8784' /8784/; +SCALAR IOF365 'Multiplier 365' /365/; +SCALAR IOF05 'Multiplier 0.5' /0.5/; +SCALAR IOF1_ 'Multiplier 1 (used with QOBJ and derivation of marginal values)' /1/;!! special, possibly to disappear in future versions +* Scalars for occational use, their meaning will be context dependent: +SCALAR ISCALAR1 '(Context dependent)'; +SCALAR ISCALAR2 '(Context dependent)'; +SCALAR ISCALAR3 '(Context dependent)'; +SCALAR ISCALAR4 '(Context dependent)'; +SCALAR ISCALAR5 '(Context dependent)'; + +* Initialisations of printing of log and error files and messages: +$INCLUDE '../../base/logerror/logerinc/error1.inc'; + +* Ensuring existence of needed output folders: +$ifi not dexist "../../simex" execute 'mkdir -p "../../simex"'; +$ifi not dexist "../logerror/logerinc" execute 'mkdir -p "../logerror/logerinc"'; +$ifi not dexist "../output/economic" execute 'mkdir -p "../output/economic"'; +$ifi not dexist "../output/inputout" execute 'mkdir -p "../output/inputout"'; +$ifi not dexist "../output/printout" execute 'mkdir -p "../output/printout"'; +$ifi not dexist "../output/temp" execute 'mkdir -p "../output/temp"'; + + +*------------------------------------------------------------------------------- +*---- End: Some generally applicable stuff ------------------------------------- +*------------------------------------------------------------------------------- + + + +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- +* In case of Model Balbase4 the following included file substitutes the remaining part of the present file Balmorel.gms: +* Use local file if it exists, otherwise use the one in folder ../../base/model/. +$ifi %BB4%==yes $ifi exist 'Balmorelbb4.inc' $include 'Balmorelbb4.inc'; +$ifi %BB4%==yes $ifi not exist 'Balmorelbb4.inc' $include '../../base/model/Balmorelbb4.inc'; +$ifi %BB4%==yes $goto ENDOFMODEL +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- + +*----- End of model:------------------------------------------------------------ +$label ENDOFMODEL +$ifi %all_endofmodelgdx%==yes execute_unload "all_endofmodel.gdx"; +*----- End of model ------------------------------------------------------------ + + +*--- Results collection for this case ------------------------------------------ + +$ifi not %system.filesys%==MSNT $goto endofMSNToutput +*The following section until $label endofMSNToutput is related to Windows output only +*Please use only backslash \ instead of forward slash / in this section until the label + +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "%relpathoutput%temp\*.gdx"'; +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move merged.gdx "%relpathoutput%%CASEID%.gdx"'; + +$ifi %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "..\output\%CASEID%.gdx"'; +$ifi %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move merged.gdx "%relpathoutput%%CASEID%-results.gdx"' + +$ifi %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access "%relpathoutput%%CASEID%-results.gdx"'; +$ifi %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i "%relpathoutput%%CASEID%-results.gdx" -o "%relpathoutput%%CASEID%-results.db"'; + +*--- Results collection and comparison for differents cases -------------------- + +$ifi not %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "%relpathoutput%%CASEID%.gdx" "%relpathModel%..\..\%MERGEWITH%/output\%MERGEWITH%.gdx"'; +$ifi not %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move merged.gdx "%relpathoutput%%CASEID%-resmerged.gdx"'; + +$ifi not %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access "%relpathoutput%%CASEID%-resmerged.gdx"'; +$ifi not %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i "%relpathoutput%%CASEID%-resmerged.gdx" -o "%relpathoutput%%CASEID%-resmerged.db"'; + +$ifi not %DIFFCASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxdiff "%relpathoutput%%CASEID%-results.gdx" "%relpathModel%..\..\%DIFFWITH%/output/%DIFFWITH%-results.gdx"'; +$ifi not %DIFFCASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move diffile.gdx "%relpathoutput%%CASEID%-diff.gdx"'; + +$label endofMSNToutput + +$ifi not %system.filesys%==UNIX $goto endofUNIXoutput +*The following section until $label endofUNIXoutput is related to UNIX output only +*Please use only forward slash / instead of backslash \ in this section until the label + +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "../output/temp/*.gdx"'; +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./merged.gdx ./"%relpathoutput%%CASEID%.gdx"'; + +$ifi %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "../output/%CASEID%.gdx"'; +$ifi %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./merged.gdx ./"%relpathoutput%%CASEID%-results.gdx"' + +$ifi %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access ./"%relpathoutput%%CASEID%-results.gdx"'; +$ifi %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i ./"%relpathoutput%%CASEID%-results.gdx" -o ./"%relpathoutput%%CASEID%-results.db"'; + +*--- Results collection and comparison for differents cases -------------------- + +$ifi not %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge ./"%relpathoutput%%CASEID%.gdx" ./"%relpathModel%../../%MERGEWITH%/output/%MERGEWITH%.gdx"'; +$ifi not %MERGECASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./merged.gdx ./"%relpathoutput%%CASEID%-resmerged.gdx"'; + +$ifi not %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access ./"%relpathoutput%%CASEID%-resmerged.gdx"'; +$ifi not %MERGECASE%==NONE +$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i ./"%relpathoutput%%CASEID%-resmerged.gdx" -o ./"%relpathoutput%%CASEID%-resmerged.db"'; + +$ifi not %DIFFCASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxdiff ./"%relpathoutput%%CASEID%-results.gdx" ./"%relpathModel%../../%DIFFWITH%/output/%DIFFWITH%-results.gdx"'; +$ifi not %DIFFCASE%==NONE +$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./diffile.gdx ./"%relpathoutput%%CASEID%-diff.gdx"'; + + +$label endofUNIXoutput + +*----- End of file:------------------------------------------------------------ +$label endoffile + + diff --git a/base/model/Balmorelbb4.inc b/base/model/Balmorelbb4.inc index ee46150c..765f2237 100644 --- a/base/model/Balmorelbb4.inc +++ b/base/model/Balmorelbb4.inc @@ -1,4 +1,7 @@ * File BalmorelBB4.inc for handling the Balbase4 model in Balmorel. +* First version 2009(hr) based on DTU Master project by Jesper Felstedt &QHYDROGEN_ Morten Middelboe Pedersen under supervision by Hans Ravn. +* Present version 20150428(hr), 20150430(Weiming), 20151217(hr), 20160810(hr), 20170419 (Juan and hr), 20240624 (Mathias and Ioannis) +* Thanks to Weiming Xiong, Andreas von Recklinghausen and others for good contributions. * The model is still under development. * It will be available from www.balmorel.com under open source conditions. @@ -31,7 +34,7 @@ * SETS: * Geography related: -SET CCCRRRAAA All geographical entities (CCC + RRR + AAA)'; +SET CCCRRRAAA All geographical entities (CCC + RRR + AAA); SET CCC(CCCRRRAAA) "All Countries"; SET C(CCC) "Countries in the simulation"; SET RRR(CCCRRRAAA) "All regions"; @@ -250,7 +253,7 @@ PARAMETER VHYRS_STL(YYY,AAA,SSS,TTT) "Hourly hydro storage level (MWh)"; PARAMETER GKACCUMNET(YYY,AAA,GGG) "Resulting technology capacity development at end of (ULTimo) previous (i.e., start of current) year (MW) (MWh for storage) to be transferred to future runs"; PARAMETER XKACCUMNET(YYY,IRRRE,IRRRI) "Resulting transmission capacity development at end (MISSING: minus any decommissioning of them due to lifetime expiration) this BB4, at end of previous (i.e., available at start of current) year (MW) to be transferred to future runs"; -* Data for handling other storage +* Data for handling storage PARAMETER ESTOVOLTS(YYY,AAA,GGG,SSS,TTT) "Inter-seasonal Electricity storage contents at beginning of time segment (MWh) to be transferred to future runs (MWh)"; PARAMETER HSTOVOLTS(YYY,AAA,GGG,SSS,TTT) "Inter-seasonal Heat storage contents at beginning of time segment (MWh) to be transferred to future runs (MWh)"; PARAMETER ESTOVOLT(YYY,AAA,GGG,SSS,TTT) "Intra-seasonal Electricity storage contents at beginning of time segment (MWh) to be transferred to future runs (MWh)"; @@ -2565,7 +2568,7 @@ $ifi %OUTPUT_SUMMARY%==yes $if EXIST '../../base/output/OUTPUT_SUMMARY.inc' *--- End of Main results calculation --------------------------------------- *----optiflow output file (TO BE MERGED IN OUTPUT_SUMMARY.inc) -$ifi %Optiflow%==yes $include '../output/Optiflow_OutputFile.inc'; +$ifi %Optiflow%==yes $if EXIST '../../base/output/Optiflow_OutputFile.inc' $include '../../base/output/Optiflow_OutputFile.inc'; $ifi not %export_results%==yes $goto result_export *------------------------------------------------------------------------------ @@ -2671,7 +2674,7 @@ ESTOVOLTVAL(Y,IA,G,S,T)=0; HSTOVOLTVAL(Y,IA,G,S,T)=0; -*Energy and commitment dispatch to be used in future runs by addon "import_results" +*Energy dispatch to be used in future runs by addon "import_results" *DE_T(Y,IR,S,T)= SUM(DEUSER, ((DE(Y,IR,DEUSER) * DE_VAR_T(IR,DEUSER,S,T)) / IDE_SUMST(IR,DEUSER))$(IDE_SUMST(IR,DEUSER) GT 0))/(1-DISLOSS_E(IR)); *DH_T(Y,IA,S,T)=SUM(DHUSER, ((DH(Y,IA,DHUSER) * DH_VAR_T(IA,DHUSER,S,T)) / IDH_SUMST(IA,DHUSER))$(IDH_SUMST(IA,DHUSER) GT 0))/(1-DISLOSS_H(IA)); GE_T(Y,IA,IGE,S,T)$IAGK_HASORPOT(Y,IA,IGE)=VGE_T.L(Y,IA,IGE,S,T); diff --git a/base/model/balcomline.opt b/base/model/balcomline.opt index b41b3ba0..657f0405 100644 --- a/base/model/balcomline.opt +++ b/base/model/balcomline.opt @@ -11,7 +11,7 @@ Eolonly 1 pw=180 * Cerr is the maximum number of compile time errors before GAMS stops. 0 (default) means no limit: -cerr=3 +cerr=1 * Errmsg controls the position of error messages in the listing file. Possible values: * 0 Place error messages at the end of compiler listing (default) diff --git a/base/model/balopt.opt b/base/model/balopt.opt index 9c57eb6f..f2a2f1f7 100644 --- a/base/model/balopt.opt +++ b/base/model/balopt.opt @@ -28,6 +28,7 @@ $setglobal reduced_vqdebug *----------------------------------------------------------------- *---- END: VQDEBUG ------------------------------------------------ + *=============================================================================== *=============================================================================== * Project and Case ID: @@ -101,18 +102,18 @@ $setglobal TechInvest yes *!option yes SCALAR GINVESTSTART 'Year from which endogenous decisions in generation capacities are allowed' ; -GINVESTSTART= 2020; +GINVESTSTART= 2025; *---- End: Generation investments options---- *---- Transmission investments options---- * Permit investments in transmission capacity: -$setglobal TransInvest yes +$setglobal TransInvest YES *!option yes SCALAR XINVESTSTART 'Year from which endogenous decisions in transmission capacities are allowed' ; -XINVESTSTART= 2025; +XINVESTSTART= 2035; *---- End: Transmission investments options---- @@ -123,7 +124,7 @@ XINVESTSTART= 2025; *overall control variable HEATTRANS must be 'yes' if any of the other heat trans controls are. * Permit heat transmission between areas. -$setglobal HEATTRANS +$setglobal HEATTRANS YES *!option yes * Permit heat transmission investments between areas. @@ -138,30 +139,30 @@ $setglobal HeatTempLift *!option yes *-----------------------END OF: HEAT TRANSFER-------------------------------------------------------- -* Include industry data in the optimization. Requires HEATTRANS add-on to be activated. -$setglobal INDUSTRY +* Include industry data in the optimization. Requires HEATTRANSFER add-on to be activated. +$setglobal INDUSTRY YES *!option yes *--------------------INDIVIDUAL USERS-------------------------------------------------------------- * overall control variable INDIVUSERS must be 'yes' if any of the other INDIVUSERS controls are: -* Include individual users data in the optimization. Requires HEATTRANS add-on to be activated. -$setglobal INDIVUSERS +* Include individual users data in the optimization. Requires HEATTRANSFER add-on to be activated. +$setglobal INDIVUSERS YES *!option yes * Include the possibility to shift exogenous heat demand between technology groups. -$setglobal GROUP_DEMAND_SHIFT +$setglobal GROUP_DEMAND_SHIFT *!option yes *-----------------END: INDIVIDUAL USERS-------------------------------------------------------------- -* Include seasonal variation of heat pump's COP. If not activated, heat pumps will have a constant COP during the year. (The current version does not support using heat pumps in addon COMBTECH) +* Include seasonal variation of heat pump's COP. If not activated, heat pumps will have a constant COP during the year. $setglobal SEASONALCOP *!option yes *------------START OF: OFFSHORE GRIDS-------------------------- * overall control variable OFFSHOREGRID must be 'yes' if any of the other OFFSHOREGRID controls are: -* Allow to build a complicated offshore grid with hubs in the sea. -$setglobal OFFSHOREGRID +* Allow to build a complicated offshore grid with hubs in the sea. Requires MIP to work as intended since technologies are modelled with economies of scale via SOS variables. If RMIP is used, it will become a linear problem. +$setglobal OFFSHOREGRID NO *!option yes *Adds Economies of Scale in generation elements that are part of offshore grids. It requires to use the MIP solver, so remember to activate it. @@ -173,30 +174,31 @@ $setglobal OFFSHOREGRID_ES_ELTRANS *!option yes *------------END OF: OFFSHORE GRIDS-------------------------- + * Allow Carbon Capture Storage (CCS) technologies. -$setglobal CCS +$setglobal CCS YES *!option yes -* Allow curtailment of variable renewable energy generators +* Allow curtailment of variable renewable energy generators (BB4) $setglobal VRESHUTDOWN yes *!option yes *------------------------------EV ADDON------------------------------------------------- * overall control variable EV must be 'yes' if any of the other EV controls are: -$setglobal EV -*!option +$setglobal EV +*!option YES * Adds DUMB charging: $setglobal DUMB *!option yes * Adds smart charging. Only smart charging, without V2G!: -$setglobal SMART +$setglobal SMART *!option yes -* Adds Vehicle-to-Grid possibility: -$setglobal V2G YES +* Adds V2G charging: +$setglobal V2G YES *!option yes * Restricts PHEV vehicles from doing V2G (only relevant if V2G is activated): @@ -234,7 +236,7 @@ $setglobal EV_INTERSEASONAL *------------------------------TRANSPORT ADDON------------------------------------------------- * overall control variable TRANSPORT must be 'yes' if any of the other TRANSPORT controls are: * Include transport data in the optimization (it is supposed to complement the EV addon, so check consistency of data before using it). -$setglobal TRANSPORT +$setglobal TRANSPORT YES *!option yes * Defines the commodity used to model synthetic fuel generation. @@ -243,7 +245,7 @@ $setglobal TRANSPORT_SYNTHFUEL_COMMODITY HYDROGEN *!option HYDROGEN !Text "The demand for synthetic fuel generation is modelled as hydrogen demand (requires hydrogen addon to be activated)" * Defines the possibility for the transport demand of synthetic fuels to be aggregated internationally instead of having to satisfy it regionally. -$setglobal TRANSPORT_DEMAND_AGGREGATED +$setglobal TRANSPORT_DEMAND_AGGREGATED *------------------------------END:TRANSPORT ADDON------------------------------------------------- @@ -316,13 +318,13 @@ $setglobal NOHEATMARKET * however, it has not been checked. Please take contact if problems. * Combine two technologies to one to achieve e.g. multifuel characteristics. -$setglobal COMBTECH +$setglobal COMBTECH *!option yes *-----------------HYDROGEN ADDON--------------------------------------- * overall control variable HYDROGEN must be 'yes' if any of the other HYDROGEN controls are: * Set demands for hydrogen consumption and enable hydrogen technologies (some options require COMBTECH to be activated too). -$Setglobal HYDROGEN +$Setglobal HYDROGEN YES *!option yes * Allows investments in hydrogen transport @@ -330,11 +332,15 @@ $Setglobal H2TransInvest YES *!option yes SCALAR XH2INVESTSTART 'Year from which endogenous decisions in H2 transmission capacities are allowed' ; -XH2INVESTSTART= 2025; +XH2INVESTSTART= 2030; -*Adds Economies of Scale in H2 transmission. It requires to use the MIP solver, so remember to activate it. +*Adds Economies of Scale in H2 transmission. It requires to use the MIP solver, so remember to activate it (Dataset not updated for new countries in pan-European system) $setglobal ES_H2TRANS + +* Allows import of Hydrogen from 3rd countries outside Balmorel +$Setglobal IMPORT_H2 YES *!option yes + *-----------------END: HYDROGEN ADDON--------------------------------------- * Use stepwise linear fuel prices @@ -448,7 +454,7 @@ $setglobal REVERT_DECOM_ENDO_G yes * Note: If either NO_DECOM_EXO_G, or LIFETIME_DECOM_EXO_G, or ECONOMIC_DECOM_EXO_Gor is activated, then the parameter GKFX will be recalculated so capacities only increase in the system. SCALAR GDECOMEXOSTART 'Year from which endogenous decisions in decommissioning of exogenous generation capacities are allowed'; -GDECOMEXOSTART= 2020; +GDECOMEXOSTART= 2025; SCALAR GDECOMEXOPOTSTART 'Starting year to calculate the yearly decommissioning capacity potential, i.e. exogenous decommissioning occuring before this year will not be considered' ; GDECOMEXOPOTSTART= 2016; diff --git a/base/model/cplex.op4 b/base/model/cplex.op4 index 194d4c61..e1b7069e 100644 --- a/base/model/cplex.op4 +++ b/base/model/cplex.op4 @@ -10,5 +10,7 @@ LPmethod 4 Threads -1 advind 0 +predual -1 + diff --git a/base/output/OUTPUT_SUMMARY.inc b/base/output/OUTPUT_SUMMARY.inc index 59c5b134..35e765ab 100644 --- a/base/output/OUTPUT_SUMMARY.inc +++ b/base/output/OUTPUT_SUMMARY.inc @@ -1,1673 +1,1683 @@ -*Create output file - -SET CATEGORY /COSTS,INCOME,PROFIT/; -SET SUBCATEGORY /ELECTRICITY_SALE,HEAT_SALE,H2_SALE,BIOMETHANE_SALE,GENERATION_CAPITAL_COSTS,GENERATION_FIXED_COSTS,GENERATION_OPERATIONAL_COSTS, - GENERATION_FUEL_COSTS,GENERATION_TAXES,GENERATION_GRID_TARIFFS,TRANSMISSION_CAPITAL_COSTS,TRANSMISSION_OPERATIONAL_COSTS, - TRANSMISSION_TRADE_INCOME,TRANSMISSION_TRADE_COSTS,HEAT_TRANSMISSION_CAPITAL_COSTS,HEAT_TRANSMISSION_OPERATIONAL_COSTS, - H2_TRANSMISSION_CAPITAL_COSTS,H2_TRANSMISSION_OPERATIONAL_COSTS,GENERATION_CO2_TAX,GENERATION_CO2_TRANSPORT,GENERATION_UC_COSTS, - GENERATION_OTHER_EMI_TAX,GENERATION_OPPORTUNITY_COSTS_STORAGE,GENERATION_REDISPATCH_COMPENSATION_COST,ELECTRICITY_REDISPATCH_COMPENSATION, - HEAT_REDISPATCH_COMPENSATION,HYDROGEN_REDISPATCH_COMPENSATION,BIOMETHANE_REDISPATCH_COMPENSATION, - HYDRO_PROFILE,TAXES,GRID_TARIFFS,TOTAL_PROFIT,ENERGY_SPECIFIC_PROFIT/; -SET COMMODITY /ELECTRICITY,HEAT,HYDROGEN,BIOMETHANE/; -SET TECH_TYPE /CONDENSING,CHP-BACK-PRESSURE,CHP-EXTRACTION,BOILERS,ELECT-TO-HEAT,INTERSEASONAL-HEAT-STORAGE,INTERSEASONAL-ELECT-STORAGE,INTRASEASONAL-HEAT-STORAGE,INTRASEASONAL-ELECT-STORAGE,HYDRO-RESERVOIRS,HYDRO-RUN-OF-RIVER,WIND-ON,WIND-OFF,SOLAR-PV,SOLAR-HEATING,HYDRO-WAVE,HEAT-PUMP,EL-BOILER,FUELCELL,ELECTROLYZER,H2-STORAGE,BIOMETH-DAC,BIOGASUPGRADING,BIOGASMETHANATION,HUB-OFF,STEAMREFORMING/; -SET GTECH_TYPE(GGG,TECH_TYPE); -SET PRICE_CATEGORY /AVERAGE,AVERAGE_WEIGHTED_BY_CONSUMPTION,AVERAGE_WEIGHTED_BY_PRODUCTION/; -SET VARIABLE_CATEGORY /EXOGENOUS,ENDOGENOUS,DECOMMISSIONING,ENDOGENOUS_ELECT2HEAT,ENDO_INTRASTO,ENDO_INTERSTO,ENDO_EV,ENDO_HEATPUMP,ENDO_ELBOILER,ENDO_OTHERTRANS,DIST_LOSSES,TRANS_LOSSES,ENDO_CCS,ENDO_H2,ENDO_BIOMETHANE,ENDO_FUELCELL/; -SET EL_BAL_TYPE /CURTAILMENT,NETEXPORT,PRICE,DEMAND_EXO,DEMAND_LOSS,DEMAND_INTERSTO,DEMAND_INTRASTO,DEMAND_P2H,DEMAND_EV,CONDENSING,CHP-BACK-PRESSURE,CHP-EXTRACTION,ELECT-TO-HEAT,INTER-STO,INTRA-STO,HYDRO-RESERVOIRS,HYDRO-RUN-OF-RIVER,WIND-ON,WIND-OFF,SOLAR-PV,HYDRO-WAVE,FUELCELL,DEMAND_OTHERTRANS,DEMAND_DISTLOSSES,DEMAND_CCS,DEMAND_P2G/; -SET H_BAL_TYPE /CURTAILMENT,NETEXPORT,PRICE,DEMAND_EXO,DEMAND_LOSS,DEMAND_INTERSTO,DEMAND_INTRASTO,BOILERS,CHP-BACK-PRESSURE,CHP-EXTRACTION,INTER-STO,INTRA-STO,SOLAR-HEATING,P2H,FUELCELL,DEMAND_P2G,DEMAND_DISTLOSSES/; -SET UNITS /GW,TWh,MWh,Money_per_MWh,Mmoney,kton,GWh/; - - - GTECH_TYPE(G,'CONDENSING')$IGCND(G) = YES; - GTECH_TYPE(G,'CHP-BACK-PRESSURE')$IGBPR(G) = YES; - GTECH_TYPE(G,'CHP-EXTRACTION')$IGEXT(G) = YES; - GTECH_TYPE(G,'BOILERS')$IGHOB(G) = YES; - GTECH_TYPE(G,'ELECT-TO-HEAT')$IGETOH(G) = YES; - GTECH_TYPE(G,'INTRASEASONAL-HEAT-STORAGE')$(GDATA(G,'GDTYPE') EQ GHSTO) = YES; - GTECH_TYPE(G,'INTERSEASONAL-HEAT-STORAGE')$(GDATA(G,'GDTYPE') EQ GHSTOS) = YES; - GTECH_TYPE(G,'INTRASEASONAL-ELECT-STORAGE')$(GDATA(G,'GDTYPE') EQ GESTO) = YES; - GTECH_TYPE(G,'INTERSEASONAL-ELECT-STORAGE')$(GDATA(G,'GDTYPE') EQ GESTOS) = YES; - GTECH_TYPE(G,'HYDRO-RESERVOIRS')$IGHYRS(G) = YES; - GTECH_TYPE(G,'HYDRO-RUN-OF-RIVER')$IGHYRR(G) = YES; - GTECH_TYPE(G,'WIND-ON')$(IGWND(G) AND GDATA(G,'GDTECHGROUP') EQ WINDTURBINE_ONSHORE) = YES; - GTECH_TYPE(G,'WIND-OFF')$(IGWND(G) AND GDATA(G,'GDTECHGROUP') EQ WINDTURBINE_OFFSHORE) = YES; - GTECH_TYPE(G,'SOLAR-PV')$IGSOLE(G) = YES; - GTECH_TYPE(G,'SOLAR-HEATING')$IGSOLH(G) = YES; - GTECH_TYPE(G,'HYDRO-WAVE')$IGWAVE(G) = YES; -$ifi %HYDROGEN%==yes GTECH_TYPE(G,'FUELCELL')$(GDATA(G,'GDTECHGROUP') EQ FUELCELL) = YES; -$ifi %HYDROGEN%==yes GTECH_TYPE(G,'ELECTROLYZER')$(GDATA(G,'GDTECHGROUP') EQ ELECTROLYZER) = YES; -$ifi %HYDROGEN%==yes GTECH_TYPE(G,'ELECTROLYZER')$(GDATA(G,'GDTECHGROUP') EQ BACKUP_HYDROGEN) = YES; -$ifi %HYDROGEN%==yes GTECH_TYPE(G,'H2-STORAGE')$(GDATA(G,'GDTYPE') EQ HYDROGEN_GH2STO) = YES; -$ifi %HYDROGEN%==yes GTECH_TYPE(G,'BIOMETH-DAC')$(GDATA(G,'GDTYPE') EQ HYDROGEN_GH2TOBIOMETH) = YES; -$ifi %HYDROGEN%==yes GTECH_TYPE(G,'BIOGASUPGRADING')$(GDATA(G,'GDTYPE') EQ HYDROGEN_GBIOGASUPGRADING) = YES; -$ifi %HYDROGEN%==yes GTECH_TYPE(G,'BIOGASMETHANATION')$(GDATA(G,'GDTYPE') EQ HYDROGEN_GBIOGASMETHANATION) = YES; -$ifi %HYDROGEN%==yes GTECH_TYPE(G,'STEAMREFORMING')$(GDATA(G,'GDTYPE') EQ HYDROGEN_GCH4TOH2) = YES; -$ifi %OFFSHOREGRID%==yes GTECH_TYPE(G,'HUB-OFF')$(GDATA(G,'GDTYPE') EQ HUB_OFF) = YES; - -PARAMETER G_CAP_YCRAF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,VARIABLE_CATEGORY,UNITS) "Generation capacity for each year, country, region, area, technology, fuel, commodity, technology type and variable type (GW)"; -PARAMETER G_STO_YCRAF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,VARIABLE_CATEGORY,UNITS) "Generation storage for each year, country, region, area, technology, fuel, commodity, technology type and variable type (GWh)"; -PARAMETER PRO_YCRAGFST(Y,C,RRR,AAA,G,FFF,SSS,TTT,COMMODITY,TECH_TYPE,UNITS) "Energy Production for each year, country, region, area, technology, fuel, season, hour, commodity and technology type (MWh)" ; -PARAMETER PRO_YCRAGF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,UNITS) "Annual Energy Production for each year, country, region, area, technology, fuel, commodity and technology type (TWh)"; -PARAMETER F_CONS_YCRAST(Y,C,RRR,AAA,G,FFF,SSS,TTT,TECH_TYPE,UNITS) "Fuel consumption for each year, country, region, area, technology, fuel, season, hour and technology type (MWh)"; -PARAMETER F_CONS_YCRA(Y,C,RRR,AAA,G,FFF,TECH_TYPE,UNITS) "Fuel consumption for each year, country, region, area, technology, fuel and technology type (TWh)"; -PARAMETER X_CAP_YCR(Y,C,IRRRE,IRRRI,VARIABLE_CATEGORY,UNITS) "Transmission capacity for each year, country, from region to region (GW)"; -PARAMETER X_FLOW_YCRST(Y,C,IRRRE,IRRRI,SSS,TTT,UNITS) "Transmission flow for each year, country, from region to region, for each season and hour (MWh)"; -PARAMETER X_FLOW_YCR(Y,C,IRRRE,IRRRI,UNITS) "Transmission flow for each year, country, from region to region (TWh)"; -PARAMETER ECO_G_YCRAG(Y,C,RRR,AAA,G,FFF,TECH_TYPE,CATEGORY,SUBCATEGORY,UNITS) "Generation Economic Output for each year, country, region, fuel, technology type, area and technology (Mmoney)"; -PARAMETER ECO_X_YCR(Y,C,RRR,IRRRI,CATEGORY,SUBCATEGORY,UNITS) "Electric Transmission Economic Output for each year, country and region (Mmoney)"; -PARAMETER OBJ_YCR(Y,C,RRR,SUBCATEGORY,UNITS) "Objective function (Mmoney)"; -PARAMETER EL_PRICE_YCR(Y,C,RRR,PRICE_CATEGORY,UNITS) "Average Electricity Prices for each region (money/MWh)"; -PARAMETER EL_PRICE_YCRST(Y,C,RRR,SSS,TTT,UNITS) "Hourly Electricity Prices for each region (money/MWh)"; -PARAMETER H_PRICE_YCRA(Y,C,RRR,AAA,PRICE_CATEGORY,UNITS) "Average heating Prices for each area(money/MWh)"; -PARAMETER H_PRICE_YCRAST(Y,C,RRR,AAA,SSS,TTT,UNITS) "Hourly heating Prices for each area (money(MWh)"; -PARAMETER EL_DEMAND_YCRST(Y,C,RRR,SSS,TTT,VARIABLE_CATEGORY,UNITS) "Aggregated Hourly Electricity Demand (MWh)"; -PARAMETER EL_DEMAND_YCR(Y,C,RRR,VARIABLE_CATEGORY,UNITS) "Aggregated annual Electricity Demand (TWh)"; -PARAMETER H_DEMAND_YCRAST(Y,C,RRR,AAA,SSS,TTT,VARIABLE_CATEGORY,UNITS) "Aggregated Hourly heat Demand (MWh)"; -PARAMETER H_DEMAND_YCRA(Y,C,RRR,AAA,VARIABLE_CATEGORY,UNITS) "Aggregated Annual heat Demand (MWh)"; -PARAMETER EMI_YCRAG(Y,C,RRR,AAA,G,FFF,TECH_TYPE,UNITS) "Annual CO2 emissions(ktons)"; -PARAMETER CURT_YCRAGFST(Y,C,RRR,AAA,G,FFF,SSS,TTT,COMMODITY,TECH_TYPE,UNITS) "Hourly energy curtailment per country, region, area, technology, fuel, hour and technology type (MWh)"; -PARAMETER CURT_YCRAGF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,UNITS) "Hourly energy curtailment per country, region, area, technology, fuel, hour and technology type (MWh)"; -PARAMETER EL_BALANCE_YCRST(Y,C,RRR,EL_BAL_TYPE,SSS,TTT,UNITS) "Hourly electricity balance per country, region, and type (MWh)"; -PARAMETER H_BALANCE_YCRAST(Y,C,RRR,AAA,H_BAL_TYPE,SSS,TTT,UNITS) "Hourly heat balance per country, region, area and type (MWh)"; -$ifi %HYDROGEN%==yes PARAMETER H2_PRICE_YCRST(Y,C,RRR,SSS,TTT,UNITS) "Hourly hydrogen Prices for each region (money/MWh)"; -$ifi %HYDROGEN%==yes PARAMETER BIOMETH_PRICE_YST(Y,SSS,TTT,UNITS) "Hourly biomethane Prices for each region (money/MWh)"; -$ifi %HYDROGEN%==yes PARAMETER XH2_CAP_YCR(Y,C,IRRRE,IRRRI,VARIABLE_CATEGORY,UNITS) "H2 Transmission capacity for each year, country, from region to region (GW)"; -$ifi %HYDROGEN%==yes PARAMETER XH2_FLOW_YCRST(Y,C,IRRRE,IRRRI,SSS,TTT,UNITS) "H2 Transmission flow for each year, country, from region to region, for each season and hour (MWh)"; -$ifi %HYDROGEN%==yes PARAMETER XH2_FLOW_YCR(Y,C,IRRRE,IRRRI,UNITS) "H2 Transmission flow for each year, country, from region to region (TWh)"; -$ifi %HYDROGEN%==yes PARAMETER ECO_XH2_YCR(Y,C,RRR,IRRRI,CATEGORY,SUBCATEGORY,UNITS) "H2 Transmission Economic Output for each year, country and region (Mmoney)"; -$ifi %HYDROGEN%==yes PARAMETER H2_DEMAND_YCRST(Y,C,RRR,SSS,TTT,VARIABLE_CATEGORY,UNITS) "Aggregated Hourly H2 Demand (MWh)"; -$ifi %HYDROGEN%==yes PARAMETER H2_DEMAND_YCR(Y,C,RRR,VARIABLE_CATEGORY,UNITS) "Aggregated annual H2 Demand (TWh)"; - -**Additional Addon output -*HEATRANS -$ifi %HEATTRANS%==yes PARAMETER XH_CAP_YCA(Y,C,IAAAE,IAAAI,VARIABLE_CATEGORY,UNITS) "Heat Transmission capacity for each year, country, from area to area (GW)"; -$ifi %HEATTRANS%==yes PARAMETER XH_FLOW_YCAST(Y,C,IAAAE,IAAAI,SSS,TTT,UNITS) "Heat Transmission flow for each year, country, from areas to area, for each season and hour (MWh)"; -$ifi %HEATTRANS%==yes PARAMETER XH_FLOW_YCA(Y,C,IAAAE,IAAAI,UNITS) "Heat Transmission flow for each year, country, from area to area (TWh)"; -$ifi %HEATTRANS%==yes PARAMETER ECO_XH_YCRA(Y,C,RRR,AAA,IAAAI,CATEGORY,SUBCATEGORY,UNITS) "Heat Transmission Economic Output for each year, country and region and area (Mmoney)"; - - -$ifi not %stointers%==all $goto NO_STOINTERS -*Modification of STORAGE sets so calculation are done correcting its type: -IGHSTO(G) = 0; -IGHSTOS(G) = 0; -IGESTO(G) = 0; -IGESTOS(G) = 0; -IGHSTO(G) = YES$(GDATA(G,'GDTYPE') EQ GHSTO); -IGHSTOS(G) = YES$(GDATA(G,'GDTYPE') EQ GHSTOS); -IGESTO(G) = YES$(GDATA(G,'GDTYPE') EQ GESTO); -IGESTOS(G) = YES$(GDATA(G,'GDTYPE') EQ GESTOS); -$label NO_STOINTERS - -*Modification of IHOURSINST -$ifi %RollingSeasons%==yes PARAMETER IHOURSINTROLLINGSEASON(S,T) "Length of time segment (hours) used in rolling season mode"; -$ifi %RollingSeasons%==yes IWEIGHSUMS=SUM(SSS$(ORD(SSS) LE ROLLINGSEASONSNUMBER), WEIGHT_S(SSS)); -$ifi %RollingSeasons%==yes IHOURSINTROLLINGSEASON(S,T)=IOF8760*WEIGHT_S(S)*WEIGHT_T(T)/(IWEIGHSUMS*SUM(ITALIAS, WEIGHT_T(ITALIAS)))/IHOURFRAC; - -IWEIGHSUMS = SUM(S, WEIGHT_S(S)); -IWEIGHSUMT = SUM(T, WEIGHT_T(T)); -IHOURSINST(SSS,TTT)=0; -IHOURSINST(S,T)=IOF8760*WEIGHT_S(S)*WEIGHT_T(T)/(IWEIGHSUMS*IWEIGHSUMT)/IHOURFRAC; - - - -* -------------------- ENERGY CAPACITY --------------------- - -* ------------- ELECTRICITY CAPACITY -------------- - -** EXOGENOUS ELECTRICITY CAPACITY - -G_CAP_YCRAF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'EXOGENOUS','GW')$((not IGETOH(IGE)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) AND NOT(IHYDROGEN(IGE)))= -IOF0001*GKFX(Y,IA,IGE)/(1$((NOT IGESTO(IGE)) AND (NOT IGESTOS(IGE)))+GDATA(IGE,'GDSTOHUNLD')$((IGESTO(IGE)) OR IGESTOS(IGE))) -; - - -** ENDOGENOUS ELECTRIC CAPACITY ACCUMULATED INVESTMENTS -G_CAP_YCRAF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'ENDOGENOUS','GW')$((not IGETOH(IGE)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) AND NOT(IHYDROGEN(IGE)))= -IOF0001*VGKNACCUMNET.L(Y,IA,IGE)/(1$((NOT IGESTO(IGE)) AND (NOT IGESTOS(IGE)))+GDATA(IGE,'GDSTOHUNLD')$((IGESTO(IGE)) OR IGESTOS(IGE))) -; - -** ENDOGENOUS ELECTRICITY CAPACITY ACCUMULATED DECOMMISSIONING -$ifi %DECOM%==yes G_CAP_YCRAF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'DECOMMISSIONING','GW')$((not IGETOH(IGE)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,IGE) AND NOT(IHYDROGEN(IGE)))= -$ifi %DECOM%==yes -IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IGE)/(1$((NOT IGESTO(IGE)) AND (NOT IGESTOS(IGE)))+GDATA(IGE,'GDSTOHUNLD')$((IGESTO(IGE)) OR IGESTOS(IGE))) -; - - -* ------------- END OF ELECTRICITY CAPACITY -------------- - -* ------------- HEAT CAPACITY -------------- - -** EXOGENOUS HEAT CAPACITY - -G_CAP_YCRAF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'EXOGENOUS','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE))= -(IOF0001*GKFX(Y,IA,IGH)/((1$((NOT IGHSTO(IGH)) AND (NOT IGHSTOS(IGH)))+GDATA(IGH,'GDSTOHUNLD')$((IGHSTO(IGH)) OR IGHSTOS(IGH)))*(GDATA(IGH,'GDCB')$IGBPR(IGH)+ (GDATA(IGH,'GDCB')+GDATA(IGH,'GDCV'))$IGEXT(IGH)+1$(NOT (IGBPR(IGH) OR IGEXT(IGH))))))$(NOT IGBYPASS(IGH) AND NOT(IHYDROGEN(IGH))) -+(IOF0001*GKFX(Y,IA,IGH)*(((GDATA(IGH,'GDCB')/GDATA(IGH,'GDBYPASSC')+1)/(GDATA(IGH,'GDCB')+GDATA(IGH,'GDCV')))$IGEXT(IGH) + (1/GDATA(IGH,'GDBYPASSC')+1/GDATA(IGH,'GDCB'))$IGBPR(IGH)))$(IGBYPASS(IGH) AND NOT(IHYDROGEN(IGH))) -$ifi %HYDROGEN%==yes +(IOF0001*GKFX(Y,IA,IGH)/GDATA(IGH,'GDCB'))$(IHYDROGEN_GETOHH2(IGH) OR IHYDROGEN_GH2TOEH(IGH)) -; - -** ENDOGENOUS HEAT CAPACITY ACCUMULATED INVESTMENTS -G_CAP_YCRAF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'ENDOGENOUS','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE))= -(IOF0001*VGKNACCUMNET.L(Y,IA,IGH)/((1$((NOT IGHSTO(IGH)) AND (NOT IGHSTOS(IGH)))+GDATA(IGH,'GDSTOHUNLD')$((IGHSTO(IGH)) OR IGHSTOS(IGH)))*(GDATA(IGH,'GDCB')$IGBPR(IGH)+ (GDATA(IGH,'GDCB')+GDATA(IGH,'GDCV'))$IGEXT(IGH)+1$(NOT (IGBPR(IGH) OR IGEXT(IGH))))))$(NOT IGBYPASS(IGH) AND NOT(IHYDROGEN(IGH))) -+(IOF0001*VGKNACCUMNET.L(Y,IA,IGH)*(((GDATA(IGH,'GDCB')/GDATA(IGH,'GDBYPASSC')+1)/(GDATA(IGH,'GDCB')+GDATA(IGH,'GDCV')))$IGEXT(IGH) + (1/GDATA(IGH,'GDBYPASSC')+1/GDATA(IGH,'GDCB'))$IGBPR(IGH)))$(IGBYPASS(IGH) AND NOT(IHYDROGEN(IGH))) -$ifi %HYDROGEN%==yes +(IOF0001*VGKNACCUMNET.L(Y,IA,IGH)/GDATA(IGH,'GDCB'))$(IHYDROGEN_GETOHH2(IGH) OR IHYDROGEN_GH2TOEH(IGH)) -; - -** ENDOGENOUS HEAT CAPACITY ACCUMULATED DECOMMISSIONING -$ifi %DECOM%==yes G_CAP_YCRAF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'DECOMMISSIONING','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,IGH))= -$ifi %DECOM%==yes -(IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IGH)/((1$((NOT IGHSTO(IGH)) AND (NOT IGHSTOS(IGH)))+GDATA(IGH,'GDSTOHUNLD')$((IGHSTO(IGH)) OR IGHSTOS(IGH)))*(GDATA(IGH,'GDCB')$IGBPR(IGH)+ (GDATA(IGH,'GDCB')+GDATA(IGH,'GDCV'))$IGEXT(IGH)+1$(NOT (IGBPR(IGH) OR IGEXT(IGH))))))$(NOT IGBYPASS(IGH) AND NOT(IHYDROGEN(IGH))) -$ifi %DECOM%==yes -(IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IGH)*(((GDATA(IGH,'GDCB')/GDATA(IGH,'GDBYPASSC')+1)/(GDATA(IGH,'GDCB')+GDATA(IGH,'GDCV')))$IGEXT(IGH) + (1/GDATA(IGH,'GDBYPASSC')+1/GDATA(IGH,'GDCB'))$IGBPR(IGH)))$(IGBYPASS(IGH) AND NOT(IHYDROGEN(IGH))) -$ifi %DECOM%==yes $ifi %HYDROGEN%==yes -(IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IGH)/GDATA(IGH,'GDCB'))$(IHYDROGEN_GETOHH2(IGH) OR IHYDROGEN_GH2TOEH(IGH)) -; - -* ------------- END OF HEAT CAPACITY -------------- - -$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_CAPACITY -* ------------- HYDROGEN CAPACITY -------------- - -** EXOGENOUS HYDROGEN CAPACITY - -G_CAP_YCRAF(Y,C,IR,IA,G,FFF,'HYDROGEN',TECH_TYPE,'EXOGENOUS','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IHYDROGEN(G))= -IOF0001*GKFX(Y,IA,G)*(1$IHYDROGEN_GH2STO(G)+GDATA(G,'GDFE')$(NOT(IHYDROGEN_GH2STO(G))))/(1$(NOT IHYDROGEN_GH2STO(G))+GDATA(G,'GDSTOHUNLD')$IHYDROGEN_GH2STO(G)) -; - - -** ENDOGENOUS HYDROGEN CAPACITY ACCUMULATED INVESTMENTS -G_CAP_YCRAF(Y,C,IR,IA,G,FFF,'HYDROGEN',TECH_TYPE,'ENDOGENOUS','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IHYDROGEN(G))= -IOF0001*VGKNACCUMNET.L(Y,IA,G)*(1$IHYDROGEN_GH2STO(G)+GDATA(G,'GDFE')$(NOT(IHYDROGEN_GH2STO(G))))/(1$(NOT IHYDROGEN_GH2STO(G))+GDATA(G,'GDSTOHUNLD')$IHYDROGEN_GH2STO(G)) -; - -** ENDOGENOUS HYDROGEN CAPACITY ACCUMULATED DECOMMISSIONING -$ifi %DECOM%==yes G_CAP_YCRAF(Y,C,IR,IA,G,FFF,'HYDROGEN',TECH_TYPE,'DECOMMISSIONING','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,G) AND IHYDROGEN(G))= -$ifi %DECOM%==yes -IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,G)*(1$IHYDROGEN_GH2STO(G)+GDATA(G,'GDFE')$(NOT(IHYDROGEN_GH2STO(G))))/(1$(NOT IHYDROGEN_GH2STO(G))+GDATA(G,'GDSTOHUNLD')$IHYDROGEN_GH2STO(G)) -; - -* ------------- END OF HYDROGEN CAPACITY -------------- - -* ------------- BIOMETHANE CAPACITY -------------- - -** EXOGENOUS BIOMETHANE CAPACITY - -G_CAP_YCRAF(Y,C,IR,IA,G,FFF,'BIOMETHANE',TECH_TYPE,'EXOGENOUS','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND (IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASMETHANATION(G) OR IHYDROGEN_GBIOGASUPGRADING(G)))= -IOF0001*GKFX(Y,IA,G) -; - -** ENDOGENOUS BIOMETHANE CAPACITY ACCUMULATED INVESTMENTS -G_CAP_YCRAF(Y,C,IR,IA,G,FFF,'BIOMETHANE',TECH_TYPE,'ENDOGENOUS','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND (IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASMETHANATION(G) OR IHYDROGEN_GBIOGASUPGRADING(G)))= -IOF0001*VGKNACCUMNET.L(Y,IA,G) -; - -** ENDOGENOUS HYDROGEN CAPACITY ACCUMULATED DECOMMISSIONING -$ifi %DECOM%==yes G_CAP_YCRAF(Y,C,IR,IA,G,FFF,'BIOMETHANE',TECH_TYPE,'DECOMMISSIONING','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,G) AND (IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASMETHANATION(G) OR IHYDROGEN_GBIOGASUPGRADING(G)))= -$ifi %DECOM%==yes -IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,G) -; - -* ------------- END OF BIOMETHANE CAPACITY -------------- - -$label NO_HYDROGEN_CAPACITY - -* ---------------- END OF ENERGY CAPACITY ----------------- - -* -------------------- ENERGY STORAGE --------------------- - -* ------------- ELECTRICITY STORAGE -------------- - -** EXOGENOUS ELECTRICITY STORAGE - -G_STO_YCRAF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'EXOGENOUS','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) AND (IGESTO(IGE) OR IGESTOS(IGE) OR IGHYRS(IGE)) )= -IOF0001*GKFX(Y,IA,IGE)*(1$(NOT IGHYRS(IGE)) + HYRSMAXVOL_G(IA,IGE)$IGHYRS(IGE)) -; - -** ENDOGENOUS ELECTRIC STORAGE ACCUMULATED INVESTMENTS -G_STO_YCRAF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'ENDOGENOUS','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) AND (IGESTO(IGE) OR IGESTOS(IGE) OR IGHYRS(IGE)) )= -IOF0001*VGKNACCUMNET.L(Y,IA,IGE)*(1$(NOT IGHYRS(IGE)) + HYRSMAXVOL_G(IA,IGE)$IGHYRS(IGE)) -; - -** ENDOGENOUS ELECTRICITY STORAGE ACCUMULATED DECOMMISSIONING -$ifi %DECOM%==yes G_STO_YCRAF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'DECOMMISSIONING','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,IGE) AND (IGESTO(IGE) OR IGESTOS(IGE) OR IGHYRS(IGE)) )= -$ifi %DECOM%==yes -IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IGE)*(1$(NOT IGHYRS(IGE)) + HYRSMAXVOL_G(IA,IGE)$IGHYRS(IGE)) -; - -* ------------- END OF ELECTRICITY CAPACITY -------------- - -* ------------- HEAT STORAGE -------------- - -** EXOGENOUS HEAT STORAGE - -G_STO_YCRAF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'EXOGENOUS','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE) AND (IGHSTO(IGH) OR IGHSTOS(IGH)))= -IOF0001*GKFX(Y,IA,IGH) -; - -** ENDOGENOUS HEAT STORAGE ACCUMULATED INVESTMENTS -G_STO_YCRAF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'ENDOGENOUS','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE) AND (IGHSTO(IGH) OR IGHSTOS(IGH)))= -IOF0001*VGKNACCUMNET.L(Y,IA,IGH) -; - -** ENDOGENOUS HEAT STORAGE ACCUMULATED DECOMMISSIONING -$ifi %DECOM%==yes G_STO_YCRAF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'DECOMMISSIONING','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,IGH) AND (IGHSTO(IGH) OR IGHSTOS(IGH)))= -$ifi %DECOM%==yes -IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IGH) -; - -* ------------- END OF HEAT STORAGE -------------- - -$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_STORAGE -* ------------- HYDROGEN STORAGE -------------- - -** EXOGENOUS HYDROGEN STORAGE - -G_STO_YCRAF(Y,C,IR,IA,IHYDROGEN_GH2STO,FFF,'HYDROGEN',TECH_TYPE,'EXOGENOUS','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO) AND IGF(IHYDROGEN_GH2STO,FFF) AND GTECH_TYPE(IHYDROGEN_GH2STO,TECH_TYPE) )= -IOF0001*GKFX(Y,IA,IHYDROGEN_GH2STO) -; - -** ENDOGENOUS HYDROGEN STORAGE ACCUMULATED INVESTMENTS -G_STO_YCRAF(Y,C,IR,IA,IHYDROGEN_GH2STO,FFF,'HYDROGEN',TECH_TYPE,'ENDOGENOUS','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO) AND IGF(IHYDROGEN_GH2STO,FFF) AND GTECH_TYPE(IHYDROGEN_GH2STO,TECH_TYPE))= -IOF0001*VGKNACCUMNET.L(Y,IA,IHYDROGEN_GH2STO) -; - -** ENDOGENOUS HYDROGEN STORAGE ACCUMULATED DECOMMISSIONING -$ifi %DECOM%==yes G_STO_YCRAF(Y,C,IR,IA,IHYDROGEN_GH2STO,FFF,'HYDROGEN',TECH_TYPE,'DECOMMISSIONING','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO) AND IGF(IHYDROGEN_GH2STO,FFF) AND GTECH_TYPE(IHYDROGEN_GH2STO,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,IHYDROGEN_GH2STO))= -$ifi %DECOM%==yes -IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IHYDROGEN_GH2STO) -; -* ------------- END OF HYDROGEN STORAGE -------------- -$label NO_HYDROGEN_STORAGE - -* ---------------- END OF ENERGY STORAGE ----------------- - - -* -------------------- ENERGY PRODUCTION --------------------- - -** ELECTRICITY PRODUCTION HOURLY -PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY',TECH_TYPE,'MWh')$((not IGETOH(IGE)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE))= - VGE_T.L(Y,IA,IGE,S,T); - -** ELECTRICITY PRODUCTION ANNUALLY -PRO_YCRAGF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'TWh')$((not IGETOH(IGE)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE))= - SUM((S,T),IHOURSINST(S,T)*VGE_T.L(Y,IA,IGE,S,T)*IHOURFRAC)/IOF1000000; - -** HEAT PRODUCTION HOURLY -PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT',TECH_TYPE,'MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE))= - VGH_T.L(Y,IA,IGH,S,T); - -** HEAT PRODUCTION ANNUALY -PRO_YCRAGF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE))= - SUM((S,T),IHOURSINST(S,T)*VGH_T.L(Y,IA,IGH,S,T)*IHOURFRAC)/IOF1000000; - -$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_PRODUCTION -** HYDROGEN PRODUCTION HOURLY -PRO_YCRAGFST(Y,C,IR,IA,G,FFF,S,T,'HYDROGEN',TECH_TYPE,'MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IHYDROGEN(G) )= - +(VGE_T.L(Y,IA,G,S,T)*GDATA(G,'GDFE'))$IHYDROGEN_GETOH2(G) - +(VGE_T.L(Y,IA,G,S,T)*GDATA(G,'GDFE'))$IHYDROGEN_GETOHH2(G) - +(VGE_T.L(Y,IA,G,S,T)*GDATA(G,'GDFE'))$IHYDROGEN_GEHTOH2(G) - +(VHYDROGEN_GH2_T.L(Y,IA,G,S,T))$(IHYDROGEN_GCH4TOH2(G) OR IHYDROGEN_GH2STO(G)) -; - -** HYDROGEN PRODUCTION ANNUALY -PRO_YCRAGF(Y,C,IR,IA,G,FFF,'HYDROGEN',TECH_TYPE,'TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IHYDROGEN(G))= - SUM((S,T),IHOURSINST(S,T)*PRO_YCRAGFST(Y,C,IR,IA,G,FFF,S,T,'HYDROGEN',TECH_TYPE,'MWh')*IHOURFRAC)/IOF1000000 -; - -** BIOMETHANE PRODUCTION HOURLY -PRO_YCRAGFST(Y,C,IR,IA,G,FFF,S,T,'BIOMETHANE',TECH_TYPE,'MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND (IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASMETHANATION(G) OR IHYDROGEN_GBIOGASUPGRADING(G)))= - VGBIOMETH_T.L(Y,IA,G,S,T)$IHYDROGEN_GH2TOBIOMETH(G) - + VGBIOGASMETHANATION_T.L(Y,IA,G,S,T)$IHYDROGEN_GBIOGASMETHANATION(G) - +VGBIOGASUPGRADING_T.L(Y,IA,G,S,T)$IHYDROGEN_GBIOGASUPGRADING(G) -; - -** BIOMETHANE PRODUCTION ANNUALY -PRO_YCRAGF(Y,C,IR,IA,G,FFF,'BIOMETHANE',TECH_TYPE,'TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND (IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASMETHANATION(G) OR IHYDROGEN_GBIOGASUPGRADING(G)))= - SUM((S,T),IHOURSINST(S,T)*PRO_YCRAGFST(Y,C,IR,IA,G,FFF,S,T,'BIOMETHANE',TECH_TYPE,'MWh')*IHOURFRAC)/IOF1000000 -; -$label NO_HYDROGEN_PRODUCTION - -* ---------------- END OF ENERGY PRODUCTION ----------------- - - -* -------------------- FUEL CONSUMPTION --------------------- - -** HOURLY FUEL CONSUMPTION -F_CONS_YCRAST(Y,C,IR,IA,G,FFF,S,T,TECH_TYPE,'MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE))= - VGF_T.L(Y,IA,G,S,T); - -** ANNUAL FUEL CONSUMPTION -F_CONS_YCRA(Y,C,IR,IA,G,FFF,TECH_TYPE,'TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE))= - SUM((S,T),IHOURSINST(S,T)*VGF_T.L(Y,IA,G,S,T)*IHOURFRAC)/IOF1000000; - - -* ---------------- END OF FUEL CONSUMPTION ----------------- - -* ---------------- ENERGY CURTAILMENT ----------------------- -*HOURLY CURTAILMENT -CURT_YCRAGFST(Y,C,IR,IA,IGHYRR,FFF,S,T,'ELECTRICITY','HYDRO-RUN-OF-RIVER','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGHYRR) AND IGF(IGHYRR,FFF) AND GTECH_TYPE(IGHYRR,'HYDRO-RUN-OF-RIVER'))= -(WTRRRFLH(IA) * ( - GKFX(Y,IA,IGHYRR) -$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGHYRR) - + VGKNACCUMNET.L(Y,IA,IGHYRR) - ) * WTRRRVAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGHYRR,S,T)) + IGKRATE(IA,IGHYRR,S,T))) / IWTRRRSUM(IA) --VGE_T.L(Y,IA,IGHYRR,S,T); - -CURT_YCRAGFST(Y,C,IR,IA,IGWND,FFF,S,T,'ELECTRICITY','WIND-ON','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGWND) AND IGF(IGWND,FFF) AND GTECH_TYPE(IGWND,'WIND-ON'))= -$ifi %WNDFLH_DOL%==AAA (WNDFLH(IA) * ( -$ifi %WNDFLH_DOL%==AAA_GGG (WNDFLH(IA,IGWND) * ( - GKFX(Y,IA,IGWND) -$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGWND) - + VGKNACCUMNET.L(Y,IA,IGWND) - ) * WND_VAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGWND,S,T)) + IGKRATE(IA,IGWND,S,T))) / IWND_SUMST(IA) --VGE_T.L(Y,IA,IGWND,S,T); - -CURT_YCRAGFST(Y,C,IR,IA,IGWND,FFF,S,T,'ELECTRICITY','WIND-OFF','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGWND) AND IGF(IGWND,FFF) AND GTECH_TYPE(IGWND,'WIND-OFF'))= -$ifi %WNDFLH_DOL%==AAA (WNDFLH(IA) * ( -$ifi %WNDFLH_DOL%==AAA_GGG (WNDFLH(IA,IGWND) * ( - GKFX(Y,IA,IGWND) -$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGWND) - + VGKNACCUMNET.L(Y,IA,IGWND) - ) * WND_VAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGWND,S,T)) + IGKRATE(IA,IGWND,S,T))) / IWND_SUMST(IA) --VGE_T.L(Y,IA,IGWND,S,T); - -CURT_YCRAGFST(Y,C,IR,IA,IGSOLE,FFF,S,T,'ELECTRICITY','SOLAR-PV','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGSOLE) AND IGF(IGSOLE,FFF) AND GTECH_TYPE(IGSOLE,'SOLAR-PV'))= -$ifi %SOLEFLH_DOL%==AAA (SOLEFLH(IA) * ( -$ifi %SOLEFLH_DOL%==AAA_GGG (SOLEFLH(IA,IGSOLE) * ( - GKFX(Y,IA,IGSOLE) -$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGSOLE) - + VGKNACCUMNET.L(Y,IA,IGSOLE) - ) * SOLE_VAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGSOLE,S,T)) + IGKRATE(IA,IGSOLE,S,T))) / ISOLESUMST(IA) --VGE_T.L(Y,IA,IGSOLE,S,T); - -CURT_YCRAGFST(Y,C,IR,IA,IGSOLH,FFF,S,T,'HEAT','SOLAR-HEATING','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGSOLH) AND IGF(IGSOLH,FFF) AND GTECH_TYPE(IGSOLH,'SOLAR-HEATING'))= -$ifi %SOLHFLH_DOL%==AAA (SOLHFLH(IA) * ( -$ifi %SOLHFLH_DOL%==AAA_GGG (SOLHFLH(IA,IGSOLH) * ( - GKFX(Y,IA,IGSOLH) -$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGSOLH) - + VGKNACCUMNET.L(Y,IA,IGSOLH) - ) * SOLH_VAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGSOLH,S,T)) + IGKRATE(IA,IGSOLH,S,T))) / ISOLHSUMST(IA) --VGH_T.L(Y,IA,IGSOLH,S,T); - - -*ANNUAL CURTAILMENT -CURT_YCRAGF(Y,C,IR,IA,IGHYRR,FFF,'ELECTRICITY','HYDRO-RUN-OF-RIVER','TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGHYRR) AND IGF(IGHYRR,FFF) AND GTECH_TYPE(IGHYRR,'HYDRO-RUN-OF-RIVER'))= -SUM((S,T),IHOURSINST(S,T)*CURT_YCRAGFST(Y,C,IR,IA,IGHYRR,FFF,S,T,'ELECTRICITY','HYDRO-RUN-OF-RIVER','MWh')*IHOURFRAC)/IOF1000000 -; -CURT_YCRAGF(Y,C,IR,IA,IGWND,FFF,'ELECTRICITY','WIND-ON','TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGWND) AND IGF(IGWND,FFF) AND GTECH_TYPE(IGWND,'WIND-ON'))= -SUM((S,T),IHOURSINST(S,T)*CURT_YCRAGFST(Y,C,IR,IA,IGWND,FFF,S,T,'ELECTRICITY','WIND-ON','MWh')*IHOURFRAC)/IOF1000000 -; -CURT_YCRAGF(Y,C,IR,IA,IGWND,FFF,'ELECTRICITY','WIND-OFF','TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGWND) AND IGF(IGWND,FFF) AND GTECH_TYPE(IGWND,'WIND-OFF'))= -SUM((S,T),IHOURSINST(S,T)*CURT_YCRAGFST(Y,C,IR,IA,IGWND,FFF,S,T,'ELECTRICITY','WIND-OFF','MWh')*IHOURFRAC)/IOF1000000 -; -CURT_YCRAGF(Y,C,IR,IA,IGSOLE,FFF,'ELECTRICITY','SOLAR-PV','TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGSOLE) AND IGF(IGSOLE,FFF) AND GTECH_TYPE(IGSOLE,'SOLAR-PV'))= -SUM((S,T),IHOURSINST(S,T)*CURT_YCRAGFST(Y,C,IR,IA,IGSOLE,FFF,S,T,'ELECTRICITY','SOLAR-PV','MWh')*IHOURFRAC)/IOF1000000 -; -CURT_YCRAGF(Y,C,IR,IA,IGSOLH,FFF,'HEAT','SOLAR-HEATING','TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGSOLH) AND IGF(IGSOLH,FFF) AND GTECH_TYPE(IGSOLH,'SOLAR-HEATING'))= -SUM((S,T),IHOURSINST(S,T)*CURT_YCRAGFST(Y,C,IR,IA,IGSOLH,FFF,S,T,'HEAT','SOLAR-HEATING','MWh')*IHOURFRAC)/IOF1000000 -; - -*Curtailment of hydro reservoirs should be analyzed by aggregating the technology set -CURT_YCRAGF(Y,C,IR,IA,IGHYRS,FFF,'ELECTRICITY','HYDRO-RESERVOIRS','TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGHYRS) AND IGF(IGHYRS,FFF) AND GTECH_TYPE(IGHYRS,'HYDRO-RESERVOIRS'))= -SUM(S, - IHYINF_S(IA,S) * - ( - GKFX(Y,IA,IGHYRS) -$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGHYRS) - +VGKNACCUMNET.L(Y,IA,IGHYRS) - ) - -SUM(T,IHYRST(S,T)*VGE_T.L(Y,IA,IGHYRS,S,T)) - )/IOF1000000*IHOURFRAC -; - - -* ---------------- END OF ENERGY CURTAILMENT ----------------------- - -* ------------------------- ENERGY DEMAND ---------------------- - -* ------------- ELECTRICITY DEMAND -------------- - -*HOURLY -** EXOGENOUS DEMAND - -EL_DEMAND_YCRST(Y,C,IR,S,T,'EXOGENOUS','MWh')$(CCCRRR(C,IR) AND SUM(DEUSER,IDE_SUMST(IR,DEUSER)))= -SUM((DEUSER)$IDE_SUMST(IR,DEUSER), DE(Y,IR,DEUSER) * DE_VAR_T(IR,DEUSER,S,T) / IDE_SUMST(IR,DEUSER)) - -$ifi not %DEMANDRESPONSE%==yes $goto NO_DEMANDRESPONSE -* Downward regulation for DR shed technologies: -- SUM(IADR(Y,IA,DR_TECH_SHED)$(RRRAAA(IR,IA) AND CCCRRR(C,IR)), VDR_DOWN.L(Y,IA,DR_TECH_SHED,S,T)) -* Balance between upward and downward regulation for DR shift technologies: -*TODO - efficiency -- SUM(IADR(Y,IA,DR_TECH_SHIFT)$(RRRAAA(IR,IA) AND CCCRRR(C,IR)), VDR_DOWN.L(Y,IA,DR_TECH_SHIFT,S,T)) + SUM(IADR(Y,IA,DR_TECH_SHIFT)$(RRRAAA(IR,IA) AND CCCRRR(C,IR)), VDR_UP.L(Y,IA,DR_TECH_SHIFT,S,T)) -$label NO_DEMANDRESPONSE -; - -** ENDOGENOUS DEMAND - -EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDOGENOUS_ELECT2HEAT','MWh')$CCCRRR(C,IR)= -* P2H CONSUMPTION - SUM(IA$RRRAAA(IR,IA), SUM(IGETOH$IAGK_HASORPOT(Y,IA,IGETOH), VGE_T.L(Y,IA,IGETOH,S,T))) -; - -EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_INTRASTO','MWh')$CCCRRR(C,IR)= -* INTRASEASONAL ELECTRICITY STORAGE -$ifi not %stointers%==all +SUM(IA$RRRAAA(IR,IA),SUM(IGESTO$IAGK_HASORPOT(Y,IA,IGESTO),VESTOLOADT.L(Y,IA,IGESTO,S,T))) -$ifi %stointers%==all +SUM(IA$RRRAAA(IR,IA),SUM(IGESTO$IAGK_HASORPOT(Y,IA,IGESTO),VESTOLOADTS.L(Y,IA,IGESTO,S,T))) -; - -EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_INTERSTO','MWh')$CCCRRR(C,IR)= -* INTERSEASONAL ELECTRICITY STORAGE - +SUM(IA$RRRAAA(IR,IA),SUM(IGESTOS$IAGK_HASORPOT(Y,IA,IGESTOS),VESTOLOADTS.L(Y,IA,IGESTOS,S,T))) -; - -$ifi not %EV%==yes $goto NO_EV -EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_EV','MWh')$CCCRRR(C,IR)= - + VEV_VG2V_BEV.L(Y,IR,S,T) - + VEV_VG2V_PHEV.L(Y,IR,S,T) -$ifi %V2G%==yes - VEV_VV2G_BEV.L(Y,IR,S,T) -$ifi %V2G%==yes - VEV_VV2G_PHEV.L(Y,IR,S,T) -$ifi %STEPCHARGSCHEME%==yes - VEV_VV2G_BEV.L(Y,IR,S,T)$(cs('v2g',IY411)) -$ifi %STEPCHARGSCHEME%==yes - VEV_VV2G_PHEV.L(Y,IR,S,T)$(cs('v2g',IY411)) -; -$label NO_EV - -$ifi %TRANSPORT%==yes EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_OTHERTRANS','MWh')$CCCRRR(C,IR)= -$ifi %TRANSPORT%==yes $ifi %TRANSPORT_SYNTHFUEL_COMMODITY%==ELECTRICITY +VTRANSDEMAND_T.L(Y,IR,S,T) -$ifi %TRANSPORT%==yes +0; - -$ifi %CCS%==yes EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_CCS','MWh')$CCCRRR(C,IR)= -$ifi %CCS%==yes SUM((IA,G)$(IAGK_HASORPOT(Y,IA,G) AND RRRAAA(IR,IA) AND CCS_G(G)),VGF_T.L(Y,IA,G,S,T) * IM_CO2(G)*IOF0001 * IOF3P6 * CCS_CO2CAPTEFF_G(G)* CCS_DECO2COMP_G(G)); - -$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_DEMAND -EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_H2','MWh')$CCCRRR(C,IR)= -+SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GETOH2)$(RRRAAA(IR,IA)), VGE_T.L(Y,IA,IHYDROGEN_GETOH2,S,T)) -+SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GETOHH2)$(RRRAAA(IR,IA)), VGE_T.L(Y,IA,IHYDROGEN_GETOHH2,S,T)) -+SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GEHTOH2)$(RRRAAA(IR,IA)), VGE_T.L(Y,IA,IHYDROGEN_GEHTOH2,S,T)) -; - -EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_BIOMETHANE','MWh')$CCCRRR(C,IR)= -*Adding electricity consumption from BIOMETHANE-DAC generation -+ SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH)$(RRRAAA(IR,IA)),VGBIOMETH_T.L(Y,IA,IHYDROGEN_GH2TOBIOMETH,S,T)*DAC_DE(IHYDROGEN_GH2TOBIOMETH)) -*Adding electricity consumption from biogas upgrading generation -+ SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASUPGRADING)$(RRRAAA(IR,IA)),VGBIOGASUPGRADING_T.L(Y,IA,IHYDROGEN_GBIOGASUPGRADING,S,T)*BIOGASUPGRADING_DE(IHYDROGEN_GBIOGASUPGRADING)) -*Adding electricity consumption from biogas methanation generation -+ SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASMETHANATION)$(RRRAAA(IR,IA)),VGBIOGASMETHANATION_T.L(Y,IA,IHYDROGEN_GBIOGASMETHANATION,S,T)*METHANATION_DE(IHYDROGEN_GBIOGASMETHANATION)) -; -$label NO_HYDROGEN_DEMAND - - -**LOSSES -EL_DEMAND_YCRST(Y,C,IR,S,T,'DIST_LOSSES','MWh')$CCCRRR(C,IR)= -* EXOGENOUS DEMAND -EL_DEMAND_YCRST(Y,C,IR,S,T,'EXOGENOUS','MWh')*DISLOSS_E(IR)/(1-DISLOSS_E(IR)) -* P2H CONSUMPTION -+SUM(IA$RRRAAA(IR,IA), SUM(IGETOH$IAGK_HASORPOT(Y,IA,IGETOH), VGE_T.L(Y,IA,IGETOH,S,T)*DISLOSS_E_AG(IA,IGETOH)/(1-DISLOSS_E_AG(IA,IGETOH)))) -* INTRASEASONAL ELECTRICITY STORAGE -$ifi not %stointers%==all +SUM(IA$RRRAAA(IR,IA),SUM(IGESTO$IAGK_HASORPOT(Y,IA,IGESTO),VESTOLOADT.L(Y,IA,IGESTO,S,T)*DISLOSS_E_AG(IA,IGESTO)/(1-DISLOSS_E_AG(IA,IGESTO)))) -$ifi %stointers%==all +SUM(IA$RRRAAA(IR,IA),SUM(IGESTO$IAGK_HASORPOT(Y,IA,IGESTO),VESTOLOADTS.L(Y,IA,IGESTO,S,T)*DISLOSS_E_AG(IA,IGESTO)/(1-DISLOSS_E_AG(IA,IGESTO)))) -* INTERSEASONAL ELECTRICITY STORAGE -+SUM(IA$RRRAAA(IR,IA),SUM(IGESTOS$IAGK_HASORPOT(Y,IA,IGESTOS),VESTOLOADT.L(Y,IA,IGESTOS,S,T)*DISLOSS_E_AG(IA,IGESTOS)/(1-DISLOSS_E_AG(IA,IGESTOS)))) -* EVs -$ifi not %EV%==yes $goto NO_EV2 - +( - + VEV_VG2V_BEV.L(Y,IR,S,T) - + VEV_VG2V_PHEV.L(Y,IR,S,T) - )*EV_DISTLOSSLOAD(IR)/(1-EV_DISTLOSSLOAD(IR)) - +( -$ifi %V2G%==yes - VEV_VV2G_BEV.L(Y,IR,S,T) -$ifi %V2G%==yes - VEV_VV2G_PHEV.L(Y,IR,S,T) -$ifi %STEPCHARGSCHEME%==yes - VEV_VV2G_BEV.L(Y,IR,S,T)$(cs('v2g',IY411)) -$ifi %STEPCHARGSCHEME%==yes - VEV_VV2G_PHEV.L(Y,IR,S,T)$(cs('v2g',IY411)) - +0)*EV_DISTLOSSUNLOAD(IR)/(1-EV_DISTLOSSUNLOAD(IR)) -$label NO_EV2 -*CCS -$ifi %CCS%==yes +SUM((IA,G)$(IAGK_HASORPOT(Y,IA,G) AND RRRAAA(IR,IA) AND CCS_G(G)),VGF_T.L(Y,IA,G,S,T) * IM_CO2(G)*IOF0001 * IOF3P6 * CCS_CO2CAPTEFF_G(G)* CCS_DECO2COMP_G(G)*DISLOSS_E_AG(IA,G)/(1-DISLOSS_E_AG(IA,G))) - -$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_DEMAND2 -+SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GETOH2)$(RRRAAA(IR,IA)), VGE_T.L(Y,IA,IHYDROGEN_GETOH2,S,T)*DISLOSS_E_AG(IA,IHYDROGEN_GETOH2)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GETOH2))) -+SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GETOHH2)$(RRRAAA(IR,IA)), VGE_T.L(Y,IA,IHYDROGEN_GETOHH2,S,T)*DISLOSS_E_AG(IA,IHYDROGEN_GETOHH2)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GETOHH2))) -+SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GEHTOH2)$(RRRAAA(IR,IA)), VGE_T.L(Y,IA,IHYDROGEN_GEHTOH2,S,T)*DISLOSS_E_AG(IA,IHYDROGEN_GEHTOH2)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GEHTOH2))) -+SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH)$(RRRAAA(IR,IA)),VGBIOMETH_T.L(Y,IA,IHYDROGEN_GH2TOBIOMETH,S,T)*DAC_DE(IHYDROGEN_GH2TOBIOMETH)*DISLOSS_E_AG(IA,IHYDROGEN_GH2TOBIOMETH)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GH2TOBIOMETH))) -+SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASUPGRADING)$(RRRAAA(IR,IA)),VGBIOGASUPGRADING_T.L(Y,IA,IHYDROGEN_GBIOGASUPGRADING,S,T)*BIOGASUPGRADING_DE(IHYDROGEN_GBIOGASUPGRADING)*DISLOSS_E_AG(IA,IHYDROGEN_GBIOGASUPGRADING)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GBIOGASUPGRADING))) -+SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASMETHANATION)$(RRRAAA(IR,IA)),VGBIOGASMETHANATION_T.L(Y,IA,IHYDROGEN_GBIOGASMETHANATION,S,T)*METHANATION_DE(IHYDROGEN_GBIOGASMETHANATION)*DISLOSS_E_AG(IA,IHYDROGEN_GBIOGASMETHANATION)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GBIOGASMETHANATION))) -$label NO_HYDROGEN_DEMAND2 - -*PRODUCTION SIDE -+SUM((IGE,IA,FFF,TECH_TYPE)$((NOT IGETOH(IGE)) AND RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY',TECH_TYPE,'MWh')*DISLOSS_E_AG(IA,IGE)) -; - -EL_DEMAND_YCRST(Y,C,IR,S,T,'TRANS_LOSSES','MWh')$CCCRRR(C,IR)= -*TRANSMISSION LOSSES -+SUM(IRE$IXK_HASORPOT(Y,IRE,IR),VX_T.L(Y,IRE,IR,S,T)*XLOSS(IRE,IR)); - - -*ANNUAL DEMAND -** EXOGENOUS DEMAND - -EL_DEMAND_YCR(Y,C,IR,'EXOGENOUS','TWh')$(CCCRRR(C,IR) AND SUM(DEUSER,IDE_SUMST(IR,DEUSER)))= -SUM((DEUSER,S,T)$IDE_SUMST(IR,DEUSER), IHOURSINST(S,T)*IHOURFRAC*DE(Y,IR,DEUSER) * DE_VAR_T(IR,DEUSER,S,T) / IDE_SUMST(IR,DEUSER))/IOF1000000; - -** ENDOGENOUS DEMAND - -EL_DEMAND_YCR(Y,C,IR,'ENDOGENOUS_ELECT2HEAT','TWh')$CCCRRR(C,IR)= -* HEAT PUMPS CONSUMPTION - SUM((S,T,IA)$RRRAAA(IR,IA), SUM(IGETOH$IAGK_HASORPOT(Y,IA,IGETOH), IHOURSINST(S,T)*IHOURFRAC*VGE_T.L(Y,IA,IGETOH,S,T)))/IOF1000000 -; - -EL_DEMAND_YCR(Y,C,IR,'ENDO_INTRASTO','TWh')$CCCRRR(C,IR)= -* INTRASEASONAL ELECTRICITY STORAGE LOSS -$ifi not %stointers%==all +SUM((S,T,IA)$RRRAAA(IR,IA),SUM(IGESTO$IAGK_HASORPOT(Y,IA,IGESTO),IHOURSINST(S,T)*IHOURFRAC*(VESTOLOADT.L(Y,IA,IGESTO,S,T)-VGE_T.L(Y,IA,IGESTO,S,T))))/IOF1000000 -$ifi %stointers%==all +SUM((S,T,IA)$RRRAAA(IR,IA),SUM(IGESTO$IAGK_HASORPOT(Y,IA,IGESTO),IHOURSINST(S,T)*IHOURFRAC*(VESTOLOADTS.L(Y,IA,IGESTO,S,T)-VGE_T.L(Y,IA,IGESTO,S,T))))/IOF1000000 -; - -EL_DEMAND_YCR(Y,C,IR,'ENDO_INTERSTO','TWh')$CCCRRR(C,IR)= -* INTERSEASONAL ELECTRICITY STORAGE LOSS - +SUM((S,T,IA)$RRRAAA(IR,IA),SUM(IGESTOS$IAGK_HASORPOT(Y,IA,IGESTOS),IHOURSINST(S,T)*IHOURFRAC*(VESTOLOADTS.L(Y,IA,IGESTOS,S,T)-VGE_T.L(Y,IA,IGESTOS,S,T))))/IOF1000000 -; - -EL_DEMAND_YCR(Y,C,IR,'ENDO_EV','TWh')$CCCRRR(C,IR)= - SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_EV','MWh')*IHOURFRAC)/IOF1000000 -; - -EL_DEMAND_YCR(Y,C,IR,'ENDO_OTHERTRANS','TWh')$CCCRRR(C,IR)= - SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_OTHERTRANS','MWh')*IHOURFRAC)/IOF1000000 -; - -EL_DEMAND_YCR(Y,C,IR,'DIST_LOSSES','TWh')$CCCRRR(C,IR)= - SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'DIST_LOSSES','MWh')*IHOURFRAC)/IOF1000000 -; - -EL_DEMAND_YCR(Y,C,IR,'TRANS_LOSSES','TWh')$CCCRRR(C,IR)= - SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'TRANS_LOSSES','MWh')*IHOURFRAC)/IOF1000000 -; - -EL_DEMAND_YCR(Y,C,IR,'ENDO_CCS','TWh')$CCCRRR(C,IR)= - SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_CCS','MWh')*IHOURFRAC)/IOF1000000 -; - -EL_DEMAND_YCR(Y,C,IR,'ENDO_H2','TWh')$CCCRRR(C,IR)= - SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_H2','MWh')*IHOURFRAC)/IOF1000000 -; - -EL_DEMAND_YCR(Y,C,IR,'ENDO_BIOMETHANE','TWh')$CCCRRR(C,IR)= - SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_BIOMETHANE','MWh')*IHOURFRAC)/IOF1000000 -; - -* ----------- END OF ELECTRICITY DEMAND -------------- - -* ------------- HEAT DEMAND -------------- -*HOURLY DEMAND -** EXOGENOUS DEMAND - -H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'EXOGENOUS','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA) AND SUM(DHUSER,IDH_SUMST(IA,DHUSER)))= -SUM((DHUSER)$IDH_SUMST(IA,DHUSER), DH(Y,IA,DHUSER) * DH_VAR_T(IA,DHUSER,S,T) / IDH_SUMST(IA,DHUSER)) - -$ifi not %INDIVUSERS%==yes $goto NO_INDIVUSERSGROUP -$ifi not %GROUP_DEMAND_SHIFT%==yes $goto NO_INDIVUSERSGROUP -+ ( -*Removing default demand - -SUM((DHUSER)$IDH_SUMST(IA,DHUSER), DH(Y,IA,DHUSER) * DH_VAR_T(IA,DHUSER,S,T) / IDH_SUMST(IA,DHUSER)) -*Introducing new allocated demand - +SUM((INDIVUSERS_GROUP,DHUSER)$INDIVUSERS_GROUP_A(IA,DHUSER,INDIVUSERS_GROUP), - VINDIVUSERS_GROUPSHARE.L(Y,IR,DHUSER,INDIVUSERS_GROUP)* DH_VAR_T(IA,DHUSER,S,T)/ IDH_SUMST(IA,DHUSER) - )*SUM((INDIVUSERS_DEMANDTYPE,DHUSER)$INDIVUSERS_DEMANDTYPE_A(IA,DHUSER,INDIVUSERS_DEMANDTYPE), - SUM(IAI$(RRRAAA(IR,IAI) AND INDIVUSERS_DEMANDTYPE_A(IAI,DHUSER,INDIVUSERS_DEMANDTYPE)), DH(Y,IAI,DHUSER)) - ) - )$(INDIVUSERS_AAA(IA) - AND SUM((INDIVUSERS_GROUP,DHUSER),INDIVUSERS_GROUP_A(IA,DHUSER,INDIVUSERS_GROUP)) - AND SUM((INDIVUSERS_DEMANDTYPE,DHUSER),INDIVUSERS_DEMANDTYPE_A(IA,DHUSER,INDIVUSERS_DEMANDTYPE))) -$label NO_INDIVUSERSGROUP -; - -** ENDOGENOUS DEMAND - -H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_INTRASTO','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= -* INTRASEASONAL HEAT STORAGE -$ifi not %stointers%==all SUM(IGHSTO$IAGK_HASORPOT(Y,IA,IGHSTO),VHSTOLOADT.L(Y,IA,IGHSTO,S,T)) -$ifi %stointers%==all SUM(IGHSTO$IAGK_HASORPOT(Y,IA,IGHSTO),VHSTOLOADTS.L(Y,IA,IGHSTO,S,T)) - -; - -H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_INTERSTO','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= -* INTERSEASONAL HEAT STORAGE - + SUM(IGHSTOS$IAGK_HASORPOT(Y,IA,IGHSTOS),VHSTOLOADTS.L(Y,IA,IGHSTOS,S,T)) -; - -**LOSSES -H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'DIST_LOSSES','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= -* EXOGENOUS DEMAND -H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'EXOGENOUS','MWh')*DISLOSS_H(IA)/(1-DISLOSS_H(IA)) -; - -$ifi %HEATTRANS%==yes H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'TRANS_LOSSES','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= -$ifi %HEATTRANS%==yes *TRANSMISSION LOSSES -$ifi %HEATTRANS%==yes +SUM(IAE$IXHK_HASORPOT(Y,IAE,IA),VXH_T.L(Y,IAE,IA,S,T)*XHLOSS(IAE,IA)); -$ifi %HEATTRANS%==yes ; - -$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_DEMAND3 -H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_H2','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= -+SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GEHTOH2)$(RRRAAA(IR,IA)), VGH_T.L(Y,IA,IHYDROGEN_GEHTOH2,S,T)) -; - -H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_BIOMETHANE','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= -+ SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH)$(RRRAAA(IR,IA)),VGBIOMETH_T.L(Y,IA,IHYDROGEN_GH2TOBIOMETH,S,T)*DAC_DH(IHYDROGEN_GH2TOBIOMETH)) -; -$label NO_HYDROGEN_DEMAND3 - - -*ANNUAL DEMAND -** EXOGENOUS DEMAND - -H_DEMAND_YCRA(Y,C,IR,IA,'EXOGENOUS','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA) AND SUM(DHUSER,IDH_SUMST(IA,DHUSER)))= -SUM((S,T),IHOURSINST(S,T)*H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'EXOGENOUS','MWh')*IHOURFRAC)/IOF1000000; -; - -** ENDOGENOUS DEMAND -H_DEMAND_YCRA(Y,C,IR,IA,'ENDO_INTRASTO','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= -* INTRASEASONAL HEAT STORAGE LOSS -$ifi not %stointers%==all SUM((S,T,IGHSTO)$IAGK_HASORPOT(Y,IA,IGHSTO),IHOURSINST(S,T)*IHOURFRAC*(VHSTOLOADT.L(Y,IA,IGHSTO,S,T)-VGH_T.L(Y,IA,IGHSTO,S,T)))/IOF1000000 -$ifi %stointers%==all SUM((S,T,IGHSTO)$IAGK_HASORPOT(Y,IA,IGHSTO),IHOURSINST(S,T)*IHOURFRAC*(VHSTOLOADTS.L(Y,IA,IGHSTO,S,T)-VGH_T.L(Y,IA,IGHSTO,S,T)))/IOF1000000 - -; - -H_DEMAND_YCRA(Y,C,IR,IA,'ENDO_INTERSTO','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= - -* INTERSEASONAL HEAT STORAGE LOSS - + SUM((S,T,IGHSTOS)$IAGK_HASORPOT(Y,IA,IGHSTOS),IHOURSINST(S,T)*IHOURFRAC*(VHSTOLOADTS.L(Y,IA,IGHSTOS,S,T)-VGH_T.L(Y,IA,IGHSTOS,S,T)))/IOF1000000 -; - -H_DEMAND_YCRA(Y,C,IR,IA,'DIST_LOSSES','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= - SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'DIST_LOSSES','MWh'))/IOF1000000 -; - -H_DEMAND_YCRA(Y,C,IR,IA,'TRANS_LOSSES','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= - SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'TRANS_LOSSES','MWh'))/IOF1000000 -; - -H_DEMAND_YCRA(Y,C,IR,IA,'ENDO_H2','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= - SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_H2','MWh'))/IOF1000000 -; - -H_DEMAND_YCRA(Y,C,IR,IA,'ENDO_BIOMETHANE','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= - SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_BIOMETHANE','MWh'))/IOF1000000 -; -* ----------- END OF HEAT DEMAND -------------- - -$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_DEMAND30 -* ------------- H2 DEMAND -------------- - -*HOURLY -** EXOGENOUS DEMAND - -H2_DEMAND_YCRST(Y,C,IR,S,T,'EXOGENOUS','MWh')$(CCCRRR(C,IR))= - IHYDROGEN_DH2_REGION_T_Y(Y,IR,S,T)+ - SUM(IA$RRRAAA(IR,IA),IHYDROGEN_DH2_AREA_T_Y(Y,IA,S,T)) -; - -H2_DEMAND_YCRST(Y,C,IR,S,T,'DIST_LOSSES','MWh')$CCCRRR(C,IR)= - IHYDROGEN_DH2_REGION_T_Y(Y,IR,S,T)*DISLOSS_H2(IR)/(1-DISLOSS_H2(IR))+ - SUM(IA$RRRAAA(IR,IA),IHYDROGEN_DH2_AREA_T_Y(Y,IA,S,T)*DISLOSS_H2(IA)/(1-DISLOSS_H2(IA))) -; - -** ENDOGENOUS DEMAND - -H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_INTERSTO','MWh')$CCCRRR(C,IR)= -* INTERSEASONAL H2 STORAGE - +SUM(IA$RRRAAA(IR,IA),SUM(IHYDROGEN_GH2STO$IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO),VHYDROGEN_STOLOADT.L(Y,IA,IHYDROGEN_GH2STO,S,T))) -; - -$ifi %TRANSPORT%==yes H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_OTHERTRANS','MWh')$CCCRRR(C,IR)= -$ifi %TRANSPORT%==yes $ifi %TRANSPORT_SYNTHFUEL_COMMODITY%==HYDROGEN +VTRANSDEMAND_T.L(Y,IR,S,T) -$ifi %TRANSPORT%==yes +0; - - -H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_FUELCELL','MWh')$CCCRRR(C,IR)= -*Adding H2 consumption from FUELCELLS generation -+ SUM(IAGK_HASORPOT(Y,IA,G)$(RRRAAA(IR,IA) AND GTECH_TYPE(G,'FUELCELL')),VGF_T.L(Y,IA,G,S,T)) -; - -H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_BIOMETHANE','MWh')$CCCRRR(C,IR)= -*Adding H2 consumption from BIOMETHANE-DAC generation -+ SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH)$(RRRAAA(IR,IA)),VGBIOMETH_T.L(Y,IA,IHYDROGEN_GH2TOBIOMETH,S,T)/GDATA(IHYDROGEN_GH2TOBIOMETH,'GDFE')) -*Adding H2 consumption from biogas methanation generation -+ SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASMETHANATION)$(RRRAAA(IR,IA)),VGBIOGASMETHANATION_T.L(Y,IA,IHYDROGEN_GBIOGASMETHANATION,S,T)*METHANATION_DH2(IHYDROGEN_GBIOGASMETHANATION)) - -; - - -**LOSSES - -H2_DEMAND_YCRST(Y,C,IR,S,T,'TRANS_LOSSES','MWh')$CCCRRR(C,IR)= -*TRANSMISSION LOSSES -+SUM(IRE$IXH2K_HASORPOT(Y,IRE,IR),VXH2_T.L(Y,IRE,IR,S,T)*XH2LOSS(IRE,IR)); - - -*ANNUAL DEMAND -** EXOGENOUS DEMAND - -H2_DEMAND_YCR(Y,C,IR,'EXOGENOUS','TWh')$(CCCRRR(C,IR))= -SUM((S,T), IHOURSINST(S,T)*IHOURFRAC*H2_DEMAND_YCRST(Y,C,IR,S,T,'EXOGENOUS','MWh'))/IOF1000000; - -** ENDOGENOUS DEMAND - -H2_DEMAND_YCR(Y,C,IR,'ENDO_INTERSTO','TWh')$CCCRRR(C,IR)= -* INTERSEASONAL H2 STORAGE LOSS - +SUM((S,T,IA)$RRRAAA(IR,IA),SUM(IHYDROGEN_GH2STO$IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO),IHOURSINST(S,T)*IHOURFRAC*(VHYDROGEN_STOLOADT.L(Y,IA,IHYDROGEN_GH2STO,S,T)-VHYDROGEN_GH2_T.L(Y,IA,IHYDROGEN_GH2STO,S,T))))/IOF1000000 -; - -H2_DEMAND_YCR(Y,C,IR,'ENDO_FUELCELL','TWh')$CCCRRR(C,IR)= - SUM((S,T),IHOURSINST(S,T)*H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_FUELCELL','MWh')*IHOURFRAC)/IOF1000000 -; - -H2_DEMAND_YCR(Y,C,IR,'ENDO_OTHERTRANS','TWh')$CCCRRR(C,IR)= - SUM((S,T),IHOURSINST(S,T)*H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_OTHERTRANS','MWh')*IHOURFRAC)/IOF1000000 -; - -H2_DEMAND_YCR(Y,C,IR,'TRANS_LOSSES','TWh')$CCCRRR(C,IR)= - SUM((S,T),IHOURSINST(S,T)*H2_DEMAND_YCRST(Y,C,IR,S,T,'TRANS_LOSSES','MWh')*IHOURFRAC)/IOF1000000 -; - -H2_DEMAND_YCR(Y,C,IR,'DIST_LOSSES','TWh')$CCCRRR(C,IR)= - SUM((S,T),IHOURSINST(S,T)*H2_DEMAND_YCRST(Y,C,IR,S,T,'DIST_LOSSES','MWh')*IHOURFRAC)/IOF1000000 -; - -H2_DEMAND_YCR(Y,C,IR,'ENDO_BIOMETHANE','TWh')$CCCRRR(C,IR)= - SUM((S,T),IHOURSINST(S,T)*H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_BIOMETHANE','MWh')*IHOURFRAC)/IOF1000000 -; - -* ----------- END OF H2 DEMAND -------------- -$label NO_HYDROGEN_DEMAND30 - -* -------------------- END OF ENERGY DEMAND ----------------------- - - -* -------------------- TRANSMISSION CAPACITY ----------------------- - -** EXOGENOUS ELECTRICITY CAPACITY - -X_CAP_YCR(Y,C,IR,IRI,'EXOGENOUS','GW')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= -IOF0001*XKFX(Y,IR,IRI) -; - - -** ENDOGENOUS ELECTRIC CAPACITY - -$ifi not %TransInvest%==yes $goto Transmission_investments_end -X_CAP_YCR(Y,C,IR,IRI,'ENDOGENOUS','GW')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= -IOF0001*VXKNACCUMNET.L(Y,IR,IRI) -; -$label Transmission_investments_end - - -* -------------------- END OF TRANSMISSION CAPACITY ----------------------- - -* -------------------- TRANSMISSION FLOW ----------------------- - -** HOURLY TRANSMISSION FLOW - -X_FLOW_YCRST(Y,C,IR,IRI,S,T,'MWh')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= -VX_T.L(Y,IR,IRI,S,T); -; - - -** ANNUAL TRANSMISSION FLOW -X_FLOW_YCR(Y,C,IR,IRI,'TWh')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= -SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*VX_T.L(Y,IR,IRI,S,T))/IOF1000000; -; - - - -* -------------------- END OF TRANSMISSION FLOW ----------------------- - -$ifi NOT %HEATTRANS%==yes $goto no_heattrans -* -------------------- HEAT TRANSMISSION CAPACITY ----------------------- - -** EXOGENOUS HEAT TRANSMISSION CAPACITY - -XH_CAP_YCA(Y,C,IA,IAI,'EXOGENOUS','GW')$(ICA(C,IA) AND (XHKFX(Y,IA,IAI) OR XHKFX_DH(Y,IA,IAI)))= -IOF0001*(XHKFX(Y,IA,IAI) + XHKFX_DH(Y,IA,IAI)) -; - -** ENDOGENOUS HEAT TRANSMISSION CAPACITY - -$ifi not %HeatTransInvest%==yes $goto Heat_Transmission_investments_end -XH_CAP_YCA(Y,C,IA,IAI,'ENDOGENOUS','GW')$(ICA(C,IA) AND IXHK_HASORPOT(Y,IA,IAI))= -IOF0001*VXHKNACCUMNET.L(Y,IA,IAI) -; -$label Heat_Transmission_investments_end - - -* -------------------- END OF HEAT TRANSMISSION CAPACITY ----------------------- - -* -------------------- HEAT TRANSMISSION FLOW ----------------------- - -** HOURLY HEAT TRANSMISSION FLOW - -XH_FLOW_YCAST(Y,C,IA,IAI,S,T,'MWh')$(ICA(C,IA) AND IXHK_HASORPOT(Y,IA,IAI))= -VXH_T.L(Y,IA,IAI,S,T); -; - -** ANNUAL HEAT TRANSMISSION FLOW -XH_FLOW_YCA(Y,C,IA,IAI,'TWh')$(ICA(C,IA) AND IXHK_HASORPOT(Y,IA,IAI))= -SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*VXH_T.L(Y,IA,IAI,S,T))/IOF1000000; -; - -* -------------------- END OF HEAT TRANSMISSION FLOW ----------------------- -$label no_heattrans - - -$ifi NOT %hydrogen%==yes $goto no_hydrogentrans -* -------------------- H2 TRANSMISSION CAPACITY ----------------------- - -** EXOGENOUS H2 TRANSMISSION CAPACITY - -XH2_CAP_YCR(Y,C,IR,IRI,'EXOGENOUS','GW')$(CCCRRR(C,IR) AND XH2KFX(Y,IR,IRI))= -IOF0001*XH2KFX(Y,IR,IRI) -; - -** ENDOGENOUS H2 TRANSMISSION CAPACITY - -$ifi not %H2TransInvest%==yes $goto H2_Transmission_investments_end -XH2_CAP_YCR(Y,C,IR,IRI,'ENDOGENOUS','GW')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= -IOF0001*(VXH2KNACCUMNET.L(Y,IR,IRI)$SUM(IYALIAS2$(YVALUE(IYALIAS2) LE YVALUE(Y)),IXH2KN(IYALIAS2,IR,IRI)) - +VXH2KNACCUMNET_NGTOH2.L(Y,IR,IRI)$SUM(IYALIAS2$(YVALUE(IYALIAS2) LE YVALUE(Y)),IXH2KN_NGTOH2(IYALIAS2,IR,IRI))) -; -$label H2_Transmission_investments_end - - -* -------------------- END OF HEAT TRANSMISSION CAPACITY ----------------------- - -* -------------------- H2 TRANSMISSION FLOW ----------------------- - -** HOURLY H2 TRANSMISSION FLOW - -XH2_FLOW_YCRST(Y,C,IR,IRI,S,T,'MWh')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= -VXH2_T.L(Y,IR,IRI,S,T); -; - -** ANNUAL H2 TRANSMISSION FLOW -XH2_FLOW_YCR(Y,C,IR,IRI,'TWh')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= -SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*VXH2_T.L(Y,IR,IRI,S,T))/IOF1000000; -; - -* -------------------- END OF H2 TRANSMISSION FLOW ----------------------- -$label no_hydrogentrans - -* ---------------------- ECONOMIC OUTPUT ---------------------- - -* ------------- COMMODITY PRICES -------------- - -** ELECTRICITY PRICES - -*HOURLY ELECTRICITY PRICE - -EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')$CCCRRR(C,IR)=OMONEY*QEEQ.M(Y,IR,S,T) -$ifi %RollingSeasons%==yes /IHOURSINTROLLINGSEASON(S,T) -$ifi not %RollingSeasons%==yes /IHOURSINST(S,T) -/IDISCOUNTFACTOR(Y)/IWEIGHTY(Y); - -*AVERAGE ELECTRICITY PRICE - -EL_PRICE_YCR(Y,C,IR,'AVERAGE','Money_per_MWh')$CCCRRR(C,IR)= -SUM((S,T),EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')/(IWEIGHSUMS*IWEIGHSUMT/(WEIGHT_S(S)*WEIGHT_T(T)))); - -$ontext -*EXCLUDED SO FAR -*AVERAGE ELECTRICITY PRICE WEIGTHED BY CONSUMPTION - -EL_PRICE_YCR(Y,C,IR,'AVERAGE_WEIGHTED_BY_CONSUMPTION','Money_per_MWh')$CCCRRR(C,IR)= - SUM((VARIABLE_CATEGORY,S,T), - EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*EL_DEMAND_YCRST(Y,C,IR,S,T,VARIABLE_CATEGORY,'MWh') - ) - /SUM((VARIABLE_CATEGORY,S,T),EL_DEMAND_YCRST(Y,C,IR,S,T,VARIABLE_CATEGORY,'MWh')) -; - -*AVERAGE ELECTRICITY PRICE WEIGTHED BY PRODUCTION - -EL_PRICE_YCR(Y,C,IR,'AVERAGE_WEIGHTED_BY_PRODUCTION','Money_per_MWh')$CCCRRR(C,IR)= - SUM((IGE,IA,FFF,S,T,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGE)), - EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY',TECH_TYPE,'MWh') - ) - /SUM((IGE,IA,FFF,S,T,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY',TECH_TYPE,'MWh')) -; -$offtext - -** END OF ELECTRICITY PRICES - -** heatING PRICES - -*HOURLY heatING PRICE - -$ifi %BalancingMarket%==yes $ifi %NOHEATMARKET%==yes $goto NO_HEATMARKET -H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA) AND (SUM(DHUSER, IDH_SUMST(IA,DHUSER)) -$ifi %HEATTRANS%==yes OR SUM(IAE,IXHK_HASORPOT(Y,IAE,IA)) OR SUM(IAI,IXHK_HASORPOT(Y,IA,IAI)) -))=OMONEY*QHEQ.M(Y,IA,S,T) -$ifi %RollingSeasons%==yes /IHOURSINTROLLINGSEASON(S,T) -$ifi not %RollingSeasons%==yes /IHOURSINST(S,T) -/IDISCOUNTFACTOR(Y)/IWEIGHTY(Y); -$label NO_HEATMARKET -$ifi %BalancingMarket%==yes $ifi %NOHEATMARKET%==yes H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=0; - - - -*AVERAGE heatING PRICE - -H_PRICE_YCRA(Y,C,IR,IA,'AVERAGE','Money_per_MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA) AND (SUM(DHUSER, IDH_SUMST(IA,DHUSER)) -$ifi %HEATTRANS%==yes OR SUM(IAE,IXHK_HASORPOT(Y,IAE,IA)) OR SUM(IAI,IXHK_HASORPOT(Y,IA,IAI)) -))= -SUM((S,T),H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')/(IWEIGHSUMS*IWEIGHSUMT/(WEIGHT_S(S)*WEIGHT_T(T)))); - -$ontext -*EXCLUDED SO FAR -*AVERAGE heatING PRICE WEIGTHED BY CONSUMPTION - -H_PRICE_YCRA(Y,C,IR,IA,'AVERAGE_WEIGHTED_BY_CONSUMPTION','Money_per_MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA) AND SUM(DHUSER, IDH_SUMST(IA,DHUSER)))= - SUM((VARIABLE_CATEGORY,S,T), - H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*H_DEMAND_YCRAST(Y,C,IR,IA,S,T,VARIABLE_CATEGORY,'MWh') - ) - /SUM((VARIABLE_CATEGORY,S,T),H_DEMAND_YCRAST(Y,C,IR,IA,S,T,VARIABLE_CATEGORY,'MWh')) -; - -*AVERAGE heatING PRICE WEIGTHED BY PRODUCTION - -H_PRICE_YCRA(Y,C,IR,IA,'AVERAGE_WEIGHTED_BY_PRODUCTION','Money_per_MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA) AND SUM(DHUSER, IDH_SUMST(IA,DHUSER)))= - SUM((IGH,FFF,S,T,TECH_TYPE)$IAGK_HASORPOT(Y,IA,IGH), - H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT',TECH_TYPE,'MWh') - ) - /SUM((IGH,FFF,S,T,TECH_TYPE)$IAGK_HASORPOT(Y,IA,IGH),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT',TECH_TYPE,'MWh')) -; -$offtext - -** END OF heatING PRICES - -$ifi NOT %HYDROGEN%==yes $goto NO_HYDROGEN_PRICE - -*HOURLY HYDROGEN PRICE -H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')$CCCRRR(C,IR)=OMONEY*QHYDROGEN_EQ.M(Y,IR,S,T) -$ifi %RollingSeasons%==yes /IHOURSINTROLLINGSEASON(S,T) -$ifi not %RollingSeasons%==yes /IHOURSINST(S,T) -/IDISCOUNTFACTOR(Y)/IWEIGHTY(Y); - -*HOURLY BIOMETHANE PRICE -BIOMETH_PRICE_YST(Y,S,T,'Money_per_MWh')=OMONEY*QBIOMETHANE_EQ.M(Y,S,T) -$ifi %RollingSeasons%==yes /IHOURSINTROLLINGSEASON(S,T) -$ifi not %RollingSeasons%==yes /IHOURSINST(S,T) -/IDISCOUNTFACTOR(Y)/IWEIGHTY(Y); - -$label NO_HYDROGEN_PRICE - -* ------------- END OF COMMODITY PRICES -------------- - - -* ------------- COSTS -------------- - -* -- GENERATION COSTS ----- - -** GENERATION INVESTMENTS - -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_CAPITAL_COSTS','Mmoney')$(CCCRRR(C,IR) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and RRRAAA(IR,IA))= - OMONEY*( - SUM(IYALIAS2$((ORD(IYALIAS2) LE ORD(Y)) AND IAGKNY(IYALIAS2,IA,G) AND (NOT IGKN_ES(G))),IYHASANNUITYG(IYALIAS2,Y,G)*VGKN.L(IYALIAS2,IA,G)*GINVCOST(IA,G)*ANNUITYCG(C,G)) -$ifi %OFFSHOREGRID%==yes + SUM(IYALIAS2$((ORD(IYALIAS2) LE ORD(Y)) AND IAGKNY(IYALIAS2,IA,G) AND IGKN_ES(G)),IYHASANNUITYG(IYALIAS2,Y,G)*SUM(XES,VGLAMBDA.L(IYALIAS2,IA,G,XES)*GINVCOST_ES(G,XES))*ANNUITYCG(C,G)) -) -; - -** GENEARTION FIXED COSTS - -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_FIXED_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and IAGK_HASORPOT(Y,IA,G))= - OMONEY/IOF1000*GOMFCOST(IA,G)*( - GKFX(Y,IA,G) - +VGKNACCUMNET.L(Y,IA,G) -$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,G)$IGDECOMEXOPOT(Y,IA,G) - ) -; - -** GENERATION O&M COSTS - -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_OPERATIONAL_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and IAGK_HASORPOT(Y,IA,G))= - OMONEY/IOF1000000* - ( - (IHOURFRAC *GOMVCOSTO(IA,G)*( - SUM((S,T), IHOURSINST(S,T) * VGE_T.L(Y,IA,G,S,T))$IGKE(G) -$ifi %HYDROGEN%==yes + SUM((S,T), IHOURSINST(S,T)*VHYDROGEN_GH2_T.L(Y,IA,G,S,T))$(IHYDROGEN_GH2STO(G) OR IHYDROGEN_GCH4TOH2(G)) -$ifi %HYDROGEN%==yes + SUM((S,T), IHOURSINST(S,T)*VGBIOMETH_T.L(Y,IA,G,S,T))$IHYDROGEN_GH2TOBIOMETH(G) -$ifi %HYDROGEN%==yes + SUM((S,T), IHOURSINST(S,T)*VGBIOGASMETHANATION_T.L(Y,IA,G,S,T))$IHYDROGEN_GBIOGASMETHANATION(G) -$ifi %HYDROGEN%==yes + SUM((S,T), IHOURSINST(S,T)*VGBIOGASUPGRADING_T.L(Y,IA,G,S,T))$IHYDROGEN_GBIOGASUPGRADING(G) - )) - + (IHOURFRAC *GOMVCOSTO(IA,G) * GDATA(G,'GDCV') * - ( - + SUM((S,T), IHOURSINST(S,T) * VGH_T.L(Y,IA,G,S,T))))$IGKH(G) - - + (IHOURFRAC *GOMVCOSTIN(IA,G)* - ( - + SUM((S,T), IHOURSINST(S,T) * VGF_T.L(Y,IA,G,S,T)))) - ) ; - -** GENERATION FUEL COSTS - -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_FUEL_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and IAGK_HASORPOT(Y,IA,G))= - OMONEY/IOF1000000*( - IHOURFRAC *FUELPRICE(Y,IA,FFF)*IOF3P6 -$ifi not %STEPWISEPRICE%==yes $goto No_STEPWISEPRICE - +(QSTEPWISEPRICE_GFTOSTEP.M(Y,C,FFF)/(IDISCOUNTFACTOR(Y)*IWEIGHTY(Y)))$ISTEPWISEPRICE_F(Y,C,FFF) -$label No_STEPWISEPRICE - )* - ( - SUM((S,T), IHOURSINST(S,T) * VGF_T.L(Y,IA,G,S,T) )$IAGK_HASORPOT(Y,IA,G) - )$(IGNOTETOH(G) and (not IGESTO(G)) and (not IGHSTO(G)) and (not IGESTOS(G)) and (not IGHSTOS(G))) - - -* Electricity storages: -$ifi not %stointers%==all +1/IOF1000000*SUM((S,T), EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VESTOLOADT.L(Y,IA,G,S,T))$IGESTO(G) -$ifi %stointers%==all +1/IOF1000000*SUM((S,T), EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VESTOLOADTS.L(Y,IA,G,S,T))$IGESTO(G) - +1/IOF1000000*SUM((S,T), EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VESTOLOADTS.L(Y,IA,G,S,T))$IGESTOS(G) -* Heat storages: -$ifi not %stointers%==all +1/IOF1000000*SUM((S,T), H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*VHSTOLOADT.L(Y,IA,G,S,T))$IGHSTO(G) -$ifi %stointers%==all +1/IOF1000000*SUM((S,T), H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*VHSTOLOADTS.L(Y,IA,G,S,T))$IGHSTO(G) - +1/IOF1000000*SUM((S,T), H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*VHSTOLOADTS.L(Y,IA,G,S,T))$IGHSTOS(G) -* Electricity to heat: - +1/IOF1000000*SUM((S,T), EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGE_T.L(Y,IA,G,S,T))$(IAGK_HASORPOT(Y,IA,G) and IGETOH(G)) - -$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_PRICE2 - +1/IOF1000000*SUM((S,T), EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGE_T.L(Y,IA,G,S,T))$(IHYDROGEN_GETOH2(G) OR IHYDROGEN_GEHTOH2(G) OR IHYDROGEN_GETOHH2(G)) - +1/IOF1000000*SUM((S,T), H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*VGH_T.L(Y,IA,G,S,T))$IHYDROGEN_GEHTOH2(G) - +1/IOF1000000*SUM((S,T), H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VHYDROGEN_STOLOADT.L(Y,IA,G,S,T))$IHYDROGEN_GH2STO(G) - +1/IOF1000000*SUM((S,T), H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGF_T.L(Y,IA,G,S,T))$IHYDROGEN_GH2FUEL(G) - +1/IOF1000000*SUM((S,T), H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGF_T.L(Y,IA,G,S,T) - + H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*VGBIOMETH_T.L(Y,IA,G,S,T)*DAC_DH(G) - + EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGBIOMETH_T.L(Y,IA,G,S,T)*DAC_DE(G) - )$IHYDROGEN_GH2TOBIOMETH(G) - +1/IOF1000000*SUM((S,T), EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGBIOGASUPGRADING_T.L(Y,IA,G,S,T)*BIOGASUPGRADING_DE(G) - )$IHYDROGEN_GBIOGASUPGRADING(G) - +1/IOF1000000*SUM((S,T), H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGBIOGASMETHANATION_T.L(Y,IA,G,S,T)*METHANATION_DH2(G) - + EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGBIOGASMETHANATION_T.L(Y,IA,G,S,T)*METHANATION_DE(G) - )$IHYDROGEN_GBIOGASMETHANATION(G) - +1/IOF1000000*SUM((S,T), BIOMETH_PRICE_YST(Y,S,T,'Money_per_MWh')*VGF_T.L(Y,IA,G,S,T))$IGBIOMETHANE(G) -$label NO_HYDROGEN_PRICE2 -; - -** CO2 EMISSIONS COST -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_CO2_TAX','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and IAGK_HASORPOT(Y,IA,G))= - SUM(GROUP$(GROUP_AAA(GROUP,IA)),OMONEY/IOF1000000*IHOURFRAC *IM_CO2(G)*IOF0001 * IOF3P6 *EMI_POL(Y,C,GROUP,"TAX_CO2") * SUM((S,T), IHOURSINST(S,T)*(VGF_T.L(Y,IA,G,S,T) -$ifi %CCS%==yes -(VGF_T.L(Y,IA,G,S,T)*CCS_CO2CAPTEFF_G(G))$CCS_G(G) -))) -; - -** CO2 TRANSPORT COST -$ifi %CCS%==yes ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_CO2_TRANSPORT','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and IAGK_HASORPOT(Y,IA,G) AND CCS_G(G))= -$ifi %CCS%==yes OMONEY/IOF1000000*IHOURFRAC *IM_CO2(G)*IOF0001 * IOF3P6 * CCS_TRANSPORTCOST * SUM((S,T), IHOURSINST(S,T)*(VGF_T.L(Y,IA,G,S,T))); - - -** OTHER EMISSIONS COST -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_OTHER_EMI_TAX','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and IAGK_HASORPOT(Y,IA,G))= - SUM(GROUP$(GROUP_AAA(GROUP,IA)),OMONEY/IOF1000000* - ( SUM((S,T), IHOURSINST(S,T) * (IHOURFRAC *IM_SO2(G)*IOF0001) * IOF3P6 * VGF_T.L(Y,IA,G,S,T))*EMI_POL(Y,C,GROUP,"TAX_SO2") - + SUM((S,T), IHOURSINST(S,T) * (IHOURFRAC *GDATA(G,'GDNOX')*IOF0000001) * IOF3P6 * VGF_T.L(Y,IA,G,S,T))* EMI_POL(Y,C,GROUP,"TAX_NOX") - )) -; - - -** UNIT COMMITMENT COST -$ifi not %UnitComm%==yes $goto No_UC -ECO_G_YCRAG(Y,C,IR,IA,IGUC,FFF,TECH_TYPE,'COSTS','GENERATION_UC_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(IGUC,FFF) AND GTECH_TYPE(IGUC,TECH_TYPE) and IAGK_HASORPOT(Y,IA,IGUC))= -OMONEY/IOF1000000*SUM((S,T), IHOURSINST(S,T)*( - VUCU.L(Y,IA,IGUC,S,T)*GDATA(IGUC,'GDUCUCOST')*GDATA(IGUC,'GDUCUNITSIZE')/(1$((NOT IGESTO(IGUC)) AND (NOT IGESTOS(IGUC)) AND (NOT IGHSTO(IGUC)) AND (NOT IGHSTOS(IGUC))) + GDATA(IGUC,'GDSTOHUNLD')$(IGESTO(IGUC) OR IGESTOS(IGUC) OR IGHSTO(IGUC) OR IGHSTOS(IGUC)))/CHRONOHOUR(S,T)*WEIGHT_T(T) !! startup cost - +VUCD.L(Y,IA,IGUC,S,T)*GDATA(IGUC,'GDUCDCOST')*GDATA(IGUC,'GDUCUNITSIZE')/(1$((NOT IGESTO(IGUC)) AND (NOT IGESTOS(IGUC)) AND (NOT IGHSTO(IGUC)) AND (NOT IGHSTOS(IGUC))) + GDATA(IGUC,'GDSTOHUNLD')$(IGESTO(IGUC) OR IGESTOS(IGUC) OR IGHSTO(IGUC) OR IGHSTOS(IGUC)))/CHRONOHOUR(S,T)*WEIGHT_T(T) !! shutdown cost - +VUCON.L(Y,IA,IGUC,S,T)*IHOURFRAC*GDATA(IGUC,'GDUCCOST0')*GDATA(IGUC,'GDUCUNITSIZE')/(1$((NOT IGESTO(IGUC)) AND (NOT IGESTOS(IGUC)) AND (NOT IGHSTO(IGUC)) AND (NOT IGHSTOS(IGUC))) + GDATA(IGUC,'GDSTOHUNLD')$(IGESTO(IGUC) OR IGESTOS(IGUC) OR IGHSTO(IGUC) OR IGHSTOS(IGUC))) !! fixed hourly cost - +VUCU_STOLOAD.L(Y,IA,IGUC,S,T)*GDATA(IGUC,'GDUCUCOST')*GDATA(IGUC,'GDUCUNITSIZE')/(1$((NOT IGESTO(IGUC)) AND (NOT IGESTOS(IGUC)) AND (NOT IGHSTO(IGUC)) AND (NOT IGHSTOS(IGUC))) + GDATA(IGUC,'GDSTOHLOAD')$(IGESTO(IGUC) OR IGESTOS(IGUC) OR IGHSTO(IGUC) OR IGHSTOS(IGUC)))/CHRONOHOUR(S,T)*WEIGHT_T(T) !! startup cost - +VUCD_STOLOAD.L(Y,IA,IGUC,S,T)*GDATA(IGUC,'GDUCDCOST')*GDATA(IGUC,'GDUCUNITSIZE')/(1$((NOT IGESTO(IGUC)) AND (NOT IGESTOS(IGUC)) AND (NOT IGHSTO(IGUC)) AND (NOT IGHSTOS(IGUC))) + GDATA(IGUC,'GDSTOHLOAD')$(IGESTO(IGUC) OR IGESTOS(IGUC) OR IGHSTO(IGUC) OR IGHSTOS(IGUC)))/CHRONOHOUR(S,T)*WEIGHT_T(T) !! shutdown cost - +VUCON_STOLOAD.L(Y,IA,IGUC,S,T)*IHOURFRAC*GDATA(IGUC,'GDUCCOST0')*GDATA(IGUC,'GDUCUNITSIZE')/(1$((NOT IGESTO(IGUC)) AND (NOT IGESTOS(IGUC)) AND (NOT IGHSTO(IGUC)) AND (NOT IGHSTOS(IGUC))) + GDATA(IGUC,'GDSTOHLOAD')$(IGESTO(IGUC) OR IGESTOS(IGUC) OR IGHSTO(IGUC) OR IGHSTOS(IGUC))) !! fixed hourly cost - -$ifi %STEPWISE_RAMPING_UC%==ramp_const_cost +SUM(RAMPING_RATE,(VRAMPING_DOWN.L(Y,IA,IGUC,RAMPING_RATE,S,T)*IRAMPING_G_COSTS_DOWN(IGUC,RAMPING_RATE)+VRAMPING_UP.L(Y,IA,IGUC,RAMPING_RATE,S,T)*IRAMPING_G_COSTS_UP(IGUC,RAMPING_RATE))/CHRONOHOUR(S,T)*WEIGHT_T(T)) !! ramping cost -)) -; -$label NO_UC - - -*TAXES -$ifi NOT %TAXES%==yes $goto No_TAXES -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_TAXES','Mmoney')$(CCCRRR(C,IR) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G))= -OMONEY/IOF1000000*( -* Tax(+)/subsidy(-) on fuel use for electricity generation - + SUM((IS3,T), - IHOURSINST(IS3,T) * IHOURFRAC *TAX_F_EL(Y,C,G) * VGE_T.L(Y,IA,G,IS3,T)/ (GDATA(G,'GDFE') * (1$(NOT GEFFRATE(IA,G))+GEFFRATE(IA,G)))) - -* Tax(+)/subsidy(-) on fuel use for heat generation - + SUM((IS3,T), - IHOURSINST(IS3,T) * IHOURFRAC *TAX_F_HEAT(Y,C,G) * GDATA(G,'GDCV') * VGH_T.L(Y,IA,G,IS3,T)/ (GDATA(G,'GDFE') * (1$(NOT GEFFRATE(IA,G))+GEFFRATE(IA,G)))) - -* Tax(+)/subsidy(-) on fuel use - + SUM((IS3,T), - IHOURSINST(IS3,T) * IHOURFRAC *TAX_F(Y,C,G) * VGF_T.L(Y,IA,G,IS3,T)) - -* Tax(+)/subsidy(-) on heat generation - + SUM((IS3,T), - IHOURSINST(IS3,T) * IHOURFRAC *TAX_G_HEAT(Y,C,G) * VGH_T.L(Y,IA,G,IS3,T)) - -* Tax(+)/subsidy(-) on heat generation - + SUM((IS3,T), - IHOURSINST(IS3,T) * IHOURFRAC *TAX_G_EL(Y,C,G) * VGE_T.L(Y,IA,G,IS3,T)) -) -; -$label NO_TAXES - - -*GRID TARIFFS -$ifi NOT %GRIDTARIFFS%==yes $goto No_GRIDTARIFFS -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_GRID_TARIFFS','Mmoney')$(CCCRRR(C,IR) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND (IGETOH(G) -$ifi %HYDROGEN%==yes OR IHYDROGEN_GEHTOH2(G) OR IHYDROGEN_GETOHH2(G) OR IHYDROGEN_GETOH2(G) -))= -OMONEY/IOF1000000*( - -* Fixed component and subscribed power (per MW installed) - + IOF1000 * - IGR_PRICE_TECH(Y,IR,'GRDSUBS') * (VGKNACCUMNET.L(Y,IA,G) -$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,G)$IGDECOMEXOPOT(Y,IA,G) - + GKFX(Y,IA,G)) /((GDATA(G,'GDFE') *(1$(NOT GEFFRATE(IA,G))+GEFFRATE(IA,G)))$IGETOH(G) + 1$(NOT IGETOH(G))) - -* Energy charge winter - + SUM((IS3,T)$IGR_TIME('GRDECW',IR,IS3,T), IHOURSINST(IS3,T) * IHOURFRAC *IGR_PRICE_TECH(Y,IR,'GRDECW') * VGE_T.L(Y,IA,G,IS3,T)) -* Energy charge summer - + SUM((IS3,T)$IGR_TIME('GRDECS',IR,IS3,T), IHOURSINST(IS3,T) * IHOURFRAC *IGR_PRICE_TECH(Y,IR,'GRDECS') * VGE_T.L(Y,IA,G,IS3,T)) -* Energy charge TOU step 1 - + SUM((IS3,T)$IGR_TIME('GRDTOU1',IR,IS3,T), IHOURSINST(IS3,T) * IHOURFRAC *IGR_PRICE_TECH(Y,IR,'GRDTOU1') * VGE_T.L(Y,IA,G,IS3,T)) -* Energy charge TOU step 2 - + SUM((IS3,T)$IGR_TIME('GRDTOU2',IR,IS3,T), IHOURSINST(IS3,T) * IHOURFRAC *IGR_PRICE_TECH(Y,IR,'GRDTOU2') * VGE_T.L(Y,IA,G,IS3,T)) -* Energy charge TOU step 3 - + SUM((IS3,T)$IGR_TIME('GRDTOU3',IR,IS3,T), IHOURSINST(IS3,T) * IHOURFRAC *IGR_PRICE_TECH(Y,IR,'GRDTOU3') * VGE_T.L(Y,IA,G,IS3,T)) -* Energy charge TOU step 4 - + SUM((IS3,T)$IGR_TIME('GRDTOU4',IR,IS3,T), IHOURSINST(IS3,T) * IHOURFRAC *IGR_PRICE_TECH(Y,IR,'GRDTOU4') * VGE_T.L(Y,IA,G,IS3,T)) -) -; -$label NO_GRIDTARIFFS - -* -- TRANSMISSION ----- -*ELECTRICITY -** TRANSMISSION INVESTMENTS -ECO_X_YCR(Y,C,IR,IRI,'COSTS','TRANSMISSION_CAPITAL_COSTS','Mmoney')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= - OMONEY*IOF0000001*( - SUM((IYALIAS2)$((IXKN(IYALIAS2,IRI,IR) OR IXKN(IYALIAS2,IR,IRI)) AND (ORD(IYALIAS2) LE ORD(Y)) AND (NOT (IXKN_ES(IR,IRI) OR IXKN_ES(IRI,IR)))), - IOF05*ANNUITYCX(C)*IYHASANNUITYX(IYALIAS2,Y)*VXKN.L(IYALIAS2,IR,IRI)*XINVCOST(IYALIAS2,IR,IRI)) -$ifi %OFFSHOREGRID%==yes +SUM((IYALIAS2)$((IXKN(IYALIAS2,IRI,IR) OR IXKN(IYALIAS2,IR,IRI)) AND (ORD(IYALIAS2) LE ORD(Y)) AND (IXKN_ES(IR,IRI) OR IXKN_ES(IRI,IR))), -$ifi %OFFSHOREGRID%==yes IOF05*ANNUITYCX(C)*IYHASANNUITYX(IYALIAS2,Y)*SUM(XES,VXLAMBDA.L(IYALIAS2,IR,IRI,XES)*XINVCOST_ES(IYALIAS2,IR,IRI,XES))) -) -; -** TRANSMISSION FLOW COSTS -ECO_X_YCR(Y,C,IR,IRI,'COSTS','TRANSMISSION_OPERATIONAL_COSTS','Mmoney')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= -OMONEY*IOF0000001* SUM((S,T), IHOURSINST(S,T) * (VX_T.L(Y,IR,IRI,S,T) * IHOURFRAC *XCOST(IR,IRI))); - -** TRANSMISSION TRADE COST -ECO_X_YCR(Y,C,IR,IRI,'COSTS','TRANSMISSION_TRADE_COSTS','Mmoney')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= -IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VX_T.L(Y,IRI,IR,S,T) * EL_PRICE_YCRST(Y,C,IRI,S,T,'Money_per_MWh'))); - -** TRANSMISSION TRADE INCOME -ECO_X_YCR(Y,C,IR,IRI,'INCOME','TRANSMISSION_TRADE_INCOME','Mmoney')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= -IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VX_T.L(Y,IR,IRI,S,T) * EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh'))); - -*HEAT -$ifi not %HEATTRANS%==yes $goto No_HEATTRANS_costs -** TRANSMISSION INVESTMENTS -ECO_XH_YCRA(Y,C,IR,IA,IAI,'COSTS','HEAT_TRANSMISSION_CAPITAL_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IXHK_HASORPOT(Y,IA,IAI))= - OMONEY*IOF0000001*SUM((IYALIAS2)$((IXHKN(IYALIAS2,IAI,IA) OR IXHKN(IYALIAS2,IA,IAI)) AND ORD(IYALIAS2) LE ORD(Y)) , - IOF05*ANNUITYCXH(C)*IYHASANNUITYXH(IYALIAS2,Y)*VXHKN.L(IYALIAS2,IA,IAI)*XHINVCOST(IYALIAS2,IA,IAI) - ); -** TRANSMISSION FLOW -ECO_XH_YCRA(Y,C,IR,IA,IAI,'COSTS','HEAT_TRANSMISSION_OPERATIONAL_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IXHK_HASORPOT(Y,IA,IAI))= -OMONEY*IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VXH_T.L(Y,IA,IAI,S,T) * IHOURFRAC *XHCOST(IA,IAI))); - -** TRANSMISSION TRADE COST -ECO_XH_YCRA(Y,C,IR,IA,IAI,'COSTS','TRANSMISSION_TRADE_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IXHK_HASORPOT(Y,IA,IAI))= -IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VXH_T.L(Y,IAI,IA,S,T) * H_PRICE_YCRAST(Y,C,IR,IAI,S,T,'Money_per_MWh'))); - -** TRANSMISSION TRADE INCOME -ECO_XH_YCRA(Y,C,IR,IA,IAI,'INCOME','TRANSMISSION_TRADE_INCOME','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IXHK_HASORPOT(Y,IA,IAI))= -IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VXH_T.L(Y,IA,IAI,S,T) * H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh'))); - - -$label No_HEATTRANS_costs - -*Hydrogen -$ifi not %HYDROGEN%==yes $goto No_H2TRANS_costs - -** TRANSMISSION INVESTMENTS -ECO_XH2_YCR(Y,C,IR,IRI,'COSTS','H2_TRANSMISSION_CAPITAL_COSTS','Mmoney')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= - OMONEY*IOF0000001*(SUM((IYALIAS2)$((IXH2KN(IYALIAS2,IRI,IR) OR IXH2KN(IYALIAS2,IR,IRI)) AND (NOT (IXKN_ES(IR,IRI) OR IXKN_ES(IRI,IR))) AND ORD(IYALIAS2) LE ORD(Y)) , - IOF05*ANNUITYCXH2(C)*IYHASANNUITYXH2(IYALIAS2,Y)*VXH2KN.L(IYALIAS2,IR,IRI)*XH2INVCOST(IYALIAS2,IR,IRI)) - + SUM((IYALIAS2)$((IXH2KN_NGTOH2(IYALIAS2,IRI,IR) OR IXH2KN_NGTOH2(IYALIAS2,IR,IRI)) AND ORD(IYALIAS2) LE ORD(Y)) , - IOF05*ANNUITYCXH2(C)*IYHASANNUITYXH2(IYALIAS2,Y)*VXH2KN_NGTOH2.L(IYALIAS2,IR,IRI)*XH2INVCOST_NGTOH2(IYALIAS2,IR,IRI)) - +SUM((IYALIAS2)$((IXH2KN(IYALIAS2,IRI,IR) OR IXH2KN(IYALIAS2,IR,IRI)) AND (ORD(IYALIAS2) LE ORD(Y)) AND (IXH2KN_ES(IR,IRI) OR IXH2KN_ES(IRI,IR))), - IOF05*ANNUITYCXH2(C)*IYHASANNUITYXH2(IYALIAS2,Y)*SUM(XES,VXH2LAMBDA.L(IYALIAS2,IR,IRI,XES)*XH2INVCOST_ES(IYALIAS2,IR,IRI,XES))) - ) -; - -** TRANSMISSION FLOW -ECO_XH2_YCR(Y,C,IR,IRI,'COSTS','H2_TRANSMISSION_OPERATIONAL_COSTS','Mmoney')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= -OMONEY*IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VXH2_T.L(Y,IR,IRI,S,T) * IHOURFRAC *XH2COST(IR,IRI))); - -** TRANSMISSION TRADE COST -ECO_XH2_YCR(Y,C,IR,IRI,'COSTS','TRANSMISSION_TRADE_COSTS','Mmoney')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= -IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VXH2_T.L(Y,IR,IRI,S,T) * H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh'))); - -** TRANSMISSION TRADE INCOME -ECO_XH2_YCR(Y,C,IR,IRI,'INCOME','TRANSMISSION_TRADE_INCOME','Mmoney')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= -IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VXH2_T.L(Y,IR,IRI,S,T) * H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh'))); - - -$label No_H2TRANS_costs - -* ------------- END OF COSTS -------------- - -* ------------- OBJECTIVE FUNCTION -------------- - -OBJ_YCR(Y,C,IR,'GENERATION_FUEL_COSTS','Mmoney')$CCCRRR(C,IR)= - -*GENERATION COSTS -SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND NOT((IGESTOALL(G) OR IGHSTOALL(G) OR IGETOH(G) -$ifi %STEPWISEPRICE%==yes OR ISTEPWISEPRICE_F(Y,C,FFF) -$ifi %HYDROGEN%==yes OR IHYDROGEN_GETOH2(G) OR IHYDROGEN_GEHTOH2(G) OR IHYDROGEN_GETOHH2(G) OR IHYDROGEN_GH2STO(G) OR IHYDROGEN_GH2TOE(G) OR IHYDROGEN_GH2TOEH(G) OR IHYDROGEN_GH2TOBIOMETH(G) OR IGBIOMETHANE(G) -))), -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_FUEL_COSTS','Mmoney') -) - -$ifi not %STEPWISEPRICE%==yes $goto No_STEPWISEPRICE2 - +SUM(STEPWISEPRICE_FFF$(ISTEPWISEPRICE_F(Y,C,STEPWISEPRICE_FFF) AND SUM(STEPWISEPRICE_L,VSTEPWISEPRICE_VGF_T_STEP.L(Y,C,STEPWISEPRICE_L,STEPWISEPRICE_FFF))), - OMONEY/IOF1000000*( - (0+ SUM((S,T,IA,G)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G) AND IGF(G,STEPWISEPRICE_FFF)), IHOURSINST(S,T) * VGF_T.L(Y,IA,G,S,T)*IOF3P6)*IHOURFRAC)* - SUM(STEPWISEPRICE_L,STEPWISEPRICE_FUELPRICE_STEP(Y,C,STEPWISEPRICE_L,STEPWISEPRICE_FFF)*VSTEPWISEPRICE_VGF_T_STEP.L(Y,C,STEPWISEPRICE_L,STEPWISEPRICE_FFF)) - /SUM(STEPWISEPRICE_L,VSTEPWISEPRICE_VGF_T_STEP.L(Y,C,STEPWISEPRICE_L,STEPWISEPRICE_FFF)) - ) - ) -$label No_STEPWISEPRICE2 - -; - -OBJ_YCR(Y,C,IR,'GENERATION_OPPORTUNITY_COSTS_STORAGE','Mmoney')$CCCRRR(C,IR)= -$ifi %import_results%==yes $ifi %ADDESTOVOLTS%==price +OMONEY*IOF0000001*SUM((S,T,IA,IGESTOS)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGESTOS)),IHOURSINST(S,T)*ESTOVOLTSVAL(Y,IA,IGESTOS,S,T)*IHOURFRAC*(VGE_T.L(Y,IA,IGESTOS,S,T)/GDATA(IGESTOS,'GDFE')-VESTOLOADTS.L(Y,IA,IGESTOS,S,T))) -$ifi %import_results%==yes $ifi %ADDHSTOVOLTS%==price +OMONEY*IOF0000001*SUM((S,T,IA,IGHSTOS)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGHSTOS)),IHOURSINST(S,T)*HSTOVOLTSVAL(Y,IA,IGHSTOS,S,T)*IHOURFRAC*(VGH_T.L(Y,IA,IGHSTOS,S,T)/GDATA(IGHSTOS,'GDFE')-VHSTOLOADTS.L(Y,IA,IGHSTOS,S,T))) -$ifi %import_results%==yes $ifi %ADDESTOVOLT%==price +OMONEY*IOF0000001*SUM((S,T,IA,IGESTO)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGESTO)),IHOURSINST(S,T)*ESTOVOLTVAL(Y,IA,IGESTO,S,T)*IHOURFRAC*(VGE_T.L(Y,IA,IGESTO,S,T)/GDATA(IGESTO,'GDFE')-VESTOLOADT.L(Y,IA,IGESTO,S,T))) -$ifi %import_results%==yes $ifi %ADDHSTOVOLT%==price +OMONEY*IOF0000001*SUM((S,T,IA,IGHSTO)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGHSTO)),IHOURSINST(S,T)*HSTOVOLTVAL(Y,IA,IGHSTO,S,T)*IHOURFRAC*(VGH_T.L(Y,IA,IGHSTO,S,T)/GDATA(IGHSTO,'GDFE')-VHSTOLOADT.L(Y,IA,IGHSTO,S,T))) -$ifi %import_results%==yes $ifi %HYDROGEN%==yes $ifi %ADDH2STOVOLTS%==price +OMONEY*IOF0000001*SUM((S,T,IA,IHYDROGEN_GH2STO)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO)),IHOURSINST(S,T)*H2STOVOLTSVAL(Y,IA,IHYDROGEN_GH2STO,S,T)*IHOURFRAC*(VHYDROGEN_GH2_T.L(Y,IA,IHYDROGEN_GH2STO,S,T)/GDATA(IHYDROGEN_GH2STO,'GDFE')-VHYDROGEN_STOLOADT.L(Y,IA,IHYDROGEN_GH2STO,S,T))) -$ifi %import_results%==yes $ifi %HYDROGEN%==yes $ifi %ADDBIOMETHSTOVOLTS%==price +OMONEY*IOF0000001*SUM((S,T)$(SUM((IA,IGBIOMETHANE),IAGK_HASORPOT(Y,IA,IGBIOMETHANE)) AND SUM((IA,IHYDROGEN_GH2TOBIOMETH),IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH))),IHOURSINST(S,T)*BIOMETHSTOVOLTSVAL(Y,S,T)*IHOURFRAC*(VBIOMETH_STOUNLOADT.L(Y,S,T)-VBIOMETH_STOLOADT.L(Y,S,T)) ) -+0; - -OBJ_YCR(Y,C,IR,'GENERATION_REDISPATCH_COMPENSATION_COST','Mmoney')$CCCRRR(C,IR)= -$ifi %import_results%==yes $ifi %ADDENERGYDISPATCH%==yes $ifi %BalancingMarket%==yes $ifi %BALANCINGRUNPURPOSE%==TSOREDISPATCH +OMONEY*IOF0000001*SUM((S,T,IAGK_HASORPOT(Y,IA,IGE))$(NOT (IGETOH(IGE)) AND RRRAAA(IR,IA) AND GE_T(Y,IA,IGE,S,T)),IHOURFRAC*ELECTRICITY_PRICE(Y,IR,S,T)*IHOURSINST(S,T)*(1-DISLOSS_E_AG(IA,IGE))*VGE_T_DOWN.L(Y,IA,IGE,S,T)) -$ifi %import_results%==yes $ifi %ADDENERGYDISPATCH%==yes $ifi %BalancingMarket%==yes $ifi %BALANCINGRUNPURPOSE%==TSOREDISPATCH +OMONEY*IOF0000001*SUM((S,T,IAGK_HASORPOT(Y,IA,IGH))$(RRRAAA(IR,IA) AND GH_T(Y,IA,IGH,S,T)),IHOURFRAC*HEAT_PRICE(Y,IA,S,T)*IHOURSINST(S,T)*VGH_T_DOWN.L(Y,IA,IGH,S,T)) -$ifi %import_results%==yes $ifi %ADDENERGYDISPATCH%==yes $ifi %BalancingMarket%==yes $ifi %BALANCINGRUNPURPOSE%==TSOREDISPATCH $ifi %HYDROGEN%==yes +OMONEY*IOF0000001*SUM((S,T,IAGK_HASORPOT(Y,IA,IHYDROGEN))$(RRRAAA(IR,IA) AND GH2_T(Y,IA,IHYDROGEN,S,T)),IHOURFRAC*HYDROGEN_PRICE(Y,IR,S,T)*IHOURSINST(S,T)*VGH2_T_DOWN.L(Y,IA,IHYDROGEN,S,T)) -$ifi %import_results%==yes $ifi %ADDENERGYDISPATCH%==yes $ifi %BalancingMarket%==yes $ifi %BALANCINGRUNPURPOSE%==TSOREDISPATCH $ifi %HYDROGEN%==yes +OMONEY*IOF0000001*SUM((S,T,IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH))$(RRRAAA(IR,IA) AND GBIOMETHANE_T(Y,IA,IHYDROGEN_GH2TOBIOMETH,S,T)),IHOURFRAC*BIOMETHANE_PRICE(Y,S,T)*IHOURSINST(S,T)*VGBIOMETHANE_T_DOWN.L(Y,IA,IHYDROGEN_GH2TOBIOMETH,S,T)) -$ifi %import_results%==yes $ifi %ADDENERGYDISPATCH%==yes $ifi %BalancingMarket%==yes $ifi %BALANCINGRUNPURPOSE%==TSOREDISPATCH $ifi %HYDROGEN%==yes +OMONEY*IOF0000001*SUM((S,T,IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASUPGRADING))$(RRRAAA(IR,IA) AND GBIOGASUPGRADING_T(Y,IA,IHYDROGEN_GBIOGASUPGRADING,S,T)),IHOURFRAC*BIOMETHANE_PRICE(Y,S,T)*IHOURSINST(S,T)*VGBIOGASUPGRADING_T_DOWN.L(Y,IA,IHYDROGEN_GBIOGASUPGRADING,S,T)) -$ifi %import_results%==yes $ifi %ADDENERGYDISPATCH%==yes $ifi %BalancingMarket%==yes $ifi %BALANCINGRUNPURPOSE%==TSOREDISPATCH $ifi %HYDROGEN%==yes +OMONEY*IOF0000001*SUM((S,T,IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASMETHANATION))$(RRRAAA(IR,IA) AND GBIOGASMETHANATION_T(Y,IA,IHYDROGEN_GBIOGASMETHANATION,S,T)),IHOURFRAC*BIOMETHANE_PRICE(Y,S,T)*IHOURSINST(S,T)*VGBIOGASMETHANATION_T_DOWN.L(Y,IA,IHYDROGEN_GBIOGASMETHANATION,S,T)) -+0; - - -OBJ_YCR(Y,C,IR,'HYDRO_PROFILE','Mmoney')$CCCRRR(C,IR)= -OMONEY*IOF0000001*SUM((IA,IGHYRS)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGHYRS)), SUM((S,T), HYPPROFILS(IA,S)*IHOURFRAC* IHOURSINST(S,T) - * VGE_T.L(Y,IA,IGHYRS,S,T))) -; - -OBJ_YCR(Y,C,IR,'GENERATION_CAPITAL_COSTS','Mmoney')$CCCRRR(C,IR)= -SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_CAPITAL_COSTS','Mmoney') -) -; - -OBJ_YCR(Y,C,IR,'GENERATION_FIXED_COSTS','Mmoney')$CCCRRR(C,IR)= -SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_FIXED_COSTS','Mmoney') -) -; - -OBJ_YCR(Y,C,IR,'GENERATION_OPERATIONAL_COSTS','Mmoney')$CCCRRR(C,IR)= -SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_OPERATIONAL_COSTS','Mmoney') -) -; - -OBJ_YCR(Y,C,IR,'GENERATION_CO2_TAX','Mmoney')$CCCRRR(C,IR)= -SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_CO2_TAX','Mmoney') -) -; - -$ifi %CCS%==yes OBJ_YCR(Y,C,IR,'GENERATION_CO2_TRANSPORT','Mmoney')$CCCRRR(C,IR)= -$ifi %CCS%==yes SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G) AND CCS_G(G)),ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_CO2_TRANSPORT','Mmoney')); - -OBJ_YCR(Y,C,IR,'GENERATION_OTHER_EMI_TAX','Mmoney')$CCCRRR(C,IR)= -SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_OTHER_EMI_TAX','Mmoney') -) -; - -OBJ_YCR(Y,C,IR,'GENERATION_UC_COSTS','Mmoney')$CCCRRR(C,IR)= -SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_UC_COSTS','Mmoney') -) -; - -OBJ_YCR(Y,C,IR,'TRANSMISSION_CAPITAL_COSTS','Mmoney')$CCCRRR(C,IR)= -SUM(IRI$IXK_HASORPOT(Y,IR,IRI),ECO_X_YCR(Y,C,IR,IRI,'COSTS','TRANSMISSION_CAPITAL_COSTS','Mmoney')) -; - -OBJ_YCR(Y,C,IR,'TRANSMISSION_OPERATIONAL_COSTS','Mmoney')$CCCRRR(C,IR)= -SUM(IRI$IXK_HASORPOT(Y,IR,IRI), ECO_X_YCR(Y,C,IR,IRI,'COSTS','TRANSMISSION_OPERATIONAL_COSTS','Mmoney')) -; - -$ifi not %HEATTRANS%==yes $goto No_HEATTRANS_costs_2 -OBJ_YCR(Y,C,IR,'HEAT_TRANSMISSION_CAPITAL_COSTS','Mmoney')$CCCRRR(C,IR)= -SUM((IA,IAI)$(RRRAAA(IR,IA) AND IXHK_HASORPOT(Y,IA,IAI)),ECO_XH_YCRA(Y,C,IR,IA,IAI,'COSTS','HEAT_TRANSMISSION_CAPITAL_COSTS','Mmoney')) -; - -OBJ_YCR(Y,C,IR,'HEAT_TRANSMISSION_OPERATIONAL_COSTS','Mmoney')$CCCRRR(C,IR)= -SUM((IA,IAI)$(RRRAAA(IR,IA) AND IXHK_HASORPOT(Y,IA,IAI)),ECO_XH_YCRA(Y,C,IR,IA,IAI,'COSTS','HEAT_TRANSMISSION_OPERATIONAL_COSTS','Mmoney')) -; -$label No_HEATTRANS_costs_2 - -$ifi not %HYDROGEN%==yes $goto No_H2_costs_2 -OBJ_YCR(Y,C,IR,'H2_TRANSMISSION_CAPITAL_COSTS','Mmoney')$CCCRRR(C,IR)= -SUM(IRI$IXH2K_HASORPOT(Y,IR,IRI),ECO_XH2_YCR(Y,C,IR,IRI,'COSTS','H2_TRANSMISSION_CAPITAL_COSTS','Mmoney')) -; - -OBJ_YCR(Y,C,IR,'H2_TRANSMISSION_OPERATIONAL_COSTS','Mmoney')$CCCRRR(C,IR)= -SUM(IRI$IXH2K_HASORPOT(Y,IR,IRI), ECO_XH2_YCR(Y,C,IR,IRI,'COSTS','H2_TRANSMISSION_OPERATIONAL_COSTS','Mmoney')) -; -$label No_H2_costs_2 - - -$ifi NOT %TAXES%==yes $goto No_TAXES2 -OBJ_YCR(Y,C,IR,'TAXES','Mmoney')$CCCRRR(C,IR)= -SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_TAXES','Mmoney')) -+OMONEY/IOF1000000*( - -* Tax on electricity consumption - + SUM(DEUSER, TAX_DE(C,DEUSER)*IHOURFRAC * SUM((IS3,T), IHOURSINST(IS3,T) * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0)) ) - -$ifi %DFLEXQUANT%==yes + SUM((DEUSER), *IHOURFRACTAX_DE(C,DEUSER) *( -$ifi %DFLEXQUANT%==yes SUM(DEF_U1,VDEF_T.L(Y,IR,DEUSER,IS3,T,DEF_U1) ) - SUM(DEF_D1,VDEF_T.L(Y,IR,DEUSER,IS3,T,DEF_D1) ) -$ifi %DFLEXQUANT%==yes + SUM(DEF_U2,VDEF_T.L(Y,IR,DEUSER,IS3,T,DEF_U2) ) - SUM(DEF_D2,VDEF_T.L(Y,IR,DEUSER,IS3,T,DEF_D2) ) -$ifi %DFLEXQUANT%==yes + SUM(DEF_U3,VDEF_T.L(Y,IR,DEUSER,IS3,T,DEF_U3) ) - SUM(DEF_D3,VDEF_T.L(Y,IR,DEUSER,IS3,T,DEF_D3) )) - - -+SUM(IA$RRRAAA(IR,IA), -* Tax on heat consumption - + SUM(DHUSER, TAX_DH(C,DHUSER)*IHOURFRAC * DH(Y,IA,DHUSER)) - -$ifi %DFLEXQUANT%==yes + SUM((DHUSER), TAX_DH(C,DHUSER)*IHOURFRAC * ( -$ifi %DFLEXQUANT%==yes SUM(DHF_U1,VDHF_T.L(Y,IA,DHUSER,IS3,T,DHF_U1) ) - SUM(DHF_D1,VDHF_T.L(Y,IA,DHUSER,IS3,T,DHF_D1) ) -$ifi %DFLEXQUANT%==yes + SUM(DHF_U2,VDHF_T.L(Y,IA,DHUSER,IS3,T,DHF_U2) ) - SUM(DHF_D2,VDHF_T.L(Y,IA,DHUSER,IS3,T,DHF_D2) ) -$ifi %DFLEXQUANT%==yes + SUM(DHF_U3,VDHF_T.L(Y,IA,DHUSER,IS3,T,DHF_U3) ) - SUM(DHF_D3,VDHF_T.L(Y,IA,DHUSER,IS3,T,DHF_D3) ) ) -) -) -; -$label NO_TAXES2 - - -*GRID TARIFFS2 -$ifi NOT %GRIDTARIFFS%==yes $goto No_GRIDTARIFFS2 -OBJ_YCR(Y,C,IR,'GRID_TARIFFS','Mmoney')$CCCRRR(C,IR)= -SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G) AND (IGETOH(G) -$ifi %HYDROGEN%==yes OR IHYDROGEN_GEHTOH2(G) OR IHYDROGEN_GETOHH2(G) OR IHYDROGEN_GETOH2(G) -)), ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_GRID_TARIFFS','Mmoney')) - -+OMONEY/IOF1000000*( - -* Demand charge winter - + IOF1000 * SUM((MMM)$(SUM((IS3,T)$IGR_TIME('GRDDCW',IR,IS3,T), ISSSTTTMMM(IS3,T,MMM))), SUM(IA$(SUM(IGETOH,IAGK_HASORPOT(Y,IA,IGETOH)) AND RRRAAA(IR,IA)), VGETOH_CAP1.L(Y,IA,MMM)) * IGR_PRICE_TECH(Y,IR,'GRDDCW'))/(IGR_MONTHSINSIM/card(MMM)) !! Demand charge winter -* Demand charge summer - + IOF1000 * SUM((MMM)$(SUM((IS3,T)$IGR_TIME('GRDDCS',IR,IS3,T), ISSSTTTMMM(IS3,T,MMM))), SUM(IA$(SUM(IGETOH,IAGK_HASORPOT(Y,IA,IGETOH)) AND RRRAAA(IR,IA)), VGETOH_CAP1.L(Y,IA,MMM)) * IGR_PRICE_TECH(Y,IR,'GRDDCS'))/(IGR_MONTHSINSIM/card(MMM)) !! Demand charge summer -* Demand charge 2 (e.g for spring and fall months) - + IOF1000 * SUM((MMM)$(SUM((IS3,T)$IGR_TIME('GRDDC2',IR,IS3,T), ISSSTTTMMM(IS3,T,MMM))), SUM(IA$(SUM(IGETOH,IAGK_HASORPOT(Y,IA,IGETOH)) AND RRRAAA(IR,IA)), VGETOH_CAP1.L(Y,IA,MMM)) * IGR_PRICE_TECH(Y,IR,'GRDDC2'))/(IGR_MONTHSINSIM/card(MMM)) !! Demand charge 2 - -* Demand charge TOU - + IOF1000 * SUM((MMM), SUM(IA$(SUM(IGETOH,IAGK_HASORPOT(Y,IA,IGETOH)) AND RRRAAA(IR,IA)), VGETOH_CAP2.L(Y,IA,MMM)) * IGR_PRICE_TECH(Y,IR,'GRDDCTOU'))/(IGR_MONTHSINSIM/card(MMM)) -* Demand charge yearly - + IOF1000 * SUM(IA$(SUM(IGETOH,IAGK_HASORPOT(Y,IA,IGETOH)) AND RRRAAA(IR,IA)), VGETOH_CAPY.L(Y,IA)) * IGR_PRICE_TECH(Y,IR,'GRDDCY') - -* Tariffs on user groups - + SUM((IS3,T,DEUSER)$IGR_TIME('GRDECW',IR,IS3,T), IHOURSINST(IS3,T)*IHOURFRAC * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDECW') * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0) ) - + SUM((IS3,T,DEUSER)$IGR_TIME('GRDECS',IR,IS3,T), IHOURSINST(IS3,T)*IHOURFRAC * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDECS') * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0) ) - + SUM((IS3,T,DEUSER)$IGR_TIME('GRDTOU1',IR,IS3,T), IHOURSINST(IS3,T)*IHOURFRAC * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDTOU1') * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0) ) - + SUM((IS3,T,DEUSER)$IGR_TIME('GRDTOU2',IR,IS3,T), IHOURSINST(IS3,T)*IHOURFRAC * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDTOU2') * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0) ) - + SUM((IS3,T,DEUSER)$IGR_TIME('GRDTOU3',IR,IS3,T), IHOURSINST(IS3,T)*IHOURFRAC * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDTOU3') * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0) ) - + SUM((IS3,T,DEUSER)$IGR_TIME('GRDTOU4',IR,IS3,T), IHOURSINST(IS3,T)*IHOURFRAC * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDTOU4') * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0) ) - - + IOF1000 * SUM((DEUSER,MMM)$(SUM((IS3,T)$IGR_TIME('GRDDCW',IR,IS3,T), ISSSTTTMMM(IS3,T,MMM))), VDEUSER_CAP1.L(Y,IR,DEUSER,MMM) * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDDCW'))/(IGR_MONTHSINSIM/CARD(MMM)) - + IOF1000 * SUM((DEUSER,MMM)$(SUM((IS3,T)$IGR_TIME('GRDDCS',IR,IS3,T), ISSSTTTMMM(IS3,T,MMM))), VDEUSER_CAP1.L(Y,IR,DEUSER,MMM) * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDDCS'))/(IGR_MONTHSINSIM/CARD(MMM)) - + IOF1000 * SUM((DEUSER,MMM)$(SUM((IS3,T)$IGR_TIME('GRDDC2',IR,IS3,T), ISSSTTTMMM(IS3,T,MMM))), VDEUSER_CAP1.L(Y,IR,DEUSER,MMM) * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDDC2'))/(IGR_MONTHSINSIM/CARD(MMM)) - + IOF1000 * SUM((DEUSER,MMM), VDEUSER_CAP2.L(Y,IR,DEUSER,MMM) * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDDCTOU'))/(IGR_MONTHSINSIM/card(MMM)) - + IOF1000 * SUM((DEUSER), VDEUSER_CAPY.L(Y,IR,DEUSER) * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDDCY')) - -) - -; -$label NO_GRIDTARIFFS2 - -*ADD OTHER COSTS(?) -* ------------- END OF OBJECTIVE FUNCTION -------------- - - -* ------------- INCOME -------------- -** ELECTRICITY SALES -ECO_G_YCRAG(Y,C,IR,IA,IGE,FFF,TECH_TYPE,'INCOME','ELECTRICITY_SALE','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) and IGNOTETOH(IGE))= - IOF0000001*( - SUM((S,T), IHOURSINST(S,T)* VGE_T.L(Y,IA,IGE,S,T)*EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh'))) -; - -** HEAT SALES -ECO_G_YCRAG(Y,C,IR,IA,IGH,FFF,TECH_TYPE,'INCOME','HEAT_SALE','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE))= - IOF0000001* - SUM((S,T), IHOURSINST(S,T)*VGH_T.L(Y,IA,IGH,S,T)*H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')) -; - -$ifi not %BalancingMarket%==yes $goto NO_TSOREDISPATCH1 -$ifi not %BALANCINGRUNPURPOSE%==TSOREDISPATCH $goto NO_TSOREDISPATCH1 -ECO_G_YCRAG(Y,C,IR,IA,IGE,FFF,TECH_TYPE,'INCOME','ELECTRICITY_REDISPATCH_COMPENSATION','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) and IGNOTETOH(IGE))= - OMONEY*IOF0000001*( - SUM((S,T), IHOURFRAC*IHOURSINST(S,T)*(1-DISLOSS_E_AG(IA,IGE))*VGE_T_DOWN.L(Y,IA,IGE,S,T)*ELECTRICITY_PRICE(Y,IR,S,T))) -; - -ECO_G_YCRAG(Y,C,IR,IA,IGH,FFF,TECH_TYPE,'INCOME','HEAT_REDISPATCH_COMPENSATION','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE))= - OMONEY*IOF0000001* - SUM((S,T), IHOURFRAC*IHOURSINST(S,T)*VGH_T_DOWN.L(Y,IA,IGH,S,T)*HEAT_PRICE(Y,IA,S,T)) -; -$label NO_TSOREDISPATCH1 - -$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_INCOME -** HYDROGEN SALES -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'INCOME','H2_SALE','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IHYDROGEN(G))= - IOF0000001* - SUM((S,T), IHOURSINST(S,T)*PRO_YCRAGFST(Y,C,IR,IA,G,FFF,S,T,'HYDROGEN',TECH_TYPE,'MWh')*H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')) -; - -** BIOMETHANE SALES -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'INCOME','BIOMETHANE_SALE','Mmoney')$((IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASUPGRADING(G) OR IHYDROGEN_GBIOGASMETHANATION(G)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE))= - IOF0000001*( - +SUM((S,T), IHOURSINST(S,T)*VGBIOMETH_T.L(Y,IA,G,S,T)*BIOMETH_PRICE_YST(Y,S,T,'Money_per_MWh'))$IHYDROGEN_GH2TOBIOMETH(G) - +SUM((S,T), IHOURSINST(S,T)*VGBIOGASMETHANATION_T.L(Y,IA,G,S,T)*BIOMETH_PRICE_YST(Y,S,T,'Money_per_MWh'))$IHYDROGEN_GBIOGASMETHANATION(G) - +SUM((S,T), IHOURSINST(S,T)*VGBIOGASUPGRADING_T.L(Y,IA,G,S,T)*BIOMETH_PRICE_YST(Y,S,T,'Money_per_MWh'))$IHYDROGEN_GBIOGASUPGRADING(G) -) -; - -$ifi not %BalancingMarket%==yes $goto NO_TSOREDISPATCH2 -$ifi not %BALANCINGRUNPURPOSE%==TSOREDISPATCH $goto NO_TSOREDISPATCH2 -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'INCOME','HYDROGEN_REDISPATCH_COMPENSATION','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IHYDROGEN(G))= - OMONEY*IOF0000001* - SUM((S,T), IHOURFRAC*IHOURSINST(S,T)*VGH2_T_DOWN.L(Y,IA,G,S,T)*HYDROGEN_PRICE(Y,IR,S,T)) -; - -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'INCOME','BIOMETHANE_REDISPATCH_COMPENSATION','Mmoney')$((IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASUPGRADING(G) OR IHYDROGEN_GBIOGASMETHANATION(G)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE))= - OMONEY*IOF0000001*( - +SUM((S,T), IHOURFRAC*IHOURSINST(S,T)*VGBIOMETHANE_T_DOWN.L(Y,IA,G,S,T)*BIOMETHANE_PRICE(Y,S,T))$IHYDROGEN_GH2TOBIOMETH(G) - +SUM((S,T), IHOURFRAC*IHOURSINST(S,T)*VGBIOGASMETHANATION_T_DOWN.L(Y,IA,G,S,T)*BIOMETHANE_PRICE(Y,S,T))$IHYDROGEN_GBIOGASMETHANATION(G) - +SUM((S,T), IHOURFRAC*IHOURSINST(S,T)*VGBIOGASUPGRADING_T_DOWN.L(Y,IA,G,S,T)*BIOMETHANE_PRICE(Y,S,T))$IHYDROGEN_GBIOGASUPGRADING(G) -) -; -$label NO_TSOREDISPATCH2 - -$label NO_HYDROGEN_INCOME - -* ------------- END OF INCOME -------------- - -* ------------- PROFIT -------------- -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'PROFIT','TOTAL_PROFIT','Mmoney')$IAGK_HASORPOT(Y,IA,G)= -SUM(SUBCATEGORY, ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'INCOME',SUBCATEGORY,'Mmoney')-ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS',SUBCATEGORY,'Mmoney') -) -; - -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'PROFIT','ENERGY_SPECIFIC_PROFIT','Money_per_MWh')$(IAGK_HASORPOT(Y,IA,G) AND SUM(COMMODITY,PRO_YCRAGF(Y,C,IR,IA,G,FFF,COMMODITY,TECH_TYPE,'TWh') > 0)) = -ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'PROFIT','TOTAL_PROFIT','Mmoney')/SUM(COMMODITY,PRO_YCRAGF(Y,C,IR,IA,G,FFF,COMMODITY,TECH_TYPE,'TWh')) -; -* ------------- END OF PROFIT -------------- - -* --------- END OF ECONOMIC OUTPUT ----------- - -* ---------------------- ENVIRONMENT ---------------------- - -* ------------- CO2 EMISSIONS ------------- - -EMI_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'kton')$(F_CONS_YCRA(Y,C,IR,IA,G,FFF,TECH_TYPE,'TWh'))= -(F_CONS_YCRA(Y,C,IR,IA,G,FFF,TECH_TYPE,'TWh') -$ifi %CCS%==yes -(F_CONS_YCRA(Y,C,IR,IA,G,FFF,TECH_TYPE,'TWh')*CCS_CO2CAPTEFF_G(G))$CCS_G(G) -)*IM_CO2(G)*IOF3P6 -; - -* ------------- END OF CO2 EMISSIONS ------------- - -* ---------------------- END OF ENVIRONMENT ---------------------- - -*---------------ELECTRICITY BALANCE------------ -*GENERATION -EL_BALANCE_YCRST(Y,C,IR,'CONDENSING',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','CONDENSING','MWh')); -EL_BALANCE_YCRST(Y,C,IR,'CHP-BACK-PRESSURE',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','CHP-BACK-PRESSURE','MWh')); -EL_BALANCE_YCRST(Y,C,IR,'CHP-EXTRACTION',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','CHP-EXTRACTION','MWh')); -EL_BALANCE_YCRST(Y,C,IR,'INTER-STO',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','INTERSEASONAL-ELECT-STORAGE','MWh')); -EL_BALANCE_YCRST(Y,C,IR,'INTRA-STO',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','INTRASEASONAL-ELECT-STORAGE','MWh')); -EL_BALANCE_YCRST(Y,C,IR,'HYDRO-RESERVOIRS',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','HYDRO-RESERVOIRS','MWh')); -EL_BALANCE_YCRST(Y,C,IR,'HYDRO-RUN-OF-RIVER',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','HYDRO-RUN-OF-RIVER','MWh')); -EL_BALANCE_YCRST(Y,C,IR,'WIND-ON',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','WIND-ON','MWh')); -EL_BALANCE_YCRST(Y,C,IR,'WIND-OFF',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','WIND-OFF','MWh')); -EL_BALANCE_YCRST(Y,C,IR,'SOLAR-PV',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','SOLAR-PV','MWh')); -EL_BALANCE_YCRST(Y,C,IR,'HYDRO-WAVE ',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','HYDRO-WAVE','MWh')); -$ifi %HYDROGEN%==yes EL_BALANCE_YCRST(Y,C,IR,'FUELCELL',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','FUELCELL','MWh')); - -*DEMAND -EL_BALANCE_YCRST(Y,C,IR,'DEMAND_EXO',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'EXOGENOUS','MWh'); -EL_BALANCE_YCRST(Y,C,IR,'DEMAND_P2H',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDOGENOUS_ELECT2HEAT','MWh'); -EL_BALANCE_YCRST(Y,C,IR,'DEMAND_INTRASTO',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_INTRASTO','MWh'); -EL_BALANCE_YCRST(Y,C,IR,'DEMAND_INTERSTO',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_INTERSTO','MWh'); -EL_BALANCE_YCRST(Y,C,IR,'DEMAND_EV',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_EV','MWh'); -EL_BALANCE_YCRST(Y,C,IR,'DEMAND_OTHERTRANS',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_OTHERTRANS','MWh'); -EL_BALANCE_YCRST(Y,C,IR,'DEMAND_DISTLOSSES',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'DIST_LOSSES','MWh'); -EL_BALANCE_YCRST(Y,C,IR,'DEMAND_CCS',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_CCS','MWh'); -$ifi %HYDROGEN%==yes EL_BALANCE_YCRST(Y,C,IR,'DEMAND_P2G',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_H2','MWh')+EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_BIOMETHANE','MWh'); - - -*TRANSMISSION -EL_BALANCE_YCRST(Y,C,IR,'NETEXPORT',S,T,'MWh')$CCCRRR(C,IR)=SUM(IRE$IXK_HASORPOT(Y,IR,IRE),VX_T.L(Y,IR,IRE,S,T))-SUM(IRI$IXK_HASORPOT(Y,IRI,IR),VX_T.L(Y,IRI,IR,S,T)*(1-XLOSS(IRI,IR))); - -*CURTAILMENT -EL_BALANCE_YCRST(Y,C,IR,'CURTAILMENT',S,T,'MWh')$CCCRRR(C,IR)= -SUM((IGE,TECH_TYPE,IA,FFF)$(RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE)), -CURT_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY',TECH_TYPE,'MWh') -); - -*PRICE -EL_BALANCE_YCRST(Y,C,IR,'PRICE',S,T,'Money_per_MWh')$CCCRRR(C,IR)=EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh'); -*---------------END ELECTRICITY BALANCE------------ - - -*---------------HEAT BALANCE------------ -*GENERATION -H_BALANCE_YCRAST(Y,C,IR,IA,'P2H',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','ELECT-TO-HEAT','MWh')); -H_BALANCE_YCRAST(Y,C,IR,IA,'CHP-BACK-PRESSURE',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','CHP-BACK-PRESSURE','MWh')); -H_BALANCE_YCRAST(Y,C,IR,IA,'CHP-EXTRACTION',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','CHP-EXTRACTION','MWh')); -H_BALANCE_YCRAST(Y,C,IR,IA,'INTER-STO',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','INTERSEASONAL-HEAT-STORAGE','MWh')); -H_BALANCE_YCRAST(Y,C,IR,IA,'INTRA-STO',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','INTRASEASONAL-HEAT-STORAGE','MWh')); -H_BALANCE_YCRAST(Y,C,IR,IA,'BOILERS',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','BOILERS','MWh')); -H_BALANCE_YCRAST(Y,C,IR,IA,'SOLAR-HEATING',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','SOLAR-HEATING','MWh')); -$ifi %HYDROGEN%==yes H_BALANCE_YCRAST(Y,C,IR,IA,'FUELCELL',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','FUELCELL','MWh')); - - -*DEMAND -H_BALANCE_YCRAST(Y,C,IR,IA,'DEMAND_EXO',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'EXOGENOUS','MWh'); -H_BALANCE_YCRAST(Y,C,IR,IA,'DEMAND_INTRASTO',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_INTRASTO','MWh'); -H_BALANCE_YCRAST(Y,C,IR,IA,'DEMAND_INTERSTO',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_INTERSTO','MWh'); -H_BALANCE_YCRAST(Y,C,IR,IA,'DEMAND_DISTLOSSES',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'DIST_LOSSES','MWh'); -$ifi %HYDROGEN%==yes H_BALANCE_YCRAST(Y,C,IR,IA,'DEMAND_P2G',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_H2','MWh')+ H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_BIOMETHANE','MWh'); - -*TRANSMISSION -$ifi %HEATTRANS%==yes H_BALANCE_YCRAST(Y,C,IR,IA,'NETEXPORT',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM(IAE$IXHK_HASORPOT(Y,IA,IAE),VXH_T.L(Y,IA,IAE,S,T))-SUM(IAI$IXHK_HASORPOT(Y,IAI,IA),VXH_T.L(Y,IAI,IA,S,T)*(1-XHLOSS(IAI,IA))); - -*CURTAILMENT -H_BALANCE_YCRAST(Y,C,IR,IA,'CURTAILMENT',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= -SUM((IGH,TECH_TYPE,FFF)$(IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE)), -CURT_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT',TECH_TYPE,'MWh') -); - -*PRICE -H_BALANCE_YCRAST(Y,C,IR,IA,'PRICE',S,T,'Money_per_MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh'); -*---------------END HEAT BALANCE------------ - - - -execute_unload "MainResults.gdx" ECO_G_YCRAG,ECO_X_YCR,OBJ_YCR,EL_PRICE_YCR,EL_PRICE_YCRST,PRO_YCRAGFST,EL_DEMAND_YCRST,EL_DEMAND_YCR, -H_DEMAND_YCRAST,H_DEMAND_YCRA,PRO_YCRAGF,H_PRICE_YCRAST,H_PRICE_YCRA,G_CAP_YCRAF,G_STO_YCRAF,X_CAP_YCR,X_FLOW_YCRST,X_FLOW_YCR,F_CONS_YCRAST,F_CONS_YCRA,EMI_YCRAG, -EL_BALANCE_YCRST,CURT_YCRAGFST,CURT_YCRAGF,H_BALANCE_YCRAST - -$ifi %HEATTRANS%==yes ,XH_CAP_YCA,XH_FLOW_YCAST,XH_FLOW_YCA,ECO_XH_YCRA -$ifi %HYDROGEN%==yes ,H2_PRICE_YCRST,BIOMETH_PRICE_YST,XH2_CAP_YCR, XH2_FLOW_YCRST,XH2_FLOW_YCR,ECO_XH2_YCR,H2_DEMAND_YCRST,H2_DEMAND_YCR -; - - -*Establishing modified sets and parameters to original for further calculations - -$ifi not %stointers%==all $goto NO_STOINTERS2 -*Modification of STORAGE -IGESTOS(IGESTO) = YES; IGHSTOS(IGHSTO) = YES; -IGESTO(IGESTO) = NO; IGHSTO(IGHSTO) = NO; -$label NO_STOINTERS2 - -$ifi not %RollingSeasons%==yes $goto NO_ROLLINGSEASONS -*Modification of IHOURSINST -IHOURSINST(SSS,TTT)=0; -LOOP(IS3LOOPSET$(ORD(IS3LOOPSET) LE (CARD(S)-ROLLINGSEASONSNUMBER+1)), - IS3(S)=NO; - IS3(S)$((ORD(S) LE (ORD(IS3LOOPSET)+ROLLINGSEASONSNUMBER-1)) AND (ORD(S) GE ORD(IS3LOOPSET)) )=YES; - IWEIGHSUMS=SUM(IS3, WEIGHT_S(IS3)); - IHOURSINST(IS3,T)=IOF8760*WEIGHT_S(IS3)*WEIGHT_T(T)/(IWEIGHSUMS*SUM(ITALIAS, WEIGHT_T(ITALIAS)))/IHOURFRAC; -); - -$label NO_ROLLINGSEASONS - -*Deleting files to release memory: -G_CAP_YCRAF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,VARIABLE_CATEGORY,UNITS)=0; -G_STO_YCRAF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,VARIABLE_CATEGORY,UNITS)=0; -PRO_YCRAGFST(Y,C,RRR,AAA,G,FFF,SSS,TTT,COMMODITY,TECH_TYPE,UNITS)=0; -PRO_YCRAGF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,UNITS)=0; -F_CONS_YCRAST(Y,C,RRR,AAA,G,FFF,SSS,TTT,TECH_TYPE,UNITS)=0; -F_CONS_YCRA(Y,C,RRR,AAA,G,FFF,TECH_TYPE,UNITS)=0; -X_CAP_YCR(Y,C,IRRRE,IRRRI,VARIABLE_CATEGORY,UNITS)=0; -X_FLOW_YCRST(Y,C,IRRRE,IRRRI,SSS,TTT,UNITS)=0; -X_FLOW_YCR(Y,C,IRRRE,IRRRI,UNITS)=0; -ECO_G_YCRAG(Y,C,RRR,AAA,G,FFF,TECH_TYPE,CATEGORY,SUBCATEGORY,UNITS)=0; -ECO_X_YCR(Y,C,RRR,IRRRI,CATEGORY,SUBCATEGORY,UNITS)=0; -OBJ_YCR(Y,C,RRR,SUBCATEGORY,UNITS)=0; -EL_PRICE_YCR(Y,C,RRR,PRICE_CATEGORY,UNITS)=0; -EL_PRICE_YCRST(Y,C,RRR,SSS,TTT,UNITS)=0; -H_PRICE_YCRA(Y,C,RRR,AAA,PRICE_CATEGORY,UNITS)=0; -H_PRICE_YCRAST(Y,C,RRR,AAA,SSS,TTT,UNITS)=0; -EL_DEMAND_YCRST(Y,C,RRR,SSS,TTT,VARIABLE_CATEGORY,UNITS)=0; -EL_DEMAND_YCR(Y,C,RRR,VARIABLE_CATEGORY,UNITS)=0; -H_DEMAND_YCRAST(Y,C,RRR,AAA,SSS,TTT,VARIABLE_CATEGORY,UNITS)=0; -H_DEMAND_YCRA(Y,C,RRR,AAA,VARIABLE_CATEGORY,UNITS)=0; -EMI_YCRAG(Y,C,RRR,AAA,G,FFF,TECH_TYPE,UNITS)=0; -CURT_YCRAGFST(Y,C,RRR,AAA,G,FFF,SSS,TTT,COMMODITY,TECH_TYPE,UNITS)=0; -CURT_YCRAGF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,UNITS)=0; -EL_BALANCE_YCRST(Y,C,RRR,EL_BAL_TYPE,SSS,TTT,UNITS)=0; -H_BALANCE_YCRAST(Y,C,RRR,AAA,H_BAL_TYPE,SSS,TTT,UNITS)=0; -$ifi %HYDROGEN%==yes H2_PRICE_YCRST(Y,C,RRR,SSS,TTT,UNITS)=0; -$ifi %HYDROGEN%==yes BIOMETH_PRICE_YST(Y,SSS,TTT,UNITS)=0; -$ifi %HEATTRANS%==yes XH_CAP_YCA(Y,C,IAAAE,IAAAI,VARIABLE_CATEGORY,UNITS)=0; -$ifi %HEATTRANS%==yes XH_FLOW_YCAST(Y,C,IAAAE,IAAAI,SSS,TTT,UNITS)=0; -$ifi %HEATTRANS%==yes XH_FLOW_YCA(Y,C,IAAAE,IAAAI,UNITS)=0; -$ifi %HEATTRANS%==yes ECO_XH_YCRA(Y,C,RRR,AAA,IAAAI,CATEGORY,SUBCATEGORY,UNITS)=0; -$ifi %HYDROGEN%==yes XH2_CAP_YCR(Y,C,IRRRE,IRRRI,VARIABLE_CATEGORY,UNITS)=0; -$ifi %HYDROGEN%==yes XH2_FLOW_YCRST(Y,C,IRRRE,IRRRI,SSS,TTT,UNITS)=0; -$ifi %HYDROGEN%==yes XH2_FLOW_YCR(Y,C,IRRRE,IRRRI,UNITS)=0; -$ifi %HYDROGEN%==yes ECO_XH2_YCR(Y,C,RRR,IRRRI,CATEGORY,SUBCATEGORY,UNITS)=0; -$ifi %HYDROGEN%==yes H2_DEMAND_YCRST(Y,C,RRR,SSS,TTT,VARIABLE_CATEGORY,UNITS)=0; -$ifi %HYDROGEN%==yes H2_DEMAND_YCR(Y,C,RRR,VARIABLE_CATEGORY,UNITS)=0; - - - - - +*Create output file + +SET CATEGORY /COSTS,INCOME,PROFIT, Market_Value/; +SET SUBCATEGORY /ELECTRICITY_SALE,HEAT_SALE,H2_SALE,BIOMETHANE_SALE,GENERATION_CAPITAL_COSTS,GENERATION_FIXED_COSTS, + GENERATION_OPERATIONAL_COSTS,GENERATION_FUEL_COSTS,GENERATION_TAXES,GENERATION_GRID_TARIFFS,TRANSMISSION_CAPITAL_COSTS,TRANSMISSION_OPERATIONAL_COSTS, + TRANSMISSION_TRADE_INCOME,TRANSMISSION_TRADE_COSTS,HEAT_TRANSMISSION_CAPITAL_COSTS,HEAT_TRANSMISSION_OPERATIONAL_COSTS, + H2_TRANSMISSION_CAPITAL_COSTS,H2_TRANSMISSION_OPERATIONAL_COSTS,GENERATION_CO2_TAX,GENERATION_CO2_TRANSPORT,GENERATION_UC_COSTS, + GENERATION_OTHER_EMI_TAX,GENERATION_OPPORTUNITY_COSTS,HYDRO_PROFILE,TAXES,GRID_TARIFFS,TOTAL_PROFIT,ENERGY_SPECIFIC_PROFIT GENERATION_OPPORTUNITY_COSTS_STORAGE,GENERATION_REDISPATCH_COMPENSATION_COST,ELECTRICITY_REDISPATCH_COMPENSATION, + HEAT_REDISPATCH_COMPENSATION,HYDROGEN_REDISPATCH_COMPENSATION,BIOMETHANE_REDISPATCH_COMPENSATION, + HYDRO_PROFILE,TAXES,GRID_TARIFFS,TOTAL_PROFIT,ENERGY_SPECIFIC_PROFIT/; +SET COMMODITY /ELECTRICITY,HEAT,HYDROGEN,BIOMETHANE/; +SET TECH_TYPE /CONDENSING,CHP-BACK-PRESSURE,CHP-EXTRACTION,BOILERS,ELECT-TO-HEAT,INTERSEASONAL-HEAT-STORAGE,INTERSEASONAL-ELECT-STORAGE,INTRASEASONAL-HEAT-STORAGE,INTRASEASONAL-ELECT-STORAGE,HYDRO-RESERVOIRS,HYDRO-RUN-OF-RIVER,WIND-ON,WIND-OFF,SOLAR-PV,SOLAR-HEATING,HYDRO-WAVE,HEAT-PUMP,EL-BOILER,FUELCELL,ELECTROLYZER,H2-STORAGE,BIOMETH-DAC,BIOGASUPGRADING,BIOGASMETHANATION,HUB-OFF,STEAMREFORMING/; +SET GTECH_TYPE(GGG,TECH_TYPE); +SET PRICE_CATEGORY /AVERAGE,AVERAGE_WEIGHTED_BY_CONSUMPTION,AVERAGE_WEIGHTED_BY_PRODUCTION/; +SET VARIABLE_CATEGORY /EXOGENOUS,ENDOGENOUS,DECOMMISSIONING,ENDOGENOUS_ELECT2HEAT,ENDO_INTRASTO,ENDO_INTERSTO,ENDO_EV,ENDO_HEATPUMP,ENDO_ELBOILER,ENDO_OTHERTRANS,DIST_LOSSES,TRANS_LOSSES,ENDO_CCS,ENDO_H2,ENDO_BIOMETHANE,ENDO_FUELCELL/; +SET EL_BAL_TYPE /CURTAILMENT,NETEXPORT,PRICE,DEMAND_EXO,DEMAND_LOSS,DEMAND_INTERSTO,DEMAND_INTRASTO,DEMAND_P2H,DEMAND_EV,CONDENSING,CHP-BACK-PRESSURE,CHP-EXTRACTION,ELECT-TO-HEAT,INTER-STO,INTRA-STO,HYDRO-RESERVOIRS,HYDRO-RUN-OF-RIVER,WIND-ON,WIND-OFF,SOLAR-PV,HYDRO-WAVE,FUELCELL,DEMAND_OTHERTRANS,DEMAND_DISTLOSSES,DEMAND_CCS,DEMAND_P2G/; +SET H_BAL_TYPE /CURTAILMENT,NETEXPORT,PRICE,DEMAND_EXO,DEMAND_LOSS,DEMAND_INTERSTO,DEMAND_INTRASTO,BOILERS,CHP-BACK-PRESSURE,CHP-EXTRACTION,INTER-STO,INTRA-STO,SOLAR-HEATING,P2H,FUELCELL,DEMAND_P2G,DEMAND_DISTLOSSES/; +SET UNITS /GW,TWh,MWh,Money_per_MWh,Mmoney,kton,GWh/; + + + GTECH_TYPE(G,'CONDENSING')$IGCND(G) = YES; + GTECH_TYPE(G,'CHP-BACK-PRESSURE')$IGBPR(G) = YES; + GTECH_TYPE(G,'CHP-EXTRACTION')$IGEXT(G) = YES; + GTECH_TYPE(G,'BOILERS')$IGHOB(G) = YES; + GTECH_TYPE(G,'ELECT-TO-HEAT')$IGETOH(G) = YES; + GTECH_TYPE(G,'INTRASEASONAL-HEAT-STORAGE')$(GDATA(G,'GDTYPE') EQ GHSTO) = YES; + GTECH_TYPE(G,'INTERSEASONAL-HEAT-STORAGE')$(GDATA(G,'GDTYPE') EQ GHSTOS) = YES; + GTECH_TYPE(G,'INTRASEASONAL-ELECT-STORAGE')$(GDATA(G,'GDTYPE') EQ GESTO) = YES; + GTECH_TYPE(G,'INTERSEASONAL-ELECT-STORAGE')$(GDATA(G,'GDTYPE') EQ GESTOS) = YES; + GTECH_TYPE(G,'HYDRO-RESERVOIRS')$IGHYRS(G) = YES; + GTECH_TYPE(G,'HYDRO-RUN-OF-RIVER')$IGHYRR(G) = YES; + GTECH_TYPE(G,'WIND-ON')$(IGWND(G) AND GDATA(G,'GDTECHGROUP') EQ WINDTURBINE_ONSHORE) = YES; + GTECH_TYPE(G,'WIND-OFF')$(IGWND(G) AND GDATA(G,'GDTECHGROUP') EQ WINDTURBINE_OFFSHORE) = YES; + GTECH_TYPE(G,'SOLAR-PV')$IGSOLE(G) = YES; + GTECH_TYPE(G,'SOLAR-HEATING')$IGSOLH(G) = YES; + GTECH_TYPE(G,'HYDRO-WAVE')$IGWAVE(G) = YES; +$ifi %HYDROGEN%==yes GTECH_TYPE(G,'FUELCELL')$(GDATA(G,'GDTECHGROUP') EQ FUELCELL) = YES; +$ifi %HYDROGEN%==yes GTECH_TYPE(G,'ELECTROLYZER')$(GDATA(G,'GDTECHGROUP') EQ ELECTROLYZER) = YES; +$ifi %HYDROGEN%==yes GTECH_TYPE(G,'ELECTROLYZER')$(GDATA(G,'GDTECHGROUP') EQ BACKUP_HYDROGEN) = YES; +$ifi %HYDROGEN%==yes GTECH_TYPE(G,'H2-STORAGE')$(GDATA(G,'GDTYPE') EQ HYDROGEN_GH2STO) = YES; +$ifi %HYDROGEN%==yes GTECH_TYPE(G,'BIOMETH-DAC')$(GDATA(G,'GDTYPE') EQ HYDROGEN_GH2TOBIOMETH) = YES; +$ifi %HYDROGEN%==yes GTECH_TYPE(G,'BIOGASUPGRADING')$(GDATA(G,'GDTYPE') EQ HYDROGEN_GBIOGASUPGRADING) = YES; +$ifi %HYDROGEN%==yes GTECH_TYPE(G,'BIOGASMETHANATION')$(GDATA(G,'GDTYPE') EQ HYDROGEN_GBIOGASMETHANATION) = YES; +$ifi %HYDROGEN%==yes GTECH_TYPE(G,'STEAMREFORMING')$(GDATA(G,'GDTYPE') EQ HYDROGEN_GCH4TOH2) = YES; +$ifi %OFFSHOREGRID%==yes GTECH_TYPE(G,'HUB-OFF')$(GDATA(G,'GDTYPE') EQ HUB_OFF) = YES; + +PARAMETER G_CAP_YCRAF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,VARIABLE_CATEGORY,UNITS) "Generation capacity for each year, country, region, area, technology, fuel, commodity, technology type and variable type (GW)"; +PARAMETER G_STO_YCRAF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,VARIABLE_CATEGORY,UNITS) "Generation storage for each year, country, region, area, technology, fuel, commodity, technology type and variable type (GWh)"; +PARAMETER PRO_YCRAGFST(Y,C,RRR,AAA,G,FFF,SSS,TTT,COMMODITY,TECH_TYPE,UNITS) "Energy Production for each year, country, region, area, technology, fuel, season, hour, commodity and technology type (MWh)" ; +PARAMETER PRO_YCRAGF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,UNITS) "Annual Energy Production for each year, country, region, area, technology, fuel, commodity and technology type (TWh)"; +PARAMETER F_CONS_YCRAST(Y,C,RRR,AAA,G,FFF,SSS,TTT,TECH_TYPE,UNITS) "Fuel consumption for each year, country, region, area, technology, fuel, season, hour and technology type (MWh)"; +PARAMETER F_CONS_YCRA(Y,C,RRR,AAA,G,FFF,TECH_TYPE,UNITS) "Fuel consumption for each year, country, region, area, technology, fuel and technology type (TWh)"; +PARAMETER X_CAP_YCR(Y,C,IRRRE,IRRRI,VARIABLE_CATEGORY,UNITS) "Transmission capacity for each year, country, from region to region (GW)"; +PARAMETER X_FLOW_YCRST(Y,C,IRRRE,IRRRI,SSS,TTT,UNITS) "Transmission flow for each year, country, from region to region, for each season and hour (MWh)"; +PARAMETER X_FLOW_YCR(Y,C,IRRRE,IRRRI,UNITS) "Transmission flow for each year, country, from region to region (TWh)"; +PARAMETER ECO_G_YCRAG(Y,C,RRR,AAA,G,FFF,TECH_TYPE,CATEGORY,SUBCATEGORY,UNITS) "Generation Economic Output for each year, country, region, fuel, technology type, area and technology (Mmoney)"; +PARAMETER ECO_X_YCR(Y,C,RRR,IRRRI,CATEGORY,SUBCATEGORY,UNITS) "Electric Transmission Economic Output for each year, country and region (Mmoney)"; +PARAMETER OBJ_YCR(Y,C,RRR,SUBCATEGORY,UNITS) "Objective function (Mmoney)"; +PARAMETER EL_PRICE_YCR(Y,C,RRR,PRICE_CATEGORY,UNITS) "Average Electricity Prices for each region (money/MWh)"; +PARAMETER EL_PRICE_YCRST(Y,C,RRR,SSS,TTT,UNITS) "Hourly Electricity Prices for each region (money/MWh)"; +PARAMETER H_PRICE_YCRA(Y,C,RRR,AAA,PRICE_CATEGORY,UNITS) "Average heating Prices for each area(money/MWh)"; +PARAMETER H_PRICE_YCRAST(Y,C,RRR,AAA,SSS,TTT,UNITS) "Hourly heating Prices for each area (money(MWh)"; +PARAMETER EL_DEMAND_YCRST(Y,C,RRR,SSS,TTT,VARIABLE_CATEGORY,UNITS) "Aggregated Hourly Electricity Demand (MWh)"; +PARAMETER EL_DEMAND_YCR(Y,C,RRR,VARIABLE_CATEGORY,UNITS) "Aggregated annual Electricity Demand (TWh)"; +PARAMETER H_DEMAND_YCRAST(Y,C,RRR,AAA,SSS,TTT,VARIABLE_CATEGORY,UNITS) "Aggregated Hourly heat Demand (MWh)"; +PARAMETER H_DEMAND_YCRA(Y,C,RRR,AAA,VARIABLE_CATEGORY,UNITS) "Aggregated Annual heat Demand (MWh)"; +PARAMETER EMI_YCRAG(Y,C,RRR,AAA,G,FFF,TECH_TYPE,UNITS) "Annual CO2 emissions(ktons)"; +PARAMETER CURT_YCRAGFST(Y,C,RRR,AAA,G,FFF,SSS,TTT,COMMODITY,TECH_TYPE,UNITS) "Hourly energy curtailment per country, region, area, technology, fuel, hour and technology type (MWh)"; +PARAMETER CURT_YCRAGF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,UNITS) "Hourly energy curtailment per country, region, area, technology, fuel, hour and technology type (MWh)"; +PARAMETER EL_BALANCE_YCRST(Y,C,RRR,EL_BAL_TYPE,SSS,TTT,UNITS) "Hourly electricity balance per country, region, and type (MWh)"; +PARAMETER H_BALANCE_YCRAST(Y,C,RRR,AAA,H_BAL_TYPE,SSS,TTT,UNITS) "Hourly heat balance per country, region, area and type (MWh)"; +$ifi %HYDROGEN%==yes PARAMETER H2_PRICE_YCR(Y,C,RRR,PRICE_CATEGORY,UNITS) "Average Hydrogen Prices for each region (money/MWh)"; +$ifi %HYDROGEN%==yes PARAMETER H2_PRICE_YCRST(Y,C,RRR,SSS,TTT,UNITS) "Hourly hydrogen Prices for each region (money/MWh)"; +$ifi %HYDROGEN%==yes PARAMETER BIOMETH_PRICE_YST(Y,SSS,TTT,UNITS) "Hourly biomethane Prices for each region (money/MWh)"; +$ifi %HYDROGEN%==yes PARAMETER XH2_CAP_YCR(Y,C,IRRRE,IRRRI,VARIABLE_CATEGORY,UNITS) "H2 Transmission capacity for each year, country, from region to region (GW)"; +$ifi %HYDROGEN%==yes PARAMETER XH2_FLOW_YCRST(Y,C,IRRRE,IRRRI,SSS,TTT,UNITS) "H2 Transmission flow for each year, country, from region to region, for each season and hour (MWh)"; +$ifi %HYDROGEN%==yes PARAMETER XH2_FLOW_YCR(Y,C,IRRRE,IRRRI,UNITS) "H2 Transmission flow for each year, country, from region to region (TWh)"; +$ifi %HYDROGEN%==yes PARAMETER ECO_XH2_YCR(Y,C,RRR,IRRRI,CATEGORY,SUBCATEGORY,UNITS) "H2 Transmission Economic Output for each year, country and region (Mmoney)"; +$ifi %HYDROGEN%==yes PARAMETER H2_DEMAND_YCRST(Y,C,RRR,SSS,TTT,VARIABLE_CATEGORY,UNITS) "Aggregated Hourly H2 Demand (MWh)"; +$ifi %HYDROGEN%==yes PARAMETER H2_DEMAND_YCR(Y,C,RRR,VARIABLE_CATEGORY,UNITS) "Aggregated annual H2 Demand (TWh)"; + + +**Additional Addon output +*HEATRANS +$ifi %HEATTRANS%==yes PARAMETER XH_CAP_YCA(Y,C,IAAAE,IAAAI,VARIABLE_CATEGORY,UNITS) "Heat Transmission capacity for each year, country, from area to area (GW)"; +$ifi %HEATTRANS%==yes PARAMETER XH_FLOW_YCAST(Y,C,IAAAE,IAAAI,SSS,TTT,UNITS) "Heat Transmission flow for each year, country, from areas to area, for each season and hour (MWh)"; +$ifi %HEATTRANS%==yes PARAMETER XH_FLOW_YCA(Y,C,IAAAE,IAAAI,UNITS) "Heat Transmission flow for each year, country, from area to area (TWh)"; +$ifi %HEATTRANS%==yes PARAMETER ECO_XH_YCRA(Y,C,RRR,AAA,IAAAI,CATEGORY,SUBCATEGORY,UNITS) "Heat Transmission Economic Output for each year, country and region and area (Mmoney)"; + + +$ifi not %stointers%==all $goto NO_STOINTERS +*Modification of STORAGE sets so calculation are done correcting its type: +IGHSTO(G) = 0; +IGHSTOS(G) = 0; +IGESTO(G) = 0; +IGESTOS(G) = 0; +IGHSTO(G) = YES$(GDATA(G,'GDTYPE') EQ GHSTO); +IGHSTOS(G) = YES$(GDATA(G,'GDTYPE') EQ GHSTOS); +IGESTO(G) = YES$(GDATA(G,'GDTYPE') EQ GESTO); +IGESTOS(G) = YES$(GDATA(G,'GDTYPE') EQ GESTOS); +$label NO_STOINTERS + +*Modification of IHOURSINST +$ifi %RollingSeasons%==yes PARAMETER IHOURSINTROLLINGSEASON(S,T) "Length of time segment (hours) used in rolling season mode"; +$ifi %RollingSeasons%==yes IWEIGHSUMS=SUM(SSS$(ORD(SSS) LE ROLLINGSEASONSNUMBER), WEIGHT_S(SSS)); +$ifi %RollingSeasons%==yes IHOURSINTROLLINGSEASON(S,T)=IOF8760*WEIGHT_S(S)*WEIGHT_T(T)/(IWEIGHSUMS*SUM(ITALIAS, WEIGHT_T(ITALIAS)))/IHOURFRAC; + +IWEIGHSUMS = SUM(S, WEIGHT_S(S)); +IWEIGHSUMT = SUM(T, WEIGHT_T(T)); +IHOURSINST(SSS,TTT)=0; +IHOURSINST(S,T)=IOF8760*WEIGHT_S(S)*WEIGHT_T(T)/(IWEIGHSUMS*IWEIGHSUMT)/IHOURFRAC; + + + +* -------------------- ENERGY CAPACITY --------------------- + +* ------------- ELECTRICITY CAPACITY -------------- + +** EXOGENOUS ELECTRICITY CAPACITY + +G_CAP_YCRAF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'EXOGENOUS','GW')$((not IGETOH(IGE)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) AND NOT(IHYDROGEN(IGE)))= +IOF0001*GKFX(Y,IA,IGE)/(1$((NOT IGESTO(IGE)) AND (NOT IGESTOS(IGE)))+GDATA(IGE,'GDSTOHUNLD')$((IGESTO(IGE)) OR IGESTOS(IGE))) +; + + +** ENDOGENOUS ELECTRIC CAPACITY ACCUMULATED INVESTMENTS +G_CAP_YCRAF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'ENDOGENOUS','GW')$((not IGETOH(IGE)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) AND NOT(IHYDROGEN(IGE)))= +IOF0001*VGKNACCUMNET.L(Y,IA,IGE)/(1$((NOT IGESTO(IGE)) AND (NOT IGESTOS(IGE)))+GDATA(IGE,'GDSTOHUNLD')$((IGESTO(IGE)) OR IGESTOS(IGE))) +; + +** ENDOGENOUS ELECTRICITY CAPACITY ACCUMULATED DECOMMISSIONING +$ifi %DECOM%==yes G_CAP_YCRAF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'DECOMMISSIONING','GW')$((not IGETOH(IGE)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,IGE) AND NOT(IHYDROGEN(IGE)))= +$ifi %DECOM%==yes -IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IGE)/(1$((NOT IGESTO(IGE)) AND (NOT IGESTOS(IGE)))+GDATA(IGE,'GDSTOHUNLD')$((IGESTO(IGE)) OR IGESTOS(IGE))) +; + + +* ------------- END OF ELECTRICITY CAPACITY -------------- + +* ------------- HEAT CAPACITY -------------- + +** EXOGENOUS HEAT CAPACITY + +G_CAP_YCRAF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'EXOGENOUS','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE))= +(IOF0001*GKFX(Y,IA,IGH)/((1$((NOT IGHSTO(IGH)) AND (NOT IGHSTOS(IGH)))+GDATA(IGH,'GDSTOHUNLD')$((IGHSTO(IGH)) OR IGHSTOS(IGH)))*(GDATA(IGH,'GDCB')$IGBPR(IGH)+ (GDATA(IGH,'GDCB')+GDATA(IGH,'GDCV'))$IGEXT(IGH)+1$(NOT (IGBPR(IGH) OR IGEXT(IGH))))))$(NOT IGBYPASS(IGH) AND NOT(IHYDROGEN(IGH))) ++(IOF0001*GKFX(Y,IA,IGH)*(((GDATA(IGH,'GDCB')/GDATA(IGH,'GDBYPASSC')+1)/(GDATA(IGH,'GDCB')+GDATA(IGH,'GDCV')))$IGEXT(IGH) + (1/GDATA(IGH,'GDBYPASSC')+1/GDATA(IGH,'GDCB'))$IGBPR(IGH)))$(IGBYPASS(IGH) AND NOT(IHYDROGEN(IGH))) +$ifi %HYDROGEN%==yes +(IOF0001*GKFX(Y,IA,IGH)/GDATA(IGH,'GDCB'))$(IHYDROGEN_GETOHH2(IGH) OR IHYDROGEN_GH2TOEH(IGH)) +; + +** ENDOGENOUS HEAT CAPACITY ACCUMULATED INVESTMENTS +G_CAP_YCRAF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'ENDOGENOUS','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE))= +(IOF0001*VGKNACCUMNET.L(Y,IA,IGH)/((1$((NOT IGHSTO(IGH)) AND (NOT IGHSTOS(IGH)))+GDATA(IGH,'GDSTOHUNLD')$((IGHSTO(IGH)) OR IGHSTOS(IGH)))*(GDATA(IGH,'GDCB')$IGBPR(IGH)+ (GDATA(IGH,'GDCB')+GDATA(IGH,'GDCV'))$IGEXT(IGH)+1$(NOT (IGBPR(IGH) OR IGEXT(IGH))))))$(NOT IGBYPASS(IGH) AND NOT(IHYDROGEN(IGH))) ++(IOF0001*VGKNACCUMNET.L(Y,IA,IGH)*(((GDATA(IGH,'GDCB')/GDATA(IGH,'GDBYPASSC')+1)/(GDATA(IGH,'GDCB')+GDATA(IGH,'GDCV')))$IGEXT(IGH) + (1/GDATA(IGH,'GDBYPASSC')+1/GDATA(IGH,'GDCB'))$IGBPR(IGH)))$(IGBYPASS(IGH) AND NOT(IHYDROGEN(IGH))) +$ifi %HYDROGEN%==yes +(IOF0001*VGKNACCUMNET.L(Y,IA,IGH)/GDATA(IGH,'GDCB'))$(IHYDROGEN_GETOHH2(IGH) OR IHYDROGEN_GH2TOEH(IGH)) +; + +** ENDOGENOUS HEAT CAPACITY ACCUMULATED DECOMMISSIONING +$ifi %DECOM%==yes G_CAP_YCRAF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'DECOMMISSIONING','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,IGH))= +$ifi %DECOM%==yes -(IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IGH)/((1$((NOT IGHSTO(IGH)) AND (NOT IGHSTOS(IGH)))+GDATA(IGH,'GDSTOHUNLD')$((IGHSTO(IGH)) OR IGHSTOS(IGH)))*(GDATA(IGH,'GDCB')$IGBPR(IGH)+ (GDATA(IGH,'GDCB')+GDATA(IGH,'GDCV'))$IGEXT(IGH)+1$(NOT (IGBPR(IGH) OR IGEXT(IGH))))))$(NOT IGBYPASS(IGH) AND NOT(IHYDROGEN(IGH))) +$ifi %DECOM%==yes -(IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IGH)*(((GDATA(IGH,'GDCB')/GDATA(IGH,'GDBYPASSC')+1)/(GDATA(IGH,'GDCB')+GDATA(IGH,'GDCV')))$IGEXT(IGH) + (1/GDATA(IGH,'GDBYPASSC')+1/GDATA(IGH,'GDCB'))$IGBPR(IGH)))$(IGBYPASS(IGH) AND NOT(IHYDROGEN(IGH))) +$ifi %DECOM%==yes $ifi %HYDROGEN%==yes -(IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IGH)/GDATA(IGH,'GDCB'))$(IHYDROGEN_GETOHH2(IGH) OR IHYDROGEN_GH2TOEH(IGH)) +; + +* ------------- END OF HEAT CAPACITY -------------- + +$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_CAPACITY +* ------------- HYDROGEN CAPACITY -------------- + +** EXOGENOUS HYDROGEN CAPACITY + +G_CAP_YCRAF(Y,C,IR,IA,G,FFF,'HYDROGEN',TECH_TYPE,'EXOGENOUS','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IHYDROGEN(G))= +IOF0001*GKFX(Y,IA,G)*(1$IHYDROGEN_GH2STO(G)+GDATA(G,'GDFE')$(NOT(IHYDROGEN_GH2STO(G))))/(1$(NOT IHYDROGEN_GH2STO(G))+GDATA(G,'GDSTOHUNLD')$IHYDROGEN_GH2STO(G)) +; + + +** ENDOGENOUS HYDROGEN CAPACITY ACCUMULATED INVESTMENTS +G_CAP_YCRAF(Y,C,IR,IA,G,FFF,'HYDROGEN',TECH_TYPE,'ENDOGENOUS','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IHYDROGEN(G))= +IOF0001*VGKNACCUMNET.L(Y,IA,G)*(1$IHYDROGEN_GH2STO(G)+GDATA(G,'GDFE')$(NOT(IHYDROGEN_GH2STO(G))))/(1$(NOT IHYDROGEN_GH2STO(G))+GDATA(G,'GDSTOHUNLD')$IHYDROGEN_GH2STO(G)) +; + +** ENDOGENOUS HYDROGEN CAPACITY ACCUMULATED DECOMMISSIONING +$ifi %DECOM%==yes G_CAP_YCRAF(Y,C,IR,IA,G,FFF,'HYDROGEN',TECH_TYPE,'DECOMMISSIONING','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,G) AND IHYDROGEN(G) AND NOT(IHYDROGEN_GH2TOBIOMETH(G)))= +$ifi %DECOM%==yes -IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,G)*(1$IHYDROGEN_GH2STO(G)+GDATA(G,'GDFE')$(NOT(IHYDROGEN_GH2STO(G))))/(1$(NOT IHYDROGEN_GH2STO(G))+GDATA(G,'GDSTOHUNLD')$IHYDROGEN_GH2STO(G)) +; + +* ------------- END OF HYDROGEN CAPACITY -------------- + +* ------------- BIOMETHANE CAPACITY -------------- + +** EXOGENOUS BIOMETHANE CAPACITY + +G_CAP_YCRAF(Y,C,IR,IA,G,FFF,'BIOMETHANE',TECH_TYPE,'EXOGENOUS','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND (IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASMETHANATION(G) OR IHYDROGEN_GBIOGASUPGRADING(G)))= +IOF0001*GKFX(Y,IA,G) +; + +** ENDOGENOUS BIOMETHANE CAPACITY ACCUMULATED INVESTMENTS +G_CAP_YCRAF(Y,C,IR,IA,G,FFF,'BIOMETHANE',TECH_TYPE,'ENDOGENOUS','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND (IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASMETHANATION(G) OR IHYDROGEN_GBIOGASUPGRADING(G)))= +IOF0001*VGKNACCUMNET.L(Y,IA,G) +; + +** ENDOGENOUS HYDROGEN CAPACITY ACCUMULATED DECOMMISSIONING +$ifi %DECOM%==yes G_CAP_YCRAF(Y,C,IR,IA,G,FFF,'BIOMETHANE',TECH_TYPE,'DECOMMISSIONING','GW')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,G) AND (IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASMETHANATION(G) OR IHYDROGEN_GBIOGASUPGRADING(G)))= +$ifi %DECOM%==yes -IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,G) +; + +* ------------- END OF BIOMETHANE CAPACITY -------------- + +$label NO_HYDROGEN_CAPACITY + +* ---------------- END OF ENERGY CAPACITY ----------------- + +* -------------------- ENERGY STORAGE --------------------- + +* ------------- ELECTRICITY STORAGE -------------- + +** EXOGENOUS ELECTRICITY STORAGE + +G_STO_YCRAF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'EXOGENOUS','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) AND (IGESTO(IGE) OR IGESTOS(IGE) OR IGHYRS(IGE)) )= +IOF0001*GKFX(Y,IA,IGE)*(1$(NOT IGHYRS(IGE)) + HYRSMAXVOL_G(IA,IGE)$IGHYRS(IGE)) +; + +** ENDOGENOUS ELECTRIC STORAGE ACCUMULATED INVESTMENTS +G_STO_YCRAF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'ENDOGENOUS','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) AND (IGESTO(IGE) OR IGESTOS(IGE) OR IGHYRS(IGE)) )= +IOF0001*VGKNACCUMNET.L(Y,IA,IGE)*(1$(NOT IGHYRS(IGE)) + HYRSMAXVOL_G(IA,IGE)$IGHYRS(IGE)) +; + +** ENDOGENOUS ELECTRICITY STORAGE ACCUMULATED DECOMMISSIONING +$ifi %DECOM%==yes G_STO_YCRAF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'DECOMMISSIONING','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,IGE) AND (IGESTO(IGE) OR IGESTOS(IGE) OR IGHYRS(IGE)) )= +$ifi %DECOM%==yes -IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IGE)*(1$(NOT IGHYRS(IGE)) + HYRSMAXVOL_G(IA,IGE)$IGHYRS(IGE)) +; + +* ------------- END OF ELECTRICITY CAPACITY -------------- + +* ------------- HEAT STORAGE -------------- + +** EXOGENOUS HEAT STORAGE + +G_STO_YCRAF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'EXOGENOUS','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE) AND (IGHSTO(IGH) OR IGHSTOS(IGH)))= +IOF0001*GKFX(Y,IA,IGH) +; + +** ENDOGENOUS HEAT STORAGE ACCUMULATED INVESTMENTS +G_STO_YCRAF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'ENDOGENOUS','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE) AND (IGHSTO(IGH) OR IGHSTOS(IGH)))= +IOF0001*VGKNACCUMNET.L(Y,IA,IGH) +; + +** ENDOGENOUS HEAT STORAGE ACCUMULATED DECOMMISSIONING +$ifi %DECOM%==yes G_STO_YCRAF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'DECOMMISSIONING','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,IGH) AND (IGHSTO(IGH) OR IGHSTOS(IGH)))= +$ifi %DECOM%==yes -IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IGH) +; + +* ------------- END OF HEAT STORAGE -------------- + +$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_STORAGE +* ------------- HYDROGEN STORAGE -------------- + +** EXOGENOUS HYDROGEN STORAGE + +G_STO_YCRAF(Y,C,IR,IA,IHYDROGEN_GH2STO,FFF,'HYDROGEN',TECH_TYPE,'EXOGENOUS','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO) AND IGF(IHYDROGEN_GH2STO,FFF) AND GTECH_TYPE(IHYDROGEN_GH2STO,TECH_TYPE) )= +IOF0001*GKFX(Y,IA,IHYDROGEN_GH2STO) +; + +** ENDOGENOUS HYDROGEN STORAGE ACCUMULATED INVESTMENTS +G_STO_YCRAF(Y,C,IR,IA,IHYDROGEN_GH2STO,FFF,'HYDROGEN',TECH_TYPE,'ENDOGENOUS','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO) AND IGF(IHYDROGEN_GH2STO,FFF) AND GTECH_TYPE(IHYDROGEN_GH2STO,TECH_TYPE))= +IOF0001*VGKNACCUMNET.L(Y,IA,IHYDROGEN_GH2STO) +; + +** ENDOGENOUS HYDROGEN STORAGE ACCUMULATED DECOMMISSIONING +$ifi %DECOM%==yes G_STO_YCRAF(Y,C,IR,IA,IHYDROGEN_GH2STO,FFF,'HYDROGEN',TECH_TYPE,'DECOMMISSIONING','GWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO) AND IGF(IHYDROGEN_GH2STO,FFF) AND GTECH_TYPE(IHYDROGEN_GH2STO,TECH_TYPE) AND IGDECOMEXOPOT(Y,IA,IHYDROGEN_GH2STO))= +$ifi %DECOM%==yes -IOF0001*VDECOM_EXO_ACCUM.L(Y,IA,IHYDROGEN_GH2STO) +; +* ------------- END OF HYDROGEN STORAGE -------------- +$label NO_HYDROGEN_STORAGE + +* ---------------- END OF ENERGY STORAGE ----------------- + + + + +* -------------------- ENERGY PRODUCTION --------------------- + +** ELECTRICITY PRODUCTION HOURLY +PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY',TECH_TYPE,'MWh')$((not IGETOH(IGE)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE))= + VGE_T.L(Y,IA,IGE,S,T); + +** ELECTRICITY PRODUCTION ANNUALLY +PRO_YCRAGF(Y,C,IR,IA,IGE,FFF,'ELECTRICITY',TECH_TYPE,'TWh')$((not IGETOH(IGE)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE))= + SUM((S,T),IHOURSINST(S,T)*VGE_T.L(Y,IA,IGE,S,T)*IHOURFRAC)/IOF1000000; + +** HEAT PRODUCTION HOURLY +PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT',TECH_TYPE,'MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE))= + VGH_T.L(Y,IA,IGH,S,T); + +** HEAT PRODUCTION ANNUALY +PRO_YCRAGF(Y,C,IR,IA,IGH,FFF,'HEAT',TECH_TYPE,'TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE))= + SUM((S,T),IHOURSINST(S,T)*VGH_T.L(Y,IA,IGH,S,T)*IHOURFRAC)/IOF1000000; + +$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_PRODUCTION +** HYDROGEN PRODUCTION HOURLY +PRO_YCRAGFST(Y,C,IR,IA,G,FFF,S,T,'HYDROGEN',TECH_TYPE,'MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IHYDROGEN(G) )= + +(VGE_T.L(Y,IA,G,S,T)*GDATA(G,'GDFE'))$IHYDROGEN_GETOH2(G) + +(VGE_T.L(Y,IA,G,S,T)*GDATA(G,'GDFE'))$IHYDROGEN_GETOHH2(G) + +(VGE_T.L(Y,IA,G,S,T)*GDATA(G,'GDFE'))$IHYDROGEN_GEHTOH2(G) + +(VHYDROGEN_GH2_T.L(Y,IA,G,S,T))$(IHYDROGEN_GCH4TOH2(G) OR IHYDROGEN_GH2STO(G)) +; + +** HYDROGEN PRODUCTION ANNUALY +PRO_YCRAGF(Y,C,IR,IA,G,FFF,'HYDROGEN',TECH_TYPE,'TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IHYDROGEN(G))= + SUM((S,T),IHOURSINST(S,T)*PRO_YCRAGFST(Y,C,IR,IA,G,FFF,S,T,'HYDROGEN',TECH_TYPE,'MWh')*IHOURFRAC)/IOF1000000 +; + +** BIOMETHANE PRODUCTION HOURLY +PRO_YCRAGFST(Y,C,IR,IA,G,FFF,S,T,'BIOMETHANE',TECH_TYPE,'MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND (IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASMETHANATION(G) OR IHYDROGEN_GBIOGASUPGRADING(G)))= + VGBIOMETH_T.L(Y,IA,G,S,T)$IHYDROGEN_GH2TOBIOMETH(G) + + VGBIOGASMETHANATION_T.L(Y,IA,G,S,T)$IHYDROGEN_GBIOGASMETHANATION(G) + +VGBIOGASUPGRADING_T.L(Y,IA,G,S,T)$IHYDROGEN_GBIOGASUPGRADING(G) +; + +** BIOMETHANE PRODUCTION ANNUALY +PRO_YCRAGF(Y,C,IR,IA,G,FFF,'BIOMETHANE',TECH_TYPE,'TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND (IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASMETHANATION(G) OR IHYDROGEN_GBIOGASUPGRADING(G)))= + SUM((S,T),IHOURSINST(S,T)*PRO_YCRAGFST(Y,C,IR,IA,G,FFF,S,T,'BIOMETHANE',TECH_TYPE,'MWh')*IHOURFRAC)/IOF1000000 +; +$label NO_HYDROGEN_PRODUCTION + +* ---------------- END OF ENERGY PRODUCTION ----------------- + + +* -------------------- FUEL CONSUMPTION --------------------- + +** HOURLY FUEL CONSUMPTION +F_CONS_YCRAST(Y,C,IR,IA,G,FFF,S,T,TECH_TYPE,'MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE))= + VGF_T.L(Y,IA,G,S,T); + +** ANNUAL FUEL CONSUMPTION +F_CONS_YCRA(Y,C,IR,IA,G,FFF,TECH_TYPE,'TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE))= + SUM((S,T),IHOURSINST(S,T)*VGF_T.L(Y,IA,G,S,T)*IHOURFRAC)/IOF1000000; + + +* ---------------- END OF FUEL CONSUMPTION ----------------- + +* ---------------- ENERGY CURTAILMENT ----------------------- +*HOURLY CURTAILMENT +CURT_YCRAGFST(Y,C,IR,IA,IGHYRR,FFF,S,T,'ELECTRICITY','HYDRO-RUN-OF-RIVER','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGHYRR) AND IGF(IGHYRR,FFF) AND GTECH_TYPE(IGHYRR,'HYDRO-RUN-OF-RIVER'))= +(WTRRRFLH(IA) * ( + GKFX(Y,IA,IGHYRR) +$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGHYRR) + + VGKNACCUMNET.L(Y,IA,IGHYRR) + ) * WTRRRVAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGHYRR,S,T)) + IGKRATE(IA,IGHYRR,S,T))) / IWTRRRSUM(IA) +-VGE_T.L(Y,IA,IGHYRR,S,T); + +CURT_YCRAGFST(Y,C,IR,IA,IGWND,FFF,S,T,'ELECTRICITY','WIND-ON','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGWND) AND IGF(IGWND,FFF) AND GTECH_TYPE(IGWND,'WIND-ON'))= +$ifi %WNDFLH_DOL%==AAA (WNDFLH(IA) * ( +$ifi %WNDFLH_DOL%==AAA_GGG (WNDFLH(IA,IGWND) * ( + GKFX(Y,IA,IGWND) +$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGWND) + + VGKNACCUMNET.L(Y,IA,IGWND) + ) * WND_VAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGWND,S,T)) + IGKRATE(IA,IGWND,S,T))) / IWND_SUMST(IA) +-VGE_T.L(Y,IA,IGWND,S,T); + +CURT_YCRAGFST(Y,C,IR,IA,IGWND,FFF,S,T,'ELECTRICITY','WIND-OFF','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGWND) AND IGF(IGWND,FFF) AND GTECH_TYPE(IGWND,'WIND-OFF'))= +$ifi %WNDFLH_DOL%==AAA (WNDFLH(IA) * ( +$ifi %WNDFLH_DOL%==AAA_GGG (WNDFLH(IA,IGWND) * ( + GKFX(Y,IA,IGWND) +$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGWND) + + VGKNACCUMNET.L(Y,IA,IGWND) + ) * WND_VAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGWND,S,T)) + IGKRATE(IA,IGWND,S,T))) / IWND_SUMST(IA) +-VGE_T.L(Y,IA,IGWND,S,T); + +CURT_YCRAGFST(Y,C,IR,IA,IGSOLE,FFF,S,T,'ELECTRICITY','SOLAR-PV','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGSOLE) AND IGF(IGSOLE,FFF) AND GTECH_TYPE(IGSOLE,'SOLAR-PV'))= +$ifi %SOLEFLH_DOL%==AAA (SOLEFLH(IA) * ( +$ifi %SOLEFLH_DOL%==AAA_GGG (SOLEFLH(IA,IGSOLE) * ( + GKFX(Y,IA,IGSOLE) +$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGSOLE) + + VGKNACCUMNET.L(Y,IA,IGSOLE) + ) * SOLE_VAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGSOLE,S,T)) + IGKRATE(IA,IGSOLE,S,T))) / ISOLESUMST(IA) +-VGE_T.L(Y,IA,IGSOLE,S,T); + +CURT_YCRAGFST(Y,C,IR,IA,IGSOLH,FFF,S,T,'HEAT','SOLAR-HEATING','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGSOLH) AND IGF(IGSOLH,FFF) AND GTECH_TYPE(IGSOLH,'SOLAR-HEATING'))= +$ifi %SOLHFLH_DOL%==AAA (SOLHFLH(IA) * ( +$ifi %SOLHFLH_DOL%==AAA_GGG (SOLHFLH(IA,IGSOLH) * ( + GKFX(Y,IA,IGSOLH) +$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGSOLH) + + VGKNACCUMNET.L(Y,IA,IGSOLH) + ) * SOLH_VAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGSOLH,S,T)) + IGKRATE(IA,IGSOLH,S,T))) / ISOLHSUMST(IA) +-VGH_T.L(Y,IA,IGSOLH,S,T); + + +*ANNUAL CURTAILMENT +CURT_YCRAGF(Y,C,IR,IA,IGHYRR,FFF,'ELECTRICITY','HYDRO-RUN-OF-RIVER','TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGHYRR) AND IGF(IGHYRR,FFF) AND GTECH_TYPE(IGHYRR,'HYDRO-RUN-OF-RIVER'))= +SUM((S,T),IHOURSINST(S,T)*CURT_YCRAGFST(Y,C,IR,IA,IGHYRR,FFF,S,T,'ELECTRICITY','HYDRO-RUN-OF-RIVER','MWh')*IHOURFRAC)/IOF1000000 +; +CURT_YCRAGF(Y,C,IR,IA,IGWND,FFF,'ELECTRICITY','WIND-ON','TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGWND) AND IGF(IGWND,FFF) AND GTECH_TYPE(IGWND,'WIND-ON'))= +SUM((S,T),IHOURSINST(S,T)*CURT_YCRAGFST(Y,C,IR,IA,IGWND,FFF,S,T,'ELECTRICITY','WIND-ON','MWh')*IHOURFRAC)/IOF1000000 +; +CURT_YCRAGF(Y,C,IR,IA,IGWND,FFF,'ELECTRICITY','WIND-OFF','TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGWND) AND IGF(IGWND,FFF) AND GTECH_TYPE(IGWND,'WIND-OFF'))= +SUM((S,T),IHOURSINST(S,T)*CURT_YCRAGFST(Y,C,IR,IA,IGWND,FFF,S,T,'ELECTRICITY','WIND-OFF','MWh')*IHOURFRAC)/IOF1000000 +; +CURT_YCRAGF(Y,C,IR,IA,IGSOLE,FFF,'ELECTRICITY','SOLAR-PV','TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGSOLE) AND IGF(IGSOLE,FFF) AND GTECH_TYPE(IGSOLE,'SOLAR-PV'))= +SUM((S,T),IHOURSINST(S,T)*CURT_YCRAGFST(Y,C,IR,IA,IGSOLE,FFF,S,T,'ELECTRICITY','SOLAR-PV','MWh')*IHOURFRAC)/IOF1000000 +; +CURT_YCRAGF(Y,C,IR,IA,IGSOLH,FFF,'HEAT','SOLAR-HEATING','TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGSOLH) AND IGF(IGSOLH,FFF) AND GTECH_TYPE(IGSOLH,'SOLAR-HEATING'))= +SUM((S,T),IHOURSINST(S,T)*CURT_YCRAGFST(Y,C,IR,IA,IGSOLH,FFF,S,T,'HEAT','SOLAR-HEATING','MWh')*IHOURFRAC)/IOF1000000 +; + +*Curtailment of hydro reservoirs should be analyzed by aggregating the technology set +CURT_YCRAGF(Y,C,IR,IA,IGHYRS,FFF,'ELECTRICITY','HYDRO-RESERVOIRS','TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGHYRS) AND IGF(IGHYRS,FFF) AND GTECH_TYPE(IGHYRS,'HYDRO-RESERVOIRS'))= +SUM(S, + IHYINF_S(IA,S) * + ( + GKFX(Y,IA,IGHYRS) +$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGHYRS) + +VGKNACCUMNET.L(Y,IA,IGHYRS) + ) + -SUM(T,IHYRST(S,T)*VGE_T.L(Y,IA,IGHYRS,S,T)) + )/IOF1000000*IHOURFRAC +; + + +* ---------------- END OF ENERGY CURTAILMENT ----------------------- + +* ------------------------- ENERGY DEMAND ---------------------- + +* ------------- ELECTRICITY DEMAND -------------- + +*HOURLY +** EXOGENOUS DEMAND + +EL_DEMAND_YCRST(Y,C,IR,S,T,'EXOGENOUS','MWh')$(CCCRRR(C,IR) AND SUM(DEUSER,IDE_SUMST(IR,DEUSER)))= +SUM((DEUSER)$IDE_SUMST(IR,DEUSER), DE(Y,IR,DEUSER) * DE_VAR_T(IR,DEUSER,S,T) / IDE_SUMST(IR,DEUSER)) + +$ifi not %DEMANDRESPONSE%==yes $goto NO_DEMANDRESPONSE +* Downward regulation for DR shed technologies: +- SUM(IADR(Y,IA,DR_TECH_SHED)$(RRRAAA(IR,IA) AND CCCRRR(C,IR)), VDR_DOWN.L(Y,IA,DR_TECH_SHED,S,T)) +* Balance between upward and downward regulation for DR shift technologies: +*TODO - efficiency +- SUM(IADR(Y,IA,DR_TECH_SHIFT)$(RRRAAA(IR,IA) AND CCCRRR(C,IR)), VDR_DOWN.L(Y,IA,DR_TECH_SHIFT,S,T)) + SUM(IADR(Y,IA,DR_TECH_SHIFT)$(RRRAAA(IR,IA) AND CCCRRR(C,IR)), VDR_UP.L(Y,IA,DR_TECH_SHIFT,S,T)) +$label NO_DEMANDRESPONSE +; + +** ENDOGENOUS DEMAND + +EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDOGENOUS_ELECT2HEAT','MWh')$CCCRRR(C,IR)= +* P2H CONSUMPTION + SUM(IA$RRRAAA(IR,IA), SUM(IGETOH$IAGK_HASORPOT(Y,IA,IGETOH), VGE_T.L(Y,IA,IGETOH,S,T))) +; + +EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_INTRASTO','MWh')$CCCRRR(C,IR)= +* INTRASEASONAL ELECTRICITY STORAGE +$ifi not %stointers%==all +SUM(IA$RRRAAA(IR,IA),SUM(IGESTO$IAGK_HASORPOT(Y,IA,IGESTO),VESTOLOADT.L(Y,IA,IGESTO,S,T))) +$ifi %stointers%==all +SUM(IA$RRRAAA(IR,IA),SUM(IGESTO$IAGK_HASORPOT(Y,IA,IGESTO),VESTOLOADTS.L(Y,IA,IGESTO,S,T))) +; + +EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_INTERSTO','MWh')$CCCRRR(C,IR)= +* INTERSEASONAL ELECTRICITY STORAGE + +SUM(IA$RRRAAA(IR,IA),SUM(IGESTOS$IAGK_HASORPOT(Y,IA,IGESTOS),VESTOLOADTS.L(Y,IA,IGESTOS,S,T))) +; + +$ifi not %EV%==yes $goto NO_EV +EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_EV','MWh')$CCCRRR(C,IR)= + + VEV_VG2V_BEV.L(Y,IR,S,T) + + VEV_VG2V_PHEV.L(Y,IR,S,T) +$ifi %V2G%==yes - VEV_VV2G_BEV.L(Y,IR,S,T) +$ifi %V2G%==yes - VEV_VV2G_PHEV.L(Y,IR,S,T) +$ifi %STEPCHARGSCHEME%==yes - VEV_VV2G_BEV.L(Y,IR,S,T)$(cs('v2g',IY411)) +$ifi %STEPCHARGSCHEME%==yes - VEV_VV2G_PHEV.L(Y,IR,S,T)$(cs('v2g',IY411)) +; +$label NO_EV + +$ifi %TRANSPORT%==yes EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_OTHERTRANS','MWh')$CCCRRR(C,IR)= +$ifi %TRANSPORT%==yes $ifi %TRANSPORT_SYNTHFUEL_COMMODITY%==ELECTRICITY +VTRANSDEMAND_T.L(Y,IR,S,T) +$ifi %TRANSPORT%==yes +0; + +$ifi %CCS%==yes EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_CCS','MWh')$CCCRRR(C,IR)= +$ifi %CCS%==yes SUM((IA,G)$(IAGK_HASORPOT(Y,IA,G) AND RRRAAA(IR,IA) AND CCS_G(G)),VGF_T.L(Y,IA,G,S,T) * IM_CO2(G)*IOF0001 * IOF3P6 * CCS_CO2CAPTEFF_G(G)* CCS_DECO2COMP_G(G)); + +$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_DEMAND +EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_H2','MWh')$CCCRRR(C,IR)= ++SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GETOH2)$(RRRAAA(IR,IA)), VGE_T.L(Y,IA,IHYDROGEN_GETOH2,S,T)) ++SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GETOHH2)$(RRRAAA(IR,IA)), VGE_T.L(Y,IA,IHYDROGEN_GETOHH2,S,T)) ++SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GEHTOH2)$(RRRAAA(IR,IA)), VGE_T.L(Y,IA,IHYDROGEN_GEHTOH2,S,T)) +; + +EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_BIOMETHANE','MWh')$CCCRRR(C,IR)= +*Adding electricity consumption from BIOMETHANE-DAC generation ++ SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH)$(RRRAAA(IR,IA)),VGBIOMETH_T.L(Y,IA,IHYDROGEN_GH2TOBIOMETH,S,T)*DAC_DE(IHYDROGEN_GH2TOBIOMETH)) +*Adding electricity consumption from biogas upgrading generation ++ SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASUPGRADING)$(RRRAAA(IR,IA)),VGBIOGASUPGRADING_T.L(Y,IA,IHYDROGEN_GBIOGASUPGRADING,S,T)*BIOGASUPGRADING_DE(IHYDROGEN_GBIOGASUPGRADING)) +*Adding electricity consumption from biogas methanation generation ++ SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASMETHANATION)$(RRRAAA(IR,IA)),VGBIOGASMETHANATION_T.L(Y,IA,IHYDROGEN_GBIOGASMETHANATION,S,T)*METHANATION_DE(IHYDROGEN_GBIOGASMETHANATION)) +; +$label NO_HYDROGEN_DEMAND + + +**LOSSES +EL_DEMAND_YCRST(Y,C,IR,S,T,'DIST_LOSSES','MWh')$CCCRRR(C,IR)= +* EXOGENOUS DEMAND +EL_DEMAND_YCRST(Y,C,IR,S,T,'EXOGENOUS','MWh')*DISLOSS_E(IR)/(1-DISLOSS_E(IR)) +* P2H CONSUMPTION ++SUM(IA$RRRAAA(IR,IA), SUM(IGETOH$IAGK_HASORPOT(Y,IA,IGETOH), VGE_T.L(Y,IA,IGETOH,S,T)*DISLOSS_E_AG(IA,IGETOH)/(1-DISLOSS_E_AG(IA,IGETOH)))) +* INTRASEASONAL ELECTRICITY STORAGE +$ifi not %stointers%==all +SUM(IA$RRRAAA(IR,IA),SUM(IGESTO$IAGK_HASORPOT(Y,IA,IGESTO),VESTOLOADT.L(Y,IA,IGESTO,S,T)*DISLOSS_E_AG(IA,IGESTO)/(1-DISLOSS_E_AG(IA,IGESTO)))) +$ifi %stointers%==all +SUM(IA$RRRAAA(IR,IA),SUM(IGESTO$IAGK_HASORPOT(Y,IA,IGESTO),VESTOLOADTS.L(Y,IA,IGESTO,S,T)*DISLOSS_E_AG(IA,IGESTO)/(1-DISLOSS_E_AG(IA,IGESTO)))) +* INTERSEASONAL ELECTRICITY STORAGE ++SUM(IA$RRRAAA(IR,IA),SUM(IGESTOS$IAGK_HASORPOT(Y,IA,IGESTOS),VESTOLOADT.L(Y,IA,IGESTOS,S,T)*DISLOSS_E_AG(IA,IGESTOS)/(1-DISLOSS_E_AG(IA,IGESTOS)))) +* EVs +$ifi not %EV%==yes $goto NO_EV2 + +( + + VEV_VG2V_BEV.L(Y,IR,S,T) + + VEV_VG2V_PHEV.L(Y,IR,S,T) + )*EV_DISTLOSSLOAD(IR)/(1-EV_DISTLOSSLOAD(IR)) + +( +$ifi %V2G%==yes - VEV_VV2G_BEV.L(Y,IR,S,T) +$ifi %V2G%==yes - VEV_VV2G_PHEV.L(Y,IR,S,T) +$ifi %STEPCHARGSCHEME%==yes - VEV_VV2G_BEV.L(Y,IR,S,T)$(cs('v2g',IY411)) +$ifi %STEPCHARGSCHEME%==yes - VEV_VV2G_PHEV.L(Y,IR,S,T)$(cs('v2g',IY411)) + +0)*EV_DISTLOSSUNLOAD(IR)/(1-EV_DISTLOSSUNLOAD(IR)) +$label NO_EV2 +*CCS +$ifi %CCS%==yes +SUM((IA,G)$(IAGK_HASORPOT(Y,IA,G) AND RRRAAA(IR,IA) AND CCS_G(G)),VGF_T.L(Y,IA,G,S,T) * IM_CO2(G)*IOF0001 * IOF3P6 * CCS_CO2CAPTEFF_G(G)* CCS_DECO2COMP_G(G)*DISLOSS_E_AG(IA,G)/(1-DISLOSS_E_AG(IA,G))) + +$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_DEMAND2 ++SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GETOH2)$(RRRAAA(IR,IA)), VGE_T.L(Y,IA,IHYDROGEN_GETOH2,S,T)*DISLOSS_E_AG(IA,IHYDROGEN_GETOH2)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GETOH2))) ++SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GETOHH2)$(RRRAAA(IR,IA)), VGE_T.L(Y,IA,IHYDROGEN_GETOHH2,S,T)*DISLOSS_E_AG(IA,IHYDROGEN_GETOHH2)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GETOHH2))) ++SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GEHTOH2)$(RRRAAA(IR,IA)), VGE_T.L(Y,IA,IHYDROGEN_GEHTOH2,S,T)*DISLOSS_E_AG(IA,IHYDROGEN_GEHTOH2)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GEHTOH2))) ++SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH)$(RRRAAA(IR,IA)),VGBIOMETH_T.L(Y,IA,IHYDROGEN_GH2TOBIOMETH,S,T)*DAC_DE(IHYDROGEN_GH2TOBIOMETH)*DISLOSS_E_AG(IA,IHYDROGEN_GH2TOBIOMETH)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GH2TOBIOMETH))) ++SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASUPGRADING)$(RRRAAA(IR,IA)),VGBIOGASUPGRADING_T.L(Y,IA,IHYDROGEN_GBIOGASUPGRADING,S,T)*BIOGASUPGRADING_DE(IHYDROGEN_GBIOGASUPGRADING)*DISLOSS_E_AG(IA,IHYDROGEN_GBIOGASUPGRADING)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GBIOGASUPGRADING))) ++SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASMETHANATION)$(RRRAAA(IR,IA)),VGBIOGASMETHANATION_T.L(Y,IA,IHYDROGEN_GBIOGASMETHANATION,S,T)*METHANATION_DE(IHYDROGEN_GBIOGASMETHANATION)*DISLOSS_E_AG(IA,IHYDROGEN_GBIOGASMETHANATION)/(1-DISLOSS_E_AG(IA,IHYDROGEN_GBIOGASMETHANATION))) +$label NO_HYDROGEN_DEMAND2 + +*PRODUCTION SIDE ++SUM((IGE,IA,FFF,TECH_TYPE)$((NOT IGETOH(IGE)) AND RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY',TECH_TYPE,'MWh')*DISLOSS_E_AG(IA,IGE)) +; + +EL_DEMAND_YCRST(Y,C,IR,S,T,'TRANS_LOSSES','MWh')$CCCRRR(C,IR)= +*TRANSMISSION LOSSES ++SUM(IRE$IXK_HASORPOT(Y,IRE,IR),VX_T.L(Y,IRE,IR,S,T)*XLOSS(IRE,IR)); + + +*ANNUAL DEMAND +** EXOGENOUS DEMAND + +EL_DEMAND_YCR(Y,C,IR,'EXOGENOUS','TWh')$(CCCRRR(C,IR) AND SUM(DEUSER,IDE_SUMST(IR,DEUSER)))= +SUM((DEUSER,S,T)$IDE_SUMST(IR,DEUSER), IHOURSINST(S,T)*IHOURFRAC*DE(Y,IR,DEUSER) * DE_VAR_T(IR,DEUSER,S,T) / IDE_SUMST(IR,DEUSER))/IOF1000000; + +** ENDOGENOUS DEMAND + +EL_DEMAND_YCR(Y,C,IR,'ENDOGENOUS_ELECT2HEAT','TWh')$CCCRRR(C,IR)= +* HEAT PUMPS CONSUMPTION + SUM((S,T,IA)$RRRAAA(IR,IA), SUM(IGETOH$IAGK_HASORPOT(Y,IA,IGETOH), IHOURSINST(S,T)*IHOURFRAC*VGE_T.L(Y,IA,IGETOH,S,T)))/IOF1000000 +; + +EL_DEMAND_YCR(Y,C,IR,'ENDO_INTRASTO','TWh')$CCCRRR(C,IR)= +* INTRASEASONAL ELECTRICITY STORAGE LOSS +$ifi not %stointers%==all +SUM((S,T,IA)$RRRAAA(IR,IA),SUM(IGESTO$IAGK_HASORPOT(Y,IA,IGESTO),IHOURSINST(S,T)*IHOURFRAC*(VESTOLOADT.L(Y,IA,IGESTO,S,T)-VGE_T.L(Y,IA,IGESTO,S,T))))/IOF1000000 +$ifi %stointers%==all +SUM((S,T,IA)$RRRAAA(IR,IA),SUM(IGESTO$IAGK_HASORPOT(Y,IA,IGESTO),IHOURSINST(S,T)*IHOURFRAC*(VESTOLOADTS.L(Y,IA,IGESTO,S,T)-VGE_T.L(Y,IA,IGESTO,S,T))))/IOF1000000 +; + +EL_DEMAND_YCR(Y,C,IR,'ENDO_INTERSTO','TWh')$CCCRRR(C,IR)= +* INTERSEASONAL ELECTRICITY STORAGE LOSS + +SUM((S,T,IA)$RRRAAA(IR,IA),SUM(IGESTOS$IAGK_HASORPOT(Y,IA,IGESTOS),IHOURSINST(S,T)*IHOURFRAC*(VESTOLOADTS.L(Y,IA,IGESTOS,S,T)-VGE_T.L(Y,IA,IGESTOS,S,T))))/IOF1000000 +; + +EL_DEMAND_YCR(Y,C,IR,'ENDO_EV','TWh')$CCCRRR(C,IR)= + SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_EV','MWh')*IHOURFRAC)/IOF1000000 +; + +EL_DEMAND_YCR(Y,C,IR,'ENDO_OTHERTRANS','TWh')$CCCRRR(C,IR)= + SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_OTHERTRANS','MWh')*IHOURFRAC)/IOF1000000 +; + +EL_DEMAND_YCR(Y,C,IR,'DIST_LOSSES','TWh')$CCCRRR(C,IR)= + SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'DIST_LOSSES','MWh')*IHOURFRAC)/IOF1000000 +; + +EL_DEMAND_YCR(Y,C,IR,'TRANS_LOSSES','TWh')$CCCRRR(C,IR)= + SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'TRANS_LOSSES','MWh')*IHOURFRAC)/IOF1000000 +; + +EL_DEMAND_YCR(Y,C,IR,'ENDO_CCS','TWh')$CCCRRR(C,IR)= + SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_CCS','MWh')*IHOURFRAC)/IOF1000000 +; + +EL_DEMAND_YCR(Y,C,IR,'ENDO_H2','TWh')$CCCRRR(C,IR)= + SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_H2','MWh')*IHOURFRAC)/IOF1000000 +; + +EL_DEMAND_YCR(Y,C,IR,'ENDO_BIOMETHANE','TWh')$CCCRRR(C,IR)= + SUM((S,T),IHOURSINST(S,T)*EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_BIOMETHANE','MWh')*IHOURFRAC)/IOF1000000 +; + +* ----------- END OF ELECTRICITY DEMAND -------------- + +* ------------- HEAT DEMAND -------------- +*HOURLY DEMAND +** EXOGENOUS DEMAND + +H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'EXOGENOUS','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA) AND SUM(DHUSER,IDH_SUMST(IA,DHUSER)))= +SUM((DHUSER)$IDH_SUMST(IA,DHUSER), DH(Y,IA,DHUSER) * DH_VAR_T(IA,DHUSER,S,T) / IDH_SUMST(IA,DHUSER)) + +$ifi not %INDIVUSERS%==yes $goto NO_INDIVUSERSGROUP +$ifi not %GROUP_DEMAND_SHIFT%==yes $goto NO_INDIVUSERSGROUP ++ ( +*Removing default demand + -SUM((DHUSER)$IDH_SUMST(IA,DHUSER), DH(Y,IA,DHUSER) * DH_VAR_T(IA,DHUSER,S,T) / IDH_SUMST(IA,DHUSER)) +*Introducing new allocated demand + +SUM((INDIVUSERS_GROUP,DHUSER)$INDIVUSERS_GROUP_A(IA,DHUSER,INDIVUSERS_GROUP), + VINDIVUSERS_GROUPSHARE.L(Y,IR,DHUSER,INDIVUSERS_GROUP)* DH_VAR_T(IA,DHUSER,S,T)/ IDH_SUMST(IA,DHUSER) + )*SUM((INDIVUSERS_DEMANDTYPE,DHUSER)$INDIVUSERS_DEMANDTYPE_A(IA,DHUSER,INDIVUSERS_DEMANDTYPE), + SUM(IAI$(RRRAAA(IR,IAI) AND INDIVUSERS_DEMANDTYPE_A(IAI,DHUSER,INDIVUSERS_DEMANDTYPE)), DH(Y,IAI,DHUSER)) + ) + )$(INDIVUSERS_AAA(IA) + AND SUM((INDIVUSERS_GROUP,DHUSER),INDIVUSERS_GROUP_A(IA,DHUSER,INDIVUSERS_GROUP)) + AND SUM((INDIVUSERS_DEMANDTYPE,DHUSER),INDIVUSERS_DEMANDTYPE_A(IA,DHUSER,INDIVUSERS_DEMANDTYPE))) +$label NO_INDIVUSERSGROUP +; + +** ENDOGENOUS DEMAND + +H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_INTRASTO','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= +* INTRASEASONAL HEAT STORAGE +$ifi not %stointers%==all SUM(IGHSTO$IAGK_HASORPOT(Y,IA,IGHSTO),VHSTOLOADT.L(Y,IA,IGHSTO,S,T)) +$ifi %stointers%==all SUM(IGHSTO$IAGK_HASORPOT(Y,IA,IGHSTO),VHSTOLOADTS.L(Y,IA,IGHSTO,S,T)) + +; + +H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_INTERSTO','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= +* INTERSEASONAL HEAT STORAGE + + SUM(IGHSTOS$IAGK_HASORPOT(Y,IA,IGHSTOS),VHSTOLOADTS.L(Y,IA,IGHSTOS,S,T)) +; + +**LOSSES +H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'DIST_LOSSES','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= +* EXOGENOUS DEMAND +H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'EXOGENOUS','MWh')*DISLOSS_H(IA)/(1-DISLOSS_H(IA)) +; + +$ifi %HEATTRANS%==yes H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'TRANS_LOSSES','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= +$ifi %HEATTRANS%==yes *TRANSMISSION LOSSES +$ifi %HEATTRANS%==yes +SUM(IAE$IXHK_HASORPOT(Y,IAE,IA),VXH_T.L(Y,IAE,IA,S,T)*XHLOSS(IAE,IA)); +$ifi %HEATTRANS%==yes ; + +$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_DEMAND3 +H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_H2','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= ++SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GEHTOH2)$(RRRAAA(IR,IA)), VGH_T.L(Y,IA,IHYDROGEN_GEHTOH2,S,T)) +; + +H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_BIOMETHANE','MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= ++ SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH)$(RRRAAA(IR,IA)),VGBIOMETH_T.L(Y,IA,IHYDROGEN_GH2TOBIOMETH,S,T)*DAC_DH(IHYDROGEN_GH2TOBIOMETH)) +; +$label NO_HYDROGEN_DEMAND3 + + +*ANNUAL DEMAND +** EXOGENOUS DEMAND + +H_DEMAND_YCRA(Y,C,IR,IA,'EXOGENOUS','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA) AND SUM(DHUSER,IDH_SUMST(IA,DHUSER)))= +SUM((S,T),IHOURSINST(S,T)*H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'EXOGENOUS','MWh')*IHOURFRAC)/IOF1000000; +; + +** ENDOGENOUS DEMAND +H_DEMAND_YCRA(Y,C,IR,IA,'ENDO_INTRASTO','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= +* INTRASEASONAL HEAT STORAGE LOSS +$ifi not %stointers%==all SUM((S,T,IGHSTO)$IAGK_HASORPOT(Y,IA,IGHSTO),IHOURSINST(S,T)*IHOURFRAC*(VHSTOLOADT.L(Y,IA,IGHSTO,S,T)-VGH_T.L(Y,IA,IGHSTO,S,T)))/IOF1000000 +$ifi %stointers%==all SUM((S,T,IGHSTO)$IAGK_HASORPOT(Y,IA,IGHSTO),IHOURSINST(S,T)*IHOURFRAC*(VHSTOLOADTS.L(Y,IA,IGHSTO,S,T)-VGH_T.L(Y,IA,IGHSTO,S,T)))/IOF1000000 + +; + +H_DEMAND_YCRA(Y,C,IR,IA,'ENDO_INTERSTO','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= + +* INTERSEASONAL HEAT STORAGE LOSS + + SUM((S,T,IGHSTOS)$IAGK_HASORPOT(Y,IA,IGHSTOS),IHOURSINST(S,T)*IHOURFRAC*(VHSTOLOADTS.L(Y,IA,IGHSTOS,S,T)-VGH_T.L(Y,IA,IGHSTOS,S,T)))/IOF1000000 +; + +H_DEMAND_YCRA(Y,C,IR,IA,'DIST_LOSSES','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= + SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'DIST_LOSSES','MWh'))/IOF1000000 +; + +H_DEMAND_YCRA(Y,C,IR,IA,'TRANS_LOSSES','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= + SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'TRANS_LOSSES','MWh'))/IOF1000000 +; + +H_DEMAND_YCRA(Y,C,IR,IA,'ENDO_H2','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= + SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_H2','MWh'))/IOF1000000 +; + +H_DEMAND_YCRA(Y,C,IR,IA,'ENDO_BIOMETHANE','TWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= + SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_BIOMETHANE','MWh'))/IOF1000000 +; +* ----------- END OF HEAT DEMAND -------------- + +$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_DEMAND30 +* ------------- H2 DEMAND -------------- + +*HOURLY +** EXOGENOUS DEMAND + +H2_DEMAND_YCRST(Y,C,IR,S,T,'EXOGENOUS','MWh')$(CCCRRR(C,IR))= + IHYDROGEN_DH2_REGION_T_Y(Y,IR,S,T)+ + SUM(IA$RRRAAA(IR,IA),IHYDROGEN_DH2_AREA_T_Y(Y,IA,S,T)) +; + +H2_DEMAND_YCRST(Y,C,IR,S,T,'DIST_LOSSES','MWh')$CCCRRR(C,IR)= + IHYDROGEN_DH2_REGION_T_Y(Y,IR,S,T)*DISLOSS_H2(IR)/(1-DISLOSS_H2(IR))+ + SUM(IA$RRRAAA(IR,IA),IHYDROGEN_DH2_AREA_T_Y(Y,IA,S,T)*DISLOSS_H2(IA)/(1-DISLOSS_H2(IA))) +; + +** ENDOGENOUS DEMAND + +H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_INTERSTO','MWh')$CCCRRR(C,IR)= +* INTERSEASONAL H2 STORAGE + +SUM(IA$RRRAAA(IR,IA),SUM(IHYDROGEN_GH2STO$IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO),VHYDROGEN_STOLOADT.L(Y,IA,IHYDROGEN_GH2STO,S,T))) +; + +$ifi %TRANSPORT%==yes H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_OTHERTRANS','MWh')$CCCRRR(C,IR)= +$ifi %TRANSPORT%==yes $ifi %TRANSPORT_SYNTHFUEL_COMMODITY%==HYDROGEN +VTRANSDEMAND_T.L(Y,IR,S,T) +$ifi %TRANSPORT%==yes +0; + + +H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_FUELCELL','MWh')$CCCRRR(C,IR)= +*Adding H2 consumption from FUELCELLS generation ++ SUM(IAGK_HASORPOT(Y,IA,G)$(RRRAAA(IR,IA) AND GTECH_TYPE(G,'FUELCELL')),VGF_T.L(Y,IA,G,S,T)) +; + +H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_BIOMETHANE','MWh')$CCCRRR(C,IR)= +*Adding H2 consumption from BIOMETHANE-DAC generation ++ SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH)$(RRRAAA(IR,IA)),VGBIOMETH_T.L(Y,IA,IHYDROGEN_GH2TOBIOMETH,S,T)/GDATA(IHYDROGEN_GH2TOBIOMETH,'GDFE')) +; + + +**LOSSES + +H2_DEMAND_YCRST(Y,C,IR,S,T,'TRANS_LOSSES','MWh')$CCCRRR(C,IR)= +*TRANSMISSION LOSSES ++SUM(IRE$IXH2K_HASORPOT(Y,IRE,IR),VXH2_T.L(Y,IRE,IR,S,T)*XH2LOSS(IRE,IR)); + + +*ANNUAL DEMAND +** EXOGENOUS DEMAND + +H2_DEMAND_YCR(Y,C,IR,'EXOGENOUS','TWh')$(CCCRRR(C,IR))= +SUM((S,T), IHOURSINST(S,T)*IHOURFRAC*H2_DEMAND_YCRST(Y,C,IR,S,T,'EXOGENOUS','MWh'))/IOF1000000; + +** ENDOGENOUS DEMAND + +H2_DEMAND_YCR(Y,C,IR,'ENDO_INTERSTO','TWh')$CCCRRR(C,IR)= +* INTERSEASONAL H2 STORAGE LOSS + +SUM((S,T,IA)$RRRAAA(IR,IA),SUM(IHYDROGEN_GH2STO$IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO),IHOURSINST(S,T)*IHOURFRAC*(VHYDROGEN_STOLOADT.L(Y,IA,IHYDROGEN_GH2STO,S,T)-VHYDROGEN_GH2_T.L(Y,IA,IHYDROGEN_GH2STO,S,T))))/IOF1000000 +; + +H2_DEMAND_YCR(Y,C,IR,'ENDO_FUELCELL','TWh')$CCCRRR(C,IR)= + SUM((S,T),IHOURSINST(S,T)*H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_FUELCELL','MWh')*IHOURFRAC)/IOF1000000 +; + +H2_DEMAND_YCR(Y,C,IR,'ENDO_OTHERTRANS','TWh')$CCCRRR(C,IR)= + SUM((S,T),IHOURSINST(S,T)*H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_OTHERTRANS','MWh')*IHOURFRAC)/IOF1000000 +; + +H2_DEMAND_YCR(Y,C,IR,'TRANS_LOSSES','TWh')$CCCRRR(C,IR)= + SUM((S,T),IHOURSINST(S,T)*H2_DEMAND_YCRST(Y,C,IR,S,T,'TRANS_LOSSES','MWh')*IHOURFRAC)/IOF1000000 +; + +H2_DEMAND_YCR(Y,C,IR,'DIST_LOSSES','TWh')$CCCRRR(C,IR)= + SUM((S,T),IHOURSINST(S,T)*H2_DEMAND_YCRST(Y,C,IR,S,T,'DIST_LOSSES','MWh')*IHOURFRAC)/IOF1000000 +; + +H2_DEMAND_YCR(Y,C,IR,'ENDO_BIOMETHANE','TWh')$CCCRRR(C,IR)= + SUM((S,T),IHOURSINST(S,T)*H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_BIOMETHANE','MWh')*IHOURFRAC)/IOF1000000 +; + +* ----------- END OF H2 DEMAND -------------- +$label NO_HYDROGEN_DEMAND30 + +* -------------------- END OF ENERGY DEMAND ----------------------- + + +* -------------------- TRANSMISSION CAPACITY ----------------------- + +** EXOGENOUS ELECTRICITY CAPACITY + +X_CAP_YCR(Y,C,IR,IRI,'EXOGENOUS','GW')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= +IOF0001*XKFX(Y,IR,IRI) +; + + +** ENDOGENOUS ELECTRIC CAPACITY + +$ifi not %TransInvest%==yes $goto Transmission_investments_end +X_CAP_YCR(Y,C,IR,IRI,'ENDOGENOUS','GW')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= +IOF0001*VXKNACCUMNET.L(Y,IR,IRI) +; +$label Transmission_investments_end + + +* -------------------- END OF TRANSMISSION CAPACITY ----------------------- + +* -------------------- TRANSMISSION FLOW ----------------------- + +** HOURLY TRANSMISSION FLOW + +X_FLOW_YCRST(Y,C,IR,IRI,S,T,'MWh')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= +VX_T.L(Y,IR,IRI,S,T); +; + + +** ANNUAL TRANSMISSION FLOW +X_FLOW_YCR(Y,C,IR,IRI,'TWh')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= +SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*VX_T.L(Y,IR,IRI,S,T))/IOF1000000; +; + + + +* -------------------- END OF TRANSMISSION FLOW ----------------------- + +$ifi NOT %HEATTRANS%==yes $goto no_heattrans +* -------------------- HEAT TRANSMISSION CAPACITY ----------------------- + +** EXOGENOUS HEAT TRANSMISSION CAPACITY + +XH_CAP_YCA(Y,C,IA,IAI,'EXOGENOUS','GW')$(ICA(C,IA) AND (XHKFX(Y,IA,IAI) OR XHKFX_DH(Y,IA,IAI)))= +IOF0001*(XHKFX(Y,IA,IAI) + XHKFX_DH(Y,IA,IAI)) +; + +** ENDOGENOUS HEAT TRANSMISSION CAPACITY + +$ifi not %HeatTransInvest%==yes $goto Heat_Transmission_investments_end +XH_CAP_YCA(Y,C,IA,IAI,'ENDOGENOUS','GW')$(ICA(C,IA) AND IXHK_HASORPOT(Y,IA,IAI))= +IOF0001*VXHKNACCUMNET.L(Y,IA,IAI) +; +$label Heat_Transmission_investments_end + + +* -------------------- END OF HEAT TRANSMISSION CAPACITY ----------------------- + +* -------------------- HEAT TRANSMISSION FLOW ----------------------- + +** HOURLY HEAT TRANSMISSION FLOW + +XH_FLOW_YCAST(Y,C,IA,IAI,S,T,'MWh')$(ICA(C,IA) AND IXHK_HASORPOT(Y,IA,IAI))= +VXH_T.L(Y,IA,IAI,S,T); +; + +** ANNUAL HEAT TRANSMISSION FLOW +XH_FLOW_YCA(Y,C,IA,IAI,'TWh')$(ICA(C,IA) AND IXHK_HASORPOT(Y,IA,IAI))= +SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*VXH_T.L(Y,IA,IAI,S,T))/IOF1000000; +; + +* -------------------- END OF HEAT TRANSMISSION FLOW ----------------------- +$label no_heattrans + + +$ifi NOT %hydrogen%==yes $goto no_hydrogentrans +* -------------------- H2 TRANSMISSION CAPACITY ----------------------- + +** EXOGENOUS H2 TRANSMISSION CAPACITY + +XH2_CAP_YCR(Y,C,IR,IRI,'EXOGENOUS','GW')$(CCCRRR(C,IR) AND XH2KFX(Y,IR,IRI))= +IOF0001*XH2KFX(Y,IR,IRI) +; + +** ENDOGENOUS H2 TRANSMISSION CAPACITY + +$ifi not %H2TransInvest%==yes $goto H2_Transmission_investments_end +XH2_CAP_YCR(Y,C,IR,IRI,'ENDOGENOUS','GW')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= +IOF0001*(VXH2KNACCUMNET.L(Y,IR,IRI)$SUM(IYALIAS2$(YVALUE(IYALIAS2) LE YVALUE(Y)),IXH2KN(IYALIAS2,IR,IRI)) + +VXH2KNACCUMNET_NGTOH2.L(Y,IR,IRI)$SUM(IYALIAS2$(YVALUE(IYALIAS2) LE YVALUE(Y)),IXH2KN_NGTOH2(IYALIAS2,IR,IRI))) +; +$label H2_Transmission_investments_end + + +* -------------------- END OF HEAT TRANSMISSION CAPACITY ----------------------- + +* -------------------- H2 TRANSMISSION FLOW ----------------------- + +** HOURLY H2 TRANSMISSION FLOW + +XH2_FLOW_YCRST(Y,C,IR,IRI,S,T,'MWh')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= +VXH2_T.L(Y,IR,IRI,S,T); +; + +** ANNUAL H2 TRANSMISSION FLOW +XH2_FLOW_YCR(Y,C,IR,IRI,'TWh')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= +SUM((S,T),IHOURSINST(S,T)*IHOURFRAC*VXH2_T.L(Y,IR,IRI,S,T))/IOF1000000; +; + +* -------------------- END OF H2 TRANSMISSION FLOW ----------------------- +$label no_hydrogentrans + +* ---------------------- ECONOMIC OUTPUT ---------------------- + +* ------------- COMMODITY PRICES -------------- + +** ELECTRICITY PRICES + +*HOURLY ELECTRICITY PRICE + +EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')$CCCRRR(C,IR)=OMONEY*QEEQ.M(Y,IR,S,T) +$ifi %RollingSeasons%==yes /IHOURSINTROLLINGSEASON(S,T) +$ifi not %RollingSeasons%==yes /IHOURSINST(S,T) +/IDISCOUNTFACTOR(Y)/IWEIGHTY(Y); + +*AVERAGE ELECTRICITY PRICE + +EL_PRICE_YCR(Y,C,IR,'AVERAGE','Money_per_MWh')$CCCRRR(C,IR)= +SUM((S,T),EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')/(IWEIGHSUMS*IWEIGHSUMT/(WEIGHT_S(S)*WEIGHT_T(T)))); + +$ontext +*EXCLUDED SO FAR +*AVERAGE ELECTRICITY PRICE WEIGTHED BY CONSUMPTION + +EL_PRICE_YCR(Y,C,IR,'AVERAGE_WEIGHTED_BY_CONSUMPTION','Money_per_MWh')$CCCRRR(C,IR)= + SUM((VARIABLE_CATEGORY,S,T), + EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*EL_DEMAND_YCRST(Y,C,IR,S,T,VARIABLE_CATEGORY,'MWh') + ) + /SUM((VARIABLE_CATEGORY,S,T),EL_DEMAND_YCRST(Y,C,IR,S,T,VARIABLE_CATEGORY,'MWh')) +; + +*AVERAGE ELECTRICITY PRICE WEIGTHED BY PRODUCTION + +EL_PRICE_YCR(Y,C,IR,'AVERAGE_WEIGHTED_BY_PRODUCTION','Money_per_MWh')$CCCRRR(C,IR)= + SUM((IGE,IA,FFF,S,T,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGE)), + EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY',TECH_TYPE,'MWh') + ) + /SUM((IGE,IA,FFF,S,T,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY',TECH_TYPE,'MWh')) +; +$offtext + +** END OF ELECTRICITY PRICES + +** heatING PRICES + +*HOURLY heatING PRICE + +$ifi %BalancingMarket%==yes $ifi %NOHEATMARKET%==yes $goto NO_HEATMARKET +H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA) AND (SUM(DHUSER, IDH_SUMST(IA,DHUSER)) +$ifi %HEATTRANS%==yes OR SUM(IAE,IXHK_HASORPOT(Y,IAE,IA)) OR SUM(IAI,IXHK_HASORPOT(Y,IA,IAI)) +))=OMONEY*QHEQ.M(Y,IA,S,T) +$ifi %RollingSeasons%==yes /IHOURSINTROLLINGSEASON(S,T) +$ifi not %RollingSeasons%==yes /IHOURSINST(S,T) +/IDISCOUNTFACTOR(Y)/IWEIGHTY(Y); +$label NO_HEATMARKET +$ifi %BalancingMarket%==yes $ifi %NOHEATMARKET%==yes H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=0; + + + +*AVERAGE heatING PRICE + +H_PRICE_YCRA(Y,C,IR,IA,'AVERAGE','Money_per_MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA) AND (SUM(DHUSER, IDH_SUMST(IA,DHUSER)) +$ifi %HEATTRANS%==yes OR SUM(IAE,IXHK_HASORPOT(Y,IAE,IA)) OR SUM(IAI,IXHK_HASORPOT(Y,IA,IAI)) +))= +SUM((S,T),H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')/(IWEIGHSUMS*IWEIGHSUMT/(WEIGHT_S(S)*WEIGHT_T(T)))); + +$ontext +*EXCLUDED SO FAR +*AVERAGE heatING PRICE WEIGTHED BY CONSUMPTION + +H_PRICE_YCRA(Y,C,IR,IA,'AVERAGE_WEIGHTED_BY_CONSUMPTION','Money_per_MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA) AND SUM(DHUSER, IDH_SUMST(IA,DHUSER)))= + SUM((VARIABLE_CATEGORY,S,T), + H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*H_DEMAND_YCRAST(Y,C,IR,IA,S,T,VARIABLE_CATEGORY,'MWh') + ) + /SUM((VARIABLE_CATEGORY,S,T),H_DEMAND_YCRAST(Y,C,IR,IA,S,T,VARIABLE_CATEGORY,'MWh')) +; + +*AVERAGE heatING PRICE WEIGTHED BY PRODUCTION + +H_PRICE_YCRA(Y,C,IR,IA,'AVERAGE_WEIGHTED_BY_PRODUCTION','Money_per_MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA) AND SUM(DHUSER, IDH_SUMST(IA,DHUSER)))= + SUM((IGH,FFF,S,T,TECH_TYPE)$IAGK_HASORPOT(Y,IA,IGH), + H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT',TECH_TYPE,'MWh') + ) + /SUM((IGH,FFF,S,T,TECH_TYPE)$IAGK_HASORPOT(Y,IA,IGH),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT',TECH_TYPE,'MWh')) +; +$offtext + +** END OF heatING PRICES + +$ifi NOT %HYDROGEN%==yes $goto NO_HYDROGEN_PRICE + +*HOURLY HYDROGEN PRICE +H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')$CCCRRR(C,IR)=OMONEY*QHYDROGEN_EQ.M(Y,IR,S,T) +$ifi %RollingSeasons%==yes /IHOURSINTROLLINGSEASON(S,T) +$ifi not %RollingSeasons%==yes /IHOURSINST(S,T) +/IDISCOUNTFACTOR(Y)/IWEIGHTY(Y); + +*AVERAGE Hydrogen PRICE + +H2_PRICE_YCR(Y,C,IR,'AVERAGE','Money_per_MWh')$CCCRRR(C,IR)= +SUM((S,T),H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')/(IWEIGHSUMS*IWEIGHSUMT/(WEIGHT_S(S)*WEIGHT_T(T)))); + + +*HOURLY BIOMETHANE PRICE +BIOMETH_PRICE_YST(Y,S,T,'Money_per_MWh')=OMONEY*QBIOMETHANE_EQ.M(Y,S,T) +$ifi %RollingSeasons%==yes /IHOURSINTROLLINGSEASON(S,T) +$ifi not %RollingSeasons%==yes /IHOURSINST(S,T) +/IDISCOUNTFACTOR(Y)/IWEIGHTY(Y); + +$label NO_HYDROGEN_PRICE + +* ------------- END OF COMMODITY PRICES -------------- + + +* ------------- COSTS -------------- + +* -- GENERATION COSTS ----- + +** GENERATION INVESTMENTS + +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_CAPITAL_COSTS','Mmoney')$(CCCRRR(C,IR) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and RRRAAA(IR,IA))= + OMONEY*( + SUM(IYALIAS2$((ORD(IYALIAS2) LE ORD(Y)) AND IAGKNY(IYALIAS2,IA,G) AND (NOT IGKN_ES(G))),IYHASANNUITYG(IYALIAS2,Y,G)*VGKN.L(IYALIAS2,IA,G)*GINVCOST(IA,G)*ANNUITYCG(C,G)) +$ifi %OFFSHOREGRID%==yes + SUM(IYALIAS2$((ORD(IYALIAS2) LE ORD(Y)) AND IAGKNY(IYALIAS2,IA,G) AND IGKN_ES(G)),IYHASANNUITYG(IYALIAS2,Y,G)*SUM(XES,VGLAMBDA.L(IYALIAS2,IA,G,XES)*GINVCOST_ES(G,XES))*ANNUITYCG(C,G)) +) +; + +** GENEARTION FIXED COSTS + +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_FIXED_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and IAGK_HASORPOT(Y,IA,G))= + OMONEY/IOF1000*GOMFCOST(IA,G)*( + GKFX(Y,IA,G) + +VGKNACCUMNET.L(Y,IA,G) +$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,G)$IGDECOMEXOPOT(Y,IA,G) + ) +; + +** GENERATION O&M COSTS + +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_OPERATIONAL_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and IAGK_HASORPOT(Y,IA,G))= + OMONEY/IOF1000000* + ( + (IHOURFRAC *GOMVCOSTO(IA,G)*( + SUM((S,T), IHOURSINST(S,T) * VGE_T.L(Y,IA,G,S,T))$IGKE(G) +$ifi %HYDROGEN%==yes + SUM((S,T), IHOURSINST(S,T)*VHYDROGEN_GH2_T.L(Y,IA,G,S,T))$(IHYDROGEN_GH2STO(G) OR IHYDROGEN_GCH4TOH2(G)) +$ifi %HYDROGEN%==yes + SUM((S,T), IHOURSINST(S,T)*VGBIOMETH_T.L(Y,IA,G,S,T))$IHYDROGEN_GH2TOBIOMETH(G) +$ifi %HYDROGEN%==yes + SUM((S,T), IHOURSINST(S,T)*VGBIOGASMETHANATION_T.L(Y,IA,G,S,T))$IHYDROGEN_GBIOGASMETHANATION(G) +$ifi %HYDROGEN%==yes + SUM((S,T), IHOURSINST(S,T)*VGBIOGASUPGRADING_T.L(Y,IA,G,S,T))$IHYDROGEN_GBIOGASUPGRADING(G) + )) + + (IHOURFRAC *GOMVCOSTO(IA,G) * GDATA(G,'GDCV') * + ( + + SUM((S,T), IHOURSINST(S,T) * VGH_T.L(Y,IA,G,S,T))))$IGKH(G) + + + (IHOURFRAC *GOMVCOSTIN(IA,G)* + ( + + SUM((S,T), IHOURSINST(S,T) * VGF_T.L(Y,IA,G,S,T)))) + ) ; + +** GENERATION FUEL COSTS + +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_FUEL_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and IAGK_HASORPOT(Y,IA,G))= + OMONEY/IOF1000000*( + IHOURFRAC *FUELPRICE(Y,IA,FFF)*IOF3P6 +$ifi not %STEPWISEPRICE%==yes $goto No_STEPWISEPRICE + +(QSTEPWISEPRICE_GFTOSTEP.M(Y,C,FFF)/(IDISCOUNTFACTOR(Y)*IWEIGHTY(Y)))$ISTEPWISEPRICE_F(Y,C,FFF) +$label No_STEPWISEPRICE + )* + ( + SUM((S,T), IHOURSINST(S,T) * VGF_T.L(Y,IA,G,S,T) )$IAGK_HASORPOT(Y,IA,G) + )$(IGNOTETOH(G) and (not IGESTO(G)) and (not IGHSTO(G)) and (not IGESTOS(G)) and (not IGHSTOS(G))) + + +* Electricity storages: +$ifi not %stointers%==all +OMONEY/IOF1000000*SUM((S,T), EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VESTOLOADT.L(Y,IA,G,S,T))$IGESTO(G) +$ifi %stointers%==all +OMONEY/IOF1000000*SUM((S,T), EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VESTOLOADTS.L(Y,IA,G,S,T))$IGESTO(G) + +OMONEY/IOF1000000*SUM((S,T), EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VESTOLOADTS.L(Y,IA,G,S,T))$IGESTOS(G) +* Heat storages: +$ifi not %stointers%==all +OMONEY/IOF1000000*SUM((S,T), H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*VHSTOLOADT.L(Y,IA,G,S,T))$IGHSTO(G) +$ifi %stointers%==all +OMONEY/IOF1000000*SUM((S,T), H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*VHSTOLOADTS.L(Y,IA,G,S,T))$IGHSTO(G) + +OMONEY/IOF1000000*SUM((S,T), H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*VHSTOLOADTS.L(Y,IA,G,S,T))$IGHSTOS(G) +* Electricity to heat: + +OMONEY/IOF1000000*SUM((S,T), EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGE_T.L(Y,IA,G,S,T))$(IAGK_HASORPOT(Y,IA,G) and IGETOH(G)) + +$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_PRICE2 + +OMONEY/IOF1000000*SUM((S,T), EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGE_T.L(Y,IA,G,S,T))$(IHYDROGEN_GETOH2(G) OR IHYDROGEN_GEHTOH2(G) OR IHYDROGEN_GETOHH2(G)) + +OMONEY/IOF1000000*SUM((S,T), H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*VGH_T.L(Y,IA,G,S,T))$IHYDROGEN_GEHTOH2(G) + +OMONEY/IOF1000000*SUM((S,T), H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VHYDROGEN_STOLOADT.L(Y,IA,G,S,T))$IHYDROGEN_GH2STO(G) + +OMONEY/IOF1000000*SUM((S,T), H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGF_T.L(Y,IA,G,S,T))$IHYDROGEN_GH2FUEL(G) + +OMONEY/IOF1000000*SUM((S,T), H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGF_T.L(Y,IA,G,S,T) + + H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')*VGBIOMETH_T.L(Y,IA,G,S,T)*DAC_DH(G) + + EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGBIOMETH_T.L(Y,IA,G,S,T)*DAC_DE(G) + )$IHYDROGEN_GH2TOBIOMETH(G) + +OMONEY/IOF1000000*SUM((S,T), EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGBIOGASUPGRADING_T.L(Y,IA,G,S,T)*BIOGASUPGRADING_DE(G) + )$IHYDROGEN_GBIOGASUPGRADING(G) + +OMONEY/IOF1000000*SUM((S,T), H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGBIOGASMETHANATION_T.L(Y,IA,G,S,T)*METHANATION_DH2(G) + + EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')*VGBIOGASMETHANATION_T.L(Y,IA,G,S,T)*METHANATION_DE(G) + )$IHYDROGEN_GBIOGASMETHANATION(G) + +OMONEY/IOF1000000*SUM((S,T), BIOMETH_PRICE_YST(Y,S,T,'Money_per_MWh')*VGF_T.L(Y,IA,G,S,T))$IGBIOMETHANE(G) +$label NO_HYDROGEN_PRICE2 +; + +** CO2 EMISSIONS COST +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_CO2_TAX','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and IAGK_HASORPOT(Y,IA,G))= + SUM(GROUP$(GROUP_AAA(GROUP,IA)),OMONEY/IOF1000000*IHOURFRAC *IM_CO2(G)*IOF0001 * IOF3P6 *EMI_POL(Y,C,GROUP,"TAX_CO2") * SUM((S,T), IHOURSINST(S,T)*(VGF_T.L(Y,IA,G,S,T) +$ifi %CCS%==yes -(VGF_T.L(Y,IA,G,S,T)*CCS_CO2CAPTEFF_G(G))$CCS_G(G) +))) +; + +** CO2 TRANSPORT COST +$ifi %CCS%==yes ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_CO2_TRANSPORT','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and IAGK_HASORPOT(Y,IA,G) AND CCS_G(G))= +$ifi %CCS%==yes OMONEY/IOF1000000*IHOURFRAC *IM_CO2(G)*IOF0001 * IOF3P6 * CCS_TRANSPORTCOST * SUM((S,T), IHOURSINST(S,T)*(VGF_T.L(Y,IA,G,S,T))); + + +** OTHER EMISSIONS COST +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_OTHER_EMI_TAX','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and IAGK_HASORPOT(Y,IA,G))= + SUM(GROUP$(GROUP_AAA(GROUP,IA)),OMONEY/IOF1000000* + ( SUM((S,T), IHOURSINST(S,T) * (IHOURFRAC *IM_SO2(G)*IOF0001) * IOF3P6 * VGF_T.L(Y,IA,G,S,T))*EMI_POL(Y,C,GROUP,"TAX_SO2") + + SUM((S,T), IHOURSINST(S,T) * (IHOURFRAC *GDATA(G,'GDNOX')*IOF0000001) * IOF3P6 * VGF_T.L(Y,IA,G,S,T))* EMI_POL(Y,C,GROUP,"TAX_NOX") + )) +; + + +** UNIT COMMITMENT COST +$ifi not %UnitComm%==yes $goto No_UC +ECO_G_YCRAG(Y,C,IR,IA,IGUC,FFF,TECH_TYPE,'COSTS','GENERATION_UC_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IGF(IGUC,FFF) AND GTECH_TYPE(IGUC,TECH_TYPE) and IAGK_HASORPOT(Y,IA,IGUC))= +OMONEY/IOF1000000*SUM((S,T), IHOURSINST(S,T)*( + VUCU.L(Y,IA,IGUC,S,T)*GDATA(IGUC,'GDUCUCOST')*GDATA(IGUC,'GDUCUNITSIZE')/(1$((NOT IGESTO(IGUC)) AND (NOT IGESTOS(IGUC)) AND (NOT IGHSTO(IGUC)) AND (NOT IGHSTOS(IGUC))) + GDATA(IGUC,'GDSTOHUNLD')$(IGESTO(IGUC) OR IGESTOS(IGUC) OR IGHSTO(IGUC) OR IGHSTOS(IGUC)))/CHRONOHOUR(S,T)*WEIGHT_T(T) !! startup cost + +VUCD.L(Y,IA,IGUC,S,T)*GDATA(IGUC,'GDUCDCOST')*GDATA(IGUC,'GDUCUNITSIZE')/(1$((NOT IGESTO(IGUC)) AND (NOT IGESTOS(IGUC)) AND (NOT IGHSTO(IGUC)) AND (NOT IGHSTOS(IGUC))) + GDATA(IGUC,'GDSTOHUNLD')$(IGESTO(IGUC) OR IGESTOS(IGUC) OR IGHSTO(IGUC) OR IGHSTOS(IGUC)))/CHRONOHOUR(S,T)*WEIGHT_T(T) !! shutdown cost + +VUCON.L(Y,IA,IGUC,S,T)*IHOURFRAC*GDATA(IGUC,'GDUCCOST0')*GDATA(IGUC,'GDUCUNITSIZE')/(1$((NOT IGESTO(IGUC)) AND (NOT IGESTOS(IGUC)) AND (NOT IGHSTO(IGUC)) AND (NOT IGHSTOS(IGUC))) + GDATA(IGUC,'GDSTOHUNLD')$(IGESTO(IGUC) OR IGESTOS(IGUC) OR IGHSTO(IGUC) OR IGHSTOS(IGUC))) !! fixed hourly cost + +VUCU_STOLOAD.L(Y,IA,IGUC,S,T)*GDATA(IGUC,'GDUCUCOST')*GDATA(IGUC,'GDUCUNITSIZE')/(1$((NOT IGESTO(IGUC)) AND (NOT IGESTOS(IGUC)) AND (NOT IGHSTO(IGUC)) AND (NOT IGHSTOS(IGUC))) + GDATA(IGUC,'GDSTOHLOAD')$(IGESTO(IGUC) OR IGESTOS(IGUC) OR IGHSTO(IGUC) OR IGHSTOS(IGUC)))/CHRONOHOUR(S,T)*WEIGHT_T(T) !! startup cost + +VUCD_STOLOAD.L(Y,IA,IGUC,S,T)*GDATA(IGUC,'GDUCDCOST')*GDATA(IGUC,'GDUCUNITSIZE')/(1$((NOT IGESTO(IGUC)) AND (NOT IGESTOS(IGUC)) AND (NOT IGHSTO(IGUC)) AND (NOT IGHSTOS(IGUC))) + GDATA(IGUC,'GDSTOHLOAD')$(IGESTO(IGUC) OR IGESTOS(IGUC) OR IGHSTO(IGUC) OR IGHSTOS(IGUC)))/CHRONOHOUR(S,T)*WEIGHT_T(T) !! shutdown cost + +VUCON_STOLOAD.L(Y,IA,IGUC,S,T)*IHOURFRAC*GDATA(IGUC,'GDUCCOST0')*GDATA(IGUC,'GDUCUNITSIZE')/(1$((NOT IGESTO(IGUC)) AND (NOT IGESTOS(IGUC)) AND (NOT IGHSTO(IGUC)) AND (NOT IGHSTOS(IGUC))) + GDATA(IGUC,'GDSTOHLOAD')$(IGESTO(IGUC) OR IGESTOS(IGUC) OR IGHSTO(IGUC) OR IGHSTOS(IGUC))) !! fixed hourly cost + +$ifi %STEPWISE_RAMPING_UC%==ramp_const_cost +SUM(RAMPING_RATE,(VRAMPING_DOWN.L(Y,IA,IGUC,RAMPING_RATE,S,T)*IRAMPING_G_COSTS_DOWN(IGUC,RAMPING_RATE)+VRAMPING_UP.L(Y,IA,IGUC,RAMPING_RATE,S,T)*IRAMPING_G_COSTS_UP(IGUC,RAMPING_RATE))/CHRONOHOUR(S,T)*WEIGHT_T(T)) !! ramping cost +)) +; +$label NO_UC + + +*TAXES +$ifi NOT %TAXES%==yes $goto No_TAXES +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_TAXES','Mmoney')$(CCCRRR(C,IR) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G))= +OMONEY/IOF1000000*( +* Tax(+)/subsidy(-) on fuel use for electricity generation + + SUM((IS3,T), + IHOURSINST(IS3,T) * IHOURFRAC *TAX_F_EL(Y,C,G) * VGE_T.L(Y,IA,G,IS3,T)/ (GDATA(G,'GDFE') * (1$(NOT GEFFRATE(IA,G))+GEFFRATE(IA,G)))) + +* Tax(+)/subsidy(-) on fuel use for heat generation + + SUM((IS3,T), + IHOURSINST(IS3,T) * IHOURFRAC *TAX_F_HEAT(Y,C,G) * GDATA(G,'GDCV') * VGH_T.L(Y,IA,G,IS3,T)/ (GDATA(G,'GDFE') * (1$(NOT GEFFRATE(IA,G))+GEFFRATE(IA,G)))) + +* Tax(+)/subsidy(-) on fuel use + + SUM((IS3,T), + IHOURSINST(IS3,T) * IHOURFRAC *TAX_F(Y,C,G) * VGF_T.L(Y,IA,G,IS3,T)) + +* Tax(+)/subsidy(-) on heat generation + + SUM((IS3,T), + IHOURSINST(IS3,T) * IHOURFRAC *TAX_G_HEAT(Y,C,G) * VGH_T.L(Y,IA,G,IS3,T)) + +* Tax(+)/subsidy(-) on heat generation + + SUM((IS3,T), + IHOURSINST(IS3,T) * IHOURFRAC *TAX_G_EL(Y,C,G) * VGE_T.L(Y,IA,G,IS3,T)) +) +; +$label NO_TAXES + + +*GRID TARIFFS +$ifi NOT %GRIDTARIFFS%==yes $goto No_GRIDTARIFFS +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_GRID_TARIFFS','Mmoney')$(CCCRRR(C,IR) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND (IGETOH(G) +$ifi %HYDROGEN%==yes OR IHYDROGEN_GEHTOH2(G) OR IHYDROGEN_GETOHH2(G) OR IHYDROGEN_GETOH2(G) +))= +OMONEY/IOF1000000*( + +* Fixed component and subscribed power (per MW installed) + + IOF1000 * + IGR_PRICE_TECH(Y,IR,'GRDSUBS') * (VGKNACCUMNET.L(Y,IA,G) +$ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,G)$IGDECOMEXOPOT(Y,IA,G) + + GKFX(Y,IA,G)) /((GDATA(G,'GDFE') *(1$(NOT GEFFRATE(IA,G))+GEFFRATE(IA,G)))$IGETOH(G) + 1$(NOT IGETOH(G))) + +* Energy charge winter + + SUM((IS3,T)$IGR_TIME('GRDECW',IR,IS3,T), IHOURSINST(IS3,T) * IHOURFRAC *IGR_PRICE_TECH(Y,IR,'GRDECW') * VGE_T.L(Y,IA,G,IS3,T)) +* Energy charge summer + + SUM((IS3,T)$IGR_TIME('GRDECS',IR,IS3,T), IHOURSINST(IS3,T) * IHOURFRAC *IGR_PRICE_TECH(Y,IR,'GRDECS') * VGE_T.L(Y,IA,G,IS3,T)) +* Energy charge TOU step 1 + + SUM((IS3,T)$IGR_TIME('GRDTOU1',IR,IS3,T), IHOURSINST(IS3,T) * IHOURFRAC *IGR_PRICE_TECH(Y,IR,'GRDTOU1') * VGE_T.L(Y,IA,G,IS3,T)) +* Energy charge TOU step 2 + + SUM((IS3,T)$IGR_TIME('GRDTOU2',IR,IS3,T), IHOURSINST(IS3,T) * IHOURFRAC *IGR_PRICE_TECH(Y,IR,'GRDTOU2') * VGE_T.L(Y,IA,G,IS3,T)) +* Energy charge TOU step 3 + + SUM((IS3,T)$IGR_TIME('GRDTOU3',IR,IS3,T), IHOURSINST(IS3,T) * IHOURFRAC *IGR_PRICE_TECH(Y,IR,'GRDTOU3') * VGE_T.L(Y,IA,G,IS3,T)) +* Energy charge TOU step 4 + + SUM((IS3,T)$IGR_TIME('GRDTOU4',IR,IS3,T), IHOURSINST(IS3,T) * IHOURFRAC *IGR_PRICE_TECH(Y,IR,'GRDTOU4') * VGE_T.L(Y,IA,G,IS3,T)) +) +; +$label NO_GRIDTARIFFS + +* -- TRANSMISSION ----- +*ELECTRICITY +** TRANSMISSION INVESTMENTS +ECO_X_YCR(Y,C,IR,IRI,'COSTS','TRANSMISSION_CAPITAL_COSTS','Mmoney')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= + OMONEY*IOF0000001*( + SUM((IYALIAS2)$((IXKN(IYALIAS2,IRI,IR) OR IXKN(IYALIAS2,IR,IRI)) AND (ORD(IYALIAS2) LE ORD(Y)) AND (NOT (IXKN_ES(IR,IRI) OR IXKN_ES(IRI,IR)))), + IOF05*ANNUITYCX(C)*IYHASANNUITYX(IYALIAS2,Y)*VXKN.L(IYALIAS2,IR,IRI)*XINVCOST(IYALIAS2,IR,IRI)) +$ifi %OFFSHOREGRID%==yes +SUM((IYALIAS2)$((IXKN(IYALIAS2,IRI,IR) OR IXKN(IYALIAS2,IR,IRI)) AND (ORD(IYALIAS2) LE ORD(Y)) AND (IXKN_ES(IR,IRI) OR IXKN_ES(IRI,IR))), +$ifi %OFFSHOREGRID%==yes IOF05*ANNUITYCX(C)*IYHASANNUITYX(IYALIAS2,Y)*SUM(XES,VXLAMBDA.L(IYALIAS2,IR,IRI,XES)*XINVCOST_ES(IYALIAS2,IR,IRI,XES))) +) +; +** TRANSMISSION FLOW COSTS +ECO_X_YCR(Y,C,IR,IRI,'COSTS','TRANSMISSION_OPERATIONAL_COSTS','Mmoney')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= +OMONEY*IOF0000001* SUM((S,T), IHOURSINST(S,T) * (VX_T.L(Y,IR,IRI,S,T) * IHOURFRAC *XCOST(IR,IRI))); + +** TRANSMISSION TRADE COST +ECO_X_YCR(Y,C,IR,IRI,'COSTS','TRANSMISSION_TRADE_COSTS','Mmoney')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= +OMONEY*IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VX_T.L(Y,IRI,IR,S,T) * EL_PRICE_YCRST(Y,C,IRI,S,T,'Money_per_MWh'))); + +** TRANSMISSION TRADE INCOME +ECO_X_YCR(Y,C,IR,IRI,'INCOME','TRANSMISSION_TRADE_INCOME','Mmoney')$(CCCRRR(C,IR) AND IXK_HASORPOT(Y,IR,IRI))= +OMONEY*IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VX_T.L(Y,IR,IRI,S,T) * EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh'))); + +*HEAT +$ifi not %HEATTRANS%==yes $goto No_HEATTRANS_costs +** TRANSMISSION INVESTMENTS +ECO_XH_YCRA(Y,C,IR,IA,IAI,'COSTS','HEAT_TRANSMISSION_CAPITAL_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IXHK_HASORPOT(Y,IA,IAI))= + OMONEY*IOF0000001*SUM((IYALIAS2)$((IXHKN(IYALIAS2,IAI,IA) OR IXHKN(IYALIAS2,IA,IAI)) AND ORD(IYALIAS2) LE ORD(Y)) , + IOF05*ANNUITYCXH(C)*IYHASANNUITYXH(IYALIAS2,Y)*VXHKN.L(IYALIAS2,IA,IAI)*XHINVCOST(IYALIAS2,IA,IAI) + ); +** TRANSMISSION FLOW +ECO_XH_YCRA(Y,C,IR,IA,IAI,'COSTS','HEAT_TRANSMISSION_OPERATIONAL_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IXHK_HASORPOT(Y,IA,IAI))= +OMONEY*IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VXH_T.L(Y,IA,IAI,S,T) * IHOURFRAC *XHCOST(IA,IAI))); + +** TRANSMISSION TRADE COST +ECO_XH_YCRA(Y,C,IR,IA,IAI,'COSTS','TRANSMISSION_TRADE_COSTS','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IXHK_HASORPOT(Y,IA,IAI))= +OMONEY*IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VXH_T.L(Y,IAI,IA,S,T) * H_PRICE_YCRAST(Y,C,IR,IAI,S,T,'Money_per_MWh'))); + +** TRANSMISSION TRADE INCOME +ECO_XH_YCRA(Y,C,IR,IA,IAI,'INCOME','TRANSMISSION_TRADE_INCOME','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) AND IXHK_HASORPOT(Y,IA,IAI))= +OMONEY*IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VXH_T.L(Y,IA,IAI,S,T) * H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh'))); + + +$label No_HEATTRANS_costs + +*Hydrogen +$ifi not %HYDROGEN%==yes $goto No_H2TRANS_costs + +** TRANSMISSION INVESTMENTS +ECO_XH2_YCR(Y,C,IR,IRI,'COSTS','H2_TRANSMISSION_CAPITAL_COSTS','Mmoney')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= + OMONEY*IOF0000001*(SUM((IYALIAS2)$((IXH2KN(IYALIAS2,IRI,IR) OR IXH2KN(IYALIAS2,IR,IRI)) AND (NOT (IXKN_ES(IR,IRI) OR IXKN_ES(IRI,IR))) AND ORD(IYALIAS2) LE ORD(Y)) , + IOF05*ANNUITYCXH2(C)*IYHASANNUITYXH2(IYALIAS2,Y)*VXH2KN.L(IYALIAS2,IR,IRI)*XH2INVCOST(IYALIAS2,IR,IRI)) + + SUM((IYALIAS2)$((IXH2KN_NGTOH2(IYALIAS2,IRI,IR) OR IXH2KN_NGTOH2(IYALIAS2,IR,IRI)) AND ORD(IYALIAS2) LE ORD(Y)) , + IOF05*ANNUITYCXH2(C)*IYHASANNUITYXH2(IYALIAS2,Y)*VXH2KN_NGTOH2.L(IYALIAS2,IR,IRI)*XH2INVCOST_NGTOH2(IYALIAS2,IR,IRI)) + +SUM((IYALIAS2)$((IXH2KN(IYALIAS2,IRI,IR) OR IXH2KN(IYALIAS2,IR,IRI)) AND (ORD(IYALIAS2) LE ORD(Y)) AND (IXH2KN_ES(IR,IRI) OR IXH2KN_ES(IRI,IR))), + IOF05*ANNUITYCXH2(C)*IYHASANNUITYXH2(IYALIAS2,Y)*SUM(XES,VXH2LAMBDA.L(IYALIAS2,IR,IRI,XES)*XH2INVCOST_ES(IYALIAS2,IR,IRI,XES))) + ) +; + +** TRANSMISSION FLOW +ECO_XH2_YCR(Y,C,IR,IRI,'COSTS','H2_TRANSMISSION_OPERATIONAL_COSTS','Mmoney')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= +OMONEY*IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VXH2_T.L(Y,IR,IRI,S,T) * IHOURFRAC *XH2COST(IR,IRI))); + +** TRANSMISSION TRADE COST +ECO_XH2_YCR(Y,C,IR,IRI,'COSTS','TRANSMISSION_TRADE_COSTS','Mmoney')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= +OMONEY*IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VXH2_T.L(Y,IR,IRI,S,T) * H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh'))); + +** TRANSMISSION TRADE INCOME +ECO_XH2_YCR(Y,C,IR,IRI,'INCOME','TRANSMISSION_TRADE_INCOME','Mmoney')$(CCCRRR(C,IR) AND IXH2K_HASORPOT(Y,IR,IRI))= +OMONEY*IOF0000001*SUM((S,T), IHOURSINST(S,T) * (VXH2_T.L(Y,IR,IRI,S,T) * H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh'))); + + +$label No_H2TRANS_costs + +* ------------- END OF COSTS -------------- + +* ------------- OBJECTIVE FUNCTION -------------- + +OBJ_YCR(Y,C,IR,'GENERATION_FUEL_COSTS','Mmoney')$CCCRRR(C,IR)= + +*GENERATION COSTS +SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND NOT((IGESTOALL(G) OR IGHSTOALL(G) OR IGETOH(G) +$ifi %STEPWISEPRICE%==yes OR ISTEPWISEPRICE_F(Y,C,FFF) +$ifi %HYDROGEN%==yes OR IHYDROGEN_GETOH2(G) OR IHYDROGEN_GEHTOH2(G) OR IHYDROGEN_GETOHH2(G) OR IHYDROGEN_GH2STO(G) OR IHYDROGEN_GH2TOE(G) OR IHYDROGEN_GH2TOEH(G) OR IHYDROGEN_GH2TOBIOMETH(G) OR IGBIOMETHANE(G) +))), +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_FUEL_COSTS','Mmoney') +) + +$ifi not %STEPWISEPRICE%==yes $goto No_STEPWISEPRICE2 + +SUM(STEPWISEPRICE_FFF$(ISTEPWISEPRICE_F(Y,C,STEPWISEPRICE_FFF) AND SUM(STEPWISEPRICE_L,VSTEPWISEPRICE_VGF_T_STEP.L(Y,C,STEPWISEPRICE_L,STEPWISEPRICE_FFF))), + OMONEY/IOF1000000*( + (0+ SUM((S,T,IA,G)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G) AND IGF(G,STEPWISEPRICE_FFF)), IHOURSINST(S,T) * VGF_T.L(Y,IA,G,S,T)*IOF3P6)*IHOURFRAC)* + SUM(STEPWISEPRICE_L,STEPWISEPRICE_FUELPRICE_STEP(Y,C,STEPWISEPRICE_L,STEPWISEPRICE_FFF)*VSTEPWISEPRICE_VGF_T_STEP.L(Y,C,STEPWISEPRICE_L,STEPWISEPRICE_FFF)) + /SUM(STEPWISEPRICE_L,VSTEPWISEPRICE_VGF_T_STEP.L(Y,C,STEPWISEPRICE_L,STEPWISEPRICE_FFF)) + ) + ) +$label No_STEPWISEPRICE2 + +; + +OBJ_YCR(Y,C,IR,'GENERATION_OPPORTUNITY_COSTS_STORAGE','Mmoney')$CCCRRR(C,IR)= +$ifi %import_results%==yes $ifi %ADDESTOVOLTS%==price +OMONEY*IOF0000001*SUM((S,T,IA,IGESTOS)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGESTOS)),IHOURSINST(S,T)*ESTOVOLTSVAL(Y,IA,IGESTOS,S,T)*IHOURFRAC*(VGE_T.L(Y,IA,IGESTOS,S,T)/GDATA(IGESTOS,'GDFE')-VESTOLOADTS.L(Y,IA,IGESTOS,S,T))) +$ifi %import_results%==yes $ifi %ADDHSTOVOLTS%==price +OMONEY*IOF0000001*SUM((S,T,IA,IGHSTOS)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGHSTOS)),IHOURSINST(S,T)*HSTOVOLTSVAL(Y,IA,IGHSTOS,S,T)*IHOURFRAC*(VGH_T.L(Y,IA,IGHSTOS,S,T)/GDATA(IGHSTOS,'GDFE')-VHSTOLOADTS.L(Y,IA,IGHSTOS,S,T))) +$ifi %import_results%==yes $ifi %ADDESTOVOLT%==price +OMONEY*IOF0000001*SUM((S,T,IA,IGESTO)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGESTO)),IHOURSINST(S,T)*ESTOVOLTVAL(Y,IA,IGESTO,S,T)*IHOURFRAC*(VGE_T.L(Y,IA,IGESTO,S,T)/GDATA(IGESTO,'GDFE')-VESTOLOADT.L(Y,IA,IGESTO,S,T))) +$ifi %import_results%==yes $ifi %ADDHSTOVOLT%==price +OMONEY*IOF0000001*SUM((S,T,IA,IGHSTO)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGHSTO)),IHOURSINST(S,T)*HSTOVOLTVAL(Y,IA,IGHSTO,S,T)*IHOURFRAC*(VGH_T.L(Y,IA,IGHSTO,S,T)/GDATA(IGHSTO,'GDFE')-VHSTOLOADT.L(Y,IA,IGHSTO,S,T))) +$ifi %import_results%==yes $ifi %HYDROGEN%==yes $ifi %ADDH2STOVOLTS%==price +OMONEY*IOF0000001*SUM((S,T,IA,IHYDROGEN_GH2STO)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO)),IHOURSINST(S,T)*H2STOVOLTSVAL(Y,IA,IHYDROGEN_GH2STO,S,T)*IHOURFRAC*(VHYDROGEN_GH2_T.L(Y,IA,IHYDROGEN_GH2STO,S,T)/GDATA(IHYDROGEN_GH2STO,'GDFE')-VHYDROGEN_STOLOADT.L(Y,IA,IHYDROGEN_GH2STO,S,T))) +$ifi %import_results%==yes $ifi %HYDROGEN%==yes $ifi %ADDBIOMETHSTOVOLTS%==price +OMONEY*IOF0000001*SUM((S,T)$(SUM((IA,IGBIOMETHANE),IAGK_HASORPOT(Y,IA,IGBIOMETHANE)) AND SUM((IA,IHYDROGEN_GH2TOBIOMETH),IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH))),IHOURSINST(S,T)*BIOMETHSTOVOLTSVAL(Y,S,T)*IHOURFRAC*(VBIOMETH_STOUNLOADT.L(Y,S,T)-VBIOMETH_STOLOADT.L(Y,S,T)) ) ++0; + +OBJ_YCR(Y,C,IR,'GENERATION_REDISPATCH_COMPENSATION_COST','Mmoney')$CCCRRR(C,IR)= +$ifi %import_results%==yes $ifi %ADDENERGYDISPATCH%==yes $ifi %BalancingMarket%==yes $ifi %BALANCINGRUNPURPOSE%==TSOREDISPATCH +OMONEY*IOF0000001*SUM((S,T,IAGK_HASORPOT(Y,IA,IGE))$(NOT (IGETOH(IGE)) AND RRRAAA(IR,IA) AND GE_T(Y,IA,IGE,S,T)),IHOURFRAC*ELECTRICITY_PRICE(Y,IR,S,T)*IHOURSINST(S,T)*(1-DISLOSS_E_AG(IA,IGE))*VGE_T_DOWN.L(Y,IA,IGE,S,T)) +$ifi %import_results%==yes $ifi %ADDENERGYDISPATCH%==yes $ifi %BalancingMarket%==yes $ifi %BALANCINGRUNPURPOSE%==TSOREDISPATCH +OMONEY*IOF0000001*SUM((S,T,IAGK_HASORPOT(Y,IA,IGH))$(RRRAAA(IR,IA) AND GH_T(Y,IA,IGH,S,T)),IHOURFRAC*HEAT_PRICE(Y,IA,S,T)*IHOURSINST(S,T)*VGH_T_DOWN.L(Y,IA,IGH,S,T)) +$ifi %import_results%==yes $ifi %ADDENERGYDISPATCH%==yes $ifi %BalancingMarket%==yes $ifi %BALANCINGRUNPURPOSE%==TSOREDISPATCH $ifi %HYDROGEN%==yes +OMONEY*IOF0000001*SUM((S,T,IAGK_HASORPOT(Y,IA,IHYDROGEN))$(RRRAAA(IR,IA) AND GH2_T(Y,IA,IHYDROGEN,S,T)),IHOURFRAC*HYDROGEN_PRICE(Y,IR,S,T)*IHOURSINST(S,T)*VGH2_T_DOWN.L(Y,IA,IHYDROGEN,S,T)) +$ifi %import_results%==yes $ifi %ADDENERGYDISPATCH%==yes $ifi %BalancingMarket%==yes $ifi %BALANCINGRUNPURPOSE%==TSOREDISPATCH $ifi %HYDROGEN%==yes +OMONEY*IOF0000001*SUM((S,T,IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH))$(RRRAAA(IR,IA) AND GBIOMETHANE_T(Y,IA,IHYDROGEN_GH2TOBIOMETH,S,T)),IHOURFRAC*BIOMETHANE_PRICE(Y,S,T)*IHOURSINST(S,T)*VGBIOMETHANE_T_DOWN.L(Y,IA,IHYDROGEN_GH2TOBIOMETH,S,T)) +$ifi %import_results%==yes $ifi %ADDENERGYDISPATCH%==yes $ifi %BalancingMarket%==yes $ifi %BALANCINGRUNPURPOSE%==TSOREDISPATCH $ifi %HYDROGEN%==yes +OMONEY*IOF0000001*SUM((S,T,IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASUPGRADING))$(RRRAAA(IR,IA) AND GBIOGASUPGRADING_T(Y,IA,IHYDROGEN_GBIOGASUPGRADING,S,T)),IHOURFRAC*BIOMETHANE_PRICE(Y,S,T)*IHOURSINST(S,T)*VGBIOGASUPGRADING_T_DOWN.L(Y,IA,IHYDROGEN_GBIOGASUPGRADING,S,T)) +$ifi %import_results%==yes $ifi %ADDENERGYDISPATCH%==yes $ifi %BalancingMarket%==yes $ifi %BALANCINGRUNPURPOSE%==TSOREDISPATCH $ifi %HYDROGEN%==yes +OMONEY*IOF0000001*SUM((S,T,IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASMETHANATION))$(RRRAAA(IR,IA) AND GBIOGASMETHANATION_T(Y,IA,IHYDROGEN_GBIOGASMETHANATION,S,T)),IHOURFRAC*BIOMETHANE_PRICE(Y,S,T)*IHOURSINST(S,T)*VGBIOGASMETHANATION_T_DOWN.L(Y,IA,IHYDROGEN_GBIOGASMETHANATION,S,T)) ++0; + + +OBJ_YCR(Y,C,IR,'HYDRO_PROFILE','Mmoney')$CCCRRR(C,IR)= +OMONEY*IOF0000001*SUM((IA,IGHYRS)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,IGHYRS)), SUM((S,T), HYPPROFILS(IA,S)*IHOURFRAC* IHOURSINST(S,T) + * VGE_T.L(Y,IA,IGHYRS,S,T))) +; + +OBJ_YCR(Y,C,IR,'GENERATION_CAPITAL_COSTS','Mmoney')$CCCRRR(C,IR)= +SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_CAPITAL_COSTS','Mmoney') +) +; + +OBJ_YCR(Y,C,IR,'GENERATION_FIXED_COSTS','Mmoney')$CCCRRR(C,IR)= +SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_FIXED_COSTS','Mmoney') +) +; + +OBJ_YCR(Y,C,IR,'GENERATION_OPERATIONAL_COSTS','Mmoney')$CCCRRR(C,IR)= +SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_OPERATIONAL_COSTS','Mmoney') +) +; + +OBJ_YCR(Y,C,IR,'GENERATION_CO2_TAX','Mmoney')$CCCRRR(C,IR)= +SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_CO2_TAX','Mmoney') +) +; + +$ifi %CCS%==yes OBJ_YCR(Y,C,IR,'GENERATION_CO2_TRANSPORT','Mmoney')$CCCRRR(C,IR)= +$ifi %CCS%==yes SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G) AND CCS_G(G)),ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_CO2_TRANSPORT','Mmoney')); + +OBJ_YCR(Y,C,IR,'GENERATION_OTHER_EMI_TAX','Mmoney')$CCCRRR(C,IR)= +SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_OTHER_EMI_TAX','Mmoney') +) +; + +OBJ_YCR(Y,C,IR,'GENERATION_UC_COSTS','Mmoney')$CCCRRR(C,IR)= +SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_UC_COSTS','Mmoney') +) +; + +OBJ_YCR(Y,C,IR,'TRANSMISSION_CAPITAL_COSTS','Mmoney')$CCCRRR(C,IR)= +SUM(IRI$IXK_HASORPOT(Y,IR,IRI),ECO_X_YCR(Y,C,IR,IRI,'COSTS','TRANSMISSION_CAPITAL_COSTS','Mmoney')) +; + +OBJ_YCR(Y,C,IR,'TRANSMISSION_OPERATIONAL_COSTS','Mmoney')$CCCRRR(C,IR)= +SUM(IRI$IXK_HASORPOT(Y,IR,IRI), ECO_X_YCR(Y,C,IR,IRI,'COSTS','TRANSMISSION_OPERATIONAL_COSTS','Mmoney')) +; + +$ifi not %HEATTRANS%==yes $goto No_HEATTRANS_costs_2 +OBJ_YCR(Y,C,IR,'HEAT_TRANSMISSION_CAPITAL_COSTS','Mmoney')$CCCRRR(C,IR)= +SUM((IA,IAI)$(RRRAAA(IR,IA) AND IXHK_HASORPOT(Y,IA,IAI)),ECO_XH_YCRA(Y,C,IR,IA,IAI,'COSTS','HEAT_TRANSMISSION_CAPITAL_COSTS','Mmoney')) +; + +OBJ_YCR(Y,C,IR,'HEAT_TRANSMISSION_OPERATIONAL_COSTS','Mmoney')$CCCRRR(C,IR)= +SUM((IA,IAI)$(RRRAAA(IR,IA) AND IXHK_HASORPOT(Y,IA,IAI)),ECO_XH_YCRA(Y,C,IR,IA,IAI,'COSTS','HEAT_TRANSMISSION_OPERATIONAL_COSTS','Mmoney')) +; +$label No_HEATTRANS_costs_2 + +$ifi not %HYDROGEN%==yes $goto No_H2_costs_2 +OBJ_YCR(Y,C,IR,'H2_TRANSMISSION_CAPITAL_COSTS','Mmoney')$CCCRRR(C,IR)= +SUM(IRI$IXH2K_HASORPOT(Y,IR,IRI),ECO_XH2_YCR(Y,C,IR,IRI,'COSTS','H2_TRANSMISSION_CAPITAL_COSTS','Mmoney')) +; + +OBJ_YCR(Y,C,IR,'H2_TRANSMISSION_OPERATIONAL_COSTS','Mmoney')$CCCRRR(C,IR)= +SUM(IRI$IXH2K_HASORPOT(Y,IR,IRI), ECO_XH2_YCR(Y,C,IR,IRI,'COSTS','H2_TRANSMISSION_OPERATIONAL_COSTS','Mmoney')) +; +$label No_H2_costs_2 + + +$ifi NOT %TAXES%==yes $goto No_TAXES2 +OBJ_YCR(Y,C,IR,'TAXES','Mmoney')$CCCRRR(C,IR)= +SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G)), ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_TAXES','Mmoney')) ++OMONEY/IOF1000000*( + +* Tax on electricity consumption + + SUM(DEUSER, TAX_DE(C,DEUSER)*IHOURFRAC * SUM((IS3,T), IHOURSINST(IS3,T) * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0)) ) + +$ifi %DFLEXQUANT%==yes + SUM((DEUSER), *IHOURFRACTAX_DE(C,DEUSER) *( +$ifi %DFLEXQUANT%==yes SUM(DEF_U1,VDEF_T.L(Y,IR,DEUSER,IS3,T,DEF_U1) ) - SUM(DEF_D1,VDEF_T.L(Y,IR,DEUSER,IS3,T,DEF_D1) ) +$ifi %DFLEXQUANT%==yes + SUM(DEF_U2,VDEF_T.L(Y,IR,DEUSER,IS3,T,DEF_U2) ) - SUM(DEF_D2,VDEF_T.L(Y,IR,DEUSER,IS3,T,DEF_D2) ) +$ifi %DFLEXQUANT%==yes + SUM(DEF_U3,VDEF_T.L(Y,IR,DEUSER,IS3,T,DEF_U3) ) - SUM(DEF_D3,VDEF_T.L(Y,IR,DEUSER,IS3,T,DEF_D3) )) + + ++SUM(IA$RRRAAA(IR,IA), +* Tax on heat consumption + + SUM(DHUSER, TAX_DH(C,DHUSER)*IHOURFRAC * DH(Y,IA,DHUSER)) + +$ifi %DFLEXQUANT%==yes + SUM((DHUSER), TAX_DH(C,DHUSER)*IHOURFRAC * ( +$ifi %DFLEXQUANT%==yes SUM(DHF_U1,VDHF_T.L(Y,IA,DHUSER,IS3,T,DHF_U1) ) - SUM(DHF_D1,VDHF_T.L(Y,IA,DHUSER,IS3,T,DHF_D1) ) +$ifi %DFLEXQUANT%==yes + SUM(DHF_U2,VDHF_T.L(Y,IA,DHUSER,IS3,T,DHF_U2) ) - SUM(DHF_D2,VDHF_T.L(Y,IA,DHUSER,IS3,T,DHF_D2) ) +$ifi %DFLEXQUANT%==yes + SUM(DHF_U3,VDHF_T.L(Y,IA,DHUSER,IS3,T,DHF_U3) ) - SUM(DHF_D3,VDHF_T.L(Y,IA,DHUSER,IS3,T,DHF_D3) ) ) +) +) +; +$label NO_TAXES2 + + +*GRID TARIFFS2 +$ifi NOT %GRIDTARIFFS%==yes $goto No_GRIDTARIFFS2 +OBJ_YCR(Y,C,IR,'GRID_TARIFFS','Mmoney')$CCCRRR(C,IR)= +SUM((IA,G,FFF,TECH_TYPE)$(RRRAAA(IR,IA) AND IAGK_HASORPOT(Y,IA,G) AND (IGETOH(G) +$ifi %HYDROGEN%==yes OR IHYDROGEN_GEHTOH2(G) OR IHYDROGEN_GETOHH2(G) OR IHYDROGEN_GETOH2(G) +)), ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS','GENERATION_GRID_TARIFFS','Mmoney')) + ++OMONEY/IOF1000000*( + +* Demand charge winter + + IOF1000 * SUM((MMM)$(SUM((IS3,T)$IGR_TIME('GRDDCW',IR,IS3,T), ISSSTTTMMM(IS3,T,MMM))), SUM(IA$(SUM(IGETOH,IAGK_HASORPOT(Y,IA,IGETOH)) AND RRRAAA(IR,IA)), VGETOH_CAP1.L(Y,IA,MMM)) * IGR_PRICE_TECH(Y,IR,'GRDDCW'))/(IGR_MONTHSINSIM/card(MMM)) !! Demand charge winter +* Demand charge summer + + IOF1000 * SUM((MMM)$(SUM((IS3,T)$IGR_TIME('GRDDCS',IR,IS3,T), ISSSTTTMMM(IS3,T,MMM))), SUM(IA$(SUM(IGETOH,IAGK_HASORPOT(Y,IA,IGETOH)) AND RRRAAA(IR,IA)), VGETOH_CAP1.L(Y,IA,MMM)) * IGR_PRICE_TECH(Y,IR,'GRDDCS'))/(IGR_MONTHSINSIM/card(MMM)) !! Demand charge summer +* Demand charge 2 (e.g for spring and fall months) + + IOF1000 * SUM((MMM)$(SUM((IS3,T)$IGR_TIME('GRDDC2',IR,IS3,T), ISSSTTTMMM(IS3,T,MMM))), SUM(IA$(SUM(IGETOH,IAGK_HASORPOT(Y,IA,IGETOH)) AND RRRAAA(IR,IA)), VGETOH_CAP1.L(Y,IA,MMM)) * IGR_PRICE_TECH(Y,IR,'GRDDC2'))/(IGR_MONTHSINSIM/card(MMM)) !! Demand charge 2 + +* Demand charge TOU + + IOF1000 * SUM((MMM), SUM(IA$(SUM(IGETOH,IAGK_HASORPOT(Y,IA,IGETOH)) AND RRRAAA(IR,IA)), VGETOH_CAP2.L(Y,IA,MMM)) * IGR_PRICE_TECH(Y,IR,'GRDDCTOU'))/(IGR_MONTHSINSIM/card(MMM)) +* Demand charge yearly + + IOF1000 * SUM(IA$(SUM(IGETOH,IAGK_HASORPOT(Y,IA,IGETOH)) AND RRRAAA(IR,IA)), VGETOH_CAPY.L(Y,IA)) * IGR_PRICE_TECH(Y,IR,'GRDDCY') + +* Tariffs on user groups + + SUM((IS3,T,DEUSER)$IGR_TIME('GRDECW',IR,IS3,T), IHOURSINST(IS3,T)*IHOURFRAC * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDECW') * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0) ) + + SUM((IS3,T,DEUSER)$IGR_TIME('GRDECS',IR,IS3,T), IHOURSINST(IS3,T)*IHOURFRAC * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDECS') * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0) ) + + SUM((IS3,T,DEUSER)$IGR_TIME('GRDTOU1',IR,IS3,T), IHOURSINST(IS3,T)*IHOURFRAC * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDTOU1') * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0) ) + + SUM((IS3,T,DEUSER)$IGR_TIME('GRDTOU2',IR,IS3,T), IHOURSINST(IS3,T)*IHOURFRAC * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDTOU2') * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0) ) + + SUM((IS3,T,DEUSER)$IGR_TIME('GRDTOU3',IR,IS3,T), IHOURSINST(IS3,T)*IHOURFRAC * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDTOU3') * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0) ) + + SUM((IS3,T,DEUSER)$IGR_TIME('GRDTOU4',IR,IS3,T), IHOURSINST(IS3,T)*IHOURFRAC * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDTOU4') * VDENET_T.L(Y,IR,DEUSER,IS3,T)$(IDE_SUMST(IR,DEUSER) GT 0) ) + + + IOF1000 * SUM((DEUSER,MMM)$(SUM((IS3,T)$IGR_TIME('GRDDCW',IR,IS3,T), ISSSTTTMMM(IS3,T,MMM))), VDEUSER_CAP1.L(Y,IR,DEUSER,MMM) * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDDCW'))/(IGR_MONTHSINSIM/CARD(MMM)) + + IOF1000 * SUM((DEUSER,MMM)$(SUM((IS3,T)$IGR_TIME('GRDDCS',IR,IS3,T), ISSSTTTMMM(IS3,T,MMM))), VDEUSER_CAP1.L(Y,IR,DEUSER,MMM) * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDDCS'))/(IGR_MONTHSINSIM/CARD(MMM)) + + IOF1000 * SUM((DEUSER,MMM)$(SUM((IS3,T)$IGR_TIME('GRDDC2',IR,IS3,T), ISSSTTTMMM(IS3,T,MMM))), VDEUSER_CAP1.L(Y,IR,DEUSER,MMM) * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDDC2'))/(IGR_MONTHSINSIM/CARD(MMM)) + + IOF1000 * SUM((DEUSER,MMM), VDEUSER_CAP2.L(Y,IR,DEUSER,MMM) * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDDCTOU'))/(IGR_MONTHSINSIM/card(MMM)) + + IOF1000 * SUM((DEUSER), VDEUSER_CAPY.L(Y,IR,DEUSER) * IGR_PRICE_DEUSER(Y,IR,DEUSER,'GRDDCY')) + +) + +; +$label NO_GRIDTARIFFS2 + +*ADD OTHER COSTS(?) + + +* ------------- END OF OBJECTIVE FUNCTION -------------- + + +* ------------- INCOME -------------- + +** ELECTRICITY SALES +ECO_G_YCRAG(Y,C,IR,IA,IGE,FFF,TECH_TYPE,'INCOME','ELECTRICITY_SALE','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) and IGNOTETOH(IGE))= + OMONEY*IOF0000001* + SUM((S,T), IHOURSINST(S,T)*VGE_T.L(Y,IA,IGE,S,T)*EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')) +; + +** HEAT SALES +ECO_G_YCRAG(Y,C,IR,IA,IGH,FFF,TECH_TYPE,'INCOME','HEAT_SALE','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE))= + OMONEY*IOF0000001* + SUM((S,T), IHOURSINST(S,T)*VGH_T.L(Y,IA,IGH,S,T)*H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh')) +; + +$ifi not %BalancingMarket%==yes $goto NO_TSOREDISPATCH1 +$ifi not %BALANCINGRUNPURPOSE%==TSOREDISPATCH $goto NO_TSOREDISPATCH1 +ECO_G_YCRAG(Y,C,IR,IA,IGE,FFF,TECH_TYPE,'INCOME','ELECTRICITY_REDISPATCH_COMPENSATION','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE) and IGNOTETOH(IGE))= + OMONEY*IOF0000001*( + SUM((S,T), IHOURFRAC*IHOURSINST(S,T)*(1-DISLOSS_E_AG(IA,IGE))*VGE_T_DOWN.L(Y,IA,IGE,S,T)*ELECTRICITY_PRICE(Y,IR,S,T))) +; + +ECO_G_YCRAG(Y,C,IR,IA,IGH,FFF,TECH_TYPE,'INCOME','HEAT_REDISPATCH_COMPENSATION','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE))= + OMONEY*IOF0000001* + SUM((S,T), IHOURFRAC*IHOURSINST(S,T)*VGH_T_DOWN.L(Y,IA,IGH,S,T)*HEAT_PRICE(Y,IA,S,T)) +; +$label NO_TSOREDISPATCH1 + +$ifi not %HYDROGEN%==yes $goto NO_HYDROGEN_INCOME +** HYDROGEN SALES +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'INCOME','H2_SALE','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IHYDROGEN(G) AND NOT(IHYDROGEN_GH2TOBIOMETH(G)))= + OMONEY*IOF0000001* + SUM((S,T), IHOURSINST(S,T)*PRO_YCRAGFST(Y,C,IR,IA,G,FFF,S,T,'HYDROGEN',TECH_TYPE,'MWh')*H2_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh')) +; + +** BIOMETHANE SALES +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'INCOME','BIOMETHANE_SALE','Mmoney')$((IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASUPGRADING(G) OR IHYDROGEN_GBIOGASMETHANATION(G)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE))= + OMONEY*IOF0000001*( + +SUM((S,T), IHOURSINST(S,T)*VGBIOMETH_T.L(Y,IA,G,S,T)*BIOMETH_PRICE_YST(Y,S,T,'Money_per_MWh'))$IHYDROGEN_GH2TOBIOMETH(G) + +SUM((S,T), IHOURSINST(S,T)*VGBIOGASMETHANATION_T.L(Y,IA,G,S,T)*BIOMETH_PRICE_YST(Y,S,T,'Money_per_MWh'))$IHYDROGEN_GBIOGASMETHANATION(G) + +SUM((S,T), IHOURSINST(S,T)*VGBIOGASUPGRADING_T.L(Y,IA,G,S,T)*BIOMETH_PRICE_YST(Y,S,T,'Money_per_MWh'))$IHYDROGEN_GBIOGASUPGRADING(G) +) +; + +$ifi not %BalancingMarket%==yes $goto NO_TSOREDISPATCH2 +$ifi not %BALANCINGRUNPURPOSE%==TSOREDISPATCH $goto NO_TSOREDISPATCH2 +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'INCOME','HYDROGEN_REDISPATCH_COMPENSATION','Mmoney')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE) AND IHYDROGEN(G))= + OMONEY*IOF0000001* + SUM((S,T), IHOURFRAC*IHOURSINST(S,T)*VGH2_T_DOWN.L(Y,IA,G,S,T)*HYDROGEN_PRICE(Y,IR,S,T)) +; + +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'INCOME','BIOMETHANE_REDISPATCH_COMPENSATION','Mmoney')$((IHYDROGEN_GH2TOBIOMETH(G) OR IHYDROGEN_GBIOGASUPGRADING(G) OR IHYDROGEN_GBIOGASMETHANATION(G)) AND CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,G) AND IGF(G,FFF) AND GTECH_TYPE(G,TECH_TYPE))= + OMONEY*IOF0000001*( + +SUM((S,T), IHOURFRAC*IHOURSINST(S,T)*VGBIOMETHANE_T_DOWN.L(Y,IA,G,S,T)*BIOMETHANE_PRICE(Y,S,T))$IHYDROGEN_GH2TOBIOMETH(G) + +SUM((S,T), IHOURFRAC*IHOURSINST(S,T)*VGBIOGASMETHANATION_T_DOWN.L(Y,IA,G,S,T)*BIOMETHANE_PRICE(Y,S,T))$IHYDROGEN_GBIOGASMETHANATION(G) + +SUM((S,T), IHOURFRAC*IHOURSINST(S,T)*VGBIOGASUPGRADING_T_DOWN.L(Y,IA,G,S,T)*BIOMETHANE_PRICE(Y,S,T))$IHYDROGEN_GBIOGASUPGRADING(G) +) +; +$label NO_TSOREDISPATCH2 + +$label NO_HYDROGEN_INCOME + +* ------------- END OF INCOME -------------- + +* ------------- PROFIT -------------- +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'PROFIT','TOTAL_PROFIT','Mmoney')$IAGK_HASORPOT(Y,IA,G)= +SUM(SUBCATEGORY, ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'INCOME',SUBCATEGORY,'Mmoney')-ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'COSTS',SUBCATEGORY,'Mmoney') +) +; + +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'PROFIT','ENERGY_SPECIFIC_PROFIT','Money_per_MWh')$(IAGK_HASORPOT(Y,IA,G) AND SUM(COMMODITY,PRO_YCRAGF(Y,C,IR,IA,G,FFF,COMMODITY,TECH_TYPE,'TWh')) > 0 ) = +ECO_G_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'PROFIT','TOTAL_PROFIT','Mmoney')/SUM(COMMODITY,PRO_YCRAGF(Y,C,IR,IA,G,FFF,COMMODITY,TECH_TYPE,'TWh')) +; +* ------------- END OF PROFIT -------------- + +* --------- END OF ECONOMIC OUTPUT ----------- + +* ---------------------- ENVIRONMENT ---------------------- + +* ------------- CO2 EMISSIONS ------------- + +EMI_YCRAG(Y,C,IR,IA,G,FFF,TECH_TYPE,'kton')$(F_CONS_YCRA(Y,C,IR,IA,G,FFF,TECH_TYPE,'TWh'))= +(F_CONS_YCRA(Y,C,IR,IA,G,FFF,TECH_TYPE,'TWh') +$ifi %CCS%==yes -(F_CONS_YCRA(Y,C,IR,IA,G,FFF,TECH_TYPE,'TWh')*CCS_CO2CAPTEFF_G(G))$CCS_G(G) +)*IM_CO2(G)*IOF3P6 +; + +* ------------- END OF CO2 EMISSIONS ------------- + +* ---------------------- END OF ENVIRONMENT ---------------------- + +*---------------ELECTRICITY BALANCE------------ +*GENERATION +EL_BALANCE_YCRST(Y,C,IR,'CONDENSING',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','CONDENSING','MWh')); +EL_BALANCE_YCRST(Y,C,IR,'CHP-BACK-PRESSURE',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','CHP-BACK-PRESSURE','MWh')); +EL_BALANCE_YCRST(Y,C,IR,'CHP-EXTRACTION',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','CHP-EXTRACTION','MWh')); +EL_BALANCE_YCRST(Y,C,IR,'INTER-STO',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','INTERSEASONAL-ELECT-STORAGE','MWh')); +EL_BALANCE_YCRST(Y,C,IR,'INTRA-STO',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','INTRASEASONAL-ELECT-STORAGE','MWh')); +EL_BALANCE_YCRST(Y,C,IR,'HYDRO-RESERVOIRS',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','HYDRO-RESERVOIRS','MWh')); +EL_BALANCE_YCRST(Y,C,IR,'HYDRO-RUN-OF-RIVER',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','HYDRO-RUN-OF-RIVER','MWh')); +EL_BALANCE_YCRST(Y,C,IR,'WIND-ON',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','WIND-ON','MWh')); +EL_BALANCE_YCRST(Y,C,IR,'WIND-OFF',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','WIND-OFF','MWh')); +EL_BALANCE_YCRST(Y,C,IR,'SOLAR-PV',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','SOLAR-PV','MWh')); +EL_BALANCE_YCRST(Y,C,IR,'HYDRO-WAVE ',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','HYDRO-WAVE','MWh')); +$ifi %HYDROGEN%==yes EL_BALANCE_YCRST(Y,C,IR,'FUELCELL',S,T,'MWh')$CCCRRR(C,IR)=SUM((IA,IGE,FFF)$(RRRAAA(IR,IA) AND IGF(IGE,FFF) AND IAGK_HASORPOT(Y,IA,IGE)),PRO_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY','FUELCELL','MWh')); + +*DEMAND +EL_BALANCE_YCRST(Y,C,IR,'DEMAND_EXO',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'EXOGENOUS','MWh'); +EL_BALANCE_YCRST(Y,C,IR,'DEMAND_P2H',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDOGENOUS_ELECT2HEAT','MWh'); +EL_BALANCE_YCRST(Y,C,IR,'DEMAND_INTRASTO',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_INTRASTO','MWh'); +EL_BALANCE_YCRST(Y,C,IR,'DEMAND_INTERSTO',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_INTERSTO','MWh'); +EL_BALANCE_YCRST(Y,C,IR,'DEMAND_EV',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_EV','MWh'); +EL_BALANCE_YCRST(Y,C,IR,'DEMAND_OTHERTRANS',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_OTHERTRANS','MWh'); +EL_BALANCE_YCRST(Y,C,IR,'DEMAND_DISTLOSSES',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'DIST_LOSSES','MWh'); +EL_BALANCE_YCRST(Y,C,IR,'DEMAND_CCS',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_CCS','MWh'); +$ifi %HYDROGEN%==yes EL_BALANCE_YCRST(Y,C,IR,'DEMAND_P2G',S,T,'MWh')$CCCRRR(C,IR)=EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_H2','MWh')+EL_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_BIOMETHANE','MWh'); + + +*TRANSMISSION +EL_BALANCE_YCRST(Y,C,IR,'NETEXPORT',S,T,'MWh')$CCCRRR(C,IR)=SUM(IRE$IXK_HASORPOT(Y,IR,IRE),VX_T.L(Y,IR,IRE,S,T))-SUM(IRI$IXK_HASORPOT(Y,IRI,IR),VX_T.L(Y,IRI,IR,S,T)*(1-XLOSS(IRI,IR))); + +*CURTAILMENT +EL_BALANCE_YCRST(Y,C,IR,'CURTAILMENT',S,T,'MWh')$CCCRRR(C,IR)= +SUM((IGE,TECH_TYPE,IA,FFF)$(RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGE) AND IGF(IGE,FFF) AND GTECH_TYPE(IGE,TECH_TYPE)), +CURT_YCRAGFST(Y,C,IR,IA,IGE,FFF,S,T,'ELECTRICITY',TECH_TYPE,'MWh') +); + +*PRICE +EL_BALANCE_YCRST(Y,C,IR,'PRICE',S,T,'Money_per_MWh')$CCCRRR(C,IR)=EL_PRICE_YCRST(Y,C,IR,S,T,'Money_per_MWh'); +*---------------END ELECTRICITY BALANCE------------ + + +*---------------HEAT BALANCE------------ +*GENERATION +H_BALANCE_YCRAST(Y,C,IR,IA,'P2H',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','ELECT-TO-HEAT','MWh')); +H_BALANCE_YCRAST(Y,C,IR,IA,'CHP-BACK-PRESSURE',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','CHP-BACK-PRESSURE','MWh')); +H_BALANCE_YCRAST(Y,C,IR,IA,'CHP-EXTRACTION',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','CHP-EXTRACTION','MWh')); +H_BALANCE_YCRAST(Y,C,IR,IA,'INTER-STO',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','INTERSEASONAL-HEAT-STORAGE','MWh')); +H_BALANCE_YCRAST(Y,C,IR,IA,'INTRA-STO',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','INTRASEASONAL-HEAT-STORAGE','MWh')); +H_BALANCE_YCRAST(Y,C,IR,IA,'BOILERS',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','BOILERS','MWh')); +H_BALANCE_YCRAST(Y,C,IR,IA,'SOLAR-HEATING',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','SOLAR-HEATING','MWh')); +$ifi %HYDROGEN%==yes H_BALANCE_YCRAST(Y,C,IR,IA,'FUELCELL',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM((IGH,FFF)$(IGF(IGH,FFF) AND IAGK_HASORPOT(Y,IA,IGH)),PRO_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT','FUELCELL','MWh')); + + +*DEMAND +H_BALANCE_YCRAST(Y,C,IR,IA,'DEMAND_EXO',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'EXOGENOUS','MWh'); +H_BALANCE_YCRAST(Y,C,IR,IA,'DEMAND_INTRASTO',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_INTRASTO','MWh'); +H_BALANCE_YCRAST(Y,C,IR,IA,'DEMAND_INTERSTO',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_INTERSTO','MWh'); +H_BALANCE_YCRAST(Y,C,IR,IA,'DEMAND_DISTLOSSES',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'DIST_LOSSES','MWh'); +$ifi %HYDROGEN%==yes H_BALANCE_YCRAST(Y,C,IR,IA,'DEMAND_P2G',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_H2','MWh')+ H_DEMAND_YCRAST(Y,C,IR,IA,S,T,'ENDO_BIOMETHANE','MWh'); + +*TRANSMISSION +$ifi %HEATTRANS%==yes H_BALANCE_YCRAST(Y,C,IR,IA,'NETEXPORT',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=SUM(IAE$IXHK_HASORPOT(Y,IA,IAE),VXH_T.L(Y,IA,IAE,S,T))-SUM(IAI$IXHK_HASORPOT(Y,IAI,IA),VXH_T.L(Y,IAI,IA,S,T)*(1-XHLOSS(IAI,IA))); + +*CURTAILMENT +H_BALANCE_YCRAST(Y,C,IR,IA,'CURTAILMENT',S,T,'MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))= +SUM((IGH,TECH_TYPE,FFF)$(IAGK_HASORPOT(Y,IA,IGH) AND IGF(IGH,FFF) AND GTECH_TYPE(IGH,TECH_TYPE)), +CURT_YCRAGFST(Y,C,IR,IA,IGH,FFF,S,T,'HEAT',TECH_TYPE,'MWh') +); + +*PRICE +H_BALANCE_YCRAST(Y,C,IR,IA,'PRICE',S,T,'Money_per_MWh')$(CCCRRR(C,IR) AND RRRAAA(IR,IA))=H_PRICE_YCRAST(Y,C,IR,IA,S,T,'Money_per_MWh'); +*---------------END HEAT BALANCE------------ + + + +execute_unload "MainResults.gdx" ECO_G_YCRAG,ECO_X_YCR,OBJ_YCR,EL_PRICE_YCR,EL_PRICE_YCRST,PRO_YCRAGFST,EL_DEMAND_YCRST,EL_DEMAND_YCR, +H_DEMAND_YCRAST,H_DEMAND_YCRA,PRO_YCRAGF,H_PRICE_YCRAST,H_PRICE_YCRA,G_CAP_YCRAF,G_STO_YCRAF,X_CAP_YCR,X_FLOW_YCRST,X_FLOW_YCR,F_CONS_YCRAST,F_CONS_YCRA,EMI_YCRAG, +EL_BALANCE_YCRST,CURT_YCRAGFST,CURT_YCRAGF,H_BALANCE_YCRAST + +$ifi %HEATTRANS%==yes ,XH_CAP_YCA,XH_FLOW_YCAST,XH_FLOW_YCA,ECO_XH_YCRA +$ifi %HYDROGEN%==yes ,H2_PRICE_YCRST,H2_PRICE_YCR,BIOMETH_PRICE_YST,XH2_CAP_YCR, XH2_FLOW_YCRST,XH2_FLOW_YCR,ECO_XH2_YCR,H2_DEMAND_YCRST,H2_DEMAND_YCR +; + + +*Establishing modified sets and parameters to original for further calculations + +$ifi not %stointers%==all $goto NO_STOINTERS2 +*Modification of STORAGE +IGESTOS(IGESTO) = YES; IGHSTOS(IGHSTO) = YES; +IGESTO(IGESTO) = NO; IGHSTO(IGHSTO) = NO; +$label NO_STOINTERS2 + +$ifi not %RollingSeasons%==yes $goto NO_ROLLINGSEASONS +*Modification of IHOURSINST +IHOURSINST(SSS,TTT)=0; +LOOP(IS3LOOPSET$(ORD(IS3LOOPSET) LE (CARD(S)-ROLLINGSEASONSNUMBER+1)), + IS3(S)=NO; + IS3(S)$((ORD(S) LE (ORD(IS3LOOPSET)+ROLLINGSEASONSNUMBER-1)) AND (ORD(S) GE ORD(IS3LOOPSET)) )=YES; + IWEIGHSUMS=SUM(IS3, WEIGHT_S(IS3)); + IHOURSINST(IS3,T)=IOF8760*WEIGHT_S(IS3)*WEIGHT_T(T)/(IWEIGHSUMS*SUM(ITALIAS, WEIGHT_T(ITALIAS)))/IHOURFRAC; +); + +$label NO_ROLLINGSEASONS + +*Deleting files to release memory: +G_CAP_YCRAF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,VARIABLE_CATEGORY,UNITS)=0; +G_STO_YCRAF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,VARIABLE_CATEGORY,UNITS)=0; +PRO_YCRAGFST(Y,C,RRR,AAA,G,FFF,SSS,TTT,COMMODITY,TECH_TYPE,UNITS)=0; +PRO_YCRAGF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,UNITS)=0; +F_CONS_YCRAST(Y,C,RRR,AAA,G,FFF,SSS,TTT,TECH_TYPE,UNITS)=0; +F_CONS_YCRA(Y,C,RRR,AAA,G,FFF,TECH_TYPE,UNITS)=0; +X_CAP_YCR(Y,C,IRRRE,IRRRI,VARIABLE_CATEGORY,UNITS)=0; +X_FLOW_YCRST(Y,C,IRRRE,IRRRI,SSS,TTT,UNITS)=0; +X_FLOW_YCR(Y,C,IRRRE,IRRRI,UNITS)=0; +ECO_G_YCRAG(Y,C,RRR,AAA,G,FFF,TECH_TYPE,CATEGORY,SUBCATEGORY,UNITS)=0; +ECO_X_YCR(Y,C,RRR,IRRRI,CATEGORY,SUBCATEGORY,UNITS)=0; +OBJ_YCR(Y,C,RRR,SUBCATEGORY,UNITS)=0; +EL_PRICE_YCR(Y,C,RRR,PRICE_CATEGORY,UNITS)=0; +EL_PRICE_YCRST(Y,C,RRR,SSS,TTT,UNITS)=0; +H_PRICE_YCRA(Y,C,RRR,AAA,PRICE_CATEGORY,UNITS)=0; +H_PRICE_YCRAST(Y,C,RRR,AAA,SSS,TTT,UNITS)=0; +EL_DEMAND_YCRST(Y,C,RRR,SSS,TTT,VARIABLE_CATEGORY,UNITS)=0; +EL_DEMAND_YCR(Y,C,RRR,VARIABLE_CATEGORY,UNITS)=0; +H_DEMAND_YCRAST(Y,C,RRR,AAA,SSS,TTT,VARIABLE_CATEGORY,UNITS)=0; +H_DEMAND_YCRA(Y,C,RRR,AAA,VARIABLE_CATEGORY,UNITS)=0; +EMI_YCRAG(Y,C,RRR,AAA,G,FFF,TECH_TYPE,UNITS)=0; +CURT_YCRAGFST(Y,C,RRR,AAA,G,FFF,SSS,TTT,COMMODITY,TECH_TYPE,UNITS)=0; +CURT_YCRAGF(Y,C,RRR,AAA,G,FFF,COMMODITY,TECH_TYPE,UNITS)=0; +EL_BALANCE_YCRST(Y,C,RRR,EL_BAL_TYPE,SSS,TTT,UNITS)=0; +H_BALANCE_YCRAST(Y,C,RRR,AAA,H_BAL_TYPE,SSS,TTT,UNITS)=0; +$ifi %HYDROGEN%==yes H2_PRICE_YCRST(Y,C,RRR,SSS,TTT,UNITS)=0; +$ifi %HYDROGEN%==yes H2_PRICE_YCR(Y,C,RRR,PRICE_CATEGORY,UNITS)=0; +$ifi %HYDROGEN%==yes BIOMETH_PRICE_YST(Y,SSS,TTT,UNITS)=0; +$ifi %HEATTRANS%==yes XH_CAP_YCA(Y,C,IAAAE,IAAAI,VARIABLE_CATEGORY,UNITS)=0; +$ifi %HEATTRANS%==yes XH_FLOW_YCAST(Y,C,IAAAE,IAAAI,SSS,TTT,UNITS)=0; +$ifi %HEATTRANS%==yes XH_FLOW_YCA(Y,C,IAAAE,IAAAI,UNITS)=0; +$ifi %HEATTRANS%==yes ECO_XH_YCRA(Y,C,RRR,AAA,IAAAI,CATEGORY,SUBCATEGORY,UNITS)=0; +$ifi %HYDROGEN%==yes XH2_CAP_YCR(Y,C,IRRRE,IRRRI,VARIABLE_CATEGORY,UNITS)=0; +$ifi %HYDROGEN%==yes XH2_FLOW_YCRST(Y,C,IRRRE,IRRRI,SSS,TTT,UNITS)=0; +$ifi %HYDROGEN%==yes XH2_FLOW_YCR(Y,C,IRRRE,IRRRI,UNITS)=0; +$ifi %HYDROGEN%==yes ECO_XH2_YCR(Y,C,RRR,IRRRI,CATEGORY,SUBCATEGORY,UNITS)=0; +$ifi %HYDROGEN%==yes H2_DEMAND_YCRST(Y,C,RRR,SSS,TTT,VARIABLE_CATEGORY,UNITS)=0; +$ifi %HYDROGEN%==yes H2_DEMAND_YCR(Y,C,RRR,VARIABLE_CATEGORY,UNITS)=0; + + + + From c08a141ef13664eb2bb016e35ea4c5ad5cb31fab Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Wed, 26 Jun 2024 19:02:51 +0200 Subject: [PATCH 06/21] Fixed minor error in output_summary --- base/output/OUTPUT_SUMMARY.inc | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/base/output/OUTPUT_SUMMARY.inc b/base/output/OUTPUT_SUMMARY.inc index 35e765ab..beb8d62e 100644 --- a/base/output/OUTPUT_SUMMARY.inc +++ b/base/output/OUTPUT_SUMMARY.inc @@ -5,9 +5,10 @@ SET SUBCATEGORY /ELECTRICITY_SALE,HEAT_SALE,H2_SALE,BIOMETHANE_SALE,GENERATION_C GENERATION_OPERATIONAL_COSTS,GENERATION_FUEL_COSTS,GENERATION_TAXES,GENERATION_GRID_TARIFFS,TRANSMISSION_CAPITAL_COSTS,TRANSMISSION_OPERATIONAL_COSTS, TRANSMISSION_TRADE_INCOME,TRANSMISSION_TRADE_COSTS,HEAT_TRANSMISSION_CAPITAL_COSTS,HEAT_TRANSMISSION_OPERATIONAL_COSTS, H2_TRANSMISSION_CAPITAL_COSTS,H2_TRANSMISSION_OPERATIONAL_COSTS,GENERATION_CO2_TAX,GENERATION_CO2_TRANSPORT,GENERATION_UC_COSTS, - GENERATION_OTHER_EMI_TAX,GENERATION_OPPORTUNITY_COSTS,HYDRO_PROFILE,TAXES,GRID_TARIFFS,TOTAL_PROFIT,ENERGY_SPECIFIC_PROFIT GENERATION_OPPORTUNITY_COSTS_STORAGE,GENERATION_REDISPATCH_COMPENSATION_COST,ELECTRICITY_REDISPATCH_COMPENSATION, - HEAT_REDISPATCH_COMPENSATION,HYDROGEN_REDISPATCH_COMPENSATION,BIOMETHANE_REDISPATCH_COMPENSATION, - HYDRO_PROFILE,TAXES,GRID_TARIFFS,TOTAL_PROFIT,ENERGY_SPECIFIC_PROFIT/; + GENERATION_OTHER_EMI_TAX,GENERATION_OPPORTUNITY_COSTS,HYDRO_PROFILE,TAXES,GRID_TARIFFS,TOTAL_PROFIT,ENERGY_SPECIFIC_PROFIT, + GENERATION_OPPORTUNITY_COSTS_STORAGE,GENERATION_REDISPATCH_COMPENSATION_COST,ELECTRICITY_REDISPATCH_COMPENSATION, + HEAT_REDISPATCH_COMPENSATION,HYDROGEN_REDISPATCH_COMPENSATION,BIOMETHANE_REDISPATCH_COMPENSATION + /; SET COMMODITY /ELECTRICITY,HEAT,HYDROGEN,BIOMETHANE/; SET TECH_TYPE /CONDENSING,CHP-BACK-PRESSURE,CHP-EXTRACTION,BOILERS,ELECT-TO-HEAT,INTERSEASONAL-HEAT-STORAGE,INTERSEASONAL-ELECT-STORAGE,INTRASEASONAL-HEAT-STORAGE,INTRASEASONAL-ELECT-STORAGE,HYDRO-RESERVOIRS,HYDRO-RUN-OF-RIVER,WIND-ON,WIND-OFF,SOLAR-PV,SOLAR-HEATING,HYDRO-WAVE,HEAT-PUMP,EL-BOILER,FUELCELL,ELECTROLYZER,H2-STORAGE,BIOMETH-DAC,BIOGASUPGRADING,BIOGASMETHANATION,HUB-OFF,STEAMREFORMING/; SET GTECH_TYPE(GGG,TECH_TYPE); From 5a3ec15125b41d789cc0a03daa228bb153d56ffb Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Wed, 26 Jun 2024 19:06:24 +0200 Subject: [PATCH 07/21] Deleted old, non-functioning addons --- base/addons/Fjernvarme/Addon vejledening.txt | 25 - base/addons/Fjernvarme/demodata/data_fv.inc | 183 - base/addons/Fjernvarme/model/cost_fv.inc | 7 - base/addons/Fjernvarme/model/eqN_fv.inc | 5 - base/addons/Fjernvarme/model/eq_fv.inc | 36 - .../Fjernvarme/model/heatbalance_fv.inc | 4 - base/addons/Fjernvarme/model/sets_fv.inc | 47 - base/addons/Fjernvarme/model/unload.inc | 6 - base/addons/Fjernvarme/model/var_fv.inc | 13 - .../addons/Fjernvarme/model/yearupdate_fv.inc | 9 - base/addons/REShareE/RESEeqns.inc | 16 - base/addons/REShareE/RESEintrn.inc | 2 - .../REShareE/demodata/RESEEDATA_demodata1.inc | 96 - .../REShareE/demodata/RESEEDATA_demodata2.inc | 59 - base/addons/REShareE/printinc/RESEPrt4.inc | 55 - base/addons/REShareEH/Comments.txt | 4 - base/addons/REShareEH/RESEHeqns.inc | 52 - base/addons/REShareEH/RESEHintrn.inc | 3 - .../REShareEH/demodata/RESEHdata_demodata.inc | 93 - base/addons/REShareEH/printinc/RESEHPrt4.inc | 120 - .../bb1234compare/bb1234_checkassumptions.inc | 9 - base/addons/bb1234compare/bb1234_hellolog.inc | 32 - .../addons/bb1234compare/bb1234_varassign.inc | 418 - .../bb1234compare/bb1234_vardeclare.inc | 37 - .../gas/DH-MR - foer varmebehandling.inc | 130 - base/addons/gas/DH-MR.inc | 154 - base/addons/gas/Export_GER.INC | 8746 ----------------- base/addons/gas/GDATA-MR-old.inc | 341 - base/addons/gas/GDATA-MR.inc | 2207 ----- base/addons/gas/GGGMRset.inc | 324 - base/addons/gas/GKFX-MR.inc | 452 - base/addons/gas/GPFX_VAR_T.INC | 8746 ----------------- base/addons/gas/MRareas.inc | 52 - base/addons/gas/MRregions2areas.inc | 7 - base/addons/gas/SE_DG_VAR_T.INC | 8746 ----------------- base/addons/gas/andetgasforbrug.inc | 26 - base/addons/gas/annualupdate.inc | 58 - base/addons/gas/dg-MR.inc | 13 - base/addons/gas/dg.inc | 23 - base/addons/gas/ensmpol.inc | 350 - base/addons/gas/flowsets.inc | 76 - base/addons/gas/fuelp.inc | 221 - base/addons/gas/gasbal_1.inc | 252 - base/addons/gas/gasbb1.inc | 10 - base/addons/gas/gasbb2.inc | 16 - base/addons/gas/gasbb3.inc | 9 - base/addons/gas/gascompprint1.inc | 12 - base/addons/gas/gascompprint2.inc | 122 - base/addons/gas/gascompprint3.inc | 221 - base/addons/gas/gascontracts.inc | 40 - base/addons/gas/gascosts.inc | 71 - base/addons/gas/gasdata.inc | 234 - base/addons/gas/gasdocompare.inc | 96 - base/addons/gas/gasequations.inc | 270 - base/addons/gas/gasfacilities.inc | 327 - base/addons/gas/gasgdx.inc | 14 - base/addons/gas/gasgeninput.inc | 26 - base/addons/gas/gasheating.inc | 2 - base/addons/gas/gasinit.inc | 8 - base/addons/gas/gasinternal.inc | 42 - base/addons/gas/gasmakecompare.inc | 22 - base/addons/gas/gasmkcompare.inc | 76 - base/addons/gas/gasopt.opt | 46 - base/addons/gas/gasproduction.inc | 13 - base/addons/gas/gasregions2areas.inc | 6 - base/addons/gas/gasscaling.inc | 44 - base/addons/gas/gassets.inc | 347 - base/addons/gas/gastech.inc | 402 - base/addons/gas/gastransit.inc | 34 - base/addons/gas/gastransprices.inc | 322 - base/addons/gas/gasvar-MR.inc | 23 - base/addons/gas/gasvar.inc | 44 - base/addons/gas/gasvariables.inc | 42 - base/addons/gas/gentariffs.inc | 0 base/addons/gas/gx3vex.inc | 273 - base/addons/gas/network.inc | 275 - .../addons/hydrogen/bb4/hydrogen_acronyms.inc | 2 - base/addons/hyrsbb123/HYRSBB123sim3.inc | 39 - base/addons/hyrsbb123/bb4/HYRSBB123sim3.inc | 39 - .../hyrsbb123/bb4/hyrsbb123_hellolog.inc | 11 - base/addons/hyrsbb123/bb4/hyrsbb123addobj.inc | 13 - .../hyrsbb123/bb4/hyrsbb123balbase3.inc | 6 - .../hyrsbb123/bb4/hyrsbb123documentation.txt | 106 - .../hyrsbb123/bb4/hyrsbb123equations.inc | 54 - .../bb4/hyrsbb123includebb12data.inc | 20 - .../hyrsbb123/bb4/hyrsbb123internals.inc | 26 - base/addons/hyrsbb123/bb4/hyrsbb123sim1.inc | 17 - base/addons/hyrsbb123/bb4/hyrsbb123sim2.inc | 24 - base/addons/hyrsbb123/bb4/hyrsbb123unload.inc | 14 - .../hyrsbb123/bb4/hyrsbb123variables.inc | 7 - base/addons/hyrsbb123/hyrsbb123_hellolog.inc | 11 - base/addons/hyrsbb123/hyrsbb123addobj.inc | 13 - base/addons/hyrsbb123/hyrsbb123balbase3.inc | 6 - .../hyrsbb123/hyrsbb123documentation.txt | 106 - base/addons/hyrsbb123/hyrsbb123equations.inc | 54 - .../hyrsbb123/hyrsbb123includebb12data.inc | 20 - base/addons/hyrsbb123/hyrsbb123internals.inc | 26 - base/addons/hyrsbb123/hyrsbb123sim1.inc | 17 - base/addons/hyrsbb123/hyrsbb123sim2.inc | 24 - base/addons/hyrsbb123/hyrsbb123unload.inc | 14 - base/addons/hyrsbb123/hyrsbb123variables.inc | 7 - base/addons/policies/Addon vejledening.txt | 30 - base/addons/policies/demodata/POLAREA.inc | 11 - base/addons/policies/demodata/POLAREAGEO.inc | 8 - base/addons/policies/demodata/POLICY.inc | 8 - base/addons/policies/demodata/POLICYTYPE.inc | 12 - base/addons/policies/demodata/POLREQ.inc | 44 - base/addons/policies/demodata/POLTECH.inc | 21 - base/addons/policies/demodata/POLTYP.inc | 15 - base/addons/policies/pol_cost.inc | 12 - base/addons/policies/pol_eq.inc | 162 - base/addons/policies/pol_eqN.inc | 7 - base/addons/policies/pol_err2.inc | 62 - base/addons/policies/pol_sets.inc | 77 - base/addons/policies/pol_var.inc | 11 - base/addons/policies/pol_yearupdate.inc | 4 - .../systemcost/SysCost addon instruction.txt | 14 - base/addons/systemcost/cost_syscost.inc | 8 - .../systemcost/demodata/BASELOADSERVICE.inc | 8 - .../systemcost/demodata/sets_syscost.inc | 7 - base/addons/systemcost/eqN_syscost.inc | 5 - base/addons/systemcost/eq_syscost.inc | 15 - base/addons/systemcost/var_syscost.inc | 8 - base/addons/welfare/welfare.opt | 0 base/addons/welfare/welfarecmp/compprint1.inc | 123 - base/addons/welfare/welfarecmp/compprint3.inc | 411 - base/addons/welfare/welfarecmp/mkcompare.inc | 167 - base/addons/welfare/welfarecmp/welfarebb3.inc | 2 - 128 files changed, 37672 deletions(-) delete mode 100644 base/addons/Fjernvarme/Addon vejledening.txt delete mode 100644 base/addons/Fjernvarme/demodata/data_fv.inc delete mode 100644 base/addons/Fjernvarme/model/cost_fv.inc delete mode 100644 base/addons/Fjernvarme/model/eqN_fv.inc delete mode 100644 base/addons/Fjernvarme/model/eq_fv.inc delete mode 100644 base/addons/Fjernvarme/model/heatbalance_fv.inc delete mode 100644 base/addons/Fjernvarme/model/sets_fv.inc delete mode 100644 base/addons/Fjernvarme/model/unload.inc delete mode 100644 base/addons/Fjernvarme/model/var_fv.inc delete mode 100644 base/addons/Fjernvarme/model/yearupdate_fv.inc delete mode 100644 base/addons/REShareE/RESEeqns.inc delete mode 100644 base/addons/REShareE/RESEintrn.inc delete mode 100644 base/addons/REShareE/demodata/RESEEDATA_demodata1.inc delete mode 100644 base/addons/REShareE/demodata/RESEEDATA_demodata2.inc delete mode 100644 base/addons/REShareE/printinc/RESEPrt4.inc delete mode 100644 base/addons/REShareEH/Comments.txt delete mode 100644 base/addons/REShareEH/RESEHeqns.inc delete mode 100644 base/addons/REShareEH/RESEHintrn.inc delete mode 100644 base/addons/REShareEH/demodata/RESEHdata_demodata.inc delete mode 100644 base/addons/REShareEH/printinc/RESEHPrt4.inc delete mode 100644 base/addons/bb1234compare/bb1234_checkassumptions.inc delete mode 100644 base/addons/bb1234compare/bb1234_hellolog.inc delete mode 100644 base/addons/bb1234compare/bb1234_varassign.inc delete mode 100644 base/addons/bb1234compare/bb1234_vardeclare.inc delete mode 100644 base/addons/gas/DH-MR - foer varmebehandling.inc delete mode 100644 base/addons/gas/DH-MR.inc delete mode 100644 base/addons/gas/Export_GER.INC delete mode 100644 base/addons/gas/GDATA-MR-old.inc delete mode 100644 base/addons/gas/GDATA-MR.inc delete mode 100644 base/addons/gas/GGGMRset.inc delete mode 100644 base/addons/gas/GKFX-MR.inc delete mode 100644 base/addons/gas/GPFX_VAR_T.INC delete mode 100644 base/addons/gas/MRareas.inc delete mode 100644 base/addons/gas/MRregions2areas.inc delete mode 100644 base/addons/gas/SE_DG_VAR_T.INC delete mode 100644 base/addons/gas/andetgasforbrug.inc delete mode 100644 base/addons/gas/annualupdate.inc delete mode 100644 base/addons/gas/dg-MR.inc delete mode 100644 base/addons/gas/dg.inc delete mode 100644 base/addons/gas/ensmpol.inc delete mode 100644 base/addons/gas/flowsets.inc delete mode 100644 base/addons/gas/fuelp.inc delete mode 100644 base/addons/gas/gasbal_1.inc delete mode 100644 base/addons/gas/gasbb1.inc delete mode 100644 base/addons/gas/gasbb2.inc delete mode 100644 base/addons/gas/gasbb3.inc delete mode 100644 base/addons/gas/gascompprint1.inc delete mode 100644 base/addons/gas/gascompprint2.inc delete mode 100644 base/addons/gas/gascompprint3.inc delete mode 100644 base/addons/gas/gascontracts.inc delete mode 100644 base/addons/gas/gascosts.inc delete mode 100644 base/addons/gas/gasdata.inc delete mode 100644 base/addons/gas/gasdocompare.inc delete mode 100644 base/addons/gas/gasequations.inc delete mode 100644 base/addons/gas/gasfacilities.inc delete mode 100644 base/addons/gas/gasgdx.inc delete mode 100644 base/addons/gas/gasgeninput.inc delete mode 100644 base/addons/gas/gasheating.inc delete mode 100644 base/addons/gas/gasinit.inc delete mode 100644 base/addons/gas/gasinternal.inc delete mode 100644 base/addons/gas/gasmakecompare.inc delete mode 100644 base/addons/gas/gasmkcompare.inc delete mode 100644 base/addons/gas/gasopt.opt delete mode 100644 base/addons/gas/gasproduction.inc delete mode 100644 base/addons/gas/gasregions2areas.inc delete mode 100644 base/addons/gas/gasscaling.inc delete mode 100644 base/addons/gas/gassets.inc delete mode 100644 base/addons/gas/gastech.inc delete mode 100644 base/addons/gas/gastransit.inc delete mode 100644 base/addons/gas/gastransprices.inc delete mode 100644 base/addons/gas/gasvar-MR.inc delete mode 100644 base/addons/gas/gasvar.inc delete mode 100644 base/addons/gas/gasvariables.inc delete mode 100644 base/addons/gas/gentariffs.inc delete mode 100644 base/addons/gas/gx3vex.inc delete mode 100644 base/addons/gas/network.inc delete mode 100644 base/addons/hydrogen/bb4/hydrogen_acronyms.inc delete mode 100644 base/addons/hyrsbb123/HYRSBB123sim3.inc delete mode 100644 base/addons/hyrsbb123/bb4/HYRSBB123sim3.inc delete mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123_hellolog.inc delete mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123addobj.inc delete mode 100644 base/addons/hyrsbb123/bb4/hyrsbb123balbase3.inc delete mode 100644 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--- a/base/addons/Fjernvarme/Addon vejledening.txt +++ /dev/null @@ -1,25 +0,0 @@ - -balmorel.gms: - -Internal sets for true addon -$ifi %FV%==yes $include '../../base/addons/fjernvarme/sets_fv.inc'; - -Parameters for true addon -$ifi %FV%==yes $INCLUDE '../../base/addons/fjernvarme/data_fj.inc'; - -Variables for true addon -$ifi %FV%==yes $include '../../base/addons/fjernavrme/var_fv.inc'; - -Objective function contributions -$ifi %FV%==yes $include '../../base/addons/fjernvarme/cost_fv.inc'; - -QHEQ -$ifi %FV%==yes $include '../../base/addons/fjernvarme/heatbalance_fv.inc'; - -Equations for true addon -$ifi %FV%==yes $include '../../base/addons/fjernvarme/eq_fv.inc'; - -Equations -$ifi %FV%==yes $include '../../base/addons/fjernvarme/eqN_fv.inc'; - - diff --git a/base/addons/Fjernvarme/demodata/data_fv.inc b/base/addons/Fjernvarme/demodata/data_fv.inc deleted file mode 100644 index 9e93e215..00000000 --- a/base/addons/Fjernvarme/demodata/data_fv.inc +++ /dev/null @@ -1,183 +0,0 @@ -* File data_fv.inc to the addon "District hearting". - -PARAMETER NETINVESTED(AAA,AAA,STEPP) 'Percentatage of possible expansion performed'; - -$ifi %USENETINVESTED%==yes execute_load '../printout/NETINVESTED.gdx',NETINVESTED=NETINVESTED; - -Table DHFV(AAA,STEPFV) 'Heat demand in extention areas GWh' -* Scenario: 20 pct heat savings - STEPFV1 STEPFV2 STEPFV3 STEPFV4 STEPFV5 STEPFV6 STEPFV7 STEPFV8 STEPFV9 -DK_E_1_IND 0.04750 0.01094 0.05543 0.06216 0.03737 0.03104 0.03541 0.04080 0.06990 -DK_E_2_IND 0.06361 0.03578 0.05736 0.07187 0.04657 0.04195 0.04577 0.06043 0.08407 -DK_E_3_IND 0.00654 0.00706 0.00799 0.00972 0.01225 0.00779 0.00512 0.00820 0.00752 -DK_E_4_IND 0.02412 0.02392 0.02904 0.04372 0.02221 0.02288 0.02927 0.03421 0.05038 -DK_E_5_IND 0.02289 0.02222 0.02006 0.02503 0.01249 0.01369 0.01273 0.01234 0.01821 -DK_E_Rural_IND 0.01124 0.00769 0.01028 0.00730 0.00531 0.00344 0.00316 0.00312 0.00460 -DK_E_Urban_IND 0.57664 0.43643 0.50292 0.20579 0.26225 0.29678 0.33728 0.36011 0.50862 -DK_W_1_IND 0.08336 0.09985 0.19439 0.21012 0.20216 0.18581 0.15126 0.15307 0.18914 -DK_W_2_IND 0.03109 0.01998 0.03903 0.01550 0.04920 0.04447 0.02479 0.02246 0.02230 -DK_W_3_IND 0.01407 0.00613 0.00974 0.00871 0.01011 0.00927 0.00723 0.00555 0.00621 -DK_W_4_IND 0.00187 0.00375 0.00015 0.00446 0.00286 0.00320 0.00220 0.00183 0.00162 -DK_W_5_IND 0.00584 0.01138 0.00970 0.000001 0.03597 0.01647 0.02384 0.01699 0.01361 -DK_W_6_IND 0.00561 0.00460 0.00729 0.00478 0.00826 0.00424 0.00409 0.00395 0.00476 -DK_W_ODENSE_IND 0.09835 0.04973 0.02396 0.03022 0.02682 0.03032 0.03135 0.03504 0.05089 -DK_W_Rural_IND 0.09881 0.09114 0.03798 0.09207 0.07854 0.04839 0.03687 0.04222 0.05377 -DK_W_TREFOR_IND 0.11307 0.05749 0.11527 0.06082 0.04306 0.04493 0.04679 0.04355 0.05418 -DK_W_Urban_IND 0.04070 0.00964 0.01595 0.01024 0.00792 0.01250 0.00793 0.01254 0.00498 -DK_W_AALBORG_IND 0.10008 0.04200 0.02073 0.01624 0.01425 0.00973 0.00970 0.01125 0.01415 -DK_W_AARHUS_IND 0.01870 0.09573 0.01133 0.05277 0.04696 0.01962 0.02094 0.01365 0.01984 -DK_E_PUREDH_IND 0.01375 0.01941 0.02157 0.04151 0.03817 0.02578 0.03013 0.01710 0.01976 -DK_W_PUREDH_IND 0.03976 0.05612 0.06236 0.11999 0.11035 0.07454 0.08709 0.04942 0.05714 - - -PARAMETER HEATPEAK(AAA); - -HEATPEAK(AIND) = DH('2010',AIND)*SMAX((S,T), DH_VAR_T(AIND,S,T))/IDH_SUMST(AIND); - -* Table currently in mDKK06/GWh -Table PHFV(AAA,STEPFV) 'Price of head extention areas' -* These price data are given in mDKK06/GWh/year (converted by assignment below). - STEPFV1 STEPFV2 STEPFV3 STEPFV4 STEPFV5 STEPFV6 STEPFV7 STEPFV8 STEPFV9 -DK_E_1_IND 1.021 1.504 1.988 2.471 2.955 3.438 3.922 4.405 4.889 -DK_E_2_IND 0.833 1.266 1.699 2.131 2.564 2.997 3.430 3.862 4.295 -DK_E_3_IND 0.970 1.287 1.604 1.921 2.238 2.555 2.872 3.189 3.506 -DK_E_4_IND 0.856 1.330 1.804 2.278 2.752 3.226 3.700 4.174 4.649 -DK_E_5_IND 1.109 1.525 1.941 2.357 2.773 3.190 3.606 4.022 4.438 -DK_E_Rural_IND 1.420 1.819 2.218 2.618 3.017 3.416 3.815 4.214 4.613 -DK_E_Urban_IND 1.000 1.655 2.310 2.965 3.620 4.275 4.930 5.586 6.241 -DK_W_1_IND 0.469 0.772 1.076 1.379 1.682 1.985 2.288 2.591 2.894 -DK_W_2_IND 0.445 0.667 0.889 1.111 1.333 1.555 1.776 1.998 2.220 -DK_W_3_IND 0.429 0.663 0.896 1.129 1.363 1.596 1.829 2.063 2.296 -DK_W_4_IND 0.754 0.957 1.161 1.365 1.569 1.772 1.976 2.180 2.384 -DK_W_5_IND 0.306 0.476 0.647 0.817 0.987 1.158 1.328 1.498 1.669 -DK_W_6_IND 0.873 1.164 1.456 1.747 2.039 2.330 2.622 2.913 3.204 -DK_W_ODENSE_IND 1.107 1.668 2.229 2.789 3.350 3.911 4.472 5.032 5.593 -DK_W_Rural_IND 0.453 0.694 0.934 1.175 1.415 1.656 1.896 2.136 2.377 -DK_W_TREFOR_IND 0.731 1.107 1.483 1.858 2.234 2.610 2.986 3.361 3.737 -DK_W_Urban_IND 0.841 0.987 1.134 1.280 1.427 1.574 1.720 1.867 2.013 -DK_W_AALBORG_IND 0.866 1.147 1.428 1.710 1.991 2.272 2.554 2.835 3.116 -DK_W_AARHUS_IND 0.964 1.217 1.471 1.724 1.978 2.231 2.485 2.738 2.992 -DK_E_PUREDH_IND 0.616 0.890 1.163 1.437 1.711 1.985 2.259 2.533 2.807 -DK_W_PUREDH_IND 0.616 0.890 1.163 1.437 1.711 1.985 2.259 2.533 2.807 - -* Conversion from mDKK06/GWh/year to EUR90/MWh/year. -PHFV(AIND,STEPFV)=PHFV(AIND,STEPFV)/10.42*1000000/1000; - - -TABLE DHCD(AAA,TECH) - NAT OLIE -DK_E_1_IND 214351 250684 -DK_E_2_IND 335212 313807 -DK_E_3_IND 15533 68172 -DK_E_4_IND 207404 167164 -DK_E_5_IND 53036 127590 -DK_E_Rural_IND 10865 39420 -DK_E_Urban_IND 2377965 2301916 -DK_W_1_IND 727438 1045574 -DK_W_2_IND 110752 214327 -DK_W_3_IND 46598 48468 -DK_W_4_IND 1999 19571 -DK_W_5_IND 26147 114057 -DK_W_6_IND 31083 25226 -DK_W_ODENSE_IND 211753 189870 -DK_W_Rural_IND 309799 367115 -DK_W_TREFOR_IND 340962 337724 -DK_W_Urban_IND 10257 80602 -DK_W_AALBORG_IND 68486 129430 -DK_W_AARHUS_IND 35172 255720 - -DK_E_IND 564 11212 -DK_W_IND 5680 18794 -DK_E_PUREDH_IND 249 1133 -DK_W_PUREDH_IND 291 194 -; - -Parameter HPRATIO(YYY)/ -2010 0 -2010 0 -2011 0.07 -2012 0.14 -2013 0.21 -2014 0.28 -2015 0.35 -2016 0.42 -2017 0.49 -2018 0.56 -2019 0.63 -2020 0.7 -2021 0.73 -2022 0.76 -2023 0.79 -2024 0.82 -2025 0.85 -2026 0.88 -2027 0.91 -2028 0.94 -2029 0.97 -2030 1 -2031 1 -2032 1 -2033 1 -2034 1 -2035 1 -2036 1 -2037 1 -2038 1 -2039 1 -2040 1 -2041 1 -2042 1 -2043 1 -2044 1 -2045 1 -2046 1 -2047 1 -2048 1 -2049 1 -2050 1 -/; - -PARAMETER HEATGROWTH(AAA,YYY); - -HEATGROWTH(AIND,YYY)=DH(YYY,AIND)/DH('2010',AIND); - -PARAMETER DHCD_SUM(AAA); - -DHCD_SUM(AIND) = SUM(TECH, DHCD(AIND,TECH)); - - -GKFX('2010',AIND,'HO-IND1-NG')$DHCD_SUM(AIND) = DHCD(AIND,'NAT')/DHCD_SUM(AIND)*HEATPEAK(AIND); -GKFX('2010',AIND,'HO-IND1-LO')$DHCD_SUM(AIND) = DHCD(AIND,'OLIE')/DHCD_SUM(AIND)*HEATPEAK(AIND); - -*GKFX(YYY,AIND,G)$(YVALUE(YYY) > 2010 and GKFX('2010',AIND,G)) = max(0, GKFX('2010',AIND,G)-(YVALUE(YYY)-YVALUE('2010'))*GKFX('2010',AIND,G)/GDATA(G,'GDLIFETIME') ); -GKFX(YYY,AIND,G)$(YVALUE(YYY) > 2010 and GKFX('2010',AIND,G)) = GKFX('2010',AIND,G)*(1- HPRATIO(YYY))*HEATGROWTH(AIND,YYY); -GKFX(YYY,AIND,'EH-IND1-GV')$(YVALUE(YYY) > 2010 ) = HPRATIO(YYY)*HEATGROWTH(AIND,YYY)*HEATPEAK(AIND); - - -FKPOT(AIND,'NAT_GAS') = GKFX('2008',AIND,'HO-IND1-NG'); - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/base/addons/Fjernvarme/model/cost_fv.inc b/base/addons/Fjernvarme/model/cost_fv.inc deleted file mode 100644 index 492cba91..00000000 --- a/base/addons/Fjernvarme/model/cost_fv.inc +++ /dev/null @@ -1,7 +0,0 @@ - -* The price of extenstions of heat are added to the objective -+SUM((IA,AIND,STEPP)$AINDN(IA,AIND), PHFV(AIND,STEPP)*VIHFV(IA,AIND,STEPP)*SUM(C$ICA(C,IA), ANNUITYC(C)) ) - -* Penalty are added if constraints are violated -+SUM((IA,IGH,S,T,IPLUSMINUS)$(AIND(IA) and IAGK_Y(IA,IGH)),VQGH_IND(IA,IGH,S,T,IPLUSMINUS))*PENALTYQ/2 -+SUM((IA,IGH,S,T,IPLUSMINUS)$(AIND(IA) and IAGKN(IA,IGH)),VQGHN_IND(IA,IGH,S,T,IPLUSMINUS))*PENALTYQ/2 diff --git a/base/addons/Fjernvarme/model/eqN_fv.inc b/base/addons/Fjernvarme/model/eqN_fv.inc deleted file mode 100644 index f50fed4f..00000000 --- a/base/addons/Fjernvarme/model/eqN_fv.inc +++ /dev/null @@ -1,5 +0,0 @@ -* Names of equations regarding district heating - -QDHFV -QGH_IND -QGHN_IND diff --git a/base/addons/Fjernvarme/model/eq_fv.inc b/base/addons/Fjernvarme/model/eq_fv.inc deleted file mode 100644 index 884f8517..00000000 --- a/base/addons/Fjernvarme/model/eq_fv.inc +++ /dev/null @@ -1,36 +0,0 @@ -* Definitions of equations regarding district heating - -Equations - QDHFV(AAA,IAAAI,S,T) 'Transmission into adjacent area limited by invested capacity (MW)' - QGH_IND(AAA,G,S,T) 'Generation on individual technologies must follow demand (MW)' - QGHN_IND(AAA,G,S,T) 'Generation on individual new technologies must follow demand (MW)' -; - -* Setting production by transmissioncapacity and demand variation -* Consider making this an less-than-constraint -QDHFV(IA,IAI,IS3,T)$AINDN(IA,IAI) .. - VDHFV(IA,IAI,IS3,T) - =L= - SUM(STEPP,VIHFV(IA,IAI,STEPP))*DH_VAR_T(IAI,IS3,T)/IDH_SUMST(IAI) -; - -QGH_IND(IA,IGH,IS3,T)$(AIND(IA) and IAGK_Y(IA,IGH) and (GDATA(IGH,'GDCOMB') < 2)) .. - VGH_IND(IA,IGH)*DH_VAR_T(IA,IS3,T)/IDH_SUMST(IA) -* Slack and surplus - +VQGH_IND(IA,IGH,IS3,T,'IPLUS') - -VQGH_IND(IA,IGH,IS3,T,'IMINUS') - =E= - VGH_T(IA,IGH,IS3,T) -$ifi %COMBINATION%==yes + SUM(IGCOMB2$GGCOMB(IGH,IGCOMB2), VGH_T(IA,IGCOMB2,IS3,T)) $ IGCOMB1(IGH) -; - -QGHN_IND(IA,IGH,IS3,T)$(AIND(IA) and IAGKN(IA,IGH) and (GDATA(IGH,'GDCOMB') < 2)) .. - VGHN_IND(IA,IGH)*DH_VAR_T(IA,IS3,T)/IDH_SUMST(IA) -* Slack and surplus - +VQGHN_IND(IA,IGH,IS3,T,'IPLUS') - -VQGHN_IND(IA,IGH,IS3,T,'IMINUS') - =E= - VGHN_T(IA,IGH,IS3,T) -$ifi %COMBINATION%==yes + SUM(IGCOMB2$GGCOMB(IGH,IGCOMB2), VGHN_T(IA,IGCOMB2,IS3,T)) $ IGCOMB1(IGH) -; - diff --git a/base/addons/Fjernvarme/model/heatbalance_fv.inc b/base/addons/Fjernvarme/model/heatbalance_fv.inc deleted file mode 100644 index 83b2919a..00000000 --- a/base/addons/Fjernvarme/model/heatbalance_fv.inc +++ /dev/null @@ -1,4 +0,0 @@ -* Additional heat production from technoligies in these areas are to be added to the heat balance equation - -+(SUM(IAE$AINDN(IA,IAE), VDHFV(IA,IAE,IS3,T))/(1-DISLOSS_H(IA)) )$(not AIND(IA)) --(SUM(IAE$AINDN(IAE,IA), VDHFV(IAE,IA,IS3,T)) )$AIND(IA) diff --git a/base/addons/Fjernvarme/model/sets_fv.inc b/base/addons/Fjernvarme/model/sets_fv.inc deleted file mode 100644 index ca0ac0fc..00000000 --- a/base/addons/Fjernvarme/model/sets_fv.inc +++ /dev/null @@ -1,47 +0,0 @@ -Sets regarding district heating - -Set AINDN(AAA,IAAAI) 'Nearest area for areas with possible extenstions on heat' -/ - DK_E_1. DK_E_1_IND - DK_E_2. DK_E_2_IND - DK_E_3. DK_E_3_IND - DK_E_4. DK_E_4_IND - DK_E_5. DK_E_5_IND - - DK_W_1. DK_W_1_IND - DK_W_2. DK_W_2_IND - DK_W_3. DK_W_3_IND - DK_W_4. DK_W_4_IND - DK_W_5. DK_W_5_IND - DK_W_6. DK_W_6_IND - - DK_W_AALBORG. DK_W_AALBORG_IND - DK_W_AARHUS. DK_W_AARHUS_IND - DK_W_TREFOR. DK_W_TREFOR_IND - DK_W_ODENSE. DK_W_ODENSE_IND - - DK_E_Urban. DK_E_Urban_IND - DK_E_Rural. DK_E_Rural_IND - DK_W_Urban. DK_W_Urban_IND - DK_W_Rural. DK_W_Rural_IND - - DK_E_PUREDH. DK_E_PUREDH_IND - DK_W_PUREDH. DK_W_PUREDH_IND - -/; - -$ifi %IND%==yes AINDN(AAA,IAAAI) = no; - -Set STEPFV 'Steps in head exstentions' /STEPFV1*STEPFV10/; - -Set STEPP(STEPFV) 'Steps for price of demand' / -STEPFV1*STEPFV9 -/; - -Set STEPD(STEPFV) 'Steps for demand' / -STEPFV1*STEPFV9 -/; - -set tech / nat,olie/; - -alias(techalias,tech); diff --git a/base/addons/Fjernvarme/model/unload.inc b/base/addons/Fjernvarme/model/unload.inc deleted file mode 100644 index 243bceb7..00000000 --- a/base/addons/Fjernvarme/model/unload.inc +++ /dev/null @@ -1,6 +0,0 @@ - -$ifi %IND%==yes $goto skip_netinvested -$ifi not %USENETINVESTED%==yes NETINVESTED(IA,IAI,STEPP)$(AINDN(IA,IAI) and VIHFV.up(IA,IAI,STEPP) and (VIHFV.up(IA,IAI,STEPP) < inf)) = VIHFV.l(IA,IAI,STEPP)/VIHFV.up(IA,IAI,STEPP); -$ifi not %USENETINVESTED%==yes DISPLAY NETINVESTED; -$ifi not %USENETINVESTED%==yes execute_unload '../printout/NETINVESTED.gdx', NETINVESTED; -$label skip_netinvested \ No newline at end of file diff --git a/base/addons/Fjernvarme/model/var_fv.inc b/base/addons/Fjernvarme/model/var_fv.inc deleted file mode 100644 index 712b1d73..00000000 --- a/base/addons/Fjernvarme/model/var_fv.inc +++ /dev/null @@ -1,13 +0,0 @@ -* Variables regarding district heating - -Positive Variables - VIHFV(AAA,IAAAI,STEPFV) 'Extenstion of heat in each step' - VDHFV(AAA,IAAAI,S,T) 'Production of heat in time period' - VGH_IND(AAA,G) 'Level of production on individual technologies' - VQGH_IND(AAA,G,S,T,IPLUSMINUS) 'Penalty on diviation from profile generation' - VGHN_IND(AAA,G) 'Level of production on individual technologies' - VQGHN_IND(AAA,G,S,T,IPLUSMINUS) 'Penalty on diviation from profile generation' -; - -* Initialize. -VIHFV.l(IA,AIND,STEPP)$AINDN(IA,AIND) =0; diff --git a/base/addons/Fjernvarme/model/yearupdate_fv.inc b/base/addons/Fjernvarme/model/yearupdate_fv.inc deleted file mode 100644 index 77965cc2..00000000 --- a/base/addons/Fjernvarme/model/yearupdate_fv.inc +++ /dev/null @@ -1,9 +0,0 @@ -* Alternative: $ (VIHFV.l(AFV,STEPP) = VIHFV.up(AFV,STEPP)); -* Do something else with: $ (VIHFV.l(AFV,STEPP) > VIHFV.l(AFV,STEPP); -* Perhaps better alternative: VIHFV.u(AFV,STEPP) = VIHFV.u(AFV,STEPP) - VIHFV.l(AFV,STEPP); -* - but still make sure that the extra investment is utilized in balance eqn (could be looking for VIHF's with ub = 0) - -$ifi not %IND%==yes VIHFV.up(IA,IAI,STEPP)$AINDN(IA,IAI) = DHFV(IAI,STEPP); -$ifi not %IND%==yes VIHFV.lo(IA,IAI,STEPP)$AINDN(IA,IAI) = VIHFV.l(IA,IAI,STEPP); - -$ifi %USENETINVESTED%==yes VIHFV.fx(IA,IAI,STEPP)$AINDN(IA,IAI) = max(0,(YVALUE(Y)-2012)/(2025-2012)*VIHFV.up(IA,IAI,STEPP)*NETINVESTED(IA,IAI,STEPP)*DH(Y,IAI)/DH('2025',IAI)); \ No newline at end of file diff --git a/base/addons/REShareE/RESEeqns.inc b/base/addons/REShareE/RESEeqns.inc deleted file mode 100644 index 378da747..00000000 --- a/base/addons/REShareE/RESEeqns.inc +++ /dev/null @@ -1,16 +0,0 @@ -* File RESEeqns.inc to the addon REShareE. - - -* Equation QRESHAREE is with inspiration from equation QEEQ: -QRESHAREE(CCCRESHAREE).. - -SUM(C$CCCRESHAREEC(CCCRESHAREE,C), - SUM(IR$CCCRRR(C,IR), - SUM((IS3,T), - IHOURSINST(IS3,T)* - (SUM(IAGK_Y(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREE(IGE)), VGE_T(IA,IGE,IS3,T) ) - + SUM(IAGKN(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREE(IGE)), VGEN_T(IA,IGE,IS3,T))) ) - -)) - -=G= RESHAREE(CCCRESHAREE)* SUM(C$CCCRESHAREEC(CCCRESHAREE,C), SUM(IR$CCCRRR(C,IR), DE_Y(IR))); diff --git a/base/addons/REShareE/RESEintrn.inc b/base/addons/REShareE/RESEintrn.inc deleted file mode 100644 index 686c06ca..00000000 --- a/base/addons/REShareE/RESEintrn.inc +++ /dev/null @@ -1,2 +0,0 @@ -* -PARAMETER DE_Y(RRR) 'Nominal electricity demand this year (MWh)'; diff --git a/base/addons/REShareE/demodata/RESEEDATA_demodata1.inc b/base/addons/REShareE/demodata/RESEEDATA_demodata1.inc deleted file mode 100644 index ee254a4d..00000000 --- a/base/addons/REShareE/demodata/RESEEDATA_demodata1.inc +++ /dev/null @@ -1,96 +0,0 @@ -* File RESEEDATA_demodata1.inc to the Balmorel addon REShareE. - -* The data given here will probably not be valid for your model due to inconsistencies with other data, -* however, this file demonstrates the idea. - -* The data file with real data is supposed to be located in the data folder, like any other input data file. - - -SET GREE(GGG) 'Generation technologies qualified as renewable electricity technologies' -/ - -ST-B9-MW -ST-B9-ST -ST-B9-WO -ST-B9-WW -ST-E0-WOsn -ST-E0-WWsn -ST-E1-WOsn -ST-B1-MWn -ST-B1-STn -ST-B1-WOn -ST-B1-WWn -CC-C1-WOn -CC-E1-WOn - HY-S01 - HY-S02 - HY-S03 - HY-S04 - HY-S05 - HY-S06 - HY-S07 - HY-S08 - HY-S09 - HY-S10 - HY-R -WI-L9 -WI-S9 -WI-L0 -WI-S0 -WI-L1 -WI-S1 -HO-B8-MW -HO-B8-ST -HO-B8-WO -HO-B0-MW -HO-B0-ST -HO-B0-WO -ST-E8-WW -ST-E9-WO -ST-B8-WW -ST-E8-ST -DK_W_CHP3 -DK_W_CHP4 -DK_E_CHP3 -DK_E_CHP4 - -/; - - - -SET GREEH(GGG) 'Generation technologies qualified as renewable electricity and heat technologies' ; - -GREEH(GGG)$IGF(GGG,'FUEL_WIND') = yes; -GREEH(GGG)$IGF(GGG,'FUEL_MUNIWASTE') = yes; -GREEH(GGG)$IGF(GGG,'FUEL_STRAW') = yes; -GREEH(GGG)$IGF(GGG,'FUEL_WOOD') = yes; -GREEH(GGG)$IGF(GGG,'FUEL_WIND') = yes; -GREEH(GGG)$IGF(GGG,'FUEL_WATER)' = yes; -GREEH(GGG)$IGF(GGG,'FUEL_SUN') = yes; -GREEH(GGG)$IGF(GGG,'FUEL_WIND') = yes; - - - - -SET CCCRESHAREE 'Group of countries with a common renewable electricity share goal' -/ - CCCRESHAREE_NORDIC - -/; - - - -SET CCCRESHAREEC(CCCRESHAREE,CCC) 'Countries within group with common renewable electricity share goal' -/ - CCCRESHAREE_NORDIC. DENMARK - CCCRESHAREE_NORDIC. Norway - CCCRESHAREE_NORDIC. Sweden - CCCRESHAREE_NORDIC. FINLAND -/; - - - -PARAMETER RESHAREE(CCCRESHAREE) 'Share of renewable electricity (fraction)' -/ - CCCRESHAREE_NORDIC 0.0 -/; diff --git a/base/addons/REShareE/demodata/RESEEDATA_demodata2.inc b/base/addons/REShareE/demodata/RESEEDATA_demodata2.inc deleted file mode 100644 index 9f690251..00000000 --- a/base/addons/REShareE/demodata/RESEEDATA_demodata2.inc +++ /dev/null @@ -1,59 +0,0 @@ -* File RESEdata.inc to the addon REShareEH. - -*$ontext -SET GREE(GGG) 'Generation technologies qualified as renewable electricity technologies' -/ - -ST-B9-MW -ST-B9-WW - HY-S01 - HY-S02 - HY-S03 - HY-S04 - HY-S05 - HY-S06 - HY-S07 - HY-S08 - HY-S09 - HY-S10 - -WI-L9 - -/; - - - -SET GREEH(GGG) 'Generation technologies qualified as renewable electricity and heat technologies' ; - -GREEH(GGG)$IGF(GGG,'WIND') = yes; -GREEH(GGG)$IGF(GGG,'STRAW') = yes; -GREEH(GGG)$IGF(GGG,'WOOD') = yes; -GREEH(GGG)$IGF(GGG,'WIND') = yes; -GREEH(GGG)$IGF(GGG,'WATER') = yes; -GREEH(GGG)$IGF(GGG,'SUN') = yes; -*$OFFTEXT - - - -SET CCCRESHAREE 'Group of countries with a common renewable electricity share goal' -/ - CCCRESHAREE_NORDIC - -/; - - - -SET CCCRESHAREEC(CCCRESHAREE,CCC) 'Countries within group with common renewable electricity share goal' -/ - CCCRESHAREE_NORDIC. DENMARK - CCCRESHAREE_NORDIC. Norway - CCCRESHAREE_NORDIC. Sweden - CCCRESHAREE_NORDIC. FINLAND -/; - - - -PARAMETER RESHAREE(CCCRESHAREE) 'Share of renewable electricity (fraction)' -/ - CCCRESHAREE_NORDIC 0.0 -/; diff --git a/base/addons/REShareE/printinc/RESEPrt4.inc b/base/addons/REShareE/printinc/RESEPrt4.inc deleted file mode 100644 index ff865b28..00000000 --- a/base/addons/REShareE/printinc/RESEPrt4.inc +++ /dev/null @@ -1,55 +0,0 @@ -* File RESEPrt4.inc to the addon REShareE. -* Hans Ravn 2007/8. - - -* ----------------------------------------------------------------------------- - -PUT REShareEPrt4; - -* First print headings: - -if ((REShareEPrt4.pw LT 32000), -REShareEPrt4.pw = 32000; -PUT "Version: " SYSTEM.TITLE /; -PUT "File name: RESEprt4.out" /; -PUT "File created: " SYSTEM.DATE " " SYSTEM.TIME /; -PUT "Case-identification: " OCASEID.TS //; - -PUT "Output related to Add-on REShareE" / ; -PUT "Control settings:"/; -PUT "$Setglobal bb1 %bb1%" /; -PUT "$Setglobal bb2 %bb2%" /; -PUT "$Setglobal REShareE %REShareE%" /; -put /; - -); - -* End of headings. - -* ----------------------------------------------------------------------------- - -PUT "Year " Y.tl/; - -PUT " Marginal cost (price) of the REShareE equation: "/; -LOOP(CCCRESHAREE, -PUT " Group " CCCRESHAREE.tl:20 ": Required share " RESHAREE(CCCRESHAREE):5:2 ", marginal cost " (QRESHAREE.m(CCCRESHAREE)/IOF0000001) " Money/MWh, Group consists of countries "; -LOOP(C$CCCRESHAREEC(CCCRESHAREE,C), PUT c.TL); PUT /; -ISCALAR1= /* Cf. the expression in the equation QRESHAREE */ -SUM(C$CCCRESHAREEC(CCCRESHAREE,C), - SUM(IR$CCCRRR(C,IR), - SUM((IS3,T), - IHOURSINST(IS3,T)* - (SUM(IAGK_Y(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREE(IGE)), VGE_T.L(IA,IGE,IS3,T) ) - + SUM(IAGKN(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREE(IGE)), VGEN_T.L(IA,IGE,IS3,T))) ) - -)); -ISCALAR2=SUM(C$CCCRESHAREEC(CCCRESHAREE,C), SUM(IR$CCCRRR(C,IR), DE_Y(IR))); /* Cf. the expression in the equation QRESHAREE */ - -PUT " Prod: " (ISCALAR1 * IOF0001):10:0 " GWh, demand " (ISCALAR2 * IOF0001):10:0 " GWh" /; - -); - - -* Tilføjelser, der er eksperimentelle, og nok ender et andet sted: - -* Fuel use for \ No newline at end of file diff --git a/base/addons/REShareEH/Comments.txt b/base/addons/REShareEH/Comments.txt deleted file mode 100644 index 81241f9e..00000000 --- a/base/addons/REShareEH/Comments.txt +++ /dev/null @@ -1,4 +0,0 @@ -Comment to RESEH, Hans Ravn, 20080603 - -1. The factor IOF1_ in file RESEHPrt4.inc may have to be replaced by IOF1000000. -2. \ No newline at end of file diff --git a/base/addons/REShareEH/RESEHeqns.inc b/base/addons/REShareEH/RESEHeqns.inc deleted file mode 100644 index b8fd1d00..00000000 --- a/base/addons/REShareEH/RESEHeqns.inc +++ /dev/null @@ -1,52 +0,0 @@ -* File equations.inc to the addon REShareEH. - - -* Equation QRESHAREEH is with inspiration from equations QEEQ and QHEQ: -QRESHAREEH(CCCRESHAREEH).. - -SUM(C$CCCRESHAREEHC(CCCRESHAREEH,C), -* SUM(IR$CCCRRR(C,IR), - SUM((IS3,T), - IHOURSINST(IS3,T)* - ( - SUM(IAGK_Y(IA,IGE)$(ICA(C,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGE_T(IA,IGE,IS3,T) ) - - SUM(IA$ICA(C,IA), VESTOLOADT(IA,IS3,T)) - + SUM(IAGKN(IA,IGE)$(ICA(C,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGEN_T(IA,IGE,IS3,T)) -* NEW? - + SUM(IAGK_Y(IA,IGH)$(ICA(C,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T(IA,IGH,IS3,T) ) - - SUM(IA$ICA(C,IA), VHSTOLOADT(IA,IS3,T)) - + SUM(IAGKN(IA,IGH)$(ICA(C,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T(IA,IGH,IS3,T)) -* NEW? - ) - -/* This (outcommneted) expresses fuel use: - ( - SUM(IAGK_Y(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGE_T(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE') * (1-GEFFDERATE(IA,IGE))) ) - + SUM(IAGKN(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGEN_T(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IA,IGE))) ) - + SUM(IAGK_Y(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T(IA,IGH,IS3,T)/(GDATA(IGH,'GDFE') * (1-GEFFDERATE(IA,IGH))) ) - + SUM(IAGKN(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T(IA,IGH,IS3,T)/(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IA,IGH))) ) - ) -*/ - ) -) - -=G= -* Distribution and transmission loss? -RESHAREEH(CCCRESHAREEH)* SUM(C$CCCRESHAREEHC(CCCRESHAREEH,C), SUM(IR$CCCRRR(C,IR), DE_Y(IR)) + SUM(IA$ICA(C,IA), DH_Y(IA)) ); - -/* -*(271569 )*1000; -271669*1000 -$ifi %NEPcase%==6 + (37*1000000) -$ifi %NEPcase%==12 + (74*1000000) -$ifi %NEPcase%==18 + (111*1000000) - -1000000000 -*/ - -; - -/* -Tillægene 6, 12, 18% giver hhv 37, 74, 111 TWh. -Jeg tror, aftalen var at dette skulle gælde for 2020. -Det kunne så være rimeligt med en lineær interpolation, f.eks. 1/3 i 2010, 1/1 i 2020, 4/3 i 2030? -*/ diff --git a/base/addons/REShareEH/RESEHintrn.inc b/base/addons/REShareEH/RESEHintrn.inc deleted file mode 100644 index d21a4a76..00000000 --- a/base/addons/REShareEH/RESEHintrn.inc +++ /dev/null @@ -1,3 +0,0 @@ -* -PARAMETER DE_Y(RRR) 'Nominal electricity demand this year (MWh)'; -PARAMETER DH_Y(AAA) 'Nominal heat demand this year (MWh)'; \ No newline at end of file diff --git a/base/addons/REShareEH/demodata/RESEHdata_demodata.inc b/base/addons/REShareEH/demodata/RESEHdata_demodata.inc deleted file mode 100644 index f89d4020..00000000 --- a/base/addons/REShareEH/demodata/RESEHdata_demodata.inc +++ /dev/null @@ -1,93 +0,0 @@ -* File data.inc to the addon REShareEH. - -SET GREE(GGG) 'Generation technologies qualified as renewable electricity technologies' -/ - -ST-B9-MW -ST-B9-ST -ST-B9-WO -ST-B9-WW -ST-E0-WOsn -ST-E0-WWsn -ST-E1-WOsn -ST-B1-MWn -ST-B1-STn -ST-B1-WOn -ST-B1-WWn -CC-C1-WOn -CC-E1-WOn - HY-S01 - HY-S02 - HY-S03 - HY-S04 - HY-S05 - HY-S06 - HY-S07 - HY-S08 - HY-S09 - HY-S10 - HY-R -WI-L9 -WI-S9 -WI-L0 -WI-S0 -WI-L1 -WI-S1 -HO-B8-MW -HO-B8-ST -HO-B8-WO -HO-B0-MW -HO-B0-ST -HO-B0-WO -ST-E8-WW -ST-E9-WO -ST-B8-WW -ST-E8-ST -DK_W_CHP3 -DK_W_CHP4 -DK_E_CHP3 -DK_E_CHP4 - -/; - - - -SET GREEH(GGG) 'Generation technologies qualified as renewable electricity and heat technologies' ; - -GREEH(GGG)$IGF(GGG,'FUEL_WIND') = yes; -GREEH(GGG)$IGF(GGG,'FUEL_MUNIWASTE') = yes; -GREEH(GGG)$IGF(GGG,'FUEL_STRAW') = yes; -GREEH(GGG)$IGF(GGG,'FUEL_WOOD') = yes; -GREEH(GGG)$IGF(GGG,'FUEL_WIND') = yes; -GREEH(GGG)$IGF(GGG,'FUEL_WATER)' = yes; -GREEH(GGG)$IGF(GGG,'FUEL_SUN') = yes; -GREEH(GGG)$IGF(GGG,'FUEL_WIND') = yes; - - - - - - -SET CCCRESHAREEH 'Group of countries with a common renewable electricity and heat share goal' -/ - CCCRESHAREEH_NORDIC -/; - - - - -SET CCCRESHAREEHC(CCCRESHAREEH,CCC) 'Countries within group with common renewable electricity share goal' -/ - CCCRESHAREEH_NORDIC. DENMARK - CCCRESHAREEH_NORDIC. Norway - CCCRESHAREEH_NORDIC. Sweden - CCCRESHAREEH_NORDIC. FINLAND - -/; - - - -PARAMETER RESHAREEH(CCCRESHAREEH) 'Share of renewable electricity (fraction)' -/ - CCCRESHAREEH_NORDIC 0.5 -/; \ No newline at end of file diff --git a/base/addons/REShareEH/printinc/RESEHPrt4.inc b/base/addons/REShareEH/printinc/RESEHPrt4.inc deleted file mode 100644 index cc37014f..00000000 --- a/base/addons/REShareEH/printinc/RESEHPrt4.inc +++ /dev/null @@ -1,120 +0,0 @@ -* File RESEHPrt4.inc to the addon REShareEH. -* Hans Ravn 2007/8. - - - -* ----------------------------------------------------------------------------- - -PUT REShareEHPrt4; - -* First print headings: - -if ((REShareEHPrt4.pw LT 32000), -REShareEHPrt4.pw = 32000; -PUT "Version: " SYSTEM.TITLE /; -PUT "File name: RESEHprt4.out" /; -PUT "File created: " SYSTEM.DATE " " SYSTEM.TIME /; -PUT "Case-identification: " OCASEID.TS //; - -PUT "Output related to Add-on REShareEH" / ; -PUT "Control settings:"/; -PUT "$Setglobal bb1 %bb1%" /; -PUT "$Setglobal bb2 %bb2%" /; -PUT "$Setglobal REShareEH %REShareEH%" /; -put /; - - - -* Here we also print some info on technologies: -PUT / / "Technologies qualified as producing renewable electricity and heat, and its fuel: "/ /; -LOOP(GREEH, -PUT GREEH.TL:OTLG; -LOOP(FFF$IGF(GREEH,FFF), PUT FFF.TL; - - - - ); PUT /; -); - -put / / ; - -); -* End of headings. - -* ----------------------------------------------------------------------------- - -PUT "Year " Y.tl/; - -PUT " Marginal cost (price) of the REShareEH equation: "/; -LOOP(CCCRESHAREEH, -PUT " Group " CCCRESHAREEH.tl:20 ": Required share: " RESHAREEH(CCCRESHAREEH):5:2 ", marginal cost: " (QRESHAREEH.m(CCCRESHAREEH)/IOF1_) " Money/MWh,"; -PUT " Group consists of countries: "; LOOP(C$CCCRESHAREEHC(CCCRESHAREEH,C), PUT C.TL); PUT /; -ISCALAR1= /* Electricity production */ /* Cf. the expression in the equation QRESHAREEH */ -SUM(C$CCCRESHAREEHC(CCCRESHAREEH,C), -* SUM(IR$CCCRRR(C,IR), - SUM((IS3,T), - IHOURSINST(IS3,T)* - ( - SUM(IAGK_Y(IA,IGE)$(ICA(C,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGE_T.L(IA,IGE,IS3,T) ) - - SUM(IA$ICA(C,IA), VESTOLOADT.L(IA,IS3,T)) - + SUM(IAGKN(IA,IGE)$(ICA(C,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGEN_T.L(IA,IGE,IS3,T)) -* NEW ESTO? -* + SUM(IAGK_Y(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T.L(IA,IGH,IS3,T) ) -* + SUM(IAGKN(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T.L(IA,IGH,IS3,T)) - ) - /* - ( - SUM(IAGK_Y(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGE_T.L(IA,IGE,IS3,T) /(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IA,IGE))) ) - + SUM(IAGKN(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGEN_T.L(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IA,IGE))) ) -* + SUM(IAGK_Y(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T.L(IA,IGH,IS3,T) /(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IA,IGH))) ) -* + SUM(IAGKN(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T.L(IA,IGH,IS3,T)/(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IA,IGH))) ) - ) - */ -)); - -ISCALAR2= /* Heat production */ /* Cf. the expression in the equation QRESHAREEH */ -SUM(C$CCCRESHAREEHC(CCCRESHAREEH,C), - SUM((IS3,T), - IHOURSINST(IS3,T)* - ( -* SUM(IAGK_Y(IA,IGE)$(ICA(C,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGE_T.L(IA,IGE,IS3,T) ) -* + SUM(IAGKN(IA,IGE)$(ICA(C,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGEN_T.L(IA,IGE,IS3,T)) - + SUM(IAGK_Y(IA,IGH)$(ICA(C,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T.L(IA,IGH,IS3,T) ) - - SUM(IA$ICA(C,IA), VHSTOLOADT.L(IA,IS3,T)) - + SUM(IAGKN(IA,IGH)$(ICA(C,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T.L(IA,IGH,IS3,T)) -* NEW? VHSTOLOADT.L ? - - ) - - /* - ( -* SUM(IAGK_Y(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGE_T.L(IA,IGE,IS3,T) /(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IA,IGE))) ) -* + SUM(IAGKN(IA,IGE)$(RRRAAA(IR,IA) AND IGNOTETOH(IGE) AND GREEH(IGE)), VGEN_T.L(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IA,IGE))) ) - + SUM(IAGK_Y(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T.L(IA,IGH,IS3,T) /(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IA,IGH))) ) - + SUM(IAGKN(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T.L(IA,IGH,IS3,T)/(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IA,IGH))) ) - ) - */ - - -)); - - -ISCALAR3=SUM(C$CCCRESHAREEHC(CCCRESHAREEH,C), SUM(IR$CCCRRR(C,IR), DE_Y(IR)) ); /* Cf. the expression in the equation QRESHAREEH */ -ISCALAR4=SUM(C$CCCRESHAREEHC(CCCRESHAREEH,C), SUM(IA$ICA(C,IA), DH_Y(IA)) ); /* Cf. the expression in the equation QRESHAREEH */ - - -PUT " Renewable production (GWh): " ((ISCALAR1+ISCALAR2)*IOF0001):10:0 " of which: electricity: "(ISCALAR1*IOF0001):10:0 " and heat: " (ISCALAR2*IOF0001):10:0 /; -PUT " Nominal demand: (GWh): " ((ISCALAR3+ISCALAR4)*IOF0001):10:0 " of which: electricity: "(ISCALAR3*IOF0001):10:0 " and heat: " (ISCALAR4*IOF0001):10:0 /; - -); - - /* -iscalar1= sum(c,sum(ia$ica(c,ia), SUM((IS3,T), - IHOURSINST(IS3,T)* ( - + SUM(IAGK_Y(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGH_T.L(IA,IGH,IS3,T) ) - + SUM(IAGKN(IA,IGH)$(RRRAAA(IR,IA) AND IGNOTETOH(IGH) AND GREEH(IGH)), VGHN_T.L(IA,IGH,IS3,T)) - - ) -))); -put "varmeialt: " iscalar1; -*/ diff --git a/base/addons/bb1234compare/bb1234_checkassumptions.inc b/base/addons/bb1234compare/bb1234_checkassumptions.inc deleted file mode 100644 index 1555f6e4..00000000 --- a/base/addons/bb1234compare/bb1234_checkassumptions.inc +++ /dev/null @@ -1,9 +0,0 @@ -* File for addon testbb1234 for the Balmorel model - -* Addon testbb1234 compares results from Balbase1 or Balbase2 against results from Balbase4 (without and with investments, respectively). -* It assumes that only a single year is considered. - -IF((CARD(Y) NE 1), -* PUT .. -* ABORT .. -); diff --git a/base/addons/bb1234compare/bb1234_hellolog.inc b/base/addons/bb1234compare/bb1234_hellolog.inc deleted file mode 100644 index 75b6badb..00000000 --- a/base/addons/bb1234compare/bb1234_hellolog.inc +++ /dev/null @@ -1,32 +0,0 @@ -* File for addon testbb1234 for the Balmorel model - -SCALAR IBB1234VERSION "" / 303.20170102 /; - -PUT " - Addon testbb1234 (incomplete, but useful anyway)" /; - -IF((IBB1234VERSION LT IBALVERSN), - PUT " *** Addon testbb1234 is older than Balmorel version. You should verify consistency. (Message from \base\addons\bb1234compare\bb1234_hellolog.inc)" /; -); - - - - -$ontext - -This addon is for comparing output from BB1/BB2/BB3 with simular output from appropriate BB4. -It is intended for use in a transitional phase, where BB4 is developed. - -It is assumed that a single year only is considered. -The input that are similar between the two model to be compared must be identical. -The additional data for BB4 must be set up to be consistent with BB1/BB2/BB3 data. -In order for marginals to be the same in BB1/BB2/BB3 as in BB4, the BB4 discount factor must be 1. - -When activate, the addon will produce a gdx file with model results and place it in simex. File naves are -BB1234_BB1.gdx/BB1234_BB2.gdx/BB1234_BB3.gdx to be compared with BB1234_BB4.gdx. -To compare the two gdx files, use GDXDiff (under Untilities in the the IDE). - -Involved files are, besides this one: -bb1234_checkassumptions, bb12234_vardecl, bb12234_varassign - - -$offtext diff --git a/base/addons/bb1234compare/bb1234_varassign.inc b/base/addons/bb1234compare/bb1234_varassign.inc deleted file mode 100644 index 9183b659..00000000 --- a/base/addons/bb1234compare/bb1234_varassign.inc +++ /dev/null @@ -1,418 +0,0 @@ -* File for addon testbb1234 for the Balmorel model - - -display infodisplay, "bb1234_varassign !! TODO"; -*todo for bb4: lave unload også for svarende til VGKN, VxKN - -$ifi %BB1%==yes $goto doBB1andBB2 -$ifi %BB2%==yes $goto doBB1andBB2 -$ifi %BB3%==yes display infodisplay, "Error: addon testbb1234 is not implemented for BB3 (message from bb1234_varassign)"; !! Large parts must be identical to BB1? -$ifi %BB3%==yes $goto endoffile -$ifi %BB4%==yes $goto doBB4 - -* BB1 and part of BB2 ========================================================== -$label doBB1andBB2 - -* Variables: -IBB1234_VOBJ.L = VOBJ.L; - -IBB1234_VGE_T.L(Y,IA,G,S,T) = VGE_T.L(ia,g,s,t); -IBB1234_VGE_T.LO(Y,IA,G,S,T) = VGE_T.LO(ia,g,s,t); -IBB1234_VGE_T.UP(Y,IA,G,S,T) = VGE_T.UP(ia,g,s,t); -IBB1234_VGE_T.M(Y,IA,G,S,T) = VGE_T.M(ia,g,s,t); - -IBB1234_VGH_T.L(Y,IA,G,S,T) = VGH_T.L(ia,g,s,t); -IBB1234_VGH_T.LO(Y,IA,G,S,T) = VGH_T.LO(ia,g,s,t); -IBB1234_VGH_T.UP(Y,IA,G,S,T) = VGH_T.UP(ia,g,s,t); -IBB1234_VGH_T.M(Y,IA,G,S,T) = VGH_T.M(ia,g,s,t); - -*IBB1234_VDECOM(YYY,IA,G) ? - -IBB1234_VGF_T.L(Y,IA,G,S,T) = VGF_T.L(IA,G,S,T); -IBB1234_VGF_T.LO(Y,IA,G,S,T) = VGF_T.LO(IA,G,S,T); -IBB1234_VGF_T.UP(Y,IA,G,S,T) = VGF_T.UP(IA,G,S,T); -IBB1234_VGF_T.M(Y,IA,G,S,T) = VGF_T.M(IA,G,S,T); - -IBB1234_VX_T.L(Y,IRE,IRI,S,T) = VX_T.L(IRE,IRI,S,T); -IBB1234_VX_T.LO(Y,IRE,IRI,S,T) = VX_T.LO(IRE,IRI,S,T); -IBB1234_VX_T.UP(Y,IRE,IRI,S,T) = VX_T.UP(IRE,IRI,S,T); -IBB1234_VX_T.M(Y,IRE,IRI,S,T) = VX_T.M(IRE,IRI,S,T); - -$ifi %dflexquant%==yes IBB1234_VDEF_T.L(Y,IR,DEUSER,S,T,DEF) = VDEF_T.L(IR,DEUSER,S,T,DEF); -$ifi %dflexquant%==yes IBB1234_VDEF_T.LO(Y,IR,DEUSER,S,T,DEF) = VDEF_T.LO(IR,DEUSER,S,T,DEF); -$ifi %dflexquant%==yes IBB1234_VDEF_T.UP(Y,IR,DEUSER,S,T,DEF) = VDEF_T.UP(IR,DEUSER,S,T,DEF); -$ifi %dflexquant%==yes IBB1234_VDEF_T.M(Y,IR,DEUSER,S,T,DEF) = VDEF_T.M(IR,DEUSER,S,T,DEF); - -$ifi %dflexquant%==yes IBB1234_VDHF_T.L(Y,IA,DHUSER,S,T,DHF) = VDHF_T.L(IA,DHUSER,S,T,DHF); -$ifi %dflexquant%==yes IBB1234_VDHF_T.LO(Y,IA,DHUSER,S,T,DHF) = VDHF_T.LO(IA,DHUSER,S,T,DHF); -$ifi %dflexquant%==yes IBB1234_VDHF_T.UP(Y,IA,DHUSER,S,T,DHF) = VDHF_T.UP(IA,DHUSER,S,T,DHF); -$ifi %dflexquant%==yes IBB1234_VDHF_T.M(Y,IA,DHUSER,S,T,DHF) = VDHF_T.M(IA,DHUSER,S,T,DHF); - -*IBB1234_VGHYPMS_T.L(Y,IA,S,T) = VGHYPMS_T.L(IA,S,T); (Contents of pumped hydro storage (MWh)) TODO: handle that no values assigned (if VGHYPMS_T is at all to be there) -*IBB1234_VGHYPMS_T.LO(Y,IA,S,T) = VGHYPMS_T.LO(IA,S,T); -*IBB1234_VGHYPMS_T.UP(Y,IA,S,T) = VGHYPMS_T.UP(IA,S,T); -*IBB1234_VGHYPMS_T.M(Y,IA,S,T) = VGHYPMS_T.M(IA,S,T); - -IBB1234_VHYRS_S.L(Y,IA,S) = VHYRS_S.L(IA,S); -IBB1234_VHYRS_S.LO(Y,IA,S) = VHYRS_S.LO(IA,S); -IBB1234_VHYRS_S.UP(Y,IA,S) = VHYRS_S.UP(IA,S); -IBB1234_VHYRS_S.M(Y,IA,S) = VHYRS_S.M(IA,S); - -IBB1234_VESTOLOADT.L(Y,IA,S,T) = VESTOLOADT.L(IA,S,T); -IBB1234_VESTOLOADT.LO(Y,IA,S,T) = VESTOLOADT.LO(IA,S,T); -IBB1234_VESTOLOADT.UP(Y,IA,S,T) = VESTOLOADT.UP(IA,S,T); -IBB1234_VESTOLOADT.M(Y,IA,S,T) = VESTOLOADT.M(IA,S,T); - -IBB1234_VESTOLOADTS.L(Y,IA,S,T) = VESTOLOADTS.L(IA,S,T); -IBB1234_VESTOLOADTS.LO(Y,IA,S,T) = VESTOLOADTS.LO(IA,S,T); -IBB1234_VESTOLOADTS.UP(Y,IA,S,T) = VESTOLOADTS.UP(IA,S,T); -IBB1234_VESTOLOADTS.M(Y,IA,S,T) = VESTOLOADTS.M(IA,S,T); - -IBB1234_VHSTOLOADT.L(Y,IA,S,T) = VHSTOLOADT.L(IA,S,T); -IBB1234_VHSTOLOADT.LO(Y,IA,S,T) = VHSTOLOADT.LO(IA,S,T); -IBB1234_VHSTOLOADT.UP(Y,IA,S,T) = VHSTOLOADT.UP(IA,S,T); -IBB1234_VHSTOLOADT.M(Y,IA,S,T) = VHSTOLOADT.M(IA,S,T); - -IBB1234_VHSTOLOADTS.L(Y,IA,S,T) = VHSTOLOADTS.L(IA,S,T); -IBB1234_VHSTOLOADTS.LO(Y,IA,S,T) = VHSTOLOADTS.LO(IA,S,T); -IBB1234_VHSTOLOADTS.UP(Y,IA,S,T) = VHSTOLOADTS.UP(IA,S,T); -IBB1234_VHSTOLOADTS.M(Y,IA,S,T) = VHSTOLOADTS.M(IA,S,T); - -IBB1234_VESTOVOLT.L(Y,IA,S,T) = VESTOVOLT.L(IA,S,T); -IBB1234_VESTOVOLT.LO(Y,IA,S,T) = VESTOVOLT.LO(IA,S,T); -IBB1234_VESTOVOLT.UP(Y,IA,S,T) = VESTOVOLT.UP(IA,S,T); -IBB1234_VESTOVOLT.M(Y,IA,S,T) = VESTOVOLT.M(IA,S,T); - -IBB1234_VESTOVOLTS.L(Y,IA,S,T) = VESTOVOLTS.L(IA,S,T); -IBB1234_VESTOVOLTS.LO(Y,IA,S,T) = VESTOVOLTS.LO(IA,S,T); -IBB1234_VESTOVOLTS.UP(Y,IA,S,T) = VESTOVOLTS.UP(IA,S,T); -IBB1234_VESTOVOLTS.M(Y,IA,S,T) = VESTOVOLTS.M(IA,S,T); - -IBB1234_VHSTOVOLT.L(Y,IA,S,T) = VHSTOVOLT.L(IA,S,T); -IBB1234_VHSTOVOLT.LO(Y,IA,S,T) = VHSTOVOLT.LO(IA,S,T); -IBB1234_VHSTOVOLT.UP(Y,IA,S,T) = VHSTOVOLT.UP(IA,S,T); -IBB1234_VHSTOVOLT.M(Y,IA,S,T) = VHSTOVOLT.M(IA,S,T); - -IBB1234_VHSTOVOLTS.L(Y,IA,S,T) = VHSTOVOLTS.L(IA,S,T); -IBB1234_VHSTOVOLTS.LO(Y,IA,S,T) = VHSTOVOLTS.LO(IA,S,T); -IBB1234_VHSTOVOLTS.UP(Y,IA,S,T) = VHSTOVOLTS.UP(IA,S,T); -IBB1234_VHSTOVOLTS.M(Y,IA,S,T) = VHSTOVOLTS.M(IA,S,T); - -/* -IBB1234_VGKN.L(Y,IA,G) = VGKN.L(IA,G); -IBB1234_VGKN.LO(Y,IA,G) = VGKN.LO(IA,G); -IBB1234_VGKN.UP(Y,IA,G) = VGKN.UP(IA,G); -IBB1234_VGKN.M(Y,IA,G) = VGKN.M(IA,G); - -IBB1234_VXKN.L(Y,IRE,IRI) = VXKN.L(IRE,IRI); -IBB1234_VXKN.LO(Y,IRE,IRI) = VXKN.LO(IRE,IRI); -IBB1234_VXKN.UP(Y,IRE,IRI) = VXKN.UP(IRE,IRI); -IBB1234_VXKN.M(Y,IRE,IRI) = VXKN.M(IRE,IRI); -*/ - -IBB1234_VGKN.L(Y,IA,G) = 0; !! Kind of dummy, nice for GDXDiff with BB4 -IBB1234_VXKN.L(Y,IRE,IRI) = 0; !! Kind of dummy, nice for GDXDiff with BB4 - - -* Equations slack vars: -BB1234_VQEEQ.L(Y,IR,S,T,IPLUSMINUS) = VQEEQ.L(IR,S,T,IPLUSMINUS); -BB1234_VQHEQ.L(Y,IA,S,T,IPLUSMINUS) = VQHEQ.L(IA,S,T,IPLUSMINUS); - -/* todo more slack vars as above -IBB1234_VQESTOVOLT(Y,IA,S,T,IPLUSMINUS) -IBB1234_VQESTOVOLTS(Y,IA,S,T,IPLUSMINUS)execute_unload "..\output\temp\testbb1234.gdx", IBB1234_VGE_T, IBB1234_VGH_T, VQEEQ, VQHEQ ; -IBB1234_VQHSTOVOLT(Y,IA,S,T,IPLUSMINUS) -IBB1234_VQHSTOVOLTS(Y,IA,S,T,IPLUSMINUS) -*/ - -* TODO: main equations, .L and .M -BB1234_QEEQ.L(Y,IR,S,T) = QEEQ.L(IR,S,T); -BB1234_QEEQ.M(Y,IR,S,T) = QEEQ.M(IR,S,T); -BB1234_QHEQ.L(Y,IA,S,T) = QHEQ.L(IA,S,T); -BB1234_QHEQ.M(Y,IA,S,T) = QHEQ.M(IA,S,T); - !! todo: more eqns - - - - -$ifi not %dflexquant%==yes execute_unload "..\..\simex\BB1234_BB1.gdx" IBB1234_VOBJ IBB1234_VGKN IBB1234_VXKN IBB1234_VGE_T IBB1234_VGH_T IBB1234_VGF_T IBB1234_VX_T IBB1234_VGHYPMS_T IBB1234_VHYRS_S IBB1234_VESTOLOADT IBB1234_VESTOLOADTS IBB1234_VHSTOLOADT IBB1234_VHSTOLOADTS IBB1234_VESTOVOLT IBB1234_VESTOVOLTS IBB1234_VHSTOVOLT IBB1234_VHSTOVOLTS BB1234_VQEEQ BB1234_VQHEQ BB1234_QEEQ BB1234_QHEQ ; -$ifi %dflexquant%==yes execute_unload "..\..\simex\BB1234_BB1.gdx" IBB1234_VOBJ IBB1234_VGKN IBB1234_VXKN IBB1234_VGE_T IBB1234_VGH_T IBB1234_VGF_T IBB1234_VX_T IBB1234_VDEF_T IBB1234_VDHF_T IBB1234_VGHYPMS_T IBB1234_VHYRS_S IBB1234_VESTOLOADT IBB1234_VESTOLOADTS IBB1234_VHSTOLOADT IBB1234_VHSTOLOADTS IBB1234_VESTOVOLT IBB1234_VESTOVOLTS IBB1234_VHSTOVOLT IBB1234_VHSTOVOLTS BB1234_VQEEQ BB1234_VQHEQ BB1234_QEEQ BB1234_QHEQ ; - -$ifi %BB2%==yes $goto domoreBB2 - -$goto endoffile - -* BB2 ========================================================================== -$label domoreBB2 - -IBB1234_VGE_T.L(Y,IA,G,S,T) = VGE_T.L(ia,g,s,t) + VGEN_T.L(ia,g,s,t); -*IBB1234_VGE_T.LO(Y,IA,G,S,T) = VGE_T.LO(ia,g,s,t); !! ?? + VGEN_T.LO(ia,g,s,t); -*IBB1234_VGE_T.UP(Y,IA,G,S,T) = VGE_T.UP(ia,g,s,t); !! ?? + VGEN_T.UP(ia,g,s,t);; -*IBB1234_VGE_T.M(Y,IA,G,S,T) = VGE_T.M(ia,g,s,t); !! ?? + VGEN_T.LO(ia,g,s,t);; - -IBB1234_VGH_T.L(Y,IA,G,S,T) = VGH_T.L(ia,g,s,t) + VGHN_T.L(ia,g,s,t); -*IBB1234_VGH_T.LO(Y,IA,G,S,T) = VGH_T.LO(ia,g,s,t); -*IBB1234_VGH_T.UP(Y,IA,G,S,T) = VGH_T.UP(ia,g,s,t); -*IBB1234_VGH_T.M(Y,IA,G,S,T) = VGH_T.M(ia,g,s,t); - -IBB1234_VGKN.L(Y,IA,G) = VGKN.L(IA,G); -*IBB1234_VGKN.LO(Y,IA,G) = VGKN.LO(IA,G); !! ?? -*IBB1234_VGKN.UP(Y,IA,G) = VGKN.UP(IA,G); -*IBB1234_VGKN.M(Y,IA,G) = VGKN.M(IA,G); - -IBB1234_VXKN.L(Y,IRE,IRI) = VXKN.L(IRE,IRI); -*IBB1234_VXKN.LO(Y,IRE,IRI) = VXKN.LO(IRE,IRI); -*IBB1234_VXKN.UP(Y,IRE,IRI) = VXKN.UP(IRE,IRI); -*IBB1234_VXKN.M(Y,IRE,IRI) = VXKN.M(IRE,IRI); - -IBB1234_VGF_T.L(Y,IA,G,S,T) = VGF_T.L(IA,G,S,T) + VGFN_T.L(IA,G,S,T); -*IBB1234_VGF_T.LO(Y,IA,G,S,T) = VGF_T.LO(IA,G,S,T); -*IBB1234_VGF_T.UP(Y,IA,G,S,T) = VGF_T.UP(IA,G,S,T); -*IBB1234_VGF_T.M(Y,IA,G,S,T) = VGF_T.M(IA,G,S,T); - - -* TODO: main equations, .L and .M - - -execute_unload "..\..\simex\BB1234_BB2.gdx" IBB1234_VOBJ IBB1234_VGKN IBB1234_VXKN IBB1234_VGE_T IBB1234_VGH_T IBB1234_VGF_T IBB1234_VX_T /*IBB1234_VDEF_T IBB1234_VDHF_T */ IBB1234_VGHYPMS_T IBB1234_VHYRS_S IBB1234_VESTOLOADT IBB1234_VESTOLOADTS IBB1234_VHSTOLOADT IBB1234_VHSTOLOADTS IBB1234_VESTOVOLT IBB1234_VESTOVOLTS IBB1234_VHSTOVOLT IBB1234_VHSTOVOLTS - BB1234_VQEEQ BB1234_VQHEQ BB1234_QEEQ BB1234_QHEQ -; - -$goto endoffile - -* BB3 ========================================================================== -$label doBB3 - -Not implemented yet - -$goto endoffile - -* BB4 ========================================================================== -$label doBB4 - -IBB1234_VOBJ.L = VOBJ.L; - -IBB1234_VGE_T.L(Y,IA,G,S,T) = VGE_T.L(Y,ia,g,s,t); -IBB1234_VGE_T.LO(Y,IA,G,S,T) = VGE_T.LO(Y,ia,g,s,t); -IBB1234_VGE_T.UP(Y,IA,G,S,T) = VGE_T.UP(Y,ia,g,s,t); -IBB1234_VGE_T.M(Y,IA,G,S,T) = VGE_T.M(Y,ia,g,s,t); - -IBB1234_VGH_T.L(Y,IA,G,S,T) = VGH_T.L(Y,ia,g,s,t); -IBB1234_VGH_T.LO(Y,IA,G,S,T) = VGH_T.LO(Y,ia,g,s,t); -IBB1234_VGH_T.UP(Y,IA,G,S,T) = VGH_T.UP(Y,ia,g,s,t); -IBB1234_VGH_T.M(Y,IA,G,S,T) = VGH_T.M(Y,ia,g,s,t); - -IBB1234_VGF_T.L(Y,IA,G,S,T) = VGF_T.L(Y,IA,G,S,T); -IBB1234_VGF_T.LO(Y,IA,G,S,T) = VGF_T.LO(Y,IA,G,S,T); -IBB1234_VGF_T.UP(Y,IA,G,S,T) = VGF_T.UP(Y,IA,G,S,T); -IBB1234_VGF_T.M(Y,IA,G,S,T) = VGF_T.M(Y,IA,G,S,T); - -IBB1234_VX_T.L(Y,IRE,IRI,S,T) = VX_T.L(Y,IRE,IRI,S,T); -IBB1234_VX_T.LO(Y,IRE,IRI,S,T) = VX_T.LO(Y,IRE,IRI,S,T); -IBB1234_VX_T.UP(Y,IRE,IRI,S,T) = VX_T.UP(Y,IRE,IRI,S,T); -IBB1234_VX_T.M(Y,IRE,IRI,S,T) = VX_T.M(Y,IRE,IRI,S,T); - -IBB1234_VGKN.L(Y,IA,G) = VGKN.L(Y,IA,G); -IBB1234_VGKN.LO(Y,IA,G) = VGKN.LO(Y,IA,G); -IBB1234_VGKN.UP(Y,IA,G) = VGKN.UP(Y,IA,G); -IBB1234_VGKN.M(Y,IA,G) = VGKN.M(Y,IA,G); - -IBB1234_VXKN.L(Y,IRE,IRI) = VXKN.L(Y,IRE,IRI); -IBB1234_VXKN.LO(Y,IRE,IRI) = VXKN.LO(Y,IRE,IRI); -IBB1234_VXKN.UP(Y,IRE,IRI) = VXKN.UP(Y,IRE,IRI); -IBB1234_VXKN.M(Y,IRE,IRI) = VXKN.M(Y,IRE,IRI); - -/* renamed option to dflexquant -$ifi %dflex%==yes IBB1234_VDEF_T.L(Y,IR,S,T,DEF) = VDEF_T.L(Y,IR,S,T,DEF); -$ifi %dflex%==yes IBB1234_VDEF_T.LO(Y,IR,S,T,DEF) = VDEF_T.LO(Y,IR,S,T,DEF); -$ifi %dflex%==yes IBB1234_VDEF_T.UP(Y,IR,S,T,DEF) = VDEF_T.UP(Y,IR,S,T,DEF); -$ifi %dflex%==yes IBB1234_VDEF_T.M(Y,IR,S,T,DEF) = VDEF_T.M(Y,IR,S,T,DEF); - -$ifi %dflex%==yes IBB1234_VDHF_T.L(Y,IA,S,T,DHF) = VDHF_T.L(Y,IA,S,T,DHF); -$ifi %dflex%==yes IBB1234_VDHF_T.LO(Y,IA,S,T,DHF) = VDHF_T.LO(Y,IA,S,T,DHF); -$ifi %dflex%==yes IBB1234_VDHF_T.UP(Y,IA,S,T,DHF) = VDHF_T.UP(Y,IA,S,T,DHF); -$ifi %dflex%==yes IBB1234_VDHF_T.M(Y,IA,S,T,DHF) = VDHF_T.M(Y,IA,S,T,DHF); -*/ -$ifi %dflexquant%==yes IBB1234_VDEF_T.L(Y,IR,DEUSER,S,T,DEF) = VDEF_T.L(Y,IR,DEUSER,S,T,DEF); -$ifi %dflexquant%==yes IBB1234_VDEF_T.LO(Y,IR,DEUSER,S,T,DEF) = VDEF_T.LO(Y,IR,DEUSER,S,T,DEF); -$ifi %dflexquant%==yes IBB1234_VDEF_T.UP(Y,IR,DEUSER,S,T,DEF) = VDEF_T.UP(Y,IR,DEUSER,S,T,DEF); -$ifi %dflexquant%==yes IBB1234_VDEF_T.M(Y,IR,DEUSER,S,T,DEF) = VDEF_T.M(Y,IR,DEUSER,S,T,DEF); - -$ifi %dflexquant%==yes IBB1234_VDHF_T.L(Y,IA,DHUSER,S,T,DHF) = VDHF_T.L(Y,IA,DHUSER,S,T,DHF); -$ifi %dflexquant%==yes IBB1234_VDHF_T.LO(Y,IA,DHUSER,S,T,DHF) = VDHF_T.LO(Y,IA,DHUSER,S,T,DHF); -$ifi %dflexquant%==yes IBB1234_VDHF_T.UP(Y,IA,DHUSER,S,T,DHF) = VDHF_T.UP(Y,IA,DHUSER,S,T,DHF); -$ifi %dflexquant%==yes IBB1234_VDHF_T.M(Y,IA,DHUSER,S,T,DHF) = VDHF_T.M(Y,IA,DHUSER,S,T,DHF); - - -*IBB1234_VGHYPMS_T.L(Y,IA,S,T) = VGHYPMS_T.L(Y,IA,S,T); TODO: handle that no alues assigned -*IBB1234_VGHYPMS_T.LO(Y,IA,S,T) = VGHYPMS_T.LO(Y,IA,S,T); -*IBB1234_VGHYPMS_T.UP(Y,IA,S,T) = VGHYPMS_T.UP(Y,IA,S,T); -*IBB1234_VGHYPMS_T.M(Y,IA,S,T) = VGHYPMS_T.M(Y,IA,S,T); - -IBB1234_VHYRS_S.L(Y,IA,S) = VHYRS_S.L(Y,IA,S); -IBB1234_VHYRS_S.LO(Y,IA,S) = VHYRS_S.LO(Y,IA,S); -IBB1234_VHYRS_S.UP(Y,IA,S) = VHYRS_S.UP(Y,IA,S); -IBB1234_VHYRS_S.M(Y,IA,S) = VHYRS_S.M(Y,IA,S); - -IBB1234_VESTOLOADT.L(Y,IA,S,T) = VESTOLOADT.L(Y,IA,S,T); -IBB1234_VESTOLOADT.LO(Y,IA,S,T) = VESTOLOADT.LO(Y,IA,S,T); -IBB1234_VESTOLOADT.UP(Y,IA,S,T) = VESTOLOADT.UP(Y,IA,S,T); -IBB1234_VESTOLOADT.M(Y,IA,S,T) = VESTOLOADT.M(Y,IA,S,T); - -IBB1234_VESTOLOADTS.L(Y,IA,S,T) = VESTOLOADTS.L(Y,IA,S,T); -IBB1234_VESTOLOADTS.LO(Y,IA,S,T) = VESTOLOADTS.LO(Y,IA,S,T); -IBB1234_VESTOLOADTS.UP(Y,IA,S,T) = VESTOLOADTS.UP(Y,IA,S,T); -IBB1234_VESTOLOADTS.M(Y,IA,S,T) = VESTOLOADTS.M(Y,IA,S,T); - -IBB1234_VHSTOLOADT.L(Y,IA,S,T) = VHSTOLOADT.L(Y,IA,S,T); -IBB1234_VHSTOLOADT.LO(Y,IA,S,T) = VHSTOLOADT.LO(Y,IA,S,T); -IBB1234_VHSTOLOADT.UP(Y,IA,S,T) = VHSTOLOADT.UP(Y,IA,S,T); -IBB1234_VHSTOLOADT.M(Y,IA,S,T) = VHSTOLOADT.M(Y,IA,S,T); - -IBB1234_VHSTOLOADTS.L(Y,IA,S,T) = VHSTOLOADTS.L(Y,IA,S,T); -IBB1234_VHSTOLOADTS.LO(Y,IA,S,T) = VHSTOLOADTS.LO(Y,IA,S,T); -IBB1234_VHSTOLOADTS.UP(Y,IA,S,T) = VHSTOLOADTS.UP(Y,IA,S,T); -IBB1234_VHSTOLOADTS.M(Y,IA,S,T) = VHSTOLOADTS.M(Y,IA,S,T); - -IBB1234_VESTOVOLT.L(Y,IA,S,T) = VESTOVOLT.L(Y,IA,S,T); -IBB1234_VESTOVOLT.LO(Y,IA,S,T) = VESTOVOLT.LO(Y,IA,S,T); -IBB1234_VESTOVOLT.UP(Y,IA,S,T) = VESTOVOLT.UP(Y,IA,S,T); -IBB1234_VESTOVOLT.M(Y,IA,S,T) = VESTOVOLT.M(Y,IA,S,T); - -IBB1234_VESTOVOLTS.L(Y,IA,S,T) = VESTOVOLTS.L(Y,IA,S,T); -IBB1234_VESTOVOLTS.LO(Y,IA,S,T) = VESTOVOLTS.LO(Y,IA,S,T); -IBB1234_VESTOVOLTS.UP(Y,IA,S,T) = VESTOVOLTS.UP(Y,IA,S,T); -IBB1234_VESTOVOLTS.M(Y,IA,S,T) = VESTOVOLTS.M(Y,IA,S,T); - -IBB1234_VHSTOVOLT.L(Y,IA,S,T) = VHSTOVOLT.L(Y,IA,S,T); -IBB1234_VHSTOVOLT.LO(Y,IA,S,T) = VHSTOVOLT.LO(Y,IA,S,T); -IBB1234_VHSTOVOLT.UP(Y,IA,S,T) = VHSTOVOLT.UP(Y,IA,S,T); -IBB1234_VHSTOVOLT.M(Y,IA,S,T) = VHSTOVOLT.M(Y,IA,S,T); - -IBB1234_VHSTOVOLTS.L(Y,IA,S,T) = VHSTOVOLTS.L(Y,IA,S,T); -IBB1234_VHSTOVOLTS.LO(Y,IA,S,T) = VHSTOVOLTS.LO(Y,IA,S,T); -IBB1234_VHSTOVOLTS.UP(Y,IA,S,T) = VHSTOVOLTS.UP(Y,IA,S,T); -IBB1234_VHSTOVOLTS.M(Y,IA,S,T) = VHSTOVOLTS.M(Y,IA,S,T); - -* todo: VQ variables -BB1234_VQEEQ.L(Y,IR,S,T,IPLUSMINUS) = VQEEQ.L(Y,IR,S,T,IPLUSMINUS); -BB1234_VQHEQ.L(Y,IA,S,T,IPLUSMINUS) = VQHEQ.L(Y,IA,S,T,IPLUSMINUS); - - - -* TODO: main equations, .L and .M: -BB1234_QEEQ.L(Y,IR,S,T) = QEEQ.L(Y,IR,S,T); -BB1234_QEEQ.M(Y,IR,S,T) = QEEQ.M(Y,IR,S,T); -BB1234_QHEQ.L(Y,IA,S,T) = QHEQ.L(Y,IA,S,T); -BB1234_QHEQ.M(Y,IA,S,T) = QHEQ.M(Y,IA,S,T); - - - - !! and more, compare BB1 - - - -$ifi not %dflexquant%==yes execute_unload "..\..\simex\BB1234_BB4.gdx" IBB1234_VOBJ IBB1234_VGE_T IBB1234_VGH_T IBB1234_VGF_T IBB1234_VX_T IBB1234_VGKN IBB1234_VXKN IBB1234_VGHYPMS_T IBB1234_VHYRS_S IBB1234_VESTOLOADT IBB1234_VESTOLOADTS IBB1234_VHSTOLOADT IBB1234_VHSTOLOADTS IBB1234_VESTOVOLT IBB1234_VESTOVOLTS IBB1234_VHSTOVOLT IBB1234_VHSTOVOLTS BB1234_VQEEQ BB1234_VQHEQ BB1234_QEEQ BB1234_QHEQ; -$ifi %dflexquant%==yes execute_unload "..\..\simex\BB1234_BB4.gdx" IBB1234_VOBJ IBB1234_VGE_T IBB1234_VGH_T IBB1234_VGF_T IBB1234_VX_T IBB1234_VGKN IBB1234_VXKN IBB1234_VDEF_T IBB1234_VDHF_T IBB1234_VGHYPMS_T IBB1234_VHYRS_S IBB1234_VESTOLOADT IBB1234_VESTOLOADTS IBB1234_VHSTOLOADT IBB1234_VHSTOLOADTS IBB1234_VESTOVOLT IBB1234_VESTOVOLTS IBB1234_VHSTOVOLT IBB1234_VHSTOVOLTS BB1234_VQEEQ BB1234_VQHEQ BB1234_QEEQ BB1234_QHEQ; - -$goto endoffile - -$label endoffile - - - -$ontext ------------------------------------------------------------------------------- - -testing field etc. - -IBB1234_VGF_T.L(Y,IA,G,S,T) = VGF_T.L(Y,IA,G,S,T); -IBB1234_VGF_T.LO(Y,IA,G,S,T) = VGF_T.LO(Y,IA,G,S,T); -IBB1234_VGF_T.UP(Y,IA,G,S,T) = VGF_T.UP(Y,IA,G,S,T); -IBB1234_VGF_T.M(Y,IA,G,S,T) = VGF_T.M(Y,IA,G,S,T); - -IBB1234_VX_T.L(Y,IRE,IRI,S,T) = VX_T.L(Y,IRE,IRI,S,T); -IBB1234_VX_T.LO(Y,IRE,IRI,S,T) = VX_T.LO(Y,IRE,IRI,S,T); -IBB1234_VX_T.UP(Y,IRE,IRI,S,T) = VX_T.UP(Y,IRE,IRI,S,T); -IBB1234_VX_T.M(Y,IRE,IRI,S,T) = VX_T.M(Y,IRE,IRI,S,T); - -IBB1234_VGKN.L(Y,IA,G) = VGKN.L(Y,IA,G); -IBB1234_VGKN.LO(Y,IA,G) = VGKN.LO(Y,IA,G); -IBB1234_VGKN.UP(Y,IA,G) = VGKN.UP(Y,IA,G); -IBB1234_VGKN.M(Y,IA,G) = VGKN.M(Y,IA,G); - -IBB1234_VXKN.L(Y,IRE,IRI) = VXKN.L(Y,IRE,IRI); -IBB1234_VXKN.LO(Y,IRE,IRI) = VXKN.LO(Y,IRE,IRI); -IBB1234_VXKN.UP(Y,IRE,IRI) = VXKN.UP(Y,IRE,IRI); -IBB1234_VXKN.M(Y,IRE,IRI) = VXKN.M(Y,IRE,IRI); - -IBB1234_VDEF_T.L(Y,IR,S,T,DEF) = VDEF_T.L(Y,IR,S,T,DEF); -IBB1234_VDEF_T.LO(Y,IR,S,T,DEF) = VDEF_T.LO(Y,IR,S,T,DEF); -IBB1234_VDEF_T.UP(Y,IR,S,T,DEF) = VDEF_T.UP(Y,IR,S,T,DEF); -IBB1234_VDEF_T.M(Y,IR,S,T,DEF) = VDEF_T.M(Y,IR,S,T,DEF); - -IBB1234_VDHF_T.L(Y,IA,S,T,DHF) = VDHF_T.L(Y,IA,S,T,DHF); -IBB1234_VDHF_T.LO(Y,IA,S,T,DHF) = VDHF_T.LO(Y,IA,S,T,DHF); -IBB1234_VDHF_T.UP(Y,IA,S,T,DHF) = VDHF_T.UP(Y,IA,S,T,DHF); -IBB1234_VDHF_T.M(Y,IA,S,T,DHF) = VDHF_T.M(Y,IA,S,T,DHF); - -*IBB1234_VGHYPMS_T.L(Y,IA,S,T) = VGHYPMS_T.L(Y,IA,S,T); TODO: handle that no alues assigned -*IBB1234_VGHYPMS_T.LO(Y,IA,S,T) = VGHYPMS_T.LO(Y,IA,S,T); -*IBB1234_VGHYPMS_T.UP(Y,IA,S,T) = VGHYPMS_T.UP(Y,IA,S,T); -*IBB1234_VGHYPMS_T.M(Y,IA,S,T) = VGHYPMS_T.M(Y,IA,S,T); - -IBB1234_VHYRS_S.L(Y,IA,S) = VHYRS_S.L(Y,IA,S); -IBB1234_VHYRS_S.LO(Y,IA,S) = VHYRS_S.LO(Y,IA,S); -IBB1234_VHYRS_S.UP(Y,IA,S) = VHYRS_S.UP(Y,IA,S); -IBB1234_VHYRS_S.M(Y,IA,S) = VHYRS_S.M(Y,IA,S); - -IBB1234_VESTOLOADT.L(Y,IA,S,T) = VESTOLOADT.L(Y,IA,S,T); -IBB1234_VESTOLOADT.LO(Y,IA,S,T) = VESTOLOADT.LO(Y,IA,S,T); -IBB1234_VESTOLOADT.UP(Y,IA,S,T) = VESTOLOADT.UP(Y,IA,S,T); -IBB1234_VESTOLOADT.M(Y,IA,S,T) = VESTOLOADT.M(Y,IA,S,T); - -IBB1234_VESTOLOADTS.L(Y,IA,S,T) = VESTOLOADTS.L(Y,IA,S,T); -IBB1234_VESTOLOADTS.LO(Y,IA,S,T) = VESTOLOADTS.LO(Y,IA,S,T); -IBB1234_VESTOLOADTS.UP(Y,IA,S,T) = VESTOLOADTS.UP(Y,IA,S,T); -IBB1234_VESTOLOADTS.M(Y,IA,S,T) = VESTOLOADTS.M(Y,IA,S,T); - -IBB1234_VHSTOLOADT.L(Y,IA,S,T) = VHSTOLOADT.L(Y,IA,S,T); -IBB1234_VHSTOLOADT.LO(Y,IA,S,T) = VHSTOLOADT.LO(Y,IA,S,T); -IBB1234_VHSTOLOADT.UP(Y,IA,S,T) = VHSTOLOADT.UP(Y,IA,S,T); -IBB1234_VHSTOLOADT.M(Y,IA,S,T) = VHSTOLOADT.M(Y,IA,S,T); - -IBB1234_VHSTOLOADTS.L(Y,IA,S,T) = VHSTOLOADTS.L(Y,IA,S,T); -IBB1234_VHSTOLOADTS.LO(Y,IA,S,T) = VHSTOLOADTS.LO(Y,IA,S,T); -IBB1234_VHSTOLOADTS.UP(Y,IA,S,T) = VHSTOLOADTS.UP(Y,IA,S,T); -IBB1234_VHSTOLOADTS.M(Y,IA,S,T) = VHSTOLOADTS.M(Y,IA,S,T); - -IBB1234_VESTOVOLT.L(Y,IA,S,T) = VESTOVOLT.L(Y,IA,S,T); -IBB1234_VESTOVOLT.LO(Y,IA,S,T) = VESTOVOLT.LO(Y,IA,S,T); -IBB1234_VESTOVOLT.UP(Y,IA,S,T) = VESTOVOLT.UP(Y,IA,S,T); -IBB1234_VESTOVOLT.M(Y,IA,S,T) = VESTOVOLT.M(Y,IA,S,T); - -IBB1234_VESTOVOLTS.L(Y,IA,S,T) = VESTOVOLTS.L(Y,IA,S,T); -IBB1234_VESTOVOLTS.LO(Y,IA,S,T) = VESTOVOLTS.LO(Y,IA,S,T); -IBB1234_VESTOVOLTS.UP(Y,IA,S,T) = VESTOVOLTS.UP(Y,IA,S,T); -IBB1234_VESTOVOLTS.M(Y,IA,S,T) = VESTOVOLTS.M(Y,IA,S,T); - -IBB1234_VHSTOVOLT.L(Y,IA,S,T) = VHSTOVOLT.L(Y,IA,S,T); -IBB1234_VHSTOVOLT.LO(Y,IA,S,T) = VHSTOVOLT.LO(Y,IA,S,T); -IBB1234_VHSTOVOLT.UP(Y,IA,S,T) = VHSTOVOLT.UP(Y,IA,S,T); -IBB1234_VHSTOVOLT.M(Y,IA,S,T) = VHSTOVOLT.M(Y,IA,S,T); - -IBB1234_VHSTOVOLTS.L(Y,IA,S,T) = VHSTOVOLTS.L(Y,IA,S,T); -IBB1234_VHSTOVOLTS.LO(Y,IA,S,T) = VHSTOVOLTS.LO(Y,IA,S,T); -IBB1234_VHSTOVOLTS.UP(Y,IA,S,T) = VHSTOVOLTS.UP(Y,IA,S,T); -IBB1234_VHSTOVOLTS.M(Y,IA,S,T) = VHSTOVOLTS.M(Y,IA,S,T); - - ------------ - -alle bal-var med ny-invest: - - - - - - -$offtext diff --git a/base/addons/bb1234compare/bb1234_vardeclare.inc b/base/addons/bb1234compare/bb1234_vardeclare.inc deleted file mode 100644 index 2e763728..00000000 --- a/base/addons/bb1234compare/bb1234_vardeclare.inc +++ /dev/null @@ -1,37 +0,0 @@ -* File for addon testbb1234 for the Balmorel model -* Last revised 20180418 (hr) - -$ifi not declared IPLUSMINUS SET IPLUSMINUS "Violation of equation" /IPLUS "Violation of equation upwards", IMINUS "Violation of equation downwards"/; - -FREE VARIABLE IBB1234_VOBJ "Objective function value (MMoney)"; -POSITIVE VARIABLE IBB1234_VGE_T(Y,AAA,G,S,T) "Electricity generation (MW)"; -POSITIVE VARIABLE IBB1234_VGH_T(Y,AAA,G,S,T) "Heat generation (MW)"; -POSITIVE VARIABLE IBB1234_VGF_T(Y,AAA,G,S,T) "Fuel consumption rate (), existing units" -POSITIVE VARIABLE IBB1234_VX_T(Y,IRRRE,IRRRI,S,T) "Electricity export from region IRRRE to IRRRI (MW)"; -POSITIVE VARIABLE IBB1234_VGKN(Y,AAA,G) "New generation capacity (MW)"; !! se kommentarer til YYY i bb4.sim Hans TODO -POSITIVE VARIABLE IBB1234_VXKN(Y,IRRRE,IRRRI) "New electricity transmission capacity (MW)"; -POSITIVE VARIABLE IBB1234_VDECOM(YYY,AAA,G) "Decommissioned capacity(MW). NOT IN USE PRESENTLY"; -$ifi %dflexquant%==yes POSITIVE VARIABLE IBB1234_VDEF_T(Y,RRR,DEUSER,S,T,DEF) "Flexible electricity demands (MW)"; -$ifi %dflexquant%==yes POSITIVE VARIABLE IBB1234_VDHF_T(Y,AAA,DHUSER,S,T,DHF) "Flexible heat demands (MW)"; -POSITIVE VARIABLE IBB1234_VGHYPMS_T(Y,AAA,S,T) "Contents of pumped hydro storage (MWh)"; -POSITIVE VARIABLE IBB1234_VHYRS_S(Y,AAA,S) "Hydro energy equivalent at the start of the season (MWh)"; -POSITIVE VARIABLE IBB1234_VESTOLOADT(Y,AAA,S,T) "Intra-seasonal electricity storage loading (MW)"; -POSITIVE VARIABLE IBB1234_VESTOLOADTS(Y,AAA,S,T) "Inter-seasonal electricity storage loading (MW)"; -POSITIVE VARIABLE IBB1234_VHSTOLOADT(Y,AAA,S,T) "Intra-seasonal heat storage loading (MW)"; -POSITIVE VARIABLE IBB1234_VHSTOLOADTS(Y,AAA,S,T) "Inter-seasonal heat storage loading (MW)"; -POSITIVE VARIABLE IBB1234_VESTOVOLT(Y,AAA,S,T) "Electricity storage contents at beginning of time segment (MWh)"; -POSITIVE VARIABLE IBB1234_VESTOVOLTS(Y,AAA,S,T) "Electricity storage contents at beginning of time segment (MWh)"; -POSITIVE VARIABLE IBB1234_VHSTOVOLT(Y,AAA,S,T) "Heat storage contents at beginning of time segment (MWh)"; -POSITIVE VARIABLE IBB1234_VHSTOVOLTS(Y,AAA,S,T) "Heat storage contents at beginning of time segment (MWh)"; - - -POSITIVE VARIABLE BB1234_VQEEQ(Y,RRR,S,T,IPLUSMINUS) "Feasibility in electricity balance QEEQ (IMINUS is short of el, IPLUS is surplus of el) (MW)"; -POSITIVE VARIABLE BB1234_VQHEQ(Y,AAA,S,T,IPLUSMINUS) "Feasibility in heat balance QHEQ (IMINUS is short of heat, IPLUS is surplus of heat) (MW)"; -POSITIVE VARIABLE IBB1234_VQESTOVOLT(Y,AAA,S,T,IPLUSMINUS) "Feasibility in intra-seasonal electricity storage equation QESTOVOLT (MWh)"; -POSITIVE VARIABLE IBB1234_VQESTOVOLTS(Y,AAA,S,T,IPLUSMINUS) "Feasibility in inter-seasonal electricity storage equation QESTOVOLTS (MWh)"; -POSITIVE VARIABLE IBB1234_VQHSTOVOLT(Y,AAA,S,T,IPLUSMINUS) "Feasibility in intra-seasonal heat storage equation VQHSTOVOLT (MWh)"; -POSITIVE VARIABLE IBB1234_VQHSTOVOLTS(Y,AAA,S,T,IPLUSMINUS) "Feasibility in inter-seasonal heat storage equation VQHSTOVOLTS (MWh)"; - -POSITIVE VARIABLE BB1234_QEEQ(Y,RRR,S,T) "Electricity balance QEEQ (MW)"; -POSITIVE VARIABLE BB1234_QHEQ(Y,AAA,S,T) "Heat balance QHEQ (MW)"; -* More eqns diff --git a/base/addons/gas/DH-MR - foer varmebehandling.inc b/base/addons/gas/DH-MR - foer varmebehandling.inc deleted file mode 100644 index 03268ef9..00000000 --- a/base/addons/gas/DH-MR - foer varmebehandling.inc +++ /dev/null @@ -1,130 +0,0 @@ -* SOURCE: Energiproducenttælling 2004. - -PARAMETER DH_MR2004(AAA) -/ -amagerf 2224904.91666667 -billesboelle 10818.3333333333 -brande 47014.8888888889 -avedoere 1055380.83611111 -dragoer 7277.27777777778 -egtved 531798.639888889 -ellidshoej 164185.830555556 -froeslev 642938.566666667 -haverslev 556340.64 -herning 6586890.71007009 -hoejby 3261777.32422222 -karlslunde 36660.3777777778 -karup 234517.878888889 -koege 217484.897777778 -koelbjerg 336272.124444444 -liselskaer 301802.232 -litorup 49496.1 -lynge 1237484.6 -middelfart 90500.5288888889 -maaloev 501278.626388889 -noerskov 295146.515777778 -nyborg 172319.452222222 -nybro 58330.5 -DK_E_Rural 835210.904111111 -pottehuse 243976 -ringsted 632562.144444445 -slagelse 302631.638888889 -soroe 833593.757666667 -standst 301744.235777778 -stenlille 6076 -HCOersted 4335603.74522778 -* Ændret manuelt fra Taulov -skaerbaek 945821.5 -terkelsboel 224073.725555556 -torslunde 415368.915555556 -ullerslev 43140 -vallensbaek 1057007.5 -varde 1357966.38188889 -DK_W_Rural 163721.528444445 -viborg 1694392.61202222 -aalborg 2754115.00622222 -/; - -DH(YYY,AAA)$ICA('Denmark',AAA) = 0; -DH(YYY,AAA)$(DH_MR2004(AAA)) = DH_MR2004(AAA); - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/base/addons/gas/DH-MR.inc b/base/addons/gas/DH-MR.inc deleted file mode 100644 index d4d996a7..00000000 --- a/base/addons/gas/DH-MR.inc +++ /dev/null @@ -1,154 +0,0 @@ -* SOURCE: Energiproducenttælling 2004. - -PARAMETER DH_MR2004(AAA) -/ -billesboelle 10818.3333333333 -brande 47014.8888888889 -dragoer 7277.27777777778 -egtved 531798.639888889 -ellidshoej 164185.830555556 -froeslev 642938.566666667 -haverslev 556340.64 - -*herning 6586890.71007009 Original fra database -* Frastrukket DK_W_AARHUS -herning 3585684 -DK_W_AARHUS 3001207 - -* hoejby 3261777.32422222 Original fra database -* Fratrukket Odense postnumre -hoejby 327055 -* Kun anlæg med Odense postnummer: -DK_W_ODENSE 2934722 - -karup 234517.878888889 -koege 217484.897777778 -koelbjerg 336272.124444444 -liselskaer 301802.232 -litorup 49496.1 -lynge 1237484.6 -middelfart 90500.5288888889 -maaloev 501278.626388889 -noerskov 295146.515777778 -nyborg 172319.452222222 -nybro 58330.5 -DK_E_Rural 835210.904111111 -pottehuse 243976 -ringsted 632562.144444445 -slagelse 302631.638888889 -soroe 833593.757666667 -standst 301744.235777778 -stenlille 6076 - -* Bør lige undersøges igen fordi DH TREFOR er større end skaerbaek/taulov -* Taulov 945821.5 -DK_W_TREFOR 987722 - -terkelsboel 224073.725555556 -varde 1357966.38188889 - -DK_W_Rural 163721.528444445 -viborg 1694392.61202222 - - - -*aalborg 2754115.00622222 Original fra database -* Fratrukket DK_W_AALBORG: -aalborg 1561429 -DK_W_AALBORG 1192686 - -* Original DK_E_Urban: -*DK_E_Urban 10056899 -* DK_E_Urban dannet udfra sum af følgende MR stationer -*torslunde 415368.9156 -*ullerslev 43140 -*vallensbaek 1057007.5 -*karlslunde 36660.37778 -*avedoere 1055380.836 -*amagerf 2224904.917 -*HCOersted 4335603.745 -DK_E_Urban 9168066.291 -/; - -DH(YYY,AAA)$ICA('Denmark',AAA) = 0; -DH(YYY,AAA)$(DH_MR2004(AAA)) = DH_MR2004(AAA); - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/base/addons/gas/Export_GER.INC b/base/addons/gas/Export_GER.INC deleted file mode 100644 index 3597c511..00000000 --- a/base/addons/gas/Export_GER.INC +++ /dev/null @@ -1,8746 +0,0 @@ -* ----------------------------------------------------- -* MDB2GMEllund.S VerEllund.Sion 2.6, AuguEllund.St 2005 -* Erwin Kalvelagen, GAMEllund.S Development Corp -* ----------------------------------------------------- -* DAO verEllund.Sion: 3.6 -* Jet verEllund.Sion: 4.0 -* DatabaEllund.Se: D:\LarEllund.SB\1\GaEllund.STranEllund.SmiEllund.SEllund.Sion.mdb -* Query: Ellund.Select * from ellund -* ----------------------------------------------------- -Ellund.S01.T001 211.8 -Ellund.S01.T002 212.2 -Ellund.S01.T003 211.6 -Ellund.S01.T004 211.7 -Ellund.S01.T005 211.9 -Ellund.S01.T006 211.5 -Ellund.S01.T007 216.6 -Ellund.S01.T008 222.4 -Ellund.S01.T009 222.8 -Ellund.S01.T010 222.7 -Ellund.S01.T011 222.7 -Ellund.S01.T012 222.8 -Ellund.S01.T013 223.1 -Ellund.S01.T014 222.6 -Ellund.S01.T015 223.2 -Ellund.S01.T016 222.8 -Ellund.S01.T017 223 -Ellund.S01.T018 222.5 -Ellund.S01.T019 222.5 -Ellund.S01.T020 223 -Ellund.S01.T021 222.6 -Ellund.S01.T022 223.2 -Ellund.S01.T023 222.9 -Ellund.S01.T024 223.1 -Ellund.S01.T025 223.2 -Ellund.S01.T026 222.8 -Ellund.S01.T027 223.2 -Ellund.S01.T028 223 -Ellund.S01.T029 223.2 -Ellund.S01.T030 222.9 -Ellund.S01.T031 220.6 -Ellund.S01.T032 210.6 -Ellund.S01.T033 213.8 -Ellund.S01.T034 213.3 -Ellund.S01.T035 213.7 -Ellund.S01.T036 213.2 -Ellund.S01.T037 213.5 -Ellund.S01.T038 213.8 -Ellund.S01.T039 213.9 -Ellund.S01.T040 214 -Ellund.S01.T041 214.1 -Ellund.S01.T042 214.1 -Ellund.S01.T043 213.7 -Ellund.S01.T044 214 -Ellund.S01.T045 213.9 -Ellund.S01.T046 213.7 -Ellund.S01.T047 210.5 -Ellund.S01.T048 209.7 -Ellund.S01.T049 209.2 -Ellund.S01.T050 206.7 -Ellund.S01.T051 204.3 -Ellund.S01.T052 205.1 -Ellund.S01.T053 206.7 -Ellund.S01.T054 208.9 -Ellund.S01.T055 210.7 -Ellund.S01.T056 210.7 -Ellund.S01.T057 210.7 -Ellund.S01.T058 211 -Ellund.S01.T059 210.9 -Ellund.S01.T060 210.9 -Ellund.S01.T061 210.8 -Ellund.S01.T062 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299.3 -Ellund.S51.T060 300.1 -Ellund.S51.T061 300.3 -Ellund.S51.T062 300.4 -Ellund.S51.T063 300.3 -Ellund.S51.T064 300.5 -Ellund.S51.T065 300.4 -Ellund.S51.T066 300.1 -Ellund.S51.T067 299.5 -Ellund.S51.T068 299.7 -Ellund.S51.T069 299.1 -Ellund.S51.T070 297.5 -Ellund.S51.T071 296.3 -Ellund.S51.T072 293.6 -Ellund.S51.T073 293.5 -Ellund.S51.T074 293.5 -Ellund.S51.T075 293.5 -Ellund.S51.T076 294 -Ellund.S51.T077 295.6 -Ellund.S51.T078 295.8 -Ellund.S51.T079 296.5 -Ellund.S51.T080 297.8 -Ellund.S51.T081 298.1 -Ellund.S51.T082 298.5 -Ellund.S51.T083 298.8 -Ellund.S51.T084 299 -Ellund.S51.T085 299.5 -Ellund.S51.T086 299.6 -Ellund.S51.T087 299.7 -Ellund.S51.T088 299.3 -Ellund.S51.T089 299.3 -Ellund.S51.T090 299.4 -Ellund.S51.T091 299 -Ellund.S51.T092 299.2 -Ellund.S51.T093 298.8 -Ellund.S51.T094 295.3 -Ellund.S51.T095 296.4 -Ellund.S51.T096 296.4 -Ellund.S51.T097 295.6 -Ellund.S51.T098 293.7 -Ellund.S51.T099 290.5 -Ellund.S51.T100 290.7 -Ellund.S51.T101 290.4 -Ellund.S51.T102 294.3 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-Ellund.S51.T147 297.6 -Ellund.S51.T148 297.9 -Ellund.S51.T149 297.9 -Ellund.S51.T150 297.9 -Ellund.S51.T151 298.3 -Ellund.S51.T152 298.1 -Ellund.S51.T153 298.2 -Ellund.S51.T154 297.8 -Ellund.S51.T155 297.3 -Ellund.S51.T156 298.1 -Ellund.S51.T157 298 -Ellund.S51.T158 298.5 -Ellund.S51.T159 298.1 -Ellund.S51.T160 298.1 -Ellund.S51.T161 298.3 -Ellund.S51.T162 298.1 -Ellund.S51.T163 298.2 -Ellund.S51.T164 298.2 -Ellund.S51.T165 298.1 -Ellund.S51.T166 297.6 -Ellund.S51.T167 297.5 -Ellund.S51.T168 297.4 -Ellund.S52.T001 297.3 -Ellund.S52.T002 298.1 -Ellund.S52.T003 297.6 -Ellund.S52.T004 297.4 -Ellund.S52.T005 296.4 -Ellund.S52.T006 297.7 -Ellund.S52.T007 298.1 -Ellund.S52.T008 297.7 -Ellund.S52.T009 297.5 -Ellund.S52.T010 297.6 -Ellund.S52.T011 297.7 -Ellund.S52.T012 298.3 -Ellund.S52.T013 299.8 -Ellund.S52.T014 301.2 -Ellund.S52.T015 301.6 -Ellund.S52.T016 301.6 -Ellund.S52.T017 301.3 -Ellund.S52.T018 300.9 -Ellund.S52.T019 300.3 -Ellund.S52.T020 299.2 -Ellund.S52.T021 297.2 -Ellund.S52.T022 294.6 -Ellund.S52.T023 295.2 -Ellund.S52.T024 295.1 -Ellund.S52.T025 295.3 -Ellund.S52.T026 295.2 -Ellund.S52.T027 294.7 -Ellund.S52.T028 294.6 -Ellund.S52.T029 294.3 -Ellund.S52.T030 297 -Ellund.S52.T031 295 -Ellund.S52.T032 294.6 -Ellund.S52.T033 294.3 -Ellund.S52.T034 294.4 -Ellund.S52.T035 294.6 -Ellund.S52.T036 294.3 -Ellund.S52.T037 294.6 -Ellund.S52.T038 296.5 -Ellund.S52.T039 299.9 -Ellund.S52.T040 300.3 -Ellund.S52.T041 300.8 -Ellund.S52.T042 302.1 -Ellund.S52.T043 298.1 -Ellund.S52.T044 297.1 -Ellund.S52.T045 296.9 -Ellund.S52.T046 296.9 -Ellund.S52.T047 297.9 -Ellund.S52.T048 299.8 -Ellund.S52.T049 299.5 -Ellund.S52.T050 299.8 -Ellund.S52.T051 300.5 -Ellund.S52.T052 300.2 -Ellund.S52.T053 300.4 -Ellund.S52.T054 300.1 -Ellund.S52.T055 299.9 -Ellund.S52.T056 299.3 -Ellund.S52.T057 299.2 -Ellund.S52.T058 299.4 -Ellund.S52.T059 298.1 -Ellund.S52.T060 297.7 -Ellund.S52.T061 298.1 -Ellund.S52.T062 297.9 -Ellund.S52.T063 298.6 -Ellund.S52.T064 298.7 -Ellund.S52.T065 298.5 -Ellund.S52.T066 298.9 -Ellund.S52.T067 298.8 -Ellund.S52.T068 298.8 -Ellund.S52.T069 299.1 -Ellund.S52.T070 298.6 -Ellund.S52.T071 297.6 -Ellund.S52.T072 298 -Ellund.S52.T073 297.6 -Ellund.S52.T074 292.2 -Ellund.S52.T075 291.3 -Ellund.S52.T076 290.8 -Ellund.S52.T077 289.8 -Ellund.S52.T078 296 -Ellund.S52.T079 299.1 -Ellund.S52.T080 299 -Ellund.S52.T081 299.1 -Ellund.S52.T082 299.1 -Ellund.S52.T083 299.1 -Ellund.S52.T084 298.5 -Ellund.S52.T085 297.3 -Ellund.S52.T086 297.5 -Ellund.S52.T087 297.6 -Ellund.S52.T088 297.3 -Ellund.S52.T089 297.6 -Ellund.S52.T090 297.9 -Ellund.S52.T091 298.6 -Ellund.S52.T092 298.5 -Ellund.S52.T093 298.5 -Ellund.S52.T094 298.7 -Ellund.S52.T095 299 -Ellund.S52.T096 298.6 -Ellund.S52.T097 298.7 -Ellund.S52.T098 298.2 -Ellund.S52.T099 297.5 -Ellund.S52.T100 297.8 -Ellund.S52.T101 297.7 -Ellund.S52.T102 297.9 -Ellund.S52.T103 297.1 -Ellund.S52.T104 296.7 -Ellund.S52.T105 296.9 -Ellund.S52.T106 296.8 -Ellund.S52.T107 297.3 -Ellund.S52.T108 297.2 -Ellund.S52.T109 297.7 -Ellund.S52.T110 298.2 -Ellund.S52.T111 298.4 -Ellund.S52.T112 298.3 -Ellund.S52.T113 297.5 -Ellund.S52.T114 296.5 -Ellund.S52.T115 296.3 -Ellund.S52.T116 296.1 -Ellund.S52.T117 296.1 -Ellund.S52.T118 296.3 -Ellund.S52.T119 296.5 -Ellund.S52.T120 296.5 -Ellund.S52.T121 226.7 -Ellund.S52.T122 226.7 -Ellund.S52.T123 226.4 -Ellund.S52.T124 220.3 -Ellund.S52.T125 217.9 -Ellund.S52.T126 218.3 -Ellund.S52.T127 222.5 -Ellund.S52.T128 222.6 -Ellund.S52.T129 222.6 -Ellund.S52.T130 222.7 -Ellund.S52.T131 222.6 -Ellund.S52.T132 222.7 -Ellund.S52.T133 223 -Ellund.S52.T134 223.5 -Ellund.S52.T135 223.6 -Ellund.S52.T136 223.7 -Ellund.S52.T137 223.5 -Ellund.S52.T138 223.4 -Ellund.S52.T139 223.5 -Ellund.S52.T140 223.3 -Ellund.S52.T141 222.9 -Ellund.S52.T142 220.4 -Ellund.S52.T143 220.2 -Ellund.S52.T144 220.1 -Ellund.S52.T145 220.1 -Ellund.S52.T146 220.2 -Ellund.S52.T147 220 -Ellund.S52.T148 221 -Ellund.S52.T149 223.2 -Ellund.S52.T150 225.7 -Ellund.S52.T151 222.6 -Ellund.S52.T152 207.1 -Ellund.S52.T153 207 -Ellund.S52.T154 206.9 -Ellund.S52.T155 206.7 -Ellund.S52.T156 206.8 -Ellund.S52.T157 207.3 -Ellund.S52.T158 209 -Ellund.S52.T159 208.9 -Ellund.S52.T160 208.9 -Ellund.S52.T161 208.7 -Ellund.S52.T162 208.8 -Ellund.S52.T163 208.8 -Ellund.S52.T164 208.8 -Ellund.S52.T165 209 -Ellund.S52.T166 211.8 -Ellund.S52.T167 211.8 -Ellund.S52.T168 212.2 -* ----------------------------------------------------- \ No newline at end of file diff --git a/base/addons/gas/GDATA-MR-old.inc b/base/addons/gas/GDATA-MR-old.inc deleted file mode 100644 index 77fbc523..00000000 --- a/base/addons/gas/GDATA-MR-old.inc +++ /dev/null @@ -1,341 +0,0 @@ -TABLE GDATA_MR(GGG,GDATASET) - GDTYPE GDFUEL GDCV GDCB GDDESO2 GDNOX GDCH4 GDOMFCOST0 GDOMVCOST0 GDFE -'amagerf_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.922418703225379 -'amagerf_ST_B0_MW' 2 10 0.00 0.18 95.90 0.10 0.00 272.00 25.00 0.821780609388745 -'amagerf_ST_E0_CO1' 3 3 0.15 0.71 96.00 0.04 16.00 1.80 0.333763061604313 -'amagerf_ST_E0_CO3' 3 3 0.15 0.75 96.00 0.04 16.00 1.80 0.439429183716792 -'amagerf_ST_E0_ST2' 3 11 0.15 0.52 0.00 0.04 25.00 2.70 0.289521678413297 -'billesboelle_GE_B0_NG' 2 2 0.00 0.58 0.17 0.40 82.00 0.02 0.96151107538341 -'billesboelle_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.93993015663171 -'brande_GE_B0_NG' 2 2 0.00 0.75 0.17 0.40 82.00 0.02 0.852915886478752 -'brande_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.880357142857143 -'brande_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.987967725091219 -'brande_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.999981661134442 -'brande_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'broendby_GE_B0_NG' 2 2 0.00 0.70 0.17 0.40 82.00 0.02 0.871020537161038 -'broendby_GE_C0_LO' 1 6 0.00 0.17 0.40 82.00 0.02 0.371755652295276 -'broendby_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.975232448220041 -'broendby_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.908757550200865 -'broendby_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.758863854556707 -'broendby_SOLE_SOL' 11 16 0.00 0.00 0.00 0.00 0.00 0.00 1 -'broendby_ST_E0_CO1' 3 3 0.15 0.75 96.00 0.04 16.00 1.80 0.428113489887122 -'dragoer_GE_B0_NG' 2 2 0.00 0.71 0.17 0.40 82.00 0.02 0.902494396211146 -'dragoer_GE_C0_NG' 1 2 0.00 0.17 0.40 82.00 0.02 0.42 -'dragoer_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.910037011174666 -'dragoer_SOLE_SOL' 11 16 0.00 0.00 0.00 0.00 0.00 0.00 1 -'egtved_CC_B0_MW' 2 10 0.00 0.62 95.90 0.10 0.00 272.00 25.00 0.788405025998439 -'egtved_GE_B0_NG' 2 2 0.00 0.78 0.17 0.40 82.00 0.02 0.913151733013408 -'egtved_GE_C0_NG' 1 2 0.00 0.17 0.40 82.00 0.02 0.42 -'egtved_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.943585538924134 -'egtved_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.940222142642606 -'egtved_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.978159629745657 -'egtved_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'ellidshoej_GE_B0_NG' 2 2 0.00 0.70 0.17 0.40 82.00 0.02 0.895364238272827 -'ellidshoej_GT_B0_NG' 2 2 0.00 0.49 0.12 1.5e-03 8.00 5.80 0.839999274540564 -'ellidshoej_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.910560869993016 -'ellidshoej_HO_NG' 4 2 0.00 0.01 0.40 0.50 1.78460333159077 -'ellidshoej_HO_ST' 4 11 0.00 0.08 20.00 0.00 1.00035242491076 -'froeslev_CC_B0_NG' 2 2 0.00 0.79 0.05 5.0e-03 10.00 2.80 0.867274248568445 -'froeslev_GE_B0_NG' 2 2 0.00 0.78 0.17 0.40 82.00 0.02 0.897783553522783 -'froeslev_HO_CO4' 4 3 0.00 0.01 0.90 0.75 0.9 -'froeslev_HO_CO5' 4 3 0.00 0.01 0.90 0.75 0.9 -'froeslev_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.882708277062975 -'froeslev_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.790018662784373 -'froeslev_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.986608710187339 -'froeslev_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.993103448275862 -'froeslev_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.879020408163265 -'froeslev_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'froeslev_ST_C0_CO3' 1 3 0.00 96.00 0.04 16.00 1.80 0.345849361910849 -'froeslev_ST_E0_CO2' 3 3 0.15 7.36 96.00 0.04 16.00 1.80 0.405884709466712 -'haverslev_GE_B0_NG' 2 2 0.00 0.68 0.17 0.40 82.00 0.02 0.905686711575149 -'haverslev_GT_B0_NG' 2 2 0.00 0.67 0.08 0.909050128836475 -'haverslev_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.999881090887909 -'haverslev_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.876344362181598 -'haverslev_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.884791276589154 -'haverslev_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.956373454297346 -'haverslev_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.978300271666168 -'haverslev_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.98899022492414 -'haverslev_HO_WW' 4 13 0.00 0.08 20.00 0.00 1.02964335760037 -'haverslev_ST_B0_MW' 2 10 0.00 0.27 95.90 0.10 0.00 272.00 25.00 1.00347563813305 -'herning_CC_B0_NG' 2 2 0.00 1.24 0.05 5.0e-03 10.00 2.80 0.86070885940225 -'herning_GE_B0_LO' 2 6 0.00 0.66 0.17 0.40 82.00 0.02 0.96949942610512 -'herning_GE_B0_NG' 2 2 0.00 0.73 0.17 0.40 82.00 0.02 0.883415027727384 -'herning_GE_B0_WW' 2 13 0.00 0.35 0.06 2.1e-03 82.00 0.02 0.866944 -'herning_GE_C0_LO1' 1 6 0.00 0.17 0.40 82.00 0.02 0.387434482710191 -'herning_GE_C0_LO2' 1 6 0.00 0.17 0.40 82.00 0.02 0.387628199951546 -'herning_GE_C0_LO3' 1 6 0.00 0.17 0.40 82.00 0.02 0.387628199951546 -'herning_GE_C0_LO4' 1 6 0.00 0.17 0.40 82.00 0.02 0.387628199951546 -'herning_GT_B0_NG' 2 2 0.00 0.52 0.09 3.88888888888889e-04 2.07 1.50 0.735095162455347 -'herning_GT_C0_LO5' 1 6 0.00 0.12 1.5e-03 8.00 5.80 0.191373626373626 -'herning_HO_CO6' 4 3 0.00 0.01 0.90 0.75 0.920715875723601 -'herning_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.893354563822181 -'herning_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.91690148202878 -'herning_HO_LO2' 4 6 0.00 0.01 0.90 0.75 0.926286937623666 -'herning_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.846542406045587 -'herning_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.939000710727021 -'herning_HO_NG2' 4 2 0.00 0.01 0.40 0.50 0.9 -'herning_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.927899094971378 -'herning_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.948928513467262 -'herning_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.957569766522318 -'herning_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'herning_ST_B0_CO' 2 3 0.00 0.22 96.00 0.04 16.00 1.80 0.908265717574404 -'herning_ST_B0_FO' 2 5 0.00 0.07 96.00 0.04 16.00 1.80 0.980933253359316 -'herning_ST_B0_MW' 2 10 0.00 0.17 95.90 0.10 0.00 272.00 25.00 0.890662055686172 -'herning_ST_B0_WW' 2 13 0.00 0.06 0.06 2.1e-03 82.00 0.02 0.854774712144254 -'herning_ST_E0_CO1' 3 3 0.15 0.46 96.00 0.04 16.00 1.80 0.332045173364601 -'herning_ST_E0_CO3' 3 3 0.15 0.76 96.00 0.04 16.00 1.80 0.400721277234751 -'herning_ST_E0_CO4' 3 3 0.15 0.76 96.00 0.04 16.00 1.80 0.422312733610805 -'herning_ST_E0_WW1' 3 13 0.15 0.51 0.00 0.04 25.00 2.70 0.386777648704521 -'hoejby_GE_B0_NG' 2 2 0.00 0.83 0.17 0.40 82.00 0.02 0.876279415597549 -'hoejby_GT_B0_NG' 2 2 0.00 0.55 0.10 1.11971830985916e-03 5.97 4.32 0.859990099530198 -'hoejby_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.927061048140357 -'hoejby_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.924166868369476 -'hoejby_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.93376554225127 -'hoejby_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.939378614229185 -'hoejby_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.950824605998983 -'hoejby_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.942698445507369 -'hoejby_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'hoejby_ST_B0_MW' 2 10 0.00 0.29 95.90 0.10 0.00 272.00 25.00 1.03514399687223 -'hoejby_ST_B0_MW1' 2 10 0.00 0.33 95.90 0.10 0.00 272.00 25.00 0.897987478601386 -'hoejby_ST_E0_NG3' 3 2 0.15 0.82 96.00 0.04 16.00 1.80 0.372287505664969 -'hoejby_ST_E0_CO2' 3 3 0.15 0.90 96.00 0.04 16.00 1.80 0.454594134055191 -'karlslunde_GE_B0_NG' 2 2 0.00 0.71 0.17 0.40 82.00 0.02 0.907004665258234 -'karlslunde_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.851535081497559 -'karlslunde_HO_NG' 4 2 0.00 0.01 0.40 0.50 1.00764081624194 -'karup_GE_B0_NG' 2 2 0.00 0.75 0.17 0.40 82.00 0.02 0.90448101396322 -'karup_HO_LO' 4 6 0.00 0.01 0.90 0.75 1.00910677709404 -'karup_HO_NG' 4 2 0.00 0.01 0.40 0.50 1.00325323734429 -'karup_HO_WW' 4 13 0.00 0.08 20.00 0.00 1.01726256220603 -'karup_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'koege_GE_B0_NG' 2 2 0.00 0.79 0.17 0.40 82.00 0.02 0.839400340135045 -'koege_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.94353060491935 -'koege_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.807434383333148 -'koege_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.910517368954099 -'koege_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.853501477832512 -'koege_ST_B0_NG' 2 2 0.00 0.16 96.00 0.04 16.00 1.80 0.984788670984333 -'koege_ST_B0_WW' 2 13 0.00 1.10 0.06 2.1e-03 82.00 0.02 0.444250579815866 -'koelbjerg_GE_B0_NG' 2 2 0.00 0.76 0.17 0.40 82.00 0.02 0.887356298743507 -'koelbjerg_HO_CO' 4 3 0.00 0.01 0.90 0.75 0.931297064049412 -'koelbjerg_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.898986105895607 -'koelbjerg_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.913813498637514 -'koelbjerg_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.939805327555591 -'koelbjerg_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.923351047202251 -'koelbjerg_HO_WW' 4 13 0.00 0.08 20.00 0.00 1.01322292984934 -'koelbjerg_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'koelbjerg_ST_B0_CO' 2 3 0.00 0.14 96.00 0.04 16.00 1.80 0.900006582890721 -'koelbjerg_ST_B0_FO' 2 5 0.00 0.03 96.00 0.04 16.00 1.80 0.919996023856402 -'koelbjerg_ST_B0_NG' 2 2 0.00 0.10 96.00 0.04 16.00 1.80 1.10568836357856 -'koelbjerg_ST_B0_WW' 2 13 0.00 0.35 0.06 2.1e-03 82.00 0.02 0.846078496553864 -'liselskaer_CC_B0_MW' 2 10 0.00 0.37 95.90 0.10 0.00 272.00 25.00 0.731216004218123 -'liselskaer_GE_B0_NG' 2 2 0.00 0.76 0.17 0.40 82.00 0.02 0.899567057921386 -'liselskaer_HO_NG' 4 2 0.00 0.01 0.40 0.50 1.0333544577565 -'liselskaer_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'liselskaer_ST_B0_NG' 2 2 0.00 0.85 96.00 0.04 16.00 1.80 0.879888695352103 -'litorup_GE_B0_NG' 2 2 0.00 0.70 0.17 0.40 82.00 0.02 0.925577913920772 -'litorup_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.971062342202369 -'litorup_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.761854109969479 -'lynge_CC_B0_NG' 2 2 0.00 0.96 0.05 5.0e-03 10.00 2.80 0.892815553843547 -'lynge_GE_B0_NG' 2 2 0.00 0.68 0.17 0.40 82.00 0.02 0.878064107180874 -'lynge_GE_B0_WW' 2 13 0.00 0.32 0.06 2.1e-03 82.00 0.02 0.270810810810811 -'lynge_GT_B0_NG' 2 2 0.00 1.05 0.05 1.49840595111583e-03 7.87 3.86 0.854537734620137 -'lynge_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.810783555749773 -'lynge_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.963350441387805 -'lynge_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.899512848133447 -'lynge_HO_WW' 4 13 0.00 0.08 20.00 0.00 1.10024310823463 -'lynge_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'lynge_ST_B0_MW' 2 10 0.00 0.29 95.90 0.10 0.00 272.00 25.00 0.87180323093409 -'middelfart_GE_B0_NG' 2 2 0.00 0.74 0.17 0.40 82.00 0.02 0.898480141568657 -'middelfart_HO_LO' 4 6 0.00 0.01 0.90 0.75 1.04616397691005 -'middelfart_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.848459101781476 -'middelfart_HO_NG' 4 2 0.00 0.01 0.40 0.50 1.05111968654446 -'middelfart_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'maaloev_GE_B0_NG' 2 2 0.00 0.76 0.17 0.40 82.00 0.02 0.882718591303977 -'maaloev_GE_C0_LO4' 1 6 0.00 0.17 0.40 82.00 0.02 0.42 -'maaloev_GT_B0_NG' 2 2 0.00 0.59 0.08 0.761216418368918 -'maaloev_GT_C0_LO6' 1 6 0.00 0.01 1.5e-03 7.80 2.50 0.178035952586545 -'maaloev_GT_C0_NG5' 1 2 0.00 0.01 1.5e-03 7.80 2.50 0.42 -'maaloev_GT_C0_NG7' 1 2 0.00 0.08 0.3 -'maaloev_HO_FO8' 4 5 0.00 0.01 0.90 0.75 0.920715875723601 -'maaloev_HO_FO9' 4 5 0.00 0.01 0.90 0.75 0.920715875723601 -'maaloev_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.886904746733735 -'maaloev_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.819028928253104 -'maaloev_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.936636643389796 -'maaloev_HO_WO' 4 12 0.00 0.08 20.00 0.00 1.10768269397674 -'maaloev_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.887549543788595 -'maaloev_ST_C0_FO2' 1 5 0.00 96.00 0.04 16.00 1.80 0.278000849451124 -'maaloev_ST_C0_FO3' 1 5 0.00 96.00 0.04 16.00 1.80 0.293051404662283 -'noerskov_GE_B0_NG' 2 2 0.00 0.81 0.17 0.40 82.00 0.02 0.885169436940683 -'noerskov_GT_B0_NG' 2 2 0.00 0.21 0.08 0.927751180623156 -'noerskov_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.826646251626881 -'noerskov_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.690475004093022 -'noerskov_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.921092092030374 -'noerskov_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.837095345104334 -'noerskov_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.852113172572511 -'noerskov_ST_B0_CO' 2 3 0.00 0.02 96.00 0.04 16.00 1.80 0.942519825149872 -'nyborg_CC_B0_MW' 2 10 0.00 0.12 95.90 0.10 0.00 272.00 25.00 0.822512066962087 -'nyborg_GE_B0_NG' 2 2 0.00 0.44 0.17 0.40 82.00 0.02 0.915810576123031 -*'nyborg_GT_B0_NG' 2 2 0.00 0.61 0.08 -'nyborg_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.914965131710414 -'nyborg_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.859102353668779 -'nyborg_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'nybro_GE_B0_NG' 2 2 0.00 0.77 0.17 0.40 82.00 0.02 0.918143080973865 -'nybro_GT_B0_NG' 2 2 0.00 0.47 0.08 0.737923743133244 -'nybro_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.93449374560109 -'nybro_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.962016761774341 -'nybro_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.983487855336756 -'nybro_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'OEST_GE_B0_LO' 2 6 0.00 0.77 0.17 0.40 82.00 0.02 0.762788664090193 -'OEST_GE_B0_NG' 2 2 0.00 0.61 0.17 0.40 82.00 0.02 0.86700599465545 -'OEST_GE_C0_LO2' 1 6 0.00 0.17 0.40 82.00 0.02 0.30806697108067 -'OEST_HO_CO' 4 3 0.00 0.01 0.90 0.75 0.800002387211707 -'OEST_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.877763037006505 -'OEST_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.89144061089545 -'OEST_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.824050280276879 -'OEST_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.882212488651046 -'OEST_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.982385327446476 -'OEST_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.952702684274961 -'OEST_ST_B0_CO4' 2 3 0.00 0.52 96.00 0.04 16.00 1.80 0.731559594344833 -'OEST_ST_B0_FO' 2 5 0.00 0.19 96.00 0.04 16.00 1.80 0.889825019263249 -'OEST_ST_B0_FO1' 2 5 0.00 0.18 96.00 0.04 16.00 1.80 0.874462974401208 -'OEST_ST_B0_MW' 2 10 0.00 0.29 95.90 0.10 0.00 272.00 25.00 0.850499627496659 -'OEST_ST_B0_ST' 2 11 0.00 0.43 0.13 5.0e-04 82.00 0.02 0.879341260500792 -'OEST_ST_C0_FO3' 1 5 0.00 96.00 0.04 16.00 1.80 0.290269529465509 -'pottehuse_GE_B0_NG' 2 2 0.00 0.72 0.17 0.40 82.00 0.02 0.898883129535391 -'pottehuse_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.934937823258441 -'pottehuse_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.910434226677641 -'pottehuse_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.872874245043883 -'pottehuse_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.995380843551156 -'pottehuse_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'pottehuse_SOLE_SOL' 11 16 0.00 0.00 0.00 0.00 0.00 0.00 1 -'pottehuse_ST_B0_MW' 2 10 0.00 0.36 95.90 0.10 0.00 272.00 25.00 0.966851750227934 -'ringsted_CC_B0_MW' 2 10 0.00 0.26 95.90 0.10 0.00 272.00 25.00 0.700725662926452 -'ringsted_GE_B0_NG' 2 2 0.00 0.80 0.17 0.40 82.00 0.02 0.855982938998647 -'ringsted_GT_C0_LO2' 1 6 0.00 0.01 1.5e-03 7.80 2.50 0.16700195165891 -'ringsted_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.872065683896249 -'ringsted_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.986121971534401 -'ringsted_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.833644223568657 -'ringsted_ST_B0_ST' 2 11 0.00 0.31 0.13 5.0e-04 82.00 0.02 0.89194590350278 -'ringsted_ST_B0_ST3' 2 11 0.00 0.38 0.13 5.0e-04 82.00 0.02 0.874841528255722 -'slagelse_GE_B0_NG' 2 2 0.00 0.65 0.17 0.40 82.00 0.02 0.892788238911068 -'slagelse_GE_C0_NG' 1 2 0.00 0.17 0.40 82.00 0.02 0.350279836853913 -'slagelse_GT_B0_NG' 2 2 0.00 0.69 0.08 0.872339878650794 -'slagelse_HO_FO3' 4 5 0.00 0.01 0.90 0.75 0.920715875723601 -'slagelse_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.87969529009311 -'slagelse_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.870875331564987 -'slagelse_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.935588571428571 -'slagelse_HO_WW' 4 13 0.00 0.08 20.00 0.00 1.03476115721014 -'slagelse_ST_B0_MW' 2 10 0.00 0.28 95.90 0.10 0.00 272.00 25.00 0.810973250811368 -'slagelse_ST_C0_CO1' 1 3 0.00 96.00 0.04 16.00 1.80 0.325972210014942 -'slagelse_ST_C0_CO2' 1 3 0.00 96.00 0.04 16.00 1.80 0.389051247558835 -'soroe_GE_B0_NG' 2 2 0.00 0.69 0.17 0.40 82.00 0.02 0.910988160400231 -'soroe_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.880813465556943 -'soroe_HO_FO6' 4 5 0.00 0.01 0.90 0.75 0.920715875723601 -'soroe_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.933746134624042 -'soroe_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.941745629514801 -'soroe_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.910397220241423 -'soroe_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.88827249564705 -'soroe_ST_E0_CO1' 3 3 0.15 4.26 96.00 0.04 16.00 1.80 0.394688418078519 -'soroe_ST_E0_CO2' 3 3 0.15 2.50 96.00 0.04 16.00 1.80 0.374375862341591 -'soroe_ST_E0_CO3' 3 3 0.15 2.50 96.00 0.04 16.00 1.80 0.403812385571048 -'soroe_ST_E0_CO4' 3 3 0.15 1.47 96.00 0.04 16.00 1.80 0.359087616713138 -'standst_GE_B0_NG' 2 2 0.00 0.77 0.17 0.40 82.00 0.02 0.906871114401447 -*'standst_GT_B0_NG' 2 2 0.00 0.30 0.08 -'standst_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.887576754421319 -'standst_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.874446295062132 -'standst_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.903957012132946 -'standst_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'standst_ST_B0_MW' 2 10 0.00 0.31 95.90 0.10 0.00 272.00 25.00 0.737592929555498 -'stenlille_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.899875364105844 -'stenlille_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.969888627065695 -'sydhavnen_CC_B0_NG' 2 2 0.00 0.63 0.05 5.0e-03 10.00 2.80 0.77359038341798 -'sydhavnen_CC_E0_NG1' 3 2 0.15 1.04 0.05 5.0e-03 14.00 1.50 0.462956542730712 -'sydhavnen_GE_B0_NG' 2 2 0.00 0.66 0.17 0.40 82.00 0.02 0.866608695652174 -'sydhavnen_GE_C0_LO3' 1 6 0.00 0.17 0.40 82.00 0.02 0.42 -'sydhavnen_GT_B0_NG2' 2 2 0.00 0.54 0.01 1.5e-03 7.80 2.50 0.848435445455657 -'sydhavnen_GT_B0_NG4' 2 2 0.00 0.53 0.12 1.5e-03 8.00 5.80 0.865962281856528 -'sydhavnen_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.810118957988255 -'sydhavnen_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.922202149179134 -'sydhavnen_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.850341390906256 -'sydhavnen_SOLE_SOL' 11 16 0.00 0.00 0.00 0.00 0.00 0.00 1 -'sydhavnen_ST_E0_NG1' 3 2 0.15 0.24 96.00 0.04 16.00 1.80 0.320067779899708 -'sydhavnen_ST_E0_NG2' 3 2 0.15 0.33 96.00 0.04 16.00 1.80 0.304958563359101 -'taulov_CC_B0_NG' 2 2 0.00 0.55 0.05 5.0e-03 10.00 2.80 0.828906586057675 -'taulov_GE_B0_NG' 2 2 0.00 0.75 0.17 0.40 82.00 0.02 0.853329120385388 -'taulov_GT_B0_NG' 2 2 0.00 0.55 0.12 1.5e-03 8.00 5.80 0.81158558554439 -'taulov_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.884937826782838 -'taulov_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.873275989252909 -'taulov_HO_NG4' 4 2 0.00 0.01 0.40 0.50 0.9 -'taulov_ST_E0_NG1' 3 2 0.15 0.68 96.00 0.04 16.00 1.80 0.403812385571048 -'taulov_ST_E0_NG3' 3 2 0.15 0.88 96.00 0.04 16.00 1.80 0.455385691020657 -'terkelsboel_CC_B0_NG' 2 2 0.00 0.86 0.05 5.0e-03 10.00 2.80 0.906489256198347 -'terkelsboel_GE_B0_NG' 2 2 0.00 0.68 0.17 0.40 82.00 0.02 0.918289619400934 -'terkelsboel_GT_B0_NG' 2 2 0.00 0.48 0.11 1.44755244755245e-03 7.72 5.59 0.912716297913876 -'terkelsboel_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.904381555293812 -'terkelsboel_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.959062437965399 -'terkelsboel_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.9266115327971 -'terkelsboel_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'terkelsboel_ST_B0_WW' 2 13 0.00 0.10 0.06 2.1e-03 82.00 0.02 0.928366129943049 -'torslunde_GE_B0_NG' 2 2 0.00 0.66 0.17 0.40 82.00 0.02 0.874391865617908 -'torslunde_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.901975404638602 -'torslunde_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.647658137473184 -'torslunde_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.921871805564678 -'torslunde_ST_B0_MW' 2 10 0.00 0.29 95.90 0.10 0.00 272.00 25.00 0.835456765768288 -'ullerslev_GE_B0_NG' 2 2 0.00 0.77 0.17 0.40 82.00 0.02 0.875124284289181 -'ullerslev_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.961044091697121 -'ullerslev_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.812902807309753 -'ullerslev_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'vallensbaek_GE_B0_NG' 2 2 0.00 0.66 0.17 0.40 82.00 0.02 0.880186656055624 -'vallensbaek_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.766045384422719 -'vallensbaek_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.756600478784132 -'vallensbaek_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.969389554240465 -'vallensbaek_HO_WO' 4 12 0.00 0.08 20.00 0.00 1 -'vallensbaek_ST_B0_MW' 2 10 0.00 0.18 95.90 0.10 0.00 272.00 25.00 0.851685559087456 -'varde_GE_B0_NG' 2 2 0.00 0.81 0.17 0.40 82.00 0.02 0.866816009531384 -'varde_GT_B0_NG' 2 2 0.00 0.46 0.08 0.852356118467559 -'varde_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.859188544152745 -'varde_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.870830326684186 -'varde_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.901953451182568 -'varde_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.94292949421061 -'varde_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.972627372284606 -'varde_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'varde_ST_B0_MW' 2 10 0.00 0.22 95.90 0.10 0.00 272.00 25.00 0.956341487675551 -'varde_ST_E0_CO1' 3 3 0.15 0.82 96.00 0.04 16.00 1.80 0.460273015274646 -'VEST_GE_B0_NG' 2 2 0.00 0.80 0.17 0.40 82.00 0.02 0.914713790552023 -'VEST_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.942438719176533 -'VEST_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.918336932388455 -'VEST_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.852676053984195 -'VEST_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.784066687218694 -'VEST_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.966738864668541 -'VEST_ST_B0_ST' 2 11 0.00 0.29 0.13 5.0e-04 82.00 0.02 0.846437569765588 -'viborg_CC_B0_NG' 2 2 0.00 0.88 0.05 5.0e-03 10.00 2.80 0.845908213446733 -'viborg_CC_B0_WW' 2 13 0.00 0.26 0.06 2.1e-03 82.00 0.02 0.895702473473154 -'viborg_GE_B0_NG' 2 2 0.00 0.76 0.17 0.40 82.00 0.02 0.876535248347928 -'viborg_GT_B0_NG' 2 2 0.00 0.41 0.11 1.14179104477612e-03 6.08 4.41 0.861980552614891 -'viborg_HO_FO' 4 5 0.00 0.01 0.90 0.75 0.838726700727277 -'viborg_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.863730019824607 -'viborg_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.959715933658655 -'viborg_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.902032609569251 -'viborg_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.861940615511178 -'viborg_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.982081596977569 -'viborg_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'viborg_ST_B0_MW' 2 10 0.00 0.34 95.90 0.10 0.00 272.00 25.00 0.804908714316671 -'aalborg_CC_B0_NG' 2 2 0.00 1.07 0.05 5.0e-03 10.00 2.80 0.86662752545309 -'aalborg_GE_B0_NG' 2 2 0.00 0.73 0.17 0.40 82.00 0.02 0.918028922807716 -'aalborg_GE_C0_NG' 1 2 0.00 0.17 0.40 82.00 0.02 0.42 -'aalborg_GT_B0_NG' 2 2 0.00 0.47 0.11 1.20870870870871e-03 6.44 4.67 0.778406676813188 -'aalborg_GT_C0_LO3' 1 6 0.00 0.12 1.5e-03 8.00 5.80 0.202625204582651 -'aalborg_HO_CO5' 4 3 0.00 0.01 0.90 0.75 0.920715875723601 -'aalborg_HO_LO' 4 6 0.00 0.01 0.90 0.75 0.901540302267283 -'aalborg_HO_MW' 4 10 0.00 95.90 0.10 0.00 49.00 5.10 0.856159768532087 -'aalborg_HO_NG' 4 2 0.00 0.01 0.40 0.50 0.942594202988504 -'aalborg_HO_ST' 4 11 0.00 0.08 20.00 0.00 0.893428294582883 -'aalborg_HO_WO' 4 12 0.00 0.08 20.00 0.00 0.991141112788141 -'aalborg_HO_WW' 4 13 0.00 0.08 20.00 0.00 0.927417892176339 -'aalborg_HYRR_HY' 9 15 0.00 0.00 0.00 0.00 0.00 0.00 1 -'aalborg_ST_B0_MW' 2 10 0.00 0.29 95.90 0.10 0.00 272.00 25.00 0.843988610233222 -'aalborg_ST_B0_NG' 2 2 0.00 0.05 96.00 0.04 16.00 1.80 0.901097662248667 -'aalborg_ST_E0_CO2' 3 3 0.15 6.78 96.00 0.04 16.00 1.80 0.374987610727755 -'aalborg_ST_E0_CO4' 3 3 0.15 0.90 96.00 0.04 16.00 1.80 0.469748583629186 -; - -GDATA(GGGMR,GDATASET)=GDATA_MR(GGGMR,GDATASET); \ No newline at end of file diff --git a/base/addons/gas/GDATA-MR.inc b/base/addons/gas/GDATA-MR.inc deleted file mode 100644 index 00f2cb54..00000000 --- a/base/addons/gas/GDATA-MR.inc +++ /dev/null @@ -1,2207 +0,0 @@ -TABLE GDATA_MR(GGG,GDATASET) - GDTYPE GDFUEL GDCV GDCB GDFE GDDESO2 GDNOX GDCH4 GDOMFCOST0 GDOMVCOST0 -'amagerf_HO_LO' 4 6 0 0.922418703225379 0.015 0.9 0.75 -'amagerf_ST_B0_MW' 2 10 0 0.183572273666027 0.803354143896783 95.9 0.109 0 272 25 -'amagerf_ST_E0_CO1' 3 3 0.15 0.715789473684211 0.297480733676603 96 0.04 16 1.8 -'amagerf_ST_E0_CO3' 3 3 0.15 0.755287009063444 0.410525757091008 96 0.04 16 1.8 -'amagerf_ST_E0_ST2' 3 11 0.15 0.529069767441861 0.242618614761152 0 0.04 25 2.7 -'billesboelle_GE_B0_NG' 2 2 0 0.580313775579008 0.960226095664813 0.17 0.4 82 0.025 -'billesboelle_HO_NG' 4 2 0 0.93993015663171 0.015 0.4 0.5 -'brande_GE_B0_NG' 2 2 0 0.75786765649876 0.782299419889603 0.17 0.4 82 0.025 -'brande_HO_LO' 4 6 0 0.880357142857143 0.015 0.9 0.75 -'brande_HO_NG' 4 2 0 0.984957378569543 0.015 0.4 0.5 -'brande_HO_WO' 4 12 0 0.980175686332044 0.08 20 0 -'brande_HYRR_HY' 9 15 0 1 0 0 0 0 0 -'broendby_GE_B0_NG' 2 2 0 0.703317168429701 0.732250194246709 0.17 0.4 82 0.025 -'broendby_GE_C0_LO' 1 6 0 0.371755652295276 0.17 0.4 82 0.025 -'broendby_HO_FO' 4 5 0 0.975232448220041 0.015 0.9 0.75 -'broendby_HO_LO' 4 6 0 0.908757550200865 0.015 0.9 0.75 -'broendby_HO_NG' 4 2 0 0.758863854556707 0.015 0.4 0.5 -'broendby_SOLE_SOL' 11 16 0 1 0 0 0 0 0 -'broendby_ST_E0_CO1' 3 3 0.15 0.755287009063444 0.397470505821311 96 0.04 16 1.8 -'dragoer_GE_B0_NG' 2 2 0 0.715649946625824 0.800444567249047 0.17 0.4 82 0.025 -'dragoer_GE_C0_NG' 1 2 0 0.42 0.17 0.4 82 0.025 -'dragoer_HO_LO' 4 6 0 0.910037011174666 0.015 0.9 0.75 -'dragoer_SOLE_SOL' 11 16 0 1 0 0 0 0 0 -'egtved_CC_B0_MW' 2 10 0 0.621030704698669 0.767652005672387 95.9 0.109 0 272 25 -'egtved_GE_B0_NG' 2 2 0 0.781169843326666 0.845184800539746 0.17 0.4 82 0.025 -'egtved_GE_C0_NG' 1 2 0 0.42 0.17 0.4 82 0.025 -'egtved_HO_LO' 4 6 0 0.865697633847704 0.015 0.9 0.75 -'egtved_HO_NG' 4 2 0 0.936230093952891 0.015 0.4 0.5 -'egtved_HO_WO' 4 12 0 0.970519185803239 0.08 20 0 -'egtved_HYRR_HY' 9 15 0 1 0 0 0 0 0 -'ellidshoej_GE_B0_NG' 2 2 0 0.700330951440733 0.890193302036873 0.17 0.4 82 0.025 -'ellidshoej_GT_B0_NG' 2 2 0 0.496943314709736 0.155991999553513 0.12 0.0015 8 5.8 -'ellidshoej_HO_LO' 4 6 0 0.910560869993016 0.015 0.9 0.75 -'ellidshoej_HO_NG' 4 2 0 1.78122727983196 0.015 0.4 0.5 -'ellidshoej_HO_ST' 4 11 0 0.977433437166049 0.08 20 0 -'froeslev_CC_B0_NG' 2 2 0 0.796058029274548 0.840468367343671 0.05 0.005 10 2.8 -'froeslev_GE_B0_NG' 2 2 0 0.781220661066641 0.782009993328424 0.17 0.4 82 0.025 -'froeslev_HO_CO4' 4 3 0 0.9 0.015 0.9 0.75 -'froeslev_HO_CO5' 4 3 0 0.9 0.015 0.9 0.75 -'froeslev_HO_FO' 4 5 0 0.882708277062975 0.015 0.9 0.75 -'froeslev_HO_LO' 4 6 0 0.790018662784373 0.015 0.9 0.75 -'froeslev_HO_NG' 4 2 0 0.986608710187339 0.015 0.4 0.5 -'froeslev_HO_ST' 4 11 0 0.993103448275862 0.08 20 0 -'froeslev_HO_WO' 4 12 0 0.879020408163265 0.08 20 0 -'froeslev_HYRR_HY' 9 15 0 1 0 0 0 0 0 -'froeslev_ST_C0_CO3' 1 3 0 0.305873457225946 96 0.04 16 1.8 -'froeslev_ST_E0_CO2' 3 3 0.15 7.36470588235294 0.387307257755467 96 0.04 16 1.8 -'haverslev_GE_B0_NG' 2 2 0 0.683482410226591 0.859659143115474 0.17 0.4 82 0.025 -'haverslev_GT_B0_NG' 2 2 0 0.67640739149119 0.909050128836475 0.08 -'haverslev_HO_FO' 4 5 0 0.999881090887909 0.015 0.9 0.75 -'haverslev_HO_LO' 4 6 0 0.876344362181598 0.015 0.9 0.75 -'haverslev_HO_MW' 4 10 0 0.767084047665826 95.9 0.109 0 49 5.1 -'haverslev_HO_NG' 4 2 0 0.95273248145122 0.015 0.4 0.5 -'haverslev_HO_ST' 4 11 0 0.975373822548164 0.08 20 0 -'haverslev_HO_WO' 4 12 0 0.98899022492414 0.08 20 0 -'haverslev_ST_B0_MW' 2 10 0 0.27331072899007 0.976680235400847 95.9 0.109 0 272 25 -'herning_CC_B0_NG' 2 2 0 1.24176724701893 0.834525348501305 0.05 0.005 10 2.8 -'herning_GE_B0_LO' 2 6 0 0.66046511627907 0.967347374104886 0.17 0.4 82 0.025 -'herning_GE_B0_NG' 2 2 0 0.737402863683826 0.733785020153837 0.17 0.4 82.0000000000001 0.025 -'herning_GE_B0_WO' 2 12 0 0.3546 0.226944 0.069 0.0021 82 0.025 -'herning_GE_C0_LO1' 1 6 0 0.42 0.17 0.4 82 0.025 -'herning_GE_C0_LO2' 1 6 0 0.42 0.17 0.4 82 0.025 -'herning_GE_C0_LO3' 1 6 0 0.42 0.17 0.4 82 0.025 -'herning_GE_C0_LO4' 1 6 0 0.42 0.17 0.4 82 0.025 -'herning_GT_B0_NG' 2 2 0 0.524083088750361 0.45606527524741 9.03703703703704E-02 3.88888888888889E-04 2.07407407407407 1.5037037037037 -'herning_GT_C0_LO5' 1 6 0 0.191373626373626 0.12 0.0015 8 5.8 -'herning_HO_CO6' 4 3 0 0.90700864029122 0.015 0.9 0.75 -'herning_HO_FO' 4 5 0 0.887583469560023 0.015 0.9 0.75 -'herning_HO_LO' 4 6 0 0.914672558513859 0.015 0.9 0.75 -'herning_HO_LO2' 4 6 0 0.926286937623666 0.015 0.9 0.75 -'herning_HO_MW' 4 10 0 0.842653760501089 95.9 0.109 0 49 5.1 -'herning_HO_NG' 4 2 0 0.938497512123432 0.015 0.4 0.5 -'herning_HO_NG2' 4 2 0 0.9 0.015 0.4 0.5 -'herning_HO_ST' 4 11 0 0.922358685020805 0.08 20 0 -'herning_HO_WO' 4 12 0 0.938961539037037 0.08 20 0 -'herning_HYRR_HY' 9 15 0 1 0 0 0 0 0 -'herning_ST_B0_CO' 2 3 0 0.226220727929744 0.883618960634134 96 0.04 16 1.8 -'herning_ST_B0_FO' 2 5 0 7.24860534124629E-02 0.712520781306027 96 0.04 16 1.8 -'herning_ST_B0_MW' 2 10 0 0.179989733916807 0.878518459120176 95.9 0.109 0 272 25 -'herning_ST_B0_WO' 2 12 0 6.84625871030464E-02 0.712520781306027 0.069 0.0021 82 0.025 -'herning_ST_E0_CO1' 3 3 0.15 0.461318051575931 0.307959473780625 96 0.04 16 1.8 -'herning_ST_E0_CO3' 3 3 0.15 0.769230769230769 0.366574133455059 96 0.04 16 1.8 -'herning_ST_E0_CO4' 3 3 0.15 0.769230769230769 0.388344440059035 96 0.04 16 1.8 -'herning_ST_E0_WO1' 3 12 0.15 0.511494252873563 0.364824761940699 0 0.04 25 2.7 -'hoejby_GE_B0_NG' 2 2 0 0.830178503379087 0.751149300167299 0.17 0.4 82 0.025 -'hoejby_GT_B0_NG' 2 2 0 0.555884620733494 0.235925359322043 0.109859154929577 1.11971830985916E-03 5.97183098591549 4.32957746478873 -'hoejby_HO_FO' 4 5 0 0.927061048140357 0.015 0.9 0.75 -'hoejby_HO_LO' 4 6 0 0.924166868369476 0.015 0.9 0.75 -'hoejby_HO_NG' 4 2 0 0.93297459708766 0.015 0.4 0.5 -'hoejby_HO_ST' 4 11 0 0.939378614229185 0.08 20 0 -'hoejby_HO_WO' 4 12 0 0.950824605998983 0.08 20 0 -'hoejby_HYRR_HY' 9 15 0 1 0 0 0 0 0 -'hoejby_ST_B0_MW' 2 10 0 0.29226694632811 1.02632332444596 95.9 0.109 0 272 25 -'hoejby_ST_B0_MW1' 2 10 0 0.336057651547266 0.870128724435103 95.9 0.109 0 272 25 -'hoejby_ST_E0_NG3' 3 2 0.15 0.825153374233129 0.341178147885138 96 0.04 16 1.8 -'hoejby_ST_E0_CO2' 3 3 0.15 0.904444444444445 0.419018303924098 96 0.04 16 1.8 -'karlslunde_GE_B0_NG' 2 2 0 0.716918859631733 0.788218104058496 0.17 0.4 82 0.025 -'karlslunde_HO_LO' 4 6 0 0.851535081497559 0.015 0.9 0.75 -'karlslunde_HO_NG' 4 2 0 1.00764081624194 0.015 0.4 0.5 -'karup_GE_B0_NG' 2 2 0 0.754275648198038 0.889716241523669 0.17 0.4 82 0.025 -'karup_HO_LO' 4 6 0 0.90700864029122 0.015 0.9 0.75 -'karup_HO_NG' 4 2 0 0.996013175042951 0.015 0.4 0.5 -'karup_HO_WO' 4 12 0 1.01726256220603 0.08 20 0 -'karup_HYRR_HY' 9 15 0 1 0 0 0 0 0 -'koege_GE_B0_NG' 2 2 0 0.792942297645533 0.684684154654352 0.17 0.4 82 0.025 -'koege_HO_FO' 4 5 0 0.94353060491935 0.015 0.9 0.75 -'koege_HO_LO' 4 6 0 0.807434383333148 0.015 0.9 0.75 -'koege_HO_NG' 4 2 0 0.910517368954099 0.015 0.4 0.5 -'koege_HO_ST' 4 11 0 0.853501477832512 0.08 20 0 -'koege_ST_B0_NG' 2 2 0 0.160511111111111 0.712520781306027 96 0.04 16 1.8 -'koege_ST_B0_WO' 2 12 0 1.10218081815537 0.419220511634931 0.069 0.0021 82 0.025 -'koelbjerg_GE_B0_NG' 2 2 0 0.766890799153077 0.757862740924676 0.17 0.4 82 0.025 -'koelbjerg_HO_CO' 4 3 0 0.931297064049412 0.015 0.9 0.75 -'koelbjerg_HO_FO' 4 5 0 0.898986105895607 0.015 0.9 0.75 -'koelbjerg_HO_LO' 4 6 0 0.912738335301215 0.015 0.9 0.75 -'koelbjerg_HO_NG' 4 2 0 0.928641225850213 0.015 0.4 0.5 -'koelbjerg_HO_WO' 4 12 0 0.923351047202251 0.08 20 0 -'koelbjerg_HYRR_HY' 9 15 0 1 0 0 0 0 0 -'koelbjerg_ST_B0_CO' 2 3 0 0.145894226994217 0.712520781306027 96 0.04 16 1.8 -'koelbjerg_ST_B0_FO' 2 5 0 3.65184999627105E-02 0.712520781306027 96 0.04 16 1.8 -'koelbjerg_ST_B0_NG' 2 2 0 0.105687678140379 1.24676697306392E-04 96 0.04 16 1.8 -'koelbjerg_ST_B0_WO' 2 12 0 0.355218879123064 0.81571704009042 0.069 0.0021 82 0.025 -'liselskaer_CC_B0_MW' 2 10 0 0.373172244151216 0.695820086074372 95.9 0.109 0 272 25 -'liselskaer_GE_B0_NG' 2 2 0 0.764755244602224 0.845448620569991 0.17 0.4 82 0.025 -'liselskaer_HO_NG' 4 2 0 1.02550016145121 0.015 0.4 0.5 -'liselskaer_HYRR_HY' 9 15 0 1 0 0 0 0 0 -'liselskaer_ST_B0_NG' 2 2 0 0.854012041884817 0.405302406010856 96 0.04 16 1.8 -'litorup_GE_B0_NG' 2 2 0 0.701791174297813 0.895295257066276 0.17 0.4 82 0.025 -'litorup_HO_LO' 4 6 0 0.922509225092251 0.015 0.9 0.75 -'litorup_HO_NG' 4 2 0 0.761854109969479 0.015 0.4 0.5 -'lynge_CC_B0_NG' 2 2 0 0.96386098930925 0.878171836725561 0.05 0.005 10 2.8 -'lynge_GE_B0_NG' 2 2 0 0.687196734942597 0.81582676813945 0.17 0.4 82 0.025 -'lynge_GE_B0_WO' 2 12 0 0.325396825396825 6.64864864864865E-02 0.069 0.0021 82 0.025 -'lynge_GT_B0_NG' 2 2 0 1.05243530255202 0.838220451276975 0.058586609989373 1.49840595111583E-03 7.87460148777896 3.86503719447396 -'lynge_HO_FO' 4 5 0 0.810783555749773 0.015 0.9 0.75 -'lynge_HO_MW' 4 10 0 0.963350441387805 95.9 0.109 0 49 5.1 -'lynge_HO_NG' 4 2 0 0.888766098931617 0.015 0.4 0.5 -'lynge_HO_WO' 4 12 0 1.10024310823463 0.08 20 0 -'lynge_HYRR_HY' 9 15 0 1 0 0 0 0 0 -'lynge_ST_B0_MW' 2 10 0 0.294855014694859 0.764830118116887 95.9 0.109 0 272 25 -'middelfart_GE_B0_NG' 2 2 0 0.745728859405476 0.856468076069397 0.17 0.4 82 0.025 -'middelfart_HO_LO' 4 6 0 1.04616397691005 0.015 0.9 0.75 -'middelfart_HO_MW' 4 10 0 0.806036146692402 95.9 0.109 0 49 5.1 -'middelfart_HO_NG' 4 2 0 1.05111968654446 0.015 0.4 0.5 -'middelfart_HYRR_HY' 9 15 0 1 0 0 0 0 0 -'maaloev_GE_B0_NG' 2 2 0 0.766980159752875 0.860489610229792 0.17 0.4 82 0.025 -'maaloev_GE_C0_LO4' 1 6 0 0.42 0.17 0.4 82 0.025 -'maaloev_GT_B0_NG' 2 2 0 0.599776567518503 0.28538970992445 0.08 -'maaloev_GT_C0_LO6' 1 6 0 0.172649658528927 0.015 0.0015 7.8 2.5 -'maaloev_GT_C0_NG5' 1 2 0 0.42 0.015 0.0015 7.8 2.5 -'maaloev_GT_C0_NG7' 1 2 0 0.3 0.08 -'maaloev_HO_FO8' 4 5 0 0.90700864029122 0.015 0.9 0.75 -'maaloev_HO_FO9' 4 5 0 0.90700864029122 0.015 0.9 0.75 -'maaloev_HO_LO' 4 6 0 0.886904746733735 0.015 0.9 0.75 -'maaloev_HO_MW' 4 10 0 0.819028928253104 95.9 0.109 0 49 5.1 -'maaloev_HO_NG' 4 2 0 0.931038407660837 0.015 0.4 0.5 -'maaloev_HO_WO' 4 12 0 0.887549543788595 0.08 20 0 -'maaloev_ST_C0_FO2' 1 5 0 0.178975414924203 96 0.04 16 1.8 -'maaloev_ST_C0_FO3' 1 5 0 0.241734148696492 96 0.04 16 1.8 -'noerskov_GE_B0_NG' 2 2 0 0.815192871505796 0.873852672118979 0.17 0.4 82 0.025 -'noerskov_GT_B0_NG' 2 2 0 0.219612840584987 0.927751180623156 0.08 -'noerskov_HO_FO' 4 5 0 0.826646251626881 0.015 0.9 0.75 -'noerskov_HO_LO' 4 6 0 0.690475004093022 0.015 0.9 0.75 -'noerskov_HO_NG' 4 2 0 0.921092092030374 0.015 0.4 0.5 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/base/addons/gas/GGGMRset.inc b/base/addons/gas/GGGMRset.inc deleted file mode 100644 index 9d44e73b..00000000 --- a/base/addons/gas/GGGMRset.inc +++ /dev/null @@ -1,324 +0,0 @@ -'amagerf_HO_LO' -'amagerf_ST_B0_MW' -'amagerf_ST_E0_CO1' -'amagerf_ST_E0_CO3' -'amagerf_ST_E0_ST2' -'billesboelle_GE_B0_NG' -'billesboelle_HO_NG' -'brande_GE_B0_NG' -'brande_HO_LO' -'brande_HO_NG' -'brande_HO_WO' -'brande_HYRR_HY' -'broendby_GE_B0_NG' -'broendby_GE_C0_LO' -'broendby_HO_FO' -'broendby_HO_LO' -'broendby_HO_NG' -'broendby_SOLE_SOL' -'broendby_ST_E0_CO1' -'dragoer_GE_B0_NG' -'dragoer_GE_C0_NG' -'dragoer_HO_LO' -'dragoer_SOLE_SOL' -'egtved_CC_B0_MW' -'egtved_GE_B0_NG' -'egtved_GE_C0_NG' -'egtved_HO_LO' -'egtved_HO_NG' -'egtved_HO_WO' -'egtved_HYRR_HY' -'ellidshoej_GE_B0_NG' -'ellidshoej_GT_B0_NG' -'ellidshoej_HO_LO' -'ellidshoej_HO_NG' -'ellidshoej_HO_ST' -'froeslev_CC_B0_NG' -'froeslev_GE_B0_NG' -'froeslev_HO_CO4' -'froeslev_HO_CO5' -'froeslev_HO_FO' -'froeslev_HO_LO' -'froeslev_HO_NG' -'froeslev_HO_ST' -'froeslev_HO_WO' -'froeslev_HYRR_HY' -'froeslev_ST_C0_CO3' -'froeslev_ST_E0_CO2' -'haverslev_GE_B0_NG' -'haverslev_GT_B0_NG' -'haverslev_HO_FO' -'haverslev_HO_LO' -'haverslev_HO_MW' -'haverslev_HO_NG' -'haverslev_HO_ST' -'haverslev_HO_WO' -'haverslev_ST_B0_MW' -'herning_CC_B0_NG' -'herning_GE_B0_LO' -'herning_GE_B0_NG' -'herning_GE_B0_WO' -'herning_GE_C0_LO1' -'herning_GE_C0_LO2' -'herning_GE_C0_LO3' -'herning_GE_C0_LO4' -'herning_GT_B0_NG' -'herning_GT_C0_LO5' -'herning_HO_CO6' -'herning_HO_FO' -'herning_HO_LO' -'herning_HO_LO2' -'herning_HO_MW' -'herning_HO_NG' -'herning_HO_NG2' -'herning_HO_ST' -'herning_HO_WO' -'herning_HYRR_HY' -'herning_ST_B0_CO' -'herning_ST_B0_FO' -'herning_ST_B0_MW' -'herning_ST_B0_WO' -'herning_ST_E0_CO1' -'herning_ST_E0_CO3' -'herning_ST_E0_CO4' -'herning_ST_E0_WO1' -'hoejby_GE_B0_NG' -'hoejby_GT_B0_NG' -'hoejby_HO_FO' -'hoejby_HO_LO' -'hoejby_HO_NG' -'hoejby_HO_ST' -'hoejby_HO_WO' -'hoejby_HYRR_HY' -'hoejby_ST_B0_MW' -'hoejby_ST_B0_MW1' -'hoejby_ST_E0_NG3' -'hoejby_ST_E0_CO2' -'karlslunde_GE_B0_NG' -'karlslunde_HO_LO' -'karlslunde_HO_NG' -'karup_GE_B0_NG' -'karup_HO_LO' -'karup_HO_NG' -'karup_HO_WO' -'karup_HYRR_HY' -'koege_GE_B0_NG' -'koege_HO_FO' -'koege_HO_LO' -'koege_HO_NG' -'koege_HO_ST' -'koege_ST_B0_NG' -'koege_ST_B0_WO' -'koelbjerg_GE_B0_NG' -'koelbjerg_HO_CO' -'koelbjerg_HO_FO' -'koelbjerg_HO_LO' -'koelbjerg_HO_NG' -'koelbjerg_HO_WO' -'koelbjerg_HYRR_HY' -'koelbjerg_ST_B0_CO' -'koelbjerg_ST_B0_FO' -'koelbjerg_ST_B0_NG' -'koelbjerg_ST_B0_WO' -'liselskaer_CC_B0_MW' -'liselskaer_GE_B0_NG' -'liselskaer_HO_NG' -'liselskaer_HYRR_HY' -'liselskaer_ST_B0_NG' -'litorup_GE_B0_NG' -'litorup_HO_LO' -'litorup_HO_NG' -'lynge_CC_B0_NG' -'lynge_GE_B0_NG' -'lynge_GE_B0_WO' -'lynge_GT_B0_NG' -'lynge_HO_FO' -'lynge_HO_MW' -'lynge_HO_NG' -'lynge_HO_WO' -'lynge_HYRR_HY' -'lynge_ST_B0_MW' -'middelfart_GE_B0_NG' -'middelfart_HO_LO' -'middelfart_HO_MW' -'middelfart_HO_NG' -'middelfart_HYRR_HY' -'maaloev_GE_B0_NG' -'maaloev_GE_C0_LO4' -'maaloev_GT_B0_NG' -'maaloev_GT_C0_LO6' -'maaloev_GT_C0_NG5' -'maaloev_GT_C0_NG7' -'maaloev_HO_FO8' -'maaloev_HO_FO9' -'maaloev_HO_LO' -'maaloev_HO_MW' -'maaloev_HO_NG' -'maaloev_HO_WO' -'maaloev_ST_C0_FO2' -'maaloev_ST_C0_FO3' -'noerskov_GE_B0_NG' -'noerskov_GT_B0_NG' -'noerskov_HO_FO' -'noerskov_HO_LO' -'noerskov_HO_NG' -'noerskov_HO_WO' -'noerskov_ST_B0_CO' -'nyborg_CC_B0_MW' -'nyborg_GE_B0_NG' -*'nyborg_GT_B0_NG' -'nyborg_HO_NG' -'nyborg_HO_WO' -'nyborg_HYRR_HY' -'nybro_GE_B0_NG' -'nybro_GT_B0_NG' -'nybro_HO_LO' -'nybro_HO_NG' -'nybro_HO_WO' -'nybro_HYRR_HY' -'OEST_GE_B0_LO' -'OEST_GE_B0_NG' -'OEST_GE_C0_LO2' -'OEST_HO_CO' -'OEST_HO_FO' -'OEST_HO_LO' -'OEST_HO_MW' -'OEST_HO_ST' -'OEST_HO_WO' -'OEST_ST_B0_CO4' -'OEST_ST_B0_FO' -'OEST_ST_B0_FO1' -'OEST_ST_B0_MW' -'OEST_ST_B0_ST' -'OEST_ST_C0_FO3' -'pottehuse_GE_B0_NG' -'pottehuse_HO_LO' -'pottehuse_HO_MW' -'pottehuse_HO_NG' -'pottehuse_HO_WO' -'pottehuse_HYRR_HY' -'pottehuse_SOLE_SOL' -'pottehuse_ST_B0_MW' -'ringsted_CC_B0_MW' -'ringsted_GE_B0_NG' -'ringsted_GT_C0_LO2' -'ringsted_HO_MW' -'ringsted_HO_NG' -'ringsted_HO_ST' -'ringsted_ST_B0_ST' -'ringsted_ST_B0_ST3' -'slagelse_GE_B0_NG' -'slagelse_GE_C0_NG' -'slagelse_GT_B0_NG' -'slagelse_HO_FO3' -'slagelse_HO_NG' -'slagelse_HO_ST' -'slagelse_HO_WO' -'slagelse_ST_B0_MW' -'slagelse_ST_C0_CO1' -'slagelse_ST_C0_CO2' -'soroe_GE_B0_NG' -'soroe_HO_FO' -'soroe_HO_FO6' -'soroe_HO_LO' -'soroe_HO_NG' -'soroe_HO_ST' -'soroe_HO_WO' -'soroe_ST_E0_CO1' -'soroe_ST_E0_CO2' -'soroe_ST_E0_CO3' -'soroe_ST_E0_CO4' -'standst_GE_B0_NG' -*'standst_GT_B0_NG' -'standst_HO_LO' -'standst_HO_NG' -'standst_HO_WO' -'standst_HYRR_HY' -'standst_ST_B0_MW' -'stenlille_HO_LO' -'stenlille_HO_ST' -'sydhavnen_CC_B0_NG' -'sydhavnen_CC_E0_NG1' -'sydhavnen_GE_B0_NG' -'sydhavnen_GE_C0_LO3' -'sydhavnen_GT_B0_NG2' -'sydhavnen_GT_B0_NG4' -'sydhavnen_HO_FO' -'sydhavnen_HO_LO' -'sydhavnen_HO_NG' -'sydhavnen_SOLE_SOL' -'sydhavnen_ST_E0_NG1' -'sydhavnen_ST_E0_NG2' -'taulov_CC_B0_NG' -'taulov_GE_B0_NG' -'taulov_GT_B0_NG' -'taulov_HO_LO' -'taulov_HO_NG' -'taulov_HO_NG4' -'taulov_ST_E0_NG1' -'taulov_ST_E0_NG3' -'terkelsboel_CC_B0_NG' -'terkelsboel_GE_B0_NG' -'terkelsboel_GT_B0_NG' -'terkelsboel_HO_LO' -'terkelsboel_HO_NG' -'terkelsboel_HO_WO' -'terkelsboel_HYRR_HY' -'terkelsboel_ST_B0_WO' -'torslunde_GE_B0_NG' -'torslunde_HO_LO' -'torslunde_HO_MW' -'torslunde_HO_NG' -'torslunde_ST_B0_MW' -'ullerslev_GE_B0_NG' -'ullerslev_HO_LO' -'ullerslev_HO_NG' -'ullerslev_HYRR_HY' -'vallensbaek_GE_B0_NG' -'vallensbaek_HO_LO' -'vallensbaek_HO_MW' -'vallensbaek_HO_NG' -'vallensbaek_HO_WO' -'vallensbaek_ST_B0_MW' -'varde_GE_B0_NG' -'varde_GT_B0_NG' -'varde_HO_FO' -'varde_HO_LO' -'varde_HO_NG' -'varde_HO_WO' -'varde_HYRR_HY' -'varde_ST_B0_MW' -'varde_ST_E0_CO1' -'VEST_GE_B0_NG' -'VEST_HO_FO' -'VEST_HO_LO' -'VEST_HO_ST' -'VEST_HO_WO' -'VEST_ST_B0_ST' -'viborg_CC_B0_NG' -'viborg_CC_B0_WO' -'viborg_GE_B0_NG' -'viborg_GT_B0_NG' -'viborg_HO_FO' -'viborg_HO_LO' -'viborg_HO_NG' -'viborg_HO_ST' -'viborg_HO_WO' -'viborg_HYRR_HY' -'viborg_ST_B0_MW' -'aalborg_CC_B0_NG' -'aalborg_GE_B0_NG' -'aalborg_GE_C0_NG' -'aalborg_GT_B0_NG' -'aalborg_GT_C0_LO3' -'aalborg_HO_CO5' -'aalborg_HO_LO' -'aalborg_HO_MW' -'aalborg_HO_NG' -'aalborg_HO_ST' -'aalborg_HO_WO' -'aalborg_HYRR_HY' -'aalborg_ST_B0_MW' -'aalborg_ST_B0_NG' -'aalborg_ST_E0_CO2' -'aalborg_ST_E0_CO4' \ No newline at end of file diff --git a/base/addons/gas/GKFX-MR.inc b/base/addons/gas/GKFX-MR.inc deleted file mode 100644 index e55bb02c..00000000 --- a/base/addons/gas/GKFX-MR.inc +++ /dev/null @@ -1,452 +0,0 @@ - -* All WW canged to WO - -PARAMETER GKFXMR(AAA,GGG)/ -* DK_W_AALBORG -DK_W_AALBORG.DK_W_AALBORG_G-HSTORE 3500 -DK_W_AALBORG.DK_W_AALBORG_HO-B8-FO 800 -DK_W_AALBORG.DK_W_Aalborg_ST-E7-CO 285 -DK_W_AALBORG.DK_W_Aalborg_ST-E9-Cosn 372 - -DK_W_Rural.DK_W_Rural_G-HSTORE 5000 -DK_W_Rural.DK_W_Rural_HO-B8-FO 100 -DK_W_Rural.DK_W_Rural_ST-B8-CO 54.8000001907349 -*DK_W_Rural.DK_W_Rural_ST-B8-NG 44.5 -DK_W_Rural.DK_W_Rural_ST-B9-MW 110 - -*DK_E_Rural.DK_E_Rural_WI 618 - -* TEST! -DK_W_Rural.DK_W_Urban_ST-E9-Cosn 980 - -* DK_E_RURAL -DK_E_Rural.DK_E_Rural_ST-B8-CO 37 -DK_E_Rural.DK_E_Rural_ST-E7-CO 415 -DK_E_Rural.DK_E_Rural_ST-B8-FO 45 -DK_E_Rural.DK_E_Rural_HO-B8-FO 30 - -* Kyndby -DK_E_Rural.DK_E_Rural_ST-C7-FO 550 -DK_E_Rural.DK_E_Rural_GT-C8-LO 196 - -* Asnæs -DK_E_Rural.DK_E_Rural_ST-C8-COs 270 -DK_E_Rural.DK_E_Rural_ST-E8-ORs 640 -* Stigsnæs -DK_E_Rural.DK_E_Rural_ST-C7-CO 143 - -*slagelse.'slagelse_ST_C0_CO1' 143.00 -*slagelse.'slagelse_ST_C0_CO2' 266.00 - -*soroe.'soroe_ST_E0_CO1' 640.00 -*soroe.'soroe_ST_E0_CO2' 270.00 -* Ikke i drift. Ingen produktion i 2004 -* *soroe.'soroe_ST_E0_CO3' 270.00 -*soroe.'soroe_ST_E0_CO4' 147.00 - -* ---- DK_E_Urban -DK_E_Urban.DK_E_Rural_ST-E8-ST 70 - -*DK_E_Urban.DK_E_Urban_CC-B8-NG 155 -*DK_E_Urban.DK_E_Urban_CC-B8-NG 0 - -DK_E_Urban.DK_E_Urban_G-HSTORE=10000 -DK_E_Urban.DK_E_Urban_HO-B8-FO=1500 - -*DK_E_Urban.DK_E_Urban_ST-B8-NG=229 -*DK_E_Urban.DK_E_Urban_ST-B8-NG 154 - -DK_E_Urban.DK_E_Urban_ST-B9-ST=35 -DK_E_Urban.DK_E_Urban_ST-E0-NGsn=267.5 -DK_E_Urban.DK_E_Urban_ST-E0-Wosn=267.5 -DK_E_Urban.DK_E_Urban_ST-E9-Cosn=500 - -* Amager værket er omfattet af DK_E_Urban -*amagerf.'amagerf_HO_LO' 102.00 -*amagerf.'amagerf_ST_B0_MW' 28.50 -* Lukket: -** amagerf.'amagerf_ST_E0_CO1' 136.00 -*amagerf.'amagerf_ST_E0_CO3' 250.00 -*amagerf.'amagerf_ST_E0_ST2' 91.00 - -billesboelle.'billesboelle_GE_B0_NG' 1.11 -billesboelle.'billesboelle_HO_NG' 3.50 -brande.'brande_GE_B0_NG' 6.27 -brande.'brande_HO_LO' 1.00 -brande.'brande_HO_NG' 5.70 -brande.'brande_HO_WO' 1.20 -brande.'brande_HYRR_HY' 0.25 -* Nogle af disse er måske på Avedøre... men det er tvivlsomt -broendby.'broendby_GE_B0_NG' 6.20 -broendby.'broendby_GE_C0_LO' 1.68 -broendby.'broendby_HO_FO' 90.00 -broendby.'broendby_HO_LO' 227.60 -broendby.'broendby_HO_NG' 111.80 -broendby.'broendby_SOLE_SOL' 0.01 -* Avedoere er omfattet af DK_E_Urban -* avedoere.'broendby_ST_E0_CO1' 250.00 -* Avedøre blok 2 længere nede. -* avedoere.DK_E_Urban_ST-E0-NGsn 380 -* avedoere.DK_E_Urban_ST-E0-WOsn 190 -dragoer.'dragoer_GE_B0_NG' 1.44 -dragoer.'dragoer_GE_C0_NG' 0.20 -dragoer.'dragoer_HO_LO' 45.00 -dragoer.'dragoer_SOLE_SOL' 0.02 -egtved.'egtved_CC_B0_MW' 33.00 -egtved.'egtved_GE_B0_NG' 46.42 -egtved.'egtved_GE_C0_NG' 0.40 -egtved.'egtved_HO_LO' 37.05 -egtved.'egtved_HO_NG' 342.36 -egtved.'egtved_HO_WO' 12.30 -egtved.'egtved_HYRR_HY' 1.19 -ellidshoej.'ellidshoej_GE_B0_NG' 19.25 -ellidshoej.'ellidshoej_GT_B0_NG' 5.20 -ellidshoej.'ellidshoej_HO_LO' 28.30 -ellidshoej.'ellidshoej_HO_NG' 34.25 -ellidshoej.'ellidshoej_HO_ST' 1.60 -froeslev.'froeslev_CC_B0_NG' 58.00 -froeslev.'froeslev_GE_B0_NG' 23.42 -*froeslev.'froeslev_HO_CO4' 0.00 -*froeslev.'froeslev_HO_CO5' 0.00 -froeslev.'froeslev_HO_FO' 115.20 -froeslev.'froeslev_HO_LO' 26.00 -froeslev.'froeslev_HO_NG' 153.97 -froeslev.'froeslev_HO_ST' 3.15 -froeslev.'froeslev_HO_WO' 1.75 -froeslev.'froeslev_HYRR_HY' 7.0e-03 -* Findes i DK_W_Rural -* Ikke i drift: -** froeslev.'froeslev_ST_C0_CO3' 327.00 -*froeslev.'froeslev_ST_E0_CO2' 626.00 -haverslev.'haverslev_GE_B0_NG' 41.25 -haverslev.'haverslev_GT_B0_NG' 0.92 -haverslev.'haverslev_HO_FO' 19.00 -haverslev.'haverslev_HO_LO' 40.00 -haverslev.'haverslev_HO_MW' 24.10 -haverslev.'haverslev_HO_NG' 72.60 -haverslev.'haverslev_HO_ST' 11.15 -haverslev.'haverslev_HO_WO' 38.60 -haverslev.'haverslev_ST_B0_MW' 2.89 -herning.'herning_CC_B0_NG' 107.00 -herning.'herning_GE_B0_LO' 0.94 -herning.'herning_GE_B0_NG' 92.65 -herning.'herning_GE_B0_WO' 0.05 -* Del af DK_W_Rural -*herning.'herning_GE_C0_LO1' 0.21 -*herning.'herning_GE_C0_LO2' 0.33 -*herning.'herning_GE_C0_LO3' 0.27 -*herning.'herning_GE_C0_LO4' 0.38 -herning.'herning_GT_B0_NG' 24.30 -*herning.'herning_GT_C0_LO5' 12.00 -*herning.'herning_HO_CO6' 0.00 -herning.'herning_HO_FO' 221.85 -*herning.'herning_HO_LO' 842.20 -*herning.'herning_HO_LO2' 69.60 -herning.'herning_HO_MW' 46.10 -herning.'herning_HO_NG' 436.00 -* No information: -* herning.'herning_HO_NG2' 0.00 -herning.'herning_HO_ST' 74.00 -herning.'herning_HO_WO' 144.64 -herning.'herning_HYRR_HY' 0.51 -*herning.'herning_ST_B0_CO' 19.60 -*herning.'herning_ST_B0_FO' 5.00 -*herning.'herning_ST_B0_MW' 27.15 -herning.'herning_ST_B0_WO' 5.00 -*herning.'herning_ST_E0_CO1' 48.30 -* Findes i DK_W_AAHURS: -*herning.'herning_ST_E0_CO3' 350.00 -*herning.'herning_ST_E0_CO4' 350.00 -*herning.'herning_ST_E0_WO1' 89.00 -* DK_W_AARHUS -DK_W_AARHUS.DK_W_AARHUS_G-HSTORE 3500 -DK_W_AARHUS.DK_W_AARHUS_HO-B8-FO 600 -DK_W_AARHUS.DK_W_Aarhus_ST-B9-MW 60 -DK_W_AARHUS.DK_W_Aarhus_ST-E8-Cos 665 -DK_W_AARHUS.DK_W_Aarhus_ST-E8-ST 35 - - - -hoejby.'hoejby_GE_B0_NG' 58.15 -hoejby.'hoejby_GT_B0_NG' 7.10 -hoejby.'hoejby_HO_FO' 159.90 -hoejby.'hoejby_HO_LO' 223.59 -hoejby.'hoejby_HO_NG' 134.05 -hoejby.'hoejby_HO_ST' 3.78 -hoejby.'hoejby_HO_WO' 187.51 -hoejby.'hoejby_HYRR_HY' 6.0e-03 -hoejby.'hoejby_ST_B0_MW' 4.50 -* Fjernet da de eksisterer i DK_W_ODENSE: -*hoejby.'hoejby_ST_B0_MW1' 24.00 -*hoejby.'hoejby_ST_E0_NG3' 269.00 -*hoejby.'hoejby_ST_E0_CO2' 407.00 -* DK_W_ODENSE -DK_W_ODENSE.DK_W_ODENSE_G-HSTORE 3500 -DK_W_ODENSE.DK_W_ODENSE_HO-B8-FO 600 -DK_W_ODENSE.DK_W_Odense_ST-B9-MW 24 -DK_W_ODENSE.DK_W_Odense_ST-E7-NG 217 -DK_W_ODENSE.DK_W_Odense_ST-E9-Cosn 339 -karlslunde.'karlslunde_GE_B0_NG' 4.22 -karlslunde.'karlslunde_HO_LO' 75.50 -karlslunde.'karlslunde_HO_NG' 11.00 -karup.'karup_GE_B0_NG' 30.23 -karup.'karup_HO_LO' 0.87 -karup.'karup_HO_NG' 120.42 -karup.'karup_HO_WO' 10.00 -karup.'karup_HYRR_HY' 3.92 -koege.'koege_GE_B0_NG' 14.05 -koege.'koege_HO_FO' 10.00 -koege.'koege_HO_LO' 12.00 -koege.'koege_HO_NG' 6.14 -koege.'koege_HO_ST' 4.00 -koege.'koege_ST_B0_NG' 5.80 -koege.'koege_ST_B0_WO' 26.00 -koelbjerg.'koelbjerg_GE_B0_NG' 59.00 -koelbjerg.'koelbjerg_HO_CO' 10.00 -koelbjerg.'koelbjerg_HO_FO' 3.60 -koelbjerg.'koelbjerg_HO_LO' 62.47 -koelbjerg.'koelbjerg_HO_NG' 77.85 -koelbjerg.'koelbjerg_HO_WO' 24.90 -koelbjerg.'koelbjerg_HYRR_HY' 0.11 -koelbjerg.'koelbjerg_ST_B0_CO' 5.30 -koelbjerg.'koelbjerg_ST_B0_FO' 4.00 -koelbjerg.'koelbjerg_ST_B0_NG' 11.00 -koelbjerg.'koelbjerg_ST_B0_WO' 4.68 -liselskaer.'liselskaer_CC_B0_MW' 5.40 -liselskaer.'liselskaer_GE_B0_NG' 40.26 -liselskaer.'liselskaer_HO_NG' 107.32 -liselskaer.'liselskaer_HYRR_HY' 0.40 -liselskaer.'liselskaer_ST_B0_NG' 3.20 -litorup.'litorup_GE_B0_NG' 8.59 -litorup.'litorup_HO_LO' 7.00 -litorup.'litorup_HO_NG' 12.90 -lynge.'lynge_CC_B0_NG' 77.00 -lynge.'lynge_GE_B0_NG' 33.69 -lynge.'lynge_GE_B0_WO' 0.09 -lynge.'lynge_GT_B0_NG' 94.10 -lynge.'lynge_HO_FO' 3.15 -lynge.'lynge_HO_MW' 21.00 -lynge.'lynge_HO_NG' 221.38 -lynge.'lynge_HO_WO' 15.00 -lynge.'lynge_HYRR_HY' 0.02 -lynge.'lynge_ST_B0_MW' 7.40 -middelfart.'middelfart_GE_B0_NG' 6.18 -middelfart.'middelfart_HO_LO' 19.30 -middelfart.'middelfart_HO_MW' 7.00 -middelfart.'middelfart_HO_NG' 16.34 -middelfart.'middelfart_HYRR_HY' 0.01 -maaloev.'maaloev_GE_B0_NG' 51.88 -*maaloev.'maaloev_GE_C0_LO4' 20.00 -maaloev.'maaloev_GT_B0_NG' 4.50 -*maaloev.'maaloev_GT_C0_LO6' 63.00 -*maaloev.'maaloev_GT_C0_NG5' 63.00 -*maaloev.'maaloev_GT_C0_NG7' 2.00 -*maaloev.'maaloev_HO_FO8' 50.00 -*maaloev.'maaloev_HO_FO9' 0.00 -maaloev.'maaloev_HO_LO' 30.40 -maaloev.'maaloev_HO_MW' 16.00 -maaloev.'maaloev_HO_NG' 68.50 -maaloev.'maaloev_HO_WO' 40.70 -*maaloev.'maaloev_ST_C0_FO2' 260.00 -*maaloev.'maaloev_ST_C0_FO3' 260.00 -noerskov.'noerskov_GE_B0_NG' 42.85 -noerskov.'noerskov_GT_B0_NG' 0.80 -noerskov.'noerskov_HO_FO' 9.90 -noerskov.'noerskov_HO_LO' 7.50 -noerskov.'noerskov_HO_NG' 59.20 -noerskov.'noerskov_HO_WO' 25.28 -noerskov.'noerskov_ST_B0_CO' 6.70 -nyborg.'nyborg_CC_B0_MW' 21.00 -nyborg.'nyborg_GE_B0_NG' 0.04 -*nyborg.'nyborg_GT_B0_NG' 4.90 -nyborg.'nyborg_HO_NG' 12.04 -nyborg.'nyborg_HO_WO' 71.90 -nyborg.'nyborg_HYRR_HY' 0.04 -nybro.'nybro_GE_B0_NG' 7.87 -nybro.'nybro_GT_B0_NG' 4.00 -nybro.'nybro_HO_LO' 4.65 -nybro.'nybro_HO_NG' 9.16 -nybro.'nybro_HO_WO' 1.90 -nybro.'nybro_HYRR_HY' 0.02 -DK_E_Rural.'OEST_GE_B0_LO' 0.35 -DK_E_Rural.'OEST_GE_B0_NG' 1.24 -*DK_E_Rural.'OEST_GE_C0_LO2' 19.60 -DK_E_Rural.'OEST_HO_CO' 26.00 -DK_E_Rural.'OEST_HO_FO' 54.40 -DK_E_Rural.'OEST_HO_LO' 108.30 -DK_E_Rural.'OEST_HO_MW' 22.00 -DK_E_Rural.'OEST_HO_ST' 87.65 -DK_E_Rural.'OEST_HO_WO' 129.00 -*DK_E_Rural.'OEST_ST_B0_CO4' 37.00 -*DK_E_Rural.'OEST_ST_B0_FO' 34.00 -*DK_E_Rural.'OEST_ST_B0_FO1' 4.00 -DK_E_Rural.'OEST_ST_B0_MW' 6.70 -DK_E_Rural.'OEST_ST_B0_ST' 11.30 -*DK_E_Rural.'OEST_ST_C0_FO3' 27.50 -pottehuse.'pottehuse_GE_B0_NG' 3.17 -pottehuse.'pottehuse_HO_LO' 43.30 -pottehuse.'pottehuse_HO_MW' 12.40 -pottehuse.'pottehuse_HO_NG' 230.10 -pottehuse.'pottehuse_HO_WO' 8.20 -pottehuse.'pottehuse_HYRR_HY' 0.95 -pottehuse.'pottehuse_SOLE_SOL' 0.10 -pottehuse.'pottehuse_ST_B0_MW' 6.26 -ringsted.'ringsted_CC_B0_MW' 38.00 -ringsted.'ringsted_GE_B0_NG' 63.86 -ringsted.'ringsted_GT_C0_LO2' 70.00 -ringsted.'ringsted_HO_MW' 10.00 -ringsted.'ringsted_HO_NG' 232.66 -ringsted.'ringsted_HO_ST' 8.00 -ringsted.'ringsted_ST_B0_ST' 5.00 -ringsted.'ringsted_ST_B0_ST3' 9.00 -slagelse.'slagelse_GE_B0_NG' 13.41 -slagelse.'slagelse_GE_C0_NG' 0.31 -slagelse.'slagelse_GT_B0_NG' 0.97 -*slagelse.'slagelse_HO_FO3' 2.00 -slagelse.'slagelse_HO_NG' 139.80 -slagelse.'slagelse_HO_ST' 4.50 -slagelse.'slagelse_HO_WO' 7.80 -slagelse.'slagelse_ST_B0_MW' 12.00 -* Sigsnæsværket fjernet fordi det er omfattet af DK_E_Rural -*slagelse.'slagelse_ST_C0_CO1' 143.00 -*slagelse.'slagelse_ST_C0_CO2' 266.00 -soroe.'soroe_GE_B0_NG' 31.83 -soroe.'soroe_HO_FO' 16.70 -*soroe.'soroe_HO_FO6' 42.00 -soroe.'soroe_HO_LO' 19.40 -soroe.'soroe_HO_NG' 62.00 -soroe.'soroe_HO_ST' 11.50 -soroe.'soroe_HO_WO' 11.10 -* Asnæsværket fjernet fordi det er omfattet af DK_E_Rural -*soroe.'soroe_ST_E0_CO1' 640.00 -*soroe.'soroe_ST_E0_CO2' 270.00 -* Ikke i drift. Ingen produktion i 2004 -* soroe.'soroe_ST_E0_CO3' 270.00 -*soroe.'soroe_ST_E0_CO4' 147.00 -standst.'standst_GE_B0_NG' 33.98 -*standst.'standst_GT_B0_NG' 0.60 -standst.'standst_HO_LO' 11.43 -standst.'standst_HO_NG' 94.50 -standst.'standst_HO_WO' 32.26 -standst.'standst_HYRR_HY' 0.10 -standst.'standst_ST_B0_MW' 2.60 -stenlille.'stenlille_HO_LO' 3.30 -stenlille.'stenlille_HO_ST' 2.50 -DK_E_Urban.'sydhavnen_CC_B0_NG' 13.30 -*Avedoere.'sydhavnen_CC_E0_NG1' 591.00 -* Effekt fordelt på naturgas og træ i forholdet 2-1 -*Avedoere.'sydhavnen_CC_E0_NG1' 380.00 -DK_E_Urban.'sydhavnen_GE_B0_NG' 0.55 -* Ses på museet Dieselhouse -*HCOersted.'sydhavnen_GE_C0_LO3' 12.00 -* Svanemølle blok 7: -*HCOersted.'sydhavnen_GT_B0_NG2' 60.00 -*HCO Blok 8: -*HCOersted.'sydhavnen_GT_B0_NG4' 24.30 -DK_E_Urban.'sydhavnen_HO_FO' 171.00 -DK_E_Urban.'sydhavnen_HO_LO' 331.90 -DK_E_Urban.'sydhavnen_HO_NG' 47.80 -DK_E_Urban.'sydhavnen_SOLE_SOL' 0.04 -* Svanemøllen blok 1 + HCØ blok 1-4 med 70 MW Skrottet i 2005: -*HCOersted.'sydhavnen_ST_E0_NG1' 155 -* HCØ Blok 7: -*HCOersted.'sydhavnen_ST_E0_NG2' 88.00 -skaerbaek.'taulov_CC_B0_NG' 6.25 -skaerbaek.'taulov_GE_B0_NG' 1.17 -skaerbaek.'taulov_GT_B0_NG' 33.40 -skaerbaek.'taulov_HO_LO' 139.41 -skaerbaek.'taulov_HO_NG' 42.00 -skaerbaek.'taulov_HO_NG4' 0.00 -*Seperat område til Skærbæk værketet -* Mangler i systemplan 2006. Er skrottet. -** skaerbaek.'taulov_ST_E0_NG1' 93.00 -*skaerbaek.'taulov_ST_E0_NG3' 392.00 -DK_W_TREFOR.DK_W_TREFOR_G-HSTORE=3500 -DK_W_TREFOR.DK_W_TREFOR_HO-B8-FO=800 -DK_W_TREFOR.DK_W_Trefor_ST-E9-NGn=391 -terkelsboel.'terkelsboel_CC_B0_NG' 6.00 -terkelsboel.'terkelsboel_GE_B0_NG' 14.60 -terkelsboel.'terkelsboel_GT_B0_NG' 14.30 -terkelsboel.'terkelsboel_HO_LO' 28.45 -terkelsboel.'terkelsboel_HO_NG' 107.81 -terkelsboel.'terkelsboel_HO_WO' 5.80 -terkelsboel.'terkelsboel_HYRR_HY' 0.03 -terkelsboel.'terkelsboel_ST_B0_WO' 0.60 -torslunde.'torslunde_GE_B0_NG' 3.33 -torslunde.'torslunde_HO_LO' 77.70 -torslunde.'torslunde_HO_MW' 28.00 -torslunde.'torslunde_HO_NG' 101.90 -torslunde.'torslunde_ST_B0_MW' 13.70 -ullerslev.'ullerslev_GE_B0_NG' 13.39 -ullerslev.'ullerslev_HO_LO' 66.82 -ullerslev.'ullerslev_HO_NG' 19.60 -ullerslev.'ullerslev_HYRR_HY' 0.02 -vallensbaek.'vallensbaek_GE_B0_NG' 4.32 -vallensbaek.'vallensbaek_HO_LO' 38.99 -vallensbaek.'vallensbaek_HO_MW' 104.00 -vallensbaek.'vallensbaek_HO_NG' 42.50 -vallensbaek.'vallensbaek_HO_WO' 32.00 -vallensbaek.'vallensbaek_ST_B0_MW' 39.00 -varde.'varde_GE_B0_NG' 16.83 -varde.'varde_GT_B0_NG' 3.74 -varde.'varde_HO_FO' 7.00 -varde.'varde_HO_LO' 202.46 -varde.'varde_HO_NG' 67.54 -varde.'varde_HO_WO' 51.45 -varde.'varde_HYRR_HY' 1.60 -*varde.'varde_ST_B0_MW' 18.00 -*varde.'varde_ST_E0_CO1' 378.00 -DK_W_Rural.'VEST_GE_B0_NG' 0.45 -DK_W_Rural.'VEST_HO_FO' 24.60 -DK_W_Rural.'VEST_HO_LO' 5.10 -DK_W_Rural.'VEST_HO_ST' 22.05 -DK_W_Rural.'VEST_HO_WO' 25.53 -DK_W_Rural.'VEST_ST_B0_ST' 2.30 -viborg.'viborg_CC_B0_NG' 87.00 -viborg.'viborg_CC_B0_WO' 1.40 -viborg.'viborg_GE_B0_NG' 90.15 -viborg.'viborg_GT_B0_NG' 20.10 -viborg.'viborg_HO_FO' 97.45 -viborg.'viborg_HO_LO' 60.90 -viborg.'viborg_HO_NG' 445.13 -viborg.'viborg_HO_ST' 8.80 -viborg.'viborg_HO_WO' 157.13 -viborg.'viborg_HYRR_HY' 0.02 -viborg.'viborg_ST_B0_MW' 32.92 -aalborg.'aalborg_CC_B0_NG' 56.15 -aalborg.'aalborg_GE_B0_NG' 145.28 -aalborg.'aalborg_GE_C0_NG' 0.05 -aalborg.'aalborg_GT_B0_NG' 39.96 -aalborg.'aalborg_GT_C0_LO3' 25.00 -aalborg.'aalborg_HO_CO5' 0.00 -aalborg.'aalborg_HO_LO' 260.15 -aalborg.'aalborg_HO_MW' 48.60 -aalborg.'aalborg_HO_NG' 582.13 -aalborg.'aalborg_HO_ST' 5.00 -aalborg.'aalborg_HO_WO' 65.86 -aalborg.'aalborg_HYRR_HY' 0.21 -aalborg.'aalborg_ST_B0_MW' 14.10 -aalborg.'aalborg_ST_B0_NG' 0.73 -*Findes i DK_W_AALBORG -*aalborg.'aalborg_ST_E0_CO2' 285.00 -*aalborg.'aalborg_ST_E0_CO4' 380.00 -/; - -GKFX(YYY,AAA,GGG)$(ICA('Denmark',AAA) and GDATA(GGG,'GDTYPE')<> 10)=0; -GKFX(YYY,MRAREAS,GGG)$(GDATA(GGG,'GDTYPE')<> 10)=GKFXMR(MRAREAS,GGG); - -GKFX(YYY,MRAREAS(AAA),'DK_E_1_G-HSTORE')$(not SUM(IGHSTO, GKFX('2006',AAA,IGHSTO))) -=8*(SUM(IGKHNOSTO, GKFX(YYY,AAA,IGKHNOSTO)) + SUM(G$(IGKE(G) and not IGH(G) and GDATA(G,'GDCB')and GKFX(YYY,AAA,G)>0), GKFX(YYY,AAA,G)/GDATA(G,'GDCB'))); - - - -SETS MR2CENTRAL(AAA,AAA,GGG) -/ -Hoejby.DK_W_Odense.DK_W_Odense_ST-E7-NG -Avedoere.DK_E_Urban.DK_E_Urban_ST-E0-NGsn -HCOersted.DK_E_Urban.DK_E_Urban_ST-B8-NG -HCOersted.DK_E_Urban.'sydhavnen_CC_B0_NG' -HCOersted.DK_E_Urban.'sydhavnen_HO_NG' -HCOersted.DK_E_Urban.'sydhavnen_GE_B0_NG' -SKAERBAEK.DK_W_TREFOR.DK_W_Trefor_ST-E9-NGn -/; \ No newline at end of file diff --git a/base/addons/gas/GPFX_VAR_T.INC b/base/addons/gas/GPFX_VAR_T.INC deleted file mode 100644 index 6b7e50c2..00000000 --- a/base/addons/gas/GPFX_VAR_T.INC +++ /dev/null @@ -1,8746 +0,0 @@ -* ----------------------------------------------------- -* MDB2GMNYBRO.S VerNYBRO.Sion 2.6, AuguNYBRO.St 2005 -* Erwin Kalvelagen, GAMNYBRO.S Development Corp -* ----------------------------------------------------- -* DAO verNYBRO.Sion: 3.6 -* Jet verNYBRO.Sion: 4.0 -* DatabaNYBRO.Se: D:\LarNYBRO.SB\1\GaNYBRO.STranNYBRO.SmiNYBRO.SNYBRO.Sion.mdb -* Query: NYBRO.Select * from nybro -* ----------------------------------------------------- -NYBRO.S01.T001 1007.69 -NYBRO.S01.T002 979.64 -NYBRO.S01.T003 965.476 -NYBRO.S01.T004 957.064 -NYBRO.S01.T005 949.957 -NYBRO.S01.T006 955.704 -NYBRO.S01.T007 969.582 -NYBRO.S01.T008 995.973 -NYBRO.S01.T009 1031.864 -NYBRO.S01.T010 1034.847 -NYBRO.S01.T011 1064.654 -NYBRO.S01.T012 1088.146 -NYBRO.S01.T013 1076.203 -NYBRO.S01.T014 1048.409 -NYBRO.S01.T015 1041.322 -NYBRO.S01.T016 1048.434 -NYBRO.S01.T017 1054.539 -NYBRO.S01.T018 1063.716 -NYBRO.S01.T019 1064.568 -NYBRO.S01.T020 1066.26 -NYBRO.S01.T021 1074.772 -NYBRO.S01.T022 1087.013 -NYBRO.S01.T023 1091.482 -NYBRO.S01.T024 1085.088 -NYBRO.S01.T025 1079.2 -NYBRO.S01.T026 1083.575 -NYBRO.S01.T027 1064.059 -NYBRO.S01.T028 1079.009 -NYBRO.S01.T029 1068.721 -NYBRO.S01.T030 1057.204 -NYBRO.S01.T031 1051.208 -NYBRO.S01.T032 1051.698 -NYBRO.S01.T033 1058.948 -NYBRO.S01.T034 1055.819 -NYBRO.S01.T035 1059.871 -NYBRO.S01.T036 1059.866 -NYBRO.S01.T037 1057.906 -NYBRO.S01.T038 1071.62 -NYBRO.S01.T039 1071.729 -NYBRO.S01.T040 1066.39 -NYBRO.S01.T041 1054.871 -NYBRO.S01.T042 1055.575 -NYBRO.S01.T043 1054.212 -NYBRO.S01.T044 1052.897 -NYBRO.S01.T045 1047.398 -NYBRO.S01.T046 1049.214 -NYBRO.S01.T047 1016.735 -NYBRO.S01.T048 654.247 -NYBRO.S01.T049 629.944 -NYBRO.S01.T050 626.15 -NYBRO.S01.T051 799.754 -NYBRO.S01.T052 796.934 -NYBRO.S01.T053 820.332 -NYBRO.S01.T054 869.729 -NYBRO.S01.T055 923.826 -NYBRO.S01.T056 968.139 -NYBRO.S01.T057 1006.846 -NYBRO.S01.T058 1022.83 -NYBRO.S01.T059 1038.026 -NYBRO.S01.T060 1062.038 -NYBRO.S01.T061 1078.294 -NYBRO.S01.T062 1088.683 -NYBRO.S01.T063 1074.816 -NYBRO.S01.T064 1068.227 -NYBRO.S01.T065 1071.418 -NYBRO.S01.T066 1077.729 -NYBRO.S01.T067 1072.506 -NYBRO.S01.T068 1076.623 -NYBRO.S01.T069 1085.198 -NYBRO.S01.T070 1095.345 -NYBRO.S01.T071 1100.293 -NYBRO.S01.T072 1086.646 -NYBRO.S01.T073 1075.166 -NYBRO.S01.T074 1067.989 -NYBRO.S01.T075 1069.135 -NYBRO.S01.T076 1045.358 -NYBRO.S01.T077 1002.38 -NYBRO.S01.T078 1000.384 -NYBRO.S01.T079 1002.88 -NYBRO.S01.T080 1034.188 -NYBRO.S01.T081 1074.118 -NYBRO.S01.T082 1099.075 -NYBRO.S01.T083 1112.947 -NYBRO.S01.T084 1126.097 -NYBRO.S01.T085 1130.96 -NYBRO.S01.T086 1127.959 -NYBRO.S01.T087 1118.558 -NYBRO.S01.T088 1099.257 -NYBRO.S01.T089 1093.737 -NYBRO.S01.T090 1099.512 -NYBRO.S01.T091 1114.069 -NYBRO.S01.T092 1103.512 -NYBRO.S01.T093 1095.923 -NYBRO.S01.T094 1090.952 -NYBRO.S01.T095 1080.801 -NYBRO.S01.T096 1068.469 -NYBRO.S01.T097 1062.563 -NYBRO.S01.T098 1052.29 -NYBRO.S01.T099 1039.421 -NYBRO.S01.T100 1043.036 -NYBRO.S01.T101 1033.287 -NYBRO.S01.T102 1027.995 -NYBRO.S01.T103 995.158 -NYBRO.S01.T104 1004.76 -NYBRO.S01.T105 1005.998 -NYBRO.S01.T106 1007.304 -NYBRO.S01.T107 1007.457 -NYBRO.S01.T108 1008.034 -NYBRO.S01.T109 1002.623 -NYBRO.S01.T110 999.524 -NYBRO.S01.T111 1001.671 -NYBRO.S01.T112 1004.292 -NYBRO.S01.T113 999.603 -NYBRO.S01.T114 999.065 -NYBRO.S01.T115 998.207 -NYBRO.S01.T116 992.284 -NYBRO.S01.T117 986.285 -NYBRO.S01.T118 985.369 -NYBRO.S01.T119 975.944 -NYBRO.S01.T120 968.924 -NYBRO.S01.T121 967.063 -NYBRO.S01.T122 961.68 -NYBRO.S01.T123 957.012 -NYBRO.S01.T124 960.224 -NYBRO.S01.T125 952.579 -NYBRO.S01.T126 951.598 -NYBRO.S01.T127 949.816 -NYBRO.S01.T128 950.097 -NYBRO.S01.T129 953.021 -NYBRO.S01.T130 954.918 -NYBRO.S01.T131 955.663 -NYBRO.S01.T132 952.528 -NYBRO.S01.T133 958.154 -NYBRO.S01.T134 952.315 -NYBRO.S01.T135 970.329 -NYBRO.S01.T136 968.113 -NYBRO.S01.T137 968.917 -NYBRO.S01.T138 975.29 -NYBRO.S01.T139 981.676 -NYBRO.S01.T140 983.565 -NYBRO.S01.T141 994.752 -NYBRO.S01.T142 1003.868 -NYBRO.S01.T143 1009.496 -NYBRO.S01.T144 1018.783 -NYBRO.S01.T145 1015.242 -NYBRO.S01.T146 1018.25 -NYBRO.S01.T147 1022.25 -NYBRO.S01.T148 1021.963 -NYBRO.S01.T149 1029.254 -NYBRO.S01.T150 1029.966 -NYBRO.S01.T151 1033.772 -NYBRO.S01.T152 1038.488 -NYBRO.S01.T153 1045.026 -NYBRO.S01.T154 1039.499 -NYBRO.S01.T155 1036.465 -NYBRO.S01.T156 1028.79 -NYBRO.S01.T157 1027.952 -NYBRO.S01.T158 995.938 -NYBRO.S01.T159 990.144 -NYBRO.S01.T160 993.92 -NYBRO.S01.T161 992.552 -NYBRO.S01.T162 1020.42 -NYBRO.S01.T163 1022.266 -NYBRO.S01.T164 1024.569 -NYBRO.S01.T165 1027.152 -NYBRO.S01.T166 1024.963 -NYBRO.S01.T167 1030.665 -NYBRO.S01.T168 1021.354 -NYBRO.S02.T001 1023.955 -NYBRO.S02.T002 1023.041 -NYBRO.S02.T003 1022.831 -NYBRO.S02.T004 1019.145 -NYBRO.S02.T005 1024.713 -NYBRO.S02.T006 1024.383 -NYBRO.S02.T007 1030.351 -NYBRO.S02.T008 1042.52 -NYBRO.S02.T009 1052.352 -NYBRO.S02.T010 1056.894 -NYBRO.S02.T011 1055.392 -NYBRO.S02.T012 1058.016 -NYBRO.S02.T013 1058.876 -NYBRO.S02.T014 1058.338 -NYBRO.S02.T015 1056.299 -NYBRO.S02.T016 1048.786 -NYBRO.S02.T017 1042.665 -NYBRO.S02.T018 1034.283 -NYBRO.S02.T019 1028.042 -NYBRO.S02.T020 1020.546 -NYBRO.S02.T021 1021.358 -NYBRO.S02.T022 1001.782 -NYBRO.S02.T023 988.62 -NYBRO.S02.T024 973.331 -NYBRO.S02.T025 978.164 -NYBRO.S02.T026 964.079 -NYBRO.S02.T027 943.844 -NYBRO.S02.T028 926.635 -NYBRO.S02.T029 921.15 -NYBRO.S02.T030 940.534 -NYBRO.S02.T031 977.977 -NYBRO.S02.T032 995.147 -NYBRO.S02.T033 1012.177 -NYBRO.S02.T034 1025.549 -NYBRO.S02.T035 1016.081 -NYBRO.S02.T036 1012.196 -NYBRO.S02.T037 1011.227 -NYBRO.S02.T038 1025.079 -NYBRO.S02.T039 1025.41 -NYBRO.S02.T040 1026.978 -NYBRO.S02.T041 1032.937 -NYBRO.S02.T042 1035.932 -NYBRO.S02.T043 1036.015 -NYBRO.S02.T044 1033.955 -NYBRO.S02.T045 1029.373 -NYBRO.S02.T046 1018.252 -NYBRO.S02.T047 1010.127 -NYBRO.S02.T048 999.723 -NYBRO.S02.T049 993.931 -NYBRO.S02.T050 967.172 -NYBRO.S02.T051 956.127 -NYBRO.S02.T052 946.395 -NYBRO.S02.T053 933.186 -NYBRO.S02.T054 930.797 -NYBRO.S02.T055 966.57 -NYBRO.S02.T056 985.368 -NYBRO.S02.T057 1005.326 -NYBRO.S02.T058 1009.509 -NYBRO.S02.T059 1000.132 -NYBRO.S02.T060 996.857 -NYBRO.S02.T061 993.489 -NYBRO.S02.T062 988.343 -NYBRO.S02.T063 987.141 -NYBRO.S02.T064 983.991 -NYBRO.S02.T065 980.087 -NYBRO.S02.T066 985.451 -NYBRO.S02.T067 995.354 -NYBRO.S02.T068 998.468 -NYBRO.S02.T069 1002.043 -NYBRO.S02.T070 999.689 -NYBRO.S02.T071 1001.373 -NYBRO.S02.T072 998.518 -NYBRO.S02.T073 999.69 -NYBRO.S02.T074 990.243 -NYBRO.S02.T075 992.184 -NYBRO.S02.T076 993.975 -NYBRO.S02.T077 993.626 -NYBRO.S02.T078 976.375 -NYBRO.S02.T079 977.284 -NYBRO.S02.T080 990.947 -NYBRO.S02.T081 1007.451 -NYBRO.S02.T082 1015.805 -NYBRO.S02.T083 1007.574 -NYBRO.S02.T084 1018.695 -NYBRO.S02.T085 1018.212 -NYBRO.S02.T086 1020.571 -NYBRO.S02.T087 1020.49 -NYBRO.S02.T088 1019.642 -NYBRO.S02.T089 1026.926 -NYBRO.S02.T090 1022.216 -NYBRO.S02.T091 1025.872 -NYBRO.S02.T092 1034.227 -NYBRO.S02.T093 1029.89 -NYBRO.S02.T094 1031.614 -NYBRO.S02.T095 1024.135 -NYBRO.S02.T096 1025.929 -NYBRO.S02.T097 1020.514 -NYBRO.S02.T098 1017.85 -NYBRO.S02.T099 1013.795 -NYBRO.S02.T100 1011.837 -NYBRO.S02.T101 1011.161 -NYBRO.S02.T102 1006.833 -NYBRO.S02.T103 998.45 -NYBRO.S02.T104 1005.049 -NYBRO.S02.T105 1018.346 -NYBRO.S02.T106 1035.908 -NYBRO.S02.T107 1043.054 -NYBRO.S02.T108 1042.061 -NYBRO.S02.T109 1051.346 -NYBRO.S02.T110 1043.562 -NYBRO.S02.T111 1044.676 -NYBRO.S02.T112 1036.01 -NYBRO.S02.T113 1038.902 -NYBRO.S02.T114 1040.068 -NYBRO.S02.T115 1022.35 -NYBRO.S02.T116 1025.535 -NYBRO.S02.T117 1028.113 -NYBRO.S02.T118 1017.725 -NYBRO.S02.T119 1013.103 -NYBRO.S02.T120 1000.904 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-NYBRO.S02.T161 1015.788 -NYBRO.S02.T162 1021.807 -NYBRO.S02.T163 1026.927 -NYBRO.S02.T164 1028.792 -NYBRO.S02.T165 1029.784 -NYBRO.S02.T166 1024.184 -NYBRO.S02.T167 1020.454 -NYBRO.S02.T168 1010.969 -NYBRO.S03.T001 1017.847 -NYBRO.S03.T002 1002.966 -NYBRO.S03.T003 976.373 -NYBRO.S03.T004 962.177 -NYBRO.S03.T005 995.349 -NYBRO.S03.T006 1034.139 -NYBRO.S03.T007 1039.53 -NYBRO.S03.T008 1043.016 -NYBRO.S03.T009 1061.011 -NYBRO.S03.T010 1064.634 -NYBRO.S03.T011 1055.551 -NYBRO.S03.T012 1058.004 -NYBRO.S03.T013 1053.327 -NYBRO.S03.T014 1053.002 -NYBRO.S03.T015 1055.414 -NYBRO.S03.T016 1050.457 -NYBRO.S03.T017 1055.219 -NYBRO.S03.T018 1058.803 -NYBRO.S03.T019 1056.866 -NYBRO.S03.T020 1053.874 -NYBRO.S03.T021 1057.571 -NYBRO.S03.T022 1057.477 -NYBRO.S03.T023 1052.782 -NYBRO.S03.T024 1053.801 -NYBRO.S03.T025 1043.832 -NYBRO.S03.T026 1046.145 -NYBRO.S03.T027 1035.228 -NYBRO.S03.T028 1032.633 -NYBRO.S03.T029 1023.144 -NYBRO.S03.T030 1019.476 -NYBRO.S03.T031 1021.77 -NYBRO.S03.T032 1026.242 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631.816 -NYBRO.S19.T007 699.76 -NYBRO.S19.T008 728.556 -NYBRO.S19.T009 744.412 -NYBRO.S19.T010 766.64 -NYBRO.S19.T011 768.192 -NYBRO.S19.T012 783.112 -NYBRO.S19.T013 782.512 -NYBRO.S19.T014 784.208 -NYBRO.S19.T015 787.848 -NYBRO.S19.T016 774.208 -NYBRO.S19.T017 772.656 -NYBRO.S19.T018 747.472 -NYBRO.S19.T019 753.112 -NYBRO.S19.T020 769.808 -NYBRO.S19.T021 766.664 -NYBRO.S19.T022 761.464 -NYBRO.S19.T023 756.824 -NYBRO.S19.T024 716.832 -NYBRO.S19.T025 695.584 -NYBRO.S19.T026 687.464 -NYBRO.S19.T027 681.648 -NYBRO.S19.T028 653.784 -NYBRO.S19.T029 661.176 -NYBRO.S19.T030 675.456 -NYBRO.S19.T031 687.008 -NYBRO.S19.T032 723.944 -NYBRO.S19.T033 768.112 -NYBRO.S19.T034 740.096 -NYBRO.S19.T035 799.712 -NYBRO.S19.T036 797.416 -NYBRO.S19.T037 790.376 -NYBRO.S19.T038 788.24 -NYBRO.S19.T039 777.272 -NYBRO.S19.T040 755.48 -NYBRO.S19.T041 740.2 -NYBRO.S19.T042 730.344 -NYBRO.S19.T043 722.688 -NYBRO.S19.T044 721.8 -NYBRO.S19.T045 718.68 -NYBRO.S19.T046 717.824 -NYBRO.S19.T047 710.472 -NYBRO.S19.T048 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666.456 -NYBRO.S19.T133 654.728 -NYBRO.S19.T134 651.312 -NYBRO.S19.T135 641.248 -NYBRO.S19.T136 633.888 -NYBRO.S19.T137 623.872 -NYBRO.S19.T138 615.44 -NYBRO.S19.T139 588.296 -NYBRO.S19.T140 597.784 -NYBRO.S19.T141 592.344 -NYBRO.S19.T142 593.528 -NYBRO.S19.T143 589.096 -NYBRO.S19.T144 589.848 -NYBRO.S19.T145 589.968 -NYBRO.S19.T146 583.016 -NYBRO.S19.T147 588.36 -NYBRO.S19.T148 586.48 -NYBRO.S19.T149 590.4 -NYBRO.S19.T150 595.04 -NYBRO.S19.T151 605.376 -NYBRO.S19.T152 612.544 -NYBRO.S19.T153 640.488 -NYBRO.S19.T154 657.576 -NYBRO.S19.T155 662.224 -NYBRO.S19.T156 667.544 -NYBRO.S19.T157 659.768 -NYBRO.S19.T158 654.448 -NYBRO.S19.T159 650.272 -NYBRO.S19.T160 638.36 -NYBRO.S19.T161 629.832 -NYBRO.S19.T162 625.288 -NYBRO.S19.T163 618.608 -NYBRO.S19.T164 619.32 -NYBRO.S19.T165 619.072 -NYBRO.S19.T166 611.648 -NYBRO.S19.T167 605.12 -NYBRO.S19.T168 588.296 -NYBRO.S20.T001 575.896 -NYBRO.S20.T002 577.328 -NYBRO.S20.T003 588.272 -NYBRO.S20.T004 594.208 -NYBRO.S20.T005 603.12 -NYBRO.S20.T006 626.224 -NYBRO.S20.T007 653.504 -NYBRO.S20.T008 698.496 -NYBRO.S20.T009 779.64 -NYBRO.S20.T010 812.024 -NYBRO.S20.T011 847.792 -NYBRO.S20.T012 830.6 -NYBRO.S20.T013 797.664 -NYBRO.S20.T014 773.84 -NYBRO.S20.T015 766.168 -NYBRO.S20.T016 763.264 -NYBRO.S20.T017 756.48 -NYBRO.S20.T018 752.192 -NYBRO.S20.T019 743.968 -NYBRO.S20.T020 735.264 -NYBRO.S20.T021 721.96 -NYBRO.S20.T022 709.936 -NYBRO.S20.T023 697.752 -NYBRO.S20.T024 691.144 -NYBRO.S20.T025 683.472 -NYBRO.S20.T026 668.312 -NYBRO.S20.T027 651.504 -NYBRO.S20.T028 627.744 -NYBRO.S20.T029 636.984 -NYBRO.S20.T030 631.824 -NYBRO.S20.T031 663.56 -NYBRO.S20.T032 697.896 -NYBRO.S20.T033 732.8 -NYBRO.S20.T034 745.192 -NYBRO.S20.T035 758.76 -NYBRO.S20.T036 763.978 -NYBRO.S20.T037 771.783 -NYBRO.S20.T038 770.709 -NYBRO.S20.T039 773.728 -NYBRO.S20.T040 777.967 -NYBRO.S20.T041 775.613 -NYBRO.S20.T042 765.266 -NYBRO.S20.T043 752.062 -NYBRO.S20.T044 738.532 -NYBRO.S20.T045 736.136 -NYBRO.S20.T046 733.119 -NYBRO.S20.T047 715.704 -NYBRO.S20.T048 721.892 -NYBRO.S20.T049 733.41 -NYBRO.S20.T050 732.07 -NYBRO.S20.T051 721.048 -NYBRO.S20.T052 714.426 -NYBRO.S20.T053 690.852 -NYBRO.S20.T054 663.459 -NYBRO.S20.T055 680.833 -NYBRO.S20.T056 743.927 -NYBRO.S20.T057 765.531 -NYBRO.S20.T058 775.152 -NYBRO.S20.T059 772.826 -NYBRO.S20.T060 769.24 -NYBRO.S20.T061 777.679 -NYBRO.S20.T062 784.722 -NYBRO.S20.T063 790.135 -NYBRO.S20.T064 782.339 -NYBRO.S20.T065 777.781 -NYBRO.S20.T066 773.526 -NYBRO.S20.T067 766.484 -NYBRO.S20.T068 761.244 -NYBRO.S20.T069 753.521 -NYBRO.S20.T070 751.245 -NYBRO.S20.T071 744.928 -NYBRO.S20.T072 739.132 -NYBRO.S20.T073 733.283 -NYBRO.S20.T074 732.804 -NYBRO.S20.T075 736.729 -NYBRO.S20.T076 734.044 -NYBRO.S20.T077 722.642 -NYBRO.S20.T078 671.965 -NYBRO.S20.T079 656.977 -NYBRO.S20.T080 645.324 -NYBRO.S20.T081 651.35 -NYBRO.S20.T082 646.815 -NYBRO.S20.T083 641.03 -NYBRO.S20.T084 634.251 -NYBRO.S20.T085 631.055 -NYBRO.S20.T086 618.161 -NYBRO.S20.T087 605.16 -NYBRO.S20.T088 603.161 -NYBRO.S20.T089 610.553 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643.756 -NYBRO.S21.T006 658.962 -NYBRO.S21.T007 697.432 -NYBRO.S21.T008 739.246 -NYBRO.S21.T009 785.584 -NYBRO.S21.T010 814.352 -NYBRO.S21.T011 867.16 -NYBRO.S21.T012 861.908 -NYBRO.S21.T013 815.25 -NYBRO.S21.T014 835.1 -NYBRO.S21.T015 843.054 -NYBRO.S21.T016 832.572 -NYBRO.S21.T017 823.34 -NYBRO.S21.T018 813.534 -NYBRO.S21.T019 805.58 -NYBRO.S21.T020 802.462 -NYBRO.S21.T021 794.68 -NYBRO.S21.T022 786.352 -NYBRO.S21.T023 780.868 -NYBRO.S21.T024 771.328 -NYBRO.S21.T025 766.94 -NYBRO.S21.T026 759.762 -NYBRO.S21.T027 750.494 -NYBRO.S21.T028 734.45 -NYBRO.S21.T029 710.862 -NYBRO.S21.T030 692.32 -NYBRO.S21.T031 702.434 -NYBRO.S21.T032 736.954 -NYBRO.S21.T033 742.184 -NYBRO.S21.T034 765.12 -NYBRO.S21.T035 738.752 -NYBRO.S21.T036 767.04 -NYBRO.S21.T037 773.376 -NYBRO.S21.T038 766.736 -NYBRO.S21.T039 793.92 -NYBRO.S21.T040 817.502 -NYBRO.S21.T041 807.18 -NYBRO.S21.T042 806.91 -NYBRO.S21.T043 805.436 -NYBRO.S21.T044 804.402 -NYBRO.S21.T045 799.538 -NYBRO.S21.T046 790.15 -NYBRO.S21.T047 779.91 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650.552 -NYBRO.S26.T008 697.216 -NYBRO.S26.T009 763.132 -NYBRO.S26.T010 793.016 -NYBRO.S26.T011 815.152 -NYBRO.S26.T012 826.996 -NYBRO.S26.T013 834.412 -NYBRO.S26.T014 824.916 -NYBRO.S26.T015 816.328 -NYBRO.S26.T016 809.368 -NYBRO.S26.T017 798.456 -NYBRO.S26.T018 792.28 -NYBRO.S26.T019 788.272 -NYBRO.S26.T020 780.28 -NYBRO.S26.T021 774.856 -NYBRO.S26.T022 764.248 -NYBRO.S26.T023 768.104 -NYBRO.S26.T024 766.628 -NYBRO.S26.T025 765.732 -NYBRO.S26.T026 736.616 -NYBRO.S26.T027 696.152 -NYBRO.S26.T028 678.168 -NYBRO.S26.T029 668.448 -NYBRO.S26.T030 662.904 -NYBRO.S26.T031 707.052 -NYBRO.S26.T032 742.388 -NYBRO.S26.T033 775.536 -NYBRO.S26.T034 800.504 -NYBRO.S26.T035 819.164 -NYBRO.S26.T036 829.82 -NYBRO.S26.T037 840.196 -NYBRO.S26.T038 849.852 -NYBRO.S26.T039 852.536 -NYBRO.S26.T040 851.008 -NYBRO.S26.T041 830.532 -NYBRO.S26.T042 820.576 -NYBRO.S26.T043 802.648 -NYBRO.S26.T044 803.864 -NYBRO.S26.T045 804.496 -NYBRO.S26.T046 806.712 -NYBRO.S26.T047 803.456 -NYBRO.S26.T048 778.372 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989.04 -NYBRO.S26.T091 958.672 -NYBRO.S26.T092 947.444 -NYBRO.S26.T093 933.004 -NYBRO.S26.T094 927.372 -NYBRO.S26.T095 916.78 -NYBRO.S26.T096 911.372 -NYBRO.S26.T097 900.16 -NYBRO.S26.T098 895.332 -NYBRO.S26.T099 893.984 -NYBRO.S26.T100 876.824 -NYBRO.S26.T101 877.328 -NYBRO.S26.T102 873.844 -NYBRO.S26.T103 893.88 -NYBRO.S26.T104 920.36 -NYBRO.S26.T105 960.264 -NYBRO.S26.T106 984.768 -NYBRO.S26.T107 1021.26 -NYBRO.S26.T108 1038.38 -NYBRO.S26.T109 1055.944 -NYBRO.S26.T110 1036.988 -NYBRO.S26.T111 1027.664 -NYBRO.S26.T112 998.68 -NYBRO.S26.T113 978.284 -NYBRO.S26.T114 970.056 -NYBRO.S26.T115 965.784 -NYBRO.S26.T116 965.548 -NYBRO.S26.T117 959.904 -NYBRO.S26.T118 937.824 -NYBRO.S26.T119 927.492 -NYBRO.S26.T120 900.684 -NYBRO.S26.T121 883.272 -NYBRO.S26.T122 865.512 -NYBRO.S26.T123 843.184 -NYBRO.S26.T124 824.032 -NYBRO.S26.T125 817.152 -NYBRO.S26.T126 806.16 -NYBRO.S26.T127 803.24 -NYBRO.S26.T128 803.816 -NYBRO.S26.T129 804.032 -NYBRO.S26.T130 802.776 -NYBRO.S26.T131 803.48 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885.128 -NYBRO.S30.T089 883.88 -NYBRO.S30.T090 884.792 -NYBRO.S30.T091 879.368 -NYBRO.S30.T092 860.48 -NYBRO.S30.T093 856.584 -NYBRO.S30.T094 860.632 -NYBRO.S30.T095 855.408 -NYBRO.S30.T096 850.888 -NYBRO.S30.T097 841.328 -NYBRO.S30.T098 840.728 -NYBRO.S30.T099 840.16 -NYBRO.S30.T100 835.232 -NYBRO.S30.T101 831.296 -NYBRO.S30.T102 836.266 -NYBRO.S30.T103 843.328 -NYBRO.S30.T104 867.296 -NYBRO.S30.T105 894.912 -NYBRO.S30.T106 909.752 -NYBRO.S30.T107 915.432 -NYBRO.S30.T108 908 -NYBRO.S30.T109 903.632 -NYBRO.S30.T110 902.512 -NYBRO.S30.T111 898.152 -NYBRO.S30.T112 893.712 -NYBRO.S30.T113 894.544 -NYBRO.S30.T114 892.768 -NYBRO.S30.T115 892.264 -NYBRO.S30.T116 880.352 -NYBRO.S30.T117 883.36 -NYBRO.S30.T118 871.976 -NYBRO.S30.T119 871.2 -NYBRO.S30.T120 858.192 -NYBRO.S30.T121 843.552 -NYBRO.S30.T122 838.32 -NYBRO.S30.T123 815.056 -NYBRO.S30.T124 790.904 -NYBRO.S30.T125 776.6 -NYBRO.S30.T126 775.576 -NYBRO.S30.T127 775.072 -NYBRO.S30.T128 772.568 -NYBRO.S30.T129 767.136 -NYBRO.S30.T130 760.2 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-NYBRO.S32.T005 811.96 -NYBRO.S32.T006 813.664 -NYBRO.S32.T007 829.288 -NYBRO.S32.T008 847.464 -NYBRO.S32.T009 877.056 -NYBRO.S32.T010 875.624 -NYBRO.S32.T011 897.688 -NYBRO.S32.T012 909.984 -NYBRO.S32.T013 911.68 -NYBRO.S32.T014 916.792 -NYBRO.S32.T015 917.952 -NYBRO.S32.T016 920.24 -NYBRO.S32.T017 893.824 -NYBRO.S32.T018 862.664 -NYBRO.S32.T019 867.648 -NYBRO.S32.T020 863.344 -NYBRO.S32.T021 868.368 -NYBRO.S32.T022 862.152 -NYBRO.S32.T023 862.2 -NYBRO.S32.T024 853.84 -NYBRO.S32.T025 853.704 -NYBRO.S32.T026 848.264 -NYBRO.S32.T027 844.92 -NYBRO.S32.T028 840.584 -NYBRO.S32.T029 840.328 -NYBRO.S32.T030 840.84 -NYBRO.S32.T031 834.944 -NYBRO.S32.T032 842.416 -NYBRO.S32.T033 867.672 -NYBRO.S32.T034 889.624 -NYBRO.S32.T035 896.36 -NYBRO.S32.T036 893.856 -NYBRO.S32.T037 891.848 -NYBRO.S32.T038 870.448 -NYBRO.S32.T039 823.632 -NYBRO.S32.T040 810.016 -NYBRO.S32.T041 828.624 -NYBRO.S32.T042 835.456 -NYBRO.S32.T043 837.856 -NYBRO.S32.T044 831.824 -NYBRO.S32.T045 824.08 -NYBRO.S32.T046 819.808 -NYBRO.S32.T047 826.576 -NYBRO.S32.T048 825.504 -NYBRO.S32.T049 828.064 -NYBRO.S32.T050 825.616 -NYBRO.S32.T051 827.6 -NYBRO.S32.T052 822.032 -NYBRO.S32.T053 817.824 -NYBRO.S32.T054 814.4 -NYBRO.S32.T055 812.352 -NYBRO.S32.T056 824.224 -NYBRO.S32.T057 838.864 -NYBRO.S32.T058 875.84 -NYBRO.S32.T059 891.392 -NYBRO.S32.T060 904.512 -NYBRO.S32.T061 906.32 -NYBRO.S32.T062 912.704 -NYBRO.S32.T063 921.584 -NYBRO.S32.T064 910.624 -NYBRO.S32.T065 897.68 -NYBRO.S32.T066 884.256 -NYBRO.S32.T067 870.672 -NYBRO.S32.T068 871.424 -NYBRO.S32.T069 866.368 -NYBRO.S32.T070 866.832 -NYBRO.S32.T071 861.584 -NYBRO.S32.T072 853.472 -NYBRO.S32.T073 849.328 -NYBRO.S32.T074 844.96 -NYBRO.S32.T075 788.032 -NYBRO.S32.T076 814.096 -NYBRO.S32.T077 797.264 -NYBRO.S32.T078 807.92 -NYBRO.S32.T079 821.008 -NYBRO.S32.T080 866.752 -NYBRO.S32.T081 888.528 -NYBRO.S32.T082 882.784 -NYBRO.S32.T083 893.44 -NYBRO.S32.T084 902.16 -NYBRO.S32.T085 888.449 -NYBRO.S32.T086 881.286 -NYBRO.S32.T087 869.68 -NYBRO.S32.T088 859.184 -NYBRO.S32.T089 846 -NYBRO.S32.T090 834.896 -NYBRO.S32.T091 839.536 -NYBRO.S32.T092 834.72 -NYBRO.S32.T093 831.904 -NYBRO.S32.T094 830.464 -NYBRO.S32.T095 836.112 -NYBRO.S32.T096 831.424 -NYBRO.S32.T097 828.016 -NYBRO.S32.T098 817.488 -NYBRO.S32.T099 812.768 -NYBRO.S32.T100 813.84 -NYBRO.S32.T101 812.352 -NYBRO.S32.T102 812.88 -NYBRO.S32.T103 824.32 -NYBRO.S32.T104 859.168 -NYBRO.S32.T105 869.952 -NYBRO.S32.T106 889.888 -NYBRO.S32.T107 906.56 -NYBRO.S32.T108 919.728 -NYBRO.S32.T109 924.16 -NYBRO.S32.T110 923.856 -NYBRO.S32.T111 925.44 -NYBRO.S32.T112 925.952 -NYBRO.S32.T113 912.944 -NYBRO.S32.T114 895.936 -NYBRO.S32.T115 886.704 -NYBRO.S32.T116 875.584 -NYBRO.S32.T117 868.848 -NYBRO.S32.T118 856.832 -NYBRO.S32.T119 851.84 -NYBRO.S32.T120 842.288 -NYBRO.S32.T121 840.336 -NYBRO.S32.T122 832.768 -NYBRO.S32.T123 824.976 -NYBRO.S32.T124 824.128 -NYBRO.S32.T125 820.4 -NYBRO.S32.T126 817.504 -NYBRO.S32.T127 811.004 -NYBRO.S32.T128 806.981 -NYBRO.S32.T129 799.282 -NYBRO.S32.T130 794.94 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779.209 -NYBRO.S33.T005 770.001 -NYBRO.S33.T006 786.504 -NYBRO.S33.T007 809.45 -NYBRO.S33.T008 827.896 -NYBRO.S33.T009 845.342 -NYBRO.S33.T010 856.13 -NYBRO.S33.T011 850.852 -NYBRO.S33.T012 698.288 -NYBRO.S33.T013 706.004 -NYBRO.S33.T014 763.94 -NYBRO.S33.T015 782.038 -NYBRO.S33.T016 801.878 -NYBRO.S33.T017 802.75 -NYBRO.S33.T018 800.344 -NYBRO.S33.T019 797.646 -NYBRO.S33.T020 797.378 -NYBRO.S33.T021 796.616 -NYBRO.S33.T022 794.026 -NYBRO.S33.T023 796.654 -NYBRO.S33.T024 795.164 -NYBRO.S33.T025 797.616 -NYBRO.S33.T026 793.352 -NYBRO.S33.T027 752.092 -NYBRO.S33.T028 777.278 -NYBRO.S33.T029 776.156 -NYBRO.S33.T030 776.86 -NYBRO.S33.T031 776.582 -NYBRO.S33.T032 780.156 -NYBRO.S33.T033 775.218 -NYBRO.S33.T034 777.064 -NYBRO.S33.T035 775.414 -NYBRO.S33.T036 771.394 -NYBRO.S33.T037 770.307 -NYBRO.S33.T038 770.562 -NYBRO.S33.T039 763.732 -NYBRO.S33.T040 763.873 -NYBRO.S33.T041 750.757 -NYBRO.S33.T042 749.94 -NYBRO.S33.T043 756.317 -NYBRO.S33.T044 761.5 -NYBRO.S33.T045 762.314 -NYBRO.S33.T046 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203.006 -NYBRO.S35.T013 209.092 -NYBRO.S35.T014 290.307 -NYBRO.S35.T015 310.043 -NYBRO.S35.T016 311.631 -NYBRO.S35.T017 311.384 -NYBRO.S35.T018 313.752 -NYBRO.S35.T019 313.581 -NYBRO.S35.T020 314.096 -NYBRO.S35.T021 315.302 -NYBRO.S35.T022 295.263 -NYBRO.S35.T023 202.942 -NYBRO.S35.T024 371.158 -NYBRO.S35.T025 430.376 -NYBRO.S35.T026 411.128 -NYBRO.S35.T027 484.952 -NYBRO.S35.T028 495.906 -NYBRO.S35.T029 496.785 -NYBRO.S35.T030 558.076 -NYBRO.S35.T031 569.054 -NYBRO.S35.T032 529.383 -NYBRO.S35.T033 551.66 -NYBRO.S35.T034 672.373 -NYBRO.S35.T035 717.092 -NYBRO.S35.T036 719.6 -NYBRO.S35.T037 713.196 -NYBRO.S35.T038 717.592 -NYBRO.S35.T039 722.124 -NYBRO.S35.T040 738.019 -NYBRO.S35.T041 723.285 -NYBRO.S35.T042 745.912 -NYBRO.S35.T043 758.68 -NYBRO.S35.T044 757.312 -NYBRO.S35.T045 755.352 -NYBRO.S35.T046 749.952 -NYBRO.S35.T047 753.608 -NYBRO.S35.T048 751.928 -NYBRO.S35.T049 749.072 -NYBRO.S35.T050 722.088 -NYBRO.S35.T051 634.384 -NYBRO.S35.T052 611.52 -NYBRO.S35.T053 609.984 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825.373 -NYBRO.S38.T051 818.985 -NYBRO.S38.T052 809.398 -NYBRO.S38.T053 802.281 -NYBRO.S38.T054 800.233 -NYBRO.S38.T055 798.845 -NYBRO.S38.T056 815.373 -NYBRO.S38.T057 846.958 -NYBRO.S38.T058 893.986 -NYBRO.S38.T059 930.008 -NYBRO.S38.T060 956.846 -NYBRO.S38.T061 966.223 -NYBRO.S38.T062 975.487 -NYBRO.S38.T063 973.654 -NYBRO.S38.T064 978.673 -NYBRO.S38.T065 980.497 -NYBRO.S38.T066 982.81 -NYBRO.S38.T067 981.369 -NYBRO.S38.T068 971.729 -NYBRO.S38.T069 971.242 -NYBRO.S38.T070 965.217 -NYBRO.S38.T071 1018.757 -NYBRO.S38.T072 982.29 -NYBRO.S38.T073 929.077 -NYBRO.S38.T074 894.605 -NYBRO.S38.T075 850.902 -NYBRO.S38.T076 841.729 -NYBRO.S38.T077 823.657 -NYBRO.S38.T078 816.726 -NYBRO.S38.T079 835.285 -NYBRO.S38.T080 863.453 -NYBRO.S38.T081 913.002 -NYBRO.S38.T082 965.229 -NYBRO.S38.T083 956.281 -NYBRO.S38.T084 1020.321 -NYBRO.S38.T085 1019.987 -NYBRO.S38.T086 1030.989 -NYBRO.S38.T087 1024.237 -NYBRO.S38.T088 1019.775 -NYBRO.S38.T089 1004.197 -NYBRO.S38.T090 995.706 -NYBRO.S38.T091 982.065 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796.285 -NYBRO.S40.T047 773.141 -NYBRO.S40.T048 733.009 -NYBRO.S40.T049 722.005 -NYBRO.S40.T050 710.761 -NYBRO.S40.T051 708.549 -NYBRO.S40.T052 701.137 -NYBRO.S40.T053 696.893 -NYBRO.S40.T054 690.237 -NYBRO.S40.T055 701.769 -NYBRO.S40.T056 741.957 -NYBRO.S40.T057 810.953 -NYBRO.S40.T058 859.201 -NYBRO.S40.T059 881.513 -NYBRO.S40.T060 901.425 -NYBRO.S40.T061 916.429 -NYBRO.S40.T062 920.145 -NYBRO.S40.T063 910.869 -NYBRO.S40.T064 897.065 -NYBRO.S40.T065 875.085 -NYBRO.S40.T066 863.257 -NYBRO.S40.T067 869.481 -NYBRO.S40.T068 869.265 -NYBRO.S40.T069 858.862 -NYBRO.S40.T070 825.765 -NYBRO.S40.T071 776.273 -NYBRO.S40.T072 757.825 -NYBRO.S40.T073 725.965 -NYBRO.S40.T074 722.978 -NYBRO.S40.T075 715.049 -NYBRO.S40.T076 713.509 -NYBRO.S40.T077 712.301 -NYBRO.S40.T078 709.057 -NYBRO.S40.T079 736.046 -NYBRO.S40.T080 788.941 -NYBRO.S40.T081 856.845 -NYBRO.S40.T082 904.093 -NYBRO.S40.T083 948.713 -NYBRO.S40.T084 939.002 -NYBRO.S40.T085 928.031 -NYBRO.S40.T086 915.87 -NYBRO.S40.T087 904.77 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750.452 -NYBRO.S40.T130 754.116 -NYBRO.S40.T131 757.744 -NYBRO.S40.T132 759.632 -NYBRO.S40.T133 762.736 -NYBRO.S40.T134 764.04 -NYBRO.S40.T135 763.444 -NYBRO.S40.T136 763.016 -NYBRO.S40.T137 763.165 -NYBRO.S40.T138 762.816 -NYBRO.S40.T139 768.284 -NYBRO.S40.T140 773.496 -NYBRO.S40.T141 780.266 -NYBRO.S40.T142 787.344 -NYBRO.S40.T143 791.365 -NYBRO.S40.T144 786.468 -NYBRO.S40.T145 788.13 -NYBRO.S40.T146 793.578 -NYBRO.S40.T147 796.594 -NYBRO.S40.T148 797.859 -NYBRO.S40.T149 798.462 -NYBRO.S40.T150 790.98 -NYBRO.S40.T151 778.433 -NYBRO.S40.T152 776.348 -NYBRO.S40.T153 779.538 -NYBRO.S40.T154 792.291 -NYBRO.S40.T155 799.574 -NYBRO.S40.T156 808.447 -NYBRO.S40.T157 821.77 -NYBRO.S40.T158 826.031 -NYBRO.S40.T159 824.251 -NYBRO.S40.T160 821.023 -NYBRO.S40.T161 551.391 -NYBRO.S40.T162 443.776 -NYBRO.S40.T163 392.111 -NYBRO.S40.T164 304.543 -NYBRO.S40.T165 307.755 -NYBRO.S40.T166 309.89 -NYBRO.S40.T167 485.036 -NYBRO.S40.T168 587.519 -NYBRO.S41.T001 721.098 -NYBRO.S41.T002 705.858 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927.292 -NYBRO.S41.T045 924.87 -NYBRO.S41.T046 916.65 -NYBRO.S41.T047 883.506 -NYBRO.S41.T048 820.794 -NYBRO.S41.T049 808.504 -NYBRO.S41.T050 805.96 -NYBRO.S41.T051 798.832 -NYBRO.S41.T052 788.81 -NYBRO.S41.T053 789.824 -NYBRO.S41.T054 789.108 -NYBRO.S41.T055 796.816 -NYBRO.S41.T056 838.58 -NYBRO.S41.T057 903.3 -NYBRO.S41.T058 932.54 -NYBRO.S41.T059 943.044 -NYBRO.S41.T060 963.668 -NYBRO.S41.T061 870.287 -NYBRO.S41.T062 837.199 -NYBRO.S41.T063 842.275 -NYBRO.S41.T064 843.836 -NYBRO.S41.T065 846.728 -NYBRO.S41.T066 846.384 -NYBRO.S41.T067 844.896 -NYBRO.S41.T068 841.78 -NYBRO.S41.T069 837.94 -NYBRO.S41.T070 877.532 -NYBRO.S41.T071 843.46 -NYBRO.S41.T072 806.048 -NYBRO.S41.T073 809.86 -NYBRO.S41.T074 795.676 -NYBRO.S41.T075 783.96 -NYBRO.S41.T076 775.632 -NYBRO.S41.T077 777.668 -NYBRO.S41.T078 772.592 -NYBRO.S41.T079 784.972 -NYBRO.S41.T080 822.64 -NYBRO.S41.T081 874.856 -NYBRO.S41.T082 927.424 -NYBRO.S41.T083 969.044 -NYBRO.S41.T084 989.268 -NYBRO.S41.T085 994.664 -NYBRO.S41.T086 994.096 -NYBRO.S41.T087 993.72 -NYBRO.S41.T088 991.872 -NYBRO.S41.T089 975.02 -NYBRO.S41.T090 966.1 -NYBRO.S41.T091 967.708 -NYBRO.S41.T092 967.536 -NYBRO.S41.T093 958.628 -NYBRO.S41.T094 937.104 -NYBRO.S41.T095 872.088 -NYBRO.S41.T096 823.76 -NYBRO.S41.T097 811.896 -NYBRO.S41.T098 805.156 -NYBRO.S41.T099 797.208 -NYBRO.S41.T100 786.496 -NYBRO.S41.T101 781.264 -NYBRO.S41.T102 778.524 -NYBRO.S41.T103 801.568 -NYBRO.S41.T104 825.848 -NYBRO.S41.T105 874.979 -NYBRO.S41.T106 904.59 -NYBRO.S41.T107 904.916 -NYBRO.S41.T108 916.339 -NYBRO.S41.T109 925.188 -NYBRO.S41.T110 937.916 -NYBRO.S41.T111 933.638 -NYBRO.S41.T112 929.628 -NYBRO.S41.T113 929.084 -NYBRO.S41.T114 951.74 -NYBRO.S41.T115 965.772 -NYBRO.S41.T116 989.208 -NYBRO.S41.T117 992.28 -NYBRO.S41.T118 979.092 -NYBRO.S41.T119 951.292 -NYBRO.S41.T120 915.86 -NYBRO.S41.T121 878.976 -NYBRO.S41.T122 838.58 -NYBRO.S41.T123 824.576 -NYBRO.S41.T124 815.904 -NYBRO.S41.T125 805.916 -NYBRO.S41.T126 791.704 -NYBRO.S41.T127 779.208 -NYBRO.S41.T128 779.344 -NYBRO.S41.T129 782.628 -NYBRO.S41.T130 784.492 -NYBRO.S41.T131 793.164 -NYBRO.S41.T132 799.58 -NYBRO.S41.T133 799 -NYBRO.S41.T134 804.208 -NYBRO.S41.T135 804.576 -NYBRO.S41.T136 808.592 -NYBRO.S41.T137 810.248 -NYBRO.S41.T138 823.312 -NYBRO.S41.T139 831.08 -NYBRO.S41.T140 842.92 -NYBRO.S41.T141 846.056 -NYBRO.S41.T142 859.288 -NYBRO.S41.T143 869.512 -NYBRO.S41.T144 870.2 -NYBRO.S41.T145 865.584 -NYBRO.S41.T146 850.664 -NYBRO.S41.T147 835.816 -NYBRO.S41.T148 820.608 -NYBRO.S41.T149 817.304 -NYBRO.S41.T150 813.216 -NYBRO.S41.T151 797.24 -NYBRO.S41.T152 792.568 -NYBRO.S41.T153 800.32 -NYBRO.S41.T154 811 -NYBRO.S41.T155 823.256 -NYBRO.S41.T156 834.744 -NYBRO.S41.T157 847.816 -NYBRO.S41.T158 849.504 -NYBRO.S41.T159 851.848 -NYBRO.S41.T160 852.2 -NYBRO.S41.T161 854.144 -NYBRO.S41.T162 858.256 -NYBRO.S41.T163 862.08 -NYBRO.S41.T164 887.68 -NYBRO.S41.T165 882.088 -NYBRO.S41.T166 889.768 -NYBRO.S41.T167 866.776 -NYBRO.S41.T168 845.992 -NYBRO.S42.T001 832.568 -NYBRO.S42.T002 822.488 -NYBRO.S42.T003 814.856 -NYBRO.S42.T004 811.608 -NYBRO.S42.T005 800.392 -NYBRO.S42.T006 805.056 -NYBRO.S42.T007 819.632 -NYBRO.S42.T008 877.48 -NYBRO.S42.T009 930.808 -NYBRO.S42.T010 974.664 -NYBRO.S42.T011 1013.632 -NYBRO.S42.T012 1033.136 -NYBRO.S42.T013 1050.248 -NYBRO.S42.T014 1076.992 -NYBRO.S42.T015 1083.4 -NYBRO.S42.T016 1086.512 -NYBRO.S42.T017 1083.184 -NYBRO.S42.T018 1082.056 -NYBRO.S42.T019 1080.736 -NYBRO.S42.T020 1068.88 -NYBRO.S42.T021 1051.72 -NYBRO.S42.T022 1020.128 -NYBRO.S42.T023 1001.16 -NYBRO.S42.T024 989.048 -NYBRO.S42.T025 991.056 -NYBRO.S42.T026 944.344 -NYBRO.S42.T027 896.536 -NYBRO.S42.T028 880.664 -NYBRO.S42.T029 870.56 -NYBRO.S42.T030 862.848 -NYBRO.S42.T031 878.496 -NYBRO.S42.T032 898.752 -NYBRO.S42.T033 948.216 -NYBRO.S42.T034 980.512 -NYBRO.S42.T035 1015.88 -NYBRO.S42.T036 1019.896 -NYBRO.S42.T037 1020.384 -NYBRO.S42.T038 1003.552 -NYBRO.S42.T039 1002.216 -NYBRO.S42.T040 1015.92 -NYBRO.S42.T041 1031.496 -NYBRO.S42.T042 1030.168 -NYBRO.S42.T043 1026.896 -NYBRO.S42.T044 1024.424 -NYBRO.S42.T045 1027.328 -NYBRO.S42.T046 1032.656 -NYBRO.S42.T047 1036.392 -NYBRO.S42.T048 1031.16 -NYBRO.S42.T049 1000.456 -NYBRO.S42.T050 979.208 -NYBRO.S42.T051 1006.424 -NYBRO.S42.T052 1029.944 -NYBRO.S42.T053 997.456 -NYBRO.S42.T054 953.864 -NYBRO.S42.T055 959.936 -NYBRO.S42.T056 963.512 -NYBRO.S42.T057 982.877 -NYBRO.S42.T058 1016.287 -NYBRO.S42.T059 1031.215 -NYBRO.S42.T060 893.879 -NYBRO.S42.T061 730.785 -NYBRO.S42.T062 806.944 -NYBRO.S42.T063 980.288 -NYBRO.S42.T064 996.824 -NYBRO.S42.T065 979.448 -NYBRO.S42.T066 962.848 -NYBRO.S42.T067 950.912 -NYBRO.S42.T068 944.464 -NYBRO.S42.T069 936.136 -NYBRO.S42.T070 925.448 -NYBRO.S42.T071 911.048 -NYBRO.S42.T072 889.056 -NYBRO.S42.T073 875.832 -NYBRO.S42.T074 868.92 -NYBRO.S42.T075 858.168 -NYBRO.S42.T076 834.344 -NYBRO.S42.T077 777.504 -NYBRO.S42.T078 778.432 -NYBRO.S42.T079 796.808 -NYBRO.S42.T080 846.496 -NYBRO.S42.T081 937.376 -NYBRO.S42.T082 957.16 -NYBRO.S42.T083 962.344 -NYBRO.S42.T084 975.504 -NYBRO.S42.T085 959.708 -NYBRO.S42.T086 936.174 -NYBRO.S42.T087 925.566 -NYBRO.S42.T088 923.078 -NYBRO.S42.T089 925.576 -NYBRO.S42.T090 934.344 -NYBRO.S42.T091 936.52 -NYBRO.S42.T092 935.048 -NYBRO.S42.T093 936.872 -NYBRO.S42.T094 935.304 -NYBRO.S42.T095 926.984 -NYBRO.S42.T096 914.528 -NYBRO.S42.T097 906.528 -NYBRO.S42.T098 889.2 -NYBRO.S42.T099 822.256 -NYBRO.S42.T100 783.872 -NYBRO.S42.T101 763.92 -NYBRO.S42.T102 725.448 -NYBRO.S42.T103 732.584 -NYBRO.S42.T104 782.088 -NYBRO.S42.T105 845.936 -NYBRO.S42.T106 910.472 -NYBRO.S42.T107 905.976 -NYBRO.S42.T108 900.52 -NYBRO.S42.T109 897.152 -NYBRO.S42.T110 897.584 -NYBRO.S42.T111 894.304 -NYBRO.S42.T112 900.816 -NYBRO.S42.T113 889.28 -NYBRO.S42.T114 888.232 -NYBRO.S42.T115 890.168 -NYBRO.S42.T116 887.08 -NYBRO.S42.T117 885.784 -NYBRO.S42.T118 877.84 -NYBRO.S42.T119 864.96 -NYBRO.S42.T120 856.44 -NYBRO.S42.T121 848.84 -NYBRO.S42.T122 842.552 -NYBRO.S42.T123 830.736 -NYBRO.S42.T124 823.176 -NYBRO.S42.T125 814.6 -NYBRO.S42.T126 810.624 -NYBRO.S42.T127 803.84 -NYBRO.S42.T128 798.2 -NYBRO.S42.T129 786.936 -NYBRO.S42.T130 779.864 -NYBRO.S42.T131 782.352 -NYBRO.S42.T132 780.832 -NYBRO.S42.T133 780.936 -NYBRO.S42.T134 778.52 -NYBRO.S42.T135 773.48 -NYBRO.S42.T136 767.768 -NYBRO.S42.T137 759.968 -NYBRO.S42.T138 755.896 -NYBRO.S42.T139 751.56 -NYBRO.S42.T140 744.16 -NYBRO.S42.T141 729.144 -NYBRO.S42.T142 727.088 -NYBRO.S42.T143 729.448 -NYBRO.S42.T144 711.848 -NYBRO.S42.T145 696.456 -NYBRO.S42.T146 697.376 -NYBRO.S42.T147 712.712 -NYBRO.S42.T148 726.352 -NYBRO.S42.T149 704.448 -NYBRO.S42.T150 688.272 -NYBRO.S42.T151 685.984 -NYBRO.S42.T152 684.144 -NYBRO.S42.T153 696.944 -NYBRO.S42.T154 709.496 -NYBRO.S42.T155 725.68 -NYBRO.S42.T156 739.608 -NYBRO.S42.T157 750.704 -NYBRO.S42.T158 763.472 -NYBRO.S42.T159 757.144 -NYBRO.S42.T160 753.264 -NYBRO.S42.T161 743.48 -NYBRO.S42.T162 755.152 -NYBRO.S42.T163 759.36 -NYBRO.S42.T164 743.816 -NYBRO.S42.T165 738.992 -NYBRO.S42.T166 720.736 -NYBRO.S42.T167 714.272 -NYBRO.S42.T168 707.64 -NYBRO.S43.T001 686.56 -NYBRO.S43.T002 682.664 -NYBRO.S43.T003 678 -NYBRO.S43.T004 666.672 -NYBRO.S43.T005 651.944 -NYBRO.S43.T006 633.048 -NYBRO.S43.T007 659.136 -NYBRO.S43.T008 699.992 -NYBRO.S43.T009 746.968 -NYBRO.S43.T010 788.48 -NYBRO.S43.T011 833.264 -NYBRO.S43.T012 849.848 -NYBRO.S43.T013 824.704 -NYBRO.S43.T014 852.672 -NYBRO.S43.T015 887.256 -NYBRO.S43.T016 887.56 -NYBRO.S43.T017 886.016 -NYBRO.S43.T018 886.88 -NYBRO.S43.T019 879.552 -NYBRO.S43.T020 869.008 -NYBRO.S43.T021 857.104 -NYBRO.S43.T022 848.344 -NYBRO.S43.T023 815.168 -NYBRO.S43.T024 765.984 -NYBRO.S43.T025 744.344 -NYBRO.S43.T026 738.832 -NYBRO.S43.T027 686.64 -NYBRO.S43.T028 657.656 -NYBRO.S43.T029 647.984 -NYBRO.S43.T030 622.544 -NYBRO.S43.T031 662.984 -NYBRO.S43.T032 701.504 -NYBRO.S43.T033 737.16 -NYBRO.S43.T034 772.352 -NYBRO.S43.T035 797.672 -NYBRO.S43.T036 817.168 -NYBRO.S43.T037 833.04 -NYBRO.S43.T038 843.52 -NYBRO.S43.T039 850.992 -NYBRO.S43.T040 852.64 -NYBRO.S43.T041 841.12 -NYBRO.S43.T042 846.816 -NYBRO.S43.T043 850.816 -NYBRO.S43.T044 862.688 -NYBRO.S43.T045 866.048 -NYBRO.S43.T046 863.632 -NYBRO.S43.T047 840.768 -NYBRO.S43.T048 826.992 -NYBRO.S43.T049 800.512 -NYBRO.S43.T050 765.824 -NYBRO.S43.T051 755.904 -NYBRO.S43.T052 746.784 -NYBRO.S43.T053 742.128 -NYBRO.S43.T054 736.56 -NYBRO.S43.T055 745.968 -NYBRO.S43.T056 778.56 -NYBRO.S43.T057 828.168 -NYBRO.S43.T058 865.2 -NYBRO.S43.T059 878.176 -NYBRO.S43.T060 892.432 -NYBRO.S43.T061 892.104 -NYBRO.S43.T062 897.416 -NYBRO.S43.T063 902.576 -NYBRO.S43.T064 919.872 -NYBRO.S43.T065 931.056 -NYBRO.S43.T066 934.776 -NYBRO.S43.T067 943.328 -NYBRO.S43.T068 942.64 -NYBRO.S43.T069 945.88 -NYBRO.S43.T070 939.608 -NYBRO.S43.T071 927.36 -NYBRO.S43.T072 921.152 -NYBRO.S43.T073 915.904 -NYBRO.S43.T074 892.96 -NYBRO.S43.T075 866.176 -NYBRO.S43.T076 849.328 -NYBRO.S43.T077 842.16 -NYBRO.S43.T078 846.6 -NYBRO.S43.T079 831.976 -NYBRO.S43.T080 855.856 -NYBRO.S43.T081 848.288 -NYBRO.S43.T082 873.752 -NYBRO.S43.T083 880.048 -NYBRO.S43.T084 888.104 -NYBRO.S43.T085 896.12 -NYBRO.S43.T086 894.48 -NYBRO.S43.T087 896.464 -NYBRO.S43.T088 900.96 -NYBRO.S43.T089 914.104 -NYBRO.S43.T090 938.048 -NYBRO.S43.T091 947.024 -NYBRO.S43.T092 953.264 -NYBRO.S43.T093 958.216 -NYBRO.S43.T094 954.664 -NYBRO.S43.T095 941.56 -NYBRO.S43.T096 904.28 -NYBRO.S43.T097 900.72 -NYBRO.S43.T098 859.92 -NYBRO.S43.T099 855.344 -NYBRO.S43.T100 849.04 -NYBRO.S43.T101 850.128 -NYBRO.S43.T102 853.92 -NYBRO.S43.T103 857.536 -NYBRO.S43.T104 864.912 -NYBRO.S43.T105 873.552 -NYBRO.S43.T106 880.496 -NYBRO.S43.T107 898.8 -NYBRO.S43.T108 906.784 -NYBRO.S43.T109 907.704 -NYBRO.S43.T110 914 -NYBRO.S43.T111 910.936 -NYBRO.S43.T112 911.848 -NYBRO.S43.T113 909.784 -NYBRO.S43.T114 908.656 -NYBRO.S43.T115 907.312 -NYBRO.S43.T116 918.784 -NYBRO.S43.T117 913.552 -NYBRO.S43.T118 911.616 -NYBRO.S43.T119 898.592 -NYBRO.S43.T120 886.36 -NYBRO.S43.T121 881.128 -NYBRO.S43.T122 874.352 -NYBRO.S43.T123 862.448 -NYBRO.S43.T124 863.24 -NYBRO.S43.T125 853.216 -NYBRO.S43.T126 844.592 -NYBRO.S43.T127 838.296 -NYBRO.S43.T128 845.96 -NYBRO.S43.T129 839.408 -NYBRO.S43.T130 787.968 -NYBRO.S43.T131 783.496 -NYBRO.S43.T132 780.44 -NYBRO.S43.T133 779.296 -NYBRO.S43.T134 757.888 -NYBRO.S43.T135 754.92 -NYBRO.S43.T136 752.368 -NYBRO.S43.T137 750.336 -NYBRO.S43.T138 755.248 -NYBRO.S43.T139 760.16 -NYBRO.S43.T140 752.168 -NYBRO.S43.T141 752.144 -NYBRO.S43.T142 754.104 -NYBRO.S43.T143 753.808 -NYBRO.S43.T144 757.112 -NYBRO.S43.T145 757.144 -NYBRO.S43.T146 755.328 -NYBRO.S43.T147 753.412 -NYBRO.S43.T148 746.56 -NYBRO.S43.T149 742.664 -NYBRO.S43.T150 727.704 -NYBRO.S43.T151 726.424 -NYBRO.S43.T152 734.856 -NYBRO.S43.T153 734.36 -NYBRO.S43.T154 736.208 -NYBRO.S43.T155 752.512 -NYBRO.S43.T156 776.248 -NYBRO.S43.T157 782.496 -NYBRO.S43.T158 776.848 -NYBRO.S43.T159 774.536 -NYBRO.S43.T160 769.296 -NYBRO.S43.T161 769.792 -NYBRO.S43.T162 775.752 -NYBRO.S43.T163 791.512 -NYBRO.S43.T164 780.448 -NYBRO.S43.T165 784.624 -NYBRO.S43.T166 784.28 -NYBRO.S43.T167 772.136 -NYBRO.S43.T168 752.32 -NYBRO.S44.T001 731.336 -NYBRO.S44.T002 731 -NYBRO.S44.T003 723.336 -NYBRO.S44.T004 719.288 -NYBRO.S44.T005 719.472 -NYBRO.S44.T006 726.432 -NYBRO.S44.T007 773.528 -NYBRO.S44.T008 838.792 -NYBRO.S44.T009 884.128 -NYBRO.S44.T010 927.128 -NYBRO.S44.T011 977.184 -NYBRO.S44.T012 987.368 -NYBRO.S44.T013 989.72 -NYBRO.S44.T014 990.44 -NYBRO.S44.T015 999.128 -NYBRO.S44.T016 999.64 -NYBRO.S44.T017 985.616 -NYBRO.S44.T018 972.816 -NYBRO.S44.T019 950.832 -NYBRO.S44.T020 946.784 -NYBRO.S44.T021 940.896 -NYBRO.S44.T022 932.192 -NYBRO.S44.T023 925.296 -NYBRO.S44.T024 915.936 -NYBRO.S44.T025 898.592 -NYBRO.S44.T026 839.616 -NYBRO.S44.T027 823.52 -NYBRO.S44.T028 815.064 -NYBRO.S44.T029 808.96 -NYBRO.S44.T030 811.808 -NYBRO.S44.T031 823.832 -NYBRO.S44.T032 857.392 -NYBRO.S44.T033 893.032 -NYBRO.S44.T034 956.4 -NYBRO.S44.T035 973.776 -NYBRO.S44.T036 977.904 -NYBRO.S44.T037 973.688 -NYBRO.S44.T038 993.24 -NYBRO.S44.T039 992.24 -NYBRO.S44.T040 1022.552 -NYBRO.S44.T041 1031.192 -NYBRO.S44.T042 1019.144 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929.208 -NYBRO.S44.T085 965.312 -NYBRO.S44.T086 964.504 -NYBRO.S44.T087 973.44 -NYBRO.S44.T088 975.544 -NYBRO.S44.T089 976.344 -NYBRO.S44.T090 974.28 -NYBRO.S44.T091 977.568 -NYBRO.S44.T092 978.912 -NYBRO.S44.T093 979.424 -NYBRO.S44.T094 975.64 -NYBRO.S44.T095 979.24 -NYBRO.S44.T096 977.928 -NYBRO.S44.T097 979.864 -NYBRO.S44.T098 982.832 -NYBRO.S44.T099 989.024 -NYBRO.S44.T100 930.944 -NYBRO.S44.T101 909.624 -NYBRO.S44.T102 904.728 -NYBRO.S44.T103 904.44 -NYBRO.S44.T104 937.728 -NYBRO.S44.T105 966.928 -NYBRO.S44.T106 989.032 -NYBRO.S44.T107 1001.288 -NYBRO.S44.T108 1016.76 -NYBRO.S44.T109 998.804 -NYBRO.S44.T110 993.301 -NYBRO.S44.T111 1000.603 -NYBRO.S44.T112 1023.73 -NYBRO.S44.T113 998.752 -NYBRO.S44.T114 980.464 -NYBRO.S44.T115 980.728 -NYBRO.S44.T116 975.208 -NYBRO.S44.T117 959.4 -NYBRO.S44.T118 945.776 -NYBRO.S44.T119 925.344 -NYBRO.S44.T120 905.448 -NYBRO.S44.T121 877.424 -NYBRO.S44.T122 843.496 -NYBRO.S44.T123 807.648 -NYBRO.S44.T124 803.152 -NYBRO.S44.T125 803.68 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1057.344 -NYBRO.S46.T039 1060.584 -NYBRO.S46.T040 1063.448 -NYBRO.S46.T041 1058.28 -NYBRO.S46.T042 1054.024 -NYBRO.S46.T043 1027.576 -NYBRO.S46.T044 998.216 -NYBRO.S46.T045 979.184 -NYBRO.S46.T046 964.696 -NYBRO.S46.T047 959.816 -NYBRO.S46.T048 956.632 -NYBRO.S46.T049 949.616 -NYBRO.S46.T050 946.872 -NYBRO.S46.T051 910.632 -NYBRO.S46.T052 881.12 -NYBRO.S46.T053 868.464 -NYBRO.S46.T054 858.712 -NYBRO.S46.T055 875.712 -NYBRO.S46.T056 943.032 -NYBRO.S46.T057 990.648 -NYBRO.S46.T058 1017.056 -NYBRO.S46.T059 1022.864 -NYBRO.S46.T060 1030.856 -NYBRO.S46.T061 1028.408 -NYBRO.S46.T062 1022.377 -NYBRO.S46.T063 1009.598 -NYBRO.S46.T064 1002.082 -NYBRO.S46.T065 994.802 -NYBRO.S46.T066 988.998 -NYBRO.S46.T067 985.573 -NYBRO.S46.T068 990.582 -NYBRO.S46.T069 994.725 -NYBRO.S46.T070 986.661 -NYBRO.S46.T071 976.774 -NYBRO.S46.T072 959.65 -NYBRO.S46.T073 950.602 -NYBRO.S46.T074 945.607 -NYBRO.S46.T075 935.323 -NYBRO.S46.T076 902.53 -NYBRO.S46.T077 873.881 -NYBRO.S46.T078 870.616 -NYBRO.S46.T079 891.437 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974.696 -NYBRO.S46.T163 978.462 -NYBRO.S46.T164 981.338 -NYBRO.S46.T165 987.411 -NYBRO.S46.T166 979.904 -NYBRO.S46.T167 974.692 -NYBRO.S46.T168 955.823 -NYBRO.S47.T001 937.221 -NYBRO.S47.T002 924.889 -NYBRO.S47.T003 907.728 -NYBRO.S47.T004 897.435 -NYBRO.S47.T005 889.05 -NYBRO.S47.T006 892.653 -NYBRO.S47.T007 909.837 -NYBRO.S47.T008 951.984 -NYBRO.S47.T009 997.434 -NYBRO.S47.T010 1016.298 -NYBRO.S47.T011 1029.402 -NYBRO.S47.T012 1037.301 -NYBRO.S47.T013 1044.346 -NYBRO.S47.T014 1044.319 -NYBRO.S47.T015 1047.632 -NYBRO.S47.T016 1050.577 -NYBRO.S47.T017 1026.042 -NYBRO.S47.T018 1027.824 -NYBRO.S47.T019 1023.231 -NYBRO.S47.T020 1012.841 -NYBRO.S47.T021 1009.232 -NYBRO.S47.T022 994.027 -NYBRO.S47.T023 984.015 -NYBRO.S47.T024 945.013 -NYBRO.S47.T025 918.992 -NYBRO.S47.T026 853.739 -NYBRO.S47.T027 808.529 -NYBRO.S47.T028 808.971 -NYBRO.S47.T029 816.786 -NYBRO.S47.T030 842.915 -NYBRO.S47.T031 918.299 -NYBRO.S47.T032 960.135 -NYBRO.S47.T033 985.303 -NYBRO.S47.T034 1000.176 -NYBRO.S47.T035 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945.664 -NYBRO.S49.T153 944.22 -NYBRO.S49.T154 978.068 -NYBRO.S49.T155 972.428 -NYBRO.S49.T156 980.676 -NYBRO.S49.T157 982.452 -NYBRO.S49.T158 988.836 -NYBRO.S49.T159 984.504 -NYBRO.S49.T160 980.276 -NYBRO.S49.T161 981.884 -NYBRO.S49.T162 988.456 -NYBRO.S49.T163 997.82 -NYBRO.S49.T164 980.948 -NYBRO.S49.T165 971.992 -NYBRO.S49.T166 965.236 -NYBRO.S49.T167 942.672 -NYBRO.S49.T168 939.544 -NYBRO.S50.T001 932.58 -NYBRO.S50.T002 920.056 -NYBRO.S50.T003 914.616 -NYBRO.S50.T004 898.764 -NYBRO.S50.T005 889.484 -NYBRO.S50.T006 896.156 -NYBRO.S50.T007 924.652 -NYBRO.S50.T008 957.46 -NYBRO.S50.T009 1006.576 -NYBRO.S50.T010 1039.36 -NYBRO.S50.T011 1067.008 -NYBRO.S50.T012 1080.432 -NYBRO.S50.T013 1084.356 -NYBRO.S50.T014 1083.596 -NYBRO.S50.T015 1085.6 -NYBRO.S50.T016 1087.592 -NYBRO.S50.T017 1074.224 -NYBRO.S50.T018 1058.64 -NYBRO.S50.T019 1056.552 -NYBRO.S50.T020 1035.624 -NYBRO.S50.T021 1026.144 -NYBRO.S50.T022 1022.94 -NYBRO.S50.T023 1019.24 -NYBRO.S50.T024 1005.172 -NYBRO.S50.T025 961.008 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-NYBRO.S50.T109 682.848 -NYBRO.S50.T110 631.016 -NYBRO.S50.T111 777.024 -NYBRO.S50.T112 957.304 -NYBRO.S50.T113 980.456 -NYBRO.S50.T114 1003.672 -NYBRO.S50.T115 1023.256 -NYBRO.S50.T116 1016.284 -NYBRO.S50.T117 998.02 -NYBRO.S50.T118 982.056 -NYBRO.S50.T119 964.232 -NYBRO.S50.T120 955.492 -NYBRO.S50.T121 949.68 -NYBRO.S50.T122 937.204 -NYBRO.S50.T123 912.568 -NYBRO.S50.T124 897.52 -NYBRO.S50.T125 869.12 -NYBRO.S50.T126 853.736 -NYBRO.S50.T127 844.94 -NYBRO.S50.T128 848.336 -NYBRO.S50.T129 865.86 -NYBRO.S50.T130 901.976 -NYBRO.S50.T131 952.968 -NYBRO.S50.T132 1000.804 -NYBRO.S50.T133 1030.964 -NYBRO.S50.T134 1023.116 -NYBRO.S50.T135 1029.4 -NYBRO.S50.T136 1028.052 -NYBRO.S50.T137 1024.8 -NYBRO.S50.T138 1026.896 -NYBRO.S50.T139 1025.328 -NYBRO.S50.T140 1025.668 -NYBRO.S50.T141 1021.252 -NYBRO.S50.T142 1014.172 -NYBRO.S50.T143 1012.156 -NYBRO.S50.T144 1013.616 -NYBRO.S50.T145 1010.608 -NYBRO.S50.T146 1001.932 -NYBRO.S50.T147 932.532 -NYBRO.S50.T148 947.86 -NYBRO.S50.T149 956.54 -NYBRO.S50.T150 955.036 -NYBRO.S50.T151 951.704 -NYBRO.S50.T152 954.652 -NYBRO.S50.T153 957.544 -NYBRO.S50.T154 966.528 -NYBRO.S50.T155 975.912 -NYBRO.S50.T156 990.68 -NYBRO.S50.T157 1005.056 -NYBRO.S50.T158 1009.6 -NYBRO.S50.T159 1008.432 -NYBRO.S50.T160 998.948 -NYBRO.S50.T161 983.332 -NYBRO.S50.T162 973.636 -NYBRO.S50.T163 965.824 -NYBRO.S50.T164 950.148 -NYBRO.S50.T165 945.052 -NYBRO.S50.T166 933.724 -NYBRO.S50.T167 923.084 -NYBRO.S50.T168 917.308 -NYBRO.S51.T001 914.328 -NYBRO.S51.T002 916.224 -NYBRO.S51.T003 929.544 -NYBRO.S51.T004 935.668 -NYBRO.S51.T005 884.256 -NYBRO.S51.T006 866.424 -NYBRO.S51.T007 896.524 -NYBRO.S51.T008 925.344 -NYBRO.S51.T009 952.06 -NYBRO.S51.T010 994.704 -NYBRO.S51.T011 984.148 -NYBRO.S51.T012 1014.4 -NYBRO.S51.T013 1031.708 -NYBRO.S51.T014 1012.62 -NYBRO.S51.T015 1005.24 -NYBRO.S51.T016 1006.844 -NYBRO.S51.T017 1018.412 -NYBRO.S51.T018 1015.264 -NYBRO.S51.T019 1007.432 -NYBRO.S51.T020 1011.836 -NYBRO.S51.T021 1013.888 -NYBRO.S51.T022 1014.28 -NYBRO.S51.T023 1014.364 -NYBRO.S51.T024 995.904 -NYBRO.S51.T025 978.436 -NYBRO.S51.T026 967.724 -NYBRO.S51.T027 965.192 -NYBRO.S51.T028 966.068 -NYBRO.S51.T029 959.764 -NYBRO.S51.T030 952.188 -NYBRO.S51.T031 951.756 -NYBRO.S51.T032 974.52 -NYBRO.S51.T033 994.28 -NYBRO.S51.T034 1002.528 -NYBRO.S51.T035 1000.408 -NYBRO.S51.T036 1006.62 -NYBRO.S51.T037 1015.676 -NYBRO.S51.T038 1010.404 -NYBRO.S51.T039 1006.18 -NYBRO.S51.T040 999.624 -NYBRO.S51.T041 990.392 -NYBRO.S51.T042 984.24 -NYBRO.S51.T043 976.544 -NYBRO.S51.T044 968.404 -NYBRO.S51.T045 969.392 -NYBRO.S51.T046 969.348 -NYBRO.S51.T047 967.768 -NYBRO.S51.T048 941.568 -NYBRO.S51.T049 909.636 -NYBRO.S51.T050 901.652 -NYBRO.S51.T051 907.172 -NYBRO.S51.T052 908.1 -NYBRO.S51.T053 909.264 -NYBRO.S51.T054 905.7 -NYBRO.S51.T055 912.108 -NYBRO.S51.T056 929.212 -NYBRO.S51.T057 948.216 -NYBRO.S51.T058 971.872 -NYBRO.S51.T059 986.228 -NYBRO.S51.T060 998.76 -NYBRO.S51.T061 992.452 -NYBRO.S51.T062 993.692 -NYBRO.S51.T063 991.456 -NYBRO.S51.T064 1000.44 -NYBRO.S51.T065 1006.2 -NYBRO.S51.T066 1008.984 -NYBRO.S51.T067 1001.968 -NYBRO.S51.T068 978.496 -NYBRO.S51.T069 954 -NYBRO.S51.T070 935.16 -NYBRO.S51.T071 818.496 -NYBRO.S51.T072 786.128 -NYBRO.S51.T073 781.592 -NYBRO.S51.T074 785.8 -NYBRO.S51.T075 789.308 -NYBRO.S51.T076 840.636 -NYBRO.S51.T077 898.332 -NYBRO.S51.T078 882.94 -NYBRO.S51.T079 885.728 -NYBRO.S51.T080 889.808 -NYBRO.S51.T081 913.944 -NYBRO.S51.T082 927.676 -NYBRO.S51.T083 965.14 -NYBRO.S51.T084 987.784 -NYBRO.S51.T085 999.544 -NYBRO.S51.T086 998.528 -NYBRO.S51.T087 997.732 -NYBRO.S51.T088 992.996 -NYBRO.S51.T089 988.416 -NYBRO.S51.T090 983.104 -NYBRO.S51.T091 973.892 -NYBRO.S51.T092 968.424 -NYBRO.S51.T093 964.532 -NYBRO.S51.T094 943.168 -NYBRO.S51.T095 915.044 -NYBRO.S51.T096 853.048 -NYBRO.S51.T097 836.156 -NYBRO.S51.T098 806.952 -NYBRO.S51.T099 794.048 -NYBRO.S51.T100 811.628 -NYBRO.S51.T101 818.116 -NYBRO.S51.T102 826.2 -NYBRO.S51.T103 858.344 -NYBRO.S51.T104 886.756 -NYBRO.S51.T105 916.332 -NYBRO.S51.T106 952.048 -NYBRO.S51.T107 982.74 -NYBRO.S51.T108 997.896 -NYBRO.S51.T109 1003.844 -NYBRO.S51.T110 1030.956 -NYBRO.S51.T111 1043.868 -NYBRO.S51.T112 1049.144 -NYBRO.S51.T113 1051.7 -NYBRO.S51.T114 1059.952 -NYBRO.S51.T115 1061.956 -NYBRO.S51.T116 1060.956 -NYBRO.S51.T117 1043.82 -NYBRO.S51.T118 1036.928 -NYBRO.S51.T119 1029.492 -NYBRO.S51.T120 996.356 -NYBRO.S51.T121 982.752 -NYBRO.S51.T122 960.32 -NYBRO.S51.T123 949.056 -NYBRO.S51.T124 941.408 -NYBRO.S51.T125 930.016 -NYBRO.S51.T126 920.8 -NYBRO.S51.T127 924.448 -NYBRO.S51.T128 928.2 -NYBRO.S51.T129 925.688 -NYBRO.S51.T130 930.292 -NYBRO.S51.T131 929.436 -NYBRO.S51.T132 939.936 -NYBRO.S51.T133 941.12 -NYBRO.S51.T134 942.94 -NYBRO.S51.T135 951.764 -NYBRO.S51.T136 949.872 -NYBRO.S51.T137 952.144 -NYBRO.S51.T138 956.496 -NYBRO.S51.T139 963.536 -NYBRO.S51.T140 972.72 -NYBRO.S51.T141 974.888 -NYBRO.S51.T142 978.712 -NYBRO.S51.T143 978.912 -NYBRO.S51.T144 975.22 -NYBRO.S51.T145 962.82 -NYBRO.S51.T146 952.704 -NYBRO.S51.T147 943.252 -NYBRO.S51.T148 942.788 -NYBRO.S51.T149 936.736 -NYBRO.S51.T150 940.428 -NYBRO.S51.T151 938.424 -NYBRO.S51.T152 951.996 -NYBRO.S51.T153 952.624 -NYBRO.S51.T154 952.712 -NYBRO.S51.T155 969.34 -NYBRO.S51.T156 983.732 -NYBRO.S51.T157 995.416 -NYBRO.S51.T158 1000.796 -NYBRO.S51.T159 1007.236 -NYBRO.S51.T160 1007.4 -NYBRO.S51.T161 1016.544 -NYBRO.S51.T162 1011.584 -NYBRO.S51.T163 1020.784 -NYBRO.S51.T164 1025.452 -NYBRO.S51.T165 1020.032 -NYBRO.S51.T166 1019.452 -NYBRO.S51.T167 1020.408 -NYBRO.S51.T168 1013.72 -NYBRO.S52.T001 1005.84 -NYBRO.S52.T002 996.32 -NYBRO.S52.T003 991.012 -NYBRO.S52.T004 981.372 -NYBRO.S52.T005 975.984 -NYBRO.S52.T006 973.856 -NYBRO.S52.T007 986.08 -NYBRO.S52.T008 1001.988 -NYBRO.S52.T009 1026.128 -NYBRO.S52.T010 1040.628 -NYBRO.S52.T011 1069.62 -NYBRO.S52.T012 1072.908 -NYBRO.S52.T013 1077.152 -NYBRO.S52.T014 1070.46 -NYBRO.S52.T015 1068.54 -NYBRO.S52.T016 1061.344 -NYBRO.S52.T017 1057.712 -NYBRO.S52.T018 1057.716 -NYBRO.S52.T019 1052.34 -NYBRO.S52.T020 1044.024 -NYBRO.S52.T021 1043.736 -NYBRO.S52.T022 1038.212 -NYBRO.S52.T023 1024.624 -NYBRO.S52.T024 1012.196 -NYBRO.S52.T025 993.64 -NYBRO.S52.T026 980.632 -NYBRO.S52.T027 978.272 -NYBRO.S52.T028 964.3 -NYBRO.S52.T029 928.16 -NYBRO.S52.T030 912.388 -NYBRO.S52.T031 904.396 -NYBRO.S52.T032 919.284 -NYBRO.S52.T033 930.876 -NYBRO.S52.T034 946.5 -NYBRO.S52.T035 920.608 -NYBRO.S52.T036 920.368 -NYBRO.S52.T037 922.52 -NYBRO.S52.T038 921.488 -NYBRO.S52.T039 920.64 -NYBRO.S52.T040 927.12 -NYBRO.S52.T041 976.324 -NYBRO.S52.T042 988.004 -NYBRO.S52.T043 988.888 -NYBRO.S52.T044 993.768 -NYBRO.S52.T045 1002.84 -NYBRO.S52.T046 1004.188 -NYBRO.S52.T047 981.164 -NYBRO.S52.T048 937.048 -NYBRO.S52.T049 945.832 -NYBRO.S52.T050 936.72 -NYBRO.S52.T051 930.028 -NYBRO.S52.T052 923.164 -NYBRO.S52.T053 917.804 -NYBRO.S52.T054 911.8 -NYBRO.S52.T055 922.464 -NYBRO.S52.T056 945.544 -NYBRO.S52.T057 970.128 -NYBRO.S52.T058 985.6 -NYBRO.S52.T059 1006.536 -NYBRO.S52.T060 999.496 -NYBRO.S52.T061 1012.736 -NYBRO.S52.T062 1011.744 -NYBRO.S52.T063 1018.416 -NYBRO.S52.T064 1031.512 -NYBRO.S52.T065 1024.176 -NYBRO.S52.T066 1015.64 -NYBRO.S52.T067 998.632 -NYBRO.S52.T068 988.64 -NYBRO.S52.T069 981.344 -NYBRO.S52.T070 981.248 -NYBRO.S52.T071 981.336 -NYBRO.S52.T072 962.624 -NYBRO.S52.T073 898.144 -NYBRO.S52.T074 875.808 -NYBRO.S52.T075 856.136 -NYBRO.S52.T076 834.616 -NYBRO.S52.T077 846.32 -NYBRO.S52.T078 852.96 -NYBRO.S52.T079 876.064 -NYBRO.S52.T080 895.216 -NYBRO.S52.T081 912.136 -NYBRO.S52.T082 928.784 -NYBRO.S52.T083 943.816 -NYBRO.S52.T084 964.168 -NYBRO.S52.T085 973.976 -NYBRO.S52.T086 1003.128 -NYBRO.S52.T087 1007.856 -NYBRO.S52.T088 1016.632 -NYBRO.S52.T089 1028.416 -NYBRO.S52.T090 1027.504 -NYBRO.S52.T091 1037.816 -NYBRO.S52.T092 1035.896 -NYBRO.S52.T093 1035.08 -NYBRO.S52.T094 1035.336 -NYBRO.S52.T095 1007.808 -NYBRO.S52.T096 978.392 -NYBRO.S52.T097 939.656 -NYBRO.S52.T098 918.864 -NYBRO.S52.T099 890.696 -NYBRO.S52.T100 882 -NYBRO.S52.T101 881.256 -NYBRO.S52.T102 875.688 -NYBRO.S52.T103 890.776 -NYBRO.S52.T104 904.976 -NYBRO.S52.T105 934.544 -NYBRO.S52.T106 961.408 -NYBRO.S52.T107 976.16 -NYBRO.S52.T108 986.384 -NYBRO.S52.T109 994.56 -NYBRO.S52.T110 1001.728 -NYBRO.S52.T111 1000.44 -NYBRO.S52.T112 1016 -NYBRO.S52.T113 1026 -NYBRO.S52.T114 1021.624 -NYBRO.S52.T115 1019.192 -NYBRO.S52.T116 1017.072 -NYBRO.S52.T117 1012.64 -NYBRO.S52.T118 1010.8 -NYBRO.S52.T119 994.064 -NYBRO.S52.T120 978.28 -NYBRO.S52.T121 1078.825 -NYBRO.S52.T122 1078.254 -NYBRO.S52.T123 1074.544 -NYBRO.S52.T124 1072.006 -NYBRO.S52.T125 1075.136 -NYBRO.S52.T126 1074.615 -NYBRO.S52.T127 1075.654 -NYBRO.S52.T128 1070.094 -NYBRO.S52.T129 1069.474 -NYBRO.S52.T130 1061.825 -NYBRO.S52.T131 1046.733 -NYBRO.S52.T132 1045.183 -NYBRO.S52.T133 1037.144 -NYBRO.S52.T134 1033.247 -NYBRO.S52.T135 1027.421 -NYBRO.S52.T136 1031.994 -NYBRO.S52.T137 1026.773 -NYBRO.S52.T138 1029.308 -NYBRO.S52.T139 1028.378 -NYBRO.S52.T140 1026.615 -NYBRO.S52.T141 1022.942 -NYBRO.S52.T142 1019.666 -NYBRO.S52.T143 1018.786 -NYBRO.S52.T144 1025.743 -NYBRO.S52.T145 1023.553 -NYBRO.S52.T146 1023.719 -NYBRO.S52.T147 1019.282 -NYBRO.S52.T148 1008.046 -NYBRO.S52.T149 988.962 -NYBRO.S52.T150 991.965 -NYBRO.S52.T151 988.904 -NYBRO.S52.T152 993.973 -NYBRO.S52.T153 1003.004 -NYBRO.S52.T154 1001.056 -NYBRO.S52.T155 1007.4 -NYBRO.S52.T156 1007.803 -NYBRO.S52.T157 1008.204 -NYBRO.S52.T158 1013.624 -NYBRO.S52.T159 1011.812 -NYBRO.S52.T160 1015.582 -NYBRO.S52.T161 1008.571 -NYBRO.S52.T162 1022.306 -NYBRO.S52.T163 1023.505 -NYBRO.S52.T164 1031.016 -NYBRO.S52.T165 1031.24 -NYBRO.S52.T166 1035.202 -NYBRO.S52.T167 1035.593 -NYBRO.S52.T168 1030.31 -* ----------------------------------------------------- \ No newline at end of file diff --git a/base/addons/gas/MRareas.inc b/base/addons/gas/MRareas.inc deleted file mode 100644 index c4097aac..00000000 --- a/base/addons/gas/MRareas.inc +++ /dev/null @@ -1,52 +0,0 @@ -BROENDBY -DRAGOER -EGTVED -ELLIDSHOEJ -HERNING -HOEJBY -HVIDOVRE -POTTEHUSE -SKAERBAEK -SYDHAVNEN -TAULOV -TORSLUNDE -AVEDOERE -AMAGERF -AALBORG -VESTAMAGER -VARDE -VALLENSBAEK -STENLILLE -HCOERSTED -ELLUND -HAVERSLEV -HELLE -SLAGELSE -SOROE -STANDST -ULLERSLEV -BILLESBOELLE -BRANDE -VIBORG -TERKELSBOEL -RINGSTED -NOERSKOV -NYBORG -NYBRO -LYNGE -MIDDELFART -MAALOEV -KARLSLUNDE -KARUP -KOEGE -KOELBJERG -LISELSKAER -LITORUP -OERESUND -FROESLEV -LILBALLE -LYNGSODDE -KONGSMARK -$ifi %HEATING%==yes VEKS_V, CTR_V, VFORBRANDING_V, NFORBRANDING_V, AALBORG_V, AARHUS_V, ODENSE_V, HERNING_V, TVIS_V - - diff --git a/base/addons/gas/MRregions2areas.inc b/base/addons/gas/MRregions2areas.inc deleted file mode 100644 index 4cc37cf4..00000000 --- a/base/addons/gas/MRregions2areas.inc +++ /dev/null @@ -1,7 +0,0 @@ -DK_E.(AMAGERF, AVEDOERE, BROENDBY,DRAGOER,HCOERSTED,KARLSLUNDE,KOEGE,MAALOEV, - SYDHAVNEN,TORSLUNDE,VALLENSBAEK,VESTAMAGER,LYNGE,RINGSTED,SLAGELSE, - SOROE, HVIDOVRE, KONGSMARK) -DK_W.(AALBORG, BILLESBOELLE, BRANDE, KARUP, LISELSKAER, HAVERSLEV, - HELLE, NOERSKOV, NYBORG, STANDST,TERKELSBOEL,ULLERSLEV,VIBORG, - ELLIDSHOEJ, HOEJBY, KOELBJERG,MIDDELFART,HERNING,SKAERBAEK,TAULOV,VARDE, - POTTEHUSE,EGTVED,LILBALLE,LYNGSODDE, FROESLEV) \ No newline at end of file diff --git a/base/addons/gas/SE_DG_VAR_T.INC b/base/addons/gas/SE_DG_VAR_T.INC deleted file mode 100644 index 0ffdcb03..00000000 --- a/base/addons/gas/SE_DG_VAR_T.INC +++ /dev/null @@ -1,8746 +0,0 @@ -* ----------------------------------------------------- -* MDB2GMSE_S_Rural.S VerSE_S_Rural.Sion 2.6, AuguSE_S_Rural.St 2005 -* Erwin Kalvelagen, GAMSE_S_Rural.S Development Corp -* ----------------------------------------------------- -* DAO verSE_S_Rural.Sion: 3.6 -* Jet verSE_S_Rural.Sion: 4.0 -* DatabaSE_S_Rural.Se: D:\LarSE_S_Rural.SB\1\GaSE_S_Rural.STranSE_S_Rural.SmiSE_S_Rural.SSE_S_Rural.Sion.mdb -* Query: SE_S_Rural.Select * from dragoer -* ----------------------------------------------------- -SE_S_Rural.S01.T001 150.125 -SE_S_Rural.S01.T002 149.898 -SE_S_Rural.S01.T003 150.066 -SE_S_Rural.S01.T004 150.176 -SE_S_Rural.S01.T005 149.676 -SE_S_Rural.S01.T006 141.695 -SE_S_Rural.S01.T007 133.703 -SE_S_Rural.S01.T008 150.206 -SE_S_Rural.S01.T009 150.529 -SE_S_Rural.S01.T010 152.749 -SE_S_Rural.S01.T011 151.875 -SE_S_Rural.S01.T012 153.087 -SE_S_Rural.S01.T013 151.558 -SE_S_Rural.S01.T014 161.914 -SE_S_Rural.S01.T015 168.093 -SE_S_Rural.S01.T016 168.627 -SE_S_Rural.S01.T017 169.617 -SE_S_Rural.S01.T018 168.342 -SE_S_Rural.S01.T019 167.985 -SE_S_Rural.S01.T020 168.054 -SE_S_Rural.S01.T021 167.897 -SE_S_Rural.S01.T022 168.309 -SE_S_Rural.S01.T023 169.432 -SE_S_Rural.S01.T024 172.244 -SE_S_Rural.S01.T025 172.024 -SE_S_Rural.S01.T026 172.051 -SE_S_Rural.S01.T027 172.015 -SE_S_Rural.S01.T028 172.196 -SE_S_Rural.S01.T029 169.183 -SE_S_Rural.S01.T030 168.499 -SE_S_Rural.S01.T031 170.076 -SE_S_Rural.S01.T032 170.418 -SE_S_Rural.S01.T033 177.012 -SE_S_Rural.S01.T034 182.981 -SE_S_Rural.S01.T035 183.057 -SE_S_Rural.S01.T036 182.879 -SE_S_Rural.S01.T037 182.762 -SE_S_Rural.S01.T038 183.75 -SE_S_Rural.S01.T039 183.346 -SE_S_Rural.S01.T040 183.244 -SE_S_Rural.S01.T041 183.063 -SE_S_Rural.S01.T042 183.621 -SE_S_Rural.S01.T043 184.433 -SE_S_Rural.S01.T044 185.356 -SE_S_Rural.S01.T045 191.254 -SE_S_Rural.S01.T046 190.797 -SE_S_Rural.S01.T047 191.826 -SE_S_Rural.S01.T048 192.395 -SE_S_Rural.S01.T049 192.457 -SE_S_Rural.S01.T050 182.455 -SE_S_Rural.S01.T051 181.405 -SE_S_Rural.S01.T052 181.14 -SE_S_Rural.S01.T053 180.935 -SE_S_Rural.S01.T054 180.131 -SE_S_Rural.S01.T055 179.838 -SE_S_Rural.S01.T056 179.521 -SE_S_Rural.S01.T057 179.659 -SE_S_Rural.S01.T058 179.407 -SE_S_Rural.S01.T059 179.226 -SE_S_Rural.S01.T060 178.899 -SE_S_Rural.S01.T061 179.45 -SE_S_Rural.S01.T062 180.355 -SE_S_Rural.S01.T063 183.419 -SE_S_Rural.S01.T064 183.028 -SE_S_Rural.S01.T065 181.909 -SE_S_Rural.S01.T066 175.876 -SE_S_Rural.S01.T067 172.439 -SE_S_Rural.S01.T068 170.075 -SE_S_Rural.S01.T069 167.876 -SE_S_Rural.S01.T070 166.506 -SE_S_Rural.S01.T071 168.864 -SE_S_Rural.S01.T072 171.102 -SE_S_Rural.S01.T073 147.86 -SE_S_Rural.S01.T074 144.036 -SE_S_Rural.S01.T075 143.917 -SE_S_Rural.S01.T076 143.872 -SE_S_Rural.S01.T077 138.183 -SE_S_Rural.S01.T078 134.298 -SE_S_Rural.S01.T079 131.458 -SE_S_Rural.S01.T080 135.723 -SE_S_Rural.S01.T081 136.329 -SE_S_Rural.S01.T082 138.192 -SE_S_Rural.S01.T083 141.925 -SE_S_Rural.S01.T084 142.015 -SE_S_Rural.S01.T085 141.75 -SE_S_Rural.S01.T086 141.886 -SE_S_Rural.S01.T087 141.936 -SE_S_Rural.S01.T088 141.584 -SE_S_Rural.S01.T089 141.805 -SE_S_Rural.S01.T090 141.567 -SE_S_Rural.S01.T091 142.447 -SE_S_Rural.S01.T092 143.788 -SE_S_Rural.S01.T093 142.092 -SE_S_Rural.S01.T094 141.027 -SE_S_Rural.S01.T095 134.027 -SE_S_Rural.S01.T096 133.804 -SE_S_Rural.S01.T097 133.56 -SE_S_Rural.S01.T098 133.351 -SE_S_Rural.S01.T099 133.545 -SE_S_Rural.S01.T100 133.231 -SE_S_Rural.S01.T101 132.771 -SE_S_Rural.S01.T102 132.272 -SE_S_Rural.S01.T103 136.874 -SE_S_Rural.S01.T104 137.631 -SE_S_Rural.S01.T105 137.119 -SE_S_Rural.S01.T106 136.652 -SE_S_Rural.S01.T107 135.858 -SE_S_Rural.S01.T108 138.493 -SE_S_Rural.S01.T109 138.338 -SE_S_Rural.S01.T110 137.044 -SE_S_Rural.S01.T111 137.176 -SE_S_Rural.S01.T112 136.875 -SE_S_Rural.S01.T113 135.722 -SE_S_Rural.S01.T114 137.918 -SE_S_Rural.S01.T115 139.08 -SE_S_Rural.S01.T116 138.691 -SE_S_Rural.S01.T117 138.433 -SE_S_Rural.S01.T118 138.496 -SE_S_Rural.S01.T119 138.299 -SE_S_Rural.S01.T120 137.816 -SE_S_Rural.S01.T121 138.2 -SE_S_Rural.S01.T122 138.245 -SE_S_Rural.S01.T123 138.025 -SE_S_Rural.S01.T124 125.252 -SE_S_Rural.S01.T125 123.546 -SE_S_Rural.S01.T126 123.373 -SE_S_Rural.S01.T127 131.126 -SE_S_Rural.S01.T128 137.299 -SE_S_Rural.S01.T129 136.332 -SE_S_Rural.S01.T130 136.608 -SE_S_Rural.S01.T131 136.143 -SE_S_Rural.S01.T132 138.689 -SE_S_Rural.S01.T133 138.157 -SE_S_Rural.S01.T134 138.457 -SE_S_Rural.S01.T135 138.615 -SE_S_Rural.S01.T136 138.306 -SE_S_Rural.S01.T137 138.824 -SE_S_Rural.S01.T138 137.623 -SE_S_Rural.S01.T139 137.295 -SE_S_Rural.S01.T140 136.764 -SE_S_Rural.S01.T141 136.706 -SE_S_Rural.S01.T142 136.131 -SE_S_Rural.S01.T143 135.753 -SE_S_Rural.S01.T144 135.868 -SE_S_Rural.S01.T145 136.154 -SE_S_Rural.S01.T146 136.525 -SE_S_Rural.S01.T147 137.369 -SE_S_Rural.S01.T148 138.025 -SE_S_Rural.S01.T149 123.499 -SE_S_Rural.S01.T150 114.735 -SE_S_Rural.S01.T151 144.964 -SE_S_Rural.S01.T152 155.707 -SE_S_Rural.S01.T153 157.014 -SE_S_Rural.S01.T154 148.169 -SE_S_Rural.S01.T155 145.368 -SE_S_Rural.S01.T156 144.962 -SE_S_Rural.S01.T157 144.825 -SE_S_Rural.S01.T158 144.559 -SE_S_Rural.S01.T159 144.425 -SE_S_Rural.S01.T160 144.464 -SE_S_Rural.S01.T161 171.844 -SE_S_Rural.S01.T162 186.851 -SE_S_Rural.S01.T163 187.03 -SE_S_Rural.S01.T164 186.905 -SE_S_Rural.S01.T165 187.13 -SE_S_Rural.S01.T166 187.067 -SE_S_Rural.S01.T167 186.67 -SE_S_Rural.S01.T168 187.294 -SE_S_Rural.S02.T001 187.167 -SE_S_Rural.S02.T002 186.834 -SE_S_Rural.S02.T003 191.219 -SE_S_Rural.S02.T004 191.71 -SE_S_Rural.S02.T005 193.727 -SE_S_Rural.S02.T006 194.342 -SE_S_Rural.S02.T007 167.679 -SE_S_Rural.S02.T008 165.488 -SE_S_Rural.S02.T009 164.554 -SE_S_Rural.S02.T010 164.774 -SE_S_Rural.S02.T011 165.184 -SE_S_Rural.S02.T012 164.637 -SE_S_Rural.S02.T013 164.93 -SE_S_Rural.S02.T014 165.64 -SE_S_Rural.S02.T015 165.139 -SE_S_Rural.S02.T016 164.745 -SE_S_Rural.S02.T017 164.967 -SE_S_Rural.S02.T018 164.656 -SE_S_Rural.S02.T019 164.954 -SE_S_Rural.S02.T020 164.879 -SE_S_Rural.S02.T021 164.903 -SE_S_Rural.S02.T022 164.748 -SE_S_Rural.S02.T023 164.886 -SE_S_Rural.S02.T024 164.864 -SE_S_Rural.S02.T025 164.768 -SE_S_Rural.S02.T026 164.781 -SE_S_Rural.S02.T027 163.201 -SE_S_Rural.S02.T028 163.442 -SE_S_Rural.S02.T029 163.38 -SE_S_Rural.S02.T030 162.965 -SE_S_Rural.S02.T031 141.532 -SE_S_Rural.S02.T032 136.634 -SE_S_Rural.S02.T033 137.256 -SE_S_Rural.S02.T034 136.686 -SE_S_Rural.S02.T035 137.049 -SE_S_Rural.S02.T036 137.799 -SE_S_Rural.S02.T037 137.05 -SE_S_Rural.S02.T038 137.073 -SE_S_Rural.S02.T039 136.384 -SE_S_Rural.S02.T040 152.665 -SE_S_Rural.S02.T041 125.61 -SE_S_Rural.S02.T042 95.514 -SE_S_Rural.S02.T043 95.548 -SE_S_Rural.S02.T044 95.463 -SE_S_Rural.S02.T045 95.371 -SE_S_Rural.S02.T046 95.359 -SE_S_Rural.S02.T047 92.573 -SE_S_Rural.S02.T048 89.835 -SE_S_Rural.S02.T049 89.983 -SE_S_Rural.S02.T050 89.796 -SE_S_Rural.S02.T051 89.989 -SE_S_Rural.S02.T052 89.759 -SE_S_Rural.S02.T053 84.543 -SE_S_Rural.S02.T054 81.211 -SE_S_Rural.S02.T055 102.587 -SE_S_Rural.S02.T056 150.477 -SE_S_Rural.S02.T057 150.826 -SE_S_Rural.S02.T058 150.191 -SE_S_Rural.S02.T059 149.272 -SE_S_Rural.S02.T060 148.773 -SE_S_Rural.S02.T061 148.46 -SE_S_Rural.S02.T062 149.206 -SE_S_Rural.S02.T063 150.834 -SE_S_Rural.S02.T064 150.171 -SE_S_Rural.S02.T065 163.197 -SE_S_Rural.S02.T066 181.349 -SE_S_Rural.S02.T067 181.135 -SE_S_Rural.S02.T068 182.506 -SE_S_Rural.S02.T069 181.747 -SE_S_Rural.S02.T070 181.446 -SE_S_Rural.S02.T071 182.839 -SE_S_Rural.S02.T072 183.105 -SE_S_Rural.S02.T073 181.927 -SE_S_Rural.S02.T074 182.944 -SE_S_Rural.S02.T075 183.12 -SE_S_Rural.S02.T076 183.135 -SE_S_Rural.S02.T077 180.956 -SE_S_Rural.S02.T078 180.841 -SE_S_Rural.S02.T079 181.647 -SE_S_Rural.S02.T080 181.792 -SE_S_Rural.S02.T081 181.786 -SE_S_Rural.S02.T082 181.397 -SE_S_Rural.S02.T083 181.427 -SE_S_Rural.S02.T084 171.419 -SE_S_Rural.S02.T085 160.924 -SE_S_Rural.S02.T086 162.071 -SE_S_Rural.S02.T087 162.919 -SE_S_Rural.S02.T088 164.115 -SE_S_Rural.S02.T089 163.393 -SE_S_Rural.S02.T090 163.011 -SE_S_Rural.S02.T091 161.955 -SE_S_Rural.S02.T092 163.916 -SE_S_Rural.S02.T093 175.009 -SE_S_Rural.S02.T094 173.638 -SE_S_Rural.S02.T095 170.546 -SE_S_Rural.S02.T096 171.442 -SE_S_Rural.S02.T097 169.334 -SE_S_Rural.S02.T098 170.686 -SE_S_Rural.S02.T099 171.216 -SE_S_Rural.S02.T100 170.099 -SE_S_Rural.S02.T101 169.389 -SE_S_Rural.S02.T102 169.466 -SE_S_Rural.S02.T103 169.611 -SE_S_Rural.S02.T104 169.587 -SE_S_Rural.S02.T105 171.486 -SE_S_Rural.S02.T106 182.101 -SE_S_Rural.S02.T107 181.774 -SE_S_Rural.S02.T108 182.198 -SE_S_Rural.S02.T109 182.021 -SE_S_Rural.S02.T110 182.045 -SE_S_Rural.S02.T111 181.741 -SE_S_Rural.S02.T112 180.596 -SE_S_Rural.S02.T113 171.201 -SE_S_Rural.S02.T114 170.555 -SE_S_Rural.S02.T115 171.513 -SE_S_Rural.S02.T116 178.115 -SE_S_Rural.S02.T117 187.049 -SE_S_Rural.S02.T118 187.299 -SE_S_Rural.S02.T119 187.541 -SE_S_Rural.S02.T120 186.799 -SE_S_Rural.S02.T121 188.874 -SE_S_Rural.S02.T122 188.992 -SE_S_Rural.S02.T123 188.805 -SE_S_Rural.S02.T124 189.005 -SE_S_Rural.S02.T125 187.399 -SE_S_Rural.S02.T126 186.752 -SE_S_Rural.S02.T127 167.332 -SE_S_Rural.S02.T128 157.794 -SE_S_Rural.S02.T129 158.564 -SE_S_Rural.S02.T130 167.68 -SE_S_Rural.S02.T131 169.174 -SE_S_Rural.S02.T132 169.244 -SE_S_Rural.S02.T133 169.709 -SE_S_Rural.S02.T134 170.097 -SE_S_Rural.S02.T135 170.585 -SE_S_Rural.S02.T136 170.073 -SE_S_Rural.S02.T137 170.504 -SE_S_Rural.S02.T138 171.16 -SE_S_Rural.S02.T139 171.197 -SE_S_Rural.S02.T140 171.369 -SE_S_Rural.S02.T141 171.527 -SE_S_Rural.S02.T142 171.355 -SE_S_Rural.S02.T143 171.204 -SE_S_Rural.S02.T144 171.597 -SE_S_Rural.S02.T145 171.729 -SE_S_Rural.S02.T146 171.717 -SE_S_Rural.S02.T147 173.613 -SE_S_Rural.S02.T148 173.968 -SE_S_Rural.S02.T149 172.696 -SE_S_Rural.S02.T150 174.737 -SE_S_Rural.S02.T151 179.02 -SE_S_Rural.S02.T152 178.966 -SE_S_Rural.S02.T153 178.38 -SE_S_Rural.S02.T154 172.176 -SE_S_Rural.S02.T155 166.498 -SE_S_Rural.S02.T156 166.389 -SE_S_Rural.S02.T157 163.642 -SE_S_Rural.S02.T158 161.583 -SE_S_Rural.S02.T159 161.759 -SE_S_Rural.S02.T160 162.919 -SE_S_Rural.S02.T161 162.214 -SE_S_Rural.S02.T162 160.849 -SE_S_Rural.S02.T163 160.4 -SE_S_Rural.S02.T164 160.42 -SE_S_Rural.S02.T165 160.613 -SE_S_Rural.S02.T166 163.441 -SE_S_Rural.S02.T167 165.224 -SE_S_Rural.S02.T168 167.33 -SE_S_Rural.S03.T001 171.398 -SE_S_Rural.S03.T002 174.472 -SE_S_Rural.S03.T003 175.893 -SE_S_Rural.S03.T004 179.448 -SE_S_Rural.S03.T005 180.134 -SE_S_Rural.S03.T006 177.455 -SE_S_Rural.S03.T007 169.556 -SE_S_Rural.S03.T008 165.813 -SE_S_Rural.S03.T009 169.088 -SE_S_Rural.S03.T010 169.608 -SE_S_Rural.S03.T011 170.77 -SE_S_Rural.S03.T012 171.788 -SE_S_Rural.S03.T013 174.827 -SE_S_Rural.S03.T014 177.011 -SE_S_Rural.S03.T015 177.035 -SE_S_Rural.S03.T016 170.505 -SE_S_Rural.S03.T017 167.357 -SE_S_Rural.S03.T018 169.097 -SE_S_Rural.S03.T019 171.393 -SE_S_Rural.S03.T020 171.215 -SE_S_Rural.S03.T021 171.766 -SE_S_Rural.S03.T022 173.372 -SE_S_Rural.S03.T023 175.974 -SE_S_Rural.S03.T024 177.475 -SE_S_Rural.S03.T025 181.758 -SE_S_Rural.S03.T026 183.731 -SE_S_Rural.S03.T027 183.67 -SE_S_Rural.S03.T028 184.754 -SE_S_Rural.S03.T029 184.488 -SE_S_Rural.S03.T030 182.81 -SE_S_Rural.S03.T031 176.551 -SE_S_Rural.S03.T032 185.104 -SE_S_Rural.S03.T033 191.791 -SE_S_Rural.S03.T034 194.287 -SE_S_Rural.S03.T035 190.757 -SE_S_Rural.S03.T036 186.505 -SE_S_Rural.S03.T037 186.208 -SE_S_Rural.S03.T038 185.96 -SE_S_Rural.S03.T039 185.102 -SE_S_Rural.S03.T040 185.463 -SE_S_Rural.S03.T041 185.612 -SE_S_Rural.S03.T042 185.811 -SE_S_Rural.S03.T043 196.8 -SE_S_Rural.S03.T044 205.245 -SE_S_Rural.S03.T045 206.478 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155.395 -SE_S_Rural.S51.T076 155.194 -SE_S_Rural.S51.T077 159.145 -SE_S_Rural.S51.T078 154.192 -SE_S_Rural.S51.T079 147.765 -SE_S_Rural.S51.T080 132.884 -SE_S_Rural.S51.T081 125.119 -SE_S_Rural.S51.T082 109.917 -SE_S_Rural.S51.T083 102.026 -SE_S_Rural.S51.T084 105.798 -SE_S_Rural.S51.T085 108.493 -SE_S_Rural.S51.T086 102.998 -SE_S_Rural.S51.T087 115.089 -SE_S_Rural.S51.T088 123.763 -SE_S_Rural.S51.T089 124.895 -SE_S_Rural.S51.T090 123.554 -SE_S_Rural.S51.T091 126.892 -SE_S_Rural.S51.T092 128.146 -SE_S_Rural.S51.T093 124.828 -SE_S_Rural.S51.T094 114.861 -SE_S_Rural.S51.T095 115.896 -SE_S_Rural.S51.T096 120.29 -SE_S_Rural.S51.T097 126.499 -SE_S_Rural.S51.T098 126.77 -SE_S_Rural.S51.T099 125.282 -SE_S_Rural.S51.T100 123.837 -SE_S_Rural.S51.T101 121.967 -SE_S_Rural.S51.T102 118.971 -SE_S_Rural.S51.T103 113.262 -SE_S_Rural.S51.T104 107.808 -SE_S_Rural.S51.T105 104.571 -SE_S_Rural.S51.T106 98.864 -SE_S_Rural.S51.T107 90.868 -SE_S_Rural.S51.T108 88.658 -SE_S_Rural.S51.T109 93.522 -SE_S_Rural.S51.T110 99.034 -SE_S_Rural.S51.T111 101.632 -SE_S_Rural.S51.T112 103.933 -SE_S_Rural.S51.T113 107.021 -SE_S_Rural.S51.T114 108.686 -SE_S_Rural.S51.T115 110.315 -SE_S_Rural.S51.T116 110.2 -SE_S_Rural.S51.T117 110.445 -SE_S_Rural.S51.T118 113.965 -SE_S_Rural.S51.T119 118.21 -SE_S_Rural.S51.T120 122.365 -SE_S_Rural.S51.T121 126.204 -SE_S_Rural.S51.T122 129.233 -SE_S_Rural.S51.T123 130.431 -SE_S_Rural.S51.T124 130.195 -SE_S_Rural.S51.T125 129.96 -SE_S_Rural.S51.T126 128.639 -SE_S_Rural.S51.T127 114.538 -SE_S_Rural.S51.T128 109.771 -SE_S_Rural.S51.T129 109.657 -SE_S_Rural.S51.T130 109.826 -SE_S_Rural.S51.T131 109.134 -SE_S_Rural.S51.T132 108.307 -SE_S_Rural.S51.T133 108.407 -SE_S_Rural.S51.T134 109.489 -SE_S_Rural.S51.T135 109.429 -SE_S_Rural.S51.T136 109.619 -SE_S_Rural.S51.T137 109.402 -SE_S_Rural.S51.T138 130.252 -SE_S_Rural.S51.T139 124.656 -SE_S_Rural.S51.T140 118.834 -SE_S_Rural.S51.T141 114.949 -SE_S_Rural.S51.T142 113.101 -SE_S_Rural.S51.T143 113.763 -SE_S_Rural.S51.T144 115.284 -SE_S_Rural.S51.T145 118.041 -SE_S_Rural.S51.T146 120.693 -SE_S_Rural.S51.T147 121.369 -SE_S_Rural.S51.T148 119.497 -SE_S_Rural.S51.T149 117.872 -SE_S_Rural.S51.T150 115.864 -SE_S_Rural.S51.T151 113.034 -SE_S_Rural.S51.T152 110.861 -SE_S_Rural.S51.T153 111.038 -SE_S_Rural.S51.T154 109.471 -SE_S_Rural.S51.T155 107.258 -SE_S_Rural.S51.T156 104.343 -SE_S_Rural.S51.T157 100.918 -SE_S_Rural.S51.T158 101.869 -SE_S_Rural.S51.T159 103.881 -SE_S_Rural.S51.T160 104.184 -SE_S_Rural.S51.T161 106.757 -SE_S_Rural.S51.T162 111.586 -SE_S_Rural.S51.T163 114.143 -SE_S_Rural.S51.T164 117.902 -SE_S_Rural.S51.T165 120.076 -SE_S_Rural.S51.T166 122.297 -SE_S_Rural.S51.T167 120.812 -SE_S_Rural.S51.T168 125.856 -SE_S_Rural.S52.T001 129.289 -SE_S_Rural.S52.T002 130.761 -SE_S_Rural.S52.T003 130.478 -SE_S_Rural.S52.T004 129.856 -SE_S_Rural.S52.T005 129.362 -SE_S_Rural.S52.T006 128.359 -SE_S_Rural.S52.T007 132.86 -SE_S_Rural.S52.T008 137.696 -SE_S_Rural.S52.T009 134.652 -SE_S_Rural.S52.T010 132.533 -SE_S_Rural.S52.T011 132.382 -SE_S_Rural.S52.T012 134.063 -SE_S_Rural.S52.T013 135.282 -SE_S_Rural.S52.T014 135.087 -SE_S_Rural.S52.T015 134.896 -SE_S_Rural.S52.T016 134.318 -SE_S_Rural.S52.T017 149.39 -SE_S_Rural.S52.T018 153.358 -SE_S_Rural.S52.T019 152.798 -SE_S_Rural.S52.T020 152.84 -SE_S_Rural.S52.T021 152.364 -SE_S_Rural.S52.T022 152.293 -SE_S_Rural.S52.T023 155.392 -SE_S_Rural.S52.T024 159.88 -SE_S_Rural.S52.T025 164.137 -SE_S_Rural.S52.T026 167.574 -SE_S_Rural.S52.T027 170.308 -SE_S_Rural.S52.T028 172.21 -SE_S_Rural.S52.T029 172.646 -SE_S_Rural.S52.T030 170.939 -SE_S_Rural.S52.T031 165.476 -SE_S_Rural.S52.T032 161.129 -SE_S_Rural.S52.T033 156.09 -SE_S_Rural.S52.T034 151.183 -SE_S_Rural.S52.T035 148.542 -SE_S_Rural.S52.T036 147.566 -SE_S_Rural.S52.T037 147.161 -SE_S_Rural.S52.T038 144.599 -SE_S_Rural.S52.T039 152.506 -SE_S_Rural.S52.T040 149.201 -SE_S_Rural.S52.T041 148.957 -SE_S_Rural.S52.T042 150.207 -SE_S_Rural.S52.T043 152.706 -SE_S_Rural.S52.T044 154.499 -SE_S_Rural.S52.T045 156.42 -SE_S_Rural.S52.T046 158.676 -SE_S_Rural.S52.T047 163.398 -SE_S_Rural.S52.T048 167.485 -SE_S_Rural.S52.T049 169.791 -SE_S_Rural.S52.T050 168.801 -SE_S_Rural.S52.T051 167.391 -SE_S_Rural.S52.T052 165.219 -SE_S_Rural.S52.T053 161.757 -SE_S_Rural.S52.T054 157.151 -SE_S_Rural.S52.T055 149.597 -SE_S_Rural.S52.T056 143.358 -SE_S_Rural.S52.T057 135.397 -SE_S_Rural.S52.T058 131.28 -SE_S_Rural.S52.T059 129.136 -SE_S_Rural.S52.T060 123.842 -SE_S_Rural.S52.T061 116.648 -SE_S_Rural.S52.T062 117.752 -SE_S_Rural.S52.T063 118.118 -SE_S_Rural.S52.T064 120.352 -SE_S_Rural.S52.T065 123.137 -SE_S_Rural.S52.T066 124.078 -SE_S_Rural.S52.T067 132.584 -SE_S_Rural.S52.T068 142.392 -SE_S_Rural.S52.T069 145.179 -SE_S_Rural.S52.T070 146.618 -SE_S_Rural.S52.T071 150.829 -SE_S_Rural.S52.T072 154.807 -SE_S_Rural.S52.T073 158.477 -SE_S_Rural.S52.T074 159.07 -SE_S_Rural.S52.T075 158.025 -SE_S_Rural.S52.T076 157.731 -SE_S_Rural.S52.T077 154.448 -SE_S_Rural.S52.T078 146.144 -SE_S_Rural.S52.T079 135.55 -SE_S_Rural.S52.T080 113.956 -SE_S_Rural.S52.T081 111.806 -SE_S_Rural.S52.T082 114.169 -SE_S_Rural.S52.T083 116.722 -SE_S_Rural.S52.T084 110.286 -SE_S_Rural.S52.T085 106.195 -SE_S_Rural.S52.T086 105.246 -SE_S_Rural.S52.T087 104.793 -SE_S_Rural.S52.T088 100.824 -SE_S_Rural.S52.T089 101.988 -SE_S_Rural.S52.T090 105.416 -SE_S_Rural.S52.T091 105.537 -SE_S_Rural.S52.T092 105.117 -SE_S_Rural.S52.T093 107.827 -SE_S_Rural.S52.T094 113.876 -SE_S_Rural.S52.T095 125.881 -SE_S_Rural.S52.T096 113.056 -SE_S_Rural.S52.T097 112.185 -SE_S_Rural.S52.T098 111.649 -SE_S_Rural.S52.T099 111.371 -SE_S_Rural.S52.T100 113.831 -SE_S_Rural.S52.T101 112.198 -SE_S_Rural.S52.T102 106.83 -SE_S_Rural.S52.T103 98.558 -SE_S_Rural.S52.T104 92.281 -SE_S_Rural.S52.T105 91.717 -SE_S_Rural.S52.T106 93.923 -SE_S_Rural.S52.T107 91.934 -SE_S_Rural.S52.T108 87.305 -SE_S_Rural.S52.T109 90.766 -SE_S_Rural.S52.T110 90.934 -SE_S_Rural.S52.T111 90.61 -SE_S_Rural.S52.T112 95.319 -SE_S_Rural.S52.T113 111.255 -SE_S_Rural.S52.T114 108.726 -SE_S_Rural.S52.T115 108.697 -SE_S_Rural.S52.T116 111.774 -SE_S_Rural.S52.T117 118.011 -SE_S_Rural.S52.T118 123.141 -SE_S_Rural.S52.T119 127.099 -SE_S_Rural.S52.T120 132.525 -SE_S_Rural.S52.T121 218.587 -SE_S_Rural.S52.T122 217.594 -SE_S_Rural.S52.T123 217.311 -SE_S_Rural.S52.T124 216.966 -SE_S_Rural.S52.T125 220.892 -SE_S_Rural.S52.T126 233.516 -SE_S_Rural.S52.T127 163.286 -SE_S_Rural.S52.T128 155.497 -SE_S_Rural.S52.T129 155.935 -SE_S_Rural.S52.T130 156.296 -SE_S_Rural.S52.T131 155.897 -SE_S_Rural.S52.T132 155.727 -SE_S_Rural.S52.T133 155.84 -SE_S_Rural.S52.T134 156.519 -SE_S_Rural.S52.T135 155.728 -SE_S_Rural.S52.T136 155.602 -SE_S_Rural.S52.T137 156.309 -SE_S_Rural.S52.T138 155.587 -SE_S_Rural.S52.T139 156.305 -SE_S_Rural.S52.T140 155.591 -SE_S_Rural.S52.T141 160.572 -SE_S_Rural.S52.T142 185.808 -SE_S_Rural.S52.T143 188.6 -SE_S_Rural.S52.T144 187.786 -SE_S_Rural.S52.T145 187.203 -SE_S_Rural.S52.T146 186.949 -SE_S_Rural.S52.T147 191.885 -SE_S_Rural.S52.T148 192.997 -SE_S_Rural.S52.T149 193.822 -SE_S_Rural.S52.T150 192.488 -SE_S_Rural.S52.T151 192.482 -SE_S_Rural.S52.T152 176.143 -SE_S_Rural.S52.T153 163.704 -SE_S_Rural.S52.T154 163.3 -SE_S_Rural.S52.T155 163.161 -SE_S_Rural.S52.T156 164.209 -SE_S_Rural.S52.T157 162.617 -SE_S_Rural.S52.T158 163.88 -SE_S_Rural.S52.T159 163.582 -SE_S_Rural.S52.T160 164.85 -SE_S_Rural.S52.T161 165.185 -SE_S_Rural.S52.T162 165.441 -SE_S_Rural.S52.T163 163.441 -SE_S_Rural.S52.T164 164.412 -SE_S_Rural.S52.T165 164.91 -SE_S_Rural.S52.T166 162.019 -SE_S_Rural.S52.T167 149.96 -SE_S_Rural.S52.T168 150.244 -* ----------------------------------------------------- \ No newline at end of file diff --git a/base/addons/gas/andetgasforbrug.inc b/base/addons/gas/andetgasforbrug.inc deleted file mode 100644 index d7da68da..00000000 --- a/base/addons/gas/andetgasforbrug.inc +++ /dev/null @@ -1,26 +0,0 @@ -SET gasanvendelser "FROM MDB2GMS" / -'Centralt kraftværk' -'Andre timeaflæste' -'Decentral kraftvarme' -'Decentral ren varmeproduktion' -Fjernvarmekedeler -'Industriel andet formål' -'Industriel kraftvarme' -residual -/; - -PARAMETER DG_HNG_VAR(gasanvendelser,SSS,TTT), - DG_MidtNord_VAR(gasanvendelser,SSS,TTT), - DG_DongOest_VAR(gasanvendelser,SSS,TTT), - DG_DongVest_VAR(gasanvendelser,SSS,TTT), - DG_NGF_VAR(gasanvendelser,SSS,TTT); - -execute_load '../../base/addons/gas/gasforbrug.gdx', DG_HNG_VAR,DG_MidtNord_VAR, DG_DongOest_VAR, DG_DongVest_VAR, DG_NGF_VAR; - - -PARAMETER DG_VAR(gasanvendelser,AAA,SSS,TTT) -/ -$ondelim -$include '../../base/addons/gas/gasforbrug.csv'; -$offdelim -/; \ No newline at end of file diff --git a/base/addons/gas/annualupdate.inc b/base/addons/gas/annualupdate.inc deleted file mode 100644 index cb96096f..00000000 --- a/base/addons/gas/annualupdate.inc +++ /dev/null @@ -1,58 +0,0 @@ -* Transfer investments from previous year by raising the lower bound on investment -* quantity. -$ifi %bb2%==yes VGINVSTORE.lo(STORE,STORETYPE) = VGINVSTORE.l(STORE,STORETYPE); -$ifi %bb2%==yes VGINVINJ.lo(STORE,STORETYPE) = VGINVINJ.l(STORE,STORETYPE); -$ifi %bb2%==yes VGINVEXT.lo(STORE,STORETYPE) = VGINVEXT.l(STORE,STORETYPE); -$ifi %bb2%==yes VGINFINV.lo(NGINV) = VGINFINV.l(NGINV); -$ifi %bb2%==yes VGNETINV.lo(C,GNETSTEPS) = VGNETINV.l(C,GNETSTEPS); - -* Load capacity reservations (BB3 only) reservations are generated in BB1 or BB2 -$ifi %bb3%==yes ITC_EX_Y(SYSEXIT) = TC_EX_Y(SYSEXIT,Y); -$ifi %bb3%==yes ITC_EX_M(SYSEXIT,S,T) = TC_EX_M(SYSEXIT,Y,S,T); -$ifi %bb3%==yes ITC_EN_Y(SYSENTRY) = TC_EN_Y(SYSENTRY,Y); -$ifi %bb3%==yes ITC_EN_M(SYSENTRY,S,T) = TC_EN_M(SYSENTRY,Y,S,T); - -$ifi %bb3%==yes IGSFXW(STORE,STORETYPE,S) = GSFXW(Y,STORE,STORETYPE,S); -$ifi %bb3%==yes ISC(SCON) = SC_Y(Y,SCON); -$ifi %bb3%==yes IHYFXRW(S,IR) = HYFXRW_GAS(Y,S,IR); -$ifi %bb3%==yes IWATERVAL(IA,S) = WATERVAL(Y,S,IA); - -* Gas consumption not instigated by generation of electricity and/or district -* heating: -IDGRES_Y(IA,S,T)= DGRES(Y,IA) * (DGRES_VAR(IA,S,T) / IDGRES_SUMVAR(IA))$(IDGRES_SUMVAR(IA) >0); -IDGOTH_Y(IA,S,T)= DGOTH(Y,IA) * (DGOTH_VAR(IA,S,T) / IDGOTH_SUMVAR(IA))$(IDGOTH_SUMVAR(IA) >0); - -* Use simple bounds for capacity of supply, export, and storage operations when -* not simulating with endogenous investments. -$ifi not %bb2%==yes VSTOCK.UP(STORE,STORETYPE,S,T)= STORAGE_MAX(Y,STORE,STORETYPE)-TSOSTORAGE(Y,STORE,STORETYPE); -$ifi not %bb2%==yes VINJ.UP(STORE,STORETYPE,S,T) = INJ_MAX(Y,STORE,STORETYPE) ; -$ifi not %bb2%==yes VEXT.UP(STORE,STORETYPE,S,T) = EXT_MAX(Y,STORE,STORETYPE) ; -$ifi not %bb2%==yes VEXP.UP(SOURCE,S) = EXP_MAX(Y,SOURCE) ; -$ifi not %BB2%==yes VTRANSIT.up(TRANSIT(AAA),S,T) = SUM(SOURCE(AAA), EXP_MAX(Y,SOURCE)); -$ifi not %bb2%==yes VSUP.UP(SOURCE,S,T) = SUPPLY_MAX(Y,SOURCE); - -* Delivery maximum is never a simple bound. -IDEV_MAX(SYSEXIT) = DEV_MAX(Y,SYSEXIT); - -* Add investments in gas infrastructure to bb1 or bb3 simulation -$ifi %ADDINVEST%==yes VSTOCK.UP(STORE,STORETYPE,S,T)= STOREACC(Y,STORE,STORETYPE)+ VSTOCK.UP(STORE,STORETYPE,S,T) ; -$ifi %ADDINVEST%==yes VINJ.UP(STORE,STORETYPE,S,T) = INJACC(Y,STORE,STORETYPE)+ VINJ.UP(STORE,STORETYPE,S,T) ; -$ifi %ADDINVEST%==yes VEXT.UP(STORE,STORETYPE,S,T) = EXTACC(Y,STORE,STORETYPE)+ VEXT.UP(STORE,STORETYPE,S,T) ; -$ifi %ADDINVEST%==yes VEXP.UP(SOURCE,S) = EXPACC(Y,SOURCE)+ VEXP.UP(SOURCE,S) ; -$ifi %ADDINVEST%==yes VTRANSIT.UP(TRANSIT(AAA),S,T) = SUM(SOURCE(AAA), EXPACC(Y,SOURCE))+ VTRANSIT.UP(TRANSIT,S,T); -$ifi %ADDINVEST%==yes VSUP.UP(SOURCE,S,T) = SUPACC(Y,SOURCE)+ VSUP.UP(SOURCE,S,T) ; -$ifi %ADDINVEST%==yes IDEV_MAX(SYSEXIT) = DEV_MAX(Y,SYSEXIT)+DEVACC(Y,SYSEXIT); - -* Parameters used for define capacity bounds of existing infrastructure. Total -* capacity also includes capacity effects of investments. -$ifi %bb2%==yes ISUPPLY_MAX(SOURCE) = SUPPLY_MAX(Y,SOURCE); -$ifi %bb2%==yes IEXP_MAX(SOURCE) = EXP_MAX(Y,SOURCE) ; -$ifi %bb2%==yes IINJ_MAX(STORE,STORETYPE) = INJ_MAX(Y,STORE,STORETYPE) ; -$ifi %bb2%==yes IEXT_MAX(STORE,STORETYPE) = EXT_MAX(Y,STORE,STORETYPE) ; -$ifi %bb2%==yes ISTORE_MAX(STORE,STORETYPE) = STORAGE_MAX(Y,STORE,STORETYPE)-TSOSTORAGE(Y,STORE,STORETYPE) ; - -* Define border price at adjacent systems. -IGEX_PRICE(SOURCE,S) = GEX_PRICE(Y,SOURCE,S); - -* Assign wellhead price. -IGEXT_COST(SOURCE,S) = GEXT_COST(Y,SOURCE,S); \ No newline at end of file diff --git a/base/addons/gas/dg-MR.inc b/base/addons/gas/dg-MR.inc deleted file mode 100644 index a527918c..00000000 --- a/base/addons/gas/dg-MR.inc +++ /dev/null @@ -1,13 +0,0 @@ - - -* No seperate tables for gas consumption not used for electricity and district heating, -* therefore the annual values are obtained by summing the variation profiles. Hereby, consumption -* by this group of consumers is expected to remain unchanged. - -DGRES(YYY,AAA)$ICA('Denmark',AAA) = sum((SSS,TTT),365/364*DG_VAR('residual',AAA,SSS,TTT)); -DGOTH(YYY,AAA)$ICA('Denmark',AAA) = sum((SSS,TTT),365/364*DG_VAR('Andre timeaflæste',AAA,SSS,TTT)) - +sum((SSS,TTT),365/364*DG_VAR('Industriel andet formål',AAA,SSS,TTT)); - -DGRES(YYY,'hoejby')= sum((SSS,TTT),365/364*DG_NGF_VAR('residual',SSS,TTT)); -DGOTH(YYY,'hoejby')= sum((SSS,TTT),365/364*(DG_NGF_VAR('Andre timeaflæste',SSS,TTT) - +DG_NGF_VAR('Industriel andet formål',SSS,TTT))); \ No newline at end of file diff --git a/base/addons/gas/dg.inc b/base/addons/gas/dg.inc deleted file mode 100644 index b43b7dbe..00000000 --- a/base/addons/gas/dg.inc +++ /dev/null @@ -1,23 +0,0 @@ - - -* No seperate tables for gas consumption not used for electricity and district heating, -* therefore the annual values are obtained by summing the variation profiles. Hereby, consumption -* by this group of consumers is expected to remain unchanged. - -DGRES(YYY,'DK_E_Urban')= sum((SSS,TTT),365/364*DG_HNG_VAR('residual',SSS,TTT)); -DGOTH(YYY,'DK_E_Urban')= sum((SSS,TTT),365/364*(DG_HNG_VAR('Andre timeaflæste',SSS,TTT) - +DG_HNG_VAR('Industriel andet formål',SSS,TTT))); -DGRES(YYY,'DK_E_Rural')= sum((SSS,TTT),365/364*DG_DongOest_VAR('residual',SSS,TTT)); -DGOTH(YYY,'DK_E_Rural')= sum((SSS,TTT),365/364*(DG_DongOest_VAR('Andre timeaflæste',SSS,TTT) - +DG_DongOest_VAR('Industriel andet formål',SSS,TTT))); -DGRES(YYY,'DK_W_AALBORG')= sum((SSS,TTT),365/364*DG_MidtNord_VAR('residual',SSS,TTT)); -DGOTH(YYY,'DK_W_AALBORG')= sum((SSS,TTT),365/364*(DG_MidtNord_VAR('Andre timeaflæste',SSS,TTT) - +DG_MidtNord_VAR('Industriel andet formål',SSS,TTT))); -DGRES(YYY,'DK_W_TREFOR')= sum((SSS,TTT),365/364*DG_DongVest_VAR('residual',SSS,TTT)); -DGOTH(YYY,'DK_W_TREFOR')= sum((SSS,TTT),365/364*(DG_DongVest_VAR('Andre timeaflæste',SSS,TTT) - +DG_DongVest_VAR('Industriel andet formål',SSS,TTT))); - -DGRES(YYY,'DK_W_ODENSE')= sum((SSS,TTT),365/364*DG_NGF_VAR('residual',SSS,TTT)); -DGOTH(YYY,'DK_W_ODENSE')= sum((SSS,TTT),365/364*(DG_NGF_VAR('Andre timeaflæste',SSS,TTT) - +DG_NGF_VAR('Industriel andet formål',SSS,TTT))); - diff --git a/base/addons/gas/ensmpol.inc b/base/addons/gas/ensmpol.inc deleted file mode 100644 index 0acbe45a..00000000 --- a/base/addons/gas/ensmpol.inc +++ /dev/null @@ -1,350 +0,0 @@ - -TABLE M_POL(YYY,MPOLSET,CCC) 'Emission policy data' - - LIM_CO2.DENMARK LIM_SO2.DENMARK LIM_NOX.DENMARK -* Units: t CO2/year t SO2/year t NOx/year -1995 INF INF INF -1996 INF INF INF -1997 INF INF INF -1998 INF INF INF -1999 INF INF INF -2000 INF INF INF -2001 INF INF INF -2002 INF INF INF -2003 INF INF INF -2004 INF INF INF -2005 INF INF INF -2006 INF INF INF -2007 INF INF INF -2008 INF INF INF -2009 INF INF INF -2010 INF INF INF -2011 INF INF INF -2012 INF INF INF -2013 INF INF INF -2014 INF INF INF -2015 INF INF INF -2016 INF INF INF -2017 INF INF INF -2018 INF INF INF -2019 INF INF INF -2020 INF INF INF -2021 INF INF INF -2022 INF INF INF -2023 INF INF INF -2024 INF INF INF -2025 INF INF INF -2026 INF INF INF -2027 INF INF INF -2028 INF INF INF -2029 INF INF INF -2030 INF INF INF - -+ TAX_CO2.DENMARK TAX_SO2.DENMARK TAX_NOX.DENMARK -* Units: EUR90/t CO2 EUR90/t SO2 EUR90/t NOx -1995 0 988 -1996 0 988 -1997 0 988 -1998 0 988 -1999 0 988 -2000 0 988 -2001 0 988 -2002 0 988 -2003 0 988 -2004 0 988 -2005 4.94071 988 -2006 4.94071 988 -2007 4.94071 988 -2008 4.94071 988 -2009 4.94071 988 -2010 4.94071 988 -2011 4.94071 988 -2012 4.94071 988 -2013 9.881423 988 -2014 9.881423 988 -2015 9.881423 988 -2016 9.881423 988 -2017 9.881423 988 -2018 9.881423 988 -2019 9.881423 988 -2020 9.881423 988 -2021 9.881423 988 -2022 9.881423 988 -2023 9.881423 988 -2024 9.881423 988 -2025 9.881423 988 -2026 9.881423 988 -2027 9.881423 988 -2028 9.881423 988 -2029 9.881423 988 -2030 9.881423 988 - -+ LIM_CO2.FINLAND LIM_SO2.FINLAND LIM_NOX.FINLAND -* Units: t CO2/year t SO2/year t NOx/year -1995 INF INF INF -1996 INF INF INF -1997 INF INF INF -1998 INF INF INF -1999 INF INF INF -2000 INF INF INF -2001 INF INF INF -2002 INF INF INF -2003 INF INF INF -2004 INF INF INF -2005 INF INF INF -2006 INF INF INF -2007 INF INF INF -2008 INF INF INF -2009 INF INF INF -2010 INF INF INF -2011 INF INF INF -2012 INF INF INF -2013 INF INF INF -2014 INF INF INF -2015 INF INF INF -2016 INF INF INF -2017 INF INF INF -2018 INF INF INF -2019 INF INF INF -2020 INF INF INF -2021 INF INF INF -2022 INF INF INF -2023 INF INF INF -2024 INF INF INF -2025 INF INF INF -2026 INF INF INF -2027 INF INF INF -2028 INF INF INF -2029 INF INF INF -2030 INF INF INF - -+ TAX_CO2.FINLAND TAX_SO2.FINLAND TAX_NOX.FINLAND -* Units: EUR90/t CO2 EUR90/t SO2 EUR90/t NOx -1995 0 0 0 -1996 0 0 0 -1997 0 0 0 -1998 0 0 0 -1999 0 0 0 -2000 0 0 0 -2001 0 0 0 -2002 0 0 0 -2003 0 0 0 -2004 0 0 0 -2005 2.96443 0 0 -2006 2.96443 0 0 -2007 2.96443 0 0 -2008 4.94071 0 0 -2009 4.94071 0 0 -2010 4.94071 0 0 -2011 4.94071 0 0 -2012 4.94071 0 0 -2013 9.881423 0 0 -2014 9.881423 0 0 -2015 9.881423 0 0 -2016 9.881423 0 0 -2017 9.881423 0 0 -2018 9.881423 0 0 -2019 9.881423 0 0 -2020 9.881423 0 0 -2021 9.881423 0 0 -2022 9.881423 0 0 -2023 9.881423 0 0 -2024 9.881423 0 0 -2025 9.881423 0 0 -2026 9.881423 0 0 -2027 9.881423 0 0 -2028 9.881423 0 0 -2029 9.881423 0 0 -2030 9.881423 0 0 - -+ LIM_CO2.NORWAY LIM_SO2.NORWAY LIM_NOX.NORWAY -* Units: t CO2/year t SO2/year t NOx/year -1995 INF INF INF -1996 INF INF INF -1997 INF INF INF -1998 INF INF INF -1999 INF INF INF -2000 INF INF INF -2001 INF INF INF -2002 INF INF INF -2003 INF INF INF -2004 INF INF INF -2005 INF INF INF -2006 INF INF INF -2007 INF INF INF -2008 INF INF INF -2009 INF INF INF -2010 INF INF INF -2011 INF INF INF -2012 INF INF INF -2013 INF INF INF -2014 INF INF INF -2015 INF INF INF -2016 INF INF INF -2017 INF INF INF -2018 INF INF INF -2019 INF INF INF -2020 INF INF INF -2021 INF INF INF -2022 INF INF INF -2023 INF INF INF -2024 INF INF INF -2025 INF INF INF -2026 INF INF INF -2027 INF INF INF -2028 INF INF INF -2029 INF INF INF -2030 INF INF INF - -+ TAX_CO2.NORWAY TAX_SO2.NORWAY TAX_NOX.NORWAY -* Units: EUR90/t CO2 EUR90/t SO2 EUR90/t NOx -1995 0 1482 0 -1996 0 1482 0 -1997 0 1482 0 -1998 0 1482 0 -1999 0 1482 0 -2000 0 1482 0 -2001 0 1482 0 -2002 0 1482 0 -2003 0 1482 0 -2004 0 1482 0 -2005 2.96443 1482 0 -2006 2.96443 1482 0 -2007 2.96443 1482 0 -2008 4.94071 1482 0 -2009 4.94071 1482 0 -2010 4.94071 1482 0 -2011 4.94071 1482 0 -2012 4.94071 1482 0 -2013 9.881423 1482 0 -2014 9.881423 1482 0 -2015 9.881423 1482 0 -2016 9.881423 1482 0 -2017 9.881423 1482 0 -2018 9.881423 1482 0 -2019 9.881423 1482 0 -2020 9.881423 1482 0 -2021 9.881423 1482 0 -2022 9.881423 1482 0 -2023 9.881423 1482 0 -2024 9.881423 1482 0 -2025 9.881423 1482 0 -2026 9.881423 1482 0 -2027 9.881423 1482 0 -2028 9.881423 1482 0 -2029 9.881423 1482 0 -2030 9.881423 1482 0 - -+ LIM_CO2.SWEDEN LIM_SO2.SWEDEN LIM_NOX.SWEDEN -* Units: t CO2/year t SO2/year t NOx/year -1995 INF INF INF -1996 INF INF INF -1997 INF INF INF -1998 INF INF INF -1999 INF INF INF -2000 INF INF INF -2001 INF INF INF -2002 INF INF INF -2003 INF INF INF -2004 INF INF INF -2005 INF INF INF -2006 INF INF INF -2007 INF INF INF -2008 INF INF INF -2009 INF INF INF -2010 INF INF INF -2011 INF INF INF -2012 INF INF INF -2013 INF INF INF -2014 INF INF INF -2015 INF INF INF -2016 INF INF INF -2017 INF INF INF -2018 INF INF INF -2019 INF INF INF -2020 INF INF INF -2021 INF INF INF -2022 INF INF INF -2023 INF INF INF -2024 INF INF INF -2025 INF INF INF -2026 INF INF INF -2027 INF INF INF -2028 INF INF INF -2029 INF INF INF -2030 INF INF INF - -+ TAX_CO2.SWEDEN TAX_SO2.SWEDEN TAX_NOX.SWEDEN -* Units: EUR90/t CO2 EUR90/t SO2 EUR90/t NOx -1995 0 1215 0 -1996 0 1215 0 -1997 0 1215 0 -1998 0 1215 0 -1999 0 1215 0 -2000 0 1215 0 -2001 0 1215 0 -2002 0 1215 0 -2003 0 1215 0 -2004 0 1215 0 -2005 2.96443 1215 0 -2006 2.96443 1215 0 -2007 2.96443 1215 0 -2008 4.94071 1215 0 -2009 4.94071 1215 0 -2010 4.94071 1215 0 -2011 4.94071 1215 0 -2012 4.94071 1215 0 -2013 9.881423 1215 0 -2014 9.881423 1215 0 -2015 9.881423 1215 0 -2016 9.881423 1215 0 -2017 9.881423 1215 0 -2018 9.881423 1215 0 -2019 9.881423 1215 0 -2020 9.881423 1215 0 -2021 9.881423 1215 0 -2022 9.881423 1215 0 -2023 9.881423 1215 0 -2024 9.881423 1215 0 -2025 9.881423 1215 0 -2026 9.881423 1215 0 -2027 9.881423 1215 0 -2028 9.881423 1215 0 -2029 9.881423 1215 0 -2030 9.881423 1215 0 -; - - -* Emission limits values not assigned a positive level in the table above. -* are here given the default value INF: - -LOOP(YYY, LOOP(CCC, M_POL(YYY,'LIM_CO2',CCC) - $(M_POL(YYY,'LIM_CO2',CCC) EQ 0) = INF )); -LOOP(YYY, LOOP(CCC, M_POL(YYY,'LIM_SO2',CCC) - $(M_POL(YYY,'LIM_SO2',CCC) EQ 0) = INF )); -LOOP(YYY, LOOP(CCC, M_POL(YYY,'LIM_NOX',CCC) - $(M_POL(YYY,'LIM_NOX',CCC) EQ 0) = INF )); - - - -PARAMETER CO2_PRICE(YYY) -/ -2005 50 -2006 50 -2007 50 -2008 50 -2009 50 -2010 50 -2011 50 -2012 50 -2013 70 -2014 90 -2015 110 -2016 130 -2017 150 -/; - -CO2_PRICE(YYY)$(YVALUE(YYY)>2017) = CO2_PRICE(YYY); -M_POL(YYY,'TAX_CO2',CCC)=CO2_PRICE(YYY)/7.42/1.3; - -$ifi %quickfix%==yes M_POL(YYY,'TAX_CO2',CCC)=150/7.42/1.3; \ No newline at end of file diff --git a/base/addons/gas/flowsets.inc b/base/addons/gas/flowsets.inc deleted file mode 100644 index 83bd9ab2..00000000 --- a/base/addons/gas/flowsets.inc +++ /dev/null @@ -1,76 +0,0 @@ -SET EDGE -/ -NYBRO_VARDE -VARDE_HELLE -HELLE_EGTVED -NYBRO_VARDE2 -VARDE_EGTVED2 -EGTVED_STANDST -STANDST_POTTEHUSE -POTTEHUSE_LISELSKAER -LISELSKAER_TERKELSBOEL -TERKELSBOEL_ELLUND -EGTVED_NOERSKOV -NOERSKOV_BRANDE -BRANDE_HERNING -HERNING_KARUP -KARUP_VIBORG -VIBORG_LITORUP -LITORUP_HAVERSLEV -HAVERSLEV_ELLIDSHOEJ -ELLIDSHOEJ_AALBORG -EGTVED_LILBALLE -LILBALLE_TAULOV -TAULOV_LYNGSODDE -LILLEBAELT_SYD -LILLEBAELT_NORD -MIDDELFART_BILLESBOELLE -BILLESBOELLE_KOELBJERG -KOELBJERG_HOEJBY -HOEJBY_ULLERSLEV -ULLERSLEV_NYBORG -TAULOV_SKAERBAEK -STOREBAELTSYD -STORBAELTNORD -KONGSMARK_SLAGELSE -SLAGELSE_SOROE -SOROE_RINGSTED -RINGSTED_KOEGE -KOEGE_KARLSLUNDE -KARLSLUNDE_TORSLUNDE -TORSLUNDE_VALLENSBAEK -VALLENSBAEK_BROENDBY -BROENDBY_HVIDOVRE -HVIDOVRE_VESTAMAGER -TORSLUNDE_MAALOEV -MAALOEV_LYNGE -TORSLUNDE_STENLILLE -VESTAMAGER_DRAGOER -HVIDOVRE_AVEDOERE -VESTAMAGER_AMAGERF -AMAGERF_SYDHAVNEN -SYDHAVNEN_HCOERSTED -DRAGOER_OERESUND -DUMMY1 -DUMMY2 -DUMMY3 -/; - -ALIAS(EDGE,EDGES,e); - -SETS DUMMY(e) /DUMMY1, DUMMY2, DUMMY3/; - - -SETS EDGEDATASET "Pipe specific datatypes" - / - L - WT - ID - KNOT - ROUGH - PRESSURE - LOSSFACT - /; - -SET l_plus1 'Steps in linearization' / l1* l3, lM/, - l(l_plus1) 'Steps in linearization' / l1*l3 /; \ No newline at end of file diff --git a/base/addons/gas/fuelp.inc b/base/addons/gas/fuelp.inc deleted file mode 100644 index d9d52c12..00000000 --- a/base/addons/gas/fuelp.inc +++ /dev/null @@ -1,221 +0,0 @@ -* File Fuelp.inc - -* This file is part of the Balmorel model and should be used with the gas addon, -* Fuel prices are updated according to World Energy Outlook 2004 -* A transportation element to gas has be subtracted from DK and Swedish gas prices -* since this is repressented seperately in the gas addon -*------------------------------------------------------------------------------ - -* PARAMETER FUELPRICE contains fuel prices. -* Data source: -* IEA World Energy Outlook -* Energistyrrelsen: Forudsætninger for samfundsøkonomiske analyser - - -$ontext -* Units: EUR90/GJ -TABLE FUELPRICE(YYY,AAA,FFF) 'Fuel prices (Money/GJ)' - - DK_E_Urban.NUCLEAR DK_E_Urban.ORIMULSION DK_E_Urban.PEAT DK_E_Urban.WOOD_WASTE DK_E_Urban.COAL DK_E_Urban.FUELOIl DK_E_Urban.LIGHTOIl DK_E_Urban.STRAW DK_E_Urban.WOOD DK_E_Urban.NAT_GAS DK_E_Urban.WIND DK_E_Urban.WATER DK_E_Urban.SUN DK_E_Urban.ELECTRIC DK_E_Urban.MUNI_WASTE -2002 0.52 1.46 1.09 0.57 1.21 2.83 4.90 3.56 4.55 2.6621634204 0 0 0 0 0 -2003 0.52 1.46 1.09 0.57 1.20 2.67 4.62 3.56 4.55 2.6846252354 0 0 0 0 0 -2004 0.51 1.43 1.24 0.57 1.56 3.12 5.43 3.56 4.55 3.1675542560 0 0 0 0 0 -2005 0.60 1.59 1.47 0.57 1.72 4.35 7.64 3.56 4.55 4.1895668346 0 0 0 0 0 -2006 0.56 1.47 1.37 0.57 1.60 3.89 6.81 3.56 4.55 3.6617141841 0 0 0 0 0 -2007 0.50 1.32 1.21 0.57 1.43 3.14 5.46 3.56 4.55 3.1450924411 0 0 0 0 0 -2008 0.44 1.16 1.06 0.57 1.26 2.41 4.17 3.56 4.55 2.650932513 0 0 0 0 0 -2009 0.40 1.06 0.96 0.57 1.15 2.02 3.45 3.56 4.55 2.1792343998 0 0 0 0 0 -2010 0.40 1.06 0.96 0.57 1.15 2.02 3.45 3.56 4.55 2.1792343998 0 0 0 0 0 -2011 0.40 1.06 0.97 0.57 1.15 2.04 3.51 3.56 4.55 2.2129271221 0 0 0 0 0 -2012 0.40 1.07 0.98 0.57 1.16 2.07 3.57 3.56 4.55 2.2466198445 0 0 0 0 0 -2013 0.40 1.07 0.99 0.57 1.16 2.11 3.61 3.56 4.55 2.2803125669 0 0 0 0 0 -2014 0.40 1.08 1.00 0.57 1.17 2.13 3.67 3.56 4.55 2.3140052893 0 0 0 0 0 -2015 0.40 1.08 1.01 0.57 1.17 2.16 3.72 3.56 4.55 2.3476980116 0 0 0 0 0 -2016 0.41 1.09 1.02 0.57 1.19 2.19 3.77 3.56 4.55 2.3813907340 0 0 0 0 0 -2017 0.40 1.09 1.03 0.57 1.19 2.22 3.82 3.56 4.55 2.4150834564 0 0 0 0 0 -2018 0.40 1.10 1.04 0.57 1.20 2.25 3.88 3.56 4.55 2.4487761787 0 0 0 0 0 -2019 0.40 1.10 1.05 0.57 1.20 2.27 3.93 3.56 4.55 2.4824689011 0 0 0 0 0 -2020 0.40 1.11 1.06 0.57 1.21 2.31 3.98 3.56 4.55 2.5161616235 0 0 0 0 0 -2021 0.40 1.11 1.06 0.57 1.21 2.33 4.04 3.56 4.55 2.5498543459 0 0 0 0 0 -2022 0.41 1.12 1.10 0.57 1.22 2.36 4.08 3.56 4.55 2.5723161608 0 0 0 0 0 -2023 0.40 1.12 1.07 0.57 1.22 2.40 4.14 3.56 4.55 2.6060088831 0 0 0 0 0 -2024 0.40 1.13 1.08 0.57 1.23 2.42 4.18 3.56 4.55 2.6397016055 0 0 0 0 0 -2025 0.40 1.13 1.08 0.57 1.23 2.45 4.24 3.56 4.55 2.6733943279 0 0 0 0 0 -2026 0.40 1.14 1.09 0.57 1.24 2.48 4.30 3.56 4.55 2.7070870503 0 0 0 0 0 -2027 0.40 1.14 1.08 0.57 1.24 2.51 4.34 3.56 4.55 2.7407797726 0 0 0 0 0 -2028 0.41 1.15 1.09 0.57 1.25 2.54 4.40 3.56 4.55 2.7744724950 0 0 0 0 0 -2029 0.40 1.15 1.09 0.57 1.25 2.57 4.45 3.56 4.55 2.8081652714 0 0 0 0 0 -2030 0.40 1.16 1.10 0.57 1.26 2.60 4.50 3.56 4.55 2.8418579398 0 0 0 0 0 - -+ FI_R_Urban.NAT_GAS FI_R_Rural.NAT_GAS -2002 2.9874213836478 2.9874213836478 -2003 3.00988319856244 3.00988319856244 -2004 3.49281221922731 3.49281221922731 -2005 4.51482479784367 4.51482479784367 -2006 3.98697214734951 3.98697214734951 -2007 3.47035040431267 3.47035040431267 -2008 2.97619047619048 2.97619047619048 -2009 2.50449236298293 2.50449236298293 -2010 2.50449236298293 2.50449236298293 -2011 2.5381850853549 2.5381850853549 -2012 2.57187780772686 2.57187780772686 -2013 2.60557053009883 2.60557053009883 -2014 2.6392632524708 2.6392632524708 -2015 2.67295597484277 2.67295597484277 -2016 2.70664869721474 2.70664869721474 -2017 2.7403414195867 2.7403414195867 -2018 2.77403414195867 2.77403414195867 -2019 2.80772686433064 2.80772686433064 -2020 2.84141958670261 2.84141958670261 -2021 2.87511230907457 2.87511230907457 -2022 2.89757412398922 2.89757412398922 -2023 2.93126684636119 2.93126684636119 -2024 2.96495956873315 2.96495956873315 -2025 2.99865229110512 2.99865229110512 -2026 3.03234501347709 3.03234501347709 -2027 3.06603773584906 3.06603773584906 -2028 3.09973045822102 3.09973045822102 -2029 3.13342318059299 3.13342318059299 -2030 3.16711590296496 3.16711590296496 - -+ NO_N_Rural.NAT_GAS NO_S_Rural.NAT_GAS NO_M_Rural.NAT_GAS -2002 2.68913991164904 2.68913991164904 2.68913991164904 -2003 2.70926298509004 2.70926298509004 2.70926298509004 -2004 3.14353099730458 3.14353099730458 3.14353099730458 -2005 4.06433458724564 4.06433458724564 4.06433458724564 -2006 3.58843498769875 3.58843498769875 3.58843498769875 -2007 3.12303751356881 3.12303751356881 3.12303751356881 -2008 2.67833314270464 2.67833314270464 2.67833314270464 -2009 2.25384260687975 2.25384260687975 2.25384260687975 -2010 2.25384260687975 2.25384260687975 2.25384260687975 -2011 2.28396623216872 2.28396623216872 2.28396623216872 -2012 2.31409052163769 2.31409052163769 2.31409052163769 -2013 2.34521298478651 2.34521298478651 2.34521298478651 -2014 2.37523729478686 2.37523729478686 2.37523729478686 -2015 2.40536194535535 2.40536194535535 2.40536194535535 -2016 2.43548719470478 2.43548719470478 2.43548719470478 -2017 2.466604817413 2.466604817413 2.466604817413 -2018 2.49672976504027 2.49672976504027 2.49672976504027 -2019 2.52675631131094 2.52675631131094 2.52675631131094 -2020 2.55787041375975 2.55787041375975 2.55787041375975 -2021 2.58799574011757 2.58799574011757 2.58799574011757 -2022 2.60801302420032 2.60801302420032 2.60801302420032 -2023 2.63902277803879 2.63902277803879 2.63902277803879 -2024 2.66826777569281 2.66826777569281 2.66826777569281 -2025 2.69839584838846 2.69839584838846 2.69839584838846 -2026 2.7285243629644 2.7285243629644 2.7285243629644 -2027 2.75962912698317 2.75962912698317 2.75962912698317 -2028 2.78965990593594 2.78965990593594 2.78965990593594 -2029 2.8197886564553 2.8197886564553 2.8197886564553 -2030 2.84991781252976 2.84991781252976 2.8499178125297 - -+ SE_N_Rural.NAT_GAS SE_M_Rural.NAT_GAS SE_S_Rural.NAT_GAS SE_M_Urban.NAT_GAS -2002 2.95932331325086 2.95932331325086 2.95932331325086 2.95932331325086 -2003 2.98412386963915 2.98412386963915 2.98412386963915 2.98412386963915 -2004 3.51571389875435 3.51571389875435 3.51571389875435 3.51571389875435 -2005 4.64092000441868 4.64092000441868 4.64092000441868 4.64092000441868 -2006 4.06073031544654 4.06073031544654 4.06073031544654 4.06073031544654 -2007 3.48989450109786 3.48989450109786 3.48989450109786 3.48989450109786 -2008 2.94647683794668 2.94647683794668 2.94647683794668 2.94647683794668 -2009 2.427759977666 2.427759977666 2.427759977666 2.427759977666 -2010 2.427759977666 2.427759977666 2.427759977666 2.427759977666 -2011 2.46502353451866 2.46502353451866 2.46502353451866 2.46502353451866 -2012 2.50228637589287 2.50228637589287 2.50228637589287 2.50228637589287 -2013 2.53954853301547 2.53954853301547 2.53954853301547 2.53954853301547 -2014 2.57690966775905 2.57690966775905 2.57690966775905 2.57690966775905 -2015 2.6141704619345 2.6141704619345 2.6141704619345 2.6141704619345 -2016 2.65043739175204 2.65043739175204 2.65043739175204 2.65043739175204 -2017 2.68769847936471 2.68769847936471 2.68769847936471 2.68769847936471 -2018 2.72495897648137 2.72495897648137 2.72495897648137 2.72495897648137 -2019 2.76231787495464 2.76231787495464 2.76231787495464 2.76231787495464 -2020 2.79957719346211 2.79957719346211 2.79957719346211 2.79957719346211 -2021 2.83683599051202 2.83683599051202 2.83683599051202 2.83683599051202 -2022 2.86173724443255 2.86173724443255 2.86173724443255 2.86173724443255 -2023 2.89811205708091 2.89811205708091 2.89811205708091 2.89811205708091 -2024 2.93429263854268 2.93429263854268 2.93429263854268 2.93429263854268 -2025 2.97155115741072 2.97155115741072 2.97155115741072 2.97155115741072 -2026 3.00880920631912 3.00880920631912 3.00880920631912 3.00880920631912 -2027 3.04606680231925 3.04606680231925 3.04606680231925 3.04606680231925 -2028 3.08342146811042 3.08342146811042 3.08342146811042 3.08342146811042 -2029 3.12067816233499 3.12067816233499 3.12067816233499 3.12067816233499 -2030 3.15696145072706 3.15696145072706 3.15696145072706 3.15696145072706 -; - - -* Unless other values are assigned in the TABLE FUELPRICE above, -* the values for 'DK_E_Urban' are used for all A: - -FUELPRICE(YYY,IA,FFF)$(FUELPRICE(YYY,IA,FFF) EQ 0)=FUELPRICE(YYY,'DK_E_Urban',FFF); -$offtext - -$ifi %GAS%==yes FUELPRICE(Y,IA,'NAT_GAS')$(ICA('Denmark',IA)) = FUELPRICE(Y,IA,'NAT_GAS')-2.91/(1.398*7.43); - - -$ifi %FREEGAS%==yes FUELPRICE(YYY,IA,'NAT_GAS')$((ICA('Denmark',IA) or ICA('Sweden',IA)) -$ifi %FREEGAS%==yes and (not sameas('Nybro',IA)) -$ifi %FREEGAS%==yes and (not sameas('Ellund',IA)) -$ifi %FREEGAS%==yes and (not sameas('Oeresund',IA))) -$ifi %FREEGAS%==yes =0; - -PARAMETER GEXT_COST(YYY,SOURCE) 'Average Extraction Cost EURO/MWh', - GEX_PRICE(YYY,SOURCE,S) 'Export Price Money/MWh'; - -GEXT_COST(YYY,'Nybro')=FUELPRICE(YYY,'Nybro','NAT_GAS')*%NG_PROD_FRAC%; -GEXT_COST(YYY,'Oeresund')=FUELPRICE(YYY,'Oeresund','NAT_GAS')*%NG_PROD_FRAC%; - -GEX_PRICE(YYY,SOURCE,S)(IFUELP_Y('Nybro','NAT_GAS')*(1+WSF(S))+%NG_EXP_ADD%; - -$ontext -TABLE GEXT_COST(YYY,SOURCE) 'Average Extraction Cost EURO97/MWh' - - Nybro Oeresund -2003 3.00 3.00 -2004 3.00 3.00 -2005 3.00 3.00 -2006 3.00 3.00 -2007 3.00 3.00 -2008 3.00 3.00 -2009 3.00 3.00 -2010 3.00 3.00 -2011 3.00 3.00 -2012 3.00 3.00 -2013 3.00 3.00 -2014 3.00 3.00 -2015 3.00 3.00 -2016 3.00 3.00 -2017 3.00 3.00 -2018 3.00 3.00 -2019 3.00 3.00 -2020 3.00 3.00 -2021 3.00 3.00 -2022 3.00 3.00 -2023 3.00 3.00 -2024 3.00 3.00 -2025 3.00 3.00 - - Nybro Oeresund -2003 0.65 0.65 -2004 0.65 0.65 -2005 0.65 0.65 -2006 0.65 0.65 -2007 0.65 0.65 -2008 0.65 0.65 -2009 0.65 0.65 -2010 0.65 0.65 -2011 0.65 0.65 -2012 0.65 0.65 -2013 0.65 0.65 -2014 0.65 0.65 -2015 0.65 0.65 -2016 0.65 0.65 -2017 0.65 0.65 -2018 0.65 0.65 -2019 0.65 0.65 -2020 0.65 0.65 -2021 0.65 0.65 -2022 0.65 0.65 -2023 0.65 0.65 -2024 0.65 0.65 -2025 0.65 0.65 -; -$offtext \ No newline at end of file diff --git a/base/addons/gas/gasbal_1.inc b/base/addons/gas/gasbal_1.inc deleted file mode 100644 index de6ecc5d..00000000 --- a/base/addons/gas/gasbal_1.inc +++ /dev/null @@ -1,252 +0,0 @@ -* File: bal_1.out - -* Modified last time: 29-05-2002 by (rus), 2003.08.07 (hrv), 16-02-2004 (ld), 20060328(hr) - -* This file will print a table containing a energy balance for each country - - - - -* ----------------------------------------------------------------------------- - -PUT bal_1; - -* First print file headings: - -if ((bal_1.pw LT 32000), -bal_1.pw = 32000; -PUT "Version: " SYSTEM.TITLE /; -PUT "File name: bal_1.out" /; -PUT "Time-stamp: '" SYSTEM.DATE " " SYSTEM.TIME "'" /; -PUT "Case-identification: '" OCASEID.TS "'" //; - - -PUT "Energy balance" //; -PUT "Units: GWh" //; - -); - -* End of file headings. - -* ------------------------------------------------------------------------------------ -* If scenarios (i.e., more than base case) print scenario heading: -IF((CARD(SCENARIO) GT 1), -PUT /"*==============================================================================="/; -PUT "* " SCENARIO.TL " " SCENARIO.TE(SCENARIO) /; -PUT "*==============================================================================="//; -); -* End of scenario heading -* ------------------------------------------------------------------------------------ - -bal_1.LJ = 1; -PUT "Electricity balance for "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; -bal_1.LJ = 2; - -PUT "Total electricity generation (excl. heat pumps) ;" ; LOOP(C, PUT ( EZGqCY(C)*IOF0001):12:0 ; PUT ";";); PUT /; -PUT " - nuclear ;" ; LOOP(C, PUT ( SUM((IA,IGNUC,S,T)$((IAGK_Y(IA,IGNUC) or AGKN(IA,IGNUC)) and ICA(C,IA)),(VGE_T.l(IA,IGNUC,S,T)+VGEN_T.l(IA,IGNUC,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; -PUT " - natural gas ;" ; LOOP(C, PUT ( SUM((IA,IGE,S,T)$((IAGK_Y(IA,IGE) or AGKN(IA,IGE)) and ICA(C,IA) and GDATA(IGE,'GDFUEL')=2),(VGE_T.l(IA,IGE,S,T)+VGEN_T.l(IA,IGE,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; -PUT " - other thermal ;" ; LOOP(C, PUT ( SUM((IA,IGE,S,T)$((IAGK_Y(IA,IGE) or AGKN(IA,IGE)) and ICA(C,IA) and not (GDATA(IGE,'GDTYPE')=5) and not (GDATA(IGE,'GDFUEL')=2) and not IGWND(IGE) and not IGHYRR(IGE) and not IGHYRS(IGE) and not IGNUC(IGE)),(VGE_T.l(IA,IGE,S,T)+VGEN_T.l(IA,IGE,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; -PUT " - hydro ;" ; LOOP(C, PUT ( SUM((IA,G,S,T)$((IAGK_Y(IA,G) or AGKN(IA,G)) and (IGHYRR(G) or IGHYRS(G)) and ICA(C,IA)),(VGE_T.l(IA,G,S,T)+VGEN_T.l(IA,G,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; -PUT " - wind ;" ; LOOP(C, PUT ( SUM((IA,IGWND,S,T)$((IAGK_Y(IA,IGWND) or AGKN(IA,IGWND)) and ICA(C,IA)),(VGE_T.l(IA,IGWND,S,T)+VGEN_T.l(IA,IGWND,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; -PUT " (hereof CHP) ;" ; LOOP(C, PUT ( SUM((IA,G,S,T)$((IAGK_Y(IA,G) or AGKN(IA,G)) and (IGEXT(G) or IGBPR(G)) and ICA(C,IA)),(VGE_T.l(IA,G,S,T)+VGEN_T.l(IA,G,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; -PUT "Consumption for heat pumps ;" ; LOOP(C, PUT (-EZG13qCY(C)*IOF0001):12:0 ; PUT ";";)PUT /; /*sarah*/ -PUT "Net import from other Nordic countries ;" ; LOOP(C, PUT ( SUM((IRE,S,T)$CCCRRR(C,IRE), SUM(IRI$(not CCCRRR(C,IRI)), (VX_T.l(IRI,IRE,S,T)-VX_T.l(IRE,IRI,S,T))*IHOURSINST(S,T)))*IOF0001 ):12:0 ; PUT ";";)PUT /; -PUT "Net import from other countries ;" ; LOOP(C, PUT ( EX3qCY(C)*IOF0001):12:0 ; PUT ";";)PUT /; -PUT "Transmission loss ;" ; LOOP(C, PUT ( EXLqCY(C)*IOF0001):12:0 ; PUT ";";)PUT /; -PUT "Distribution loss ;" ; LOOP(C, PUT ( ELqCY(C)*IOF0001):12:0 ; PUT ";";)PUT /; -PUT "Electricity demand ;" ; LOOP(C, PUT ( EDqCY(C)*IOF0001):12:0 ; PUT ";";); PUT //; - - -bal_1.LJ = 1; -PUT "Natural gas balance for "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; -bal_1.LJ = 2; - -PUT "Total endogenous gas production ;" ; LOOP(C, PUT ( 0+IOF0001*SUM((S,T), VSUP.l('Nybro',s,t)*IHOURSINST(S,T))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; -PUT "Consumption of natural gas ;" ; LOOP(C, PUT ( IOF0001*SUM((IA,S,T), VGUD.l(IA,s,t)*IHOURSINST(S,T))$SAMEAS(C,'Denmark')+IOF0001*SUM((TRANSIT,S,T), VTRANSIT.l(TRANSIT,s,t)*IHOURSINST(S,T))$SAMEAS(C,'Sweden') ):12:0 ; PUT ";";); PUT /; -PUT " - for electricity and district heating ;" ; LOOP(C, PUT ( IOF0001*SUM((IA,S,T)$ICA(C,IA), - IHOURSINST(S,T)*sum(IGE$(IAGKN(IA,IGE) and GDATA(IGE,'GDFUEL') EQ 2), VGEN_T.l(IA,IGE,S,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IGE,IA))) ) - +IHOURSINST(S,T)*sum(IGH$(IAGKN(IA,IGH) and GDATA(IGH,'GDFUEL') EQ 2), GDATA(IGH,'GDCV')*VGHN_T.l(IA,IGH,S,T)/(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IGH,IA))) ) - +IHOURSINST(S,T)*sum(IGE$(IAGK_Y(IA,IGE) and GDATA(IGE,'GDFUEL') EQ 2),VGE_T.l(IA,IGE,S,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IGE,IA))) ) - +IHOURSINST(S,T)*sum(IGH$(IAGK_Y(IA,IGH) and GDATA(IGH,'GDFUEL') EQ 2),GDATA(IGH,'GDCV')*VGH_T.l(IA,IGH,S,T) /(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IGH,IA))) ) ) ):12:0 ; PUT ";";); PUT /; -PUT " - other consumption ;" ; LOOP(C, PUT ( IOF0001*SUM((IA,S,T)$ICA(C,IA), (IDGRES_Y(IA,S,T)+IDGOTH_Y(IA,S,T))*IHOURSINST(S,T)) ):12:0 ; PUT ";";); PUT /; -PUT "Exports of natural gas ;" ; LOOP(C, PUT ( 0+IOF0001*SUM((SOURCE,S,T), VEXP.l(SOURCE,S)*IHOURSINST(S,T))$SAMEAS(C,'Denmark')+IOF0001*SUM((TRANSIT,S,T), VTRANSIT.l(TRANSIT,S,T)*IHOURSINST(S,T))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; -PUT "Imports of natural gas ;" ; LOOP(C, PUT ( 0+IOF0001*SUM((SOURCE,S,T)$(not SAMEAS(SOURCE,'Nybro')), VSUP.l(SOURCE,S,T)*IHOURSINST(S,T))$SAMEAS(C,'Denmark')+IOF0001*SUM((TRANSIT,S,T), VTRANSIT.l(TRANSIT,S,T)*IHOURSINST(S,T))$SAMEAS(C,'Sweden') ):12:0 ; PUT ";";); PUT /; - -PUT /; - -bal_1.LJ = 1; -PUT "District heating balance for "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; -bal_1.LJ = 2; - -PUT "Total heat generation ;" ; LOOP(C, PUT ( HZGqCY(C)*IOF0001):12:0 ; PUT ";";); PUT /; -PUT " - natural gas ;" ; LOOP(C, PUT ( SUM((IA,IGH,S,T)$((IAGK_Y(IA,IGH) or AGKN(IA,IGH)) and ICA(C,IA) and (GDATA(IGH,'GDFUEL')=2)),(VGH_T.l(IA,IGH,S,T)+VGHN_T.l(IA,IGH,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; -PUT " - other ;" ; LOOP(C, PUT ( SUM((IA,IGH,S,T)$((IAGK_Y(IA,IGH) or AGKN(IA,IGH)) and ICA(C,IA) and not (GDATA(IGH,'GDFUEL')=2) and not (GDATA(IGH,'GDTYPE')=6)),(VGH_T.l(IA,IGH,S,T)+VGHN_T.l(IA,IGH,S,T))*IHOURSINST(S,T))*IOF0001):12:0 ; PUT ";";); PUT /; -PUT "Distribution loss ;" ; LOOP(C, PUT ( HLqCY(C)*IOF0001):12:0 ; PUT ";";)PUT /; -PUT "Heat demand ;" ; LOOP(C, PUT ( HDqCY(C)*IOF0001):12:0 ; PUT ";";); PUT //; - - - -*----------------------------------- -* Investments: -*----------------------------------- - -$ifi not %BB2%==yes $goto BAL1_INVEST_END - - -PUT "Investments" //; -PUT "Units: Mio EURO6" //; - - - -* End of file headings. - -* ------------------------------------------------------------------------------------ -* If scenarios (i.e., more than base case) print scenario heading: -IF((CARD(SCENARIO) GT 1), -PUT /"*==============================================================================="/; -PUT "* " SCENARIO.TL " " SCENARIO.TE(SCENARIO) /; -PUT "*==============================================================================="//; -); -* End of scenario heading -* ------------------------------------------------------------------------------------ - -bal_1.LJ = 1; -PUT "Electricity generation and infrastructure MW el "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; -bal_1.LJ = 2; - -PUT "Total electricity generation (excl. heat pumps) ;" ; LOOP(C, PUT ( SUM((IA,IGE)$((not IGETOH(IGE)) and IAGKN(IA,IGE) and ICA(C,IA)), VGKN.l(IA,IGE))):12:0 ;PUT ";"; ); PUT /; -PUT " - nuclear ;" ; LOOP(C, PUT ( SUM((IA,IGNUC)$(IAGKN(IA,IGNUC) and ICA(C,IA)),VGKN.l(IA,IGNUC))):12:0 ; PUT ";";); PUT /; -PUT " - natural gas ;" ; LOOP(C, PUT ( SUM((IA,IGE)$(IAGKN(IA,IGE) and ICA(C,IA) and GDATA(IGE,'GDFUEL')=2),VGKN.l(IA,IGE))):12:0 ; PUT ";";); PUT /; -PUT " - other thermal ;" ; LOOP(C, PUT ( SUM((IA,IGE)$(IAGKN(IA,IGE) and ICA(C,IA) and not (GDATA(IGE,'GDTYPE')=5) and not (GDATA(IGE,'GDFUEL')=2) and not IGWND(IGE) and not IGHYRR(IGE) and not IGHYRS(IGE) and not IGNUC(IGE)),VGKN.l(IA,IGE))):12:0 ; PUT ";";); PUT /; -PUT " - hydro ;" ; LOOP(C, PUT ( SUM((IA,G)$(IAGKN(IA,G) and (IGHYRR(G) or IGHYRS(G)) and ICA(C,IA)),VGKN.l(IA,G))):12:0 ; PUT ";";); PUT /; -PUT " - wind ;" ; LOOP(C, PUT ( SUM((IA,IGWND)$(IAGKN(IA,IGWND) and ICA(C,IA)),VGKN.l(IA,IGWND))):12:0 ; PUT ";";); PUT /; -PUT " (hereof CHP) ;" ; LOOP(C, PUT ( SUM((IA,G)$(IAGKN(IA,G) and (IGEXT(G) or IGBPR(G)) and ICA(C,IA)),VGKN.l(IA,G))):12:0 ; PUT ";";); PUT /; -PUT "Electric heating ;" ; LOOP(C, PUT ( SUM((IA,IGETOH)$(IAGKN(IA,IGETOH) and ICA(C,IA)), VGKN.l(IA,IGETOH))):12:0 ; PUT ";";); PUT /; -*PUT "Transmission capacity ;" ; LOOP(C, PUT ( SUM(IRE$CCCRRR(C,IRE), SUM(VXKN.l(IRE,IRE) ))):12:0 ; PUT ";";)PUT /; - -PUT //; - -bal_1.LJ = 1; -PUT "Natural gas capacity gained from investments "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; -bal_1.LJ = 2; - -PUT "Total new supply capacity (MWh/h) ;" ; LOOP(C, PUT ( sum((NGINV,SOURCE),SUPEFFECT(NGINV,SOURCE)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; -PUT "Total new delivery capacity (MWh/h) ;" ; LOOP(C, PUT ( sum((GNETSTEPS,SYSEXIT), GNET_DEV_EFFECT(C,SYSEXIT)*VGNETINV.l(C,GNETSTEPS))+ sum((NGINV,SYSEXIT),DEVEFFECT(NGINV,SYSEXIT)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; -PUT " - onshore gas transmission network ;" ; LOOP(C, PUT ( sum((GNETSTEPS,SYSEXIT), GNET_DEV_EFFECT(C,SYSEXIT)*VGNETINV.l(C,GNETSTEPS)) ):12:0 ; PUT ";";); PUT /; -PUT " - compressors and interconnectors ;" ; LOOP(C, PUT ( sum((NGINV,SYSEXIT),DEVEFFECT(NGINV,SYSEXIT)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; -PUT "Total new export/transit capacity (MWh/h) ;" ; LOOP(C, PUT ( sum((GNETSTEPS,SOURCE), GNET_EXP_EFFECT(C,SOURCE)*VGNETINV.l(C,GNETSTEPS)) + sum((NGINV,SOURCE),EXPEFFECT(NGINV,SOURCE)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ):12:0 ;PUT ";"; ); PUT /; -PUT " - onshore gas transmission network ;" ; LOOP(C, PUT ( sum((GNETSTEPS,SOURCE), GNET_EXP_EFFECT(C,SOURCE)*VGNETINV.l(C,GNETSTEPS)) ):12:0 ; PUT ";";); PUT /; -PUT " - compressors and interconnectors ;" ; LOOP(C, PUT ( sum((NGINV,SOURCE),EXPEFFECT(NGINV,SOURCE)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; -PUT "Gas storage capacity ;" ; LOOP(C, PUT "-" ; ); PUT /; -PUT " - volumetric capacity (MWh) ;" ; LOOP(C, PUT ( sum((STORE,STORETYPE)$ICA(C,STORE),VGINVSTORE.l(STORE,STORETYPE)) ):12:0 ; PUT ";";); PUT /; -PUT " - injection capacity (MWh/h) ;" ; LOOP(C, PUT ( sum((STORE,STORETYPE)$ICA(C,STORE),GSTOREDATA(STORETYPE,'Injeffect')*VGINVSTORE.l(STORE,STORETYPE)+VGINVINJ.l(STORE,STORETYPE)) ):12:0 ; PUT ";";); PUT /; -PUT " - extraction capacity (MWh/h) ;" ; LOOP(C, PUT ( sum((STORE,STORETYPE)$ICA(C,STORE),GSTOREDATA(STORETYPE,'Exteffect')*VGINVSTORE.l(STORE,STORETYPE)+VGINVEXT.l(STORE,STORETYPE)) ):12:0 ; PUT ";";); PUT /; -PUT "Total gas consumption capacity (MWh/h) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,IGCND)$(ICA(C,IA) and GDATA(IGCND,'GDFUEL')=2), VGKN.l(IA,IGCND)/GDATA(IGCND,'GDFE') ) + SUM(IAGKN(IA,IGE)$(ICA(C,IA) and (IGBPR(IGE) or IGEXT(IGE)) and GDATA(IGE,'GDFUEL')=2), ( (VGKN.l(IA,IGE)+VGKN.l(IA,IGE)/GDATA(IGE,'GDCB'))$IGBPR(IGE) + VGKN.l(IA,IGE)$IGEXT(IGE) )/GDATA(IGE,'GDFE') ) + SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and GDATA(IGHH,'GDFUEL')=2), VGKN.l(IA,IGHH)/GDATA(IGHH,'GDFE') ) ):12:0 ; PUT ";";); PUT /; -PUT " - condensing gas-fired electricity generation ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,IGCND)$(ICA(C,IA) and GDATA(IGCND,'GDFUEL')=2), VGKN.l(IA,IGCND)/GDATA(IGCND,'GDFE') ) ):12:0 ; PUT ";";); PUT /; -PUT " - combined heat and power (gas-fired) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,IGE)$(ICA(C,IA) and (IGBPR(IGE) or IGEXT(IGE)) and GDATA(IGE,'GDFUEL')=2), ( (VGKN.l(IA,IGE)+VGKN.l(IA,IGE)/GDATA(IGE,'GDCB'))$IGBPR(IGE) + VGKN.l(IA,IGE)$IGEXT(IGE) )/GDATA(IGE,'GDFE') ) ):12:0 ; PUT ";";); PUT /; -PUT " - heat boilers (gas-fired) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and GDATA(IGHH,'GDFUEL')=2), VGKN.l(IA,IGHH)/GDATA(IGHH,'GDFE') ) ):12:0 ; PUT ";";); PUT /; - -PUT //; - -bal_1.LJ = 1; -PUT "District heating capacity gained from investments "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; -bal_1.LJ = 2; - -PUT "Total heat generation capacity from investments ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,G)$(ICA(C,IA) and (IGEXT(G) or IGBPR(G))), VGKN.l(IA,G)/GDATA(G,'GDCB') ) + SUM(IAGKN(IA,IGHH), VGKN.l(IA,IGHH)) ):12:0 ; PUT ";";); PUT /; -PUT " - CHP (natural gas-fired) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,G)$(ICA(C,IA) and (IGEXT(G) or IGBPR(G)) and (GDATA(G,'GDFUEL') =2)), VGKN.l(IA,G)/GDATA(G,'GDCB') ) ):12:0 ; PUT ";";); PUT /; -PUT " - CHP (other fuels) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,G)$(ICA(C,IA) and (IGEXT(G) or IGBPR(G)) and (not GDATA(G,'GDFUEL') =2) ), VGKN.l(IA,G)/GDATA(G,'GDCB')) ):12:0 ; PUT ";";); PUT /; -PUT " - heat only (natural gas-fired) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and GDATA(IGHH,'GDFUEL') =2), VGKN.l(IA,IGHH) ) ):12:0 ; PUT ";";); PUT /; -PUT " - heat only (other fuels) ;" ; LOOP(C, PUT ( SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and not GDATA(IGHH,'GDFUEL') =2), VGKN.l(IA,IGHH) ) ):12:0 ; PUT ";";); PUT /; -PUT //; - -PUT "*==============================================================================="//; - - -bal_1.LJ = 1; -PUT "Electricity generation and infrastructure Mio EURO06 "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; -bal_1.LJ = 2; - -PUT "Total electricity generation (excl. heat pumps) ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,IGE)$((not IGETOH(IGE)) and IAGKN(IA,IGE) and ICA(C,IA)), GDATA(IGE,'GDINVCOST0')*VGKN.l(IA,IGE))):12:0 ; PUT ";";); PUT /; -PUT " - nuclear ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,IGNUC)$(IAGKN(IA,IGNUC) and ICA(C,IA)),GDATA(IGNUC,'GDINVCOST0')*VGKN.l(IA,IGNUC))):12:0 ; PUT ";";); PUT /; -PUT " - natural gas ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,IGE)$(IAGKN(IA,IGE) and ICA(C,IA) and GDATA(IGE,'GDFUEL')=2),GDATA(IGE,'GDINVCOST0')*VGKN.l(IA,IGE))):12:0 ; PUT ";";); PUT /; -PUT " - other thermal ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,IGE)$(IAGKN(IA,IGE) and ICA(C,IA) and not (GDATA(IGE,'GDTYPE')=5) and not (GDATA(IGE,'GDFUEL')=2) and not IGWND(IGE) and not IGHYRR(IGE) and not IGHYRS(IGE) and not IGNUC(IGE)),GDATA(IGE,'GDINVCOST0')*VGKN.l(IA,IGE))):12:0 ; PUT ";";); PUT /; -PUT " - hydro ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,G)$(IAGKN(IA,G) and (IGHYRR(G) or IGHYRS(G)) and ICA(C,IA)),GDATA(G,'GDINVCOST0')*VGKN.l(IA,G))):12:0 ; PUT ";";); PUT /; -PUT " - wind ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,IGWND)$(IAGKN(IA,IGWND) and ICA(C,IA)),GDATA(IGWND,'GDINVCOST0')*VGKN.l(IA,IGWND))):12:0 ; PUT ";";); PUT /; -PUT " (hereof CHP) ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,G)$(IAGKN(IA,G) and (IGEXT(G) or IGBPR(G)) and ICA(C,IA)),GDATA(G,'GDINVCOST0')*VGKN.l(IA,G))):12:0 ; PUT ";";); PUT /; -PUT "Electric heating ;" ; LOOP(C, PUT ( OMONEY*SUM((IA,IGETOH)$(IAGKN(IA,IGETOH) and ICA(C,IA)), GDATA(IGETOH,'GDINVCOST0')*VGKN.l(IA,IGETOH))):12:0 ; PUT ";";); PUT /; -*PUT "Transmission capacity ;" ; LOOP(C, PUT ( OMONEY*SUM(IRE$CCCRRR(C,IRE), SUM(VXKN.l(IRE,IRE) ))):12:0 ; PUT ";";)PUT /; MANGLER INVCOST - -PUT //; - -bal_1.LJ = 1; -PUT "Natural gas infrastructure Mio EURO06 "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; -bal_1.LJ = 2; - -PUT "Total investments in transmision infrastructure ;" ; LOOP(C, PUT ( OMONEY*IOF0000001*( sum(GNETSTEPS, GNET_COST(C,GNETSTEPS)*VGNETINV.l(C,GNETSTEPS)) + sum(NGINV,COST_NGINV(NGINV)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ) ):12:0 ; PUT ";";); PUT /; -PUT " - onshore gas transmission network ;" ; LOOP(C, PUT ( OMONEY*IOF0000001* sum(GNETSTEPS, GNET_COST(C,GNETSTEPS)*VGNETINV.l(C,GNETSTEPS)) ):12:0 ; PUT ";";); PUT /; -PUT " - compressors and interconnectors ;" ; LOOP(C, PUT ( OMONEY*IOF0000001* sum(NGINV,COST_NGINV(NGINV)*VGINFINV.l(NGINV))$SAMEAS(C,'Denmark') ):12:0 ; PUT ";";); PUT /; -PUT "Total investments in natural gas storage ;" ; LOOP(C, PUT ( OMONEY*IOF0000001* sum((STORE,STORETYPE)$ICA(C,STORE),GSTOREDATA(STORETYPE,'MargExtInvCost')*VGINVEXT.l(STORE,STORETYPE)+GSTOREDATA(STORETYPE,'MargInjInvCost')*VGINVINJ.l(STORE,STORETYPE)+GSTOREDATA(STORETYPE,'INVCOST')*VGINVSTORE.l(STORE,STORETYPE))/GASANNUITY ):12:0 ; PUT ";";); PUT /; -PUT " - gas storage facilities ;" ; LOOP(C, PUT ( OMONEY*IOF0000001* sum((STORE,STORETYPE)$ICA(C,STORE),GSTOREDATA(STORETYPE,'INVCOST')*VGINVSTORE.l(STORE,STORETYPE))/GASANNUITY ):12:0 ; PUT ";";); PUT /; -PUT " - injection capacity upgrades ;" ; LOOP(C, PUT ( OMONEY*IOF0000001* sum((STORE,STORETYPE)$ICA(C,STORE),GSTOREDATA(STORETYPE,'MargInjInvCost')*VGINVINJ.l(STORE,STORETYPE))/GASANNUITY ):12:0 ; PUT ";";); PUT /; -PUT " - extraction capacity expansion ;" ; LOOP(C, PUT ( OMONEY*IOF0000001* sum((STORE,STORETYPE)$ICA(C,STORE),GSTOREDATA(STORETYPE,'MargExtInvCost')*VGINVEXT.l(STORE,STORETYPE))/GASANNUITY ):12:0 ; PUT ";";); PUT /; -PUT "Total gas consumption capacity ;" ; LOOP(C, PUT ( OMONEY* SUM(IAGKN(IA,G)$(ICA(C,IA) and GDATA(G,'GDFUEL')=2), GDATA(G,'GDINVCOST0')*VGKN.l(IA,G)) ):12:0 ; PUT ";";); PUT /; -PUT " - condensing gas-fired electricity generation ;" ; LOOP(C, PUT ( OMONEY* SUM(IAGKN(IA,IGCND)$(ICA(C,IA) and GDATA(IGCND,'GDFUEL')=2), GDATA(IGCND,'GDINVCOST0')*VGKN.l(IA,IGCND) ) ):12:0 ; PUT ";";); PUT /; -PUT " - combined heat and power (gas-fired) ;" ; LOOP(C, PUT ( OMONEY* SUM(IAGKN(IA,IGE)$(ICA(C,IA) and (IGBPR(IGE) or IGEXT(IGE)) and GDATA(IGE,'GDFUEL')=2), GDATA(IGE,'GDINVCOST0')*VGKN.l(IA,IGE) ) ):12:0 ; PUT ";";); PUT /; -PUT " - heat boilers (gas-fired) ;" ; LOOP(C, PUT ( OMONEY* SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and GDATA(IGHH,'GDFUEL')=2), GDATA(IGHH,'GDINVCOST0')*VGKN.l(IA,IGHH) ) ):12:0 ; PUT ";";); PUT /; - -PUT //; - -bal_1.LJ = 1; -PUT "Investments in capacity for district heating "PUT Y.TL:4:0; PUT " ;" ; LOOP(C, PUT C.TL:12 PUT ";";) ;PUT /; -bal_1.LJ = 2; - -PUT "Total heat generation capacity from investments ;" ; LOOP(C, PUT ( OMONEY*SUM(IAGKN(IA,G)$(ICA(C,IA) and (IGEXT(G) or IGBPR(G))), GDATA(G,'GDINVCOST0')*VGKN.l(IA,G) ) + SUM(IAGKN(IA,IGHH), GDATA(IGHH,'GDINVCOST0')*VGKN.l(IA,IGHH)) ):12:0 ; PUT ";";); PUT /; -PUT " - CHP (natural gas-fired) ;" ; LOOP(C, PUT ( OMONEY*SUM(IAGKN(IA,G)$(ICA(C,IA) and (IGEXT(G) or IGBPR(G)) and (GDATA(G,'GDFUEL') =2)), GDATA(G,'GDINVCOST0')*VGKN.l(IA,G) ) ):12:0 ; PUT ";";); PUT /; -PUT " - CHP (other fuels) ;" ; LOOP(C, PUT ( OMONEY*SUM(IAGKN(IA,G)$(ICA(C,IA) and (IGEXT(G) or IGBPR(G)) and (not GDATA(G,'GDFUEL') =2) ), GDATA(G,'GDINVCOST0')*VGKN.l(IA,G) ) ):12:0 ; PUT ";";); PUT /; -PUT " - heat only (natural gas-fired) ;" ; LOOP(C, PUT ( OMONEY*SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and GDATA(IGHH,'GDFUEL') =2), GDATA(IGHH,'GDINVCOST0')*VGKN.l(IA,IGHH) ) ):12:0 ; PUT ";";); PUT /; -PUT " - heat only (other fuels) ;" ; LOOP(C, PUT ( OMONEY*SUM(IAGKN(IA,IGHH)$(ICA(C,IA) and not GDATA(IGHH,'GDFUEL') =2), GDATA(IGHH,'GDINVCOST0')*VGKN.l(IA,IGHH) ) ):12:0 ; PUT ";";); PUT /; -PUT //; - -$label BAL1_INVEST_END - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/base/addons/gas/gasbb1.inc b/base/addons/gas/gasbb1.inc deleted file mode 100644 index 495ca538..00000000 --- a/base/addons/gas/gasbb1.inc +++ /dev/null @@ -1,10 +0,0 @@ -QGEQ -QCONSUMPTION -QStoreVol -QStoreInj -QStoreExt -QTransEntryCap -QTransExitCap -QGSTOREBAL -QGSVERIGE -QGDEV_MAX \ No newline at end of file diff --git a/base/addons/gas/gasbb2.inc b/base/addons/gas/gasbb2.inc deleted file mode 100644 index e3163a65..00000000 --- a/base/addons/gas/gasbb2.inc +++ /dev/null @@ -1,16 +0,0 @@ -QGEQ -QCONSUMPTION -$ifi %BB2KAPPROD%==yes QStoreVol -$ifi %BB2KAPPROD%==yes QStoreInj -$ifi %BB2KAPPROD%==yes QStoreExt -$ifi %BB2KAPPROD%==yes QTransEntryCap -$ifi %BB2KAPPROD%==yes QTransExitCap -QGSTOREBAL -QGSVERIGE -QGSUPPLY_MAX -QGINJ_MAX -QGEXT_MAX -QGSTORE_MAX -QGEXP_MAX -QGTRANSIT_MAX -QGDEV_MAX \ No newline at end of file diff --git a/base/addons/gas/gasbb3.inc b/base/addons/gas/gasbb3.inc deleted file mode 100644 index 48d9120a..00000000 --- a/base/addons/gas/gasbb3.inc +++ /dev/null @@ -1,9 +0,0 @@ -QGEQ -QCONSUMPTION -QStoreInj -QStoreExt -QTransEntryCap -QTransExitCap -QGSFXW -QGSVERIGE -QGDEV_MAX \ No newline at end of file diff --git a/base/addons/gas/gascompprint1.inc b/base/addons/gas/gascompprint1.inc deleted file mode 100644 index b9a9ea28..00000000 --- a/base/addons/gas/gascompprint1.inc +++ /dev/null @@ -1,12 +0,0 @@ -* File gascompprint1.inc. -* This file is an extension to the welfare calculation module for the Balmorel model. -* Created by Jens Hauch and Thomas E. Pedersen, COWI A/S (DK). -* Adapted for Balmorel version 2.10, 2.13 by Hans Ravn. -* Extended to for the gas add-on by Lars Bregnbæk, X-mas 2006. - - -* These files are used for comparison between scenarios -File gascompfile /..\compare\GAS%CASEID%.MED/; - - - diff --git a/base/addons/gas/gascompprint2.inc b/base/addons/gas/gascompprint2.inc deleted file mode 100644 index 2b062826..00000000 --- a/base/addons/gas/gascompprint2.inc +++ /dev/null @@ -1,122 +0,0 @@ -* File gascompprint2.inc. -* This file is an extension to the welfare calculation module for the Balmorel model. -* Created by Jens Hauch and Thomas E. Pedersen, COWI A/S (DK). -* Adapted for Balmorel version 2.10, 2.13 by Hans Ravn. -* Extended to for the gas add-on by Lars Bregnbæk, X-mas 2006. - - - -*=============================================================================== -* Overview -*=============================================================================== - -* This file contains -* data -* declarations of internal parameters. - -* Data: -* scalar irt Interest rate; -* parameter tlifetime Life time for transmission capacity - years; - - -* Comments on terminology: -* p: prices -* e: electricity (pe: electricity price) -* h: heat -* psur: producers surplus -* earnings: producers surplus (at national level - so the terminology seems slightly inconsistent) -* cw: consumers welfare (cwe: electricity, cwh: heat) -* dc: discounted -* dlt, delta: difference - -* Note that internal parameters do not obey to the conventions that their name should start with 'I'. - - - -*=============================================================================== -* Overview end -*=============================================================================== - -PARAMETERS - residualcosts(Y,C) 'Gas costs for residual consumers (Input currency)' - residualcosts00(Y,C) 'Gas costs for residual consumers (Input currency)' - dresidualbenifit(C) 'Discounted welfare change for residual consumers (Input currency)' - - othercosts(Y,C) 'Gas costs for other consumers (Input currency)' - othercosts00(Y,C) 'Gas costs for other consumers (Input currency)' - dotherbenifit(C) 'Discounted welfare change for residual consumers (Input currency)' - - DSOrevenue(Y,C) 'Revenue for distribution companies' - DSOcosts(Y,C) - DSOprofit(Y,C) 'Profits for distribution companies (Input currency)' - DSOprofit00(Y,C) 'Profits for distribution companies (Input currency)' - dcdltDSOprofit(C) 'Discounted welfare change for distribution companies (Input currency)' - - TSOCAPrev(Y,C) 'TSO Revenue from capacity products' - TSOCAPrev00(Y,C) 'TSO Revenue from capacity products' - dcdltTSOCAPrevbenefit(C) 'Discounted welfare change in TSO from Capacity products (Input currency)' - - TSOVOLrev(Y,C) 'TSO volumetric revenue' - TSOVOLrev00(Y,C) 'TSO volumetric revenue' - dcdltTSOVOLrevbenefit(C) 'Discounted welfare change in TSO from Volumetic tariffs (Input currency)' - - TSOCAPRent(Y,C) 'TSO Capacity rent' - TSOCAPRent00(Y,C) 'TSO Capacity rent' - dcdltTSOCAPRentbenefit(C) 'Discounted welfare change in TSO Capacity rent (Input currency)' - - TSOfixedcosts(Y,C) - TSOfixedcosts00(Y,C) - TSOvariablecosts(Y,C) - TSOvariablecosts00(Y,C) - TSOprofit(Y,C) - TSOprofit00(Y,C) - - dcdltTSOProfit(C) 'Discounted welfare change in TSO profit (Input currency)' - - SSOCAPrev(Y,C) 'SSO Capacity revenue' - SSOCAPrev00(Y,C) 'SSO Capacity revenue' - dcdltSSOCAPrevbenefit(C) 'Discounted welfare change in SSO Capacity revenue (Input currency)' - - SSOVOLrev(Y,C) 'SSO volumetric revenue' - SSOVOLrev00(Y,C) 'SSO volumetric revenue' - dcdltSSOVOLrevbenefit(C) 'Discounted welfare change in SSO volumetric revenue (Input currency)' - - SSOCAPrent(Y,C) 'SSO Capacity rent' - SSOCAPrent00(Y,C) 'SSO Capacity rent' - dcdltSSOCAPrentbenefit(C) 'Discountred welfare change in SSO Capacity rent (Input currency)' - - SSOfixedcosts(Y,C) - SSOvariablecosts(Y,C) - - SSOprofit(Y,C) - SSOprofit00(Y,C) - dcdltSSOProfit(C) 'Discounted welfare change in SSO profits (Input currency)' - - GasSupplierCosts(Y,C) - GasSupplierRevenue(Y,C) - GasSupplierProfit(Y,C) - GasSupplierProfit00(Y,C) - dcdltGasSupplierProfit(C) 'Discounted welfare change for gas suppliers (Input currency)' - - GasShipperCosts(Y,C) - GasShipperRevenue(Y,C) - GasShipperProfit(Y,C) - GasShipperProfit00(Y,C) - dcdltGasShipperProfit(C) 'Discounted welfare change for gas shippers (Input currency)' - - GasProducerCosts(Y,C) - GasProducerRevenue(Y,C) - GasProducerProfit(Y,C) - GasProducerProfit00(Y,C) - dcdltGasProducerProfit(C) 'Discounted welfare change for gas shippers (Input currency)' - - GasStorageUserCost(Y,C) - GasStorageUserRevenue(Y,C) - GasStorageUserProfit(Y,C) - GasStorageUserProfit00(Y,C) - dcdltGasStorageUserProfit(C) - - ShipperCapRent(Y,C) - ShipperCapRent00(Y,C) - dcdltShipperCapRent(C) -; \ No newline at end of file diff --git a/base/addons/gas/gascompprint3.inc b/base/addons/gas/gascompprint3.inc deleted file mode 100644 index fab34af9..00000000 --- a/base/addons/gas/gascompprint3.inc +++ /dev/null @@ -1,221 +0,0 @@ -* File gascompprint2.inc. -* This file is an extension to the welfare calculation module for the Balmorel model. -* Created by Jens Hauch and Thomas E. Pedersen, COWI A/S (DK). -* Adapted for Balmorel version 2.10, 2.13 by Hans Ravn. -* Extended to for the gas add-on by Lars Bregnbæk, X-mas 2006. - - -*=============================================================================== -* Overview -*=============================================================================== - -* This file contains calculation of intermediate parameter values. - -* There are 6 parts: gas producers, gasconsumers (except heat and power generators), -* TSOs, Distributors, Suppliers, Storageoperators, Exporers. - -* Much of the code in this file may seem superfluous. The development phylosophy -* has been to prepare the code for any forseeable additions with regard to agent -* detail. For instance many agentgroups have associated costs or profits which are -* set to zero as there is at present no available data, nor an accurate repressentation -* of these factors in the model. - -*=============================================================================== -* End: Overview -*=============================================================================== - -*=============================================================================== -* Gas Producers -*=============================================================================== - -* Only Danish natural gas production considered. Should be modified for new investments. -GasProducerRevenue(Y,'Denmark')= Sum((S,T),(IGEXT_COST('Nybro',S)*IOF3P6 + IOF1000000*(-1*VSUP.m('Nybro',S,T)/IHOURSINST(S,T)))*VSUP.l('Nybro',S,T)*IHOURSINST(S,T)); - -* Artificially set to zero -GasProducerCosts(Y,C) = 0; - -GasProducerProfit(Y,C) = GasProducerRevenue(Y,C)-GasProducerCosts(Y,C); - - - -*=============================================================================== -* Gas Consumers -*=============================================================================== - - -* Gasconsumers (except heat and power generators) - -* Costs to residual consumers (e.g. private homes and small businesses) (Input currency) -residualcosts(Y,C) = SUM((IA,S,T)$ICA(C,IA), IDGRES_Y(IA,S,T)*IOF1000000 *(QCONSUMPTION.m(IA,S,T)$ICA('Denmark',IA) - +QGSVERIGE.m(S,T)$ICA('Sweden',IA))); - -* Costs to other consumers (e.g. Industry) (Input currency) -othercosts(Y,C) = SUM((IA,S,T)$ICA(C,IA), IDGOTH_Y(IA,S,T)*IOF1000000 *(QCONSUMPTION.m(IA,S,T)$ICA('Denmark',IA) - +QGSVERIGE.m(S,T)$ICA('Sweden',IA))); - -*=============================================================================== -* Gas Distributors -*=============================================================================== - -* Distributioners revenue. -DSOrevenue(Y,C) = 1/7.43/1.389*SUM((DDD,IA,S,T)$(DDDNODE(DDD,IA) and ICA(C,IA)), TAU_DIST(DDD)*VGUD.l(IA,S,T)*IHOURSINST(S,T))$SAMEAS(C,'Denmark') - +1/7.43/1.389*SUM((TRANSIT,S,T), TAU_DIST('SE_S_DIST')*VTRANSIT.l(TRANSIT,S,T)*IHOURSINST(S,T))$SAMEAS(C,'Sweden'); - -* Distributor costs artificially set to zero. -DSOcosts(Y,C) = 0; - -DSOprofit(Y,C) = DSOrevenue(Y,C)-DSOcosts(Y,C); - -*=============================================================================== -* Gas TSOs -*=============================================================================== - -* TSO capacity revenue (Input currency) -TSOCAPrev(Y,'Denmark') = +1/7.43/1.389*( -$ifi %bb1%==yes + SUM(SYSENTRY, TAU_EN_Y*VTC_EN_Y.l(SYSENTRY) ) -$ifi %bb1%==yes + SUM(SYSEXIT, TAU_EX_Y*VTC_EX_Y.l(SYSEXIT) ) -$ifi %bb1%==yes + SUM(SYSENTRY, SUM(IMONTH, TAU_EN_M(IMONTH)*VTC_EN_M.l(SYSENTRY,IMONTH)*MONTHSCALE(IMONTH)) ) -$ifi %bb1%==yes + SUM(SYSEXIT, SUM(IMONTH, TAU_EX_M(IMONTH)*VTC_EX_M.l(SYSEXIT,IMONTH)*MONTHSCALE(IMONTH)) ) -$ifi %bb2%==yes + SUM(SYSENTRY, TAU_EN_Y*VTC_EN_Y.l(SYSENTRY) ) -$ifi %bb2%==yes + SUM(SYSEXIT, TAU_EX_Y*VTC_EX_Y.l(SYSEXIT) ) -$ifi %bb2%==yes + SUM(SYSENTRY, SUM(IMONTH, TAU_EN_M(IMONTH)*VTC_EN_M.l(SYSENTRY,IMONTH)*MONTHSCALE(IMONTH)) ) -$ifi %bb2%==yes + SUM(SYSEXIT, SUM(IMONTH, TAU_EX_M(IMONTH)*VTC_EX_M.l(SYSEXIT,IMONTH)*MONTHSCALE(IMONTH)) ) -+ SUM(SYSENTRY, sum(IS3,IDAYSIN_S(IS3)/7*(TAU_EN_W(IS3)*VTC_EN_W.l(SYSENTRY,IS3))) ) -+ SUM(SYSEXIT, sum(IS3,IDAYSIN_S(IS3)/7*(TAU_EX_W(IS3)*VTC_EX_W.l(SYSEXIT,IS3))) ) -$ifi %bb3%==yes + SUM(SYSENTRY, TAU_EN_Y*ITC_EN_Y(SYSENTRY) ) -$ifi %bb3%==yes + SUM(SYSEXIT, TAU_EX_Y*ITC_EX_Y(SYSEXIT) ) -$ifi %bb3%==yes + SUM(SYSENTRY, SUM(IMONTH, TAU_EN_M(IMONTH)*ITC_EN_M(SYSENTRY,IMONTH)*MONTHSCALE(IMONTH)) ) -$ifi %bb3%==yes + SUM(SYSEXIT, SUM(IMONTH, TAU_EX_M(IMONTH)*ITC_EX_M(SYSEXIT,IMONTH)*MONTHSCALE(IMONTH)) ) -$ifi %bb3%==yes + SUM(SYSENTRY, sum((IS3,DAY), TAU_EN_D(IS3,DAY)*VTC_EN_D.l(SYSENTRY,IS3,DAY)) ) -$ifi %bb3%==yes + SUM(SYSEXIT, sum((IS3,DAY), TAU_EX_D(IS3,DAY)*VTC_EX_D.l(SYSEXIT,IS3,DAY)) ) -); - -* TSO volumetric revenue -TSOVOLrev(Y,'Denmark') = TAU_VOL/7.43/1.389*sum((S,T), Sum(SOURCE, VSUP.l(SOURCE,S,T)*IHOURSINST(S,T))-VEXP.l('Nybro',S)*IHOURSINST(S,T)); - -* TSO capacity rent (i.e. additional congestion charges that could be applied without changing production, transmission or consumption). -TSOCAPRent(Y,'Denmark') = -$ifi %BB1%==yes +SUM((SOURCE,S,T)$(not SAMEAS(SOURCE,'Nybro')), VSUP.l(SOURCE,S,T)*(-1)*VSUP.m(SOURCE,S,T)*IOF1000000) -$ifi %BB2%==yes +SUM((SOURCE,S,T)$(not SAMEAS(SOURCE,'Nybro')), VSUP.l(SOURCE,S,T)*QSUPPLYMAX.m(SOURCE,S,T)*IOF1000000) -$ifi %BB3%==yes +SUM((SOURCE,S,T)$(not SAMEAS(SOURCE,'Nybro')), VSUP.l(SOURCE,S,T)*(-1)*VSUP.m(SOURCE,S,T)*IOF1000000) -$ifi %BB1%==yes +SUM((SOURCE,S)$(not SAMEAS(SOURCE,'Nybro')), VEXP.l(SOURCE,S)*(-1)*VEXP.m(SOURCE,S)*IOF1000000) -$ifi %BB2%==yes +SUM((SOURCE,S)$(not SAMEAS(SOURCE,'Nybro')), VEXP.l(SOURCE,S)*QEXPMAX.m(SOURCE,S)*IOF1000000) -$ifi %BB3%==yes +SUM((SOURCE,S)$(not SAMEAS(SOURCE,'Nybro')), VEXP.l(SOURCE,S)*(-1)*VEXP.m(SOURCE,S)*IOF1000000) -$ifi %BB1%==yes +SUM((TRANSIT,S,T), VTRANSIT.l(TRANSIT,S,T)*(-1)*VTRANSIT.m(TRANSIT,S,T)*IOF1000000) -$ifi %BB2%==yes +SUM((TRANSIT,S,T), VTRANSIT.l(TRANSIT,S,T)*QGTRANSIT_MAX.m(TRANSIT,S,T)*IOF1000000) -$ifi %BB3%==yes +SUM((TRANSIT,S,T), VTRANSIT.l(TRANSIT,S,T)*(-1)*VTRANSIT.m(TRANSIT,S,T)*IOF1000000) -; - -* Artificially set to zero baring investments (investments pending). -TSOfixedcosts(Y,C)=0; - -* Artificially set to zero. -TSOvariablecosts(Y,C)=0; - -TSOprofit(Y,C) = TSOCAPrev(Y,C) + TSOVOLrev(Y,C) -TSOfixedcosts(Y,C) - TSOvariablecosts(Y,C); - -*=============================================================================== -* Gas SSOs -*=============================================================================== - -* Storage costs -*NOTICE ERROR HERE. STORAGE CAPACITY MAY EXIST IN OTHER COURTRIES i.e. SWEDEN) -SSOCAPrev(Y,'Denmark')= -$ifi %bb1%==yes +1/7.43/1.389*sum(SCON, VSC.l(SCON)*SCONDATA(SCON,'TARRIF')) -$ifi %bb2%==yes +1/7.43/1.389*sum(SCON, VSC.l(SCON)*SCONDATA(SCON,'TARRIF')) -$ifi %bb3%==yes +1/7.43/1.389*sum(SCON, ISC_Y(SCON)*SCONDATA(SCON,'TARRIF')) -; - -SSOVOLrev(Y,C) = SUM((STORE(IA),S,T)$ICA(C,IA), - +SUM((IR,STORETYPE)$RRRAAA(IR,STORE),VINJ.l(STORE,STORETYPE,S,T)*GSTOREDATA(STORETYPE,'ELECT')*QEEQ.m(IR,S,T)) - +SUM(STORETYPE, VEXT.l(STORE,STORETYPE,S,T)*GSTOREDATA(STORETYPE,'GAS')*QGEQ.m(S,T)) - )*IOF1000000; - -SSOCAPrent(Y,C) = SUM((STORE(IA),S,T)$ICA(C,IA), -$ifi %bb1%==yes SUM(STORETYPE, VEXT.l(STORE,STORETYPE,S,T)*VEXT.m(STORE,STORETYPE,S,T)) -$ifi %bb2%==yes SUM(STORETYPE, VEXT.l(STORE,STORETYPE,S,T)*QGEXT_MAX.m(STORE,STORETYPE,S,T)) -$ifi %bb3%==yes SUM(STORETYPE, VEXT.l(STORE,STORETYPE,S,T)*VEXT.m(STORE,STORETYPE,S,T)) -$ifi %bb1%==yes +SUM(STORETYPE, VINJ.l(STORE,STORETYPE,S,T)*(-1*VINJ.m(STORE,STORETYPE,S,T))) -$ifi %bb2%==yes +SUM(STORETYPE, VINJ.l(STORE,STORETYPE,S,T)*QGINJ_MAX.m(STORE,STORETYPE,S,T))) -$ifi %bb3%==yes +SUM(STORETYPE, VINJ.l(STORE,STORETYPE,S,T)*(-1*VINJ.m(STORE,STORETYPE,S,T)))) -$ifi %bb1%==yes +SUM(STORETYPE, VSTOCK.l(STORE,STORETYPE,S,T)*(-1*VSTOCK.m(STORE,STORETYPE,S,T))) -$ifi %bb2%==yes +SUM(STORETYPE, VSTOCK.l(STORE,STORETYPE,S,T)*QGSTORE_MAX.m(STORE,STORETYPE,S,T)) -$ifi %bb3%==yes +SUM(STORETYPE, VSTOCK.l(STORE,STORETYPE,S,T)*(-1*VSTOCK.m(STORE,STORETYPE,S,T))) - )*IOF1000000; - -* Artificially set to zero unless investments are made (pending) -SSOfixedcosts(Y,C)= 0; - -* Set electricity costs = volumetric revenue -SSOvariablecosts(Y,C)=SSOVOLrev(Y,C); - -SSOprofit(Y,C) = SSOCAPrev(Y,C)+SSOVOLrev(Y,C)-(SSOfixedcosts(Y,C)+SSOvariablecosts(Y,C)); - -*=============================================================================== -* Gas Shippers -*=============================================================================== - -GasShipperCosts(Y,C) = -* Køb fra producenter - GasProducerRevenue(Y,C)$SAMEAS(C,'Denmark') -* Import - +Sum((SOURCE,S,T)$(ICA(C,SOURCE) and not SAMEAS(SOURCE,'Nybro')),(IOF3P6*IGEX_PRICE(SOURCE,S))*VSUP.l(SOURCE,S,T)*IHOURSINST(S,T))$SAMEAS(C,'Denmark') -* Gas transport - +TSOCAPrev(Y,C) + TSOVOLrev(Y,C) -* Køb på det danske marked: - +IOF1000000*Sum((STORE,STORETYPE,S,T)$ICA(C,STORE), VEXT.l(STORE,STORETYPE,S,T)*QGEQ.m(S,T)) -; - -GasShipperRevenue(Y,C) = IOF1000000*Sum((IA,S,T)$ICA('Denmark',IA),QCONSUMPTION.m(IA,S,T)*VGUD.l(IA,S,T))$SAMEAS(C,'Denmark')-DSOrevenue(Y,'Denmark')$SAMEAS(C,'Denmark') - +IOF1000000*Sum((STORE,STORETYPE,S,T)$ICA(C,STORE), VINJ.l(STORE,STORETYPE,S,T)*QGEQ.m(S,T))$SAMEAS(C,'Denmark') - +Sum((SOURCE,S,T),IHOURSINST(S,T)*(IGEX_PRICE(SOURCE,S)*IOF3P6)*VEXP.l(SOURCE,S))$SAMEAS(C,'Denmark') - +IOF1000000*Sum((TRANSIT,S,T)$ICA(C,TRANSIT),QGSVERIGE.m(S,T)*VTRANSIT.l(TRANSIT,S,T))$SAMEAS(C,'Denmark')-DSOrevenue(Y,'Sweden')$SAMEAS(C,'Denmark'); - -GasShipperProfit(Y,C) = GasShipperRevenue(Y,C) - GasShipperCosts(Y,C) ; - -$ifi %BB1%==yes ShipperCapRent(Y,'Denmark') = +SUM(S, VEXP.l('Nybro',S)*(-1)*VEXP.m('Nybro',S)*IOF1000000); -$ifi %BB2%==yes ShipperCapRent(Y,'Denmark') = +SUM(S, VEXP.l('Nybro',S)*QEXPMAX.m('Nybro',S)*IOF1000000); -$ifi %BB3%==yes ShipperCapRent(Y,'Denmark') = +SUM(S, VEXP.l('Nybro',S)*(-1)*VEXP.m('Nybro',S)*IOF1000000); - -*=============================================================================== -* Gas Suppliers -*=============================================================================== -* This agent's economics appear rather artificial. The supplier is considered to be -* an arbitrageur, purchasing gas from the shipper upon exit from the transmission -* system and upon entry to the distribution system, and reselling to the enduser -* thereby making a profit. However, the models fundamentals ensure that there are -* no excess profits and that the markets are arbitrag-free. Thereby, the supplier -* should never make a profit, and is only included here for the sake of generality. - -* Suppliercosts are defined as retail less distribution costs. The distribution -* costs are then added to the costs. -GasSupplierCosts(Y,C)= IOF1000000*Sum((IA,S,T)$ICA(C,IA),QCONSUMPTION.m(IA,S,T)*VGUD.l(IA,S,T)) - +IOF1000000*Sum((TRANSIT,S,T)$ICA(C,TRANSIT),QGSVERIGE.m(S,T)*VTRANSIT.l(TRANSIT,S,T))$SAMEAS(C,'Sweden') - -DSOrevenue(Y,C)+DSOrevenue(Y,C); - -* Supplier revenue is the retail value of gas. -GasSupplierRevenue(Y,C) = IOF1000000*Sum((IA,S,T)$ICA(C,IA),QCONSUMPTION.m(IA,S,T)*VGUD.l(IA,S,T)) - +IOF1000000*Sum((TRANSIT,S,T)$ICA(C,TRANSIT),QGSVERIGE.m(S,T)*VTRANSIT.l(TRANSIT,S,T))$SAMEAS(C,'Sweden'); - - -GasSupplierProfit(Y,C) = GasSupplierRevenue(Y,C) - GasSupplierCosts(Y,C); - -*=============================================================================== -* Gas Storage User -*=============================================================================== - - -GasStorageUserCost(Y,C) = SSOCAPrev(Y,C) + SSOVOLrev(Y,C) - +SUM((STORE,STORETYPE,S,T)$ICA(C,STORE), - QGEQ.m(S,T)*VINJ.l(STORE,STORETYPE,S,T) -* +QGSTOREBAL.m(STORE,STORETYPE,S,T)*VEXT.l(STORE,STORETYPE,S,T)*IHOURSINST(S,T) - )*IOF1000000 -; - -GasStorageUserRevenue(Y,C) = +SUM((STORE,STORETYPE,S,T)$ICA(C,STORE), - VEXT.l(STORE,STORETYPE,S,T)*QGEQ.m(S,T) -* +VINJ.l(STORE,STORETYPE,S,T)*QGSTOREBAL.m(STORE,STORETYPE,S,T)*IHOURSINST(S,T) - )*IOF1000000 -; - -GasStorageUserProfit(Y,C) = GasStorageUserRevenue(Y,C)-GasStorageUserCost(Y,C); \ No newline at end of file diff --git a/base/addons/gas/gascontracts.inc b/base/addons/gas/gascontracts.inc deleted file mode 100644 index 8c986ca2..00000000 --- a/base/addons/gas/gascontracts.inc +++ /dev/null @@ -1,40 +0,0 @@ -*-----------------------------------------------------------------------------------------------* -* File : gascontracts.inc -* Author : -* Version : 1.0 -* Remarks : # -* Date : 06/10/2005 10:39:54 -* Changed : 27-06-2006 13:47:04 -* Changed by: -*-----------------------------------------------------------------------------------------------* -* Volumen- Udtrækskapacitet Injektionskapacitet Tarif uden -* kapacitet pr. time pr. time fyldningskrav -* kWh % af volumen % af volumen kr/MWh - -TABLE SCONDATA(SCON,SCONDATASET) - VOLCAP EXTCAP INJCAP TARRIF -STORE1 1 0.00042 0.00021 35.4 -STORE2 1 0.00209 0.00104 71.7 -; - - - - -$ontext -TABLE SCONDATA(SCON,SCONDATASET) - VOLCAP EXTCAP INJCAP TARRIF -STORE1 1 0.00042 0.00021 0.0303 -STORE2 1 0.00209 0.00104 0.0655 -; - - -Gamle tariffer: -TABLE SCONDATA(SCON,SCONDATASET) - VOLCAP INJCAP EXTCAP TARRIF -* kWh of vol of vol kr./MWh -STORE1 0.001 0.00042 0.00021 29.5 -STORE2 0.001 0.00209 0.00104 65.5; - -$offtext -$ifi not %FREESTORAGE%==yes TAU_INJ = 0.123*1000/100; -$ifi %FREESTORAGE%==yes TAU_INJ = 0; diff --git a/base/addons/gas/gascosts.inc b/base/addons/gas/gascosts.inc deleted file mode 100644 index 19757297..00000000 --- a/base/addons/gas/gascosts.inc +++ /dev/null @@ -1,71 +0,0 @@ -* -------- GAS COSTS -------- * -* Gas expenses in kr. exchange rate 7.43 used -$ontext -$ifi %bb1%==yes + CTC_EN_Y *VTC_EN_Y -$ifi %bb1%==yes + CTC_EX_Y *VTC_EX_Y -$ifi %bb1%==yes + SUM(IMONTH, CTC_EN_M(IMONTH) *VTC_EN_M(IMONTH)) -$ifi %bb1%==yes + SUM(IMONTH, CTC_EX_M(IMONTH) *VTC_EX_M(IMONTH)) -$ifi %bb2%==yes + CTC_EN_Y *VTC_EN_Y -$ifi %bb2%==yes + CTC_EX_Y *VTC_EX_Y -$ifi %bb2%==yes + SUM(IMONTH, CTC_EN_M(IMONTH) *VTC_EN_M(IMONTH)) -$ifi %bb2%==yes + SUM(IMONTH, CTC_EX_M(IMONTH) *VTC_EX_M(IMONTH)) - + SUM(IS3, CTC_EN_W(IS3) *VTC_EN_W(IS3)) - + SUM(IS3, CTC_EX_W(IS3) *VTC_EX_W(IS3)) -$ifi %bb3%==yes + SUM((IS3,DAY), CTC_EN_D(IS3,DAY) *VTC_EN_D(IS3,DAY)) -$ifi %bb3%==yes + SUM((IS3,DAY), CTC_EX_D(IS3,DAY) *VTC_EX_D(IS3,DAY)) - + SUM((SOURCE,IS3,T), CSUP(SOURCE,IS3,T) *VSUP(SOURCE,IS3,T)) -$ifi %bb1%==yes + SUM(SCON, CSC(SCON) *VSC(SCON)) -$ifi %bb2%==yes + SUM(SCON, CSC(SCON) *VSC(SCON)) - + SUM((STORE,IS3,T), CINJ(STORE, IS3,T) *VINJ(STORE, IS3,T)) - + SUM((SOURCE,IS3,T), CEXP(SOURCE,IS3,T) *VEXP(SOURCE,IS3,T)) -$offtext - -+1/7.43/1.389*( -* Transmission costs -$ifi %bb1%==yes + SUM(SYSENTRY, TAU_EN_Y*VTC_EN_Y(SYSENTRY) ) -$ifi %bb1%==yes + SUM(SYSEXIT, TAU_EX_Y*VTC_EX_Y(SYSEXIT) ) -$ifi %bb1%==yes + SUM(SYSENTRY, SUM(IMONTH, TAU_EN_M(IMONTH)*VTC_EN_M(SYSENTRY,IMONTH)*MONTHSCALE(IMONTH)) ) -$ifi %bb1%==yes + SUM(SYSEXIT, SUM(IMONTH, TAU_EX_M(IMONTH)*VTC_EX_M(SYSEXIT,IMONTH)*MONTHSCALE(IMONTH)) ) -$ifi %bb2%==yes + SUM(SYSENTRY, TAU_EN_Y*VTC_EN_Y(SYSENTRY) ) -$ifi %bb2%==yes + SUM(SYSEXIT, TAU_EX_Y*VTC_EX_Y(SYSEXIT) ) -$ifi %bb2%==yes + SUM(SYSENTRY, SUM(IMONTH, TAU_EN_M(IMONTH)*VTC_EN_M(SYSENTRY,IMONTH)*MONTHSCALE(IMONTH)) ) -$ifi %bb2%==yes + SUM(SYSEXIT, SUM(IMONTH, TAU_EX_M(IMONTH)*VTC_EX_M(SYSEXIT,IMONTH)*MONTHSCALE(IMONTH)) ) -+ SUM(SYSENTRY, sum(IS3,IDAYSIN_S(IS3)/7*(TAU_EN_W(IS3)*VTC_EN_W(SYSENTRY,IS3))) ) -+ SUM(SYSEXIT, sum(IS3,IDAYSIN_S(IS3)/7*(TAU_EX_W(IS3)*VTC_EX_W(SYSEXIT,IS3))) ) -+ SUM((SYSENTRY,IS3,T), VTC_EN_FLEX(SYSENTRY,IS3,T)*IHOURSINST(IS3,T)*TAU_EN_FLEX(SYSENTRY) ) -+ SUM((SYSEXIT,IS3,T), VTC_EX_FLEX(SYSEXIT,IS3,T) *IHOURSINST(IS3,T)*TAU_EX_FLEX(SYSEXIT) ) -$ifi %bb3%==yes + SUM(SYSENTRY, sum((IS3,DAY), TAU_EN_D(IS3,DAY)*VTC_EN_D(SYSENTRY,IS3,DAY)) ) -$ifi %bb3%==yes + SUM(SYSEXIT, sum((IS3,DAY), TAU_EX_D(IS3,DAY)*VTC_EX_D(SYSEXIT,IS3,DAY)) ) -+ SUM((IA,IS3,T)$ICA('Denmark',IA), TAU_VOL*VGUD(IA,IS3,T)) -+ SUM((SOURCE,IS3,T)$(not sameas(SOURCE,'Nybro')), IHOURSINST(IS3,T)*VEXP(SOURCE,IS3)) -+ SUM((TRANSIT,IS3,T),IHOURSINST(IS3,T)*VTRANSIT(TRANSIT,IS3,T)) -* Storage costs -$ifi %bb1%==yes +sum(SCON, VSC(SCON)*SCONDATA(SCON,'TARRIF')) -$ifi %bb2%==yes +sum(SCON, VSC(SCON)*SCONDATA(SCON,'TARRIF')) -* Udkommenteret fordi variable omkostninger kobles til el og gas priser -* +TAU_INJ*sum((STORE,IS3,T), IHOURSINST(IS3,T)*VINJ(STORE, IS3,T)) - -* Distribution costs -+ sum((DDD,AAA,IS3,T)$(DDDNODE(DDD,AAA) and IA(AAA)) , IHOURSINST(IS3,T)*TAU_DIST(DDD)*VGUD(AAA,IS3,T)) -+ sum((DDD,TRANSIT,IS3,T)$DDDNODE(DDD,TRANSIT), IHOURSINST(IS3,T)*TAU_DIST(DDD)*VTRANSIT(TRANSIT,IS3,T)) -) -* Consumer utility - -* Value of exports -- sum((SOURCE,IS3,T),IHOURSINST(IS3,T)*(IGEX_PRICE(SOURCE,IS3)*IOF3P6)*VEXP(SOURCE,IS3)) - -* Cost of imports -+ sum((SOURCE,IS3,T)$(not sameas(SOURCE,'Nybro')),IHOURSINST(IS3,T)*(IGEX_PRICE(SOURCE,IS3)*IOF3P6)*VSUP(SOURCE,IS3,T)) - -* Cost of extraction -+ sum((SOURCE,IS3,T),IHOURSINST(IS3,T)*IGEXT_COST(SOURCE,IS3)*IOF3P6*VSUP(SOURCE,IS3,T)) - -* Investment costs -$ifi %bb2%==yes + sum(NGINV,COST_NGINV(NGINV)*VGINFINV(NGINV))*GASANNUITY - -$ifi %bb2%==yes + sum((C,GNETSTEPS), GNET_COST(C,GNETSTEPS)*VGNETINV(C,GNETSTEPS))*GASANNUITY - -$ifi %bb2%==yes + sum((STORE,STORETYPE),GSTOREDATA(STORETYPE,'INVCOST')*VGINVSTORE(STORE,STORETYPE)) -$ifi %bb2%==yes + sum((STORE,STORETYPE),GSTOREDATA(STORETYPE,'FOMK')*VGINVSTORE(STORE,STORETYPE)) -$ifi %bb2%==yes + sum((STORE,STORETYPE),GSTOREDATA(STORETYPE,'MargExtInvCost')*VGINVEXT(STORE,STORETYPE)) -$ifi %bb2%==yes + sum((STORE,STORETYPE),GSTOREDATA(STORETYPE,'MargInjInvCost')*VGINVINJ(STORE,STORETYPE)) diff --git a/base/addons/gas/gasdata.inc b/base/addons/gas/gasdata.inc deleted file mode 100644 index b91d205d..00000000 --- a/base/addons/gas/gasdata.inc +++ /dev/null @@ -1,234 +0,0 @@ -* This file contains and references data required for running the Balmorel model -* with the GAS add-on. - - -SCALAR kNm3ToMWh /11/; - -* If simulating with high geografical detail alternative values for district -* heating demand must be used. - -*------------------------------------------------------------------------------- -* Include demand for district heating at MR geografical level. -*------------------------------------------------------------------------------- - -$ifi %MR%==yes $INCLUDE '../../base/addons/gas/DH-MR.inc'; - -* Heat demand variation profile used: -$ifi %MR%==yes DH_VAR_T(MRAREAS(AAA),SSS,TTT)$DDDNODE('HNG',AAA) = DH_VAR_T('DK_E_Urban',SSS,TTT); -$ifi %MR%==yes DH_VAR_T(MRAREAS(AAA),SSS,TTT)$DDDNODE('DONG_OEST',AAA) = DH_VAR_T('DK_E_Rural',SSS,TTT); -$ifi %MR%==yes DH_VAR_T(MRAREAS(AAA),SSS,TTT)$DDDNODE('DONG_VEST',AAA) = DH_VAR_T('DK_W_Rural',SSS,TTT); -$ifi %MR%==yes DH_VAR_T(MRAREAS(AAA),SSS,TTT)$DDDNODE('NGF',AAA) = DH_VAR_T('DK_E_Urban',SSS,TTT); -$ifi %MR%==yes DH_VAR_T(MRAREAS(AAA),SSS,TTT)$DDDNODE('Midt_Nord',AAA) = DH_VAR_T('DK_W_Urban',SSS,TTT); - -*------------------------------------------------------------------------------- -* Swedish gas consumption which is not instigated by generation of district heating -* or electricity. -*------------------------------------------------------------------------------- - -PARAMETER SE_DGRES(YYY,AAA,IMONTH)/ -2004.SE_S_Rural.JAN 104764 -2004.SE_S_Rural.FEB 123812 -2004.SE_S_Rural.MAR 85716 -2004.SE_S_Rural.APR 47620 -2004.SE_S_Rural.MAY 28572 -2004.SE_S_Rural.JUN 28572 -2004.SE_S_Rural.JUL 19048 -2004.SE_S_Rural.AUG 28572 -2004.SE_S_Rural.SEP 28572 -2004.SE_S_Rural.OCT 47620 -2004.SE_S_Rural.NOV 76192 -2004.SE_S_Rural.DEC 104764 -/; - -PARAMETER SE_DGOTH(YYY,AAA,IMONTH)/ -2004.SE_S_Rural.JAN 514285 -2004.SE_S_Rural.FEB 485713 -2004.SE_S_Rural.MAR 495237 -2004.SE_S_Rural.APR 409523 -2004.SE_S_Rural.MAY 380952 -2004.SE_S_Rural.JUN 371428 -2004.SE_S_Rural.JUL 323809 -2004.SE_S_Rural.AUG 371428 -2004.SE_S_Rural.SEP 380952 -2004.SE_S_Rural.OCT 409523 -2004.SE_S_Rural.NOV 466666 -2004.SE_S_Rural.DEC 523809 -/; - -SE_DGRES(Y,AAA,IMONTH)=SE_DGRES('2004',AAA,IMONTH) ; -SE_DGOTH(Y,AAA,IMONTH)=SE_DGOTH('2004',AAA,IMONTH) ; - - - - -PARAMETER - DGRES(YYY,AAA) 'Residual gasforbrug, årligt per område', - DGOTH(YYY,AAA) 'Gasforbrug andre timeaflæste, årligt per område' - DGRES_VAR(AAA,SSS,TTT) 'Residual gasforbrug, timevariation', - DGOTH_VAR(AAA,SSS,TTT) 'Gasforbrug andre timeaflæste, timevariation' - DGRESF_STEPS(AAA,SSS,TTT,DF_QP,DGRESF) 'Elastic residual gas demands' - DGOTHF_STEPS(AAA,SSS,TTT,DF_QP,DGOTHF) 'Elastic gas demands from "other" large consumers' - SE_DGRES_VAR_T(AAA,SSS,TTT) 'Variation in Swedish residual consumption', - SE_DGOTH_VAR_T(AAA,SSS,TTT) 'Variation in Swedish "other" large consumers demand' - ISE_DGRES_SUMVAR(AAA,IMONTH) 'Residual gas consumption, weightsum over the variation' - ISE_DGOTH_SUMVAR(AAA,IMONTH) 'Other large gas consumers, weightsum over the variation' -; - - - - - - -DGRES(YYY,'SE_S_Rural')= sum(IMONTH, SE_DGRES(YYY,'SE_S_Rural',IMONTH)); -DGOTH(YYY,'SE_S_Rural')= sum(IMONTH, SE_DGOTH(YYY,'SE_S_Rural',IMONTH)); - -* Load variation profiles for gas consumption which is not instigated by generation of -* electricity or district heating. -$if %MR%==yes $INCLUDE '../../base/addons/gas/gasvar-MR.inc'; -$if not %MR%==yes $INCLUDE '../../base/addons/gas/gasvar.inc'; - -* To help make the variation profiles for Swedish gas consumption we use the profiles from NGF since these are based on Danish national averages. -$if %MR%==yes ISE_DGRES_SUMVAR('SE_S_Rural',IMONTH) = sum((SSS,TTT)$MONTHSSS(IMONTH,SSS), DGRES_VAR('hoejby',SSS,TTT)); -$if %MR%==yes ISE_DGOTH_SUMVAR('SE_S_Rural',IMONTH) = sum((SSS,TTT)$MONTHSSS(IMONTH,SSS), DGOTH_VAR('hoejby',SSS,TTT)); -$if not %MR%==yes ISE_DGRES_SUMVAR('SE_S_Rural',IMONTH) = sum((SSS,TTT)$MONTHSSS(IMONTH,SSS), DGRES_VAR('DK_W_ODENSE',SSS,TTT)); -$if not %MR%==yes ISE_DGOTH_SUMVAR('SE_S_Rural',IMONTH) = sum((SSS,TTT)$MONTHSSS(IMONTH,SSS), DGOTH_VAR('DK_W_ODENSE',SSS,TTT)); - -$if %MR%==yes DGRES_VAR('SE_S_Rural',SSS,TTT) = DGRES_VAR('hoejby',SSS,TTT)/SUM(IMONTH$MONTHSSS(IMONTH,SSS),ISE_DGRES_SUMVAR('SE_S_Rural',IMONTH))*SUM(IMONTH$MONTHSSS(IMONTH,SSS), SE_DGRES('2004','SE_S_Rural',IMONTH)/DGRES('2004','SE_S_Rural')); -$if %MR%==yes DGOTH_VAR('SE_S_Rural',SSS,TTT) = DGOTH_VAR('hoejby',SSS,TTT)/SUM(IMONTH$MONTHSSS(IMONTH,SSS),ISE_DGOTH_SUMVAR('SE_S_Rural',IMONTH))*SUM(IMONTH$MONTHSSS(IMONTH,SSS), SE_DGOTH('2004','SE_S_Rural',IMONTH)/DGOTH('2004','SE_S_Rural')); -$if not %MR%==yes DGRES_VAR('SE_S_Rural',SSS,TTT) = DGRES_VAR('DK_W_ODENSE',SSS,TTT)/SUM(IMONTH$MONTHSSS(IMONTH,SSS),ISE_DGRES_SUMVAR('SE_S_Rural',IMONTH))*SUM(IMONTH$MONTHSSS(IMONTH,SSS), SE_DGRES('2004','SE_S_Rural',IMONTH)/DGRES('2004','SE_S_Rural')); -$if not %MR%==yes DGOTH_VAR('SE_S_Rural',SSS,TTT) = DGOTH_VAR('DK_W_ODENSE',SSS,TTT)/SUM(IMONTH$MONTHSSS(IMONTH,SSS),ISE_DGOTH_SUMVAR('SE_S_Rural',IMONTH))*SUM(IMONTH$MONTHSSS(IMONTH,SSS), SE_DGOTH('2004','SE_S_Rural',IMONTH)/DGOTH('2004','SE_S_Rural')); - - -* Load annual values for gas consumption which is not instigated by generation of -* electricity or district heating. -$if not %MR%==yes $INCLUDE '../../base/addons/gas/dg.inc'; -$if %MR%==yes $INCLUDE '../../base/addons/gas/dg-MR.inc'; - -PARAMETER - SCONDATA(SCON,SCONDATASET) 'Storage contracts', - TAU_INJ 'Injection tarrif', - TAU_EN_Y 'Annual entry tarrif', - TAU_EX_Y 'Annual exit tarrif', - TAU_EN_M(IMONTH) 'Monthly entry tarrif', - TAU_EX_M(IMONTH) 'Monthly exit tarrif', - TAU_EN_W(SSS) 'Weekly entry tarrif', - TAU_EX_W(SSS) 'Weekly exit tarrif', - TAU_EN_D(SSS,DAY) 'Daily entry tarrif', - TAU_EX_D(SSS,DAY) 'Daily exit tarrif', - TAU_VOL 'Tranmission volume tarrif', - TAU_DIST(DDD) 'Distribution tariff', - TC_EN_Y(SYSENTRY,YYY) 'Yearly transmissions capacity reserved exogenously' - TC_EX_Y(SYSEXIT,YYY) 'Yearly transmissions capacity reserved exogenously' - TC_EN_M(SYSENTRY,YYY,SSS,TTT) 'Monthly transmissions capacity reserved exogenously' - TC_EX_M(SYSEXIT,YYY,SSS,TTT) 'Monthly transmissions capacity reserved exogenously' -; -$ifi not EXIST 'gascontracts.inc' $INCLUDE '../../base/addons/gas/gascontracts.inc'; -$ifi EXIST 'gascontracts.inc' $INCLUDE 'gascontracts.inc'; -$ifi not EXIST 'gastransprices.inc' $INCLUDE '../../base/addons/gas/gastransprices.inc'; -$ifi EXIST 'gastransprices.inc' $INCLUDE 'gastransprices.inc'; -$ifi not EXIST 'gx3vex.inc' $INCLUDE '../../base/addons/gas/gx3vex.inc'; -$ifi EXIST 'gx3vex.inc' $INCLUDE 'gx3vex.inc'; - - -* In the base scenario, gasprices are adjusted to reflect price difference with continent, and -* cleaned of transportation expenses. -FUELPRICE(YYY,IA,'NAT_GAS')$((ICA('Denmark',IA) or ICA('Sweden',IA)) - and (not sameas('Nybro',IA)) - and (not sameas('Ellund',IA)) - and (not sameas('Oeresund',IA))) - =0; - - -* Priser justeres i overenstemmelse med forudsætninger i gas-el rapport. -$ifi %LOWGAS%==yes FUELPRICE(Y,'Nybro','NAT_GAS') = 1.5/0.25*0.343/1.398; -$ifi %LOWGAS%==yes FUELPRICE(Y,'Oeresund','NAT_GAS') = 1.5/0.25*0.343/1.398; - -$ifi %MEDGAS%==yes FUELPRICE(Y,'Nybro','NAT_GAS') = FUELPRICE(Y,'Nybro','NAT_GAS') -1.026/1.398; -$ifi %MEDGAS%==yes FUELPRICE(Y,'Oeresund','NAT_GAS') = FUELPRICE(Y,'Oeresund','NAT_GAS') -1.026/1.398; - -$ifi %HIGHGAS%==yes FUELPRICE(Y,'Nybro','NAT_GAS') = FUELPRICE(Y,'Nybro','NAT_GAS') -0.418/1.398; -$ifi %HIGHGAS%==yes FUELPRICE(Y,'Oeresund','NAT_GAS') = FUELPRICE(Y,'Oeresund','NAT_GAS') -0.418/1.398; - - -PARAMETER GEXT_COST(YYY,SOURCE,S) 'Average Extraction Cost EURO/MWh', - GEX_PRICE(YYY,SOURCE,S) 'Export Price Money/MWh'; - -GEXT_COST(YYY,'Nybro',S) =FUELPRICE(YYY,'Nybro','NAT_GAS'); -GEXT_COST(YYY,'Oeresund',S) =FUELPRICE(YYY,'Oeresund','NAT_GAS'); -GEX_PRICE(YYY,SOURCE,S)$(not SAMEAS(SOURCE,'Oeresund')) =FUELPRICE(YYY,'Ellund','NAT_GAS')*WSF(S); - - - - -PARAMETER - SUPPLY_MAX(YYY,SOURCE) 'Hourly exogenous natural gas supply capacity' - EXP_MAX(YYY,SOURCE) 'Hourly exogenous natural gas export capacity' - COST_NGINV(NGINV0) 'Investment costs' - SUPEFFECT(NGINV0,SOURCE) 'Effect on supply capacity from investment' - EXPEFFECT(NGINV0,SOURCE) 'Effect on export capacity from investment' - INJ_MAX(YYY,STORE,STORETYPE) 'Injection capacity, exogenous' - EXT_MAX(YYY,STORE,STORETYPE) 'Extraction capacity, exogenous' - STORAGE_MAX(YYY,STORE,STORETYPE) 'Storage volumetric capacity, exogenous' - GSTOREDATA(STORETYPE,*) 'Storage investment data' - MAXINVEST(STORE,STORETYPE) 'Maximum possible capacity at storage location' - TSOSTORAGE(YYY,STORE,STORETYPE) 'Storage reserved for emergencies by the TSO' - ; - -$ifi EXIST 'gasfacilities.inc' $INCLUDE 'gasfacilities.inc'; -$ifi not EXIST 'gasfacilities.inc' $INCLUDE '../../base/addons/gas/gasfacilities.inc'; - -PARAMETER - GSFXW(YYY,STORE,STORETYPE,SSS) 'Allokeret netto gasudtræk fra lager per uge' - SC_Y(YYY,SCON) 'Bestilte gaslagerprodukter' - GPFXW(YYY,SOURCE,SSS) 'Allokering af gasproduktion' - HYFXRW_GAS(YYY,SSS,RRR) 'Allokering af vandkraftmængder til uger' - STOREACC(YYY,STORE,STORETYPE) 'Storage volumetric investment from bb2' - INJACC(YYY,STORE,STORETYPE) 'Storage injection investment from bb2' - EXTACC(YYY,STORE,STORETYPE) 'Storage extraction investment from bb2' - EXPACC(YYY,SOURCE) 'Gas export capacity investment from bb2' - SUPACC(YYY,SOURCE) 'Gas supply capacity investment from bb2' - DEVACC(YYY,SYSEXIT) 'Gas delivery capacity investment from bb2' -; - -* Load bb1/bb2 allocations to bb3 model -$ifi %bb3%==yes execute_load '../../base/addons/gas/GSFXW.gdx', GSFXW; -$ifi %bb3%==yes execute_load '../../base/addons/gas/reserved_capacity.gdx', TC_EN_Y,TC_EX_Y,TC_EN_M,TC_EX_M; -$ifi %bb3%==yes execute_load '../../base/addons/gas/SC_Y.gdx', SC_Y; - -* Load investments undertaken in BB2 to BB1 or BB3 simulations. -$ifi %ADDINVEST%==yes execute_load '../../base/addons/gas/gasinvest.gdx',STOREACC,INJACC,EXTACC,EXPACC,SUPACC,DEVACC; - -PARAMETER - GASANNUITY / 0.117 / - ; - -* The following are currently unused functional representations of the cost -* elements of the objective function using gams-f. These are especially applicable -* in a MCP framework. Note that elements may not match that of the objective function -* in this model and therefore should be verified before use. - -$ontext -* FUNCTIONS using gams-f -CTC_EN_YEAR == IOF0000001/7.43*TAU_EN_Y; -CTC_EX_Y == IOF0000001/7.43*TAU_EX_Y; -CTC_EN_M(IMONTH) == IOF0000001/7.43*TAU_EN_M(IMONTH)*MONTHSCALE(IMONTH); -CTC_EX_M(IMONTH) == IOF0000001/7.43*TAU_EX_M(IMONTH)*MONTHSCALE(IMONTH); -CTC_EN_W(IS3) == IOF0000001/7.43*TAU_EN_W(IS3); -CTC_EX_W(IS3) == IOF0000001/7.43*TAU_EX_W(IS3); -CTC_EN_D(IS3,DAY) == IOF0000001/7.43*TAU_EN_D(IS3,DAY); -CTC_EX_D(IS3,DAY) == IOF0000001/7.43*TAU_EX_D(IS3,DAY); -CSUP(SOURCE,IS3,T) == IOF0000001/7.43*IHOURSINST(IS3,T)*TAU_VOL; -CSC(SCON) == IOF0000001/7.43*SCONDATA(SCON,'TARRIF'); -CINJ(STORE, IS3,T) == IOF0000001/7.43*IHOURSINST(IS3,T)*TAU_INJ; -CGUD(AAA,IS3,T) == IOF0000001/7.43*TAU_DIST(DDD); -CEXP(SOURCE,IS3,T) == -IOF0000001/7.43*IHOURSINST(IS3,T)*(IGEX_PRICE(SOURCE,IS3)-IFUELP_Y('Nybro','NAT_GAS')); -$offtext - - -* The following concerns a possible future endogenous implementation of gas -* transmissions constrains based on modelling physical flows. - -$ifi %PIPELINE%==yes $INCLUDE '../../base/addons/gasprops.inc'; -$ifi %PIPELINE%==yes $INCLUDE '../../base/addons/gastech.inc'; -$ifi %PIPELINE%==yes $INCLUDE '../../base/addons/network.inc'; -$ifi %PIPELINE%==yes $INCLUDE '../../base/addons/linearize.inc'; \ No newline at end of file diff --git a/base/addons/gas/gasdocompare.inc b/base/addons/gas/gasdocompare.inc deleted file mode 100644 index afc0ec33..00000000 --- a/base/addons/gas/gasdocompare.inc +++ /dev/null @@ -1,96 +0,0 @@ -* File gasdocompare.INC. -* This file is part of the welfare calculation module for the Balmorel model. -* Created by Jens Hauch and Thomas E. Pedersen, COWI A/S (DK). -* Adapted for Balmorel version 2.10, 2.13 by Hans Ravn. -* Extended to for the gas add-on by Lars Bregnbæk, X-mas 2006. - -* mangler: Base er næsten hårdkodet (men som det ses, er der mulighed for at vælge et andet base !) -* i welfare.inc ses, at der ikke er mellemvariabler for trans - ... - -*=============================================================================== -* Overview -*=============================================================================== - -* This file contains: - -* calculation of differences between cases -* discounting of welfare changes for groups - - - -*=============================================================================== -* Overview end -*=============================================================================== - -*=============================================================================== -* Gas Consumers -*=============================================================================== - -* Residual consumers (discounted welfare gains for consumers) -dresidualbenifit(C) = Sum(Y, -(residualcosts(Y,C)-residualcosts00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - -* Other consumers (discounted welfare gains for consumers) -dotherbenifit(C) = Sum(Y, -(othercosts(Y,C)-othercosts00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - - -*=============================================================================== -* Distribution Companies -*=============================================================================== - - -dcdltDSOprofit(C) = Sum(Y, (DSOprofit(Y,C)-DSOprofit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - - -*=============================================================================== -* TSO -*=============================================================================== - -dcdltTSOVOLrevbenefit(C) = Sum(Y, (TSOVOLrev(Y,C)-TSOVOLrev00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - -dcdltTSOCAPrevbenefit(C) = Sum(Y, (TSOCAPrev(Y,C)-TSOCAPrev00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - -dcdltTSOCAPRentbenefit(C) = Sum(Y, (TSOCAPRent(Y,C)-TSOCAPRent00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - -dcdltTSOProfit(C) = Sum(Y, (TSOProfit(Y,C) - TSOProfit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - -*=============================================================================== -* SSO -*=============================================================================== - -dcdltSSOCAPrevbenefit(C) = Sum(Y, (SSOCAPrev(Y,C)-SSOCAPrev00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - - -dcdltSSOVOLrevbenefit(C) = Sum(Y, (SSOVOLrev(Y,C)-SSOVOLrev00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - - -dcdltSSOCAPrentbenefit(C) = Sum(Y, (SSOCAPrent(Y,C)-SSOCAPrent00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - - -dcdltSSOProfit(C) = Sum(Y, (SSOProfit(Y,C) - SSOProfit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - - -*=============================================================================== -* Producers -*=============================================================================== - -dcdltGasProducerProfit(C) = Sum(Y,(GasProducerProfit(Y,C) - GasProducerProfit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - -*=============================================================================== -* Suppliers -*=============================================================================== - -dcdltGasSupplierProfit(C) = Sum(Y, (GasSupplierProfit(Y,C)-GasSupplierProfit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - -*=============================================================================== -* Shippers -*=============================================================================== - -dcdltGasShipperProfit(C) = Sum(Y, (GasShipperProfit(Y,C) - GasShipperProfit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - -dcdltShipperCapRent(C) = Sum(Y, (ShipperCapRent(Y,C) - ShipperCapRent00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; - -*=============================================================================== -* Gas Storage User -*=============================================================================== - -dcdltGasStorageUserProfit(C) = Sum(Y, (GasStorageUserProfit(Y,C) - GasStorageUserProfit00(Y,C))/(1+diskrate)**(ord(y)-1))/IOF1000000; diff --git a/base/addons/gas/gasequations.inc b/base/addons/gas/gasequations.inc deleted file mode 100644 index a50bcf3b..00000000 --- a/base/addons/gas/gasequations.inc +++ /dev/null @@ -1,270 +0,0 @@ -Equations - QGEQ(s,t) 'Gas balance at Gas Pool' - QCONSUMPTION(i,s,t) 'Gas balance at consumption node' - QTransEntryCap(SYSENTRY,s,t) 'Entry capacity reservation constraint' - QTransExitCap(SYSEXIT,s,t) 'Exit capacity reservation constraint' - QStoreVol(s,T) 'Volumetric storage capacity reservation constraint' - QStoreInj(s,t) 'Injection capacity reservation constraint' - QStoreExt(s,t) 'Extraction capacity reservation constraint' - QGSTOREBAL(STORE,STORETYPE,S,T) 'Gas storage balance' - QGSFXW(STORE,STORETYPE,S) 'Net gas storage for week' - QGSVERIGE(S,T) 'Gas balance in Sweden' -; -ALIAS (IA, IAALIAS); - - -QGSVERIGE(IS3,T) .. - VTRANSIT('OERESUND',IS3,T) - +sum((STORE,STORETYPE)$ICA('Sweden',STORE), - VEXT(STORE,STORETYPE,IS3,T) - ) - - =G= - +sum(IA$ICA('Sweden',IA), - +sum(IGE$(IAGKN(IA,IGE) and GDATA(IGE,'GDFUEL') EQ 2), - VGEN_T(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IGE,IA))) - ) - +sum(IGH$(IAGKN(IA,IGH) and GDATA(IGH,'GDFUEL') EQ 2), - GDATA(IGH,'GDCV')*VGHN_T(IA,IGH,IS3,T)/(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IGH,IA))) - ) - +sum(IGE$(IAGK_Y(IA,IGE) and GDATA(IGE,'GDFUEL') EQ 2), - VGE_T(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IGE,IA))) - ) - +sum(IGH$(IAGK_Y(IA,IGH) and GDATA(IGH,'GDFUEL') EQ 2), - GDATA(IGH,'GDCV')*VGH_T(IA,IGH,IS3,T) /(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IGH,IA))) - ) - -* +sum(IA$ICA('Sweden',IA),ISE_DGRES_Y('SE_S_Rural',IS3,T)) -* +sum(IA$ICA('Sweden',IA),ISE_DGOTH_Y('SE_S_Rural',IS3,T)) - +IDGRES_Y(IA,IS3,T) - +IDGOTH_Y(IA,IS3,T) - - ) - +sum((STORE,STORETYPE)$ICA('Sweden',STORE), - VINJ(STORE,STORETYPE,IS3,T) - ) - +sum((STORE,STORETYPE)$ICA('Sweden',STORE), - + VEXT(STORE,STORETYPE,IS3,T)*GSTOREDATA(STORETYPE,'GAS') - ) -; - -QGEQ(IS3,T) .. - sum(SOURCE, - VSUP(SOURCE,IS3,T) - ) - +sum((STORE,STORETYPE)$ICA('Denmark',STORE), - + VEXT(STORE,STORETYPE,IS3,T) - ) - -sum((STORE,STORETYPE)$ICA('Denmark',STORE), - + VEXT(STORE,STORETYPE,IS3,T)*GSTOREDATA(STORETYPE,'GAS') - ) - =G= - sum(IA$ICA('Denmark',IA), - VGUD(IA,IS3,T) - ) - +sum((STORE,STORETYPE)$ICA('Denmark',STORE), - VINJ(STORE,STORETYPE,IS3,T) - ) - +sum(SOURCE, - VEXP(SOURCE,IS3) - ) - +sum(TRANSIT, - VTRANSIT(TRANSIT,IS3,T) - ) - ; - - -$ifi not %MR%==yes QCONSUMPTION(IA,IS3,T)$ICA('Denmark',IA) .. -$ifi %MR%==yes QCONSUMPTION(IA,IS3,T)$(ICA('Denmark',IA) and not NOTMR(IA)) .. - - VGUD(IA,IS3,T) - =G= - IDGRES_Y(IA,IS3,T) - + IDGOTH_Y(IA,IS3,T) - +sum(IGE$(IAGKN(IA,IGE) and GDATA(IGE,'GDFUEL') EQ 2), - VGEN_T(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IGE,IA))) - ) - +sum(IGH$(IAGKN(IA,IGH) and GDATA(IGH,'GDFUEL') EQ 2), - GDATA(IGH,'GDCV')*VGHN_T(IA,IGH,IS3,T)/(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IGH,IA))) - ) - +sum(IGE$(IAGK_Y(IA,IGE) and GDATA(IGE,'GDFUEL') EQ 2), - VGE_T(IA,IGE,IS3,T)/(GDATA(IGE,'GDFE')* (1-GEFFDERATE(IGE,IA))) - ) - +sum(IGH$(IAGK_Y(IA,IGH) and GDATA(IGH,'GDFUEL') EQ 2), - GDATA(IGH,'GDCV')*VGH_T(IA,IGH,IS3,T) /(GDATA(IGH,'GDFE')* (1-GEFFDERATE(IGH,IA))) - ) -$ifi %MR%==yes +SUM((IAALIAS,G)$MR2CENTRAL(IA,IAALIAS,G), -$ifi %MR%==yes +( -$ifi %MR%==yes VGEN_T(IAALIAS,G,IS3,T)/(GDATA(G,'GDFE')* (1-GEFFDERATE(G,IA))) -$ifi %MR%==yes )$(IGE(G) and IAGKN(IAALIAS,G) and GDATA(G,'GDFUEL') EQ 2) -$ifi %MR%==yes +( -$ifi %MR%==yes GDATA(G,'GDCV')*VGHN_T(IAALIAS,G,IS3,T)/(GDATA(G,'GDFE')* (1-GEFFDERATE(G,IAALIAS))) -$ifi %MR%==yes )$(IGH(G) and IAGKN(IAALIAS,G) and GDATA(G,'GDFUEL') EQ 2) -$ifi %MR%==yes +( -$ifi %MR%==yes VGE_T(IAALIAS,G,IS3,T)/(GDATA(G,'GDFE')* (1-GEFFDERATE(G,IAALIAS))) -$ifi %MR%==yes )$(IGE(G) and IAGK_Y(IAALIAS,G) and GDATA(G,'GDFUEL') EQ 2) -$ifi %MR%==yes +( -$ifi %MR%==yes GDATA(G,'GDCV')*VGH_T(IAALIAS,G,IS3,T) /(GDATA(G,'GDFE')* (1-GEFFDERATE(G,IAALIAS))) -$ifi %MR%==yes )$(IGH(G) and IAGK_Y(IAALIAS,G) and GDATA(G,'GDFUEL') EQ 2) -$ifi %MR%==yes ) - -$ifi %HEATING%==yes + sum(HEAT$HEATMR(HEAT,IA), VGX(IA,HEAT,IS3,T)) - ; - -QTransEntryCap(SYSENTRY,IS3,T) .. -$ifi %bb3%== yes ITC_EN_Y(SYSENTRY) -$ifi %bb3%== yes + ITC_EN_M(SYSENTRY,IS3,T) -$ifi %bb3%== yes + sum(DAY$DAYT(DAY,T), VTC_EN_D(SYSENTRY,IS3,DAY)) -$ifi %bb1%== yes VTC_EN_Y(SYSENTRY) -$ifi %bb1%== yes + sum(IMONTH$MONTHSSS(IMONTH,IS3), VTC_EN_M(SYSENTRY,IMONTH)) -$ifi %bb2%== yes VTC_EN_Y(SYSENTRY) -$ifi %bb2%== yes + sum(IMONTH$MONTHSSS(IMONTH,IS3), VTC_EN_M(SYSENTRY,IMONTH)) - + VTC_EN_W(SYSENTRY,IS3) - + VTC_EN_FLEX(SYSENTRY,IS3,T) - =G= - (VSUP('Nybro',IS3,T)-VEXP('Nybro',IS3))$(SAMEAS(SYSENTRY,'Nybro')) - +VSUP('Ellund',IS3,T)$(SAMEAS(SYSENTRY,'Ellund')) ; - -QTransExitCap(SYSEXIT,IS3,T) .. -$ifi %bb3%== yes ITC_EX_Y(SYSEXIT) -$ifi %bb3%== yes + ITC_EX_M(SYSEXIT,IS3,T) -$ifi %bb3%== yes + sum(DAY$DAYT(DAY,T), VTC_EX_D(SYSEXIT,IS3,DAY)) -$ifi %bb1%== yes VTC_EX_Y(SYSEXIT) -$ifi %bb1%== yes + sum(IMONTH$MONTHSSS(IMONTH,IS3), VTC_EX_M(SYSEXIT,IMONTH)) -$ifi %bb2%== yes VTC_EX_Y(SYSEXIT) -$ifi %bb2%== yes + sum(IMONTH$MONTHSSS(IMONTH,IS3), VTC_EX_M(SYSEXIT,IMONTH)) - + VTC_EX_W(SYSEXIT,IS3) - + VTC_EX_FLEX(SYSEXIT,IS3,T) - =G= - - sum(IA$ICA('Denmark',IA), - VGUD(IA,IS3,T) - )$(SAMEAS(SYSEXIT,'DK-EXIT')) - + - VEXP('Ellund',IS3)$(SAMEAS(SYSEXIT,'Ellund')) -* + VEXP('Oeresund',IS3)$(SAMEAS(SYSEXIT,'Oeresund')) - + VTRANSIT('Oeresund',IS3,T)$(SAMEAS(SYSEXIT,'Oeresund')) -; - -QStoreVol(IS3,T) .. - sum(SCON, - VSC(SCON)*SCONDATA(SCON,'VOLCAP') - ) -*$ifi %bb2%==yes +SUM((STORE,STORETYPE),VGINVSTORE(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) -*$ifi %bb2%==yes -VGINVSTORE.l(STORE,STORETYPE)$STOREINV(STORE, STORETYPE)) - =G= sum((STORE,STORETYPE), - VSTOCK(STORE,STORETYPE,IS3,T) - ); - -QStoreInj(IS3,T) .. - sum(SCON, -$ifi %bb1%== yes VSC(SCON) -$ifi %bb2%== yes VSC(SCON) -$ifi %bb3%== yes ISC(SCON) - *SCONDATA(SCON,'INJCAP')*SCONDATA(SCON,'VOLCAP') - ) -*$ifi %bb2%==yes +SUM((STORE,STORETYPE),VGINVINJ(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) -*$ifi %bb2%==yes +(GSTOREDATA(STORETYPE,'INJEFFECT')*VGINVSTORE(STORE,STORETYPE))$STOREINV(STORE, STORETYPE) -*$ifi %bb2%==yes -VGINVINJ.l(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) -*$ifi %bb2%==yes -(GSTOREDATA(STORETYPE,'INJEFFECT')*VGINVSTORE.l(STORE,STORETYPE))$STOREINV(STORE, STORETYPE)) - =G= - sum((STORE,STORETYPE), - VINJ(STORE,STORETYPE,IS3,T) - ); - -QStoreExt(IS3,T) .. - sum(SCON, -$ifi %bb1%== yes VSC(SCON) -$ifi %bb2%== yes VSC(SCON) -$ifi %bb3%== yes ISC(SCON) - *SCONDATA(SCON,'EXTCAP')*SCONDATA(SCON,'VOLCAP') - ) -*$ifi %bb2%==yes +SUM((STORE,STORETYPE),VGINVEXT(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) -*$ifi %bb2%==yes +(GSTOREDATA(STORETYPE,'EXTEFFECT')*VGINVSTORE(STORE,STORETYPE))$STOREINV(STORE, STORETYPE) -*$ifi %bb2%==yes -VGINVEXT.l(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) -*$ifi %bb2%==yes -(GSTOREDATA(STORETYPE,'EXTEFFECT')*VGINVSTORE.l(STORE,STORETYPE))$STOREINV(STORE, STORETYPE)) - =G= - sum((STORE,STORETYPE), - VEXT(STORE,STORETYPE,IS3,T) - ); - -QGSTOREBAL(STORE,STORETYPE,S,T).. - VSTOCK(STORE,STORETYPE,S,T) - +IHOURSINST(S,T)*(VINJ(STORE,STORETYPE,S,T)-VEXT(STORE,STORETYPE,S,T)) -=G= VSTOCK(STORE,STORETYPE,S,T+1)$(ord(T) 0) .. - IDEV_MAX(SYSEXIT) -$ifi %BB2%==yes +sum(NGINV,DEVEFFECT(NGINV,SYSEXIT)*VGINFINV(NGINV)) - +sum((C,GNETSTEPS), GNET_DEV_EFFECT(C,SYSEXIT)*VGNETINV(C,GNETSTEPS)) - =G= - 1.21*sum(IA$ICA('Denmark',IA), - VGUD(IA,IS3,T) - )$(SAMEAS(SYSEXIT,'DK-EXIT')) - ; - - -QGINJ_MAX(STORE,STORETYPE,IS3,T).. - IINJ_MAX(STORE,STORETYPE) - +VGINVINJ(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) - +(GSTOREDATA(STORETYPE,'INJEFFECT')*VGINVSTORE(STORE,STORETYPE))$STOREINV(STORE, STORETYPE) - =G= - VINJ(STORE,STORETYPE,IS3,T) - ; - -QGEXT_MAX(STORE,STORETYPE,IS3,T).. - IEXT_MAX(STORE,STORETYPE) - +VGINVEXT(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) - +(GSTOREDATA(STORETYPE,'EXTEFFECT')*VGINVSTORE(STORE,STORETYPE))$STOREINV(STORE, STORETYPE) - =G= - VEXT(STORE,STORETYPE,IS3,T) - ; - -QGSTORE_MAX(STORE,STORETYPE,IS3,T).. - ISTORE_MAX(STORE,STORETYPE) - +VGINVSTORE(STORE,STORETYPE)$STOREINV(STORE, STORETYPE) - =G= - VSTOCK(STORE,STORETYPE,IS3,T) - ; diff --git a/base/addons/gas/gasfacilities.inc b/base/addons/gas/gasfacilities.inc deleted file mode 100644 index a1aa6ee3..00000000 --- a/base/addons/gas/gasfacilities.inc +++ /dev/null @@ -1,327 +0,0 @@ -*------------------------------------------------------------------------------* -* Transmission posibilities -*------------------------------------------------------------------------------* - -TABLE SUPPLY_MAX(YYY,SOURCE) 'Hourly exogenous natural gas supply capacity' - Nybro Ellund Oeresund -2003 1002.029427 344 eps -2004 1059.107052 344 eps -2005 1116.184678 344 eps -2006 1116.184678 344 eps -2007 1116.184678 344 eps -2008 1116.184678 344 eps -2009 1116.184678 344 eps -2010 1116.184678 344 eps -2011 1045.154744 344 eps -2012 974.1248097 344 eps -2013 903.0948757 344 eps -2014 832.0649417 344 eps -2015 761.0350076 344 eps -2016 731.2278031 344 eps -2017 701.4205987 344 eps -2018 671.6133942 344 eps -2019 641.8061898 344 eps -2020 611.9989853 344 eps -2021 582.1917808 344 eps -2022 552.3845764 344 eps -2023 522.5773719 344 eps -2024 492.7701674 344 eps -2025 462.962963 344 eps -2026 462.962963 344 eps -2027 462.962963 344 eps -2028 462.962963 344 eps -2029 462.962963 344 eps -2030 462.962963 344 eps -* 2005.ELLUND 344, -* 2005.OERESUND 0, -* 2005.NYBRO 1040 -; - -TABLE EXP_MAX(YYY,SOURCE) 'Hourly exogenous natural gas export capacity' - OERESUND NYBRO ELLUND -2005 286 303 344 -2006 358 303 344 -2007 358 303 344 -2008 358 303 344 -2009 358 303 344 -2010 358 303 344 -2011 358 303 344 -2012 358 303 344 -2013 358 303 344 -2014 358 303 344 -2015 358 303 344 -2016 358 303 344 -2017 358 303 344 -2018 358 303 344 -2019 358 303 344 -2020 358 303 344 -2021 358 303 344 -2022 358 303 344 -2023 358 303 344 -2024 358 303 344 -2025 358 303 344 -2026 358 303 344 -2027 358 303 344 -2028 358 303 344 -2029 358 303 344 -2030 358 303 344 -; - -TABLE DEV_MAX(YYY,SYSEXIT) 'Hourly exogenous natural gas delivery capacity' - DK-EXIT -2005 1191 -2006 1191 -2007 1191 -2008 1191 -2009 1191 -2010 1191 -2011 1191 -2012 1191 -2013 1191 -2014 1191 -2015 1191 -2016 1191 -2017 1191 -2018 1191 -2019 1191 -2020 1191 -2021 1191 -2022 1191 -2023 1191 -2024 1191 -2025 1191 -2026 1191 -2027 1191 -2028 1191 -2029 1191 -2030 1191 -; - -DEV_MAX(YYY,SYSEXIT) = DEV_MAX(YYY,SYSEXIT) * kNm3ToMWh; -SUPPLY_MAX(YYY,SOURCE) = SUPPLY_MAX(YYY,SOURCE) * kNm3ToMWh; -EXP_MAX(YYY,SOURCE) = EXP_MAX(YYY,SOURCE) * kNm3ToMWh; - - -* TAL FOR ELLUND FORSTÆRKELSE OG BGI KAPACITET ER TAGET UD AF LUFTEN - -PARAMETER COST_NGINV(NGINV0) 'Investment costs' -/ -* Investment cost in Bill. DKkr 2006 -BGI 2.0 -NO_SYDARNE 0.8 -NO_NYBRO 1.2 -NO_TYRA 0.8 -DK_COMPR 0.255 -NEGP_ELLUND 1.0 -/; -* Omregning til EURO90 -COST_NGINV(NGINV0)=1/(7.43)/(1.389)*1000*1000*1000*COST_NGINV(NGINV0); - -PARAMETER GNET_COST(C,GNETSTEPS0) 'Marginal cost of gas transmissionnetwork expansion EURO90/(Unit expansion)' -* Data source: Kapacitetsudvidelse i transmissionssystemet - Simulering med Pipeline studio. -* Den anvendte pris er en gennemsnitsværdi, da omkostningsstrukturen i notat er konkav kapacitetsforøgelsen. -* Døgnbelastningsfaktor på 1.07 er anvendt til at skalere døgnmængder til timemængder. -* COST -* EURO90/MWh/h -/ -Denmark.GNETSTEP1 28923 -Denmark.GNETSTEP2 28923 -Denmark.GNETSTEP3 28923 -/; - -PARAMETER GNET_MAX(C,GNETSTEPS0) 'Steplength for marginal expansion cost curve (Unit expansion)' -* Unit cost must be increasing with step number -* Data source: Kapacitetsudvidelse i transmissionssystemet - Simulering med Pipeline studio. (Energinet.dk) -* Max_Effect -* kNm3/d -/ -Denmark.GNETSTEP1 4757 -Denmark.GNETSTEP2 0 -Denmark.GNETSTEP3 0 -/; - -TABLE GNET_DEV_EFFECT(C,SYSEXIT) 'Increased delivery capacity from network expansion (MWh/h)/(Unit expansion)' - DK-EXIT -Denmark 1 -; - -TABLE GNET_EXP_EFFECT(C,SOURCE) 'Increased export capacity from network expansion (MWh/h)/(Unit expansion)' - OERESUND -Denmark 1 -; - -TABLE DEVEFFECT(NGINV0,SYSEXIT) 'Increased delivery capacity from infrastrukture investments (MWh/h)' - DK-EXIT -BGI eps -NO_SYDARNE eps -NO_NYBRO eps -NO_TYRA eps -DK_COMPR 1 -NEGP_ELLUND eps -; - - -TABLE SUPEFFECT(NGINV0,SOURCE) -* Additional hourly supply capacity gained from investment - NYBRO ELLUND OERESUND -BGI eps eps 542 -NO_SYDARNE 542 eps eps -NO_NYBRO 1125 eps eps -NO_TYRA 420 eps eps -DK_COMPR eps eps eps -NEGP_ELLUND eps 378.84 eps -; -SUPEFFECT(NGINV0,SOURCE) = SUPEFFECT(NGINV0,SOURCE)*kNm3ToMWh; - -TABLE EXPEFFECT(NGINV0,SOURCE) -* Additional hourly export capacity gained from investment MNm3/h - NYBRO ELLUND OERESUND -BGI eps eps 542 -NO_SYDARNE eps eps eps -NO_NYBRO eps eps eps -NO_TYRA eps eps eps -DK_COMPR eps eps 200 -NEGP_ELLUND eps eps 378.84 -; -EXPEFFECT(NGINV0,SOURCE) = EXPEFFECT(NGINV0,SOURCE)*kNm3ToMWh; - -*------------------------------------------------------------------------------* -* STORAGE FACILITIES -*------------------------------------------------------------------------------* - -TABLE INJ_MAX(YYY,STORE,STORETYPE) -* kNm3/h - LITORUP.Cavern STENLILLE.Aquifer -2005 165 110 -2006 165 110 -2007 165 110 -2008 165 110 -2009 165 110 -2010 165 110 -2011 165 110 -2012 165 110 -2013 165 110 -2014 165 110 -2015 165 110 -2016 165 110 -2017 165 110 -2018 165 110 -2019 165 110 -2020 165 110 -2021 165 110 -2022 165 110 -2023 165 110 -2024 165 110 -2025 165 110 -2026 165 110 -2027 165 110 -2028 165 110 -2029 165 110 -2030 165 110 -; - -TABLE EXT_MAX(YYY,STORE,STORETYPE) -* kNm3/h - LITORUP.Cavern STENLILLE.Aquifer -2005 600 600 -2006 600 600 -2007 600 600 -2008 600 600 -2009 600 600 -2010 600 600 -2011 600 600 -2012 600 600 -2013 600 600 -2014 600 600 -2015 600 600 -2016 600 600 -2017 600 600 -2018 600 600 -2019 600 600 -2020 600 600 -2021 600 600 -2022 600 600 -2023 600 600 -2024 600 600 -2025 600 600 -2026 600 600 -2027 600 600 -2028 600 600 -2029 600 600 -2030 600 600 -; - -TABLE STORAGE_MAX(YYY,STORE,STORETYPE) -* kNm3 - LITORUP.Cavern STENLILLE.Aquifer -2005 400000 300000 -2006 400000 300000 -2007 400000 300000 -2008 400000 300000 -2009 400000 300000 -2010 400000 300000 -2011 400000 300000 -2012 400000 300000 -2013 400000 300000 -2014 400000 300000 -2015 400000 300000 -2016 400000 300000 -2017 400000 300000 -2018 400000 300000 -2019 400000 300000 -2020 400000 300000 -2021 400000 300000 -2022 400000 300000 -2023 400000 300000 -2024 400000 300000 -2025 400000 300000 -2026 400000 300000 -2027 400000 300000 -2028 400000 300000 -2029 400000 300000 -2030 400000 300000 -; - - -TABLE GSTOREDATA(STORETYPE,*) 'Storage investment data' - INVCOST FOMK INJEFFECT EXTEFFECT ELECT GAS MargInjInvCost MargExtInvCost -* EURO02/kNm3 EURO02/kN3 -Ratio to volume capacity- fraction fraction Marginal cost Marginal cost -* of INJ of EXT of inj capacity of ext capacity -* EURO02/kNm3/h EURO02/kNm3/h -Dep_Field 42.40 13 0.0002 0.0006 0.00341 0.002 10320 10320 -Aquifer 49.10 15 0.0002 0.000458 0.00341 0.002 10560 10560 -Cavern 83.90 11 0.00041 0.00198 0.0033 0.0009 10080 10080 -LNG 150.60 45 0.0004557 0.00651 0.00526 0.00319 7104 7104 -; - -* INVCOST is the yearly annuity payment. -* DATASOURCES: The 2002 European Gas Storage Study, Lille Torup Naturgaslager - Grønt regnskab 2004, -* Stenlille Naturgaslager - Grønt regnskab 2004 - -* Faktor 1.302272195 brug til omregning af EURO02 til EURO90 -* Translate costs to EURO90/MWh -GSTOREDATA(STORETYPE,'INVCOST') = GSTOREDATA(STORETYPE,'INVCOST')/1.302272195/kNm3ToMWh; -GSTOREDATA(STORETYPE,'MargInjInvCost') = GSTOREDATA(STORETYPE,'MargInjInvCost')/1.302272195/kNm3ToMWh; -GSTOREDATA(STORETYPE,'MargExtInvCost') = GSTOREDATA(STORETYPE,'MargExtInvCost')/1.302272195/kNm3ToMWh; -GSTOREDATA(STORETYPE,'FOMK') = GSTOREDATA(STORETYPE,'FOMK')/1.302272195/kNm3ToMWh; - -PARAMETER MAXINVEST(STORE,STORETYPE) 'Maximum possible capacity at storage location' -/ -LITORUP.Cavern 300000 -STENLILLE.Aquifer 100000 -STENLILLE.Cavern 1000000 -STENLILLE.LNG 1000000 -SE_S_RURAL.LNG 1000000 -/; -* Location of LNG investment in Denmark is irrelevant for the model. -* STENLILLE.Cavern repressents investment in a storage facility in Tønder. Location is irrelevant for model so no individual location node is defined. - -PARAMETER TSOSTORAGE(YYY,STORE,STORETYPE) 'Storage reserved for emergencies by the TSO'; - -TSOSTORAGE(Y,'Stenlille','Aquifer') = STORAGE_MAX(Y,'Stenlille','Aquifer')/sum((STORE,STORETYPE), STORAGE_MAX(Y,STORE,STORETYPE))*(sum((STORE,STORETYPE), STORAGE_MAX(Y,STORE,STORETYPE))-605000); -TSOSTORAGE(Y,'LiTorup','Cavern') = STORAGE_MAX(Y,'LiTorup','Cavern')/sum((STORE,STORETYPE), STORAGE_MAX(Y,STORE,STORETYPE))*(sum((STORE,STORETYPE), STORAGE_MAX(Y,STORE,STORETYPE))-605000); - -TSOSTORAGE(YYY,STORE,STORETYPE) = TSOSTORAGE(YYY,STORE,STORETYPE) * kNm3ToMWh; -INJ_MAX(YYY,STORE,STORETYPE) = INJ_MAX(YYY,STORE,STORETYPE) * kNm3ToMWh; -EXT_MAX(YYY,STORE,STORETYPE) = EXT_MAX(YYY,STORE,STORETYPE) * kNm3ToMWh; -STORAGE_MAX(YYY,STORE,STORETYPE) = STORAGE_MAX(YYY,STORE,STORETYPE) * kNm3ToMWh; \ No newline at end of file diff --git a/base/addons/gas/gasgdx.inc b/base/addons/gas/gasgdx.inc deleted file mode 100644 index b3eed4f3..00000000 --- a/base/addons/gas/gasgdx.inc +++ /dev/null @@ -1,14 +0,0 @@ - - -$ifi %bb3%==yes $goto outbb3 -$ifi %STORAGEALLOCATIONS%==yes execute_unload '../../base/addons/gas/GSFXW.gdx', GSFXW; -$ifi %STORAGEALLOCATIONS%==yes execute_unload '../../base/addons/gas/SC_Y.gdx',SC_Y; -$ifi %CAPACITYALLOCATIONS%==yes execute_unload '../../base/addons/gas/reserved_capacity.gdx', TC_EX_Y, TC_EX_M, TC_EN_Y, TC_EN_M; - -$ifi %MAKEINVEST%==yes execute_unload '../../base/addons/gas/gasinvest.gdx',SUPACC,EXPACC ,INJACC ,EXTACC ,STOREACC, DEVACC; - -$goto end_out - -$label outbb3 - -$label end_out \ No newline at end of file diff --git a/base/addons/gas/gasgeninput.inc b/base/addons/gas/gasgeninput.inc deleted file mode 100644 index 80d00bdf..00000000 --- a/base/addons/gas/gasgeninput.inc +++ /dev/null @@ -1,26 +0,0 @@ - - -* RUN only in BB1 or BB2 -$ifi %BB3%==yes $goto not_bb1orbb2 - -$ifi %STORAGEALLOCATIONS%==yes GSFXW(Y,STORE,STORETYPE,S) = SUM(T,IHOURSINST(S,T)*(VINJ.l(STORE,STORETYPE,S,T)-VEXT.l(STORE,STORETYPE,S,T))); -$ifi %STORAGEALLOCATIONS%==yes SC_Y(Y,SCON) = VSC.l(SCON); - - -$ifi %CAPACITYALLOCATIONS%==yes TC_EX_Y(SYSEXIT,Y) = VTC_EX_Y.l(SYSEXIT); -$ifi %CAPACITYALLOCATIONS%==yes TC_EX_M(SYSEXIT,Y,SSS,TTT) = SUM((IMONTH,DAY)$(DAYT(DAY,TTT) and IWHEN(IMONTH,SSS,DAY)), VTC_EX_M.l(SYSEXIT,IMONTH)); - -$ifi %CAPACITYALLOCATIONS%==yes TC_EN_Y(SYSENTRY,Y) = VTC_EN_Y.l(SYSENTRY); -$ifi %CAPACITYALLOCATIONS%==yes TC_EN_M(SYSENTRY,Y,SSS,TTT) = SUM((IMONTH,DAY)$(DAYT(DAY,TTT) and IWHEN(IMONTH,SSS,DAY)), VTC_EN_M.l(SYSENTRY,IMONTH)); - - -$ifi %MAKEWATER%==yes HYFXRW_GAS(Y,S,IR) = SUM(IA$RRRAAA(IR,IA), SUM(IGHYRR$IAGK_Y(IA,IGHYRR),SUM(T,IHOURSINST(S,T)*(VGE_T.l(IA,IGHYRR,S,T))))+SUM(IGHYRS$IAGK_Y(IA,IGHYRS),SUM(T,IHOURSINST(S,T)*(VGE_T.l(IA,IGHYRS,S,T))))); - -$ifi %MAKEINVEST%==yes SUPACC(Y,SOURCE) = SUM(NGINV,SUPEFFECT(NGINV,SOURCE)*VGINFINV.l(NGINV)); -$ifi %MAKEINVEST%==yes EXPACC(Y,SOURCE) = SUM(NGINV,EXPEFFECT(NGINV,SOURCE)*VGINFINV.l(NGINV)); -$ifi %MAKEINVEST%==yes INJACC(Y,STORE,STORETYPE) = VGINVINJ.l(STORE,STORETYPE)+VGINVSTORE.l(STORE,STORETYPE)*GSTOREDATA(STORETYPE,'INJEFFECT'); -$ifi %MAKEINVEST%==yes EXTACC(Y,STORE,STORETYPE) = VGINVEXT.l(STORE,STORETYPE)+VGINVSTORE.l(STORE,STORETYPE)*GSTOREDATA(STORETYPE,'EXTEFFECT'); -$ifi %MAKEINVEST%==yes STOREACC(Y,STORE,STORETYPE) = VGINVSTORE.l(STORE,STORETYPE); -$ifi %MAKEINVEST%==yes DEVACC(Y,SYSEXIT) = SUM((C,GNETSTEPS), GNET_DEV_EFFECT(C,SYSEXIT)*VGNETINV.l(C,GNETSTEPS)) + SUM(NGINV,DEVEFFECT(NGINV,SYSEXIT)*VGINFINV.l(NGINV)); - -$label not_bb1orbb2 diff --git a/base/addons/gas/gasheating.inc b/base/addons/gas/gasheating.inc deleted file mode 100644 index 5c09a052..00000000 --- a/base/addons/gas/gasheating.inc +++ /dev/null @@ -1,2 +0,0 @@ -$ifi %HEATING%==yes + sum(HEAT$IHX(HEAT,AAA), VHX(HEAT,IAURBH,IS3,T) )$HEAT(AAA) -$ifi %HEATING%==yes - sum(HEAT$IHX(AAA,HEAT), VHX(IAURBH,IAURBH,IS3,T) )$HEAT(AAA) \ No newline at end of file diff --git a/base/addons/gas/gasinit.inc b/base/addons/gas/gasinit.inc deleted file mode 100644 index 71e1c0bb..00000000 --- a/base/addons/gas/gasinit.inc +++ /dev/null @@ -1,8 +0,0 @@ -$ifi %bb2%==yes VGINFINV.l(NGINV) = 0; -$ifi %bb2%==yes VGINVSTORE.l(STORE,STORETYPE) = 0; -$ifi %bb2%==yes VGINVINJ.l(STORE,STORETYPE) = 0; -$ifi %bb2%==yes VGINVEXT.l(STORE,STORETYPE) = 0; -$ifi %bb2%==yes VGINVSTORE.up(STORE,STORETYPE) = MAXINVEST(STORE,STORETYPE); -$ifi %bb2%==yes STOREINV(STORE, STORETYPE) = YES$MAXINVEST(STORE,STORETYPE); -$ifi %bb2%==yes VGNETINV.up(C,GNETSTEPS) = 0; -$ifi %bb2%==yes VGNETINV.up(C,GNETSTEPS) = GNET_MAX(C,GNETSTEPS); \ No newline at end of file diff --git a/base/addons/gas/gasinternal.inc b/base/addons/gas/gasinternal.inc deleted file mode 100644 index 0f5350ea..00000000 --- a/base/addons/gas/gasinternal.inc +++ /dev/null @@ -1,42 +0,0 @@ -$ifi %MR%==yes IA(AAA)$ICA('Denmark',AAA)=yes$MRAREAS(AAA); -$ifi %MR%==yes IA('DK_E_Rural') = yes; -$ifi %MR%==yes IA('DK_W_Rural') = yes; -$ifi %MR%==yes IA('DK_E_Offshore') = yes; -$ifi %MR%==yes IA('DK_W_Offshore') = yes; - -PARAMETERS - ITC_EX_Y(SYSEXIT) 'Reserved yearly exit capacity' - ITC_EX_M(SYSEXIT,s,t) 'Reserved monthly exit capacity' - ITC_EN_Y(SYSENTRY) 'Reserved yearly entry capacity' - ITC_EN_M(SYSENTRY,s,t) 'Reserved monthly entry capacity' - IDGRES_Y(AAA,s,t) 'Residual gas demand' - IDGOTH_Y(AAA,s,t) 'Gas demand from other large consumers' - IDGRES_SUMVAR(AAA) 'Residual gasforbrug, weightsum over the variation' - IDGOTH_SUMVAR(AAA) 'Gasforbrug andre timeaflæste, vægtsum over variation' - IGSFXW(STORE,STORETYPE,SSS) 'Net quantity gas stored in present week (MWh)' - ISC(SCON) 'Storage allowances procured' - IGEX_PRICE(SOURCE,SSS) 'Export value of natural gas' - - ISE_DGRES_Y(AAA,S,T) - ISE_DGOTH_Y(AAA,S,T) - - - IGEXT_COST(SOURCE,S) 'Gas extraction variable costs' - - - ISUPPLY_MAX(SOURCE) 'Exogenous maximum supply capacity' - IEXP_MAX(SOURCE) 'Exogenous maximum supply capacity' - IDEV_MAX(SYSEXIT) 'Exogenous maximum delivery capacity' - ISTORE_MAX(STORE,STORETYPE) 'Exogenous maximum storage capacity' - IINJ_MAX(STORE,STORETYPE) 'Exogenous maximum injection capacity' - IEXT_MAX(STORE,STORETYPE) 'Exogenous maximum extraction capacity' - ; - -$ifi %bb1%==yes IDGRES_SUMVAR(IA) = sum((S,T), IHOURSINST(S,T)*DGRES_VAR(IA,S,T)); -$ifi %bb1%==yes IDGOTH_SUMVAR(IA) = sum((S,T), IHOURSINST(S,T)*DGOTH_VAR(IA,S,T)); - -$ifi %bb2%==yes IDGRES_SUMVAR(IA) = sum((S,T), IHOURSINST(S,T)*DGRES_VAR(IA,S,T)); -$ifi %bb2%==yes IDGOTH_SUMVAR(IA) = sum((S,T), IHOURSINST(S,T)*DGOTH_VAR(IA,S,T)); - -$ifi %bb3%==yes IDGRES_SUMVAR(IA) = sum((SSS,T), IHOURSINST(SSS,T)*DGRES_VAR(IA,SSS,T)); -$ifi %bb3%==yes IDGOTH_SUMVAR(IA) = sum((SSS,T), IHOURSINST(SSS,T)*DGOTH_VAR(IA,SSS,T)); \ No newline at end of file diff --git a/base/addons/gas/gasmakecompare.inc b/base/addons/gas/gasmakecompare.inc deleted file mode 100644 index 34e276e8..00000000 --- a/base/addons/gas/gasmakecompare.inc +++ /dev/null @@ -1,22 +0,0 @@ -* File mkcompare.inc. -* This file is an extension to the welfare calculation module for the Balmorel model. -* Created by Jens Hauch and Thomas E. Pedersen, COWI A/S (DK). -* Adapted for Balmorel version 2.10, 2.13 by Hans Ravn. -* Extended to for the gas add-on by Lars Bregnbæk, X-mas 2006. - - -*=============================================================================== -* Overview -*=============================================================================== - -* Intermediate parameter values are printed to files for later use. -* The parameter values were calculated in gascompprint3.inc, as ..,..,.., etc. -* They are here saved under names ...00, ...00, ...00, etc. - -*=============================================================================== -* Overview end -*=============================================================================== - - -$libinclude gams2prm residualcosts residualcosts00 -$libinclude gams2prm othercosts othercosts00 diff --git a/base/addons/gas/gasmkcompare.inc b/base/addons/gas/gasmkcompare.inc deleted file mode 100644 index 0b219aa7..00000000 --- a/base/addons/gas/gasmkcompare.inc +++ /dev/null @@ -1,76 +0,0 @@ -* File gasmkcompare.inc. -* This file is an extension to the welfare calculation module for the Balmorel model. -* Created by Jens Hauch and Thomas E. Pedersen, COWI A/S (DK). -* Adapted for Balmorel version 2.10, 2.13 by Hans Ravn. -* Extended to for the gas add-on by Lars Bregnbæk, X-mas 2006. - - -*=============================================================================== -* Overview -*=============================================================================== - -* Intermediate parameter values are printed to files for later use. -* The parameter values were calculated in gascompprint3.inc, as ..,..,.., etc. -* They are here saved under names ...00, ...00, ...00, etc. - -*=============================================================================== -* Overview end -*=============================================================================== - -*=============================================================================== -* Consumers -*=============================================================================== - -$libinclude gams2prm residualcosts residualcosts00 -$libinclude gams2prm othercosts othercosts00 - -*=============================================================================== -* Distribution companies -*=============================================================================== - -$libinclude gams2prm DSOprofit DSOprofit00 - -*=============================================================================== -* TSO -*=============================================================================== - -$libinclude gams2prm TSOCAPrev TSOCAPrev00 -$libinclude gams2prm TSOVOLrev TSOVOLrev00 -$libinclude gams2prm TSOCAPRent TSOCAPRent00 -$libinclude gams2prm TSOprofit TSOprofit00 - - -*=============================================================================== -* SSO -*=============================================================================== - -$libinclude gams2prm SSOCAPrev SSOCAPrev00 -$libinclude gams2prm SSOVOLrev SSOVOLrev00 -$libinclude gams2prm SSOCAPrent SSOCAPrent00 -$libinclude gams2prm SSOProfit SSOProfit00 - -*=============================================================================== -* Producers -*=============================================================================== - -$libinclude gams2prm GasProducerProfit GasProducerProfit00 - -*=============================================================================== -* Suppliers -*=============================================================================== - -$libinclude gams2prm GasSupplierProfit GasSupplierProfit00 - -*=============================================================================== -* Shippers -*=============================================================================== - -$libinclude gams2prm GasShipperProfit GasShipperProfit00 -$libinclude gams2prm ShipperCapRent ShipperCapRent00 - - -*=============================================================================== -* Gas Storage Customers -*=============================================================================== - -$libinclude gams2prm GasStorageUserProfit GasStorageUserProfit00 \ No newline at end of file diff --git a/base/addons/gas/gasopt.opt b/base/addons/gas/gasopt.opt deleted file mode 100644 index 6a87470f..00000000 --- a/base/addons/gas/gasopt.opt +++ /dev/null @@ -1,46 +0,0 @@ -* The following variables define options pertaining to the gas add-on module. - -* Geographical resolution at MR level -$Setglobal MR -* Employ special nodes for larger district heating areas -$Setglobal HEATING -* Run pipeline simulation -$Setglobal PIPELINE -* (bb1 only) Create an input file for gas made available in specific weeks for -* use in BB3 -$Setglobal STORAGEALLOCATIONS yes -* (bb1 only) Create an input file for yearly and monthly capacity reservations -* for use in BB3 -$Setglobal CAPACITYALLOCATIONS yes -* (bb1 only) Create an input file for gas to be produced in specific weeks for -* use in BB3 - -* Include microCHP in the model -$Setglobal micro -* Include CAES in the model -$Setglobal CAES - -* Fjernvarme områder med gasforsyning er tvungen til at fyre med gas? -$Setglobal MUSTGAS yes - -* Fjernvarme områder uden gasforsyning kan udbygges med gas? -$Setglobal EXPANDGAS - -* Allow electricity for Danish district heating -$Setglobal ALLOWEH yes - - -* Gasprice level at production nodes. Defaults to MEDGAS. -$Setglobal LOWGAS -$Setglobal MEDGAS yes -$Setglobal HIGHGAS - -* No exogenous capacity charge on gas storage. However kapacitet tariffs kan be interpretted from the -* marginal of the storage level value VSTOCK. -$Setglobal FREESTORAGE yes -$Setglobal FREEGAS yes - -$Setglobal NOCAPACITY - -* Set to yes if using descrete investments. -$Setglobal SOLVEMIP yes \ No newline at end of file diff --git a/base/addons/gas/gasproduction.inc b/base/addons/gas/gasproduction.inc deleted file mode 100644 index 57b03c69..00000000 --- a/base/addons/gas/gasproduction.inc +++ /dev/null @@ -1,13 +0,0 @@ - -TABLE GP(YYY,SOURCE) -* PJ/år - NYBRO -2005 376 -2006 377 -2007 380 -2008 387 -2009 392 -; -GP(YYY,SOURCE)=GP(YYY,SOURCE)/3.6*1000000; - - diff --git a/base/addons/gas/gasregions2areas.inc b/base/addons/gas/gasregions2areas.inc deleted file mode 100644 index c0e79094..00000000 --- a/base/addons/gas/gasregions2areas.inc +++ /dev/null @@ -1,6 +0,0 @@ -DK_E.(STENLILLE,OERESUND) -DK_W.(LITORUP, ELLUND, NYBRO) - -$ifi %HEATING%==yes DK_E.(VEKS_V,CTR_V,VFORBRANDING_V,NFORBRANDING_V) -$ifi %HEATING%==yes DK_W.(AALBORG_V,AARHUS_V,ODENSE_V,HERNING_V,TVIS_V) -$ifi %MR%==yes $INCLUDE '../../base/addons/gas/MRregions2areas.inc'; \ No newline at end of file diff --git a/base/addons/gas/gasscaling.inc b/base/addons/gas/gasscaling.inc deleted file mode 100644 index bf7ab437..00000000 --- a/base/addons/gas/gasscaling.inc +++ /dev/null @@ -1,44 +0,0 @@ -$ifi %bb1%==yes balbase1.scaleopt=1; -$ifi %bb1%==yes balbase1.scaleopt=1; -$ifi %bb1%==yes balbase1.scaleopt=1; - - -VGUD.scale(IA,S,T)$ICA('Denmark',IA) = 1/(1+2*(IDGRES_Y(IA,S,T)+IDGOTH_Y(IA,S,T))); -VSTOCK.scale(STORE,STORETYPE,S,T) = 1/(1+ISTORE_MAX(STORE,STORETYPE)); -VINJ.scale(STORE,STORETYPE,S,T) = 1/(1+IINJ_MAX(STORE,STORETYPE)); -VEXT.scale(STORE,STORETYPE,S,T) = 1/(1+IEXT_MAX(STORE,STORETYPE)); -VEXP.scale(SOURCE,S,T) = 1/(1+IEXP_MAX(SOURCE)); -VSUP.scale(SOURCE,S,T) = 1/(1+ISUPPLY_MAX(SOURCE)); -VTC_EN_Y.scale = 1/10000; -VTC_EN_M.scale(IMONTH) = 1/1000; -VTC_EN_W.scale(S) = 1/1000; -VTC_EN_D.scale(S,DAY) = 1/1000; -VTC_EX_Y.scale = 1/10000; -VTC_EX_M.scale(IMONTH) = 1/1000; -VTC_EX_W.scale(S) = 1/1000; -VTC_EX_D.scale(S,DAY) = 1/1000; -VSC.scale(SCON) = 1/(1+SUM((STORE,STORETYPE),ISTORE_MAX(STORE,STORETYPE))); -* VGINFINV(NGINV) - Well scaled -VGINVSTORE.scale(STORE,STORETYPE) = 1/10000; -VGINVEXT.scale(STORE,STORETYPE) = 1/1000; -VGINVINJ.scale(STORE,STORETYPE) = 1/1000; - - - VOBJ.scale =1/1000; - VGE_T.scale(IAGK_Y(IA,G),S,T) =1/(1+VGE_T.up(IA,G,S,T)$(VGE_T.up(IA,G,S,T) LE INF)); - VGH_T.scale(IAGK_Y(IA,G),S,T) =1/(1+VGH_T.up(IA,G,S,T)$(VGH_T.up(IA,G,S,T) LE INF)); - VX_T.scale(IRE,IRI,S,T) =1/(1+IXKINI_Y(IRE,IRI)); - VGEN_T.scale(IAGKN(IA,G),S,T) =1/1000; - VGHN_T.scale(IAGKN(IA,G),S,T) =1/1000; - VGKN.scale(IAGKN(IA,G)) =1/1000; - VXKN.scale(IRE,IRI) =1/(1+IXKINI_Y(IRE,IRI)); - VHYRS_S.scale(IA,S) =1/1000000; - VGE2LEVEL.scale(IAGK_Y(IA,IG2LEVEL),S,DAYTYPE) =1/(1+Sum(t,VGE_T.up(IA,IG2LEVEL,S,T)$(VGE_T.up(IA,IG2LEVEL,S,T) LE INF))); - VESTOLOADT.scale(IA,S,T) =1/(1+Sum(IAGK_Y(IA,IGESTO),VGE_T.up(IA,IGESTO,S,T)$(VGE_T.up(IA,IGESTO,S,T) LE INF))); - VHSTOLOADT.scale(IA,S,T) =1/(1+Sum(IAGK_Y(IA,IGHSTO),VGH_T.up(IA,IGHSTO,S,T)$(VGH_T.up(IA,IGHSTO,S,T) LE INF))); - VESTOVOLT.scale(IA,S,T) =1/(1+Sum(IAGK_Y(IA,IGESTO),VGE_T.up(IA,IGESTO,S,T)$(VGE_T.up(IA,IGESTO,S,T) LE INF))); - VHSTOVOLT.scale(IA,S,T) =1/(1+Sum(IAGK_Y(IA,IGHSTO),VGH_T.up(IA,IGHSTO,S,T)$(VGH_T.up(IA,IGHSTO,S,T) LE INF))); -$ifi %X3V%==yes VX3VIM_T.scale(X3VX(IR,X3VPLACE),X3VSTEP,S,T) = 1/(1+VX3VIM_T.up(IR,X3VPLACE,X3VSTEP,S,T)); -$ifi %X3V%==yes VX3VEX_T.scale(X3VX(IR,X3VPLACE),X3VSTEP,S,T) = 1/(1+VX3VEX_T.up(IR,X3VPLACE,X3VSTEP,S,T)); - -balbase2.optfile=1; \ No newline at end of file diff --git a/base/addons/gas/gassets.inc b/base/addons/gas/gassets.inc deleted file mode 100644 index 20992037..00000000 --- a/base/addons/gas/gassets.inc +++ /dev/null @@ -1,347 +0,0 @@ - - - -$ifi %MR%==yes SET MRAREAS(AAA) / -$ifi %MR%==yes $include '../../base/addons/gas/MRareas.inc'; -$ifi %MR%==yes DK_W_Urban -$ifi %MR%==yes DK_W_Rural -$ifi %MR%==yes DK_W_Odense -$ifi %MR%==yes DK_W_Trefor -$ifi %MR%==yes DK_W_Aarhus -$ifi %MR%==yes DK_W_Aalborg -$ifi %MR%==yes DK_E_Urban -$ifi %MR%==yes DK_E_Rural -$ifi %MR%==yes /; - - - - -ALIAS(AAA,NODE,i,j,NODES); - -SETS DDD "Regional NAT_GAS Distribution", - NODEDATASET "MR Station specific datatypes" -; - -SETS DDDAAA(DDD,AAA) "MR Stations in distribution area", - RRRDDD(RRR, DDD) "NAT_GAS Distribution areas in region"; - -SETS DDD "Regional NAT_GAS Distribution" - / DONG_VEST, - MIDT_NORD, - NGF, - DONG_OEST, - HNG, - SE_S_DIST - /; - -SETS RRRDDD(RRR,DDD) "NAT_GAS Distribution areas in region" - / DK_E.(DONG_OEST,HNG), - DK_W.(DONG_VEST,MIDT_NORD,NGF) - /; - -* Depending on geografical resolution different areas (MR) much be contained in -* the distribition areas. -$ifi %MR%==yes $goto MRDist -SETS DDDNODE(DDD,AAA) "MR Stations in distribution area" -/ - MIDT_NORD.(DK_W_Urban, DK_W_AALBORG,DK_W_AARHUS,DK_W_1, DK_W_Rural), - DONG_VEST.(DK_W_TREFOR,DK_W_2), - NGF.(DK_W_ODENSE), - DONG_OEST.(DK_E_2,DK_E_1,DK_E_Rural), - HNG.(DK_E_Urban ) - SE_S_DIST.SE_S_Rural -/; -$goto end_dist -$Label MRDist -SETS DDDNODE(DDD,AAA) "MR Stations in distribution area" -/ - MIDT_NORD.(BRANDE, ELLIDSHOEJ, HAVERSLEV, HERNING, KARUP, LITORUP, VIBORG, AALBORG), - DONG_VEST.(EGTVED,ELLUND,HELLE,LILBALLE,LISELSKAER,NYBRO,NOERSKOV,POTTEHUSE,STANDST,TAULOV,TERKELSBOEL,FROESLEV), - NGF.(MIDDELFART,BILLESBOELLE,KOELBJERG,HOEJBY,ULLERSLEV,NYBORG), - DONG_OEST.(SLAGELSE,SOROE,RINGSTED,STENLILLE), - HNG.(BROENDBY,DRAGOER,KARLSLUNDE,KOEGE,LYNGE,MAALOEV,TORSLUNDE,VALLENSBAEK,SYDHAVNEN,AMAGERF) - SE_S_Dist.SE_S_Rural -/; -$label end_dist - -SETS NODEDATASET "MR Station specific datatypes" - / - X - Y - Capacity - PRESSURE - /; - - -* The following will probably not be implemented. - -$ifi %MR%==yes SET NOTMR(AAA)/ DK_E_Urban, DK_W_Odense, DK_W_TREFOR/; - - -$ifi NOT %HEATING%==yes $goto end_heat -SET HEAT(AAA) / -VEKS_V -CTR_V -VFORBRANDING_V -NFORBRANDING_V -AALBORG_V -AARHUS_V -ODENSE_V -HERNING_V -TVIS_V -/; -ALIAS(HEAT,HEAT2); - -SET HX(i,j) / -VEKS_V.CTR_V -VEKS_V.VFORBRANDING_V -CTR_V.VEKS_V -CTR_V.VFORBRANDING_V -VFORBRANDING_V.CTR_V -VFORBRANDING_V.VEKS_V -/; - -SET HEATMR(HEAT,AAA)/ -VEKS_V.(TORSLUNDE), -CTR_V.(HCOERSTED), -VFORBRANDING_V.(VALLENSBAEK) -NFORBRANDING_V.(LYNGE) -AALBORG_V.(AALBORG, ELLIDSHOEJ) -AARHUS_V.(HERNING) -ODENSE_V.(HOEJBY) -HERNING_V.(HERNING) -TVIS_V.(TAULOV, SKAERBAEK) -/; - -SETS - IHX(i,j) - ; - -IHX(i,j)=YES$(HEAT(i)*HEAT(j)*HX(i,j)); -$label end_heat - - - -SET SOURCE(AAA) -/ -NYBRO -OERESUND -ELLUND -/; - -SET TRANSIT(AAA) -/ -OERESUND -/; - - -SET STORE(AAA) -/ -STENLILLE -LITORUP -SE_S_RURAL -/; - -$ifi %MR%==yes SET GGGMR(GGG) / -$ifi %MR%==yes $include '../../base/addons/gas/GGGMRset.inc'; -$ifi %MR%==yes /; - -SET SYSENTRY -/ -Nybro -Ellund -Oresund -/; - -SET SYSEXIT -/ -Oeresund -Ellund -DK-EXIT -/; - - - - - -* ----- Storage ------* -SET SCON / STORE1, - STORE2/; - -SET SCONDATASET -/ -VOLCAP 'Volume capacity in the storage facilities' -INJCAP 'Injection capcity' -EXTCAP 'Withdrawal capacity' -TARRIF 'Package tarrif' -/; - - -SET DAY 'Days of the week' -/ -MONDAY -TUESDAY -WEDNESDAY -THURSDAY -FRIDAY -SATURDAY -SUNDAY -/; - -SET DAYT 'Hours to days relationship' -/ - MONDAY.(T1*T24), - TUESDAY.(T25*T48), - WEDNESDAY.(T49*T72), - THURSDAY.(T73*T96), - FRIDAY.(T97*T130), - SATURDAY.(T131*T154), - SUNDAY.(T155*T168) -/; - - -* Definition of individual steps in the residual gas demand function: - -SET DGRESF 'Steps in elastic residual gas demand' -/ DGRESF_D1_9,DGRESF_D1_8,DGRESF_D1_7,DGRESF_D1_6,DGRESF_D1_5,DGRESF_D1_4,DGRESF_D1_3,DGRESF_D1_2,DGRESF_D1_1, - DGRESF_U1_1,DGRESF_U1_2,DGRESF_U1_3,DGRESF_U1_4,DGRESF_U1_5,DGRESF_U1_6,DGRESF_U1_7,DGRESF_U1_8,DGRESF_U1_9, - DGRESF_D2_9,DGRESF_D2_8,DGRESF_D2_7,DGRESF_D2_6,DGRESF_D2_5,DGRESF_D2_4,DGRESF_D2_3,DGRESF_D2_2,DGRESF_D2_1, - DGRESF_U2_1,DGRESF_U2_2,DGRESF_U2_3,DGRESF_U2_4,DGRESF_U2_5,DGRESF_U2_6,DGRESF_U2_7,DGRESF_U2_8,DGRESF_U2_9 -/; - - - - -* Definition of subsets of steps for regulation downwards (decreased demand) -* and upwards (increased demands) of electricity demand: - -SET DGRESF_D1(DGRESF) 'Downwards steps in elastic residual gas demand, relative data format' -/ -*DGRESF_D1_9,DGRESF_D1_8,DGRESF_D1_7,DGRESF_D1_6,DGRESF_D1_5,DGRESF_D1_4,DGRESF_D1_3,DGRESF_D1_2,DGRESF_D1_1 -/; - -SET DGRESF_U1(DGRESF) 'Upwards steps in elastic residual gas demand, relative data format' -/ -*DGRESF_U1_1,DGRESF_U1_2,DGRESF_U1_3,DGRESF_U1_4,DGRESF_U1_5,DGRESF_U1_6,DGRESF_U1_7,DGRESF_U1_8,DGRESF_U1_9 -/; - -SET DGRESF_D2(DGRESF) 'Downwards steps in elastic residual gas demand, absolute and MW-incremental data format' -/ -* DGRESF_D2_9,DGRESF_D2_8,DGRESF_D2_7,DGRESF_D2_6,DGRESF_D2_5,DGRESF_D2_4,DGRESF_D2_3,DGRESF_D2_2,DGRESF_D2_1 -/; - -SET DGRESF_U2(DGRESF) 'Upwards steps in elastic residual gas demand, absolute and MW-incremental data format' -/ -* DGRESF_U2_1,DGRESF_U2_2,DGRESF_U2_3,DGRESF_U2_4,DGRESF_U2_5,DGRESF_U2_6,DGRESF_U2_7,DGRESF_U2_8,DGRESF_U2_9 -/; - - -* Definition of individual steps in the other large consumers gas demand function: - -SET DGOTHF 'Steps in elastic other large consumers gas demand' -/ DGOTHF_D1_9,DGOTHF_D1_8,DGOTHF_D1_7,DGOTHF_D1_6,DGOTHF_D1_5,DGOTHF_D1_4,DGOTHF_D1_3,DGOTHF_D1_2,DGOTHF_D1_1, - DGOTHF_U1_1,DGOTHF_U1_2,DGOTHF_U1_3,DGOTHF_U1_4,DGOTHF_U1_5,DGOTHF_U1_6,DGOTHF_U1_7,DGOTHF_U1_8,DGOTHF_U1_9, - DGOTHF_D2_9,DGOTHF_D2_8,DGOTHF_D2_7,DGOTHF_D2_6,DGOTHF_D2_5,DGOTHF_D2_4,DGOTHF_D2_3,DGOTHF_D2_2,DGOTHF_D2_1, - DGOTHF_U2_1,DGOTHF_U2_2,DGOTHF_U2_3,DGOTHF_U2_4,DGOTHF_U2_5,DGOTHF_U2_6,DGOTHF_U2_7,DGOTHF_U2_8,DGOTHF_U2_9 -/; - - - - -* Definition of subsets of steps for regulation downwards (decreased demand) -* and upwards (increased demands) of electricity demand: - -SET DGOTHF_D1(DGOTHF) 'Downwards steps in elastic other large consumers gas demand, relative data format' -/ -*DGOTHF_D1_9,DGOTHF_D1_8,DGOTHF_D1_7,DGOTHF_D1_6,DGOTHF_D1_5,DGOTHF_D1_4,DGOTHF_D1_3,DGOTHF_D1_2,DGOTHF_D1_1 -/; - -SET DGOTHF_U1(DGOTHF) 'Upwards steps in elastic other large consumers gas demand, relative data format' -/ -*DGOTHF_U1_1,DGOTHF_U1_2,DGOTHF_U1_3,DGOTHF_U1_4,DGOTHF_U1_5,DGOTHF_U1_6,DGOTHF_U1_7,DGOTHF_U1_8,DGOTHF_U1_9 -/; - -SET DGOTHF_D2(DGOTHF) 'Downwards steps in elastic other large consumers gas demand, absolute and MW-incremental data format' -/ -* DGOTHF_D2_9,DGOTHF_D2_8,DGOTHF_D2_7,DGOTHF_D2_6,DGOTHF_D2_5,DGOTHF_D2_4,DGOTHF_D2_3,DGOTHF_D2_2,DGOTHF_D2_1 -/; - -SET DGOTHF_U2(DGOTHF) 'Upwards steps in elastic other large consumers gas demand, absolute and MW-incremental data format' -/ -* DGOTHF_U2_1,DGOTHF_U2_2,DGOTHF_U2_3,DGOTHF_U2_4,DGOTHF_U2_5,DGOTHF_U2_6,DGOTHF_U2_7,DGOTHF_U2_8,DGOTHF_U2_9 -/; - -$ifi %PIPELINE%==yes $include '../../base/addons/gas/flowsets.inc'; - - -SET - IMONTH /JAN, FEB, MAR, APR, MAY, JUN, JUL, AUG, SEP, OCT, NOV, DEC/; - -PARAMETER IMONTH_W(IMONTH) -/ -JAN 31, -FEB 28, -MAR 31, -APR 30, -MAY 31, -JUN 30, -JUL 31, -AUG 31, -SEP 30, -OCT 31, -NOV 30, -DEC 31 -/; -ALIAS(IMONTH,IMONTHALIAS) -PARAMETER IFROMDAY(IMONTH), - ITODAY(IMONTH); - -IFROMDAY(IMONTH)=sum(IMONTHALIAS$(ord(IMONTH)0); - - -SOS1 VARIABLE MN_TURN(s,t,MNLEG); - -SOS1 VARIABLE S_TURN(s,t,SLEG); - -SOS1 VARIABLE FYN_TURN(s,t,FYNLEG); - -PARAMETER BigM(e), - P_PIPES(e,s,t) "Pressure in pipes", - P_NODES(i,s,t) "Pressure at nodes"; - -BigM(e) = 4375; -* ---------------------------------------- * -* BOUNDS -* ---------------------------------------- * - -* --- SUPPLY LIMITS --- * -VSUP.UP(SOURCE,IST) = SUPPLY_MAX(SOURCE)*MWHperkNM3; -VEXP.UP(SOURCE,IST) = EXP_MAX(SOURCE)*MWHperkNM3; -VSUP.fx(i,IST)$(not SOURCE(i)) = 0; -VEXP.fx(i,IST)$(not SOURCE(i)) = 0; - -* --- d LIMITS --- * -VGUD.UP(IA,IST) = NODEDATA(IA,'Capacity')*MWHperkNM3; - -* ---PRESSURE BOUNDS AND REGULATORS--- * -VPs_NODES.LO(IA,IST) = power(NODEDATA(IA,'Pressure'),2); -VPs_NODES.UP(IA,IST) = 80*80; -VPs_PIPES.UP(e,IST)$(not dummy(e)) = power(EDGEDATA(e,'PRESSURE'),2); - -* --- STORAGE BOUNDS --- * -VINJ.UP(STORE,IST) = INJ_MAX(STORE)*MWHperkNM3; -VEXT.UP(STORE,IST) = EXT_MAX(STORE)*MWHperkNM3; -VSTOCK.UP(STORE,IST) = STORAGE_MAX(STORE)*MWHperkNM3; - -* --- FINGER BOUNDS --- * -*uu.lo(i,j,IST)$ARC(i,j) = 0; -uu.lo(i,j,IST)$ARC(i,j) = -sqrt((80*80-45*45)/sum(e$SEMIARC(e,i,j), cc(e))); -uu.up(i,j,IST)$ARC(i,j) = sqrt((80*80-45*45)/sum(e$SEMIARC(e,i,j), cc(e))); - - -u.lo(i,e,IST)$N(i,e) = -sqrt((80*80-45*45)/cc(e)); -u.up(i,e,IST)$N(i,e) = sqrt((80*80-45*45)/cc(e)); - -u.lo(i,fingers(e),IST)$(ARC_NODE_INC(e,i)=1) = 0; -u.up(i,fingers(e),IST)$(ARC_NODE_INC(e,i)=-1)= 0; - - -* ---------------------------------------- * -* EQUATIONS -* ---------------------------------------- * - -Equations - massbalance(i,s,t) "Node continuity", - ssmassbalance(i,s,t) "SS-Node contunuity", - linepack(e,s,t) "Linepack", - storagebalance(i,s,t) "Storage balances", - pipepressure(e,s,t,l) "Mean pressure level in the pipes", - flowdir_pos_MN(i,MNLEG,s,t) - flowdir_neg_MN(i,MNLEG,s,t) - flowdir_pos_S(i,SLEG,s,t) - flowdir_neg_S(i,SLEG,s,t) - flowdir_pos_FYN(i,FYNLEG,s,t) - flowdir_neg_FYN(i,FYNLEG,s,t) - - ssflowdir_pos_MN(i,j,MNLEG,s,t) - ssflowdir_neg_MN(i,j,MNLEG,s,t) - ssflowdir_pos_S(i,j,SLEG,s,t) - ssflowdir_neg_S(i,j,SLEG,s,t) - ssflowdir_pos_FYN(i,j,FYNLEG,s,t) - ssflowdir_neg_FYN(i,j,FYNLEG,s,t) - - linepackss(e,s,t) - seasonstock(i,s), - seasonlinepack(e,s) ; - -ssmassbalance(IA,IST) .. - VSUP(IA,IST)$SOURCE(IA) - - VEXP(IA,IST)$SOURCE(IA) - + VEXT(IA,IST)$STORE(IA) - - VINJ(IA,IST)$STORE(IA) - =E= sum(j$(ARC(IA,j)), - uu(IA,j,IST) - ) - -sum(j$(ARC(j,IA)), - uu(j,IA,IST) - ) - + VGUD(IA,IST); - - - -massbalance(IA,IST) .. - VSUP(IA,IST)$SOURCE(IA) - - VEXP(IA,IST)$SOURCE(IA) - + VEXT(IA,IST)$STORE(IA) - - VINJ(IA,IST)$STORE(IA) - =E= sum(e$(N(IA,e)), - u(IA,e,IST) - ) - + VGUD(IA,IST); - -linepack(e,IST(s,t)) .. - VO(e,s,t++1) - =E= VO(e,s,t) - +sum(i$(N(i,e)), - u(i,e,s,t) - ); - -linepackss(e,IST(s,t)) .. - 0 - =E= - +sum(i$(N(i,e)), - u(i,e,s,t) - ); - -pipepressure(e,IST,l)$(not DUMMY(e)) .. - 1000*VO(e,IST)/(K(IST)*VOL(e)) - =G= aa(l)*VPs_PIPES(e,IST)+bb(l); - -storagebalance(STORE,IST(s,t))$(ord(t)0); -NN(i,j) = YES$(ARC(i,j) or ARC(j,i)); \ No newline at end of file diff --git a/base/addons/hydrogen/bb4/hydrogen_acronyms.inc b/base/addons/hydrogen/bb4/hydrogen_acronyms.inc deleted file mode 100644 index c7d8eecb..00000000 --- a/base/addons/hydrogen/bb4/hydrogen_acronyms.inc +++ /dev/null @@ -1,2 +0,0 @@ -ACRONYM HYDROGEN_GH2STO,H2_STORAGE, HYDROGEN_GETOH2, HYDROGEN_GETOHH2, HYDROGEN_GEHTOH2, HYDROGEN_GCH4TOH2, HYDROGEN_GH2FUEL, HYDROGEN_GH2TOE, HYDROGEN_GH2TOEH, HYDROGEN_GH2TOBIOMETH,FUELCELL, BIOMETHDAC, ELECTROLYZER,STEAMREFORMING,HYDROGEN_GIMPORTH2 -; diff --git a/base/addons/hyrsbb123/HYRSBB123sim3.inc b/base/addons/hyrsbb123/HYRSBB123sim3.inc deleted file mode 100644 index 5f3233a8..00000000 --- a/base/addons/hyrsbb123/HYRSBB123sim3.inc +++ /dev/null @@ -1,39 +0,0 @@ -* File HYRSBB123sim3.inc for the Balmorel model. -* For use with addon HYRSBB123. -* Included into bb123.sim after SOLVE BALBASE3 statement, inside the loop over seasons. - - -* Update accumulated hydro amounts used: - -IHYRSBB123BB3GACCUM(IA) = IHYRSBB123BB3GACCUM(IA) + SUM(IGHYRS, SUM(T, (IHOURSINST(IS3LOOPSET,T) * VGE_T.L(IA,IGHYRS,IS3LOOPSET,T))$IAGK_Y(IA,IGHYRS) )); -IHYRSBB123BB12GACCUM(IA) = IHYRSBB123BB12GACCUM(IA) + HYRSG(Y,IA,IS3LOOPSET); - - -*hydroinflow(ia,IS3LOOPSET) = IHYINF_S(IA,IS3LOOPSET) * SUM(IGHYRS$IAGK_Y(IA,IGHYRS), (IGKVACCTOY(IA,IGHYRS)+IGKFX_Y(IA,IGHYRS))); -*hydroinflow(ia,s) = IHYINF_S(IA,S) * SUM(IGHYRS$IAGK_Y(IA,IGHYRS), (IGKVACCTOY(IA,IGHYRS)+IGKFX_Y(IA,IGHYRS))); - - -* Set volume deviation relative to BB1/BB2for the next season: -* The following adjustment distributes the hydro energy deviation evenly over the remaining seasons. -* It may need adaptation to specific circumstancs, and may also be generally improved. But it is simple. -* Todo: if S and SSS are different then a correction might be needed (not implemented). So for now: make sure that S and SSS are identical, see file hyrsbb123internals.inc. -IHYRSBB123DELTAVOL(IA) = ((IHYRSBB123BB12GACCUM(IA) - IHYRSBB123BB3GACCUM(IA))/(CARD(IS3LOOPSET)+1 - ORD(IS3LOOPSET)))$IHYRSBB123G_Y(IA,IS3LOOPSET); - -DISPLAY "FØR UPDATE I SIM3", IHYRSBB123VOLINI ; -* IHYRSBB123VOLINI: next season initial volume level. -* With equation QWATERVOLINI it will make VHYRS_S.L = IHYRSBB123VOLINI, which will be part of the information generated through positive savepoint value. -IHYRSBB123VOLINI(IA,IS3LOOPSET+1) = ( IHYRSBB123VOLINI(IA,IS3LOOPSET) - + IHYINF_S(IA,IS3LOOPSET)*SUM(IGHYRS, IGKFX_Y(IA,IGHYRS)) - - SUM(IGHYRS, SUM(T, (IHOURSINST(IS3LOOPSET,T) * VGE_T.L(IA,IGHYRS,IS3LOOPSET,T))$IAGK_Y(IA,IGHYRS) )) )$IHYRSBB123G_Y(IA,IS3LOOPSET); -* Since variable VHYRS_S is declared as positive it will create infeasibilities if IHYRSBB123VOLINI were negative, therefore correct if negative. -ISCALAR1 = SMIN(IA,IHYRSBB123VOLINI(IA,IS3LOOPSET+1)); -IF((ISCALAR1 < 0), - DISPLAY INFODISPLAY, "Warning: SMIN(IA,IS3LOOPSET+1) is negative " ; - PUT ERRORFILE "Warning from addon hyrsbb123, file hyrsbb123sim3.inc:"/; - PUT " BB3 simulation would like to have negative hydro storage volume after simulation of Season ", IS3LOOPSET.TL:4, ", most negative value would be ", ISCALAR1:6:0 /; - PUT " Hydro starage levels were artificially increased by that amount."//; -); - - -IHYRSBB123VOLINI(IA,IS3LOOPSET+1) = MAX(0,IHYRSBB123VOLINI(IA,IS3LOOPSET+1)); - diff --git a/base/addons/hyrsbb123/bb4/HYRSBB123sim3.inc b/base/addons/hyrsbb123/bb4/HYRSBB123sim3.inc deleted file mode 100644 index 5f3233a8..00000000 --- a/base/addons/hyrsbb123/bb4/HYRSBB123sim3.inc +++ /dev/null @@ -1,39 +0,0 @@ -* File HYRSBB123sim3.inc for the Balmorel model. -* For use with addon HYRSBB123. -* Included into bb123.sim after SOLVE BALBASE3 statement, inside the loop over seasons. - - -* Update accumulated hydro amounts used: - -IHYRSBB123BB3GACCUM(IA) = IHYRSBB123BB3GACCUM(IA) + SUM(IGHYRS, SUM(T, (IHOURSINST(IS3LOOPSET,T) * VGE_T.L(IA,IGHYRS,IS3LOOPSET,T))$IAGK_Y(IA,IGHYRS) )); -IHYRSBB123BB12GACCUM(IA) = IHYRSBB123BB12GACCUM(IA) + HYRSG(Y,IA,IS3LOOPSET); - - -*hydroinflow(ia,IS3LOOPSET) = IHYINF_S(IA,IS3LOOPSET) * SUM(IGHYRS$IAGK_Y(IA,IGHYRS), (IGKVACCTOY(IA,IGHYRS)+IGKFX_Y(IA,IGHYRS))); -*hydroinflow(ia,s) = IHYINF_S(IA,S) * SUM(IGHYRS$IAGK_Y(IA,IGHYRS), (IGKVACCTOY(IA,IGHYRS)+IGKFX_Y(IA,IGHYRS))); - - -* Set volume deviation relative to BB1/BB2for the next season: -* The following adjustment distributes the hydro energy deviation evenly over the remaining seasons. -* It may need adaptation to specific circumstancs, and may also be generally improved. But it is simple. -* Todo: if S and SSS are different then a correction might be needed (not implemented). So for now: make sure that S and SSS are identical, see file hyrsbb123internals.inc. -IHYRSBB123DELTAVOL(IA) = ((IHYRSBB123BB12GACCUM(IA) - IHYRSBB123BB3GACCUM(IA))/(CARD(IS3LOOPSET)+1 - ORD(IS3LOOPSET)))$IHYRSBB123G_Y(IA,IS3LOOPSET); - -DISPLAY "FØR UPDATE I SIM3", IHYRSBB123VOLINI ; -* IHYRSBB123VOLINI: next season initial volume level. -* With equation QWATERVOLINI it will make VHYRS_S.L = IHYRSBB123VOLINI, which will be part of the information generated through positive savepoint value. -IHYRSBB123VOLINI(IA,IS3LOOPSET+1) = ( IHYRSBB123VOLINI(IA,IS3LOOPSET) - + IHYINF_S(IA,IS3LOOPSET)*SUM(IGHYRS, IGKFX_Y(IA,IGHYRS)) - - SUM(IGHYRS, SUM(T, (IHOURSINST(IS3LOOPSET,T) * VGE_T.L(IA,IGHYRS,IS3LOOPSET,T))$IAGK_Y(IA,IGHYRS) )) )$IHYRSBB123G_Y(IA,IS3LOOPSET); -* Since variable VHYRS_S is declared as positive it will create infeasibilities if IHYRSBB123VOLINI were negative, therefore correct if negative. -ISCALAR1 = SMIN(IA,IHYRSBB123VOLINI(IA,IS3LOOPSET+1)); -IF((ISCALAR1 < 0), - DISPLAY INFODISPLAY, "Warning: SMIN(IA,IS3LOOPSET+1) is negative " ; - PUT ERRORFILE "Warning from addon hyrsbb123, file hyrsbb123sim3.inc:"/; - PUT " BB3 simulation would like to have negative hydro storage volume after simulation of Season ", IS3LOOPSET.TL:4, ", most negative value would be ", ISCALAR1:6:0 /; - PUT " Hydro starage levels were artificially increased by that amount."//; -); - - -IHYRSBB123VOLINI(IA,IS3LOOPSET+1) = MAX(0,IHYRSBB123VOLINI(IA,IS3LOOPSET+1)); - diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123_hellolog.inc b/base/addons/hyrsbb123/bb4/hyrsbb123_hellolog.inc deleted file mode 100644 index d465f122..00000000 --- a/base/addons/hyrsbb123/bb4/hyrsbb123_hellolog.inc +++ /dev/null @@ -1,11 +0,0 @@ -* File HYRSBB123_hellolog.inc for the Balmorel model. -* For use with addon HYRSBB123. - -PUT " - Addon hyrsbb123" /; - - -$ontext - -Explanation - todo. - -$offtext \ No newline at end of file diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123addobj.inc b/base/addons/hyrsbb123/bb4/hyrsbb123addobj.inc deleted file mode 100644 index e25947f8..00000000 --- a/base/addons/hyrsbb123/bb4/hyrsbb123addobj.inc +++ /dev/null @@ -1,13 +0,0 @@ -* File HYRSBB123addobj.inc for the Balmorel model. -* For use with addon HYRSBB123. -* Included into the objective function QOBJ in Balmorel.gms. - -$ifi not %BB3%==yes $goto label_HYRSBB123addobj - -$ifi %BB3%==yes $ifi %HYRSBB123%==price + SUM(IAGK_Y(IA,IGHYRS),SUM((IS3,T), IWATERVAL_Y(IA,IS3)* IHOURSINST(IS3,T) * VGE_T(IA,IGHYRS,IS3,T))) !! no investments in BB3 - -$ifi %BB3%==yes $ifi %HYRSBB123%==quantprice + SUM(IAGK_Y(IA,IGHYRS),SUM((IS3,T), IWATERVAL_Y(IA,IS3)* IHOURSINST(IS3,T) * VGE_T(IA,IGHYRS,IS3,T))) !! no investments in BB3 -$ifi %BB3%==yes $ifi %HYRSBB123%==quantprice + SUM((IA,IS3)$IHYRSBB123G_Y(IA,IS3), (SUM(T,IHOURSINST(IS3,T)))* SUM(IHYRSBB12DELTASET, VIHYRSBB12DELTAMHW(IA,IHYRSBB12DELTASET) *IHYRSBB123EXTRAVAL(IA,IS3,IHYRSBB12DELTASET))) - -$label label_HYRSBB123addobj; -$ifi not %BB3%==yes !! print some error message - todo. diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123balbase3.inc b/base/addons/hyrsbb123/bb4/hyrsbb123balbase3.inc deleted file mode 100644 index 0f141dcd..00000000 --- a/base/addons/hyrsbb123/bb4/hyrsbb123balbase3.inc +++ /dev/null @@ -1,6 +0,0 @@ -* Equations for addon hyrsbb123 used in Balbase3 model definition -* Latest revision 20170419 (hr) - -$ifi %HYRSBB123%==quant QHYRSG -$ifi %HYRSBB123%==quantprice QHYRSG -$ifi %HYRSBB123%==quantprice QWATERVOLINI diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123documentation.txt b/base/addons/hyrsbb123/bb4/hyrsbb123documentation.txt deleted file mode 100644 index e71de7d2..00000000 --- a/base/addons/hyrsbb123/bb4/hyrsbb123documentation.txt +++ /dev/null @@ -1,106 +0,0 @@ -$ontext -Hans Ravn -Preliminary. Revised 20170412. - -Addon HYDROBB123 - - -* For model BB3 it is possible to use values found from BB1 or BB2 solutions to achieve use of hydro power -* that corresponds to that found in a BB1 or BB2 solution. -$setglobal HYDROBB123 quantprice -*!option quant "Use seasonal quantities of hydro generation (from HYRSG.gdx) in BB3 ('primal decomposition')" -*!mayif %BB3%==yes -*!option price "Use seasonal prices for hydro generation (from WATERVAL.gdx) in BB3 ('dual decomposition')" -*!mayif %BB3%==yes -*!option quantprice "Use seasonal quantities and prices for hydro generation (from HYRSG.gdx?? and WATERVAL.gdx) in BB3" -*!mayif %BB3%==yes -*!option none "Do not not use this hydro addon" -*!mustif not %BB3%==yes -$ifi not %BB3%==yes $setglobal HYDROBB123 none - - -The option replaces controls: MAKEWATER and WATERVAL. - -This option variable has influence on hydro with storage and no influence on hydro run of river. - -For any of the options it makes sense to use it only if the seasons in S (SET S) are the same for BB3 and BB1/BB2, and have the same relative weights (WEIGHT_S). -(might be permitted in later versions) - -Allocates a fixed hydro quantity WATERVOL for use in each season, this volume was generated by a BB1 or BB2 solution and saved as simex\WATERVOL.gdx. -That amount may be exceeded at a marginal cost. That cost depends on the water values WATERVAL found in the BB1 or BB2 solution and saved as simex\WATERVAL.gdx. -The cost will be somewhat higher that the cost given in WATERVAL and will increase by increasing hydro amounts used. -General: - -After successful solution from BB1 or BB2 the water values, hydro production and beginning-of-season storage volumes will be saved to files WATERVAL.gdx, HYRSG.gdx and WATERVOLINI.gdx. -These parameters are declared in Balmorel and saved irrespective of the chosen option for HYDROBB123. -Values saved (unconditionally) to folder simex as gdx files: - HYRSG.gdx', HYRSG; - VHYRS_SL.gdx', VHYRS_S.L; - WATERVAL.gdx', WATERVAL; - -When BB3 is to be solved these values are loaded into Balmorel. What happens next depends in the option chosen. -Option specific: - -For %HYDROBB123%==quant: -Equation QWATERVOLINI is part of model BALBASE3. This limits the hydro energy generation to the same as found in the BB1/BB2 solution, known from VHYRS_SL.gdx. -Values saved (conditionally) to folder simex af gdx files with option quant : - HYRSG.gdx',HYRSG; - WATERVAL.gdx',WATERVAL; - -For %HYDROBB123%==price: -Equation QWATERVOLINI is not part of model BALBASE3. In the objective function QOBJ the water values found in BB1/BB2, known from VHYRS_SL.gdx, -are used as marginal cost for hydro power generation. -Values saved to folder simex af gdx files with option price: - HYRSG.gdx',HYRSG; - WATERVAL.gdx',WATERVAL; - -For %HYDROBB123%==quantprice: - -Before entering the loop over seasons the hydro volumes for the beginning of the first season are fixed to that of the BB1/BB2 solution, -which is known from VHYRS_SL.gdx (containing values VHYRS_S.L). - -When BB3 is solved for a season the equation QWATERVOLINI attempts to limit the use of hydro power each season -to not exceed what was found in BB1/BB2. QWATERVOLINI is located in Balmorel.gms. todo-noget inkonsistent... -The limit may be exceeded at marginal costs which for smaller amounts are not much higher than the water values found in BB1/BB2, -known from WATERVAL.gdx, but if more is used the marginal costs increase. -In the objective function QOBJ the cost of hydro power generation is the same as given as variable costs XXX . -If the hydro amounts used exceed what was found in BB1/BB2 an additional variable cost component is added in QOBJ. - -In the loop over seasons a bookkeeping mechanism tracks the amounts of water used in current season and accumulated over the simulated seasons. -Similarly the amounts of water use for BB1/BB2 by season (known from VHYRS_SL.gdx) and accumulated over the simulated seasons are kept track of. - -If after the solution for a particular season it is found that there is a deviation between the accumulated amounts for BB3 and BB1/BB2 -then data is manipulated with the aim to restore accumulated amounts for the remaining seasons. -The available main mechanisms are tightening of equation QWATERVOLINI and increase of the marginal cost for exceeding that level. - -If variable VHYRS_S (hydro energy equivalent (storage level) at the start of the season (MWh)) -were declared as a positive variable an error would be issued in case of negative values of IHYDROBB123VOLINI, -cf. equation QWATERVOLINI which essentially states that VHYRS_S =E= IHYDROBB123VOLINI. -To avoid this, the variable VHYRS_S is declared as a free variable for model BB3 (while positive in models BB1 and BB2). -The sole intention of equation IHYDROBB123VOLINI is to permit convenient comparison with the BB1/BB2 solution through VHYRS_S. - -Values saved to folder simex af gdx files with option quantprice: - HYRSG_bb3.gdx',HYRSG; - VHYRS_SL_bb3.gdx',VHYRS_S.L; - WATERVAL_bb3.gdx',WATERVAL; ? - - -Some details: -PARAMETER IHYRSG_Y(AAA,SSS) "Water (hydro) generation quantity of the seasons transferred from model BB1/BB2 for use in model Balbase3 (MWh)" ; -PARAMETER IWATERVOL_Y(AAA,SSS) "Water quantity (storage level at beginning of season) transferred from model BB1/BB2 for use in model Balbase3 (MWh)" ; -PARAMETER IWATERVAL_Y(AAA,SSS) "Water value (in input Money) transferred from model BB1/BB2 for use in model Balbase3 (MWh)" ; -SET IHYDROBB12DELTASET "Increase steps of hydro generation above BB1 level" / IBB123HYG1*IBB123HYG5 /; -PARAMETER IHYDROBB123BB3GACCUM(AAA) "The amount of water used so far during present year during BB3 solutions (MWh)"; -PARAMETER IHYDROBB123BB12GACCUM(AAA) "The amount of water used so far during present year according to BB1/BB2 solution (MWh)"; -PARAMETER IHYDROBB123DELTAVOL(AAA) "Amount of additional hydro energy (relative to BB1/BB2) pre-allocated this season (may be negative) in model BB3 (MWh)"; -PARAMETER IHYDROBB123VOLINI(AAA,S) "Initial storage level (MWh)"; -PARAMETER VHYRS_SL(Y,AAA,S) "To be saved for comparison with BB1/BB2 solution value for VHYRS_S.L (declared as a parameter) (MWh)"; -PARAMETER IHYDROBB123EXTRAVAL(AAA,S,IHYDROBB12DELTASET) ""; - -POSITIVE VARIABLE VIHYRSBB12DELTAMHW(AAA,IHYDROBB12DELTASET) "Amount of additional hydro energy allocated this season during optimization of model Balbase3 (MWh)"; - -EQUATION QWATERVOLINI(AAA,S) "Hydro energy (volume) in storage at the beginning of season in model Balbase3 (MWh)" ; -EQUATION QHYRSG(AAA,S) "Hydro energy available for this region and season in model Balbase3 (MWh)"; - - -$offtext diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123equations.inc b/base/addons/hyrsbb123/bb4/hyrsbb123equations.inc deleted file mode 100644 index 00ec70ee..00000000 --- a/base/addons/hyrsbb123/bb4/hyrsbb123equations.inc +++ /dev/null @@ -1,54 +0,0 @@ -* File HYRSBB123equations.inc for the Balmorel model. -* For use with addon HYRSBB123. -* Latest revision 20170419 (hr) - -$ifi not %BB3%==yes $goto label_hydrobb123equations -$ifi %HYRSBB123%==price $goto label_hydrobb123equations - -$ifi %BB4%==yes $goto BB4begin - -EQUATION QWATERVOLINI(AAA,S) 'Hydro energy (volume) in storage at the beginning of season in model Balbase3 (MWh)' ; -EQUATION QHYRSG(AAA,S) 'Hydro energy available for this region and season in model Balbase3 (MWh)'; - -* The equation QWATERVOLINI atttempts to achieve start-of-season values (MWh) for VHYRS_S.L that are comparable for those from BB1/BB2. -* Note that VHYRS_S is declared as a positive variable, therefore it is elsewhere artificially assured that IHYRSBB123VOLINI is non-negative. - -QWATERVOLINI(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. VHYRS_S(IA,IS3) =E= IHYRSBB123VOLINI(IA,IS3); - -* The equation QHYRSG tries to limit the HYRS generation to same level as in BB1/BB2. -* The equation will not be infeasible since VIHYRSBB12DELTAMHW.UP is INF for the last element wrt. IHYRSBB12DELTASET. -QHYRSG(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. - IHYRSBB123G_Y(IA,IS3) !! Unit: (MWh) -$ifi %HYRSBB123%==quantprice + IHYRSBB123DELTAVOL(IA) + SUM(IHYRSBB12DELTASET, VIHYRSBB12DELTAMHW(IA,IHYRSBB12DELTASET)) - =G= - SUM(IGHYRS$IAGK_Y(IA,IGHYRS), SUM(T,IHOURSINST(IS3,T)*VGE_T(IA,IGHYRS,IS3,T))) -; - -$goto label_hydrobb123equations - -$label BB4begin - -* Equations not verified for BB4! - -EQUATION QWATERVOLINI(Y,AAA,S) 'Hydro energy (volume) in storage at the beginning of season in model Balbase3 (MWh)' ; -EQUATION QHYRSG(Y,AAA,S) 'Hydro energy available for this region and season in model Balbase3 (MWh)'; - -* The equation QWATERVOLINI atttempts to achieve start-of-season values (MWh) for VHYRS_S.L that are comparable for those from BB1/BB2. -* Note that VHYRS_S is declared as a positive variable, therefore it is elsewhere artificially assured that IHYRSBB123VOLINI is non-negative. - -TODO: - - -QWATERVOLINI(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. VHYRS_S(IA,IS3) =E= IHYRSBB123VOLINI(IA,IS3); - -* The equation QHYRSG tries to limit the HYRS generation to same level as in BB1/BB2. -* The equation will not be infeasible since VIHYRSBB12DELTAMHW.UP is INF for the last element wrt. IHYRSBB12DELTASET. -QHYRSG(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. - IHYRSBB123G_Y(IA,IS3) !! Unit: (MWh) -$ifi %HYRSBB123%==quantprice + IHYRSBB123DELTAVOL(IA) + SUM(IHYRSBB12DELTASET, VIHYRSBB12DELTAMHW(IA,IHYRSBB12DELTASET)) - =G= - SUM(IGHYRS$IAGK_Y(IA,IGHYRS), SUM(T,IHOURSINST(IS3,T)*VGE_T(IA,IGHYRS,IS3,T))) -; - - -$label label_hydrobb123equations diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123includebb12data.inc b/base/addons/hyrsbb123/bb4/hyrsbb123includebb12data.inc deleted file mode 100644 index 2866dd70..00000000 --- a/base/addons/hyrsbb123/bb4/hyrsbb123includebb12data.inc +++ /dev/null @@ -1,20 +0,0 @@ -* File HYRSBB123includebb12data.inc for the Balmorel model. -* For use with addon HYRSBB123. - -* Data for handling of annual hydro constraints in BB3. -* Supposed to be saved by a previous execution of model BALBASE1 or BALBASE2 (BB1 or BB2). - -$ifi not %BB3%==yes $goto label_enofhyrsbb123includebb12data -$ifi %HYRSBB123%==none $goto label_enofhyrsbb123includebb12data - -execute_load '../../simex/HYRSG.gdx', HYRSG; -execute_load '../../simex/WATERVAL.gdx', WATERVAL; -execute_load '../../simex/VHYRS_SL.gdx', VHYRS_SL; - -* Trim with respect to S for the case that S in BB3 is a proper subset of the S used in BB1\BB2, for nicety of output only: -HYRSG(Y,AAA,SSS)$(not S(SSS)) = 0; -WATERVAL(Y,AAA,SSS)$(not S(SSS)) = 0; -VHYRS_SL(Y,AAA,SSS)$(not S(SSS)) = 0; -* The indexes Y and IA(AAA) are simply assumed to be the same. - -$label label_enofhyrsbb123includebb12data diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123internals.inc b/base/addons/hyrsbb123/bb4/hyrsbb123internals.inc deleted file mode 100644 index 4ad0c3a8..00000000 --- a/base/addons/hyrsbb123/bb4/hyrsbb123internals.inc +++ /dev/null @@ -1,26 +0,0 @@ -* The three parameters WATERVOLINI, WATERVAL and VHYRS_SLare declared as standard in the Balmorel model. -* Additional declaration and definitions for HYRSBB123 only are given here. - -* In BB3 the following set and parameters are used to control the use of hydro with storage, conditioned by HYRSBB123: - -SET IHYRSBB12DELTASET "Increase steps of hydro generation above BB1 level" / IHYRSBB123G1*IHYRSBB123G5 /; - -PARAMETER IHYRSBB123G_Y(AAA,SSS) "Water (hydro) generation quantity of the seasons transferred from model BB1/BB2 for use in model Balbase3 (MWh)"; -PARAMETER IWATERVAL_Y(AAA,SSS) "Water value (in input Money) transferred from model BB1/BB2 for use in model Balbase3 (MWh)"; -PARAMETER IHYRSBB123BB3GACCUM(AAA) "The amount of water used so far during present year during BB3 solutions (MWh)"; -PARAMETER IHYRSBB123BB12GACCUM(AAA) "The amount of water used so far during present year according to BB1/BB2 solution (MWh)"; -PARAMETER IHYRSBB123DELTAVOL(AAA) "Amount of additional hydro energy (relative to BB1/BB2) pre-allocated this season (may be negative) in model BB3 (MWh)"; -PARAMETER IHYRSBB123VOLINI(AAA,S) "Initial storage level (MWh)"; -PARAMETER IHYRSBB123EXTRAVAL(AAA,S,IHYRSBB12DELTASET); - - -* Test that Card(S) is equal to Card(SSS): -IF((CARD(S) NE CARD(SSS)), -* PUT ERRORFILE "Warning from addon hyrsbb123, file hyrsbb123sim3.inc:"/; -* PUT "Card(S) is not equal to Card(SSS), it must be." /; - DISPLAY "Warning from addon hyrsbb123, file hyrsbb123sim3.inc:"; - DISPLAY "Card(S) is not equal to Card(SSS), it must be." ; -); - - -$label label_hydrobb123internals \ No newline at end of file diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123sim1.inc b/base/addons/hyrsbb123/bb4/hyrsbb123sim1.inc deleted file mode 100644 index 489cc976..00000000 --- a/base/addons/hyrsbb123/bb4/hyrsbb123sim1.inc +++ /dev/null @@ -1,17 +0,0 @@ -* File HYRSBB123sim1.inc for the Balmorel model. -* For use with addon HYRSBB123. -* Included into bb123.sim before loop over seasons, inside the loop over years. - -IHYRSBB123BB3GACCUM(IA) = 0; -IHYRSBB123BB12GACCUM(IA) = 0; -IHYRSBB123DELTAVOL(IA) = 0; -IHYRSBB123VOLINI(IA,S)$(ORD(S) EQ 1) = VHYRS_SL(Y,IA,S); - -* Todo: if S and SSS are different then a rescaling might be useful for comparison of results (HYRSG). -* So for now: make sure that S and SSS are identical. - - - - - - diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123sim2.inc b/base/addons/hyrsbb123/bb4/hyrsbb123sim2.inc deleted file mode 100644 index b3f05ba4..00000000 --- a/base/addons/hyrsbb123/bb4/hyrsbb123sim2.inc +++ /dev/null @@ -1,24 +0,0 @@ -* File HYRSBB123sim2.inc for the Balmorel model. -* For use with addon HYRSBB123. -* Included into bb123.sim before SOLVE BALBASE3 statement, inside the loop over seasons. - -* Set values relative to the possible hydro use reletive to BB1/BB2: - - -ISCALAR1 = 0.1; !! Positive. Defines the largest individual increment, as share of the hydro power used in the BB1/BB2 solution. -VIHYRSBB12DELTAMHW.UP(IA,IHYRSBB12DELTASET) = IHYRSBB123G_Y(IA,IS3LOOPSET)*ISCALAR1*ORD(IHYRSBB12DELTASET)/CARD(IHYRSBB12DELTASET); !! Increments get larger with increased hydro use -VIHYRSBB12DELTAMHW.UP(IA,IHYRSBB12DELTASET)$(ORD(IHYRSBB12DELTASET) EQ CARD(IHYRSBB12DELTASET)) = INF; !! No limit, to ensure feasibility in equation QWATERVOL - -* Water Values: -ISCALAR1 = 2.0; !! Positive. With e.g. 2.0 the increments (above the BB1 values) on the Water Values will be doubled for each step, to reach a maximum of 2 times the BB1 values. -ISCALAR2=POWER(1/ISCALAR1,CARD(IHYRSBB12DELTASET)); -ISCALAR3=1.0; !! Additional increase may be attained by increasing ISCALAR3 above 1.0. -LOOP(IHYRSBB12DELTASET, - ISCALAR2=ISCALAR2*ISCALAR1; - IHYRSBB123EXTRAVAL(IA,IS3LOOPSET,IHYRSBB12DELTASET) = IWATERVAL_Y(IA,IS3LOOPSET)*ISCALAR3*(1+ISCALAR2); -); - - - - - diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123unload.inc b/base/addons/hyrsbb123/bb4/hyrsbb123unload.inc deleted file mode 100644 index 3cdd118a..00000000 --- a/base/addons/hyrsbb123/bb4/hyrsbb123unload.inc +++ /dev/null @@ -1,14 +0,0 @@ -* File HYRSBB123unlaod.inc for the Balmorel model. -* For use with addon HYRSBB123. - -$ifi not %BB3%==yes $goto label_hydrobb123unload - -$ifi %HYRSBB123%==quant execute_unload '../../simex/HYRSG_BB3.gdx',HYRSG; -$ifi %HYRSBB123%==quant execute_unload '../../simex/WATERVAL_BB3.gdx',WATERVAL; -$ifi %HYRSBB123%==price execute_unload '../../simex/HYRSG_BB3.gdx',HYRSG; -$ifi %HYRSBB123%==price execute_unload '../../simex/WATERVAL_BB3.gdx',WATERVAL; -$ifi %HYRSBB123%==quantprice execute_unload '../../simex/HYRSG_BB3.gdx',HYRSG; -$ifi %HYRSBB123%==quantprice execute_unload '../../simex/VHYRS_SL_BB3.gdx',VHYRS_SL; -$ifi %HYRSBB123%==quantprice execute_unload '../../simex/WATERVAL_BB3.gdx',WATERVAL; - -$label label_hydrobb123unload \ No newline at end of file diff --git a/base/addons/hyrsbb123/bb4/hyrsbb123variables.inc b/base/addons/hyrsbb123/bb4/hyrsbb123variables.inc deleted file mode 100644 index 3603e249..00000000 --- a/base/addons/hyrsbb123/bb4/hyrsbb123variables.inc +++ /dev/null @@ -1,7 +0,0 @@ -* File HYRSBB123internals.inc for the Balmorel model. -* For use with addon HYRSBB123. -* Latest revision 20170419 (hr) - -$ifi not %BB4%==yes POSITIVE VARIABLE VIHYRSBB12DELTAMHW(AAA,IHYRSBB12DELTASET) "Amount of additional hydro energy allocated this season during optimization of model Balbase3 (MWh)"; - -$ifi %BB4%==yes POSITIVE VARIABLE VIHYRSBB12DELTAMHW(Y,AAA,IHYRSBB12DELTASET) "Amount of additional hydro energy allocated this season during optimization of model Balbase3 (MWh)"; diff --git a/base/addons/hyrsbb123/hyrsbb123_hellolog.inc b/base/addons/hyrsbb123/hyrsbb123_hellolog.inc deleted file mode 100644 index d465f122..00000000 --- a/base/addons/hyrsbb123/hyrsbb123_hellolog.inc +++ /dev/null @@ -1,11 +0,0 @@ -* File HYRSBB123_hellolog.inc for the Balmorel model. -* For use with addon HYRSBB123. - -PUT " - Addon hyrsbb123" /; - - -$ontext - -Explanation - todo. - -$offtext \ No newline at end of file diff --git a/base/addons/hyrsbb123/hyrsbb123addobj.inc b/base/addons/hyrsbb123/hyrsbb123addobj.inc deleted file mode 100644 index e25947f8..00000000 --- a/base/addons/hyrsbb123/hyrsbb123addobj.inc +++ /dev/null @@ -1,13 +0,0 @@ -* File HYRSBB123addobj.inc for the Balmorel model. -* For use with addon HYRSBB123. -* Included into the objective function QOBJ in Balmorel.gms. - -$ifi not %BB3%==yes $goto label_HYRSBB123addobj - -$ifi %BB3%==yes $ifi %HYRSBB123%==price + SUM(IAGK_Y(IA,IGHYRS),SUM((IS3,T), IWATERVAL_Y(IA,IS3)* IHOURSINST(IS3,T) * VGE_T(IA,IGHYRS,IS3,T))) !! no investments in BB3 - -$ifi %BB3%==yes $ifi %HYRSBB123%==quantprice + SUM(IAGK_Y(IA,IGHYRS),SUM((IS3,T), IWATERVAL_Y(IA,IS3)* IHOURSINST(IS3,T) * VGE_T(IA,IGHYRS,IS3,T))) !! no investments in BB3 -$ifi %BB3%==yes $ifi %HYRSBB123%==quantprice + SUM((IA,IS3)$IHYRSBB123G_Y(IA,IS3), (SUM(T,IHOURSINST(IS3,T)))* SUM(IHYRSBB12DELTASET, VIHYRSBB12DELTAMHW(IA,IHYRSBB12DELTASET) *IHYRSBB123EXTRAVAL(IA,IS3,IHYRSBB12DELTASET))) - -$label label_HYRSBB123addobj; -$ifi not %BB3%==yes !! print some error message - todo. diff --git a/base/addons/hyrsbb123/hyrsbb123balbase3.inc b/base/addons/hyrsbb123/hyrsbb123balbase3.inc deleted file mode 100644 index 0f141dcd..00000000 --- a/base/addons/hyrsbb123/hyrsbb123balbase3.inc +++ /dev/null @@ -1,6 +0,0 @@ -* Equations for addon hyrsbb123 used in Balbase3 model definition -* Latest revision 20170419 (hr) - -$ifi %HYRSBB123%==quant QHYRSG -$ifi %HYRSBB123%==quantprice QHYRSG -$ifi %HYRSBB123%==quantprice QWATERVOLINI diff --git a/base/addons/hyrsbb123/hyrsbb123documentation.txt b/base/addons/hyrsbb123/hyrsbb123documentation.txt deleted file mode 100644 index e71de7d2..00000000 --- a/base/addons/hyrsbb123/hyrsbb123documentation.txt +++ /dev/null @@ -1,106 +0,0 @@ -$ontext -Hans Ravn -Preliminary. Revised 20170412. - -Addon HYDROBB123 - - -* For model BB3 it is possible to use values found from BB1 or BB2 solutions to achieve use of hydro power -* that corresponds to that found in a BB1 or BB2 solution. -$setglobal HYDROBB123 quantprice -*!option quant "Use seasonal quantities of hydro generation (from HYRSG.gdx) in BB3 ('primal decomposition')" -*!mayif %BB3%==yes -*!option price "Use seasonal prices for hydro generation (from WATERVAL.gdx) in BB3 ('dual decomposition')" -*!mayif %BB3%==yes -*!option quantprice "Use seasonal quantities and prices for hydro generation (from HYRSG.gdx?? and WATERVAL.gdx) in BB3" -*!mayif %BB3%==yes -*!option none "Do not not use this hydro addon" -*!mustif not %BB3%==yes -$ifi not %BB3%==yes $setglobal HYDROBB123 none - - -The option replaces controls: MAKEWATER and WATERVAL. - -This option variable has influence on hydro with storage and no influence on hydro run of river. - -For any of the options it makes sense to use it only if the seasons in S (SET S) are the same for BB3 and BB1/BB2, and have the same relative weights (WEIGHT_S). -(might be permitted in later versions) - -Allocates a fixed hydro quantity WATERVOL for use in each season, this volume was generated by a BB1 or BB2 solution and saved as simex\WATERVOL.gdx. -That amount may be exceeded at a marginal cost. That cost depends on the water values WATERVAL found in the BB1 or BB2 solution and saved as simex\WATERVAL.gdx. -The cost will be somewhat higher that the cost given in WATERVAL and will increase by increasing hydro amounts used. -General: - -After successful solution from BB1 or BB2 the water values, hydro production and beginning-of-season storage volumes will be saved to files WATERVAL.gdx, HYRSG.gdx and WATERVOLINI.gdx. -These parameters are declared in Balmorel and saved irrespective of the chosen option for HYDROBB123. -Values saved (unconditionally) to folder simex as gdx files: - HYRSG.gdx', HYRSG; - VHYRS_SL.gdx', VHYRS_S.L; - WATERVAL.gdx', WATERVAL; - -When BB3 is to be solved these values are loaded into Balmorel. What happens next depends in the option chosen. -Option specific: - -For %HYDROBB123%==quant: -Equation QWATERVOLINI is part of model BALBASE3. This limits the hydro energy generation to the same as found in the BB1/BB2 solution, known from VHYRS_SL.gdx. -Values saved (conditionally) to folder simex af gdx files with option quant : - HYRSG.gdx',HYRSG; - WATERVAL.gdx',WATERVAL; - -For %HYDROBB123%==price: -Equation QWATERVOLINI is not part of model BALBASE3. In the objective function QOBJ the water values found in BB1/BB2, known from VHYRS_SL.gdx, -are used as marginal cost for hydro power generation. -Values saved to folder simex af gdx files with option price: - HYRSG.gdx',HYRSG; - WATERVAL.gdx',WATERVAL; - -For %HYDROBB123%==quantprice: - -Before entering the loop over seasons the hydro volumes for the beginning of the first season are fixed to that of the BB1/BB2 solution, -which is known from VHYRS_SL.gdx (containing values VHYRS_S.L). - -When BB3 is solved for a season the equation QWATERVOLINI attempts to limit the use of hydro power each season -to not exceed what was found in BB1/BB2. QWATERVOLINI is located in Balmorel.gms. todo-noget inkonsistent... -The limit may be exceeded at marginal costs which for smaller amounts are not much higher than the water values found in BB1/BB2, -known from WATERVAL.gdx, but if more is used the marginal costs increase. -In the objective function QOBJ the cost of hydro power generation is the same as given as variable costs XXX . -If the hydro amounts used exceed what was found in BB1/BB2 an additional variable cost component is added in QOBJ. - -In the loop over seasons a bookkeeping mechanism tracks the amounts of water used in current season and accumulated over the simulated seasons. -Similarly the amounts of water use for BB1/BB2 by season (known from VHYRS_SL.gdx) and accumulated over the simulated seasons are kept track of. - -If after the solution for a particular season it is found that there is a deviation between the accumulated amounts for BB3 and BB1/BB2 -then data is manipulated with the aim to restore accumulated amounts for the remaining seasons. -The available main mechanisms are tightening of equation QWATERVOLINI and increase of the marginal cost for exceeding that level. - -If variable VHYRS_S (hydro energy equivalent (storage level) at the start of the season (MWh)) -were declared as a positive variable an error would be issued in case of negative values of IHYDROBB123VOLINI, -cf. equation QWATERVOLINI which essentially states that VHYRS_S =E= IHYDROBB123VOLINI. -To avoid this, the variable VHYRS_S is declared as a free variable for model BB3 (while positive in models BB1 and BB2). -The sole intention of equation IHYDROBB123VOLINI is to permit convenient comparison with the BB1/BB2 solution through VHYRS_S. - -Values saved to folder simex af gdx files with option quantprice: - HYRSG_bb3.gdx',HYRSG; - VHYRS_SL_bb3.gdx',VHYRS_S.L; - WATERVAL_bb3.gdx',WATERVAL; ? - - -Some details: -PARAMETER IHYRSG_Y(AAA,SSS) "Water (hydro) generation quantity of the seasons transferred from model BB1/BB2 for use in model Balbase3 (MWh)" ; -PARAMETER IWATERVOL_Y(AAA,SSS) "Water quantity (storage level at beginning of season) transferred from model BB1/BB2 for use in model Balbase3 (MWh)" ; -PARAMETER IWATERVAL_Y(AAA,SSS) "Water value (in input Money) transferred from model BB1/BB2 for use in model Balbase3 (MWh)" ; -SET IHYDROBB12DELTASET "Increase steps of hydro generation above BB1 level" / IBB123HYG1*IBB123HYG5 /; -PARAMETER IHYDROBB123BB3GACCUM(AAA) "The amount of water used so far during present year during BB3 solutions (MWh)"; -PARAMETER IHYDROBB123BB12GACCUM(AAA) "The amount of water used so far during present year according to BB1/BB2 solution (MWh)"; -PARAMETER IHYDROBB123DELTAVOL(AAA) "Amount of additional hydro energy (relative to BB1/BB2) pre-allocated this season (may be negative) in model BB3 (MWh)"; -PARAMETER IHYDROBB123VOLINI(AAA,S) "Initial storage level (MWh)"; -PARAMETER VHYRS_SL(Y,AAA,S) "To be saved for comparison with BB1/BB2 solution value for VHYRS_S.L (declared as a parameter) (MWh)"; -PARAMETER IHYDROBB123EXTRAVAL(AAA,S,IHYDROBB12DELTASET) ""; - -POSITIVE VARIABLE VIHYRSBB12DELTAMHW(AAA,IHYDROBB12DELTASET) "Amount of additional hydro energy allocated this season during optimization of model Balbase3 (MWh)"; - -EQUATION QWATERVOLINI(AAA,S) "Hydro energy (volume) in storage at the beginning of season in model Balbase3 (MWh)" ; -EQUATION QHYRSG(AAA,S) "Hydro energy available for this region and season in model Balbase3 (MWh)"; - - -$offtext diff --git a/base/addons/hyrsbb123/hyrsbb123equations.inc b/base/addons/hyrsbb123/hyrsbb123equations.inc deleted file mode 100644 index 00ec70ee..00000000 --- a/base/addons/hyrsbb123/hyrsbb123equations.inc +++ /dev/null @@ -1,54 +0,0 @@ -* File HYRSBB123equations.inc for the Balmorel model. -* For use with addon HYRSBB123. -* Latest revision 20170419 (hr) - -$ifi not %BB3%==yes $goto label_hydrobb123equations -$ifi %HYRSBB123%==price $goto label_hydrobb123equations - -$ifi %BB4%==yes $goto BB4begin - -EQUATION QWATERVOLINI(AAA,S) 'Hydro energy (volume) in storage at the beginning of season in model Balbase3 (MWh)' ; -EQUATION QHYRSG(AAA,S) 'Hydro energy available for this region and season in model Balbase3 (MWh)'; - -* The equation QWATERVOLINI atttempts to achieve start-of-season values (MWh) for VHYRS_S.L that are comparable for those from BB1/BB2. -* Note that VHYRS_S is declared as a positive variable, therefore it is elsewhere artificially assured that IHYRSBB123VOLINI is non-negative. - -QWATERVOLINI(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. VHYRS_S(IA,IS3) =E= IHYRSBB123VOLINI(IA,IS3); - -* The equation QHYRSG tries to limit the HYRS generation to same level as in BB1/BB2. -* The equation will not be infeasible since VIHYRSBB12DELTAMHW.UP is INF for the last element wrt. IHYRSBB12DELTASET. -QHYRSG(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. - IHYRSBB123G_Y(IA,IS3) !! Unit: (MWh) -$ifi %HYRSBB123%==quantprice + IHYRSBB123DELTAVOL(IA) + SUM(IHYRSBB12DELTASET, VIHYRSBB12DELTAMHW(IA,IHYRSBB12DELTASET)) - =G= - SUM(IGHYRS$IAGK_Y(IA,IGHYRS), SUM(T,IHOURSINST(IS3,T)*VGE_T(IA,IGHYRS,IS3,T))) -; - -$goto label_hydrobb123equations - -$label BB4begin - -* Equations not verified for BB4! - -EQUATION QWATERVOLINI(Y,AAA,S) 'Hydro energy (volume) in storage at the beginning of season in model Balbase3 (MWh)' ; -EQUATION QHYRSG(Y,AAA,S) 'Hydro energy available for this region and season in model Balbase3 (MWh)'; - -* The equation QWATERVOLINI atttempts to achieve start-of-season values (MWh) for VHYRS_S.L that are comparable for those from BB1/BB2. -* Note that VHYRS_S is declared as a positive variable, therefore it is elsewhere artificially assured that IHYRSBB123VOLINI is non-negative. - -TODO: - - -QWATERVOLINI(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. VHYRS_S(IA,IS3) =E= IHYRSBB123VOLINI(IA,IS3); - -* The equation QHYRSG tries to limit the HYRS generation to same level as in BB1/BB2. -* The equation will not be infeasible since VIHYRSBB12DELTAMHW.UP is INF for the last element wrt. IHYRSBB12DELTASET. -QHYRSG(IA,IS3)$IHYRSBB123G_Y(IA,IS3).. - IHYRSBB123G_Y(IA,IS3) !! Unit: (MWh) -$ifi %HYRSBB123%==quantprice + IHYRSBB123DELTAVOL(IA) + SUM(IHYRSBB12DELTASET, VIHYRSBB12DELTAMHW(IA,IHYRSBB12DELTASET)) - =G= - SUM(IGHYRS$IAGK_Y(IA,IGHYRS), SUM(T,IHOURSINST(IS3,T)*VGE_T(IA,IGHYRS,IS3,T))) -; - - -$label label_hydrobb123equations diff --git a/base/addons/hyrsbb123/hyrsbb123includebb12data.inc b/base/addons/hyrsbb123/hyrsbb123includebb12data.inc deleted file mode 100644 index 2866dd70..00000000 --- a/base/addons/hyrsbb123/hyrsbb123includebb12data.inc +++ /dev/null @@ -1,20 +0,0 @@ -* File HYRSBB123includebb12data.inc for the Balmorel model. -* For use with addon HYRSBB123. - -* Data for handling of annual hydro constraints in BB3. -* Supposed to be saved by a previous execution of model BALBASE1 or BALBASE2 (BB1 or BB2). - -$ifi not %BB3%==yes $goto label_enofhyrsbb123includebb12data -$ifi %HYRSBB123%==none $goto label_enofhyrsbb123includebb12data - -execute_load '../../simex/HYRSG.gdx', HYRSG; -execute_load '../../simex/WATERVAL.gdx', WATERVAL; -execute_load '../../simex/VHYRS_SL.gdx', VHYRS_SL; - -* Trim with respect to S for the case that S in BB3 is a proper subset of the S used in BB1\BB2, for nicety of output only: -HYRSG(Y,AAA,SSS)$(not S(SSS)) = 0; -WATERVAL(Y,AAA,SSS)$(not S(SSS)) = 0; -VHYRS_SL(Y,AAA,SSS)$(not S(SSS)) = 0; -* The indexes Y and IA(AAA) are simply assumed to be the same. - -$label label_enofhyrsbb123includebb12data diff --git a/base/addons/hyrsbb123/hyrsbb123internals.inc b/base/addons/hyrsbb123/hyrsbb123internals.inc deleted file mode 100644 index 4ad0c3a8..00000000 --- a/base/addons/hyrsbb123/hyrsbb123internals.inc +++ /dev/null @@ -1,26 +0,0 @@ -* The three parameters WATERVOLINI, WATERVAL and VHYRS_SLare declared as standard in the Balmorel model. -* Additional declaration and definitions for HYRSBB123 only are given here. - -* In BB3 the following set and parameters are used to control the use of hydro with storage, conditioned by HYRSBB123: - -SET IHYRSBB12DELTASET "Increase steps of hydro generation above BB1 level" / IHYRSBB123G1*IHYRSBB123G5 /; - -PARAMETER IHYRSBB123G_Y(AAA,SSS) "Water (hydro) generation quantity of the seasons transferred from model BB1/BB2 for use in model Balbase3 (MWh)"; -PARAMETER IWATERVAL_Y(AAA,SSS) "Water value (in input Money) transferred from model BB1/BB2 for use in model Balbase3 (MWh)"; -PARAMETER IHYRSBB123BB3GACCUM(AAA) "The amount of water used so far during present year during BB3 solutions (MWh)"; -PARAMETER IHYRSBB123BB12GACCUM(AAA) "The amount of water used so far during present year according to BB1/BB2 solution (MWh)"; -PARAMETER IHYRSBB123DELTAVOL(AAA) "Amount of additional hydro energy (relative to BB1/BB2) pre-allocated this season (may be negative) in model BB3 (MWh)"; -PARAMETER IHYRSBB123VOLINI(AAA,S) "Initial storage level (MWh)"; -PARAMETER IHYRSBB123EXTRAVAL(AAA,S,IHYRSBB12DELTASET); - - -* Test that Card(S) is equal to Card(SSS): -IF((CARD(S) NE CARD(SSS)), -* PUT ERRORFILE "Warning from addon hyrsbb123, file hyrsbb123sim3.inc:"/; -* PUT "Card(S) is not equal to Card(SSS), it must be." /; - DISPLAY "Warning from addon hyrsbb123, file hyrsbb123sim3.inc:"; - DISPLAY "Card(S) is not equal to Card(SSS), it must be." ; -); - - -$label label_hydrobb123internals \ No newline at end of file diff --git a/base/addons/hyrsbb123/hyrsbb123sim1.inc b/base/addons/hyrsbb123/hyrsbb123sim1.inc deleted file mode 100644 index 489cc976..00000000 --- a/base/addons/hyrsbb123/hyrsbb123sim1.inc +++ /dev/null @@ -1,17 +0,0 @@ -* File HYRSBB123sim1.inc for the Balmorel model. -* For use with addon HYRSBB123. -* Included into bb123.sim before loop over seasons, inside the loop over years. - -IHYRSBB123BB3GACCUM(IA) = 0; -IHYRSBB123BB12GACCUM(IA) = 0; -IHYRSBB123DELTAVOL(IA) = 0; -IHYRSBB123VOLINI(IA,S)$(ORD(S) EQ 1) = VHYRS_SL(Y,IA,S); - -* Todo: if S and SSS are different then a rescaling might be useful for comparison of results (HYRSG). -* So for now: make sure that S and SSS are identical. - - - - - - diff --git a/base/addons/hyrsbb123/hyrsbb123sim2.inc b/base/addons/hyrsbb123/hyrsbb123sim2.inc deleted file mode 100644 index b3f05ba4..00000000 --- a/base/addons/hyrsbb123/hyrsbb123sim2.inc +++ /dev/null @@ -1,24 +0,0 @@ -* File HYRSBB123sim2.inc for the Balmorel model. -* For use with addon HYRSBB123. -* Included into bb123.sim before SOLVE BALBASE3 statement, inside the loop over seasons. - -* Set values relative to the possible hydro use reletive to BB1/BB2: - - -ISCALAR1 = 0.1; !! Positive. Defines the largest individual increment, as share of the hydro power used in the BB1/BB2 solution. -VIHYRSBB12DELTAMHW.UP(IA,IHYRSBB12DELTASET) = IHYRSBB123G_Y(IA,IS3LOOPSET)*ISCALAR1*ORD(IHYRSBB12DELTASET)/CARD(IHYRSBB12DELTASET); !! Increments get larger with increased hydro use -VIHYRSBB12DELTAMHW.UP(IA,IHYRSBB12DELTASET)$(ORD(IHYRSBB12DELTASET) EQ CARD(IHYRSBB12DELTASET)) = INF; !! No limit, to ensure feasibility in equation QWATERVOL - -* Water Values: -ISCALAR1 = 2.0; !! Positive. With e.g. 2.0 the increments (above the BB1 values) on the Water Values will be doubled for each step, to reach a maximum of 2 times the BB1 values. -ISCALAR2=POWER(1/ISCALAR1,CARD(IHYRSBB12DELTASET)); -ISCALAR3=1.0; !! Additional increase may be attained by increasing ISCALAR3 above 1.0. -LOOP(IHYRSBB12DELTASET, - ISCALAR2=ISCALAR2*ISCALAR1; - IHYRSBB123EXTRAVAL(IA,IS3LOOPSET,IHYRSBB12DELTASET) = IWATERVAL_Y(IA,IS3LOOPSET)*ISCALAR3*(1+ISCALAR2); -); - - - - - diff --git a/base/addons/hyrsbb123/hyrsbb123unload.inc b/base/addons/hyrsbb123/hyrsbb123unload.inc deleted file mode 100644 index 3cdd118a..00000000 --- a/base/addons/hyrsbb123/hyrsbb123unload.inc +++ /dev/null @@ -1,14 +0,0 @@ -* File HYRSBB123unlaod.inc for the Balmorel model. -* For use with addon HYRSBB123. - -$ifi not %BB3%==yes $goto label_hydrobb123unload - -$ifi %HYRSBB123%==quant execute_unload '../../simex/HYRSG_BB3.gdx',HYRSG; -$ifi %HYRSBB123%==quant execute_unload '../../simex/WATERVAL_BB3.gdx',WATERVAL; -$ifi %HYRSBB123%==price execute_unload '../../simex/HYRSG_BB3.gdx',HYRSG; -$ifi %HYRSBB123%==price execute_unload '../../simex/WATERVAL_BB3.gdx',WATERVAL; -$ifi %HYRSBB123%==quantprice execute_unload '../../simex/HYRSG_BB3.gdx',HYRSG; -$ifi %HYRSBB123%==quantprice execute_unload '../../simex/VHYRS_SL_BB3.gdx',VHYRS_SL; -$ifi %HYRSBB123%==quantprice execute_unload '../../simex/WATERVAL_BB3.gdx',WATERVAL; - -$label label_hydrobb123unload \ No newline at end of file diff --git a/base/addons/hyrsbb123/hyrsbb123variables.inc b/base/addons/hyrsbb123/hyrsbb123variables.inc deleted file mode 100644 index 3603e249..00000000 --- a/base/addons/hyrsbb123/hyrsbb123variables.inc +++ /dev/null @@ -1,7 +0,0 @@ -* File HYRSBB123internals.inc for the Balmorel model. -* For use with addon HYRSBB123. -* Latest revision 20170419 (hr) - -$ifi not %BB4%==yes POSITIVE VARIABLE VIHYRSBB12DELTAMHW(AAA,IHYRSBB12DELTASET) "Amount of additional hydro energy allocated this season during optimization of model Balbase3 (MWh)"; - -$ifi %BB4%==yes POSITIVE VARIABLE VIHYRSBB12DELTAMHW(Y,AAA,IHYRSBB12DELTASET) "Amount of additional hydro energy allocated this season during optimization of model Balbase3 (MWh)"; diff --git a/base/addons/policies/Addon vejledening.txt b/base/addons/policies/Addon vejledening.txt deleted file mode 100644 index 41cfa601..00000000 --- a/base/addons/policies/Addon vejledening.txt +++ /dev/null @@ -1,30 +0,0 @@ -Policies addon enable predefined constraints on a userdefined geografy and regarding specific technologies or technology groups. -These flexible constraints can be used to model a lot of different policies to constrain the model. - -User guide: - -Each policy consist of a Policy Area - a Polycy Type - a group of Technologies (if nessesary) - -Files for defining Policies: - -In POLICY.inc the names of the different Policies should be stated -In POLAREA.inc the names of the different Policy Area should be stated -In POLAREAGEO.inc the Policy Areas should be defined by a subset of CCCRRRAAA -In POLTYP.inc one of the predefined Policy Type should be assigned to each Policy -In pol_data.inc the yearly data for the different Policies should be stated - -If a Policy Type is choosen which requre specification of technology -In POLTECH.inc the group of technologies concerning the Policy should be stated - - - - - - - - - - - diff --git a/base/addons/policies/demodata/POLAREA.inc b/base/addons/policies/demodata/POLAREA.inc deleted file mode 100644 index e32c5de5..00000000 --- a/base/addons/policies/demodata/POLAREA.inc +++ /dev/null @@ -1,11 +0,0 @@ -* All Policy areas -* The names of each Policy Area - -SET POLAREA 'Group of Regimes that share some common requerments' -/ -POLA1 -POLA2 -POLA3 -POLA4 -/; - diff --git a/base/addons/policies/demodata/POLAREAGEO.inc b/base/addons/policies/demodata/POLAREAGEO.inc deleted file mode 100644 index e9eb876f..00000000 --- a/base/addons/policies/demodata/POLAREAGEO.inc +++ /dev/null @@ -1,8 +0,0 @@ -* Specification of geografi in Policy Areas -* Any combination of Areas, Regions and Contries can be choosen. -* Double reprecentation of a geografy will be identifyed and removed automaticly (leads to error message in printout) - -SET POLAREAGEO(POLAREA,CCCRRRAAA) 'Defining the geografi for each Policy Area' -/ -POLA1.(DK_E) -/; diff --git a/base/addons/policies/demodata/POLICY.inc b/base/addons/policies/demodata/POLICY.inc deleted file mode 100644 index 61547a8b..00000000 --- a/base/addons/policies/demodata/POLICY.inc +++ /dev/null @@ -1,8 +0,0 @@ -* All Policies - -SET POLICY 'Group of policies' -/ -POL1 -POL2 -/; - diff --git a/base/addons/policies/demodata/POLICYTYPE.inc b/base/addons/policies/demodata/POLICYTYPE.inc deleted file mode 100644 index 50ca7de5..00000000 --- a/base/addons/policies/demodata/POLICYTYPE.inc +++ /dev/null @@ -1,12 +0,0 @@ -* All Policy types -* These types should correspond to the equations in pol_eq.inc - -SET POLICYTYPE 'Set of different policy types' -/ -SHAREGROSS -SHARENETTO -MAXCO2 -MINPROD -CAPTARGET -/; - diff --git a/base/addons/policies/demodata/POLREQ.inc b/base/addons/policies/demodata/POLREQ.inc deleted file mode 100644 index cef9951a..00000000 --- a/base/addons/policies/demodata/POLREQ.inc +++ /dev/null @@ -1,44 +0,0 @@ -* Addon for securing that Policy requrements are forfilled -* Policy requrements are given for each Policy and userdefined Policy Areas - - -* Policy requrements -* Construct the wished requrement by choosing a policy area 'POLAREA', a 'POLICY' and then state the data in the table. -* A Policy can be one of the following Policy Types. DO mind the units and specify technologies in POLTECH if requered -* SHAREGROSS implies Minimal share of electricity generation from specified technologies compared to gross electricity demand. NOTE: technologies have to be specified. Unit: Proportion -* SHARENETTO implies Minimal share of electricity generation from specified technologies compared to netto electricity demand. NOTE: technologies have to be specified. Unit: Proportion -* MAXCO2 implies Maximal CO2 emission. NO technologies need to be defined. Unit: Ton CO2 -* MINPROD implies Minimal electricity generation in from specified technologies in policy area. Unit: MWh -* CAPTARGET implies Minimal electricity generating capacity in policy area of specified technologies. NOTE: technologies have to be specified. Unit: MW -* ... implies ... -* ... implies ... - - -TABLE POLREQ(YYY,POLAREA,POLICY) - - POLA1.POL1 POLA1.POL2 -2010 -2011 -2012 -2013 -2014 -2015 -2016 -2017 -2018 -2019 -2020 0.5 0.7 -2021 -2022 -2023 -2024 -2025 -2030 -2035 -2040 -2045 -2050 - -; - -PARAMETER IPOLREQ_Y(POLAREA,POLICY) ''; diff --git a/base/addons/policies/demodata/POLTECH.inc b/base/addons/policies/demodata/POLTECH.inc deleted file mode 100644 index 0009a2f7..00000000 --- a/base/addons/policies/demodata/POLTECH.inc +++ /dev/null @@ -1,21 +0,0 @@ -* Specify which tecnologies the policies cover - -* Defined by individual tecnologi -SET POLTECH(POLICY,G) -/ - POL1.(CC-C1-NGn) - POL2.(CC-C1-NGn) - -/; - -* OR by assigning from group of technologies. -* Choose between: -* Predefined: IGCND, IGBPR, IGEXT, IGHOB, IGETOH, IGHSTO, IGHSTO_S, IGHSTOALL, IGESTO, IGHYRS, IGHYRR, IGWND, IGSOLE, IGSOLH, IGWAVE, IGHH, -* IGHHNOSTO, IGNOTETOH, IGKH, IGKHNOSTO, IGKE, IGKENOSTO, IGDISPATCH, IGEE, IGEENOSTO, IGKKNOWN, IGKFIND, IGEH, IGE or IGH. -* Addon defined: VETECH,.... -* Userdefined: USERTECH (is defined in pol_set.inc) - -*POLTECH('POL1',G) = YES$( IGWND(G) ); - - - diff --git a/base/addons/policies/demodata/POLTYP.inc b/base/addons/policies/demodata/POLTYP.inc deleted file mode 100644 index eced7c8b..00000000 --- a/base/addons/policies/demodata/POLTYP.inc +++ /dev/null @@ -1,15 +0,0 @@ -* Assignment of Policy Type to Policies -* Assign one Policy Type to each Policy -* Choose between: -* SHAREGROSS implies Minimal share of electricity generation from specified technologies compared to total electricity generation. NOTE: technologies have to be specified. Unit: Proportion -* SHARENETTO implies Minimal share of electricity generation from specified technologies compared to electricity demand. NOTE: technologies have to be specified. Unit: Proportion -* MAXCO2 implies Maximal CO2 emission. NO technologies need to be defined. Unit: Ton CO2 -* MINPROD implies Minimal electricity generation in from specified technologies in policy area. NOTE: technologies have to be specified. Unit: MWh. -* CAPTARGET implies Minimal electricity generating capacity in policy area of specified technologies. NOTE: technologies have to be specified. Unit: MW - - -SET POLTYP(POLICY,POLICYTYPE) 'Define policy type for each policy' -/ -POL1.SHAREGROSS -POL2.SHARENETTO -/; diff --git a/base/addons/policies/pol_cost.inc b/base/addons/policies/pol_cost.inc deleted file mode 100644 index 9abb6ee7..00000000 --- a/base/addons/policies/pol_cost.inc +++ /dev/null @@ -1,12 +0,0 @@ -* Addon for securing that policy requrements are forfilled -* Slack variables are penaliced to insure that the model works and minimize errors -* in case the requrement for specific policy area and policy can not be forfilled - -+ SUM((POLAREA,POLICY), VQPOLMAXCO2(POLAREA,POLICY)+VQPOLMINPROD(POLAREA,POLICY)+VQPOLCAPTARGET(POLAREA,POLICY) )*PENALTYQ/2 -+ SUM((POLAREA,POLICY), VQPOLSHAREGROSS(POLAREA,POLICY)+VQPOLSHARENET(POLAREA,POLICY) )*PENALTYQ*1000 - - - - - - diff --git a/base/addons/policies/pol_eq.inc b/base/addons/policies/pol_eq.inc deleted file mode 100644 index 680e7789..00000000 --- a/base/addons/policies/pol_eq.inc +++ /dev/null @@ -1,162 +0,0 @@ -* Addon for securing that Policy requrements are forfilled - -EQUATIONS - -QPOLSHAREGROSS (POLAREA,POLICY) 'Inshures requrements on share of electricity generation for policy area and tecnology group, compared to electricity demand in the area' - -QPOLSHARENET (POLAREA,POLICY) 'Inshures requrements on share of electricity generation for policy area and tecnology group, compared to total electricity generation in the area' - -QPOLMAXCO2 (POLAREA,POLICY) 'Inshures requrements on maximal CO2 emmision for policy area' - -QPOLMINPROD (POLAREA,POLICY) 'Inshures requrements on minimal production for policy ares and tecnology group' - -QPOLCAPTARGET (POLAREA,POLICY) 'Inshures requrements on electricity generation capacity in policy area and technology group' - -; - - -QPOLSHAREGROSS(POLAREA,POLICY)$(IPOLREQ_Y(POLAREA,POLICY) and POLTYP(POLICY,'SHAREGROSS')) .. - - - (SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), - SUM(G$(POLTECH(POLICY,G)), - SUM(C$(sameas(C,CCCRRRAAA)), - SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) - + SUM((IA,IS3,T)$(IAGKN(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) - ) - + SUM(IR$(sameas(IR,CCCRRRAAA)), - SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) - + SUM((IA,IS3,T)$(IAGKN(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) - ) - + SUM(IA$(sameas(IA,CCCRRRAAA)), - SUM((IS3,T)$(IAGK_Y(IA,G)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) - + SUM((IS3,T)$(IAGKN(IA,G)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) - ) - ) - )) - / - (SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), - SUM(C$(sameas(C,CCCRRRAAA)), - SUM((IR,IS3,T)$( CCCRRR(C,IR) ), IHOURSINST(IS3,T)* IDE_T_Y(IR,IS3,T)/(1-DISLOSS_E(IR)) ) - ) - + SUM(IR$(sameas(IR,CCCRRRAAA)), - SUM((IS3,T), IHOURSINST(IS3,T)* IDE_T_Y(IR,IS3,T)/(1-DISLOSS_E(IR)) ) - ) - )) - + VQPOLSHAREGROSS(POLAREA,POLICY) - =G= - IPOLREQ_Y(POLAREA,POLICY) -; - - - -QPOLSHARENET(POLAREA,POLICY)$(IPOLREQ_Y(POLAREA,POLICY) and POLTYP(POLICY,'SHARENETTO')) .. - - - (SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), - SUM(G$(POLTECH(POLICY,G)), - SUM(C$(sameas(C,CCCRRRAAA)), - SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) - + SUM((IA,IS3,T)$(IAGKN(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) - ) - + SUM(IR$(sameas(IR,CCCRRRAAA)), - SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) - + SUM((IA,IS3,T)$(IAGKN(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) - ) - + SUM(IA$(sameas(IA,CCCRRRAAA)), - SUM((IS3,T)$(IAGK_Y(IA,G)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) - + SUM((IS3,T)$(IAGKN(IA,G)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) - ) - ) - )) - / - (SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), - SUM(C$(sameas(C,CCCRRRAAA)), - SUM((IR,IS3,T)$( CCCRRR(C,IR) ), IHOURSINST(IS3,T)* IDE_T_Y(IR,IS3,T) ) - ) - + SUM(IR$(sameas(IR,CCCRRRAAA)), - SUM((IS3,T), IHOURSINST(IS3,T)* IDE_T_Y(IR,IS3,T)) - ) - )) - + VQPOLSHARENET(POLAREA,POLICY) - =G= - IPOLREQ_Y(POLAREA,POLICY) -; - - - - -QPOLMAXCO2(POLAREA,POLICY)$(IPOLREQ_Y(POLAREA,POLICY) and POLTYP(POLICY,'MAXCO2')) .. - - SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), - SUM(G$(IGE(G) and IM_CO2(G)), - SUM(C$(sameas(C,CCCRRRAAA)), - SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T) * (IM_CO2(G)/IOF1000) * IOF3P6 * VGF_T(IA,G,IS3,T)) - + SUM((IA,IS3,T)$(IAGKN(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T) * (IM_CO2(G)/IOF1000) * IOF3P6 * VGFN_T(IA,G,IS3,T)) - ) - + SUM(IR$(sameas(IR,CCCRRRAAA)), - SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T) * (IM_CO2(G)/IOF1000) * IOF3P6 * VGF_T(IA,G,IS3,T)) - + SUM((IA,IS3,T)$(IAGKN(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T) * (IM_CO2(G)/IOF1000) * IOF3P6 * VGFN_T(IA,G,IS3,T)) - ) - + SUM(IA$(sameas(IA,CCCRRRAAA)), - SUM((IS3,T)$(IAGK_Y(IA,G)), IHOURSINST(IS3,T) * (IM_CO2(G)/IOF1000) * IOF3P6 * VGF_T(IA,G,IS3,T)) - + SUM((IS3,T)$(IAGKN(IA,G)), IHOURSINST(IS3,T) * (IM_CO2(G)/IOF1000) * IOF3P6 * VGFN_T(IA,G,IS3,T)) - ) - ) - ) - - =L= - IPOLREQ_Y(POLAREA,POLICY) - + VQPOLMAXCO2(POLAREA,POLICY) -; - - - -QPOLMINPROD (POLAREA,POLICY)$(IPOLREQ_Y(POLAREA,POLICY) and POLTYP(POLICY,'MINPROD')) .. - - SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), - SUM(G$(POLTECH(POLICY,G)), - SUM(C$(sameas(C,CCCRRRAAA)), - SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) - + SUM((IA,IS3,T)$(IAGKN(IA,G) AND ICA(C,IA)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) - ) - + SUM(IR$(sameas(IR,CCCRRRAAA)), - SUM((IA,IS3,T)$(IAGK_Y(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) - + SUM((IA,IS3,T)$(IAGKN(IA,G) AND RRRAAA(IR,IA)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) - ) - + SUM(IA$(sameas(IA,CCCRRRAAA)), - SUM((IS3,T)$(IAGK_Y(IA,G)), IHOURSINST(IS3,T)*VGE_T(IA,G,IS3,T) ) - + SUM((IS3,T)$(IAGKN(IA,G)), IHOURSINST(IS3,T)*VGEN_T(IA,G,IS3,T)) - ) - ) - ) - + VQPOLMINPROD(POLAREA,POLICY) - =G= - IPOLREQ_Y(POLAREA,POLICY) -; - - -QPOLCAPTARGET (POLAREA,POLICY)$(IPOLREQ_Y(POLAREA,POLICY) and POLTYP(POLICY,'CAPTARGET')) .. - - - SUM(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), - SUM(G$(POLTECH(POLICY,G)), - SUM(C$(sameas(C,CCCRRRAAA)), - SUM((IA)$(IAGK_Y(IA,G) AND ICA(C,IA)), IGKFX_Y(IA,G) + IGKVACCTOY(IA,G)) - + SUM((IA)$(IAGKN(IA,G) AND ICA(C,IA)), VGKN(IA,G) ) - ) - + SUM(IR$(sameas(IR,CCCRRRAAA)), - SUM((IA)$(IAGK_Y(IA,G) AND RRRAAA(IR,IA)), IGKFX_Y(IA,G) + IGKVACCTOY(IA,G)) - + SUM((IA)$(IAGKN(IA,G) AND RRRAAA(IR,IA)), VGKN(IA,G) ) - ) - + SUM(IA$(sameas(IA,CCCRRRAAA)), - (IGKFX_Y(IA,G) + IGKVACCTOY(IA,G))$(IAGK_Y(IA,G)) - +VGKN(IA,G)$(IAGKN(IA,G)) - ) - ) - ) - + VQPOLCAPTARGET(POLAREA,POLICY) - =G= - IPOLREQ_Y(POLAREA,POLICY) -; - diff --git a/base/addons/policies/pol_eqN.inc b/base/addons/policies/pol_eqN.inc deleted file mode 100644 index 924ae9ee..00000000 --- a/base/addons/policies/pol_eqN.inc +++ /dev/null @@ -1,7 +0,0 @@ -* Addon for securing policy requirements are forfilled - -QPOLSHAREGROSS -QPOLSHARENET -QPOLMAXCO2 -QPOLMINPROD -QPOLCAPTARGET diff --git a/base/addons/policies/pol_err2.inc b/base/addons/policies/pol_err2.inc deleted file mode 100644 index d8e19b47..00000000 --- a/base/addons/policies/pol_err2.inc +++ /dev/null @@ -1,62 +0,0 @@ -* File pol_err2.inc -* This file is part of the policies addon - -* Geografy defining Policy Area is checked for dobbelt reprecentation of areas and regionss -* Writing to ERRORFILE. - -PUT ERRORFILE / "Now checking input data for addon Policies." /; - -*------------------------------------------------------------------------------ -*--- DATA IN THE FILE POLAREAGEO.inc:------------------------------------------------- -* Check Area and Regions in Countries -LOOP( POLAREA, - LOOP(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), - LOOP(C$(sameas(C,CCCRRRAAA)), - LOOP(CCCRRRAAAALIAS$(POLAREAGEO(POLAREA,CCCRRRAAAALIAS)), - LOOP(IA$(sameas(IA,CCCRRRAAAALIAS)), - If (ICA(C,IA), - PUT "Invalid geografy for Policy Area: " POLAREA.TL " Area: " IA.TL " is already countained in " C.TL; - PUT "Error is automaticly corrected"; - POLAREAGEO(POLAREA,IA) = NO; - PUT //; -ERRCOUNT2=ERRCOUNT2+1; - ) - ) - ) - LOOP(CCCRRRAAAALIAS$(POLAREAGEO(POLAREA,CCCRRRAAAALIAS)), - LOOP(IR$(sameas(IR,CCCRRRAAAALIAS)), - If (CCCRRR(C,IR), - PUT "Invalid geografy for Policy Area: " POLAREA.TL " Region: " IR.TL " is already countained in " C.TL; - PUT "Error is automaticly corrected"; - POLAREAGEO(POLAREA,IR) = NO; - PUT //; -ERRCOUNT2=ERRCOUNT2+1; - ) - ) - ) - - ) - ) -); - - - -* Check Area in Region -LOOP( POLAREA, - LOOP(CCCRRRAAA$(POLAREAGEO(POLAREA,CCCRRRAAA)), - LOOP(IR$(sameas(IR,CCCRRRAAA)), - LOOP(CCCRRRAAAALIAS$(POLAREAGEO(POLAREA,CCCRRRAAAALIAS)), - LOOP(IA$(sameas(IA,CCCRRRAAAALIAS)), - If (RRRAAA(IR,IA), - PUT "Invalid geografy for Policy Area: " POLAREA.TL " Area: " IA.TL " is already countained in Region " IR.TL; - PUT "Error is automaticly corrected"; - POLAREAGEO(POLAREA,IA) = NO; -ERRCOUNT2=ERRCOUNT2+1; - ) - ) - ) - ) - ) -); -PUT //; - diff --git a/base/addons/policies/pol_sets.inc b/base/addons/policies/pol_sets.inc deleted file mode 100644 index 492bca1a..00000000 --- a/base/addons/policies/pol_sets.inc +++ /dev/null @@ -1,77 +0,0 @@ -* Addon for securing that policy requirements are forfilled -* Policy requirements are given for userdefined policy area and policy type - -SET POLAREA 'Group of policy areas'; -SET POLAREAGEO(POLAREA,CCCRRRAAA) 'Defining the geografy of each policy area by CCC, RRR and AAA' ; -SET POLICY 'Group of policies'; -SET POLICYTYPE 'Different policy types'; -SET POLTYP(POLICY,POLICYTYPE) 'Policy type assigned to each policy'; -SET POLTECH(POLICY,G) 'Technologies related to each policy'; - -* Addon defined technology sets -SET VETECH(G) '' -/ -WI-L15 -WI-L20 -WI-L25 -WI-S15 -WI-S20 -WI-S25 - -/ -* Userdefined technology set -SET USERTECH(G) '' -/ - -ST-B8-NG - - -/ - - -SET POLAREA 'Group of policy areas' %semislash% -$if EXIST '../data/POLAREA.inc' $INCLUDE '../data/POLAREA.inc'; -$if not EXIST '../data/POLAREA.inc' $INCLUDE '../../base/addons/policies/data/POLAREA.inc'; -%semislash%; - -SET POLAREAGEO(POLAREA,CCCRRRAAA) 'Defining the geografi for each regime' %semislash% -$if EXIST '../data/POLAREAGEO.inc' $INCLUDE '../data/POLAREAGEO.inc'; -$if not EXIST '../data/POLAREAGEO.inc' $INCLUDE '../../base/addons/policies/data/POLAREAGEO.inc'; -%semislash%; - - - -SET POLICY 'Group of policies' %semislash% -$if EXIST '../data/POLICY.inc' $INCLUDE '../data/POLICY.inc'; -$if not EXIST '../data/POLICY.inc' $INCLUDE '../../base/addons/policies/data/POLICY.inc'; -%semislash%; - -SET POLICYTYPE 'Different policy types' %semislash% -$if EXIST '../data/POLICYTYPE.inc' $INCLUDE '../data/POLICYTYPE.inc'; -$if not EXIST '../data/POLICYTYPE.inc' $INCLUDE '../../base/addons/policies/data/POLICYTYPE.inc'; -%semislash%; - -SET POLTYP(POLICY,POLICYTYPE) 'Policy type assigned to each policy' %semislash% -$if EXIST '../data/POLTYP.inc' $INCLUDE '../data/POLTYP.inc'; -$if not EXIST '../data/POLTYP.inc' $INCLUDE '../../base/addons/policies/data/POLTYP.inc'; -%semislash%; - -SET POLTECH(POLICY,G) 'Technologies related to each policy' %semislash% -$if EXIST '../data/POLTECH.inc' $INCLUDE '../data/POLTECH.inc'; -$if not EXIST '../data/POLTECH.inc' $INCLUDE '../../base/addons/policies/data/POLTECH.inc'; -%semislash%; - - - - - - - - - - - - - - - diff --git a/base/addons/policies/pol_var.inc b/base/addons/policies/pol_var.inc deleted file mode 100644 index a0c138fd..00000000 --- a/base/addons/policies/pol_var.inc +++ /dev/null @@ -1,11 +0,0 @@ -* Addon for securing policy requrements are forfilled -* Slack variables are created to insure that the model works -* even if the policy requirements is not forfilled - -Positive Variables - VQPOLSHAREGROSS(POLAREA,POLICY) 'Penalty on not forefilling the gross share requirement for policy area' - VQPOLSHARENET (POLAREA,POLICY) 'Penalty on not forefilling the net share requirement for policy area' - VQPOLMAXCO2 (POLAREA,POLICY) 'Penalty on not forefilling the maximum CO2 requirement for policy area' - VQPOLMINPROD (POLAREA,POLICY) 'Penalty on not forefilling the minimum production requirement for policy area' - VQPOLCAPTARGET (POLAREA,POLICY) 'Penalty on not forefilling the minimum capacity requirement for policy area' -; diff --git a/base/addons/policies/pol_yearupdate.inc b/base/addons/policies/pol_yearupdate.inc deleted file mode 100644 index 064b0200..00000000 --- a/base/addons/policies/pol_yearupdate.inc +++ /dev/null @@ -1,4 +0,0 @@ -* Addon for securing policy requrements are forfilled -* Requerements are updated on a yearly basis - -IPOLREQ_Y(POLAREA,POLICY)=POLREQ(Y,POLAREA,POLICY); diff --git a/base/addons/systemcost/SysCost addon instruction.txt b/base/addons/systemcost/SysCost addon instruction.txt deleted file mode 100644 index f55f0e6e..00000000 --- a/base/addons/systemcost/SysCost addon instruction.txt +++ /dev/null @@ -1,14 +0,0 @@ - - -User guide: - - - - - - - - - - - diff --git a/base/addons/systemcost/cost_syscost.inc b/base/addons/systemcost/cost_syscost.inc deleted file mode 100644 index 8b2b5361..00000000 --- a/base/addons/systemcost/cost_syscost.inc +++ /dev/null @@ -1,8 +0,0 @@ -* Addon for securing that System Constraints are forfilled -* Slack variables are created to insure that the model works -* in case the System Constraints can not be forfilled - -+ SUM((C,IS3,T), VQBASELOAD(C,IS3,T))*PENALTYQ/2 - - - diff --git a/base/addons/systemcost/demodata/BASELOADSERVICE.inc b/base/addons/systemcost/demodata/BASELOADSERVICE.inc deleted file mode 100644 index c9ed46e4..00000000 --- a/base/addons/systemcost/demodata/BASELOADSERVICE.inc +++ /dev/null @@ -1,8 +0,0 @@ -* Addon for securing that System Constraints are forfilled - - -PARAMETER BASELOADSERVICE(CCC) 'Minimum production (MW) on certain generation facilities providing baseload services' -/ - Denmark 5000 -/; - diff --git a/base/addons/systemcost/demodata/sets_syscost.inc b/base/addons/systemcost/demodata/sets_syscost.inc deleted file mode 100644 index 088661b7..00000000 --- a/base/addons/systemcost/demodata/sets_syscost.inc +++ /dev/null @@ -1,7 +0,0 @@ -* Addon for securing that System Constraints are forfilled - - -SET IGEBASE(G) 'Power plants capable of supplying base load'; - - -IGEBASE(IGE)=YES$(IGEXT(IGE) or IGCND(IGE)); diff --git a/base/addons/systemcost/eqN_syscost.inc b/base/addons/systemcost/eqN_syscost.inc deleted file mode 100644 index f3f6353f..00000000 --- a/base/addons/systemcost/eqN_syscost.inc +++ /dev/null @@ -1,5 +0,0 @@ -* Addon for securing that System Constraints are forfilled - -QBASELOAD - - diff --git a/base/addons/systemcost/eq_syscost.inc b/base/addons/systemcost/eq_syscost.inc deleted file mode 100644 index a5f3a24a..00000000 --- a/base/addons/systemcost/eq_syscost.inc +++ /dev/null @@ -1,15 +0,0 @@ -* Addon for securing that System Constraints are forfilled - -Equations - -QBASELOAD(C,S,T) 'Minimum production for baseload service providers (MW)' -; - -QBASELOAD(C,IS3,T)$(BASELOADSERVICE(C) > 0) .. - SUM(IAGK_Y(IA,IGE)$(ICA(C,IA) AND IGEBASE(IGE)), VGE_T(IA,IGE,IS3,T)) - + SUM(IAGKN(IA,IGE)$(ICA(C,IA) AND IGEBASE(IGE)), VGEN_T(IA,IGE,IS3,T)) - - +VQBASELOAD(C,IS3,T) - =G= - BASELOADSERVICE(C) -; \ No newline at end of file diff --git a/base/addons/systemcost/var_syscost.inc b/base/addons/systemcost/var_syscost.inc deleted file mode 100644 index f8d9aaf9..00000000 --- a/base/addons/systemcost/var_syscost.inc +++ /dev/null @@ -1,8 +0,0 @@ -* Addon for securing that System Constraints are forfilled -* Slack variables are created to insure that the model works -* even if the System Constraints are not forfilled - -Positive Variables - - VQBASELOAD(C,S,T) 'Penalty on not forefilling the baseload' -; diff --git a/base/addons/welfare/welfare.opt b/base/addons/welfare/welfare.opt deleted file mode 100644 index e69de29b..00000000 diff --git a/base/addons/welfare/welfarecmp/compprint1.inc b/base/addons/welfare/welfarecmp/compprint1.inc deleted file mode 100644 index fab5eecd..00000000 --- a/base/addons/welfare/welfarecmp/compprint1.inc +++ /dev/null @@ -1,123 +0,0 @@ -* File compprint1.inc. -* This file is part of the redesigned welfare module for Balmorel. -* Author: Lars Bregnbæk -* Revision: 1.0 LB - - -* These files are used for comparison between scenarios -File compfile /..\output\economic\%CASEID%.MED/; -File compfile1 /..\output\economic\%CASEID%lr.MED/; - -File file_e_rev /..\output\economic\%CASEID% e_rev.med/; -File file_h_rev /..\output\economic\%CASEID% h_rev.med/; -File file_fxcost /..\output\economic\%CASEID% fxcost.med/; -File file_fuelcost /..\output\economic\%CASEID% fuelcost.med/; -File file_vcost /..\output\economic\%CASEID% vcost.med/; -*Erkki -File file_UCstartcost /..\output\economic\%CASEID% UCstartcost.med/; -File file_UCshutcost /..\output\economic\%CASEID% UCshutcost.med/; -File file_UCfixedcost /..\output\economic\%CASEID% UCfixedcost.med/; -*Erkki -File file_capcost_ex /..\output\economic\%CASEID% capcost_ex.med/; -File file_capcost_en /..\output\economic\%CASEID% capcost_en.med/; -File file_capcost_trans_ex /..\output\economic\%CASEID% capcost_trans_ex.med/; -File file_capcost_trans_en /..\output\economic\%CASEID% capcost_trans_en.med/; -File file_co2cost /..\output\economic\%CASEID% co2cost.med/; -File file_so2cost /..\output\economic\%CASEID% so2cost.med/; -File file_noxcost /..\output\economic\%CASEID% noxcost.med/; -File file_fueltax /..\output\economic\%CASEID% fueltax.med/; -File file_heattaxGH /..\output\economic\%CASEID% heattaxGH.med/; -File file_vindSubsidy /..\output\economic\%CASEID% vindSubsidy.med/; - -File file_e_cost /..\output\economic\%CASEID% e_cost.med/; -File file_h_cost /..\output\economic\%CASEID% h_cost.med/; -File file_e_util /..\output\economic\%CASEID% e_util.med/; -File file_h_util /..\output\economic\%CASEID% h_util.med/; - -File file_bottleneck /..\output\economic\%CASEID% bottleneck.med/; -File file_tradesurp /..\output\economic\%CASEID% tradesurp.med/; -File file_transcost /..\output\economic\%CASEID% transcost.med/; -File file_watervaluevar /..\output\economic\%CASEID% watervaluevar.med/; -File file_watervalue /..\output\economic\%CASEID% watervalue.med/; - -File file_NOxemission /..\output\economic\%CASEID% NOxemission.med/; -File file_SO2emission /..\output\economic\%CASEID% SO2emission.med/; -File file_CO2shadowval /..\output\economic\%CASEID% CO2shadowval.med/; -File file_VEShadowval /..\output\economic\%CASEID% VEShadowval.med/; -File file_Fosshadowval /..\output\economic\%CASEID% Fosshadowval.med/; - -File file_e_tax /..\output\economic\%CASEID% e_tax.med/; -File file_h_tax /..\output\economic\%CASEID% h_tax.med/; - -File file_dist_e /..\output\economic\%CASEID% dist_e.med/; -File file_dist_h /..\output\economic\%CASEID% dist_h.med/; - -File file_reacEf /..\output\economic\%CASEID% reacEf.med/; - -put compfile; put "Dummy"; - -Parameters - e_rev(Y,C) - h_rev(Y,C) - fxcost(Y,C) - fuelcost(Y,C) - vcost(Y,C) -*Erkki - UCstartcost(Y,C) - UCshutcost(Y,C) - UCfixedcost(Y,C) -*Erkki - capcost_ex(Y,C) - capcost_en(Y,C) - capcost_trans_ex(Y,C) - capcost_trans_en(Y,C) - - co2cost(Y,C) - so2cost(Y,C) - noxcost(Y,C) - fueltax(Y,C) - heattax_GH(Y,C) - vindSubsidy(Y,C) - - e_cost(Y,C) - h_cost(Y,C) - e_util(Y,C) - h_util(Y,C) - - bottleneck(Y,C) - tradesurp(Y,C) - transcost(Y,C) - - watervaluevar(Y,C) - watervalue(Y,C) - - NOxemission(Y,C) - SO2emission(Y,C) - CO2shadowval(Y,C) - VEShadowval(Y,C) - Fosshadowval(Y,C) - - e_tax(Y,C) - h_tax(Y,C) - - dist_e(Y,C) - dist_h(Y,C) - - reacEf(Y,C) - - -*Erkki- hourly welfare - hourlygenerationcost(Y,C,S,T) - hourlye_rev(Y,C,S,T) - hourlye_cost(Y,C,S,T) - hourlybottleneck(Y,C,S,T) - hourlyX3V(Y,C,S,T) -*Erkki -; - -*------------------------------------------------------------------------------- -* Add output for addon modules. It is best if addon code is in include files. -*------------------------------------------------------------------------------- - -$ifi %H2%==yes $include '../../base/addons/Hydrogen/compare/hycompprint1.inc'; -$ifi %transport%==yes $include '../../base/addons/transport/compare/trcompprint1.inc'; diff --git a/base/addons/welfare/welfarecmp/compprint3.inc b/base/addons/welfare/welfarecmp/compprint3.inc deleted file mode 100644 index 1e9b73a8..00000000 --- a/base/addons/welfare/welfarecmp/compprint3.inc +++ /dev/null @@ -1,411 +0,0 @@ -* File compprint3.INC. -* This file is part of the new welfare calculation module for the Balmorel model. -* Created by Lars Bregnbæk - - -* This file contains routines for handling of intermediate information that may -* be usefull output. - - -*=============================================================================== -* Overview -*=============================================================================== - - - -*=============================================================================== -* Overview end -*=============================================================================== - - e_rev(Y,C) =eps+ OMONEY*SUM((IR,S,T)$CCCRRR(C,IR), - IOF1000000*QEEQ.m(IR,S,T)*( - SUM(IAGK_Y(IA,IGE)$((RRRAAA(IR,IA)) AND IGNOTETOH(IGE)), VGE_T.l(IA,IGE,S,T)) - + SUM(IAGKN(IA,IGE)$((RRRAAA(IR,IA)) AND IGNOTETOH(IGE)), VGEN_T.l(IA,IGE,S,T)) - - SUM(IA$(RRRAAA(IR,IA) AND sum(IGESTO,IAGK_Y(IA,IGESTO)+IAGKN(IA,IGESTO))),VESTOLOADT.l(IA,S,T)) - ) - )/IOF1000000; - - h_rev(Y,C) =eps+ OMONEY*Sum((IA,S,T)$(IDH_SUMST(IA) NE 0 and ICA(C,IA)), - IOF1000000*QHEQ.m(IA,S,T)*( - SUM(IGBPR$IAGK_Y(IA,IGBPR),VGH_T.l(IA,IGBPR,S,T)) - + SUM(IGBPR$IAGKN(IA,IGBPR),VGHN_T.l(IA,IGBPR,S,T)) - + SUM(IGEXT$IAGK_Y(IA,IGEXT),VGH_T.l(IA,IGEXT,S,T)) - + SUM(IGEXT$IAGKN(IA,IGEXT),VGHN_T.l(IA,IGEXT,S,T)) - + SUM(IGHH$IAGK_Y(IA,IGHH),VGH_T.l(IA,IGHH,S,T)) - + SUM(IGHH$IAGKN(IA,IGHH),VGHN_T.l(IA,IGHH,S,T)) - + SUM(IGETOH$IAGK_Y(IA,IGETOH),VGH_T.l(IA,IGETOH,S,T)) - + SUM(IGETOH$IAGKN(IA,IGETOH),VGHN_T.l(IA,IGETOH,S,T)) - + SUM(IGESTO$(IAGK_Y(IA,IGESTO) and GDATA(IGESTO,'GDCB')),VGE_T.l(IA,IGESTO,S,T)/GDATA(IGESTO,'GDCB')) - - SUM(IGHSTO$(IAGK_Y(IA,IGHSTO) or IAGKN(IA,IGHSTO)),VHSTOLOADT.l(IA,S,T)) - ) - )/IOF1000000; - - fxcost(Y,C) =eps+ OMONEY*IOF1000*(SUM(IAGK_Y(IA,G)$ICA(C,IA),(IGKVACCTOY(IA,G) + IGKFX_Y(IA,G) -$ifi %DECOMPEFF%==yes -VDECOM.l(IA,G) - )*GOMFCOST(IA,G)) - + SUM(IAGKN(IA,G)$ICA(C,IA),VGKN.l(IA,G)*GOMFCOST(IA,G)))/IOF1000000 ; - - fuelcost(Y,C) =eps+ OMONEY*( - - SUM((IAGK_Y(IA,G),FFF)$(GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB') and ICA(C,IA) and IGNOTETOH(G)), - IFUELP_Y(IA,FFF) * IOF3P6 * SUM((S,T), IHOURSINST(S,T)*VGF_T.l(IA,G,S,T)) ) - +SUM((IAGKN(IA,G),FFF)$(GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB') and ICA(C,IA) and IGNOTETOH(G)), - IFUELP_Y(IA,FFF) * IOF3P6 * SUM((S,T), IHOURSINST(S,T)*VGFN_T.l(IA,G,S,T)) ) - - +SUM((IR,S,T)$CCCRRR(C,IR), - IOF1000000*QEEQ.m(IR,S,T)*( - SUM(IAGK_Y(IA,IGE)$((RRRAAA(IR,IA)) AND IGETOH(IGE) and ICA(C,IA)), VGE_T.l(IA,IGE,S,T)) - + SUM(IAGKN(IA,IGE)$((RRRAAA(IR,IA)) AND IGETOH(IGE) and ICA(C,IA)), VGEN_T.l(IA,IGE,S,T)) - ) - ) - )/IOF1000000; - - - - vcost(Y,C) =eps+ OMONEY*( - SUM(IAGK_Y(IA,IGE)$ICA(C,IA), GOMVCOST(IA,IGE) * SUM((S,T), IHOURSINST(S,T)*VGE_T.l(IA,IGE,S,T))) - + SUM(IAGK_Y(IA,IGNOTETOH(IGH))$ICA(C,IA), GOMVCOST(IA,IGNOTETOH) * SUM((S,T), IHOURSINST(S,T)*GDATA(IGNOTETOH,'GDCV')*(VGH_T.l(IA,IGNOTETOH,S,T)))) - + SUM(IAGKN(IA,IGE)$ICA(C,IA), GOMVCOST(IA,IGE) * SUM((S,T), IHOURSINST(S,T)*VGEN_T.l(IA,IGE,S,T))) - + SUM(IAGKN(IA,IGNOTETOH(IGH))$ICA(C,IA), GOMVCOST(IA,IGNOTETOH) * SUM((S,T), IHOURSINST(S,T)*GDATA(IGNOTETOH,'GDCV')*(VGHN_T.l(IA,IGNOTETOH,S,T)))) - )/IOF1000000; - -*Erkki UC welfare - - UCstartcost(Y,C) =eps -$ifi %UnitComm%==yes + OMONEY*( -$ifi %UnitComm%==yes SUM(IAGK_Y(IA,IGUC)$ICA(C,IA),GDATA(IGUC,'GDUCUCOST')*GDATA(IGUC,'GDUCUNITSIZE')*SUM((S,T),VUCU.L(IA,IGUC,S,T) -$ifi %UnitComm%==yes + VUCUN.L(IA,IGUC,S,T)))) -$ifi %UnitComm%==yes /IOF1000000 - ; - - UCshutcost(Y,C) =eps -$ifi %UnitComm%==yes + OMONEY*( -$ifi %UnitComm%==yes SUM(IAGK_Y(IA,IGUC)$ICA(C,IA),GDATA(IGUC,'GDUCDCOST')*GDATA(IGUC,'GDUCUNITSIZE')*SUM((S,T),VUCD.L(IA,IGUC,S,T) -$ifi %UnitComm%==yes + VUCDN.L(IA,IGUC,S,T)))) -$ifi %UnitComm%==yes /IOF1000000 - ; - - - UCfixedcost(Y,C) =eps -$ifi %UnitComm%==yes + OMONEY*( -$ifi %UnitComm%==yes SUM(IAGK_Y(IA,IGUC)$ICA(C,IA),GDATA(IGUC,'GDUCCOST0')*GDATA(IGUC,'GDUCUNITSIZE')*SUM((S,T),VUCON.L(IA,IGUC,S,T) -$ifi %UnitComm%==yes + VUCONN.L(IA,IGUC,S,T)))) -$ifi %UnitComm%==yes /IOF1000000 - ; -*Erkki - - - capcost_ex(Y,C) =eps+ OMONEY*IOF1000000*(SUM(IAGK_Y(IA,G)$ICA(C,IA),(IGKFX_Y(IA,G))*GINVCOST(IA,G)))/IOF1000000; - capcost_en(Y,C) =eps+ ( OMONEY*IOF1000000*(SUM(IAGKN(IA,G)$ICA(C,IA), VGKN.l(IA,G)*GINVCOST(IA,G))) - +OMONEY*IOF1000000*(SUM(IAGK_Y(IA,G)$ICA(C,IA),(IGKVACCTOY(IA,G))*GINVCOST(IA,G))) - )/IOF1000000; -* LARS20070829: Still no capital costs on exogenous transmission capacity. - capcost_trans_ex(Y,C) =eps+ OMONEY*( 0 )/IOF1000000; - capcost_trans_en(Y,C) =eps+ OMONEY*( SUM(IRE$CCCRRR(C,IRE), SUM(IRI$(IXKN(IRI,IRE) OR IXKN(IRE,IRI)), VXKN.l(IRE,IRI)*XINVCOST(IRE,IRI)*IOF05)) - +SUM(IRI$CCCRRR(C,IRI), SUM(IRE$(IXKN(IRI,IRE) OR IXKN(IRE,IRI)), VXKN.l(IRE,IRI)*XINVCOST(IRE,IRI)*IOF05)) - )/IOF1000000; - - co2cost(Y,C) =eps+ OMONEY*ITAX_CO2_Y(C)*SUM((IA,G,S,T)$(ICA(C,IA) and (IAGKN(IA,G) or IAGK_Y(IA,G))), IHOURSINST(S,T) * (IM_CO2(G)/1000) * IOF3P6 * - (VGF_T.l(IA,G,S,T)$IAGK_Y(IA,G)+VGFN_T.l(IA,G,S,T)$IAGKN(IA,G)))/IOF1000000 ; - - so2cost(Y,C) =eps+ OMONEY*ITAX_SO2_Y(C)*SUM((IA,G,S,T)$(ICA(C,IA) and (IAGKN(IA,G) or IAGK_Y(IA,G))), IHOURSINST(S,T) * (IM_SO2(G)/1000) * IOF3P6 * - (VGF_T.l(IA,G,S,T)$IAGK_Y(IA,G)+VGFN_T.l(IA,G,S,T)$IAGKN(IA,G)))/IOF1000000 ; - - noxcost(Y,C) =eps+ OMONEY*ITAX_NOX_Y(C)*SUM((IA,G,S,T)$(ICA(C,IA) and (IAGKN(IA,G) or IAGK_Y(IA,G))), IHOURSINST(S,T) * (GDATA(G,'GDNOX')/1000000)*IOF3P6* - (VGF_T.l(IA,G,S,T)$IAGK_Y(IA,G)+VGFN_T.l(IA,G,S,T)$IAGKN(IA,G)))/IOF1000000 ; - - fueltax(Y,C) =eps+ OMONEY*( - SUM((FFF,S,T), SUM(IAGK_Y(IA,G)$((GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB')) AND ICA(C,IA)), - IHOURSINST(S,T) * (TAX_F(FFF,C)+ITAX_GF(IA,G)) * IOF3P6 *VGF_T.l(IA,G,S,T))) - + SUM((FFF,S,T), SUM(IAGKN(IA,G)$((GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB')) AND ICA(C,IA)), - IHOURSINST(S,T) * (TAX_F(FFF,C)+ITAX_GF(IA,G)) * IOF3P6 *VGFN_T.l(IA,G,S,T))) - )/IOF1000000; - - heattax_GH(Y,C) =eps+ OMONEY*( + SUM((IA,IGH,S,T)$(IAGK_Y(IA,IGH) and ITAX_GH(IA,IGH) AND ICA(C,IA)), - ITAX_GH(IA,IGH)*IOF3P6*VGH_T.l(IA,IGH,S,T)*IHOURSINST(S,T)) - + SUM((IA,IGH,S,T)$(IAGKN(IA,IGH) and ITAX_GH(IA,IGH) AND ICA(C,IA)), - ITAX_GH(IA,IGH)*IOF3P6*VGHN_T.l(IA,IGH,S,T)*IHOURSINST(S,T)) - )/IOF1000000; - - - vindSubsidy(Y,C) =eps -$ifi %WINDTARGET%==yes + OMONEY*QWNDTARGET.m(C)*IOF1000000*SUM((IA,IGWND,S,T)$ICA(C,IA), -$ifi %WINDTARGET%==yes ( VGE_T.l(IA,IGWND,S,T)$IAGK_Y(IA,IGWND) -$ifi %WINDTARGET%==yes +VGEN_T.l(IA,IGWND,S,T)$IAGKN(IA,IGWND))*IHOURSINST(S,T) -$ifi %WINDTARGET%==yes )/IOF1000000 - ; - - e_cost(Y,C) =eps+ OMONEY*SUM((IR,S,T)$CCCRRR(C,IR), IOF1000000*QEEQ.m(IR,S,T)*( (IDE_T_Y(IR,S,T) - + SUM(DEF_U1,VDEF_T.l(IR,S,T,DEF_U1) ) - - SUM(DEF_D1,VDEF_T.l(IR,S,T,DEF_D1) ) - + SUM(DEF_U2,VDEF_T.l(IR,S,T,DEF_U2) ) - - SUM(DEF_D2,VDEF_T.l(IR,S,T,DEF_D2) ) - + SUM(DEF_U3,VDEF_T.l(IR,S,T,DEF_U3) ) - - SUM(DEF_D3,VDEF_T.l(IR,S,T,DEF_D3) ) - )/(1-DISLOSS_E(IR))) - )/IOF1000000; - - h_cost(Y,C) =eps+ OMONEY*Sum((IA,S,T)$(IDH_SUMST(IA) NE 0 and ICA(C,IA)), - IOF1000000*QHEQ.m(IA,S,T)*( - (IDH_T_Y(IA,S,T) - + SUM(DHF_U1,VDHF_T.l(IA,S,T,DHF_U1) ) - - SUM(DHF_D1,VDHF_T.l(IA,S,T,DHF_D1) ) - + SUM(DHF_U2,VDHF_T.l(IA,S,T,DHF_U2) ) - - SUM(DHF_D2,VDHF_T.l(IA,S,T,DHF_D2) ) - + SUM(DHF_U3,VDHF_T.l(IA,S,T,DHF_U3) ) - - SUM(DHF_D3,VDHF_T.l(IA,S,T,DHF_D3) ) - )/(1-DISLOSS_H(IA)) - ) - )/IOF1000000; - - - e_util(Y,C) =eps+OMONEY*( - - SUM(IR$CCCRRR(C,IR), - SUM((S,T), IHOURSINST(S,T)* - (SUM(DEF_D1, VDEF_T.l(IR,S,T,DEF_D1)* (IDEFP_T(IR,S,T,DEF_D1)+TAX_DE(C)+DISCOST_E(IR)) ) - + SUM(DEF_D2, VDEF_T.l(IR,S,T,DEF_D2)* (IDEFP_T(IR,S,T,DEF_D2)+TAX_DE(C)+DISCOST_E(IR)) ) - + SUM(DEF_D3, VDEF_T.l(IR,S,T,DEF_D3)* (IDEFP_T(IR,S,T,DEF_D3)+TAX_DE(C)+DISCOST_E(IR)) ) - ) - ) - - +SUM((S,T), IHOURSINST(S,T)* - (SUM(DEF_U1, VDEF_T.l(IR,S,T,DEF_U1)* (IDEFP_T(IR,S,T,DEF_U1)+TAX_DE(C)+DISCOST_E(IR)) ) - + SUM(DEF_U2, VDEF_T.l(IR,S,T,DEF_U2)* (IDEFP_T(IR,S,T,DEF_U2)+TAX_DE(C)+DISCOST_E(IR)) ) - + SUM(DEF_U3, VDEF_T.l(IR,S,T,DEF_U3)* (IDEFP_T(IR,S,T,DEF_U3)+TAX_DE(C)+DISCOST_E(IR)) ) - ) - ) - ))/IOF1000000; - - h_util(Y,C) =eps+OMONEY*( - - SUM(IA$ICA(C,IA), - SUM((S,T), IHOURSINST(S,T) - * SUM(DHF_D1, VDHF_T.l(IA,S,T,DHF_D1)* (IDHFP_T(IA,S,T,DHF_D1)+TAX_DH(C) ) ))) - - + SUM(IA$ICA(C,IA), - SUM((S,T), IHOURSINST(S,T) - * SUM(DHF_U1, VDHF_T.l(IA,S,T,DHF_U1)* (IDHFP_T(IA,S,T,DHF_U1)+TAX_DH(C) ) ))) - )/IOF1000000; - - - bottleneck(Y,C) =eps+ OMONEY*( - Sum((S,T), -* Udgående strøm - Sum(irrre$(CCCRRR(C,IRRRe) and ire(irrre)), - Sum(irrri$(iri(irrri) and (not sameas(irrre,irrri)) and ((VXKN.l(IRRRE,IRRRI) > 0) or (IXKINI_Y(IRRRE,IRRRI) > 0 ))), - IOF1000000*(qeeq.m(IRRRi,S,T)*vx_t.l(irRRe,irRRi,S,t)*(1-XLOSS(IRRRE,IRRRI)) - qeeq.m(IRRRe,S,T)*vx_t.l(irRRe,irRRi,S,t))))/2 -* Indgående strøm - +Sum(irrri$(CCCRRR(C,IRRRi) and iri(irrri)), - Sum(irrre$(ire(irrre) and (not sameas(irrre,irrri)) and ((VXKN.l(IRRRE,IRRRI) > 0) or (IXKINI_Y(IRRRE,IRRRI) > 0 ))), - IOF1000000*(qeeq.m(IRRRi,S,T)*vx_t.l(irRRe,irRRi,S,t)*(1-XLOSS(IRRRE,IRRRI)) - qeeq.m(IRRRe,S,T)*vx_t.l(irRRe,irRRi,S,t))))/2 - ) - )/IOF1000000; - - tradesurp(Y,C) =eps+ OMONEY*SUM((IR,S,T)$CCCRRR(C,IR), IOF1000000*QEEQ.m(IR,S,T)*(-IX3FX_T_Y(IR,S,T)))/IOF1000000; - - transcost(Y,C) =eps+ OMONEY*( SUM((IRE,IRI)$((IXKINI_Y(IRE,IRI) OR IXKN(IRE,IRI) OR IXKN(IRI,IRE)) and (CCCRRR(C,IRI) or CCCRRR(C,IRE))), - SUM((S,T), IHOURSINST(S,T) * (VX_T.l(IRE,IRI,S,T) * XCOST(IRE,IRI)))))/2/IOF1000000; - - - - watervaluevar(Y,C) =eps+ OMONEY*( - SUM(IAGK_Y(IA,IGHYRS)$ICA(C,IA), SUM((S,T), HYPPROFILS(IA,S)* IHOURSINST(S,T)*VGE_T.l(IA,IGHYRS,S,T))) - + SUM(IAGKN(IA,IGHYRS)$ICA(C,IA), SUM((S,T), HYPPROFILS(IA,S)* IHOURSINST(S,T)*VGEN_T.l(IA,IGHYRS,S,T))) - )/IOF1000000; - - watervalue(Y,C) =eps -$ifi not %bb3%==yes $goto not_waterval_CP -$ifi not %WATERVAL%==yes $goto not_waterval_CP - +OMONEY*( - SUM(IAGK_Y(IA,IGHYRS)$ICA(C,IA),SUM((S,T), IWATERVAL(IA,S)* IHOURSINST(S,T)*VGE_T.l(IA,IGHYRS,S,T))) - + SUM(IAGKN(IA,IGHYRS)$ICA(C,IA), SUM((S,T), IWATERVAL(IA,S)* IHOURSINST(S,T)*VGEN_T.l(IA,IGHYRS,S,T))) - )/IOF1000000 -$label not_waterval_CP - ; - - NOxemission(Y,C) =eps+ SUM((IA,G,S,T)$(ICA(C,IA) and (IAGKN(IA,G) or IAGK_Y(IA,G))), IHOURSINST(S,T) * (GDATA(G,'GDNOX')/1000000)*IOF3P6* - (VGF_T.l(IA,G,S,T)$IAGK_Y(IA,G)+VGFN_T.l(IA,G,S,T)$IAGKN(IA,G)))/IOF1000000 ; - SO2emission(Y,C) =eps+ SUM((IA,G,S,T)$(ICA(C,IA) and (IAGKN(IA,G) or IAGK_Y(IA,G))), IHOURSINST(S,T) * (IM_SO2(G)/1000) * IOF3P6 * - (VGF_T.l(IA,G,S,T)$IAGK_Y(IA,G)+VGFN_T.l(IA,G,S,T)$IAGKN(IA,G)))/IOF1000000 ; - -$ifi %BB3%==yes CO2shadowval(Y,C) =eps -$ifi not %BB3%==yes CO2shadowval(Y,C)$QLIMCO2.M(C) =eps -$ifi not %BB3%==yes + OMONEY*IOF1000000* -$ifi not %BB3%==yes (-QLIMCO2.M(C))* -$ifi not %BB3%==yes SUM((G,S,T), IHOURSINST(S,T) * (IM_CO2(G)/1000) * IOF3P6 * -$ifi not %BB3%==yes SUM(IA$ICA(C,IA),(VGE_T.L(IA,G,S,T) + VGEN_T.L(IA,G,S,T) -$ifi not %BB3%==yes + GDATA(G,'GDCV')*(VGH_T.L(IA,G,S,T) + VGHN_T.L(IA,G,S,T))) / -$ifi not %BB3%==yes (GDATA(G,'GDFE')* GEFFDERATE(IA,G))))/IOF1000000 - ; - - Fosshadowval(Y,C)=eps -$ifi %FOSSILTARGET%==yes +OMONEY*IOF1000000*SUM(CCCFOSSIL$CCCFOSSILEC(CCCFOSSIL,C), -$ifi %FOSSILTARGET%==yes QFOSSILTARGET.m(CCCFOSSIL) -$ifi %FOSSILTARGET%==yes )* -$ifi %FOSSILTARGET%==yes ( -$ifi %FOSSILTARGET%==yes + SUM((S,T), SUM(IAGK_Y(IA,IGE)$(IGFOSSIL(IGE) AND ICA(C,IA)), -$ifi %FOSSILTARGET%==yes IHOURSINST(S,T) * IOF3P6 * -$ifi %FOSSILTARGET%==yes (VGE_T.l(IA,IGE,S,T) / (GDATA(IGE,'GDFE') * GEFFDERATE(IA,IGE))))) -$ifi %FOSSILTARGET%==yes + SUM((S,T), SUM(IAGK_Y(IA,IGH)$(IGFOSSIL(IGH) AND ICA(C,IA)), -$ifi %FOSSILTARGET%==yes IHOURSINST(S,T) * IOF3P6 * -$ifi %FOSSILTARGET%==yes GDATA(IGH,'GDCV')* VGH_T.l(IA,IGH,S,T)/(GDATA(IGH,'GDFE') * GEFFDERATE(IA,IGH)))) -$ifi %FOSSILTARGET%==yes + SUM((S,T), SUM(IAGKN(IA,IGE)$(IGFOSSIL(IGE) AND ICA(C,IA)), -$ifi %FOSSILTARGET%==yes IHOURSINST(S,T) * IOF3P6 * -$ifi %FOSSILTARGET%==yes (VGEN_T.l(IA,IGE,S,T) / (GDATA(IGE,'GDFE') * GEFFDERATE(IA,IGE))))) -$ifi %FOSSILTARGET%==yes + SUM((S,T), SUM(IAGKN(IA,IGH)$(IGFOSSIL(IGH) AND ICA(C,IA)), -$ifi %FOSSILTARGET%==yes IHOURSINST(S,T) * IOF3P6 * -$ifi %FOSSILTARGET%==yes GDATA(IGH,'GDCV')*VGHN_T.l(IA,IGH,S,T)/(GDATA(IGH,'GDFE')*GEFFDERATE(IA,IGH)))) -$ifi %FOSSILTARGET%==yes )/IOF1000000 - ; - - VEShadowval(Y,C) =eps -$ifi %RENEWTARGET%==yes +OMONEY*IOF1000000*SUM(CCCRESHAREE$CCCRESHAREEC(CCCRESHAREE,C), -$ifi %RENEWTARGET%==yes QRENEW.m(CCCRESHAREE) -$ifi %RENEWTARGET%==yes )*SUM((S,T), IHOURSINST(S,T) * -$ifi %RENEWTARGET%==yes (SUM(IAGK_Y(IA,IGRENEW(IGE))$ICA(C,IA), -$ifi %RENEWTARGET%==yes VGE_T.l(IA,IGE,S,T) -$ifi %RENEWTARGET%==yes ) -$ifi %RENEWTARGET%==yes +SUM(IAGKN(IA,IGRENEW(IGE))$ICA(C,IA), -$ifi %RENEWTARGET%==yes VGEN_T.l(IA,IGE,S,T) -$ifi %RENEWTARGET%==yes )) -$ifi %RENEWTARGET%==yes )/IOF1000000 - ; - - -* e_tax(Y,C) =eps+ OMONEY*TAX_DE(C)*SUM((IR,S,T)$CCCRRR(C,IR), IHOURSINST(S,T)* - e_tax(Y,C) =eps+ OMONEY*SUM((IR,S,T)$CCCRRR(C,IR), TAX_DE(C) * IHOURSINST(S,T)* - (IDE_T_Y(IR,S,T) - + SUM(DEF_U1,VDEF_T.l(IR,S,T,DEF_U1) ) - - SUM(DEF_D1,VDEF_T.l(IR,S,T,DEF_D1) ) - + SUM(DEF_U2,VDEF_T.l(IR,S,T,DEF_U2) ) - - SUM(DEF_D2,VDEF_T.l(IR,S,T,DEF_D2) ) - + SUM(DEF_U3,VDEF_T.l(IR,S,T,DEF_U3) ) - - SUM(DEF_D3,VDEF_T.l(IR,S,T,DEF_D3) ) - ) - )/IOF1000000; - - h_tax(Y,C) =eps+ OMONEY*TAX_DH(C)*SUM((IA,S,T)$ICA(C,IA), IHOURSINST(S,T)* - (IDH_T_Y(IA,S,T) - + SUM(DHF_U1,VDHF_T.l(IA,S,T,DHF_U1) ) - - SUM(DHF_D1,VDHF_T.l(IA,S,T,DHF_D1) ) - ) - )/IOF1000000; - - - dist_e(Y,C) =eps+OMONEY*( - - SUM(IR$CCCRRR(C,IR), DISCOST_E(IR)* - SUM((S,T), IHOURSINST(S,T) - * (SUM(DEF_D1, VDEF_T.l(IR,S,T,DEF_D1) ) - + SUM(DEF_D2, VDEF_T.l(IR,S,T,DEF_D2) ) - + SUM(DEF_D3, VDEF_T.l(IR,S,T,DEF_D3) ) - ) - ) - ) - + SUM(IR$CCCRRR(C,IR),DISCOST_E(IR)* - SUM((S,T), IHOURSINST(S,T)* - ( IDE_T_Y(IR,S,T) - + SUM(DEF_U1, VDEF_T.l(IR,S,T,DEF_U1) ) - + SUM(DEF_U2, VDEF_T.l(IR,S,T,DEF_U2) ) - + SUM(DEF_U3, VDEF_T.l(IR,S,T,DEF_U3) ) - ) - ) - ) - )/IOF1000000; - - dist_h(Y,C) =eps+OMONEY*( - - SUM(IA$ICA(C,IA), DISCOST_H(IA)* - SUM((S,T), IHOURSINST(S,T) - * SUM(DHF_D1, VDHF_T.l(IA,S,T,DHF_D1) )))) - - + SUM(IA$ICA(C,IA), DISCOST_H(IA)* - SUM((S,T), IHOURSINST(S,T) - * (IDH_T_Y(IA,S,T)+SUM(DHF_U1, VDHF_T.l(IA,S,T,DHF_U1) ))) - )/IOF1000000; -$ontext - reacEf(Y,C)$BASELOADSERVICE(C) =eps - +OMONEY*IOF1000000*Sum((S,T), QBASELOAD.m(C,S,T)*IHOURSINST(S,T)*( - SUM(IAGK_Y(IA,IGE)$(ICA(C,IA) AND IGEBASE(IGE)), VGE_T.l(IA,IGE,S,T)) - + SUM(IAGKN(IA,IGE)$(ICA(C,IA) AND IGEBASE(IGE)), VGEN_T.l(IA,IGE,S,T))) - )/IOF1000000 - ; -$offtext -*------------------------------------------------------------------------------- -* Add output for addon modules. It is best if addon code is in include files. -*------------------------------------------------------------------------------- - -$ifi %H2%==yes $include '../../base/addons/Hydrogen/compare/hycompprint3.inc'; -$ifi %transport%==yes $include '../../base/addons/transport/compare/trcompprint3.inc'; - -*Erkki- Calculation of welfare results in hourly resolution. Allows for analysis of when exactly the bulk of welfare is won/lost -$ifi not %hourlywelfare%==yes $goto nothourlywelfare - -hourlygenerationcost(Y,C,S,T) =eps+ OMONEY*( - - SUM((IAGK_Y(IA,G),FFF)$(GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB') and ICA(C,IA) and IGNOTETOH(G)), - IFUELP_Y(IA,FFF) * IOF3P6 * IHOURSINST(S,T)*VGF_T.l(IA,G,S,T) ) - +SUM((IAGKN(IA,G),FFF)$(GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB') and ICA(C,IA) and IGNOTETOH(G)), - IFUELP_Y(IA,FFF) * IOF3P6 * IHOURSINST(S,T)*VGFN_T.l(IA,G,S,T) ) - - +SUM((IR)$CCCRRR(C,IR), - IOF1000000*QEEQ.m(IR,S,T)*( - SUM(IAGK_Y(IA,IGE)$((RRRAAA(IR,IA)) AND IGETOH(IGE) and ICA(C,IA)), VGE_T.l(IA,IGE,S,T)) - + SUM(IAGKN(IA,IGE)$((RRRAAA(IR,IA)) AND IGETOH(IGE) and ICA(C,IA)), VGEN_T.l(IA,IGE,S,T)) - ) - ) - )/IOF1000000 - - + OMONEY*( - SUM(IAGK_Y(IA,IGE)$ICA(C,IA), GOMVCOST(IA,IGE) * IHOURSINST(S,T)*VGE_T.l(IA,IGE,S,T)) - + SUM(IAGK_Y(IA,IGNOTETOH(IGH))$ICA(C,IA), GOMVCOST(IA,IGNOTETOH) * IHOURSINST(S,T)*GDATA(IGNOTETOH,'GDCV')*(VGH_T.l(IA,IGNOTETOH,S,T))) - + SUM(IAGKN(IA,IGE)$ICA(C,IA), GOMVCOST(IA,IGE) * IHOURSINST(S,T)*VGEN_T.l(IA,IGE,S,T)) - + SUM(IAGKN(IA,IGNOTETOH(IGH))$ICA(C,IA), GOMVCOST(IA,IGNOTETOH) * IHOURSINST(S,T)*GDATA(IGNOTETOH,'GDCV')*(VGHN_T.l(IA,IGNOTETOH,S,T))) - )/IOF1000000 - - + OMONEY*ITAX_CO2_Y(C)*SUM((IA,G)$(ICA(C,IA) and (IAGKN(IA,G) or IAGK_Y(IA,G))), IHOURSINST(S,T) * (IM_CO2(G)/1000) * IOF3P6 * - (VGF_T.l(IA,G,S,T)$IAGK_Y(IA,G)+VGFN_T.l(IA,G,S,T)$IAGKN(IA,G)))/IOF1000000 - - + OMONEY*( - SUM((FFF), SUM(IAGK_Y(IA,G)$((GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB')) AND ICA(C,IA)), - IHOURSINST(S,T) * (TAX_F(FFF,C)+ITAX_GF(IA,G)) * IOF3P6 *VGF_T.l(IA,G,S,T))) - + SUM((FFF), SUM(IAGKN(IA,G)$((GDATA(G,'GDFUEL') EQ FDATA(FFF,'FDNB')) AND ICA(C,IA)), - IHOURSINST(S,T) * (TAX_F(FFF,C)+ITAX_GF(IA,G)) * IOF3P6 *VGFN_T.l(IA,G,S,T))) - )/IOF1000000; - -hourlye_rev(Y,C,S,T) =eps+ OMONEY*SUM((IR)$CCCRRR(C,IR), - IOF1000000*QEEQ.m(IR,S,T)*( - SUM(IAGK_Y(IA,IGE)$((RRRAAA(IR,IA)) AND IGNOTETOH(IGE)), VGE_T.l(IA,IGE,S,T)) - + SUM(IAGKN(IA,IGE)$((RRRAAA(IR,IA)) AND IGNOTETOH(IGE)), VGEN_T.l(IA,IGE,S,T)) - - SUM(IA$(RRRAAA(IR,IA) AND sum(IGESTO,IAGK_Y(IA,IGESTO)+IAGKN(IA,IGESTO))),VESTOLOADT.l(IA,S,T)) - ) - )/IOF1000000; - -hourlye_cost(Y,C,S,T) =eps+ OMONEY*SUM((IR)$CCCRRR(C,IR), IOF1000000*QEEQ.m(IR,S,T)*( (IDE_T_Y(IR,S,T) - + SUM(DEF_U1,VDEF_T.l(IR,S,T,DEF_U1) ) - - SUM(DEF_D1,VDEF_T.l(IR,S,T,DEF_D1) ) - + SUM(DEF_U2,VDEF_T.l(IR,S,T,DEF_U2) ) - - SUM(DEF_D2,VDEF_T.l(IR,S,T,DEF_D2) ) - + SUM(DEF_U3,VDEF_T.l(IR,S,T,DEF_U3) ) - - SUM(DEF_D3,VDEF_T.l(IR,S,T,DEF_D3) ) - )/(1-DISLOSS_E(IR))) - )/IOF1000000; - -hourlybottleneck(Y,C,S,T) =eps+ OMONEY*( - -* Udgående strøm - Sum(irrre$(CCCRRR(C,IRRRe) and ire(irrre)), - Sum(irrri$(iri(irrri) and (not sameas(irrre,irrri)) and ((VXKN.l(IRRRE,IRRRI) > 0) or (IXKINI_Y(IRRRE,IRRRI) > 0 ))), - IOF1000000*(qeeq.m(IRRRi,S,T)*vx_t.l(irRRe,irRRi,S,t)*(1-XLOSS(IRRRE,IRRRI)) - qeeq.m(IRRRe,S,T)*vx_t.l(irRRe,irRRi,S,t))))/2 -* Indgående strøm - +Sum(irrri$(CCCRRR(C,IRRRi) and iri(irrri)), - Sum(irrre$(ire(irrre) and (not sameas(irrre,irrri)) and ((VXKN.l(IRRRE,IRRRI) > 0) or (IXKINI_Y(IRRRE,IRRRI) > 0 ))), - IOF1000000*(qeeq.m(IRRRi,S,T)*vx_t.l(irRRe,irRRi,S,t)*(1-XLOSS(IRRRE,IRRRI)) - qeeq.m(IRRRe,S,T)*vx_t.l(irRRe,irRRi,S,t))))/2 - - )/IOF1000000; - - -$ifi %x3v%==yes hourlyX3V(Y,C,S,T) =eps + OMONEY*sum((IR)$CCCRRR(C,IR),sum((X3VPLACE0,X3VSTEP0), IOF1000000*QEEQ.m(IR,S,T)*VX3VIM_T.l(IR,X3VPLACE0,X3VSTEP0,S,T))) - -$ifi %x3v%==yes /IOF1000000; - - -$label nothourlywelfare -*Erkki \ No newline at end of file diff --git a/base/addons/welfare/welfarecmp/mkcompare.inc b/base/addons/welfare/welfarecmp/mkcompare.inc deleted file mode 100644 index 5a429b54..00000000 --- a/base/addons/welfare/welfarecmp/mkcompare.inc +++ /dev/null @@ -1,167 +0,0 @@ -$SETGLOBAL format prn - - - -put file_e_rev ; -Put "Generator revenue from electricity generation Mio OMONEY"/; -$libinclude gams2tbl e_rev - -put file_h_rev ; -Put "Generator revenue from heat generation"/; -$libinclude gams2tbl h_rev - -put file_fxcost ; -Put "Generator fixed costs"/; -$libinclude gams2tbl fxcost - -put file_fuelcost ; -Put "Generator fuel costs"/; -$libinclude gams2tbl fuelcost - -put file_vcost ; -Put "Generator variable costs"/; -$libinclude gams2tbl vcost -*Erkki -put file_UCstartcost ; -Put "Generator start-up costs"/; -$libinclude gams2tbl UCstartcost - -put file_UCshutcost ; -Put "Generator shut-down costs"/; -$libinclude gams2tbl UCshutcost - -put file_UCfixedcost ; -Put "Generator fixed UC costs"/; -$libinclude gams2tbl UCfixedcost -*Erkki -put file_capcost_ex ; -Put "Generator total capital costs - exogenous units"/; -$libinclude gams2tbl capcost_ex - -put file_capcost_en ; -Put "Generator total capital costs - endogenous units"/; -$libinclude gams2tbl capcost_en - -put file_capcost_trans_ex ; -Put "Transmission total capital costs - exogenous units"/; -$libinclude gams2tbl capcost_trans_ex - -put file_capcost_trans_en ; -Put "Transmission total capital costs - endogenous units"/; -$libinclude gams2tbl capcost_trans_en - -put file_co2cost ; -Put "Value of CO2 emissions"/; -$libinclude gams2tbl co2cost - -put file_so2cost ; -Put "Value of SO2 emissions"/; -$libinclude gams2tbl so2cost - -put file_noxcost ; -Put "Value of NOx emissions"/; -$libinclude gams2tbl noxcost - -put file_fueltax ; -Put "Value of fuel taxes"/; -$libinclude gams2tbl fueltax - -put file_heattaxGH ; -Put "Value of heat taxes on units"/; -$libinclude gams2tbl heattax_GH - -put file_vindSubsidy ; -Put "Value of vind subsidies"/; -$libinclude gams2tbl vindSubsidy - -put file_e_cost ; -Put "Consumer electricity costs"/; -$libinclude gams2tbl e_cost - -put file_h_cost ; -Put "Consumer heating costs"/; -$libinclude gams2tbl h_cost - -put file_e_util ; -Put "Consumer utility of electricity consumption"/; -$libinclude gams2tbl e_util - -put file_h_util ; -Put "Consumer utility heat consumption"/; -$libinclude gams2tbl h_util - -put file_bottleneck ; -Put "TSO bottleneck income net of transmission losses"/; -$libinclude gams2tbl bottleneck - -put file_tradesurp ; -Put "Trade value of exogenous imports from 3rd countries"/; -$libinclude gams2tbl tradesurp - -put file_transcost ; -Put "Transmission variable costs"/; -$libinclude gams2tbl transcost - -put file_watervaluevar ; -Put "Seasonal calibration value of water"/; -$libinclude gams2tbl watervaluevar - - -put file_watervalue ; -Put "Water value transfered and used"/; -$libinclude gams2tbl watervalue - -put file_NOxemission ; -Put "NOx emissions (mio tonnes)"/; -$libinclude gams2tbl NOxemission - -put file_SO2emission ; -Put "SO2 emissions (mio tonnes)"/; -$libinclude gams2tbl SO2emission - -put file_CO2shadowval ; -Put "Shadow value of CO2 constraint"/; -$libinclude gams2tbl CO2shadowval - -put file_VEShadowval ; -Put "Shadow value of VE constraint"/; -$libinclude gams2tbl VEShadowval - -put file_Fosshadowval ; -Put "Shadow value of fossil fuel constraint"/; -$libinclude gams2tbl Fosshadowval - -put file_e_tax ; -Put "Consumer tax on electricity"/; -$libinclude gams2tbl e_tax - -put file_h_tax ; -Put "Consumer tax on heat"/; -$libinclude gams2tbl h_tax - -put file_dist_e ; -Put "Electricity distribution costs"/; -$libinclude gams2tbl dist_e - -put file_dist_h ; -Put "Heat distribution costs"/; -$libinclude gams2tbl dist_h - -*Erkki -$ontext -put file_reacEf ; -Put "Base load services"/; -$libinclude gams2tbl reacEf -$offtext -*Erkki - - - -*------------------------------------------------------------------------------- -* Add output for addon modules. It is best if addon code is in include files. -*------------------------------------------------------------------------------- - -$ifi %H2%==yes $include '../../base/addons/Hydrogen/compare/hymkcompare.inc'; -$ifi %transport%==yes $include '../../base/addons/transport/compare/trmkcompare.inc'; - - diff --git a/base/addons/welfare/welfarecmp/welfarebb3.inc b/base/addons/welfare/welfarecmp/welfarebb3.inc deleted file mode 100644 index de8a8c4b..00000000 --- a/base/addons/welfare/welfarecmp/welfarebb3.inc +++ /dev/null @@ -1,2 +0,0 @@ -* LB: Disse er fjernet, da de bruger elementer fra det gamle velfærdsmodul. Bør nok opdateres. Dette skal der tænkes lidt over. -$include "..\..\base\addons\welfare\welfarecmp\mkcompare.inc"; \ No newline at end of file From 1dcbbd80daa6dbf2325e17c1fe4f993a962938e3 Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Wed, 26 Jun 2024 19:13:23 +0200 Subject: [PATCH 08/21] Implemented XMAXINV as functioning constraint --- base/model/Balmorelbb4.inc | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/base/model/Balmorelbb4.inc b/base/model/Balmorelbb4.inc index 765f2237..5cb1eb92 100644 --- a/base/model/Balmorelbb4.inc +++ b/base/model/Balmorelbb4.inc @@ -2128,6 +2128,11 @@ QXK_UP(IY411,IRE,IRI,IS3,T)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411)),IXKN(Y,IRE,IRI)). $include "../../base/addons/_hooks/qxk_up.inc" ; + +* Put a cap on the maximum accummulated transmission capacity, if a cap is specified +VXKNACCUMNET.UP(Y,IRRRE,IRRRI)$(XINVCOST(Y,IRRRE,IRRRI) AND XMAXINV(IRRRE,IRRRI))= XMAXINV(IRRRE,IRRRI); + + * Accumulated endogenous transmission investments during present BB4 model ----- * Note that it is assumed that there are no endogenous decommissioning * of previous endogenous investment (cf. QGKACCUMNET). From d790c43c36789a109979197136bbac3ba76cb93d Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Wed, 26 Jun 2024 19:18:48 +0200 Subject: [PATCH 09/21] Added possibility of using T-dependancy on GKRATE --- base/model/Balmorelbb4.inc | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/base/model/Balmorelbb4.inc b/base/model/Balmorelbb4.inc index 5cb1eb92..f415504d 100644 --- a/base/model/Balmorelbb4.inc +++ b/base/model/Balmorelbb4.inc @@ -291,9 +291,9 @@ PARAMETER FUELPRICE_EXC(YYY,AAA,FFF) "Fuel prices to be transferred $ifi exist "bb4datainc.inc" $include "bb4datainc.inc"; $ifi not exist "bb4datainc.inc" $include "../../base/model/bb4datainc.inc"; -* UPS! Forgot DOL! TODO -*GKNMAX(YYY,AAA,GGG)$((NOT Y(YYY)) OR (NOT IA(AAA))) = INF; Presently not implemented. -GKRATE(IA,G,S)$(GKRATE(IA,G,S) EQ 1) = 0; !! Should give no reduction in size unless the data files does not exploit that default value is 1. +* GKRATE +$ifi %GKRATE_DOL%==AAA_GGG_SSS GKRATE(IA,G,S)$(GKRATE(IA,G,S) EQ 1) = 0; !! Should give no reduction in size unless the data files does not exploit that default value is 1. +$ifi %GKRATE_DOL%==AAA_GGG_SSS_TTT GKRATE(IA,G,S,T)$(GKRATE(IA,G,S,T) EQ 1) = 0; * The two specifications of fuel type 'FDACRONYM' and 'FDNB', have relative merits. * Under most circumstances 'FDACRONYM' is preferable, because it is more transparent. @@ -1078,7 +1078,8 @@ $ifi %HYDROGEN%==yes HYDROGEN_DH2_VAR_T(AAA,SSS,TTT)$((NOT IA(AAA)) OR (NOT S(SS *DHF_STEPS(AAA,SSS,TTT,DF_QP,DHF)$((NOT IA(AAA)) OR (NOT S(SSS)) OR (NOT T(TTT))) = 0; *DHF_STEPS(IA,S,T,DF_QP,DHF)$((NOT DHF_D1(DHF) AND (NOT DHF_D2(DHF)) AND (NOT DHF_D3(DHF)) AND (NOT DHF_U1(DHF)) AND (NOT DHF_U2(DHF)) AND (NOT DHF_U3(DHF)))) = 0; -GKRATE(AAA,GGG,SSS)$((NOT IA(AAA)) OR (NOT G(GGG)) OR (NOT S(SSS))) = 0; +$ifi %GKRATE_DOL%==AAA_GGG_SSS GKRATE(AAA,GGG,SSS)$((NOT IA(AAA)) OR (NOT G(GGG)) OR (NOT S(SSS))) = 0; +$ifi %GKRATE_DOL%==AAA_GGG_SSS_TTT GKRATE(AAA,GGG,SSS,TTT)$((NOT IA(AAA)) OR (NOT G(GGG)) OR (NOT S(SSS)) OR (NOT T(TTT))) = 0; FUELPRICE(YYY,AAA,FFF)$(NOT(Y(YYY)))=0; * Discounting From bf2b9f204d496d656b489a1abe086798f07e7ff4 Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Wed, 26 Jun 2024 19:50:53 +0200 Subject: [PATCH 10/21] Updated gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index c0ae047f..4d4eb265 100644 --- a/.gitignore +++ b/.gitignore @@ -23,4 +23,7 @@ base/data/README.md *.log *.lxi *.pf +*.log~1 +*.log~2 +*.log~3 %batfileMERGEWITHBASE% \ No newline at end of file From a90f489f56422963f6b7b5a919265a849635f151 Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Thu, 27 Jun 2024 15:45:34 +0200 Subject: [PATCH 11/21] Finished master_merge_tests --- .../master_merge_tests/merge_tests.ipynb | 657 +++++++++++++++--- .../auxils/master_merge_tests/merge_tests.pdf | Bin 0 -> 3038384 bytes 2 files changed, 556 insertions(+), 101 deletions(-) create mode 100644 base/auxils/master_merge_tests/merge_tests.pdf diff --git a/base/auxils/master_merge_tests/merge_tests.ipynb b/base/auxils/master_merge_tests/merge_tests.ipynb index 9dabec9c..6ebdbed1 100644 --- a/base/auxils/master_merge_tests/merge_tests.ipynb +++ b/base/auxils/master_merge_tests/merge_tests.ipynb @@ -10,20 +10,27 @@ "\n", "Please follow the procedure described in the [readme](../../../README.md), and be critical if the tests performed here is enough to verify and validate the changes made. Furthermore, note the following:\n", "- Make sure that the choice of addons is comparable, unless the change is a new addon\n", - "- If the changes are in files that by default are loaded in the base scenario (such as addons) you will need to apply the changes through options. Or, more simply, run the four tests manually in other folders and copy+paste the relevant MainResults to the paths expected by this script and append _lowtemphighspace and _hightemplowspace to the respective files. Skip to the analysis section below in that case.\n", + "- If the changes are in files that by default are loaded in the base scenario (such as addons) you will need to apply the changes through options. Or, more simply, run the four tests manually in other folders and copy+paste the relevant MainResults to the paths expected by this script and append -lowtemphighspace and -hightemplowspace to the respective files. Skip to the analysis section below in that case.\n", "- If a GamsExecutionError occur in the cell running Balmorel, check the _gams_py_gjo0.lst file, or maybe try to run the scenario in GAMS studio for easier debugging\n", - "- All tests may take around 20 minutes.\n", + "- All tests may take around 45 minutes.\n", "\n", "The [pybalmorel](https://pypi.org/project/pybalmorel/) package is required to run this notebook. In a terminal, create a new python environment, activate it and install version 0.2.2 by running the following command:\n", "```\n", "pip install pybalmorel==0.2.2\n", "```\n", - "Documentation on creating and activating virtual environments for standalone python can be found [here](https://docs.python.org/3/library/venv.html) and for a conda installation [here](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)" + "Documentation on creating and activating virtual environments for standalone python can be found [here](https://docs.python.org/3/library/venv.html) and for a conda installation [here](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html).\n", + "\n", + "You may also consider installing nbconvert for converting the results from the notebook into a distributable .pdf, using the following commands:\n", + "```\n", + "pip install nbconvert\n", + "cd base/auxils/master_merge_tests\n", + "jupyter nbconvert --to pdf merge_tests.ipynb\n", + "```\n" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +41,7 @@ "import os\n", "\n", "## Scenario and test case names\n", - "scenarios = ['base', 'changes']\n", + "scenarios = ['base', 'merge']\n", "test_cases = ['lowtemphighspace', 'hightemplowspace']" ] }, @@ -154,29 +161,36 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--- Warning: The GAMS version (45.5.0) differs from the API version (45.7.0).\n" - ] - } - ], + "outputs": [], "source": [ - "scenarios = ['base', 'base_changes']\n", - "\n", "### 2.0 Load Results\n", - "mr = MainResults(files=['MainResults_%s.gdx'%test_case for test_case in test_cases]*2,\n", - " paths=['../../../%s/model'%scenario for scenario in scenarios for i in range(2) ],\n", - " scenario_names=['%s_%s'%(scenario, test_case) for scenario in scenarios for test_case in test_cases])" + "mr = MainResults(files=['MainResults_%s-%s.gdx'%(scenario, test_case) for test_case in test_cases for scenario in scenarios],\n", + " paths=['../../../base/model'])\n", + "\n", + "\n", + "# A convenient function for printing scenario results\n", + "def print_results(res: pd.DataFrame,\n", + " name: str,\n", + " unit: str,\n", + " scenarios: list,\n", + " test_cases: list,\n", + " seperator: str = '-'):\n", + " print('All %s [%s]:\\n---------------------\\n'%(name, unit))\n", + " print(scenarios[0]+seperator+test_cases[0]+'\\n', res[scenarios[0]+seperator+test_cases[0]].to_string(), '\\n')\n", + " print(scenarios[1]+seperator+test_cases[0]+'\\n', res[scenarios[1]+seperator+test_cases[0]].to_string(), '\\n')\n", + " print(scenarios[0]+seperator+test_cases[1]+'\\n', res[scenarios[0]+seperator+test_cases[1]].to_string(), '\\n')\n", + " print(scenarios[1]+seperator+test_cases[1]+'\\n', res[scenarios[1]+seperator+test_cases[1]].to_string(), '\\n')\n", + " for test_case in test_cases:\n", + " print('\\nDifference in %s in %s between %s and %s in for %s'%tuple([name, unit] + scenarios + [test_case]),\n", + " '\\n-------------------------------------------------------------\\n')\n", + " print((res['%s%s%s'%(scenarios[1], seperator, test_case)] - res['%s%s%s'%(scenarios[0], seperator, test_case)]).to_string() + '\\n')" ] }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -185,50 +199,65 @@ "text": [ "All production [TWh]:\n", "---------------------\n", - " Scenario base_changes_hightemplowspace base_changes_lowtemphighspace base_hightemplowspace base_lowtemphighspace \n", - "Commodity ELECTRICITY HEAT HYDROGEN ELECTRICITY HEAT HYDROGEN ELECTRICITY HEAT HYDROGEN ELECTRICITY HEAT HYDROGEN\n", - "Year \n", - "2030 NaN NaN NaN 204.885725 141.036449 31.666812 NaN NaN NaN 146.877826 97.535908 7.353390\n", - "2050 251.124885 140.016999 53.919089 246.528420 143.795472 52.446507 180.074473 95.697868 30.731138 179.786263 97.253493 30.801129 \n", "\n", + "base-lowtemphighspace\n", + " Commodity ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 2869.276366 3796.358465 462.710060\n", + "2050 4667.655580 3761.749433 2271.927144 \n", "\n", - "Difference in production in TWh between base and base_changes in for lowtemphighspace \n", - "-------------------------------------------------------------\n", + "merge-lowtemphighspace\n", + " Commodity ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 4077.090418 6270.953211 380.805021\n", + "2050 6399.080581 6354.479388 2054.303374 \n", "\n", - "Commodity ELECTRICITY HEAT HYDROGEN\n", + "base-hightemplowspace\n", + " Commodity ELECTRICITY HEAT HYDROGEN\n", "Year \n", - "2030 58.007899 43.500540 24.313422\n", - "2050 66.742156 46.541979 21.645378\n", + "2030 156.270829 96.187924 14.371776\n", + "2050 181.412084 96.652686 30.780696 \n", + "\n", + "merge-hightemplowspace\n", + " Commodity ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 202.787943 141.563107 30.989462\n", + "2050 255.147313 142.961660 52.440355 \n", + "\n", + "\n", + "Difference in production in TWh between base and merge in for lowtemphighspace \n", + "-------------------------------------------------------------\n", + "\n", + "Commodity ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 1207.814052 2474.594747 -81.905038\n", + "2050 1731.425001 2592.729955 -217.623770\n", "\n", "\n", - "Difference in production in TWh between base and base_changes in for hightemplowspace \n", + "Difference in production in TWh between base and merge in for hightemplowspace \n", "-------------------------------------------------------------\n", "\n", "Commodity ELECTRICITY HEAT HYDROGEN\n", "Year \n", - "2030 NaN NaN NaN\n", - "2050 71.050412 44.319131 23.187951\n", + "2030 46.517113 45.375183 16.617686\n", + "2050 73.735228 46.308974 21.659658\n", "\n" ] } ], "source": [ - "### 2.1 Calculate Difference in capduction \n", + "### 2.1 Calculate Difference in production \n", "pro = mr.get_result('PRO_YCRAGF').pivot_table(index='Year',\n", " columns=['Scenario', 'Commodity'],\n", " values='Value',\n", " aggfunc='sum')\n", - "\n", - "print('All production [TWh]:\\n---------------------\\n', pro.to_string(), '\\n')\n", - "for test_case in test_cases:\n", - " print('\\nDifference in production in TWh between %s and %s in for %s'%tuple(scenarios + [test_case]),\n", - " '\\n-------------------------------------------------------------\\n')\n", - " print((pro['%s_%s'%(scenarios[1], test_case)] - pro['%s_%s'%(scenarios[0], test_case)]).to_string() + '\\n')" + "print_results(pro, 'production', 'TWh', scenarios, \n", + " test_cases, '-')" ] }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -236,28 +265,49 @@ "output_type": "stream", "text": [ "All generation capacities [GW]:\n", - "-------------------------------\n", - " Scenario base_changes_hightemplowspace base_changes_lowtemphighspace base_hightemplowspace base_lowtemphighspace \n", - "Commodity ELECTRICITY HEAT HYDROGEN ELECTRICITY HEAT HYDROGEN BIOMETHANE ELECTRICITY HEAT HYDROGEN BIOMETHANE ELECTRICITY HEAT HYDROGEN\n", - "Year \n", - "2030 0.000000 0.000000 0.000000 62.22878 27.296902 4.084225 0.0 0.000000 0.000000 0.000000 0.0 35.418515 17.154851 0.877514\n", - "2050 92.052875 30.571459 7.951106 84.53370 25.208215 7.378618 0.0 54.292423 20.573951 4.317686 0.0 52.441727 16.601460 4.571381 \n", + "---------------------\n", "\n", + "base-lowtemphighspace\n", + " Commodity BIOMETHANE ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 0.0 1184.316253 872.802042 78.378683\n", + "2050 0.0 2400.204859 904.004898 720.042070 \n", "\n", - "Difference in generation capacities in GW between base and base_changes in for lowtemphighspace: \n", - "-------------------------------------------------------------\n", - "Commodity BIOMETHANE ELECTRICITY HEAT HYDROGEN\n", + "merge-lowtemphighspace\n", + " Commodity BIOMETHANE ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 0.0 1517.904883 1433.982852 69.632680\n", + "2050 0.0 2948.762527 1486.044890 496.115835 \n", + "\n", + "base-hightemplowspace\n", + " Commodity BIOMETHANE ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 0.0 43.789295 22.090175 2.002775\n", + "2050 0.0 55.038155 20.737092 4.588077 \n", + "\n", + "merge-hightemplowspace\n", + " Commodity BIOMETHANE ELECTRICITY HEAT HYDROGEN\n", "Year \n", - "2030 NaN 26.810265 10.142051 3.206710\n", - "2050 NaN 32.091973 8.606754 2.807238\n", + "2030 0.0 60.925620 32.144467 3.668236\n", + "2050 0.0 93.068592 30.823739 7.277832 \n", "\n", "\n", - "Difference in generation capacities in GW between base and base_changes in for hightemplowspace: \n", + "Difference in generation capacities in GW between base and merge in for lowtemphighspace \n", "-------------------------------------------------------------\n", - "Commodity BIOMETHANE ELECTRICITY HEAT HYDROGEN\n", - "Year \n", - "2030 NaN 0.000000 0.000000 0.00000\n", - "2050 NaN 37.760452 9.997508 3.63342\n", + "\n", + "Commodity BIOMETHANE ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 0.0 333.588630 561.180810 -8.746004\n", + "2050 0.0 548.557668 582.039992 -223.926235\n", + "\n", + "\n", + "Difference in generation capacities in GW between base and merge in for hightemplowspace \n", + "-------------------------------------------------------------\n", + "\n", + "Commodity BIOMETHANE ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 0.0 17.136325 10.054292 1.665461\n", + "2050 0.0 38.030436 10.086647 2.689755\n", "\n" ] } @@ -265,7 +315,7 @@ "source": [ "### 2.2 Calculate Difference in Generation Capacities\n", "cap = mr.get_result('G_CAP_YCRAF')\n", - "cap = cap[(cap.Technology != 'INTRASEASONAL-ELECT-STORAGE') &\\\n", + "sto = cap[(cap.Technology != 'INTRASEASONAL-ELECT-STORAGE') &\\\n", " (cap.Technology != 'INTRASEASONAL-HEAT-STORAGE') &\\\n", " (cap.Technology != 'INTERSEASONAL-HEAT-STORAGE') &\\\n", " (cap.Technology != 'H2-STORAGE')].pivot_table(index='Year',\n", @@ -273,16 +323,13 @@ " values='Value',\n", " aggfunc='sum').fillna(0)\n", "\n", - "print('All generation capacities [GW]:\\n-------------------------------\\n', cap.to_string(), '\\n')\n", - "for test_case in test_cases:\n", - " print('\\nDifference in generation capacities in GW between %s and %s in for %s:'%tuple(scenarios + [test_case]),\n", - " '\\n-------------------------------------------------------------')\n", - " print((cap['%s_%s'%(scenarios[1], test_case)] - cap['%s_%s'%(scenarios[0], test_case)]).to_string() + '\\n')" + "print_results(cap, 'generation capacities', 'GW',\n", + " scenarios, test_cases, '-')" ] }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -290,49 +337,67 @@ "output_type": "stream", "text": [ "All storage capacities [GWh]:\n", - "-----------------------------\n", - " Scenario base_changes_hightemplowspace base_changes_lowtemphighspace base_hightemplowspace base_lowtemphighspace \n", - "Commodity ELECTRICITY HEAT HYDROGEN ELECTRICITY HEAT HYDROGEN ELECTRICITY HEAT ELECTRICITY HEAT\n", - "Year \n", - "2030 0.0 0.000000 0.000000 82224.0 395.604526 238.621993 0.0 0.000000 82224.0 671.524689\n", - "2050 82224.0 500.063685 342.978697 82224.0 550.614625 238.621993 82224.0 263.948093 82224.0 692.441894 \n", + "---------------------\n", + "\n", + "base-lowtemphighspace\n", + " Commodity ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 150785.604830 5658.100859 133.260319\n", + "2050 150811.535739 6295.868270 1877.621283 \n", "\n", + "merge-lowtemphighspace\n", + " Commodity ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 154516.600913 3386.836873 1321.136951\n", + "2050 154516.591163 5483.830111 37996.758448 \n", "\n", - "Difference in storage capacities in GWh between base and base_changes in for lowtemphighspace: \n", + "base-hightemplowspace\n", + " Commodity ELECTRICITY HEAT\n", + "Year \n", + "2030 82224.0 276.061581\n", + "2050 82224.0 282.360567 \n", + "\n", + "merge-hightemplowspace\n", + " Commodity ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 82224.000000 346.112568 160.332297\n", + "2050 82227.718875 384.193272 161.453957 \n", + "\n", + "\n", + "Difference in storage capacities in GWh between base and merge in for lowtemphighspace \n", "-------------------------------------------------------------\n", - "Commodity ELECTRICITY HEAT HYDROGEN\n", - "Year \n", - "2030 0.0 -275.920163 NaN\n", - "2050 0.0 -141.827269 NaN\n", "\n", + "Commodity ELECTRICITY HEAT HYDROGEN\n", + "Year \n", + "2030 3730.996083 -2271.263986 1187.876632\n", + "2050 3705.055425 -812.038159 36119.137165\n", "\n", - "Difference in storage capacities in GWh between base and base_changes in for hightemplowspace: \n", + "\n", + "Difference in storage capacities in GWh between base and merge in for hightemplowspace \n", "-------------------------------------------------------------\n", + "\n", "Commodity ELECTRICITY HEAT HYDROGEN\n", "Year \n", - "2030 0.0 0.000000 NaN\n", - "2050 0.0 236.115593 NaN\n", + "2030 0.000000 70.050987 NaN\n", + "2050 3.718875 101.832705 NaN\n", "\n" ] } ], "source": [ "### 2.3 Calculate Difference in Storage Capacities\n", - "cap = mr.get_result('G_STO_YCRAF').pivot_table(index='Year',\n", + "sto = mr.get_result('G_STO_YCRAF').pivot_table(index='Year',\n", " columns=['Scenario', 'Commodity'],\n", " values='Value',\n", " aggfunc='sum').fillna(0)\n", "\n", - "print('All storage capacities [GWh]:\\n-----------------------------\\n', cap.to_string(), '\\n')\n", - "for test_case in test_cases:\n", - " print('\\nDifference in storage capacities in GWh between %s and %s in for %s:'%tuple(scenarios + [test_case]),\n", - " '\\n-------------------------------------------------------------')\n", - " print((cap['%s_%s'%(scenarios[1], test_case)] - cap['%s_%s'%(scenarios[0], test_case)]).to_string() + '\\n')" + "print_results(sto, 'storage capacities', 'GWh',\n", + " scenarios, test_cases, '-')" ] }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -340,41 +405,431 @@ "output_type": "stream", "text": [ "All electricity transmission capacities [GW]:\n", - "---------------------------------------------\n", - " Scenario base_changes_hightemplowspace base_changes_lowtemphighspace base_hightemplowspace base_lowtemphighspace\n", - "Year \n", - "2030 0.00000 11.077000 0.000000 15.069722\n", - "2050 18.98553 14.256002 15.986844 15.069722 \n", + "---------------------\n", + "\n", + "base-lowtemphighspace\n", + " Year\n", + "2030 150.755000\n", + "2050 160.333522 \n", "\n", + "merge-lowtemphighspace\n", + " Year\n", + "2030 188.510500\n", + "2050 541.084679 \n", "\n", - "Difference in electricity transmission capacities in GW between base and base_changes in for lowtemphighspace: \n", + "base-hightemplowspace\n", + " Year\n", + "2030 11.077000\n", + "2050 15.760916 \n", + "\n", + "merge-hightemplowspace\n", + " Year\n", + "2030 11.077000\n", + "2050 16.103629 \n", + "\n", + "\n", + "Difference in electricity transmission capacities in GW between base and merge in for lowtemphighspace \n", "-------------------------------------------------------------\n", + "\n", "Year\n", - "2030 -3.992722\n", - "2050 -0.813720\n", + "2030 37.755500\n", + "2050 380.751157\n", "\n", "\n", - "Difference in electricity transmission capacities in GW between base and base_changes in for hightemplowspace: \n", + "Difference in electricity transmission capacities in GW between base and merge in for hightemplowspace \n", "-------------------------------------------------------------\n", + "\n", "Year\n", "2030 0.000000\n", - "2050 2.998686\n", + "2050 0.342713\n", "\n" ] } ], "source": [ "### 2.4 Calculate Difference in Transmission Capacities\n", - "cap = mr.get_result('X_CAP_YCR').pivot_table(index='Year',\n", + "eltran = mr.get_result('X_CAP_YCR').pivot_table(index='Year',\n", " columns=['Scenario'],\n", " values='Value',\n", " aggfunc=lambda x: np.sum(x)/2).fillna(0) # Remember to divide by two\n", "\n", - "print('All electricity transmission capacities [GW]:\\n---------------------------------------------\\n', cap.to_string(), '\\n')\n", + "print_results(eltran, 'electricity transmission capacities',\n", + " 'GW', scenarios, test_cases, '-')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All System Costs [M€]:\n", + "---------------------\n", + "\n", + "base-lowtemphighspace\n", + " Year\n", + "2030 127992.022313\n", + "2050 177372.000352 \n", + "\n", + "merge-lowtemphighspace\n", + " Year\n", + "2030 229190.203460\n", + "2050 306418.752372 \n", + "\n", + "base-hightemplowspace\n", + " Year\n", + "2030 2626.881407\n", + "2050 3121.342709 \n", + "\n", + "merge-hightemplowspace\n", + " Year\n", + "2030 5062.975367\n", + "2050 6482.092058 \n", + "\n", + "\n", + "Difference in System Costs in M€ between base and merge in for lowtemphighspace \n", + "-------------------------------------------------------------\n", + "\n", + "Year\n", + "2030 101198.181147\n", + "2050 129046.752020\n", + "\n", + "\n", + "Difference in System Costs in M€ between base and merge in for hightemplowspace \n", + "-------------------------------------------------------------\n", + "\n", + "Year\n", + "2030 2436.093960\n", + "2050 3360.749349\n", + "\n" + ] + } + ], + "source": [ + "### 2.5 Objective Costs\n", + "obj = mr.get_result('OBJ_YCR').pivot_table(index='Year',\n", + " columns='Scenario',\n", + " values='Value',\n", + " aggfunc='sum').fillna(0) \n", + "\n", + "print_results(obj, 'System Costs',\n", + " 'M€', scenarios, test_cases, '-')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### 2.6 Profiles\n", + "for test_case in test_cases:\n", + " for carrier in ['electricity', 'heat', 'hydrogen']:\n", + " for scenario in scenarios:\n", + " mr.plot_profile(carrier, 2050, '%s-%s'%(scenario, test_case))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found MainResults in ../../../base/model\\MainResults_base-lowtemphighspace.gdx\n", + "Found MainResults in ../../../base/model\\MainResults_merge-lowtemphighspace.gdx\n", + "Found MainResults in ../../../base/model\\MainResults_base-lowtemphighspace.gdx\n", + "Found MainResults in ../../../base/model\\MainResults_merge-lowtemphighspace.gdx\n", + "Found MainResults in ../../../base/model\\MainResults_base-hightemplowspace.gdx\n", + "Found MainResults in ../../../base/model\\MainResults_merge-hightemplowspace.gdx\n", + "Found MainResults in ../../../base/model\\MainResults_base-hightemplowspace.gdx\n", + "Found MainResults in ../../../base/model\\MainResults_merge-hightemplowspace.gdx\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### 2.7 Maps\n", "for test_case in test_cases:\n", - " print('\\nDifference in electricity transmission capacities in GW between %s and %s in for %s:'%tuple(scenarios + [test_case]),\n", - " '\\n-------------------------------------------------------------')\n", - " print((cap['%s_%s'%(scenarios[1], test_case)] - cap['%s_%s'%(scenarios[0], test_case)]).to_string() + '\\n')" + " for carrier in ['Electricity', 'Hydrogen']:\n", + " for scenario in scenarios:\n", + " mr.plot_map('%s-%s'%(scenario, test_case), carrier, 2050)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# KPI's\n", + "\n", + "Key performance indicators are calculated here, i.e. the total change in production, generation- storage- and transmission capacities and objective costs" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Change in KPIs in absolute units for each test case (changed scenario - base scenario)\n", + " lowtemphighspace hightemplowspace\n", + "2030 Production (TWh) 3601 109\n", + " Generation Capacities (GW) 886 29\n", + " Storage Capacities (GWh) 2648 230\n", + " El. Transmission Cap (GW) 38 0\n", + " System Costs (M€) 101198 2436\n", + "2050 Production (TWh) 4107 142\n", + " Generation Capacities (GW) 907 51\n", + " Storage Capacities (GWh) 39012 267\n", + " El. Transmission Cap (GW) 381 0\n", + " System Costs (M€) 129047 3361\n" + ] + } + ], + "source": [ + "KPI = pd.DataFrame(index=pd.MultiIndex.from_product((\n", + " [2030, 2050], \n", + " ['Production (TWh)',\n", + " 'Generation Capacities (GW)',\n", + " 'Storage Capacities (GWh)',\n", + " 'El. Transmission Cap (GW)',\n", + " 'System Costs (M€)']\n", + " )),\n", + " columns=test_cases)\n", + "for year in [2030, 2050]:\n", + " for test_case in test_cases:\n", + " \n", + " # Changed scenario\n", + " pro0 = pro.loc[str(year), (scenarios[1]+'-'+test_case, slice(None))].sum()\n", + " cap0 = cap.loc[str(year), (scenarios[1]+'-'+test_case, slice(None))].sum()\n", + " sto0 = sto.loc[str(year), (scenarios[1]+'-'+test_case, slice(None))].sum()\n", + " eltran0 = eltran.loc[str(year), (scenarios[1]+'-'+test_case)].sum()\n", + " obj0 = obj.loc[str(year), (scenarios[1]+'-'+test_case)].sum()\n", + " \n", + " # Relative to base\n", + " pro0 -= pro.loc[str(year), (scenarios[0]+'-'+test_case, slice(None))].sum()\n", + " cap0 -= cap.loc[str(year), (scenarios[0]+'-'+test_case, slice(None))].sum()\n", + " sto0 -= sto.loc[str(year), (scenarios[0]+'-'+test_case, slice(None))].sum()\n", + " eltran0 -= eltran.loc[str(year), (scenarios[0]+'-'+test_case)].sum()\n", + " obj0 -= obj.loc[str(year), (scenarios[0]+'-'+test_case)].sum()\n", + " \n", + " # Document\n", + " KPI.loc[(year, 'Production (TWh)'), test_case] = pro0\n", + " KPI.loc[(year, 'Generation Capacities (GW)'), test_case] = cap0\n", + " KPI.loc[(year, 'Storage Capacities (GWh)'), test_case] = sto0\n", + " KPI.loc[(year, 'El. Transmission Cap (GW)'), test_case] = eltran0\n", + " KPI.loc[(year, 'System Costs (M€)'), test_case] = obj0\n", + "\n", + "print('Change in KPIs in absolute units for each test case (changed scenario - base scenario)')\n", + "print(KPI.astype(float).round(0).astype(int).to_string())" ] } ], diff --git a/base/auxils/master_merge_tests/merge_tests.pdf b/base/auxils/master_merge_tests/merge_tests.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c252f159c29a14692d86126877fe5082036ba344 GIT binary patch literal 3038384 zcma&MQ;;Yym#*EmZQJ&4+qP}nwr$(CwcECB+x_qP&$*bXGZ)`YDyXDZl1kS5B#TsD zSd^BLjundZ=67&)>y|bF-ea zcC72|ho{D~>Q(*g2ZW~!LET*0KD=Z{v65;9DvGz0%euSq=Z!fRhPb{z-;G~S&RND? z<~^pjuoALXQ>Gqs6V)2rXf^@YkiBvg7xx31080RpAZ5v%5|UF-Hq2ED!*3#8%boJW z6bO4)nMrj(JPgbAbVmtzO@B~0Kl-bH0Ot?e)5NsER7`-Vka6MS?A3_5cvil7500gE z`V+)3aPA9}eNVWfjTOEZXC3A(r>QP0n*&Hev|QjKOu3bw$I3AuC_R^S7+>xONR)Nj 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z!Ysghr+;xtfsmegzZMg0<&Gs*y>xJ}YZur6*P4&2uSYS9Nm=*`fk%W;*o7)TkvGQD^Y7FK^RDSY@iC?C+IOqy#r5|fbz~l~Wj0s$v+iDeWjhgflq=>I6=weu*wV`@k1grxO5AYds+ewWTFyTe43*lsLkLrevnon z^sZrMf%chZ)(uQzhl3*c_z%@?kdkZgt6-69=Url3ziQrfZ~LdKl}}v{JZ0|p^o9M? z8?{fZuZusk$$Vd5Z0CEcUFY0eqx+BVxZG+vwlg^Gy8Tn0OnbZHcS>)U&n-5-Y13s9 zKmF#{w0Wx0E9Mn%KNC79`h-~Qsh#1^63?wo@s@Z})1LF{qvH07x|L3~OFuuk`j6Qu ieX$s3hAb{gEGnreN=@T3HZV3fF)`;-Rdw}u;{pI@J1~v_ literal 0 HcmV?d00001 From d1b529776ff3549319f628a7f698af7e7ece2037 Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Fri, 28 Jun 2024 17:15:07 +0200 Subject: [PATCH 12/21] Made an option for flexible regional hydrogen demand --- .../addons/hydrogen/bb4/hydrogen_balbase4.inc | 3 +- .../hydrogen/bb4/hydrogen_eqndecdef.inc | 29 +++++++++---------- .../hydrogen/bb4/hydrogen_ipardecdef.inc | 23 +++++---------- .../hydrogen/bb4/hydrogen_pardeclare.inc | 4 +-- .../hydrogen/bb4/hydrogen_pardefine.inc | 8 ++--- .../hydrogen/bb4/hydrogen_vardeclare.inc | 9 +++--- base/model/balopt.opt | 4 +++ base/output/OUTPUT_SUMMARY.inc | 15 +++++++++- 8 files changed, 53 insertions(+), 42 deletions(-) diff --git a/base/addons/hydrogen/bb4/hydrogen_balbase4.inc b/base/addons/hydrogen/bb4/hydrogen_balbase4.inc index f1e44269..5af303d1 100644 --- a/base/addons/hydrogen/bb4/hydrogen_balbase4.inc +++ b/base/addons/hydrogen/bb4/hydrogen_balbase4.inc @@ -29,11 +29,12 @@ $ifi %H2TransInvest%==yes QVXH2KN_ES $ifi %H2TransInvest%==yes QXH2LAMBDA_ES * Flexible hydrogen demand in space by Ioannis -* If-statements here for flexible demand in space option +$ifi not %flexible_h2_space%==yes $goto no_flexible_h2_space QSYNTHETIC_EQ_PRODUCTION QSYNTHETIC_EQ_SHIFT QSYNTHETIC_EQ_SHIFT_UP QSYNTHETIC_EQ_SHIFT_DOWN QSYNTHETIC_EQ_SHIFT_BALANCE_MOVE QSYNTHETIC_EQ_SHIFT_BALANCE_OVERALL +$label no_flexible_h2_space diff --git a/base/addons/hydrogen/bb4/hydrogen_eqndecdef.inc b/base/addons/hydrogen/bb4/hydrogen_eqndecdef.inc index a7405e60..54703067 100644 --- a/base/addons/hydrogen/bb4/hydrogen_eqndecdef.inc +++ b/base/addons/hydrogen/bb4/hydrogen_eqndecdef.inc @@ -60,8 +60,8 @@ QHYDROGEN_EQ(IY411,IR,IS3,T).. - SUM((IA,IHYDROGEN_GH2STO)$(RRRAAA(IR,IA) AND (IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2STO))),VHYDROGEN_STOLOADT(IY411,IA,IHYDROGEN_GH2STO,IS3,T)) =E= IHYDROGEN_DH2_REGION_T_Y(IY411,IR,IS3,T)/(1-DISLOSS_H2(IR))+ -*IK changes for synthetic fuel - VGE_T_SYNFUEL_ST(IY411,IR,IS3,T)/(1-DISLOSS_H2(IR)) + + +$ifi %flexible_h2_space%==yes VGE_T_FLEXDH2(IY411,IR,IS3,T)/(1-DISLOSS_H2(IR)) + SUM(IA$RRRAAA(IR,IA),IHYDROGEN_DH2_AREA_T_Y(IY411,IA,IS3,T)/(1-DISLOSS_H2(IA))) + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2FUEL)$(RRRAAA(IR,IA)),VGE_T(IY411,IA,IHYDROGEN_GH2FUEL,IS3,T)/GDATA(IHYDROGEN_GH2FUEL,'GDFE')) + SUM(IAGK_HASORPOT(IY411,IA,IHYDROGEN_GH2TOBIOMETH)$(RRRAAA(IR,IA)),VGBIOMETH_T(IY411,IA,IHYDROGEN_GH2TOBIOMETH,IS3,T)/GDATA(IHYDROGEN_GH2TOBIOMETH,'GDFE')) @@ -81,48 +81,47 @@ $ONTEXT *give it a try give the same demand *Initiate the total -IHYDROGEN_DH2SYNFUEL_REGION_Y_TOT(YYY,RRR) = HYDROGEN_DH2(YYY,RRR); +IHYDROGEN_DH2FLEX_REGION_Y_TOT(YYY,RRR) = HYDROGEN_DH2(YYY,RRR); *Initiate the first value -IHYDROGEN_DH2SYNFUEL_REGION_Y(YYY,RRR)$((ORD(YYY) EQ 1)) = IHYDROGEN_DH2SYNFUEL_REGION_Y_TOT(YYY,RRR); +IHYDROGEN_DH2FLEX_REGION_Y(YYY,RRR)$((ORD(YYY) EQ 1)) = IHYDROGEN_DH2FLEX_REGION_Y_TOT(YYY,RRR); *The difference LOOP(YYY$(NOT(ORD(YYY) EQ 1)), -IHYDROGEN_DH2SYNFUEL_REGION_Y(YYY,RRR) = IHYDROGEN_DH2SYNFUEL_REGION_Y_TOT(YYY,RRR) - IHYDROGEN_DH2SYNFUEL_REGION_Y_TOT(YYY-1,RRR); +IHYDROGEN_DH2FLEX_REGION_Y(YYY,RRR) = IHYDROGEN_DH2FLEX_REGION_Y_TOT(YYY,RRR) - IHYDROGEN_DH2FLEX_REGION_Y_TOT(YYY-1,RRR); ); $OFFTEXT -*Additional EQs for synthetic fuel demand shift - +*Additional EQs for hydrogen demand shift +$ifi not %flexible_h2_space%==yes $goto no_flexible_h2_space QSYNTHETIC_EQ_PRODUCTION(IY411,IR,IS3,T).. -VGE_T_SYNFUEL_ST(IY411,IR,IS3,T) =E= (VGE_T_SYNFUEL(IY411,IR)*HYDROGEN_DH2_VAR_T(IR,IS3,T)/IHYDROGEN_DH2_REGION_SUMST(IR))$(IHYDROGEN_DH2_REGION_SUMST(IR) GT 0) +VGE_T_FLEXDH2(IY411,IR,IS3,T) =E= (VGE_FLEXDH2(IY411,IR)*HYDROGEN_DH2_VAR_T(IR,IS3,T)/IHYDROGEN_DH2_REGION_SUMST(IR))$(IHYDROGEN_DH2_REGION_SUMST(IR) GT 0) ; QSYNTHETIC_EQ_SHIFT(IY411,IR).. -VGE_T_SYNFUEL(IY411,IR) =G= VSYN_FUEL_SHIFT(IY411,IR) +VGE_FLEXDH2(IY411,IR) =G= VFLEXDH2_SHIFT(IY411,IR) ; QSYNTHETIC_EQ_SHIFT_UP(IY411,IR).. -VSYN_FUEL_SHIFT(IY411,IR) =G= (1-THETA_FLEX_SYNFUEL)*IHYDROGEN_DH2SYNFUEL_REGION_Y(IY411,IR) +VFLEXDH2_SHIFT(IY411,IR) =G= (1-THETA_FLEX_DH2)*IHYDROGEN_DH2FLEX_REGION_Y(IY411,IR) ; QSYNTHETIC_EQ_SHIFT_DOWN(IY411,IR).. -(1+THETA_FLEX_SYNFUEL)*IHYDROGEN_DH2SYNFUEL_REGION_Y(IY411,IR) =G= VSYN_FUEL_SHIFT(IY411,IR) +(1+THETA_FLEX_DH2)*IHYDROGEN_DH2FLEX_REGION_Y(IY411,IR) =G= VFLEXDH2_SHIFT(IY411,IR) ; QSYNTHETIC_EQ_SHIFT_BALANCE_MOVE(IY411).. -sum(IR,VSYN_FUEL_SHIFT(IY411,IR)) =E= sum(IR,IHYDROGEN_DH2SYNFUEL_REGION_Y(IY411,IR)) +sum(IR,VFLEXDH2_SHIFT(IY411,IR)) =E= sum(IR,IHYDROGEN_DH2FLEX_REGION_Y(IY411,IR)) ; QSYNTHETIC_EQ_SHIFT_BALANCE_OVERALL(IY411).. -sum(IR,VGE_T_SYNFUEL(IY411,IR)) =E= sum(IR,IHYDROGEN_DH2SYNFUEL_REGION_Y(IY411,IR)) - +sum(IR,VGE_FLEXDH2(IY411,IR)) =E= sum(IR,IHYDROGEN_DH2FLEX_REGION_Y(IY411,IR)) ; -*Stops here +$label no_flexible_h2_space * Hydrogen production equal demand on area level: * Prod + OutputStorage - InputStorage = DemandTransport + DemandPower + DemandBioRefinery + other diff --git a/base/addons/hydrogen/bb4/hydrogen_ipardecdef.inc b/base/addons/hydrogen/bb4/hydrogen_ipardecdef.inc index 79526d03..4c156bd2 100644 --- a/base/addons/hydrogen/bb4/hydrogen_ipardecdef.inc +++ b/base/addons/hydrogen/bb4/hydrogen_ipardecdef.inc @@ -7,27 +7,20 @@ PARAMETERS PARAMETER IHYDROGEN_DH2_REGION_T_Y(YYY,RRR,SSS,TTT) 'Variation of H2 demand in each region along the year'; PARAMETER IHYDROGEN_DH2_AREA_T_Y(YYY,AAA,SSS,TTT) 'Variation of H2 demand in each region along the year'; -*IK addd +$ifi not %flexible_h2_space%==yes $goto no_flexible_h2_space PARAMETERS -*IHYDROGEN_DH2SYNFUEL_REGION_Y_TOT(YYY,RRR) -IHYDROGEN_DH2SYNFUEL_REGION_Y(YYY,RRR) 'demand for synthetic fuel to shift per year' +*IHYDROGEN_DH2FLEX_REGION_Y_TOT(YYY,RRR) +IHYDROGEN_DH2FLEX_REGION_Y(YYY,RRR) 'demand for synthetic fuel to shift per year' ; -* If this is zero, no hydrogen demand related to synthetic fuels can be relocated endogenously -* Make two options here, that: -* 1. Activates synthetic fuel data -* 2. Allocate the fraction of demand that can be allocated (THETA_FLEX_SYNFUEL) -Scalar -THETA_FLEX_SYNFUEL /0/ -; -*Assing Demand for synthetic fuels -IHYDROGEN_DH2SYNFUEL_REGION_Y(Y,IR) = HYDROGEN_DH2_SYN(Y,IR); +*Assing demand for flexible fuels +IHYDROGEN_DH2FLEX_REGION_Y(Y,IR) = HYDROGEN_FLEXDH2(Y,IR); -*Deduct the synthetic fuels, from overall demand -HYDROGEN_DH2(Y,IR) = HYDROGEN_DH2(Y,IR) - HYDROGEN_DH2_SYN(Y,IR); +*Deduct the flexible demand, from overall demand +HYDROGEN_DH2(Y,IR) = HYDROGEN_DH2(Y,IR) - HYDROGEN_FLEXDH2(Y,IR); +$label no_flexible_h2_space -*IK finish here IHYDROGEN_DH2_REGION_T_Y(Y,IR,S,T) = (HYDROGEN_DH2(Y,IR)*HYDROGEN_DH2_VAR_T(IR,S,T)/IHYDROGEN_DH2_REGION_SUMST(IR))$(IHYDROGEN_DH2_REGION_SUMST(IR) GT 0); IHYDROGEN_DH2_AREA_T_Y(Y,IA,S,T) = (HYDROGEN_DH2(Y,IA)*HYDROGEN_DH2_VAR_T(IA,S,T)/IHYDROGEN_DH2_AREA_SUMST(IA))$(IHYDROGEN_DH2_AREA_SUMST(IA) GT 0); diff --git a/base/addons/hydrogen/bb4/hydrogen_pardeclare.inc b/base/addons/hydrogen/bb4/hydrogen_pardeclare.inc index dfbb63b1..51ce3c33 100644 --- a/base/addons/hydrogen/bb4/hydrogen_pardeclare.inc +++ b/base/addons/hydrogen/bb4/hydrogen_pardeclare.inc @@ -1,7 +1,7 @@ PARAMETERS HYDROGEN_DH2 (YYY,CCCRRRAAA) 'Yearly demand for hydrogen from the transport sector' -*IK add -HYDROGEN_DH2_SYN (YYY,CCCRRRAAA) 'Yearly demand for hydrogen but synthetic fuels' + +HYDROGEN_FLEXDH2 (YYY,CCCRRRAAA) 'Yearly demand for hydrogen but synthetic fuels' HYDROGEN_DH2_VAR_T (CCCRRRAAA,SSS,TTT) 'Variation profile of H2 demand per region' HYDROGEN_STOVOL_START (Y,AAA,G,SSS) 'Start level of hydrogen storage' diff --git a/base/addons/hydrogen/bb4/hydrogen_pardefine.inc b/base/addons/hydrogen/bb4/hydrogen_pardefine.inc index 6e8fbb84..1054a35f 100644 --- a/base/addons/hydrogen/bb4/hydrogen_pardefine.inc +++ b/base/addons/hydrogen/bb4/hydrogen_pardefine.inc @@ -3,10 +3,10 @@ $if EXIST '../data/HYDROGEN_DH2.inc' $INCLUDE '../data/HYDROGEN_DH2. $if not EXIST '../data/HYDROGEN_DH2.inc' $INCLUDE '../../base/data/HYDROGEN_DH2.inc'; %semislash%; -*IK add -PARAMETER HYDROGEN_DH2_SYN(YYY,CCCRRRAAA) 'Yearly demand for hydrogen' %semislash% -$if EXIST '../data/HYDROGEN_DH2_SYN.inc' $INCLUDE '../data/HYDROGEN_DH2_SYN.inc'; -$if not EXIST '../data/HYDROGEN_DH2_SYN.inc' $INCLUDE '../../base/data/HYDROGEN_DH2_SYN.inc'; + +PARAMETER HYDROGEN_FLEXDH2(YYY,CCCRRRAAA) 'Yearly demand for hydrogen' %semislash% +$if EXIST '../data/HYDROGEN_FLEXDH2.inc' $INCLUDE '../data/HYDROGEN_FLEXDH2.inc'; +$if not EXIST '../data/HYDROGEN_FLEXDH2.inc' $INCLUDE '../../base/data/HYDROGEN_FLEXDH2.inc'; %semislash%; PARAMETER HYDROGEN_DH2_VAR_T(CCCRRRAAA,SSS,TTT) 'Variation profile of H2 demand per region' %semislash% diff --git a/base/addons/hydrogen/bb4/hydrogen_vardeclare.inc b/base/addons/hydrogen/bb4/hydrogen_vardeclare.inc index d280f15a..f27c25b2 100644 --- a/base/addons/hydrogen/bb4/hydrogen_vardeclare.inc +++ b/base/addons/hydrogen/bb4/hydrogen_vardeclare.inc @@ -17,12 +17,13 @@ POSITIVE VARIABLES ; -*Demand shift IK +$ifi not %flexible_h2_space%==yes $goto no_flexible_h2_space Positive variable -VGE_T_SYNFUEL_ST(Y,RRR,S,T) 'Hourly hydrogen for synthetic fuel' -VGE_T_SYNFUEL(Y,RRR) 'Total hydrogen for synthetic fuel' -VSYN_FUEL_SHIFT(Y,RRR) 'how much to shift from one area to area' +VGE_T_FLEXDH2(Y,RRR,S,T) 'Hourly hydrogen for flexible hydrogen demand' +VGE_FLEXDH2(Y,RRR) 'Total hydrogen for flexible hydrogen demand' +VFLEXDH2_SHIFT(Y,RRR) 'how much to shift from one area to area' ; +$label no_flexible_h2_space SOS2 VARIABLE VXH2LAMBDA(Y,IRRRE,IRRRI,XES) 'SOS2 Variable for modelling economy of scale'; diff --git a/base/model/balopt.opt b/base/model/balopt.opt index f2a2f1f7..8fd23f17 100644 --- a/base/model/balopt.opt +++ b/base/model/balopt.opt @@ -341,6 +341,10 @@ $setglobal ES_H2TRANS $Setglobal IMPORT_H2 YES *!option yes +* Allows flexible demand of H2 in regions (set ratio of flexible demand in HYDROGEN_FLEXDH2 with the scalar THETA_FLEX_DH2) +$setglobal flexible_h2_space +*!option yes + *-----------------END: HYDROGEN ADDON--------------------------------------- * Use stepwise linear fuel prices diff --git a/base/output/OUTPUT_SUMMARY.inc b/base/output/OUTPUT_SUMMARY.inc index beb8d62e..2e7133e3 100644 --- a/base/output/OUTPUT_SUMMARY.inc +++ b/base/output/OUTPUT_SUMMARY.inc @@ -13,7 +13,7 @@ SET COMMODITY /ELECTRICITY,HEAT,HYDROGEN,BIOMETHANE/; SET TECH_TYPE /CONDENSING,CHP-BACK-PRESSURE,CHP-EXTRACTION,BOILERS,ELECT-TO-HEAT,INTERSEASONAL-HEAT-STORAGE,INTERSEASONAL-ELECT-STORAGE,INTRASEASONAL-HEAT-STORAGE,INTRASEASONAL-ELECT-STORAGE,HYDRO-RESERVOIRS,HYDRO-RUN-OF-RIVER,WIND-ON,WIND-OFF,SOLAR-PV,SOLAR-HEATING,HYDRO-WAVE,HEAT-PUMP,EL-BOILER,FUELCELL,ELECTROLYZER,H2-STORAGE,BIOMETH-DAC,BIOGASUPGRADING,BIOGASMETHANATION,HUB-OFF,STEAMREFORMING/; SET GTECH_TYPE(GGG,TECH_TYPE); SET PRICE_CATEGORY /AVERAGE,AVERAGE_WEIGHTED_BY_CONSUMPTION,AVERAGE_WEIGHTED_BY_PRODUCTION/; -SET VARIABLE_CATEGORY /EXOGENOUS,ENDOGENOUS,DECOMMISSIONING,ENDOGENOUS_ELECT2HEAT,ENDO_INTRASTO,ENDO_INTERSTO,ENDO_EV,ENDO_HEATPUMP,ENDO_ELBOILER,ENDO_OTHERTRANS,DIST_LOSSES,TRANS_LOSSES,ENDO_CCS,ENDO_H2,ENDO_BIOMETHANE,ENDO_FUELCELL/; +SET VARIABLE_CATEGORY /EXOGENOUS,ENDOGENOUS,DECOMMISSIONING,ENDOGENOUS_ELECT2HEAT,ENDO_INTRASTO,ENDO_INTERSTO,ENDO_EV,ENDO_HEATPUMP,ENDO_ELBOILER,ENDO_OTHERTRANS,DIST_LOSSES,TRANS_LOSSES,ENDO_CCS,ENDO_H2,ENDO_BIOMETHANE,ENDO_FUELCELL,ENDO_REGIONAL/; SET EL_BAL_TYPE /CURTAILMENT,NETEXPORT,PRICE,DEMAND_EXO,DEMAND_LOSS,DEMAND_INTERSTO,DEMAND_INTRASTO,DEMAND_P2H,DEMAND_EV,CONDENSING,CHP-BACK-PRESSURE,CHP-EXTRACTION,ELECT-TO-HEAT,INTER-STO,INTRA-STO,HYDRO-RESERVOIRS,HYDRO-RUN-OF-RIVER,WIND-ON,WIND-OFF,SOLAR-PV,HYDRO-WAVE,FUELCELL,DEMAND_OTHERTRANS,DEMAND_DISTLOSSES,DEMAND_CCS,DEMAND_P2G/; SET H_BAL_TYPE /CURTAILMENT,NETEXPORT,PRICE,DEMAND_EXO,DEMAND_LOSS,DEMAND_INTERSTO,DEMAND_INTRASTO,BOILERS,CHP-BACK-PRESSURE,CHP-EXTRACTION,INTER-STO,INTRA-STO,SOLAR-HEATING,P2H,FUELCELL,DEMAND_P2G,DEMAND_DISTLOSSES/; SET UNITS /GW,TWh,MWh,Money_per_MWh,Mmoney,kton,GWh/; @@ -699,9 +699,16 @@ H2_DEMAND_YCRST(Y,C,IR,S,T,'EXOGENOUS','MWh')$(CCCRRR(C,IR))= SUM(IA$RRRAAA(IR,IA),IHYDROGEN_DH2_AREA_T_Y(Y,IA,S,T)) ; +$ifi not %flexible_h2_space%==yes $goto no_flexible_h2_space +H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_REGIONAL','MWh')$(CCCRRR(C,IR))= + VGE_T_FLEXDH2.L(Y,IR,S,T) +; +$label no_flexible_h2_space + H2_DEMAND_YCRST(Y,C,IR,S,T,'DIST_LOSSES','MWh')$CCCRRR(C,IR)= IHYDROGEN_DH2_REGION_T_Y(Y,IR,S,T)*DISLOSS_H2(IR)/(1-DISLOSS_H2(IR))+ SUM(IA$RRRAAA(IR,IA),IHYDROGEN_DH2_AREA_T_Y(Y,IA,S,T)*DISLOSS_H2(IA)/(1-DISLOSS_H2(IA))) +$ifi %flexible_h2_space%==yes + VGE_T_FLEXDH2.L(Y,IR,S,T)*DISLOSS_H2(IR)/(1-DISLOSS_H2(IR)) ; ** ENDOGENOUS DEMAND @@ -767,6 +774,12 @@ H2_DEMAND_YCR(Y,C,IR,'ENDO_BIOMETHANE','TWh')$CCCRRR(C,IR)= SUM((S,T),IHOURSINST(S,T)*H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_BIOMETHANE','MWh')*IHOURFRAC)/IOF1000000 ; +$ifi not %flexible_h2_space%==yes $goto no_flexible_h2_space2 +H2_DEMAND_YCR(Y,C,IR,'ENDO_REGIONAL','TWh')$CCCRRR(C,IR)= + SUM((S,T),IHOURSINST(S,T)*H2_DEMAND_YCRST(Y,C,IR,S,T,'ENDO_REGIONAL','MWh')*IHOURFRAC)/IOF1000000 +; +$label no_flexible_h2_space2 + * ----------- END OF H2 DEMAND -------------- $label NO_HYDROGEN_DEMAND30 From 91311a75b974dea1a95a98439b8451a133ecb48c Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Mon, 1 Jul 2024 16:20:22 +0200 Subject: [PATCH 13/21] Made MIP hydrogen variables an option and made LP problem choice default, to enhance clarity --- .../addons/hydrogen/bb4/hydrogen_balbase4.inc | 80 +- base/addons/hydrogen/bb4/hydrogen_qobj.inc | 177 +- .../hydrogen/bb4/hydrogen_varassign.inc | 14 +- base/model/balopt.opt | 2088 ++++++++--------- base/output/OUTPUT_SUMMARY.inc | 17 +- 5 files changed, 1189 insertions(+), 1187 deletions(-) diff --git a/base/addons/hydrogen/bb4/hydrogen_balbase4.inc b/base/addons/hydrogen/bb4/hydrogen_balbase4.inc index 5af303d1..faea6d28 100644 --- a/base/addons/hydrogen/bb4/hydrogen_balbase4.inc +++ b/base/addons/hydrogen/bb4/hydrogen_balbase4.inc @@ -1,40 +1,40 @@ - - - QHYDROGEN_HGEHTOH2 - QHYDROGEN_HGETOHH2 - QHYDROGEN_H2TOEH - QHYDROGEN_EQ - QHYDROGEN_EQ_AREA - QHYDROGEN_STOMAXCON - QHYDROGEN_STOMAXLOAD - QHYDROGEN_STOMAXUNLD - QHYDROGEN_STOVOL - QHYDROGEN_GCH4KT_UP -$ifi %COMBTECH%==yes QHYDROGENGKE_UP_ADD - QBIOMETHANE_EQ - QBIOMETHANE_STOVOLTS - QGKBIOMETHANE_UP - QGKBIOGASUPGRADING_UP - QGKBIOGASMETHANATION_UP - - - - QXH2K_UP -$ifi %H2TransInvest%==yes QXH2KNACCUMNET -$ifi %H2TransInvest%==yes QXH2KNACCUMNET_NGTOH2 -$ifi %H2TransInvest%==yes QXH2INVSYMMETRY -$ifi %H2TransInvest%==yes QXH2INVSYMMETRY_NGTOH2 -$ifi %H2TransInvest%==yes QXH2KMAX_NGTOH2 -$ifi %H2TransInvest%==yes QVXH2KN_ES -$ifi %H2TransInvest%==yes QXH2LAMBDA_ES - -* Flexible hydrogen demand in space by Ioannis -$ifi not %flexible_h2_space%==yes $goto no_flexible_h2_space -QSYNTHETIC_EQ_PRODUCTION -QSYNTHETIC_EQ_SHIFT -QSYNTHETIC_EQ_SHIFT_UP -QSYNTHETIC_EQ_SHIFT_DOWN -QSYNTHETIC_EQ_SHIFT_BALANCE_MOVE -QSYNTHETIC_EQ_SHIFT_BALANCE_OVERALL -$label no_flexible_h2_space - + + + QHYDROGEN_HGEHTOH2 + QHYDROGEN_HGETOHH2 + QHYDROGEN_H2TOEH + QHYDROGEN_EQ + QHYDROGEN_EQ_AREA + QHYDROGEN_STOMAXCON + QHYDROGEN_STOMAXLOAD + QHYDROGEN_STOMAXUNLD + QHYDROGEN_STOVOL + QHYDROGEN_GCH4KT_UP +$ifi %COMBTECH%==yes QHYDROGENGKE_UP_ADD + QBIOMETHANE_EQ + QBIOMETHANE_STOVOLTS + QGKBIOMETHANE_UP + QGKBIOGASUPGRADING_UP + QGKBIOGASMETHANATION_UP + + + + QXH2K_UP +$ifi %H2TransInvest%==yes QXH2KNACCUMNET +$ifi %H2TransInvest%==yes QXH2KNACCUMNET_NGTOH2 +$ifi %H2TransInvest%==yes QXH2INVSYMMETRY +$ifi %H2TransInvest%==yes QXH2INVSYMMETRY_NGTOH2 +$ifi %H2TransInvest%==yes QXH2KMAX_NGTOH2 +$ifi %H2TransInvest%==yes $ifi %ES_H2TRANS%==yes QVXH2KN_ES +$ifi %H2TransInvest%==yes $ifi %ES_H2TRANS%==yes QXH2LAMBDA_ES + +* Flexible hydrogen demand in space by Ioannis +$ifi not %flexible_h2_space%==yes $goto no_flexible_h2_space +QSYNTHETIC_EQ_PRODUCTION +QSYNTHETIC_EQ_SHIFT +QSYNTHETIC_EQ_SHIFT_UP +QSYNTHETIC_EQ_SHIFT_DOWN +QSYNTHETIC_EQ_SHIFT_BALANCE_MOVE +QSYNTHETIC_EQ_SHIFT_BALANCE_OVERALL +$label no_flexible_h2_space + diff --git a/base/addons/hydrogen/bb4/hydrogen_qobj.inc b/base/addons/hydrogen/bb4/hydrogen_qobj.inc index 8099b08e..9e29ccb5 100644 --- a/base/addons/hydrogen/bb4/hydrogen_qobj.inc +++ b/base/addons/hydrogen/bb4/hydrogen_qobj.inc @@ -1,88 +1,89 @@ -* -------- HYDROGEN COSTS -------- * - -* Change in revenue when deviating from producing the fixed hydrogen demand -* in the transport sector specified by DH2_T_Y(AAA,S,T). Introduced to make -* it possible to specify more advanced hydrogen supply functions. -* SHOULD THE SUPPLY FUNCTION BE YEAR DEPENDANT? -*No steam reforming plants considered -$ontext - + SUM(IA, - SUM((IS3,T), IHOURSINST(IS3,T) - * (SUM(DH2F_D,VDH2F_T(IA,IS3,T,DH2F_D)* IDEFP_T(IR,IS3,T,DEF_D1) ) - - - ) - - ) - ) - - - SUM(IR, - SUM((IS3,T), IHOURSINST(IS3,T) - * (SUM(DEF_U1, VDEF_T(IR,IS3,T,DEF_U1)* IDEFP_T(IR,IS3,T,DEF_U1) ) - + SUM(DEF_U2, VDEF_T(IR,IS3,T,DEF_U2)* IDEFP_T(IR,IS3,T,DEF_U2) )) - ) - ) - -revenue sale of hydrogen to the transport sector -+ SUM(DH2F_U,VDH2F_T(IA,IS3,T,DH2F_U) ) -* - SUM(DH2F_D,VDH2F_T(IA,IS3,T,DH2F_D) ) -* -$offtext - -* Variable Operation and maintainance cost of steam reforming: - - + SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GCH4TOH2), IHOURFRAC*GOMVCOSTO(IA,IHYDROGEN_GCH4TOH2) * SUM((IS3,T), IHOURSINST(IS3,T)*VHYDROGEN_GH2_T(Y,IA,IHYDROGEN_GCH4TOH2,IS3,T))) - -* Variable Operation and maintainance cost of hydrogen storage: - - + SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO), IHOURFRAC*GOMVCOSTO(IA,IHYDROGEN_GH2STO) * SUM((IS3,T), IHOURSINST(IS3,T)*VHYDROGEN_GH2_T(Y,IA,IHYDROGEN_GH2STO,IS3,T))) - -* Variable Operation and maintainance cost of methanation-DAC: - - + SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH), IHOURFRAC*GOMVCOSTO(IA,IHYDROGEN_GH2TOBIOMETH) * SUM((IS3,T), IHOURSINST(IS3,T)*VGBIOMETH_T(Y,IA,IHYDROGEN_GH2TOBIOMETH,IS3,T))) - -* Variable Operation and maintainance cost of biogas methanation: - - + SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASMETHANATION), IHOURFRAC*GOMVCOSTO(IA,IHYDROGEN_GBIOGASMETHANATION) * SUM((IS3,T), IHOURSINST(IS3,T)*VGBIOGASMETHANATION_T(Y,IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T))) - -* Variable Operation and maintainance cost of biogas upgrading: - - + SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASUPGRADING), IHOURFRAC*GOMVCOSTO(IA,IHYDROGEN_GBIOGASUPGRADING) * SUM((IS3,T), IHOURSINST(IS3,T)*VGBIOGASUPGRADING_T(Y,IA,IHYDROGEN_GBIOGASUPGRADING,IS3,T))) - - -* Fixed O&M and investment costs are included in -* Balmorel.gms - - -* H2 transmission cost: -+ SUM((IRE,IRI)$IXH2K_HASORPOT(Y,IRE,IRI), - SUM((IS3,T), IHOURFRAC*IHOURSINST(IS3,T) * (VXH2_T(Y,IRE,IRI,IS3,T) * XH2COST(IRE,IRI)))) - -* Investment costs, new H2 transmission capacity -* (the average of the annuities for the two countries in question is used for international transmission): - + SUM((IRE,IRI)$((IXH2KN(Y,IRI,IRE) OR IXH2KN(Y,IRE,IRI)) AND (NOT (IXH2KN_ES(IRE,IRI) OR IXH2KN_ES(IRI,IRE)))),IOF05*VXH2KN(Y,IRE,IRI)*XH2INVCOST(Y,IRE,IRI)* - (IOF05* (SUM(C$CCCRRR(C,IRE),ANNUITYCXH2(C))+SUM(C$CCCRRR(C,IRI),ANNUITYCXH2(C))))*SUM(IYALIAS2$(ORD(IYALIAS2) EQ ORD(Y)),IYHASANNUITYXH2(IYALIAS2,Y))) - - + SUM((IRE,IRI)$(IXH2KN_NGTOH2(Y,IRI,IRE) OR IXH2KN_NGTOH2(Y,IRE,IRI)),IOF05*VXH2KN_NGTOH2(Y,IRE,IRI)*XH2INVCOST_NGTOH2(Y,IRE,IRI)* - (IOF05* (SUM(C$CCCRRR(C,IRE),ANNUITYCXH2(C))+SUM(C$CCCRRR(C,IRI),ANNUITYCXH2(C))))*SUM(IYALIAS2$(ORD(IYALIAS2) EQ ORD(Y)),IYHASANNUITYXH2(IYALIAS2,Y))) - -*Investment costs, H2 transmission capacity invested in previous years - + SUM((IYALIAS2,IRE,IRI)$((IXH2KN(IYALIAS2,IRI,IRE) OR IXH2KN(IYALIAS2,IRE,IRI)) AND (NOT (IXH2KN_ES(IRE,IRI) OR IXH2KN_ES(IRI,IRE))) AND (ORD(IYALIAS2) LT ORD(Y))),IOF05*VXH2KN(IYALIAS2,IRE,IRI)*XH2INVCOST(IYALIAS2,IRE,IRI)* - (IOF05* (SUM(C$CCCRRR(C,IRE),ANNUITYCXH2(C))+SUM(C$CCCRRR(C,IRI),ANNUITYCXH2(C))))*IYHASANNUITYXH2(IYALIAS2,Y)) - - + SUM((IYALIAS2,IRE,IRI)$((IXH2KN_NGTOH2(IYALIAS2,IRI,IRE) OR IXH2KN_NGTOH2(IYALIAS2,IRE,IRI)) AND (ORD(IYALIAS2) LT ORD(Y))),IOF05*VXH2KN_NGTOH2(IYALIAS2,IRE,IRI)*XH2INVCOST_NGTOH2(IYALIAS2,IRE,IRI)* - (IOF05* (SUM(C$CCCRRR(C,IRE),ANNUITYCXH2(C))+SUM(C$CCCRRR(C,IRI),ANNUITYCXH2(C))))*IYHASANNUITYXH2(IYALIAS2,Y)) - - -*ECONOMIES OF SCALE -* Investment costs, new transmission capacity - - + SUM((IRE,IRI,XES)$((IXH2KN(Y,IRI,IRE) OR IXH2KN(Y,IRE,IRI)) AND (IXH2KN_ES(IRE,IRI) OR IXH2KN_ES(IRI,IRE))), - VXH2LAMBDA(Y,IRE,IRI,XES)*XH2INVCOST_ES(Y,IRE,IRI,XES)*IOF05* - (IOF05* (SUM(C$CCCRRR(C,IRE),ANNUITYCXH2(C))+SUM(C$CCCRRR(C,IRI),ANNUITYCXH2(C))))*SUM(IYALIAS2$(ORD(IYALIAS2) EQ ORD(Y)),IYHASANNUITYXH2(IYALIAS2,Y))) - -*Investment costs, transmission capacity invested in previous years - - + SUM((IYALIAS2,IRE,IRI,XES)$((IXH2KN(IYALIAS2,IRI,IRE) OR IXH2KN(IYALIAS2,IRE,IRI)) AND (IXH2KN_ES(IRE,IRI) OR IXH2KN_ES(IRI,IRE)) AND (ORD(IYALIAS2) LT ORD(Y))), - VXH2LAMBDA(IYALIAS2,IRE,IRI,XES)*XH2INVCOST_ES(IYALIAS2,IRE,IRI,XES)*IOF05* - (IOF05* (SUM(C$CCCRRR(C,IRE),ANNUITYCXH2(C))+SUM(C$CCCRRR(C,IRI),ANNUITYCXH2(C))))*IYHASANNUITYXH2(IYALIAS2,Y)) +* -------- HYDROGEN COSTS -------- * + +* Change in revenue when deviating from producing the fixed hydrogen demand +* in the transport sector specified by DH2_T_Y(AAA,S,T). Introduced to make +* it possible to specify more advanced hydrogen supply functions. +* SHOULD THE SUPPLY FUNCTION BE YEAR DEPENDANT? +*No steam reforming plants considered +$ontext + + SUM(IA, + SUM((IS3,T), IHOURSINST(IS3,T) + * (SUM(DH2F_D,VDH2F_T(IA,IS3,T,DH2F_D)* IDEFP_T(IR,IS3,T,DEF_D1) ) + + + ) + + ) + ) + + - SUM(IR, + SUM((IS3,T), IHOURSINST(IS3,T) + * (SUM(DEF_U1, VDEF_T(IR,IS3,T,DEF_U1)* IDEFP_T(IR,IS3,T,DEF_U1) ) + + SUM(DEF_U2, VDEF_T(IR,IS3,T,DEF_U2)* IDEFP_T(IR,IS3,T,DEF_U2) )) + ) + ) + +revenue sale of hydrogen to the transport sector ++ SUM(DH2F_U,VDH2F_T(IA,IS3,T,DH2F_U) ) +* - SUM(DH2F_D,VDH2F_T(IA,IS3,T,DH2F_D) ) +* +$offtext + +* Variable Operation and maintainance cost of steam reforming: + + + SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GCH4TOH2), IHOURFRAC*GOMVCOSTO(IA,IHYDROGEN_GCH4TOH2) * SUM((IS3,T), IHOURSINST(IS3,T)*VHYDROGEN_GH2_T(Y,IA,IHYDROGEN_GCH4TOH2,IS3,T))) + +* Variable Operation and maintainance cost of hydrogen storage: + + + SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2STO), IHOURFRAC*GOMVCOSTO(IA,IHYDROGEN_GH2STO) * SUM((IS3,T), IHOURSINST(IS3,T)*VHYDROGEN_GH2_T(Y,IA,IHYDROGEN_GH2STO,IS3,T))) + +* Variable Operation and maintainance cost of methanation-DAC: + + + SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GH2TOBIOMETH), IHOURFRAC*GOMVCOSTO(IA,IHYDROGEN_GH2TOBIOMETH) * SUM((IS3,T), IHOURSINST(IS3,T)*VGBIOMETH_T(Y,IA,IHYDROGEN_GH2TOBIOMETH,IS3,T))) + +* Variable Operation and maintainance cost of biogas methanation: + + + SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASMETHANATION), IHOURFRAC*GOMVCOSTO(IA,IHYDROGEN_GBIOGASMETHANATION) * SUM((IS3,T), IHOURSINST(IS3,T)*VGBIOGASMETHANATION_T(Y,IA,IHYDROGEN_GBIOGASMETHANATION,IS3,T))) + +* Variable Operation and maintainance cost of biogas upgrading: + + + SUM(IAGK_HASORPOT(Y,IA,IHYDROGEN_GBIOGASUPGRADING), IHOURFRAC*GOMVCOSTO(IA,IHYDROGEN_GBIOGASUPGRADING) * SUM((IS3,T), IHOURSINST(IS3,T)*VGBIOGASUPGRADING_T(Y,IA,IHYDROGEN_GBIOGASUPGRADING,IS3,T))) + + +* Fixed O&M and investment costs are included in +* Balmorel.gms + + +* H2 transmission cost: ++ SUM((IRE,IRI)$IXH2K_HASORPOT(Y,IRE,IRI), + SUM((IS3,T), IHOURFRAC*IHOURSINST(IS3,T) * (VXH2_T(Y,IRE,IRI,IS3,T) * XH2COST(IRE,IRI)))) + +* Investment costs, new H2 transmission capacity +* (the average of the annuities for the two countries in question is used for international transmission): + + SUM((IRE,IRI)$((IXH2KN(Y,IRI,IRE) OR IXH2KN(Y,IRE,IRI)) AND (NOT (IXH2KN_ES(IRE,IRI) OR IXH2KN_ES(IRI,IRE)))),IOF05*VXH2KN(Y,IRE,IRI)*XH2INVCOST(Y,IRE,IRI)* + (IOF05* (SUM(C$CCCRRR(C,IRE),ANNUITYCXH2(C))+SUM(C$CCCRRR(C,IRI),ANNUITYCXH2(C))))*SUM(IYALIAS2$(ORD(IYALIAS2) EQ ORD(Y)),IYHASANNUITYXH2(IYALIAS2,Y))) + + + SUM((IRE,IRI)$(IXH2KN_NGTOH2(Y,IRI,IRE) OR IXH2KN_NGTOH2(Y,IRE,IRI)),IOF05*VXH2KN_NGTOH2(Y,IRE,IRI)*XH2INVCOST_NGTOH2(Y,IRE,IRI)* + (IOF05* (SUM(C$CCCRRR(C,IRE),ANNUITYCXH2(C))+SUM(C$CCCRRR(C,IRI),ANNUITYCXH2(C))))*SUM(IYALIAS2$(ORD(IYALIAS2) EQ ORD(Y)),IYHASANNUITYXH2(IYALIAS2,Y))) + +*Investment costs, H2 transmission capacity invested in previous years + + SUM((IYALIAS2,IRE,IRI)$((IXH2KN(IYALIAS2,IRI,IRE) OR IXH2KN(IYALIAS2,IRE,IRI)) AND (NOT (IXH2KN_ES(IRE,IRI) OR IXH2KN_ES(IRI,IRE))) AND (ORD(IYALIAS2) LT ORD(Y))),IOF05*VXH2KN(IYALIAS2,IRE,IRI)*XH2INVCOST(IYALIAS2,IRE,IRI)* + (IOF05* (SUM(C$CCCRRR(C,IRE),ANNUITYCXH2(C))+SUM(C$CCCRRR(C,IRI),ANNUITYCXH2(C))))*IYHASANNUITYXH2(IYALIAS2,Y)) + + + SUM((IYALIAS2,IRE,IRI)$((IXH2KN_NGTOH2(IYALIAS2,IRI,IRE) OR IXH2KN_NGTOH2(IYALIAS2,IRE,IRI)) AND (ORD(IYALIAS2) LT ORD(Y))),IOF05*VXH2KN_NGTOH2(IYALIAS2,IRE,IRI)*XH2INVCOST_NGTOH2(IYALIAS2,IRE,IRI)* + (IOF05* (SUM(C$CCCRRR(C,IRE),ANNUITYCXH2(C))+SUM(C$CCCRRR(C,IRI),ANNUITYCXH2(C))))*IYHASANNUITYXH2(IYALIAS2,Y)) + + +*ECONOMIES OF SCALE +* Investment costs, new transmission capacity +$ifi not %ES_H2TRANS%==yes $goto no_economy_of_scale + + SUM((IRE,IRI,XES)$((IXH2KN(Y,IRI,IRE) OR IXH2KN(Y,IRE,IRI)) AND (IXH2KN_ES(IRE,IRI) OR IXH2KN_ES(IRI,IRE))), + VXH2LAMBDA(Y,IRE,IRI,XES)*XH2INVCOST_ES(Y,IRE,IRI,XES)*IOF05* + (IOF05* (SUM(C$CCCRRR(C,IRE),ANNUITYCXH2(C))+SUM(C$CCCRRR(C,IRI),ANNUITYCXH2(C))))*SUM(IYALIAS2$(ORD(IYALIAS2) EQ ORD(Y)),IYHASANNUITYXH2(IYALIAS2,Y))) + +*Investment costs, transmission capacity invested in previous years + + + SUM((IYALIAS2,IRE,IRI,XES)$((IXH2KN(IYALIAS2,IRI,IRE) OR IXH2KN(IYALIAS2,IRE,IRI)) AND (IXH2KN_ES(IRE,IRI) OR IXH2KN_ES(IRI,IRE)) AND (ORD(IYALIAS2) LT ORD(Y))), + VXH2LAMBDA(IYALIAS2,IRE,IRI,XES)*XH2INVCOST_ES(IYALIAS2,IRE,IRI,XES)*IOF05* + (IOF05* (SUM(C$CCCRRR(C,IRE),ANNUITYCXH2(C))+SUM(C$CCCRRR(C,IRI),ANNUITYCXH2(C))))*IYHASANNUITYXH2(IYALIAS2,Y)) +$label no_economy_of_scale \ No newline at end of file diff --git a/base/addons/hydrogen/bb4/hydrogen_varassign.inc b/base/addons/hydrogen/bb4/hydrogen_varassign.inc index b1e30b7d..4f84e495 100644 --- a/base/addons/hydrogen/bb4/hydrogen_varassign.inc +++ b/base/addons/hydrogen/bb4/hydrogen_varassign.inc @@ -1,7 +1,7 @@ -* File hydrogen_varassign.inc for addon hydrogen. - -$ifi %H2TransInvest%==yes VXH2KN.FX(IY411FIRST,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411FIRST)),IXH2KN(Y,IRE,IRI))= VXH2KN.L(IY411FIRST,IRE,IRI); -$ifi %H2TransInvest%==yes VXH2KNACCUMNET.FX(IY411FIRST,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411FIRST)),IXH2KN(Y,IRE,IRI))= VXH2KNACCUMNET.L(IY411FIRST,IRE,IRI); -$ifi %H2TransInvest%==yes VXH2KN_NGTOH2.FX(IY411FIRST,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411FIRST)),IXH2KN_NGTOH2(Y,IRE,IRI))= VXH2KN_NGTOH2.L(IY411FIRST,IRE,IRI); -$ifi %H2TransInvest%==yes VXH2KNACCUMNET_NGTOH2.FX(IY411FIRST,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411FIRST)),IXH2KN_NGTOH2(Y,IRE,IRI))= VXH2KNACCUMNET_NGTOH2.L(IY411FIRST,IRE,IRI); -$ifi %H2TransInvest%==yes VXH2LAMBDA.FX(IY411FIRST,IRE,IRI,XES)$(SUM(Y$(YVALUE(Y) LE YVALUE(IY411FIRST)),IXH2KN(Y,IRE,IRI)) AND IXH2KN_ES(IRE,IRI))=VXH2LAMBDA.L(IY411FIRST,IRE,IRI,XES); \ No newline at end of file +* File hydrogen_varassign.inc for addon hydrogen. + +$ifi %H2TransInvest%==yes VXH2KN.FX(IY411FIRST,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411FIRST)),IXH2KN(Y,IRE,IRI))= VXH2KN.L(IY411FIRST,IRE,IRI); +$ifi %H2TransInvest%==yes VXH2KNACCUMNET.FX(IY411FIRST,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411FIRST)),IXH2KN(Y,IRE,IRI))= VXH2KNACCUMNET.L(IY411FIRST,IRE,IRI); +$ifi %H2TransInvest%==yes VXH2KN_NGTOH2.FX(IY411FIRST,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411FIRST)),IXH2KN_NGTOH2(Y,IRE,IRI))= VXH2KN_NGTOH2.L(IY411FIRST,IRE,IRI); +$ifi %H2TransInvest%==yes VXH2KNACCUMNET_NGTOH2.FX(IY411FIRST,IRE,IRI)$SUM(Y$(YVALUE(Y) LE YVALUE(IY411FIRST)),IXH2KN_NGTOH2(Y,IRE,IRI))= VXH2KNACCUMNET_NGTOH2.L(IY411FIRST,IRE,IRI); +$ifi %H2TransInvest%==yes $ifi %ES_H2TRANS%==yes VXH2LAMBDA.FX(IY411FIRST,IRE,IRI,XES)$(SUM(Y$(YVALUE(Y) LE YVALUE(IY411FIRST)),IXH2KN(Y,IRE,IRI)) AND IXH2KN_ES(IRE,IRI))=VXH2LAMBDA.L(IY411FIRST,IRE,IRI,XES); \ No newline at end of file diff --git a/base/model/balopt.opt b/base/model/balopt.opt index 8fd23f17..cf730b6d 100644 --- a/base/model/balopt.opt +++ b/base/model/balopt.opt @@ -1,1044 +1,1044 @@ -* This file holds options that are used to control model functionality, -* including extentions in the form of addons, and interplay between models. - -* Note: in final version all relevant control settings will be indicated -* by an *!option statement, e.g. "*!option yes" and other instructions. - -* Note: If you work with BUI, the above is mandatory. -* Moreover, the only way to enter a comment in this file -* is to use a '*' as the first character on the line; -* thus, you can not use e.g., $ontext..$offtext or /*..*/ or // or !!. -* Further, if you work with BUI, this file will not be $included into Balmorel.gms, -* instead the file Balopt.bui will be $included. See the description elsewhere. - -*---- START: VQDEBUG ------------------------------------------------ -*----------------------------------------------------------------- -* overall control variable vqdebug must be 'yes' if any of the other vqdebug controls are: -* Debugging hints are available if you include feasiblity-ensuring variables. -* However, adding more variables will increase calculation time. -* Hence, if calculation time is an issue, it is recommended as default to not use them, -* and then, if the solver declares the problem infeasible, use them and resolve. -* Information of ... in the .log file? -$setglobal vqdebug -*!option yes - -*Reduce number of infeasibility variables. By default, infeasibility variables are added to many equations, which is not always required. -$setglobal reduced_vqdebug -*!option yes -*----------------------------------------------------------------- -*---- END: VQDEBUG ------------------------------------------------ - - -*=============================================================================== -*=============================================================================== -* Project and Case ID: -*=============================================================================== -*=============================================================================== - -* PROJECTID is the identification of the Project. -* Convenient, and additioanally needed if results shall be compared among projects. -* -$setglobal PROJECTID -*!option !fixedvalue -* -* -* CASEID is the Case identification string. It can be assigned freely, however, if -* the case is a base case to which other cases should be compared the name should be -* BASE. Normally the model in the base directory will be called BASE while -* any variants will have names as appropriate. Model results in the form of GDX files -* (or Access MDBs) will have the CASEID as filename with the appropriate extension. -* Note that CASEID MUST be identical to folder name of the diretory holding the case. -* -$setglobal CASEID BASE -*!option !fixedvalue - - -*=============================================================================== -*=============================================================================== -* Model choices: -*=============================================================================== -*=============================================================================== - -*------------------------------------------------------------------------------- -* The following global variables concern the choice of model. -* If $Setglobal bb4 yes, BB4 is chosen. -$setglobal bb4 yes -*!option yes - -*=============================================================================== -*=============================================================================== -* Basic models details and Addon modules: -*=============================================================================== -*=============================================================================== - -*------------------------------------------------------------------------------- -* The following global variables concern the choice of details in some of the models. -* Note that some of the choices are mutually dependent. - -*-------- Seasonal rolling horizon --------- -* Optimize seasons with a rolling horizon approach instead of all together. -*NOTE: This option is supposed to be combined with the option "import_results", which must contain information for all S and at least the first element in T -$setglobal RollingSeasons -*!option yes - -SCALAR ROLLINGSEASONSNUMBER 'Number of seasons to be optimized at a time in each rolling season optimization'; -ROLLINGSEASONSNUMBER= 1; - -*-------- END: Seasonal rolling horizon --------- - -*-------- QDENET --------- -* Introduces the calculation of the net demand of different users (e.g. households, industry, etc). Relevant for taxes, grid tariffs, demand response, etc. -$setglobal NETDEMAND -*!option yes -*-------- END: QDENET --------- - -*----------------------------------------------------------------- -*---- Investments ------------------------------------------------ - -*---- Generation investments options---- - -* Permit investments in generation and storage capacity: -$setglobal TechInvest yes -*!option yes - -SCALAR GINVESTSTART 'Year from which endogenous decisions in generation capacities are allowed' ; -GINVESTSTART= 2025; - -*---- End: Generation investments options---- - -*---- Transmission investments options---- - -* Permit investments in transmission capacity: -$setglobal TransInvest YES -*!option yes - -SCALAR XINVESTSTART 'Year from which endogenous decisions in transmission capacities are allowed' ; -XINVESTSTART= 2035; - -*---- End: Transmission investments options---- - -*----END: Investments--------------------------------------------------- -*----------------------------------------------------------------------- - -*-----------------------HEAT TRANSFER-------------------------------------------------------- -*overall control variable HEATTRANS must be 'yes' if any of the other heat trans controls are. - -* Permit heat transmission between areas. -$setglobal HEATTRANS YES -*!option yes - -* Permit heat transmission investments between areas. -$setglobal HeatTransInvest -*!option yes - -SCALAR XHINVESTSTART 'Year from which endogenous decisions in heat transmission capacities are allowed' ; -XHINVESTSTART= 2025; - -* Permit temperature lift from heat pumps to move energy from low temperature areas to high temperature ones (NOT WORKING, TO BE CHECKED). -$setglobal HeatTempLift -*!option yes -*-----------------------END OF: HEAT TRANSFER-------------------------------------------------------- - -* Include industry data in the optimization. Requires HEATTRANSFER add-on to be activated. -$setglobal INDUSTRY YES -*!option yes - -*--------------------INDIVIDUAL USERS-------------------------------------------------------------- -* overall control variable INDIVUSERS must be 'yes' if any of the other INDIVUSERS controls are: - -* Include individual users data in the optimization. Requires HEATTRANSFER add-on to be activated. -$setglobal INDIVUSERS YES -*!option yes - -* Include the possibility to shift exogenous heat demand between technology groups. -$setglobal GROUP_DEMAND_SHIFT -*!option yes -*-----------------END: INDIVIDUAL USERS-------------------------------------------------------------- - -* Include seasonal variation of heat pump's COP. If not activated, heat pumps will have a constant COP during the year. -$setglobal SEASONALCOP -*!option yes - -*------------START OF: OFFSHORE GRIDS-------------------------- -* overall control variable OFFSHOREGRID must be 'yes' if any of the other OFFSHOREGRID controls are: -* Allow to build a complicated offshore grid with hubs in the sea. Requires MIP to work as intended since technologies are modelled with economies of scale via SOS variables. If RMIP is used, it will become a linear problem. -$setglobal OFFSHOREGRID NO -*!option yes - -*Adds Economies of Scale in generation elements that are part of offshore grids. It requires to use the MIP solver, so remember to activate it. -$setglobal OFFSHOREGRID_ES_GEN -*!option yes - -*Adds Economies of Scale in electricity transmission elements that are part of offshore grids. It requires to use the MIP solver, so remember to activate it. -$setglobal OFFSHOREGRID_ES_ELTRANS -*!option yes -*------------END OF: OFFSHORE GRIDS-------------------------- - - -* Allow Carbon Capture Storage (CCS) technologies. -$setglobal CCS YES -*!option yes - -* Allow curtailment of variable renewable energy generators (BB4) -$setglobal VRESHUTDOWN yes -*!option yes - -*------------------------------EV ADDON------------------------------------------------- -* overall control variable EV must be 'yes' if any of the other EV controls are: - -$setglobal EV -*!option YES - -* Adds DUMB charging: -$setglobal DUMB -*!option yes - -* Adds smart charging. Only smart charging, without V2G!: -$setglobal SMART -*!option yes - -* Adds V2G charging: -$setglobal V2G YES -*!option yes - -* Restricts PHEV vehicles from doing V2G (only relevant if V2G is activated): -$setglobal NO_PHEV_V2G yes -*!option yes - -* Adds step charging scheme (NOT SURE IT IS WORKING ANY MORE): -$setglobal STEPCHARGSCHEME -*!option yes - -* Adds cycle degradation (NOT SURE IT IS WORKING ANY MORE, COSTS SHOULD BE ANNUALIZED): -$setglobal DEGCYCLE -*!option yes - -* Adds calendar degradation (NOT SURE IT IS WORKING ANY MORE, COSTS SHOULD BE ANNUALIZED): -$setglobal DEGCAL -*!option yes - -* EV timeseries will be averaged (relevant if less than all time steps in TTT are used). -* NOTE: It currently only works if T are equally distanced from each other and if the first T chosen is equal to the first element int TTT). If this is not desired, one can replace the timeseries with new averaged-with-another-procedude timeseries -* NOTE: It might lead to infeasibilities though -$setglobal EV_PROFILE_AVG yes -*!option yes - -*Adds EV infeasibility variables (added here on purpose, since if not all TTT are used it is very likely to run into small infeasibilities) -*Note: The value of the parameter PENALTQ should be in line with the cost assumptions in the model to obtain meaninful results -$setglobal EV_infeas yes -*!option - -* Models EV as inter-seasonal storage (if it is not activated, then EVs are model as intra-seasonal storage): -$setglobal EV_INTERSEASONAL -*!option yes -*------------------------------END: EV ADDON------------------------------------------------- - -*------------------------------TRANSPORT ADDON------------------------------------------------- -* overall control variable TRANSPORT must be 'yes' if any of the other TRANSPORT controls are: -* Include transport data in the optimization (it is supposed to complement the EV addon, so check consistency of data before using it). -$setglobal TRANSPORT YES -*!option yes - -* Defines the commodity used to model synthetic fuel generation. -$setglobal TRANSPORT_SYNTHFUEL_COMMODITY HYDROGEN -*!option ELECTRICITY !Text "The demand for synthetic fuel generation is modelled as electricity demand" -*!option HYDROGEN !Text "The demand for synthetic fuel generation is modelled as hydrogen demand (requires hydrogen addon to be activated)" - -* Defines the possibility for the transport demand of synthetic fuels to be aggregated internationally instead of having to satisfy it regionally. -$setglobal TRANSPORT_DEMAND_AGGREGATED - - -*------------------------------END:TRANSPORT ADDON------------------------------------------------- - -*----------------------------------------------------------- -*---------------HYDRO RESERVOIRS OPTIONS-------------------- -*Interpretation of the seasonal level of hydro reservoirs (QHYRSSEQ) -$setglobal HYRS_AGG hydro_year_agg -*!option hydro_year_agg !Text "The storage level of hydro reservoirs (QHYRSSEQ) will be scaled so the Seasons included in the optimization represent an annual behaviour" -*!option hydro_season_agg !Text "The storage level of hydro reservoirs (QHYRSSEQ) will be scaled so the Seasons included in the optimization represent a seasonal behaviour" - -* Relax seasonal limits for hydropower storages when running the model with less than 26 seasons (in HYRSDATA.inc) -$setglobal RELAX_HYDROSTORAGE yes -*!option yes -*---------------END: HYDRO RESERVOIRS OPTIONS-------------- -*---------------------------------------------------------- - -*------------------------------------------------- -*---- Timeseries scaling ------ -*Allows to modify the time series scaling approach -$setglobal timeseries_scaling usedtimesteps_timseries_scaling -*!option usedtimesteps_timseries_scaling !Text "Timeseries values are scaled linearly by the following factor: scaling factor = annual energy content / sum of the energy in the used part of the timeseries" -*!option full_timeseries_scaling !Text "Timeseries values are scaled linearly by the following factor: scaling factor = annual energy content / sum of the energy in the full timeseries" - -*----END: Timeseries scaling -*------------------------------------------------- - -*------------------------------------------------- -*---- IGKRATE data ------ -*Includes stochastic outages. If activated, IGKRATE will take the values in file IGKRATE.gdx, else it will be built based on GKRATE -$setglobal IGKRATE_STOCHASTIC -*!option yes -*---- end: IGKRATE calculation ------ -*------------------------------------------------- - -*-------- Balancing market --------- -* overall control variable BalancingMarket must be 'yes' if any of the other BalancingMarket controls are: -* Peforms the balancing market optimization -*Note: it is meant to be combined with Unit Commitment and import_results add-ons. It can also be combined with ancilliary services -$setglobal BalancingMarket -*!option yes - -*Define the purpose of the balancing run. -$setglobal BALANCINGRUNPURPOSE TSOREDISPATCH -*!option INTRADAYMARKET !Text "The run will simulate INTRADAY markets" -*!option TSOREDISPATCH !Text "The run will simulate the re-dispatch optimization from the TSO perspective (congetion management), adding penalties to compensate for forced re-dispatch of the units based on the TSO needs" - -*If activated the TSO will also compensate generators for the forced down re-dispatch due to forecast errors, ramping limits, etc. (only relevant if BALANCINGRUNPURPOSE=TSOREDISPATCH) (to be finished) -$setglobal FORCEDDOWNREDISPATCH -*!option yes - -*Define which units will be allowed to change their commitment status (on/off) (requires Unit Commitment addon to be on) -$setglobal CommitmentStatus NoChange -*!option NoChange !Text "The units will not be allowed to change their commitment, which will be taken from previous runs " -*!option FastUnitsChange !Text "The slow units will not be allowed to change their commitment, which will be taken from previous runs " -*!option None !Text "All the units will be allowed to change their commitment with respect to previous runs" - -*Restricts heat market from participating in the balancing market by restricting relevant heat generation variables from previous runs and not including the heat balance equation -$setglobal NOHEATMARKET -*!option yes -*-------- END: Balancing market --------- - -*------------------------------------------------------------------------------- -* The following global variables concern the choice of add-on modules to include. -* Include a module by assigning yes . -* Note that some of the choices are mutually dependent. - -* Presently some of the addons are commented out - -* they may or may not be working (at least with an appropriate data set), -* however, it has not been checked. Please take contact if problems. - -* Combine two technologies to one to achieve e.g. multifuel characteristics. -$setglobal COMBTECH -*!option yes - -*-----------------HYDROGEN ADDON--------------------------------------- -* overall control variable HYDROGEN must be 'yes' if any of the other HYDROGEN controls are: -* Set demands for hydrogen consumption and enable hydrogen technologies (some options require COMBTECH to be activated too). -$Setglobal HYDROGEN YES -*!option yes - -* Allows investments in hydrogen transport -$Setglobal H2TransInvest YES -*!option yes - -SCALAR XH2INVESTSTART 'Year from which endogenous decisions in H2 transmission capacities are allowed' ; -XH2INVESTSTART= 2030; - -*Adds Economies of Scale in H2 transmission. It requires to use the MIP solver, so remember to activate it (Dataset not updated for new countries in pan-European system) -$setglobal ES_H2TRANS - -* Allows import of Hydrogen from 3rd countries outside Balmorel -$Setglobal IMPORT_H2 YES -*!option yes - -* Allows flexible demand of H2 in regions (set ratio of flexible demand in HYDROGEN_FLEXDH2 with the scalar THETA_FLEX_DH2) -$setglobal flexible_h2_space -*!option yes - -*-----------------END: HYDROGEN ADDON--------------------------------------- - -* Use stepwise linear fuel prices -$Setglobal STEPWISEPRICE - -* Set a common maximum for fuels. (it should be checked before using it) -$Setglobal LIMITSFORFUELS -*!option yes - -* Allow demand elasticity (it should be checked before using it) -$setglobal DFLEXQUANT -*!option yes - -* Allow demand response (requires NETDEMAND) (it should be checked before using it) -$setglobal DEMANDRESPONSE -*!option yes - -* Permit exchange of electricity with third regions using a price interface (in contrast to X3FX) (it should be checked before using it) -$setglobal X3V -*!option yes - -* Grid tariffs on electricity use (requires NETDEMAND) -$setglobal GRIDTARIFFS -*!option yes - -* Taxes addon (requires NETDEMAND) -$setglobal TAXES -*!option yes - -* Transmission ramping addon (it asssumes seasonal linking) -$setglobal TRANS_RAMP -*!option yes - -*-----------------------ANCILLARY SERVICES---------------------------------------------------------------------------------------------------- -* Ancillary services addon (it requires UnitComm to be active). The other options require this one to be activated. -$setglobal ANC_SERV -*!option yes - -* FCR reserve requirement activated -$setglobal FCR -*!option yes - -* AFRR reserve requirement activated -$setglobal AFRR yes -*!option yes - -* MFRR reserve requirement activated (NOT FINISHED) -$setglobal MFRR -*!option yes - -* MRR reserve requirement activated (NOT FINISHED) -$setglobal MRR -*!option yes - -* Allow VRE to provide for reserves -$setglobal ANC_SERV_VRE YES -*!option yes - -*Include down regulation reserves. For simplification, down regulation of spinning reserves can be disactivated, since they are generally less expensive than up regulation. -$setglobal DOWN_REGULATION_INCLUDED -*!option yes -*-----------------------END: ANCILLIARY SERVICES---------------------------------------------------------------------------------------------------- - -* Make investments in technologies only at specified capacities. (it should be checked before using it) -$setglobal AGKNDISC -*!option yes -*!onlyif %BB2%==yes -*!onlyif %solvetype%==MIP - - -* Use the flow-based capacity allocation methodology -$Setglobal FlowBased -*!option yes - -* Set short storages to be inter- or intra-seasonal -* according to GDSTOINTERS in GDATA (default) or all or none -$setglobal stointers -*! option default Use as specified by GDSTOINTERS in GDATA -*! option all Let all storage technologies be inter-seasonal -*! option none Let no storage technologies be inter-seasonal (i.e., let all be intra-seasonal) - - -* Bypass of 'turbine' (electricity production) on CHP units. -$setglobal bypass yes -*!option yes - - -*-----------------------TECHNOLOGY DECOMMISSIONING-------------------------------------------------------- -* overall control variable DECOM must be 'yes' if any of the other decommissioning controls are. -$setglobal DECOM yes -*!option yes - -*-------ENDOGENOUS CAPACITIES---------- -* Note: If no option for endogenous capacities is activated, endogenous investments will not be decommissioned - -* Adds Endogenous capacities will maximum stay in the system as much as their lifetimes: -$setglobal LIFETIME_DECOM_ENDO_G YES -*!option yes - -* Adds Endogenous capacities can be decommissioned based on their profitability: -$setglobal ECONOMIC_DECOM_ENDO_G yes -*!option yes - -* Adds Endogenous decommissioned endogenous capacities can be brought back to the energy system. To be combined with the other decommissioning options: -$setglobal REVERT_DECOM_ENDO_G yes -*!option yes - -*--------END OF ENDOGENOUS CAPACITIES----- - -*--------EXOGENOUS CAPACITIES------------ -* Note: If either NO_DECOM_EXO_G, or LIFETIME_DECOM_EXO_G, or ECONOMIC_DECOM_EXO_Gor is activated, then the parameter GKFX will be recalculated so capacities only increase in the system. - -SCALAR GDECOMEXOSTART 'Year from which endogenous decisions in decommissioning of exogenous generation capacities are allowed'; -GDECOMEXOSTART= 2025; - -SCALAR GDECOMEXOPOTSTART 'Starting year to calculate the yearly decommissioning capacity potential, i.e. exogenous decommissioning occuring before this year will not be considered' ; -GDECOMEXOPOTSTART= 2016; - -* Adds No decommissioning of exogenous capacities will take place at all. The capacities in the recalculated GKFX will take place in the optimization -* Note: This option is not compatible with the other exogenous capacities decommissioning options. -$setglobal NO_DECOM_EXO_G -*!option yes - -* Adds Exogenous capacities will maximum stay in the system as much as their lifetimes (or as much as political decissions suggest it, for instance), which should be represented in the yearly development of capacities of the input parameter GKFX: -$setglobal LIFETIME_DECOM_EXO_G YES -*!option yes - -* Adds Exogenous capacities can be decommissioned based on their profitability: -$setglobal ECONOMIC_DECOM_EXO_G yes -*!option yes - -* Adds Endogenous decommissioned exogenous capacities can be brought back to the energy system. To be combined with the other decommissioning options: -$setglobal REVERT_DECOM_EXO_G yes -*!option yes -*--------END OF ENDOGENOUS CAPACITIES----- -*-----------------------End of TECHNOLOGY DECOMMISSIONING-------------------------------------------------------- - - -*-------------------- UNIT COMMITMENT ----------------------------------------------------------- -* overall control variable UnitComm must be 'yes' if any of the other unit commitment controls are: - -$setglobal UnitComm -*!option yes - -* Adds minimal production-when-on: -$setglobal MINFUELCONS YES -*!option yes -* Adds respecting minimum off time of the capacities: -$setglobal MINSTARTUPTIME yes -*!option yes -* Adds respecting minimum on time of the capacities: -$setglobal MINSHUTDOWNTIME yes -*!option yes -* Adds ramping technical limitations and stepwise costs to the Power plants when-on: -$setglobal STEPWISE_RAMPING_UC ramp_const -*!option ramp_const_cost !Text "With ramping constraints and ramping costs" -*!option ramp_const !Text "With ramping constraints but without ramping costs" -*!option no_ramping !Text "Neither ramping constraints nor ramping costs" -* Adds optimisation of planned maintenance of the generation and storage technologies -$setglobal PLANNED_MAINTENANCE -*!option yes - -* Seasonal link is on for all UC options activated. This means that past seasons will influence future seasons. -* This option increases computanional time but assures continuity in the fuel consumption profile. -$setglobal SEASONALLINKUC -*!option yes - -* The production interval is segmented into a number of disjunct pieces, each with its own costs: NOT IMPLEMENTED -$setglobal UnitCseg -*!option yes -*-------------------- End of UNIT COMMITMENT ----------------------------------------------------------- - -*--------------------STEPWISE RAMPING (LINEAR VERSION)----------------------------------------------------------- -* overall control variable Stepwise_ramping must be 'yes' if any of the other stepwise ramping controls are: -* This add-on is an alternative to Unit Commitment to model ramping constraints without using MIP. This add-on uses currently the relevant data and sets from the Unit Commitment add-on -* This add-on is not compatible with the Unit Commitment stepwiseramping option -* The use of this add-on togeter with the rest of options of Unit Commitment add-on has not been tested, so, unless tested, this add-on should not be activated if UnitComm is on. - -$setglobal STEPWISE_RAMPING -*!option yes - -* Seasonal ramping link is on. This means that the fuel consumption of the last element ot the set T of the set S will be linked to the first element of the set T of the set S+1. -* This option increases computanional time but assures continuity in the fuel consumption profile. -$setglobal SEASONALLINKRAMPING -*!option yes - -*----------------END OF STEPWISE RAMPING (LINEAR VERSION)---------------------------------------------------------- - -* Options timeaggr control automatic aggregation of time (it should be checked before using it). -$setglobal timeaggr -*!option yes - - -*------------------------------------------------------------------------------- -* OptiFlow model. -*------------------------------------------------------------------------------- -$setglobal OPTIFLOW -*!option yes -* Addon APKNdisc: discrete size investments in PROC. -$setglobal APKNdisc -*!option yes -*Investments in Optiflow technologies -$setglobal inv yes -*! option yes -*Model solved as MIP -$setglobal solveOptiFlowmip -*! option yes - -* Debug possibilities: -$Setglobal addEPStoL -*! option yes -* Not needed if there are no capacity bounds on internal proc -* It will be needed if there are capacity bounds on some internal proc, therefore add them only in that case (requires some coding, TODO). -* With such minimal use the infeasibilities will be moved to SOSIBU proc whenever possible. -$Setglobal useQNODEslack -*! option -$Setglobal useSSBslack -*! option yes -$Setglobal useShareSlack -*! option yes -* Option uniquesol permit to use quadratic terms in QOBJ in order to attain unique solution. -* Should be used for debug on small systems only. -$Setglobal uniquesol -*! option yes -* Testing ONLY: -$setglobal singlearea -*! option yes -* Testing ONLY: -$setglobal addUP -*! option yes -*------------------------------------------------------------------------------- -*Optiflow end -*------------------------------------------------------------------------------- - -* In model Balbase4 (BB4) indicate the strategy for relative weighting of years -$setglobal bb4weighty shareYYY -*!option same !Text "Use same weights for all Y" -*!option shareYYY !Text "Years in YYY but not in Y are shared between two nearest (first and last years double the shared weight contribution)" - -*=============================================================================== -*=============================================================================== -* Relative paths -*=============================================================================== -*=============================================================================== -* No choices, they must be set as shown. Note the format. -* They are to be used as illustrated below and elsewhere in the code. -* -$ifi not %system.filesys%==UNIX $goto endofUNIXrelpaths -$setglobal relpathModel "" -*!fixedvalue -$setglobal relpathoutput "../output/" -*!fixedvalue -$setglobal relpathInputdata2GDX "../output/inputout/" -*!fixedvalue -$setglobal relpathtemp "../output/temp/" -*!fixedvalue -$label endofUNIXrelpaths - -$ifi not %system.filesys%==MSNT $goto endofMSNTrelpaths -$setglobal relpathModel "" -*!fixedvalue -$setglobal relpathoutput "..\output\" -*!fixedvalue -$setglobal relpathInputdata2GDX "..\output\inputout\" -*!fixedvalue -$setglobal relpathtemp "..\output\temp\" -*!fixedvalue -$label endofMSNTrelpaths - -*=============================================================================== -*=============================================================================== -* Some interaction between models: -*=============================================================================== -*=============================================================================== - -* The following options are related to interplay between models, -* e.g. one model output serving as input to another model. - -* Overall control to export results so they can be used in future runs : -$Setglobal export_results yes -*!option yes - -*-------------------------------------------- -*--------------IMPORT OF RESULTS---------------- -*-------------------------------------------- -* Overall control to import results so they can be used in future runs: -*NOTE: In order for results to work fine, the set S used in the runs should be contained in the import results. -*NOTE: In order for results to work fine, the first element in set T of the import results should match the one used in the current run. -$Setglobal import_results -*!option yes - -*------------ IMPORT OPTIONS ---------------- - -* -----------------ENERGY DISPATCH-------------------------- - -*Adds energy dispatch (demand, generation, transmission) from previous runs -$setglobal ADDENERGYDISPATCH -*!option yes -* -----------------END OF ENERGY DISPATCH-------------------------- - -* ------------------HYDRO--------------------------------- -* For this model run it is possible to use values found from previuos solutions to specify use of hydro power -$setglobal HYRSBB123 none -*!option quant !Text "Use seasonal quantities of hydro generation (from HYRSG.gdx??) in this run ('primal decomposition')(not implemented in BB4)" -*!option price !Text "Use seasonal prices for hydro generation (from WATERVAL.gdx) in this run ('dual decomposition')" -*!option quantprice !Text "Use seasonal quantities and prices for hydro generation (from HYRSG.gdx?? and WATERVAL.gdx) in in this run (not implemented in BB4)" -*!option cont !Text "Use seasonal content for hydro generation (from VHYRS_SL.gdx) in in this run" -*!option none !Text "Do not not use this hydro addon" -* ------------------ END OF HYDRO--------------------------------- - -*--------------------INTERSEASONAL STORAGE----------------------------------------------------------- -*Import inter-seasonal electricity storage data from previous runs -$Setglobal ADDESTOVOLTS none -*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition')" -*!option price !Text "Import electricity storage contents at beginning of time segment (ESTOVOLTS.gdx) for the first element in set T and S, and puts a price to use with ESTOVOLTSVAL.gdx from previous runs. " -*!option none !Text "Do not not use this addon" - -*Import inter-seasonal heat storage data from previous runs -$Setglobal ADDHSTOVOLTS none -*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition')" -*!option price !Text "Import Heat storage contents at beginning of time segment (HSTOVOLTS.gdx) for the first element in set T and S, and puts a price to use with HSTOVOLTSVAL.gdx from previous runs." -*!option none !Text "Do not not use this addon" - -*Import intra-seasonal electricity storage data from previous runs -$Setglobal ADDESTOVOLT none -*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition')" -*!option price !Text "(to be finished) Import electricity storage contents at beginning of time segment (ESTOVOLT.gdx) for the first element in set T and S, and puts a price to generation (ESTOVOLTVAL) from previous runs." -*!option none !Text "Do not not use this addon" - -*Import intra-seasonal heat storage data from previous runs -$Setglobal ADDHSTOVOLT none -*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition')" -*!option price !Text "(to be finished) Import Heat storage contents at beginning of time segment (HSTOVOLT.gdx) for the first element in set T and S, and puts a price to generation (HSTOVOLTVAL) from previous runs" -*!option none !Text "Do not not use this addon" - -*Import inter-seasonal hydrogen storage data from previous runs (requires HYDROGEN addon) -$Setglobal ADDH2STOVOLTS none -*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition')" -*!option price !Text "Import hydrogen storage contents at beginning of time segment (H2STOVOLTS.gdx) for the first element in set T and S, and puts a price to use with H2STOVOLTSVAL.gdx from previous runs." -*!option none !Text "Do not not use this addon" - -*Import inter-seasonal biomethane storage data from previous runs (requires HYDROGEN addon) -$Setglobal ADDBIOMETHSTOVOLTS none -*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition')" -*!option price !Text "Import biomethane storage contents at beginning of time segment (BIOMETHSTOVOLTS.gdx) for the first element in set T and S, and puts a price to use with BIOMETHSTOVOLTSVAL.gdx from previous runs." -*!option none !Text "Do not not use this addon" - -*Import EV state of charge from previous runs (requires EV addon) -$Setglobal ADDEVSTOS none -*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition') (only works if EV_INTERSEASONAL is activated)" -*!option netcharging !Text "Use seasonal net charging of EV in this run ('primal decomposition') (only works if EV_INTERSEASONAL is NOT activated)" -*!option none !Text "Do not not use this addon" - - - -*--------------------END OF INTERSEASONAL STORAGE----------------------------------------------------------- - -*--------------------INVESTMENTS AND DECOMMISSIONING----------------------------------------------------------- -*NOTE: The set Y should be the same in the optimization should be the same as the one in the import results. - -*Import generation and storage technology capacity development from previous runs -$setglobal ADDTECHDEV -*!option yes - -*Add expensive to operate backup generation technologies (electricity, heat, ...). It often occurs that the investment optimization underestimates the need for power. -$setglobal ADDBACKUPGEN -*!option yes - -*Import transmission development from previous runs -$setglobal ADDTRANSDEV -*!option yes - -*Import heat transmission development from previous runs -$setglobal ADDHEATTRANSDEV -*!option yes - -*Import H2 transmission development from previous runs -$setglobal ADDH2TRANSDEV -*!option yes -*--------------------END OF INVESTMENTS AND DECOMMISSIONING----------------------------------------------------------- - -*--------------------FUEL PRICES----------------------------------------------------------- -*NOTE: The set Y should be the same in the optimization should be the same as the one in the import results. -*Import fuel prices development from previous runs -$setglobal ADDFUELPRICE -*!option yes -*--------------------END OF FUEL PRICES----------------------------------------------------------- - -*--------------------FUEL USE----------------------------------------------------------- -*NOTE: The set Y should be the same in the optimization should be the same as the one in the import results. -*Import seasonal fuel use upper limit from previous runs -$setglobal ADDMAXFUELUSES -*!option yes -*--------------------END OF FUEL USE----------------------------------------------------------- - -*--------------------PLANNED MAINTENANCE----------------------------------------------------------- -*NOTE: The set Y should be the same in the optimization should be the same as the one in the import results. -*Import optimized planned maintenance from previous runs (investments and/or decomissioning must not be allowed if this option is activated) -$setglobal ADDPLANNEDMAINTENANCE -*!option yes -*--------------------END OF PLANNED MAINTENANCE----------------------------------------------------------- - -*--------------------TRANSPORT DEMAND----------------------------------------------------------- -*NOTE: The set Y should be the same in the optimization should be the same as the one in the import results. -*Import demand per term (T) from the transport sector -$setglobal ADDTERMTRANSPORTDEMAND -*!option yes - -*Import seasonal demand from the transport sector -$setglobal ADDSEASONALTRANSPORTDEMAND -*!option yes - -*Import peak demand from the transport sector -$setglobal ADDPEAKTRANSPORTDEMAND -*!option yes -*--------------------END OF TRANSPORT DEMAND----------------------------------------------------------- - -*--------------------INDIVIDUAL USERS----------------------------------------------------------- -*Import Share of heat demand in an individual user group. Requires GROUP_DEMAND_SHIFT to be activated -$setglobal ADDGROUP_DEMAND_SHIFT -*!option yes -*--------------------END OF INDIVIDUAL USERS----------------------------------------------------------- - - -*------------- END OF IMPORT OPTIONS ---------------- -*-------------------------------------------- -*--------------END OF IMPORT OF RESULTS---------------- -*-------------------------------------------- - - - - -*=============================================================================== -*=============================================================================== -* Output: -*=============================================================================== -*=============================================================================== - -* ------------------------------------------------------------------------------ -* Alternative presentations of input data -* ------------------------------------------------------------------------------ - -* It may be convenient under some circumstances to have the applied input data in other forms, -* this may be achieved by the following mechanism. - -* Make a summary of the input data in text format to file inputout.out: -$setglobal inputdatatxt "" -*!option "" !text do not make it -*!option yes !text make it when solving -*!option yesnosolve !text make it but do not solve - - -* Save the input data in gdx format for further processing by Gdx utilities, -* e.g. viewing (Gdxviewer), comparing (Gdxdiff), or transferring to other data formats like Excel or Access. -* Note that this will be done as a compile time operation, -* such that only the 'direct' data definitions (and no assignments) are reflected. -$setglobal INPUTDATA2GDX YES -*!option yes - - -* Transfer the input data in gdx format to a Microsoft Access database ../output/inputout/inputdataout.mdb: -$setglobal INPUTDATAGDX2MDB -*!option yes !onlyif %INPUTDATA2GDX%==yes -* Note: if you have established a link from another Access database to inputdataout.mdb -* close all tables in it before executing Balmorel in order to ensure updating of the links. - -* Merge input data with the output into the gdx file %CASEID%.gdx -$setglobal MERGEINPUTDATA -*!option yes - - -* Transfer the input data in gdx format to Microsoft Excel format: -* NOTWORKING: presently. -$setglobal INPUTDATAGDX2XLS -*!option yes - - -*------------------------------------------------------------------------------- -* Output from model solutions: -*------------------------------------------------------------------------------- - -* A quick way to get (some of) the relevant information is to make a gdx file with all information. -* This option will provide unloading of all that is available at the end of Balmorel.gms. -* -$setglobal all_endofmodelgdx yes -*!option yes !Save the file all_endofmodel.gdx to the model folder - - - - -* Make some output in text format to show some results of an execution. -* The detailed code for the writing of the files is given in /output/printout/printinc. -* Setting "yes" for PRINTFILES will make some standard printout files to folder /output/printout/. -$setglobal PRINTFILES -*!option yes - -$setglobal OUTPUT_SUMMARY yes -*!option yes - -* PRINTSEPARATOR is a single character used for separating columns in printfiles, -* convenient e.g. to use ';' for copy-paste to Excel. -$setglobal PRINTSEPARATOR ';' -*!option ' ' -*!option ';' -*!option ',' - -*------------------------------------------------------------------------------- -* Results collection for the present case -*------------------------------------------------------------------------------- - -* The following will establish a mechanism that will save annual optimization results -* from bb1 and bb2 (seasonal for bb3) as gdx files; at the end they may be merged to one gdx file. -* This merged file may then be transferred to an Access database or to Excel. -* It is to be seen in conjunction with saving annual results in file bb123.sim, -* which is accomplished using savepoint with value 1, cf '$Setglobal SAVEPOINTVALUE'. -* -* The following closely linked controls are used: -* SAVEPOINTVALUE, cf. above -* MERGESAVEPOINTRESULTS yes -* MERGEDSAVEPOINTRESULTS2MDB -* MERGEDSAVEPOINTRESULTS2SQLITE -* MERGEDSAVEPOINTRESULTS2XLS: presently not working, we are waiting for GAMS version 22.7, which has better support for this. -* MERGECASE/MERGEWITH -* DIFFCASE/DIFFWITH -* -* Savepoint: the GAMS command "Modelname.Savepoint=number;" -* The savepoint attribute tells GAMS to save a GDX file that contains the information on the current solution point. -* One can save the information from just the last solve or from every solve. -* The points that are saved can be used to provide an advanced basis, an integer program starting point or a NLP starting point. -* Numeric input is expected with the allowable numeric values being: -* 0 no point gdx file is to be saved -* 1 a point gdx file is to be saved from the last solve in the GAMS model -* 2 a point gdx file is to be saved from every solve in the GAMS model -* The value specified by $Setglobal SAVEPOINTVALUE will be used for savepoint. -* To be used with subsequent handling of output. -* -$setglobal SAVEPOINTVALUE 0 -*!option 0 -*!option 1 !mustif %MERGESAVEPOINTRESULTS%==yes !mustif %MERGEDSAVEPOINTRESULTS2GDX%==yes !mustif %MERGEDSAVEPOINTRESULTS2XLS%==yes !mustif not %MERGECASE%==none !mustif not %DIFFCASE%==none -*!option 2 !text Note: nothing implemented nor tested, so do not use it. - -* Use the $setglobal MERGESAVEPOINTRESULTS to choose whether to merge annual/seasonal results or not: -$setglobal MERGESAVEPOINTRESULTS -*!option yes !onlyif %SAVEPOINTVALUE%==1 -* Note: make sure that it is possible to make the new merged gdx file, -* e.g. make sure that the file *.gdx is not open in another application (like GAMD IDE). -* -* Then prepare for the necessary details: -$ifi %system.filesys%==MSNT -$setglobal batfileMERGESAVEPOINTRESULTS %relpathModel%MERGESAVEPOINTRESULTS.bat -*!option !fixedvalue -$ifi %system.filesys%==MSNT -file fileMERGESAVEPOINTRESULTSbat / %batfileMERGESAVEPOINTRESULTS% /; -$ifi %system.filesys%==MSNT -putclose fileMERGESAVEPOINTRESULTSbat; - -$ifi %system.filesys%==UNIX -$setglobal bashfileMERGESAVEPOINTRESULTS %relpathModel%MERGESAVEPOINTRESULTS.sh -*!option !fixedvalue -$ifi %system.filesys%==UNIX -file fileMERGESAVEPOINTRESULTSbash / %bashfileMERGESAVEPOINTRESULTS% /; -$ifi %system.filesys%==UNIX -putclose fileMERGESAVEPOINTRESULTSbash; - -$setglobal MERGEDSAVEPOINTRESULTS2MDB -*!option yes !onlyif %SAVEPOINTVALUE%==1 !onlyif %MERGESAVEPOINTRESULTS%==yes - -$setglobal MERGEDSAVEPOINTRESULTS2SQLITE -*!option yes !onlyif %SAVEPOINTVALUE%==1 !onlyif %MERGESAVEPOINTRESULTS%==yes - - -*------------------------------------------------------------------------------- -* Comparison between Cases: -*------------------------------------------------------------------------------- - -* The following will merge and/or compare two results. -* With option MERGECASE is set to NONE this will be not be done, otherwise it will. -* If done, the result will be in the output folder of the present case in the form of a GDX file. -* With option MERGEDSAVEPOINTRESULTS2MDB" this may then be transferred to a MDB file. -* All output go to the output folder of the present case. -* Similar considerations for DIFFCASE. - -$setglobal MERGECASE none -*!option none !text Comparison will not be made -*!option -* MERGEWITH defines the case with which to merge results. -$setglobal MERGEWITH CaseNonversioned -*!option !mustif not %MERGECASE%==none - -* The diff uses gdxdiff. The diffile only shows items that meaningfull can be compared, -* non-compared items are reported in the Balmorel.log file. -$setglobal DIFFCASE none -*!option none !text Comparison will not be made -*!option - -$setglobal DIFFWITH CaseNonversioned -*!option none !text Comparison will not be made -*!option !mustif not %DIFFCASE%==none - -* Then prepare for the necessary details: TODO -file fileMERGEWITHBASEbat / %batfileMERGEWITHBASE% /; -putclose fileMERGEWITHBASEbat; - - - - -*=============================================================================== -*=============================================================================== -* The model type's implication for solver type -*=============================================================================== -*=============================================================================== - -* The model type has implications for the solver type. -* Classical model types are LP (Linear Programming), QP (Quadratic Programming), MIP (Mixed Integer Programming). -$setglobal SOLVETYPE RMIP -*!option LP "Linear Programming" -*!option RMIP "Relaxed Mixed Integer Programming" -*!option MIP "Mixed Integer Programming" - -*!option yes -*!mustif %AGKNDISC%==yes -*!mustif %UnitComm%==yes - - - -*=============================================================================== -*=============================================================================== -* Input data: -*=============================================================================== -*=============================================================================== - -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- -* Option semislash is used to control input data format for text files. -* With "$Setglobal semislash ;" the input files must include -* the declaration statement initially and must terminate with a "/;". -* With "$Setglobal semislash /" the input files do not contain this, but only data. -* Note that parameters and tables are identical declarations, however, -* when using "$Setglobal semislash /" make sure -* that the use of either "parameter" or "table" in Balmorel.gms for any data file -* corresponds to the data format used in the file. -* NOTE: Rev.3.03: Not fully implemented yet, you MUST use "$Setglobal semislash ;". -$setglobal semislash ";" -*!fixedvalue ; -**!option ; - - - - -* Domain overloading: The options specify the domain (indexes) that the identifiyer is declared on. -$setglobal GMINF_DOL YYY_CCCRRRAAA_FFF -*!option CCCRRRAAA_FFF -*!option YYY_CCCRRRAAA_FFF -* -$setglobal GMAXF_DOL YYY_CCCRRRAAA_FFF -*!option CCCRRRAAA_FFF -*!option YYY_CCCRRRAAA_FFF -* -$setglobal GEQF_DOL YYY_CCCRRRAAA_FFF -*!option CCCRRRAAA_FFF -*!option YYY_CCCRRRAAA_FFF -* -$setglobal XKRATE_DOL IRRRE_IRRRI_SSS -*!option IRRRE_IRRRI -*!option IRRRE_IRRRI_SSS -*!option IRRRE_IRRRI_SSS_TTT -* -$setglobal GKRATE_DOL AAA_GGG_SSS -*!option AAA_GGG_SSS -*!option AAA_GGG_SSS_TTT - -$setglobal WNDFLH_DOL AAA -*!option AAA_GGG -*!option AAA - -$setglobal SOLEFLH_DOL AAA -*!option AAA_GGG -*!option AAA - -$setglobal SOLHFLH_DOL AAA -*!option AAA_GGG -*!option AAA - -$setglobal GWAVE_DOL AAA -*!option AAA_GGG -*!option AAA - - - -*------------------------------------------------------------------------------- -* Active options will be reported to log file via file hellolog.inc -*------------------------------------------------------------------------------- - - -*------------------------------------------------------------------------------- -* End of Balopt.opt -*------------------------------------------------------------------------------- +* This file holds options that are used to control model functionality, +* including extentions in the form of addons, and interplay between models. + +* Note: in final version all relevant control settings will be indicated +* by an *!option statement, e.g. "*!option yes" and other instructions. + +* Note: If you work with BUI, the above is mandatory. +* Moreover, the only way to enter a comment in this file +* is to use a '*' as the first character on the line; +* thus, you can not use e.g., $ontext..$offtext or /*..*/ or // or !!. +* Further, if you work with BUI, this file will not be $included into Balmorel.gms, +* instead the file Balopt.bui will be $included. See the description elsewhere. + +*---- START: VQDEBUG ------------------------------------------------ +*----------------------------------------------------------------- +* overall control variable vqdebug must be 'yes' if any of the other vqdebug controls are: +* Debugging hints are available if you include feasiblity-ensuring variables. +* However, adding more variables will increase calculation time. +* Hence, if calculation time is an issue, it is recommended as default to not use them, +* and then, if the solver declares the problem infeasible, use them and resolve. +* Information of ... in the .log file? +$setglobal vqdebug +*!option yes + +*Reduce number of infeasibility variables. By default, infeasibility variables are added to many equations, which is not always required. +$setglobal reduced_vqdebug +*!option yes +*----------------------------------------------------------------- +*---- END: VQDEBUG ------------------------------------------------ + + +*=============================================================================== +*=============================================================================== +* Project and Case ID: +*=============================================================================== +*=============================================================================== + +* PROJECTID is the identification of the Project. +* Convenient, and additioanally needed if results shall be compared among projects. +* +$setglobal PROJECTID +*!option !fixedvalue +* +* +* CASEID is the Case identification string. It can be assigned freely, however, if +* the case is a base case to which other cases should be compared the name should be +* BASE. Normally the model in the base directory will be called BASE while +* any variants will have names as appropriate. Model results in the form of GDX files +* (or Access MDBs) will have the CASEID as filename with the appropriate extension. +* Note that CASEID MUST be identical to folder name of the diretory holding the case. +* +$setglobal CASEID BASE +*!option !fixedvalue + + +*=============================================================================== +*=============================================================================== +* Model choices: +*=============================================================================== +*=============================================================================== + +*------------------------------------------------------------------------------- +* The following global variables concern the choice of model. +* If $Setglobal bb4 yes, BB4 is chosen. +$setglobal bb4 yes +*!option yes + +*=============================================================================== +*=============================================================================== +* Basic models details and Addon modules: +*=============================================================================== +*=============================================================================== + +*------------------------------------------------------------------------------- +* The following global variables concern the choice of details in some of the models. +* Note that some of the choices are mutually dependent. + +*-------- Seasonal rolling horizon --------- +* Optimize seasons with a rolling horizon approach instead of all together. +*NOTE: This option is supposed to be combined with the option "import_results", which must contain information for all S and at least the first element in T +$setglobal RollingSeasons +*!option yes + +SCALAR ROLLINGSEASONSNUMBER 'Number of seasons to be optimized at a time in each rolling season optimization'; +ROLLINGSEASONSNUMBER= 1; + +*-------- END: Seasonal rolling horizon --------- + +*-------- QDENET --------- +* Introduces the calculation of the net demand of different users (e.g. households, industry, etc). Relevant for taxes, grid tariffs, demand response, etc. +$setglobal NETDEMAND +*!option yes +*-------- END: QDENET --------- + +*----------------------------------------------------------------- +*---- Investments ------------------------------------------------ + +*---- Generation investments options---- + +* Permit investments in generation and storage capacity: +$setglobal TechInvest yes +*!option yes + +SCALAR GINVESTSTART 'Year from which endogenous decisions in generation capacities are allowed' ; +GINVESTSTART= 2025; + +*---- End: Generation investments options---- + +*---- Transmission investments options---- + +* Permit investments in transmission capacity: +$setglobal TransInvest YES +*!option yes + +SCALAR XINVESTSTART 'Year from which endogenous decisions in transmission capacities are allowed' ; +XINVESTSTART= 2035; + +*---- End: Transmission investments options---- + +*----END: Investments--------------------------------------------------- +*----------------------------------------------------------------------- + +*-----------------------HEAT TRANSFER-------------------------------------------------------- +*overall control variable HEATTRANS must be 'yes' if any of the other heat trans controls are. + +* Permit heat transmission between areas. +$setglobal HEATTRANS YES +*!option yes + +* Permit heat transmission investments between areas. +$setglobal HeatTransInvest +*!option yes + +SCALAR XHINVESTSTART 'Year from which endogenous decisions in heat transmission capacities are allowed' ; +XHINVESTSTART= 2025; + +* Permit temperature lift from heat pumps to move energy from low temperature areas to high temperature ones (NOT WORKING, TO BE CHECKED). +$setglobal HeatTempLift +*!option yes +*-----------------------END OF: HEAT TRANSFER-------------------------------------------------------- + +* Include industry data in the optimization. Requires HEATTRANSFER add-on to be activated. +$setglobal INDUSTRY YES +*!option yes + +*--------------------INDIVIDUAL USERS-------------------------------------------------------------- +* overall control variable INDIVUSERS must be 'yes' if any of the other INDIVUSERS controls are: + +* Include individual users data in the optimization. Requires HEATTRANSFER add-on to be activated. +$setglobal INDIVUSERS YES +*!option yes + +* Include the possibility to shift exogenous heat demand between technology groups. +$setglobal GROUP_DEMAND_SHIFT +*!option yes +*-----------------END: INDIVIDUAL USERS-------------------------------------------------------------- + +* Include seasonal variation of heat pump's COP. If not activated, heat pumps will have a constant COP during the year. +$setglobal SEASONALCOP +*!option yes + +*------------START OF: OFFSHORE GRIDS-------------------------- +* overall control variable OFFSHOREGRID must be 'yes' if any of the other OFFSHOREGRID controls are: +* Allow to build a complicated offshore grid with hubs in the sea. Requires MIP to work as intended since technologies are modelled with economies of scale via SOS variables. If RMIP is used, it will become a linear problem. +$setglobal OFFSHOREGRID NO +*!option yes + +*Adds Economies of Scale in generation elements that are part of offshore grids. It requires to use the MIP solver, so remember to activate it. +$setglobal OFFSHOREGRID_ES_GEN +*!option yes + +*Adds Economies of Scale in electricity transmission elements that are part of offshore grids. It requires to use the MIP solver, so remember to activate it. +$setglobal OFFSHOREGRID_ES_ELTRANS +*!option yes +*------------END OF: OFFSHORE GRIDS-------------------------- + + +* Allow Carbon Capture Storage (CCS) technologies. +$setglobal CCS YES +*!option yes + +* Allow curtailment of variable renewable energy generators (BB4) +$setglobal VRESHUTDOWN yes +*!option yes + +*------------------------------EV ADDON------------------------------------------------- +* overall control variable EV must be 'yes' if any of the other EV controls are: + +$setglobal EV +*!option YES + +* Adds DUMB charging: +$setglobal DUMB +*!option yes + +* Adds smart charging. Only smart charging, without V2G!: +$setglobal SMART +*!option yes + +* Adds V2G charging: +$setglobal V2G YES +*!option yes + +* Restricts PHEV vehicles from doing V2G (only relevant if V2G is activated): +$setglobal NO_PHEV_V2G yes +*!option yes + +* Adds step charging scheme (NOT SURE IT IS WORKING ANY MORE): +$setglobal STEPCHARGSCHEME +*!option yes + +* Adds cycle degradation (NOT SURE IT IS WORKING ANY MORE, COSTS SHOULD BE ANNUALIZED): +$setglobal DEGCYCLE +*!option yes + +* Adds calendar degradation (NOT SURE IT IS WORKING ANY MORE, COSTS SHOULD BE ANNUALIZED): +$setglobal DEGCAL +*!option yes + +* EV timeseries will be averaged (relevant if less than all time steps in TTT are used). +* NOTE: It currently only works if T are equally distanced from each other and if the first T chosen is equal to the first element int TTT). If this is not desired, one can replace the timeseries with new averaged-with-another-procedude timeseries +* NOTE: It might lead to infeasibilities though +$setglobal EV_PROFILE_AVG yes +*!option yes + +*Adds EV infeasibility variables (added here on purpose, since if not all TTT are used it is very likely to run into small infeasibilities) +*Note: The value of the parameter PENALTQ should be in line with the cost assumptions in the model to obtain meaninful results +$setglobal EV_infeas yes +*!option + +* Models EV as inter-seasonal storage (if it is not activated, then EVs are model as intra-seasonal storage): +$setglobal EV_INTERSEASONAL +*!option yes +*------------------------------END: EV ADDON------------------------------------------------- + +*------------------------------TRANSPORT ADDON------------------------------------------------- +* overall control variable TRANSPORT must be 'yes' if any of the other TRANSPORT controls are: +* Include transport data in the optimization (it is supposed to complement the EV addon, so check consistency of data before using it). +$setglobal TRANSPORT YES +*!option yes + +* Defines the commodity used to model synthetic fuel generation. +$setglobal TRANSPORT_SYNTHFUEL_COMMODITY HYDROGEN +*!option ELECTRICITY !Text "The demand for synthetic fuel generation is modelled as electricity demand" +*!option HYDROGEN !Text "The demand for synthetic fuel generation is modelled as hydrogen demand (requires hydrogen addon to be activated)" + +* Defines the possibility for the transport demand of synthetic fuels to be aggregated internationally instead of having to satisfy it regionally. +$setglobal TRANSPORT_DEMAND_AGGREGATED + + +*------------------------------END:TRANSPORT ADDON------------------------------------------------- + +*----------------------------------------------------------- +*---------------HYDRO RESERVOIRS OPTIONS-------------------- +*Interpretation of the seasonal level of hydro reservoirs (QHYRSSEQ) +$setglobal HYRS_AGG hydro_year_agg +*!option hydro_year_agg !Text "The storage level of hydro reservoirs (QHYRSSEQ) will be scaled so the Seasons included in the optimization represent an annual behaviour" +*!option hydro_season_agg !Text "The storage level of hydro reservoirs (QHYRSSEQ) will be scaled so the Seasons included in the optimization represent a seasonal behaviour" + +* Relax seasonal limits for hydropower storages when running the model with less than 26 seasons (in HYRSDATA.inc) +$setglobal RELAX_HYDROSTORAGE yes +*!option yes +*---------------END: HYDRO RESERVOIRS OPTIONS-------------- +*---------------------------------------------------------- + +*------------------------------------------------- +*---- Timeseries scaling ------ +*Allows to modify the time series scaling approach +$setglobal timeseries_scaling usedtimesteps_timseries_scaling +*!option usedtimesteps_timseries_scaling !Text "Timeseries values are scaled linearly by the following factor: scaling factor = annual energy content / sum of the energy in the used part of the timeseries" +*!option full_timeseries_scaling !Text "Timeseries values are scaled linearly by the following factor: scaling factor = annual energy content / sum of the energy in the full timeseries" + +*----END: Timeseries scaling +*------------------------------------------------- + +*------------------------------------------------- +*---- IGKRATE data ------ +*Includes stochastic outages. If activated, IGKRATE will take the values in file IGKRATE.gdx, else it will be built based on GKRATE +$setglobal IGKRATE_STOCHASTIC +*!option yes +*---- end: IGKRATE calculation ------ +*------------------------------------------------- + +*-------- Balancing market --------- +* overall control variable BalancingMarket must be 'yes' if any of the other BalancingMarket controls are: +* Peforms the balancing market optimization +*Note: it is meant to be combined with Unit Commitment and import_results add-ons. It can also be combined with ancilliary services +$setglobal BalancingMarket +*!option yes + +*Define the purpose of the balancing run. +$setglobal BALANCINGRUNPURPOSE TSOREDISPATCH +*!option INTRADAYMARKET !Text "The run will simulate INTRADAY markets" +*!option TSOREDISPATCH !Text "The run will simulate the re-dispatch optimization from the TSO perspective (congetion management), adding penalties to compensate for forced re-dispatch of the units based on the TSO needs" + +*If activated the TSO will also compensate generators for the forced down re-dispatch due to forecast errors, ramping limits, etc. (only relevant if BALANCINGRUNPURPOSE=TSOREDISPATCH) (to be finished) +$setglobal FORCEDDOWNREDISPATCH +*!option yes + +*Define which units will be allowed to change their commitment status (on/off) (requires Unit Commitment addon to be on) +$setglobal CommitmentStatus NoChange +*!option NoChange !Text "The units will not be allowed to change their commitment, which will be taken from previous runs " +*!option FastUnitsChange !Text "The slow units will not be allowed to change their commitment, which will be taken from previous runs " +*!option None !Text "All the units will be allowed to change their commitment with respect to previous runs" + +*Restricts heat market from participating in the balancing market by restricting relevant heat generation variables from previous runs and not including the heat balance equation +$setglobal NOHEATMARKET +*!option yes +*-------- END: Balancing market --------- + +*------------------------------------------------------------------------------- +* The following global variables concern the choice of add-on modules to include. +* Include a module by assigning yes . +* Note that some of the choices are mutually dependent. + +* Presently some of the addons are commented out - +* they may or may not be working (at least with an appropriate data set), +* however, it has not been checked. Please take contact if problems. + +* Combine two technologies to one to achieve e.g. multifuel characteristics. +$setglobal COMBTECH +*!option yes + +*-----------------HYDROGEN ADDON--------------------------------------- +* overall control variable HYDROGEN must be 'yes' if any of the other HYDROGEN controls are: +* Set demands for hydrogen consumption and enable hydrogen technologies (some options require COMBTECH to be activated too). +$Setglobal HYDROGEN YES +*!option yes + +* Allows investments in hydrogen transport +$Setglobal H2TransInvest YES +*!option yes + +SCALAR XH2INVESTSTART 'Year from which endogenous decisions in H2 transmission capacities are allowed' ; +XH2INVESTSTART= 2030; + +*Adds Economies of Scale in H2 transmission. It requires to use the MIP solver, so remember to activate it (Dataset not updated for new countries in pan-European system) +$setglobal ES_H2TRANS + +* Allows import of Hydrogen from 3rd countries outside Balmorel +$Setglobal IMPORT_H2 YES +*!option yes + +* Allows flexible demand of H2 in regions (set ratio of flexible demand in HYDROGEN_FLEXDH2 with the scalar THETA_FLEX_DH2) +$setglobal flexible_h2_space +*!option yes + +*-----------------END: HYDROGEN ADDON--------------------------------------- + +* Use stepwise linear fuel prices +$Setglobal STEPWISEPRICE + +* Set a common maximum for fuels. (it should be checked before using it) +$Setglobal LIMITSFORFUELS +*!option yes + +* Allow demand elasticity (it should be checked before using it) +$setglobal DFLEXQUANT +*!option yes + +* Allow demand response (requires NETDEMAND) (it should be checked before using it) +$setglobal DEMANDRESPONSE +*!option yes + +* Permit exchange of electricity with third regions using a price interface (in contrast to X3FX) (it should be checked before using it) +$setglobal X3V +*!option yes + +* Grid tariffs on electricity use (requires NETDEMAND) +$setglobal GRIDTARIFFS +*!option yes + +* Taxes addon (requires NETDEMAND) +$setglobal TAXES +*!option yes + +* Transmission ramping addon (it asssumes seasonal linking) +$setglobal TRANS_RAMP +*!option yes + +*-----------------------ANCILLARY SERVICES---------------------------------------------------------------------------------------------------- +* Ancillary services addon (it requires UnitComm to be active). The other options require this one to be activated. +$setglobal ANC_SERV +*!option yes + +* FCR reserve requirement activated +$setglobal FCR +*!option yes + +* AFRR reserve requirement activated +$setglobal AFRR yes +*!option yes + +* MFRR reserve requirement activated (NOT FINISHED) +$setglobal MFRR +*!option yes + +* MRR reserve requirement activated (NOT FINISHED) +$setglobal MRR +*!option yes + +* Allow VRE to provide for reserves +$setglobal ANC_SERV_VRE YES +*!option yes + +*Include down regulation reserves. For simplification, down regulation of spinning reserves can be disactivated, since they are generally less expensive than up regulation. +$setglobal DOWN_REGULATION_INCLUDED +*!option yes +*-----------------------END: ANCILLIARY SERVICES---------------------------------------------------------------------------------------------------- + +* Make investments in technologies only at specified capacities. (it should be checked before using it) +$setglobal AGKNDISC +*!option yes +*!onlyif %BB2%==yes +*!onlyif %solvetype%==MIP + + +* Use the flow-based capacity allocation methodology +$Setglobal FlowBased +*!option yes + +* Set short storages to be inter- or intra-seasonal +* according to GDSTOINTERS in GDATA (default) or all or none +$setglobal stointers +*! option default Use as specified by GDSTOINTERS in GDATA +*! option all Let all storage technologies be inter-seasonal +*! option none Let no storage technologies be inter-seasonal (i.e., let all be intra-seasonal) + + +* Bypass of 'turbine' (electricity production) on CHP units. +$setglobal bypass yes +*!option yes + + +*-----------------------TECHNOLOGY DECOMMISSIONING-------------------------------------------------------- +* overall control variable DECOM must be 'yes' if any of the other decommissioning controls are. +$setglobal DECOM yes +*!option yes + +*-------ENDOGENOUS CAPACITIES---------- +* Note: If no option for endogenous capacities is activated, endogenous investments will not be decommissioned + +* Adds Endogenous capacities will maximum stay in the system as much as their lifetimes: +$setglobal LIFETIME_DECOM_ENDO_G YES +*!option yes + +* Adds Endogenous capacities can be decommissioned based on their profitability: +$setglobal ECONOMIC_DECOM_ENDO_G yes +*!option yes + +* Adds Endogenous decommissioned endogenous capacities can be brought back to the energy system. To be combined with the other decommissioning options: +$setglobal REVERT_DECOM_ENDO_G yes +*!option yes + +*--------END OF ENDOGENOUS CAPACITIES----- + +*--------EXOGENOUS CAPACITIES------------ +* Note: If either NO_DECOM_EXO_G, or LIFETIME_DECOM_EXO_G, or ECONOMIC_DECOM_EXO_Gor is activated, then the parameter GKFX will be recalculated so capacities only increase in the system. + +SCALAR GDECOMEXOSTART 'Year from which endogenous decisions in decommissioning of exogenous generation capacities are allowed'; +GDECOMEXOSTART= 2025; + +SCALAR GDECOMEXOPOTSTART 'Starting year to calculate the yearly decommissioning capacity potential, i.e. exogenous decommissioning occuring before this year will not be considered' ; +GDECOMEXOPOTSTART= 2016; + +* Adds No decommissioning of exogenous capacities will take place at all. The capacities in the recalculated GKFX will take place in the optimization +* Note: This option is not compatible with the other exogenous capacities decommissioning options. +$setglobal NO_DECOM_EXO_G +*!option yes + +* Adds Exogenous capacities will maximum stay in the system as much as their lifetimes (or as much as political decissions suggest it, for instance), which should be represented in the yearly development of capacities of the input parameter GKFX: +$setglobal LIFETIME_DECOM_EXO_G YES +*!option yes + +* Adds Exogenous capacities can be decommissioned based on their profitability: +$setglobal ECONOMIC_DECOM_EXO_G yes +*!option yes + +* Adds Endogenous decommissioned exogenous capacities can be brought back to the energy system. To be combined with the other decommissioning options: +$setglobal REVERT_DECOM_EXO_G yes +*!option yes +*--------END OF ENDOGENOUS CAPACITIES----- +*-----------------------End of TECHNOLOGY DECOMMISSIONING-------------------------------------------------------- + + +*-------------------- UNIT COMMITMENT ----------------------------------------------------------- +* overall control variable UnitComm must be 'yes' if any of the other unit commitment controls are: + +$setglobal UnitComm +*!option yes + +* Adds minimal production-when-on: +$setglobal MINFUELCONS YES +*!option yes +* Adds respecting minimum off time of the capacities: +$setglobal MINSTARTUPTIME yes +*!option yes +* Adds respecting minimum on time of the capacities: +$setglobal MINSHUTDOWNTIME yes +*!option yes +* Adds ramping technical limitations and stepwise costs to the Power plants when-on: +$setglobal STEPWISE_RAMPING_UC ramp_const +*!option ramp_const_cost !Text "With ramping constraints and ramping costs" +*!option ramp_const !Text "With ramping constraints but without ramping costs" +*!option no_ramping !Text "Neither ramping constraints nor ramping costs" +* Adds optimisation of planned maintenance of the generation and storage technologies +$setglobal PLANNED_MAINTENANCE +*!option yes + +* Seasonal link is on for all UC options activated. This means that past seasons will influence future seasons. +* This option increases computanional time but assures continuity in the fuel consumption profile. +$setglobal SEASONALLINKUC +*!option yes + +* The production interval is segmented into a number of disjunct pieces, each with its own costs: NOT IMPLEMENTED +$setglobal UnitCseg +*!option yes +*-------------------- End of UNIT COMMITMENT ----------------------------------------------------------- + +*--------------------STEPWISE RAMPING (LINEAR VERSION)----------------------------------------------------------- +* overall control variable Stepwise_ramping must be 'yes' if any of the other stepwise ramping controls are: +* This add-on is an alternative to Unit Commitment to model ramping constraints without using MIP. This add-on uses currently the relevant data and sets from the Unit Commitment add-on +* This add-on is not compatible with the Unit Commitment stepwiseramping option +* The use of this add-on togeter with the rest of options of Unit Commitment add-on has not been tested, so, unless tested, this add-on should not be activated if UnitComm is on. + +$setglobal STEPWISE_RAMPING +*!option yes + +* Seasonal ramping link is on. This means that the fuel consumption of the last element ot the set T of the set S will be linked to the first element of the set T of the set S+1. +* This option increases computanional time but assures continuity in the fuel consumption profile. +$setglobal SEASONALLINKRAMPING +*!option yes + +*----------------END OF STEPWISE RAMPING (LINEAR VERSION)---------------------------------------------------------- + +* Options timeaggr control automatic aggregation of time (it should be checked before using it). +$setglobal timeaggr +*!option yes + + +*------------------------------------------------------------------------------- +* OptiFlow model. +*------------------------------------------------------------------------------- +$setglobal OPTIFLOW +*!option yes +* Addon APKNdisc: discrete size investments in PROC. +$setglobal APKNdisc +*!option yes +*Investments in Optiflow technologies +$setglobal inv yes +*! option yes +*Model solved as MIP +$setglobal solveOptiFlowmip +*! option yes + +* Debug possibilities: +$Setglobal addEPStoL +*! option yes +* Not needed if there are no capacity bounds on internal proc +* It will be needed if there are capacity bounds on some internal proc, therefore add them only in that case (requires some coding, TODO). +* With such minimal use the infeasibilities will be moved to SOSIBU proc whenever possible. +$Setglobal useQNODEslack +*! option +$Setglobal useSSBslack +*! option yes +$Setglobal useShareSlack +*! option yes +* Option uniquesol permit to use quadratic terms in QOBJ in order to attain unique solution. +* Should be used for debug on small systems only. +$Setglobal uniquesol +*! option yes +* Testing ONLY: +$setglobal singlearea +*! option yes +* Testing ONLY: +$setglobal addUP +*! option yes +*------------------------------------------------------------------------------- +*Optiflow end +*------------------------------------------------------------------------------- + +* In model Balbase4 (BB4) indicate the strategy for relative weighting of years +$setglobal bb4weighty shareYYY +*!option same !Text "Use same weights for all Y" +*!option shareYYY !Text "Years in YYY but not in Y are shared between two nearest (first and last years double the shared weight contribution)" + +*=============================================================================== +*=============================================================================== +* Relative paths +*=============================================================================== +*=============================================================================== +* No choices, they must be set as shown. Note the format. +* They are to be used as illustrated below and elsewhere in the code. +* +$ifi not %system.filesys%==UNIX $goto endofUNIXrelpaths +$setglobal relpathModel "" +*!fixedvalue +$setglobal relpathoutput "../output/" +*!fixedvalue +$setglobal relpathInputdata2GDX "../output/inputout/" +*!fixedvalue +$setglobal relpathtemp "../output/temp/" +*!fixedvalue +$label endofUNIXrelpaths + +$ifi not %system.filesys%==MSNT $goto endofMSNTrelpaths +$setglobal relpathModel "" +*!fixedvalue +$setglobal relpathoutput "..\output\" +*!fixedvalue +$setglobal relpathInputdata2GDX "..\output\inputout\" +*!fixedvalue +$setglobal relpathtemp "..\output\temp\" +*!fixedvalue +$label endofMSNTrelpaths + +*=============================================================================== +*=============================================================================== +* Some interaction between models: +*=============================================================================== +*=============================================================================== + +* The following options are related to interplay between models, +* e.g. one model output serving as input to another model. + +* Overall control to export results so they can be used in future runs : +$Setglobal export_results yes +*!option yes + +*-------------------------------------------- +*--------------IMPORT OF RESULTS---------------- +*-------------------------------------------- +* Overall control to import results so they can be used in future runs: +*NOTE: In order for results to work fine, the set S used in the runs should be contained in the import results. +*NOTE: In order for results to work fine, the first element in set T of the import results should match the one used in the current run. +$Setglobal import_results +*!option yes + +*------------ IMPORT OPTIONS ---------------- + +* -----------------ENERGY DISPATCH-------------------------- + +*Adds energy dispatch (demand, generation, transmission) from previous runs +$setglobal ADDENERGYDISPATCH +*!option yes +* -----------------END OF ENERGY DISPATCH-------------------------- + +* ------------------HYDRO--------------------------------- +* For this model run it is possible to use values found from previuos solutions to specify use of hydro power +$setglobal HYRSBB123 none +*!option quant !Text "Use seasonal quantities of hydro generation (from HYRSG.gdx??) in this run ('primal decomposition')(not implemented in BB4)" +*!option price !Text "Use seasonal prices for hydro generation (from WATERVAL.gdx) in this run ('dual decomposition')" +*!option quantprice !Text "Use seasonal quantities and prices for hydro generation (from HYRSG.gdx?? and WATERVAL.gdx) in in this run (not implemented in BB4)" +*!option cont !Text "Use seasonal content for hydro generation (from VHYRS_SL.gdx) in in this run" +*!option none !Text "Do not not use this hydro addon" +* ------------------ END OF HYDRO--------------------------------- + +*--------------------INTERSEASONAL STORAGE----------------------------------------------------------- +*Import inter-seasonal electricity storage data from previous runs +$Setglobal ADDESTOVOLTS none +*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition')" +*!option price !Text "Import electricity storage contents at beginning of time segment (ESTOVOLTS.gdx) for the first element in set T and S, and puts a price to use with ESTOVOLTSVAL.gdx from previous runs. " +*!option none !Text "Do not not use this addon" + +*Import inter-seasonal heat storage data from previous runs +$Setglobal ADDHSTOVOLTS none +*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition')" +*!option price !Text "Import Heat storage contents at beginning of time segment (HSTOVOLTS.gdx) for the first element in set T and S, and puts a price to use with HSTOVOLTSVAL.gdx from previous runs." +*!option none !Text "Do not not use this addon" + +*Import intra-seasonal electricity storage data from previous runs +$Setglobal ADDESTOVOLT none +*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition')" +*!option price !Text "(to be finished) Import electricity storage contents at beginning of time segment (ESTOVOLT.gdx) for the first element in set T and S, and puts a price to generation (ESTOVOLTVAL) from previous runs." +*!option none !Text "Do not not use this addon" + +*Import intra-seasonal heat storage data from previous runs +$Setglobal ADDHSTOVOLT none +*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition')" +*!option price !Text "(to be finished) Import Heat storage contents at beginning of time segment (HSTOVOLT.gdx) for the first element in set T and S, and puts a price to generation (HSTOVOLTVAL) from previous runs" +*!option none !Text "Do not not use this addon" + +*Import inter-seasonal hydrogen storage data from previous runs (requires HYDROGEN addon) +$Setglobal ADDH2STOVOLTS none +*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition')" +*!option price !Text "Import hydrogen storage contents at beginning of time segment (H2STOVOLTS.gdx) for the first element in set T and S, and puts a price to use with H2STOVOLTSVAL.gdx from previous runs." +*!option none !Text "Do not not use this addon" + +*Import inter-seasonal biomethane storage data from previous runs (requires HYDROGEN addon) +$Setglobal ADDBIOMETHSTOVOLTS none +*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition')" +*!option price !Text "Import biomethane storage contents at beginning of time segment (BIOMETHSTOVOLTS.gdx) for the first element in set T and S, and puts a price to use with BIOMETHSTOVOLTSVAL.gdx from previous runs." +*!option none !Text "Do not not use this addon" + +*Import EV state of charge from previous runs (requires EV addon) +$Setglobal ADDEVSTOS none +*!option cont !Text "Use seasonal content from previous runs in this run ('primal decomposition') (only works if EV_INTERSEASONAL is activated)" +*!option netcharging !Text "Use seasonal net charging of EV in this run ('primal decomposition') (only works if EV_INTERSEASONAL is NOT activated)" +*!option none !Text "Do not not use this addon" + + + +*--------------------END OF INTERSEASONAL STORAGE----------------------------------------------------------- + +*--------------------INVESTMENTS AND DECOMMISSIONING----------------------------------------------------------- +*NOTE: The set Y should be the same in the optimization should be the same as the one in the import results. + +*Import generation and storage technology capacity development from previous runs +$setglobal ADDTECHDEV +*!option yes + +*Add expensive to operate backup generation technologies (electricity, heat, ...). It often occurs that the investment optimization underestimates the need for power. +$setglobal ADDBACKUPGEN +*!option yes + +*Import transmission development from previous runs +$setglobal ADDTRANSDEV +*!option yes + +*Import heat transmission development from previous runs +$setglobal ADDHEATTRANSDEV +*!option yes + +*Import H2 transmission development from previous runs +$setglobal ADDH2TRANSDEV +*!option yes +*--------------------END OF INVESTMENTS AND DECOMMISSIONING----------------------------------------------------------- + +*--------------------FUEL PRICES----------------------------------------------------------- +*NOTE: The set Y should be the same in the optimization should be the same as the one in the import results. +*Import fuel prices development from previous runs +$setglobal ADDFUELPRICE +*!option yes +*--------------------END OF FUEL PRICES----------------------------------------------------------- + +*--------------------FUEL USE----------------------------------------------------------- +*NOTE: The set Y should be the same in the optimization should be the same as the one in the import results. +*Import seasonal fuel use upper limit from previous runs +$setglobal ADDMAXFUELUSES +*!option yes +*--------------------END OF FUEL USE----------------------------------------------------------- + +*--------------------PLANNED MAINTENANCE----------------------------------------------------------- +*NOTE: The set Y should be the same in the optimization should be the same as the one in the import results. +*Import optimized planned maintenance from previous runs (investments and/or decomissioning must not be allowed if this option is activated) +$setglobal ADDPLANNEDMAINTENANCE +*!option yes +*--------------------END OF PLANNED MAINTENANCE----------------------------------------------------------- + +*--------------------TRANSPORT DEMAND----------------------------------------------------------- +*NOTE: The set Y should be the same in the optimization should be the same as the one in the import results. +*Import demand per term (T) from the transport sector +$setglobal ADDTERMTRANSPORTDEMAND +*!option yes + +*Import seasonal demand from the transport sector +$setglobal ADDSEASONALTRANSPORTDEMAND +*!option yes + +*Import peak demand from the transport sector +$setglobal ADDPEAKTRANSPORTDEMAND +*!option yes +*--------------------END OF TRANSPORT DEMAND----------------------------------------------------------- + +*--------------------INDIVIDUAL USERS----------------------------------------------------------- +*Import Share of heat demand in an individual user group. Requires GROUP_DEMAND_SHIFT to be activated +$setglobal ADDGROUP_DEMAND_SHIFT +*!option yes +*--------------------END OF INDIVIDUAL USERS----------------------------------------------------------- + + +*------------- END OF IMPORT OPTIONS ---------------- +*-------------------------------------------- +*--------------END OF IMPORT OF RESULTS---------------- +*-------------------------------------------- + + + + +*=============================================================================== +*=============================================================================== +* Output: +*=============================================================================== +*=============================================================================== + +* ------------------------------------------------------------------------------ +* Alternative presentations of input data +* ------------------------------------------------------------------------------ + +* It may be convenient under some circumstances to have the applied input data in other forms, +* this may be achieved by the following mechanism. + +* Make a summary of the input data in text format to file inputout.out: +$setglobal inputdatatxt "" +*!option "" !text do not make it +*!option yes !text make it when solving +*!option yesnosolve !text make it but do not solve + + +* Save the input data in gdx format for further processing by Gdx utilities, +* e.g. viewing (Gdxviewer), comparing (Gdxdiff), or transferring to other data formats like Excel or Access. +* Note that this will be done as a compile time operation, +* such that only the 'direct' data definitions (and no assignments) are reflected. +$setglobal INPUTDATA2GDX YES +*!option yes + + +* Transfer the input data in gdx format to a Microsoft Access database ../output/inputout/inputdataout.mdb: +$setglobal INPUTDATAGDX2MDB +*!option yes !onlyif %INPUTDATA2GDX%==yes +* Note: if you have established a link from another Access database to inputdataout.mdb +* close all tables in it before executing Balmorel in order to ensure updating of the links. + +* Merge input data with the output into the gdx file %CASEID%.gdx +$setglobal MERGEINPUTDATA +*!option yes + + +* Transfer the input data in gdx format to Microsoft Excel format: +* NOTWORKING: presently. +$setglobal INPUTDATAGDX2XLS +*!option yes + + +*------------------------------------------------------------------------------- +* Output from model solutions: +*------------------------------------------------------------------------------- + +* A quick way to get (some of) the relevant information is to make a gdx file with all information. +* This option will provide unloading of all that is available at the end of Balmorel.gms. +* +$setglobal all_endofmodelgdx yes +*!option yes !Save the file all_endofmodel.gdx to the model folder + + + + +* Make some output in text format to show some results of an execution. +* The detailed code for the writing of the files is given in /output/printout/printinc. +* Setting "yes" for PRINTFILES will make some standard printout files to folder /output/printout/. +$setglobal PRINTFILES +*!option yes + +$setglobal OUTPUT_SUMMARY yes +*!option yes + +* PRINTSEPARATOR is a single character used for separating columns in printfiles, +* convenient e.g. to use ';' for copy-paste to Excel. +$setglobal PRINTSEPARATOR ';' +*!option ' ' +*!option ';' +*!option ',' + +*------------------------------------------------------------------------------- +* Results collection for the present case +*------------------------------------------------------------------------------- + +* The following will establish a mechanism that will save annual optimization results +* from bb1 and bb2 (seasonal for bb3) as gdx files; at the end they may be merged to one gdx file. +* This merged file may then be transferred to an Access database or to Excel. +* It is to be seen in conjunction with saving annual results in file bb123.sim, +* which is accomplished using savepoint with value 1, cf '$Setglobal SAVEPOINTVALUE'. +* +* The following closely linked controls are used: +* SAVEPOINTVALUE, cf. above +* MERGESAVEPOINTRESULTS yes +* MERGEDSAVEPOINTRESULTS2MDB +* MERGEDSAVEPOINTRESULTS2SQLITE +* MERGEDSAVEPOINTRESULTS2XLS: presently not working, we are waiting for GAMS version 22.7, which has better support for this. +* MERGECASE/MERGEWITH +* DIFFCASE/DIFFWITH +* +* Savepoint: the GAMS command "Modelname.Savepoint=number;" +* The savepoint attribute tells GAMS to save a GDX file that contains the information on the current solution point. +* One can save the information from just the last solve or from every solve. +* The points that are saved can be used to provide an advanced basis, an integer program starting point or a NLP starting point. +* Numeric input is expected with the allowable numeric values being: +* 0 no point gdx file is to be saved +* 1 a point gdx file is to be saved from the last solve in the GAMS model +* 2 a point gdx file is to be saved from every solve in the GAMS model +* The value specified by $Setglobal SAVEPOINTVALUE will be used for savepoint. +* To be used with subsequent handling of output. +* +$setglobal SAVEPOINTVALUE 0 +*!option 0 +*!option 1 !mustif %MERGESAVEPOINTRESULTS%==yes !mustif %MERGEDSAVEPOINTRESULTS2GDX%==yes !mustif %MERGEDSAVEPOINTRESULTS2XLS%==yes !mustif not %MERGECASE%==none !mustif not %DIFFCASE%==none +*!option 2 !text Note: nothing implemented nor tested, so do not use it. + +* Use the $setglobal MERGESAVEPOINTRESULTS to choose whether to merge annual/seasonal results or not: +$setglobal MERGESAVEPOINTRESULTS +*!option yes !onlyif %SAVEPOINTVALUE%==1 +* Note: make sure that it is possible to make the new merged gdx file, +* e.g. make sure that the file *.gdx is not open in another application (like GAMD IDE). +* +* Then prepare for the necessary details: +$ifi %system.filesys%==MSNT +$setglobal batfileMERGESAVEPOINTRESULTS %relpathModel%MERGESAVEPOINTRESULTS.bat +*!option !fixedvalue +$ifi %system.filesys%==MSNT +file fileMERGESAVEPOINTRESULTSbat / %batfileMERGESAVEPOINTRESULTS% /; +$ifi %system.filesys%==MSNT +putclose fileMERGESAVEPOINTRESULTSbat; + +$ifi %system.filesys%==UNIX +$setglobal bashfileMERGESAVEPOINTRESULTS %relpathModel%MERGESAVEPOINTRESULTS.sh +*!option !fixedvalue +$ifi %system.filesys%==UNIX +file fileMERGESAVEPOINTRESULTSbash / %bashfileMERGESAVEPOINTRESULTS% /; +$ifi %system.filesys%==UNIX +putclose fileMERGESAVEPOINTRESULTSbash; + +$setglobal MERGEDSAVEPOINTRESULTS2MDB +*!option yes !onlyif %SAVEPOINTVALUE%==1 !onlyif %MERGESAVEPOINTRESULTS%==yes + +$setglobal MERGEDSAVEPOINTRESULTS2SQLITE +*!option yes !onlyif %SAVEPOINTVALUE%==1 !onlyif %MERGESAVEPOINTRESULTS%==yes + + +*------------------------------------------------------------------------------- +* Comparison between Cases: +*------------------------------------------------------------------------------- + +* The following will merge and/or compare two results. +* With option MERGECASE is set to NONE this will be not be done, otherwise it will. +* If done, the result will be in the output folder of the present case in the form of a GDX file. +* With option MERGEDSAVEPOINTRESULTS2MDB" this may then be transferred to a MDB file. +* All output go to the output folder of the present case. +* Similar considerations for DIFFCASE. + +$setglobal MERGECASE none +*!option none !text Comparison will not be made +*!option +* MERGEWITH defines the case with which to merge results. +$setglobal MERGEWITH CaseNonversioned +*!option !mustif not %MERGECASE%==none + +* The diff uses gdxdiff. The diffile only shows items that meaningfull can be compared, +* non-compared items are reported in the Balmorel.log file. +$setglobal DIFFCASE none +*!option none !text Comparison will not be made +*!option + +$setglobal DIFFWITH CaseNonversioned +*!option none !text Comparison will not be made +*!option !mustif not %DIFFCASE%==none + +* Then prepare for the necessary details: TODO +file fileMERGEWITHBASEbat / %batfileMERGEWITHBASE% /; +putclose fileMERGEWITHBASEbat; + + + + +*=============================================================================== +*=============================================================================== +* The model type's implication for solver type +*=============================================================================== +*=============================================================================== + +* The model type has implications for the solver type. +* Classical model types are LP (Linear Programming), QP (Quadratic Programming), MIP (Mixed Integer Programming). +$setglobal SOLVETYPE LP +*!option LP "Linear Programming" +*!option RMIP "Relaxed Mixed Integer Programming" +*!option MIP "Mixed Integer Programming" + +*!option yes +*!mustif %AGKNDISC%==yes +*!mustif %UnitComm%==yes + + + +*=============================================================================== +*=============================================================================== +* Input data: +*=============================================================================== +*=============================================================================== + +*------------------------------------------------------------------------------- +*------------------------------------------------------------------------------- +* Option semislash is used to control input data format for text files. +* With "$Setglobal semislash ;" the input files must include +* the declaration statement initially and must terminate with a "/;". +* With "$Setglobal semislash /" the input files do not contain this, but only data. +* Note that parameters and tables are identical declarations, however, +* when using "$Setglobal semislash /" make sure +* that the use of either "parameter" or "table" in Balmorel.gms for any data file +* corresponds to the data format used in the file. +* NOTE: Rev.3.03: Not fully implemented yet, you MUST use "$Setglobal semislash ;". +$setglobal semislash ";" +*!fixedvalue ; +**!option ; + + + + +* Domain overloading: The options specify the domain (indexes) that the identifiyer is declared on. +$setglobal GMINF_DOL YYY_CCCRRRAAA_FFF +*!option CCCRRRAAA_FFF +*!option YYY_CCCRRRAAA_FFF +* +$setglobal GMAXF_DOL YYY_CCCRRRAAA_FFF +*!option CCCRRRAAA_FFF +*!option YYY_CCCRRRAAA_FFF +* +$setglobal GEQF_DOL YYY_CCCRRRAAA_FFF +*!option CCCRRRAAA_FFF +*!option YYY_CCCRRRAAA_FFF +* +$setglobal XKRATE_DOL IRRRE_IRRRI_SSS +*!option IRRRE_IRRRI +*!option IRRRE_IRRRI_SSS +*!option IRRRE_IRRRI_SSS_TTT +* +$setglobal GKRATE_DOL AAA_GGG_SSS +*!option AAA_GGG_SSS +*!option AAA_GGG_SSS_TTT + +$setglobal WNDFLH_DOL AAA +*!option AAA_GGG +*!option AAA + +$setglobal SOLEFLH_DOL AAA +*!option AAA_GGG +*!option AAA + +$setglobal SOLHFLH_DOL AAA +*!option AAA_GGG +*!option AAA + +$setglobal GWAVE_DOL AAA +*!option AAA_GGG +*!option AAA + + + +*------------------------------------------------------------------------------- +* Active options will be reported to log file via file hellolog.inc +*------------------------------------------------------------------------------- + + +*------------------------------------------------------------------------------- +* End of Balopt.opt +*------------------------------------------------------------------------------- diff --git a/base/output/OUTPUT_SUMMARY.inc b/base/output/OUTPUT_SUMMARY.inc index 2e7133e3..bfdaba32 100644 --- a/base/output/OUTPUT_SUMMARY.inc +++ b/base/output/OUTPUT_SUMMARY.inc @@ -340,7 +340,7 @@ F_CONS_YCRA(Y,C,IR,IA,G,FFF,TECH_TYPE,'TWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and * ---------------- ENERGY CURTAILMENT ----------------------- *HOURLY CURTAILMENT -CURT_YCRAGFST(Y,C,IR,IA,IGHYRR,FFF,S,T,'ELECTRICITY','HYDRO-RUN-OF-RIVER','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGHYRR) AND IGF(IGHYRR,FFF) AND GTECH_TYPE(IGHYRR,'HYDRO-RUN-OF-RIVER'))= +CURT_YCRAGFST(Y,C,IR,IA,IGHYRR,FFF,S,T,'ELECTRICITY','HYDRO-RUN-OF-RIVER','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGHYRR) AND IGF(IGHYRR,FFF) AND GTECH_TYPE(IGHYRR,'HYDRO-RUN-OF-RIVER') AND IWTRRRSUM(IA) > 0)= (WTRRRFLH(IA) * ( GKFX(Y,IA,IGHYRR) $ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGHYRR) @@ -348,7 +348,7 @@ $ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGHYRR) ) * WTRRRVAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGHYRR,S,T)) + IGKRATE(IA,IGHYRR,S,T))) / IWTRRRSUM(IA) -VGE_T.L(Y,IA,IGHYRR,S,T); -CURT_YCRAGFST(Y,C,IR,IA,IGWND,FFF,S,T,'ELECTRICITY','WIND-ON','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGWND) AND IGF(IGWND,FFF) AND GTECH_TYPE(IGWND,'WIND-ON'))= +CURT_YCRAGFST(Y,C,IR,IA,IGWND,FFF,S,T,'ELECTRICITY','WIND-ON','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGWND) AND IGF(IGWND,FFF) AND GTECH_TYPE(IGWND,'WIND-ON') AND IWND_SUMST(IA) > 0)= $ifi %WNDFLH_DOL%==AAA (WNDFLH(IA) * ( $ifi %WNDFLH_DOL%==AAA_GGG (WNDFLH(IA,IGWND) * ( GKFX(Y,IA,IGWND) @@ -357,7 +357,7 @@ $ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGWND) ) * WND_VAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGWND,S,T)) + IGKRATE(IA,IGWND,S,T))) / IWND_SUMST(IA) -VGE_T.L(Y,IA,IGWND,S,T); -CURT_YCRAGFST(Y,C,IR,IA,IGWND,FFF,S,T,'ELECTRICITY','WIND-OFF','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGWND) AND IGF(IGWND,FFF) AND GTECH_TYPE(IGWND,'WIND-OFF'))= +CURT_YCRAGFST(Y,C,IR,IA,IGWND,FFF,S,T,'ELECTRICITY','WIND-OFF','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGWND) AND IGF(IGWND,FFF) AND GTECH_TYPE(IGWND,'WIND-OFF') AND IWND_SUMST(IA) > 0)= $ifi %WNDFLH_DOL%==AAA (WNDFLH(IA) * ( $ifi %WNDFLH_DOL%==AAA_GGG (WNDFLH(IA,IGWND) * ( GKFX(Y,IA,IGWND) @@ -366,7 +366,7 @@ $ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGWND) ) * WND_VAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGWND,S,T)) + IGKRATE(IA,IGWND,S,T))) / IWND_SUMST(IA) -VGE_T.L(Y,IA,IGWND,S,T); -CURT_YCRAGFST(Y,C,IR,IA,IGSOLE,FFF,S,T,'ELECTRICITY','SOLAR-PV','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGSOLE) AND IGF(IGSOLE,FFF) AND GTECH_TYPE(IGSOLE,'SOLAR-PV'))= +CURT_YCRAGFST(Y,C,IR,IA,IGSOLE,FFF,S,T,'ELECTRICITY','SOLAR-PV','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGSOLE) AND IGF(IGSOLE,FFF) AND GTECH_TYPE(IGSOLE,'SOLAR-PV') AND ISOLESUMST(IA) > 0)= $ifi %SOLEFLH_DOL%==AAA (SOLEFLH(IA) * ( $ifi %SOLEFLH_DOL%==AAA_GGG (SOLEFLH(IA,IGSOLE) * ( GKFX(Y,IA,IGSOLE) @@ -375,7 +375,7 @@ $ifi %DECOM%==yes -VDECOM_EXO_ACCUM.L(Y,IA,IGSOLE) ) * SOLE_VAR_T(IA,S,T) * (1$(NOT IGKRATE(IA,IGSOLE,S,T)) + IGKRATE(IA,IGSOLE,S,T))) / ISOLESUMST(IA) -VGE_T.L(Y,IA,IGSOLE,S,T); -CURT_YCRAGFST(Y,C,IR,IA,IGSOLH,FFF,S,T,'HEAT','SOLAR-HEATING','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGSOLH) AND IGF(IGSOLH,FFF) AND GTECH_TYPE(IGSOLH,'SOLAR-HEATING'))= +CURT_YCRAGFST(Y,C,IR,IA,IGSOLH,FFF,S,T,'HEAT','SOLAR-HEATING','MWh')$(CCCRRR(C,IR) and RRRAAA(IR,IA) and IAGK_HASORPOT(Y,IA,IGSOLH) AND IGF(IGSOLH,FFF) AND GTECH_TYPE(IGSOLH,'SOLAR-HEATING') AND ISOLHSUMST(IA) > 0)= $ifi %SOLHFLH_DOL%==AAA (SOLHFLH(IA) * ( $ifi %SOLHFLH_DOL%==AAA_GGG (SOLHFLH(IA,IGSOLH) * ( GKFX(Y,IA,IGSOLH) @@ -1246,9 +1246,10 @@ ECO_XH2_YCR(Y,C,IR,IRI,'COSTS','H2_TRANSMISSION_CAPITAL_COSTS','Mmoney')$(CCCRRR IOF05*ANNUITYCXH2(C)*IYHASANNUITYXH2(IYALIAS2,Y)*VXH2KN.L(IYALIAS2,IR,IRI)*XH2INVCOST(IYALIAS2,IR,IRI)) + SUM((IYALIAS2)$((IXH2KN_NGTOH2(IYALIAS2,IRI,IR) OR IXH2KN_NGTOH2(IYALIAS2,IR,IRI)) AND ORD(IYALIAS2) LE ORD(Y)) , IOF05*ANNUITYCXH2(C)*IYHASANNUITYXH2(IYALIAS2,Y)*VXH2KN_NGTOH2.L(IYALIAS2,IR,IRI)*XH2INVCOST_NGTOH2(IYALIAS2,IR,IRI)) - +SUM((IYALIAS2)$((IXH2KN(IYALIAS2,IRI,IR) OR IXH2KN(IYALIAS2,IR,IRI)) AND (ORD(IYALIAS2) LE ORD(Y)) AND (IXH2KN_ES(IR,IRI) OR IXH2KN_ES(IRI,IR))), - IOF05*ANNUITYCXH2(C)*IYHASANNUITYXH2(IYALIAS2,Y)*SUM(XES,VXH2LAMBDA.L(IYALIAS2,IR,IRI,XES)*XH2INVCOST_ES(IYALIAS2,IR,IRI,XES))) - ) +$ifi %ES_H2TRANS%==yes +SUM((IYALIAS2)$((IXH2KN(IYALIAS2,IRI,IR) OR IXH2KN(IYALIAS2,IR,IRI)) AND (ORD(IYALIAS2) LE ORD(Y)) AND (IXH2KN_ES(IR,IRI) OR IXH2KN_ES(IRI,IR))), +$ifi %ES_H2TRANS%==yes IOF05*ANNUITYCXH2(C)*IYHASANNUITYXH2(IYALIAS2,Y)*SUM(XES,VXH2LAMBDA.L(IYALIAS2,IR,IRI,XES)*XH2INVCOST_ES(IYALIAS2,IR,IRI,XES))) +$ifi %ES_H2TRANS%==yes ) + ) ; ** TRANSMISSION FLOW From 959eeca4bf338caf668ff0b8585e1116520feb2f Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Mon, 1 Jul 2024 16:20:39 +0200 Subject: [PATCH 14/21] Deleted changes scenario for testing changes --- changes/model/Balmorel.gms | 210 ------------------------------------- changes/model/cplex.op4 | 14 --- 2 files changed, 224 deletions(-) delete mode 100644 changes/model/Balmorel.gms delete mode 100644 changes/model/cplex.op4 diff --git a/changes/model/Balmorel.gms b/changes/model/Balmorel.gms deleted file mode 100644 index 753013d6..00000000 --- a/changes/model/Balmorel.gms +++ /dev/null @@ -1,210 +0,0 @@ -* File Balmorel.gms -$TITLE Balmorel version 4.03 (March 2022; latest 20220318) - -SCALAR IBALVERSN 'This version of Balmorel' /403.20220318/; -* Efforts have been made to make a good model. -* However, most probably the model is incomplete and subject to errors. -* It is distributed with the idea that it will be usefull anyway, -* and with the purpose of getting the essential feedback, -* which in turn will permit the development of improved versions -* to the benefit of other user. -* Hopefully it will be applied in that spirit. - -* All GAMS code of the Balmorel model is distributed under ICS license, -* see the license file in the base/model folder. - - -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- - - -* This is a preliminary version of Balmorel 3.03. -* It is intended for review and commenting. -* See a short list of changes since previous version 3.02 in base/documentation/Balmorel303.txt. -* It is scheduled that a final version 3.03 will be available early 2017. - -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- - - -* GAMS options are $included from file balgams.opt. -* In order to make them apply globally, the option $ONGLOBAL will first be set here: -$ONGLOBAL - -$ifi %system.filesys%==UNIX -execute 'chmod -R ug+rw "../.."'; - -* The balgams file holds control settings for GAMS. -* Use local file if it exists, otherwise use the one in folder ../../base/model/. -$ifi exist 'balgams.opt' $include 'balgams.opt'; -$ifi not exist 'balgams.opt' $include '../../base/model/balgams.opt'; - -* The balopt file holds option (control) settings for the Balmorel model. -* Use local file if it exists, otherwise use the one in folder ../../base/model/. - - -$ifi exist 'balopt.opt' $include 'balopt.opt'; -$ifi not exist 'balopt.opt' $include '../../base/model/balopt.opt'; - -$ifi %system.filesys%==UNIX -execute 'chmod -R ug+rw "../.."'; - -* If merging of savepoint files is to be performed, -* make sure that there are no gdx files initially in applied folders: -$ifi %system.filesys%==UNIX -$ifi %MERGESAVEPOINTRESULTS%==yes execute "rm *.gdx"; -$ifi %system.filesys%==MSNT -$ifi %MERGESAVEPOINTRESULTS%==yes execute "del *.gdx"; -$ifi %system.filesys%==UNIX -$ifi %MERGESAVEPOINTRESULTS%==yes execute "rm ../output/temp/*.gdx"; -$ifi %system.filesys%==MSNT -$ifi %MERGESAVEPOINTRESULTS%==yes execute "del ..\output\temp\*.gdx"; - -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- -*---- Some generally applicable stuff: ----------------------------------------- -*------------------------------------------------------------------------------- - - -* The following may be used in display, put or execute_unload statements (the text, not the value, holds relevant information) -SCALAR SystemDateTime "%system.date%, %system.time%" /NA/; - -* Displaying only a text string will not be accessible through the 'table of content' on the .lst file, therefore this -SCALAR INFODISPLAY "See information next:" /NA/; -DISPLAY INFODISPLAY, "Balmorel run stared at", SystemDateTime; - -* Convenient Factors, typically relating Output and Input: -SCALAR IOF100 'Multiplier 100' /100/; -SCALAR IOF1000 'Multiplier 1000' /1000/; -SCALAR IOF1000000 'Multiplier 1000000' /1000000/; -SCALAR IOF0001 'Multiplier 0.001' /0.001/; -SCALAR IOF0000001 'Multiplier 0.000001' /0.000001/; -SCALAR IOF3P6 'Multiplier 3.6' /3.6/; -SCALAR IOF24 'Multiplier 24' /24/; -SCALAR IOF8760 'Multiplier 8760' /8760/; -SCALAR IOF8784 'Multiplier 8784' /8784/; -SCALAR IOF365 'Multiplier 365' /365/; -SCALAR IOF05 'Multiplier 0.5' /0.5/; -SCALAR IOF1_ 'Multiplier 1 (used with QOBJ and derivation of marginal values)' /1/;!! special, possibly to disappear in future versions -* Scalars for occational use, their meaning will be context dependent: -SCALAR ISCALAR1 '(Context dependent)'; -SCALAR ISCALAR2 '(Context dependent)'; -SCALAR ISCALAR3 '(Context dependent)'; -SCALAR ISCALAR4 '(Context dependent)'; -SCALAR ISCALAR5 '(Context dependent)'; - -* Initialisations of printing of log and error files and messages: -$INCLUDE '../../base/logerror/logerinc/error1.inc'; - -* Ensuring existence of needed output folders: -$ifi not dexist "../../simex" execute 'mkdir -p "../../simex"'; -$ifi not dexist "../logerror/logerinc" execute 'mkdir -p "../logerror/logerinc"'; -$ifi not dexist "../output/economic" execute 'mkdir -p "../output/economic"'; -$ifi not dexist "../output/inputout" execute 'mkdir -p "../output/inputout"'; -$ifi not dexist "../output/printout" execute 'mkdir -p "../output/printout"'; -$ifi not dexist "../output/temp" execute 'mkdir -p "../output/temp"'; - - -*------------------------------------------------------------------------------- -*---- End: Some generally applicable stuff ------------------------------------- -*------------------------------------------------------------------------------- - - - -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- -* In case of Model Balbase4 the following included file substitutes the remaining part of the present file Balmorel.gms: -* Use local file if it exists, otherwise use the one in folder ../../base/model/. -$ifi %BB4%==yes $ifi exist 'Balmorelbb4.inc' $include 'Balmorelbb4.inc'; -$ifi %BB4%==yes $ifi not exist 'Balmorelbb4.inc' $include '../../base/model/Balmorelbb4.inc'; -$ifi %BB4%==yes $goto ENDOFMODEL -*------------------------------------------------------------------------------- -*------------------------------------------------------------------------------- - -*----- End of model:------------------------------------------------------------ -$label ENDOFMODEL -$ifi %all_endofmodelgdx%==yes execute_unload "all_endofmodel.gdx"; -*----- End of model ------------------------------------------------------------ - - -*--- Results collection for this case ------------------------------------------ - -$ifi not %system.filesys%==MSNT $goto endofMSNToutput -*The following section until $label endofMSNToutput is related to Windows output only -*Please use only backslash \ instead of forward slash / in this section until the label - -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "%relpathoutput%temp\*.gdx"'; -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move merged.gdx "%relpathoutput%%CASEID%.gdx"'; - -$ifi %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "..\output\%CASEID%.gdx"'; -$ifi %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move merged.gdx "%relpathoutput%%CASEID%-results.gdx"' - -$ifi %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access "%relpathoutput%%CASEID%-results.gdx"'; -$ifi %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i "%relpathoutput%%CASEID%-results.gdx" -o "%relpathoutput%%CASEID%-results.db"'; - -*--- Results collection and comparison for differents cases -------------------- - -$ifi not %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "%relpathoutput%%CASEID%.gdx" "%relpathModel%..\..\%MERGEWITH%/output\%MERGEWITH%.gdx"'; -$ifi not %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move merged.gdx "%relpathoutput%%CASEID%-resmerged.gdx"'; - -$ifi not %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access "%relpathoutput%%CASEID%-resmerged.gdx"'; -$ifi not %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i "%relpathoutput%%CASEID%-resmerged.gdx" -o "%relpathoutput%%CASEID%-resmerged.db"'; - -$ifi not %DIFFCASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxdiff "%relpathoutput%%CASEID%-results.gdx" "%relpathModel%..\..\%DIFFWITH%/output/%DIFFWITH%-results.gdx"'; -$ifi not %DIFFCASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'move diffile.gdx "%relpathoutput%%CASEID%-diff.gdx"'; - -$label endofMSNToutput - -$ifi not %system.filesys%==UNIX $goto endofUNIXoutput -*The following section until $label endofUNIXoutput is related to UNIX output only -*Please use only forward slash / instead of backslash \ in this section until the label - -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "../output/temp/*.gdx"'; -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./merged.gdx ./"%relpathoutput%%CASEID%.gdx"'; - -$ifi %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge "../output/%CASEID%.gdx"'; -$ifi %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./merged.gdx ./"%relpathoutput%%CASEID%-results.gdx"' - -$ifi %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access ./"%relpathoutput%%CASEID%-results.gdx"'; -$ifi %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i ./"%relpathoutput%%CASEID%-results.gdx" -o ./"%relpathoutput%%CASEID%-results.db"'; - -*--- Results collection and comparison for differents cases -------------------- - -$ifi not %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxmerge ./"%relpathoutput%%CASEID%.gdx" ./"%relpathModel%../../%MERGEWITH%/output/%MERGEWITH%.gdx"'; -$ifi not %MERGECASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./merged.gdx ./"%relpathoutput%%CASEID%-resmerged.gdx"'; - -$ifi not %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2MDB%==yes execute '=gdx2access ./"%relpathoutput%%CASEID%-resmerged.gdx"'; -$ifi not %MERGECASE%==NONE -$ifi %MERGEDSAVEPOINTRESULTS2SQLITE%==yes execute '=gdx2sqlite -i ./"%relpathoutput%%CASEID%-resmerged.gdx" -o ./"%relpathoutput%%CASEID%-resmerged.db"'; - -$ifi not %DIFFCASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'gdxdiff ./"%relpathoutput%%CASEID%-results.gdx" ./"%relpathModel%../../%DIFFWITH%/output/%DIFFWITH%-results.gdx"'; -$ifi not %DIFFCASE%==NONE -$ifi %MERGESAVEPOINTRESULTS%==yes execute 'mv ./diffile.gdx ./"%relpathoutput%%CASEID%-diff.gdx"'; - - -$label endofUNIXoutput - -*----- End of file:------------------------------------------------------------ -$label endoffile - - diff --git a/changes/model/cplex.op4 b/changes/model/cplex.op4 deleted file mode 100644 index 194d4c61..00000000 --- a/changes/model/cplex.op4 +++ /dev/null @@ -1,14 +0,0 @@ -* Option file 4 for CPLEX solver. For possible options, see the GAMS Solver Manual. -* To use with CPLEX, set '$Setglobal USEOPTIONFILE 4' in balgams.opt. -* Do not change this file, it is predefined. -* To use other options with CPLEX, make and/or use some other file, see USEOPTIONFILE in balgams.opt. -* "LPmethod 4": Barrier method -* "Threads -1": Use all available threads except 1. -* If memory tight, set the Threads parameter to 1. -* advind is set to 0 by default since for large problems a better performance has been experienced. If 0, the optimization will not use the previous solution to try to find the next run faster. - -LPmethod 4 -Threads -1 -advind 0 - - From a4fbaa61bc791fbba59cc1bb0c75b20153d50f2f Mon Sep 17 00:00:00 2001 From: Mathias Berg Rosendal <77012503+Mathias157@users.noreply.github.com> Date: Mon, 1 Jul 2024 16:41:09 +0200 Subject: [PATCH 15/21] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 19f87b12..d9ab1ec9 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@ The model is developed and distributed under open source ideals. The source code ### Procedure for Merging Changes to Master -The DTU Balmorel team has decided on the following procedure for merging new changes to the master: +The Balmorel team of DTU Management, Energy Economics and Modelling has decided on the following procedure for merging new changes to the master: 1. A decision to merge should be agreed upon in a Balmorel meeting with the admins 2. Create a new branch from master, which will be used as a temporary branch to do all the changes 3. Make the desired changes in a new scenario: From 909acc3ee46df400652fc4cc831d90253454665c Mon Sep 17 00:00:00 2001 From: Mathias157 Date: Mon, 1 Jul 2024 16:46:45 +0200 Subject: [PATCH 16/21] Updated readme --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index d9ab1ec9..471a7d3d 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,7 @@ # Balmorel +The Balmorel energy system model. Data is included in the [Balmorel_data repository](https://github.com/balmorelcommunity/Balmorel_data), which we recommend cloning to base/ for version control of both base data and framework. Note that the cloned folder should be renamed from Balmorel_data to data in order to function properly. + A scenario that's possible to run with an academic trial license of GAMS can be found in the balmorel-demo branch. ## What is Balmorel? From 7cf824ec6557dfd2ccb7988a7840efb9fd81be89 Mon Sep 17 00:00:00 2001 From: Mathias Berg Rosendal <77012503+Mathias157@users.noreply.github.com> Date: Mon, 1 Jul 2024 16:50:33 +0200 Subject: [PATCH 17/21] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 471a7d3d..8c6a82f4 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Balmorel -The Balmorel energy system model. Data is included in the [Balmorel_data repository](https://github.com/balmorelcommunity/Balmorel_data), which we recommend cloning to base/ for version control of both base data and framework. Note that the cloned folder should be renamed from Balmorel_data to data in order to function properly. +The Balmorel energy system model. Data is included in the [Balmorel_data repository](https://github.com/balmorelcommunity/Balmorel_data), which we recommend cloning to base/ for version control of both base data and framework. Note: The cloned folder should be renamed from "Balmorel_data" to "data" to function properly. A scenario that's possible to run with an academic trial license of GAMS can be found in the balmorel-demo branch. From e69243ed715da36c2381d48e1753e267295f5159 Mon Sep 17 00:00:00 2001 From: TheisUM Date: Tue, 9 Jul 2024 13:21:51 +0200 Subject: [PATCH 18/21] Delete base/auxils/annuity_calculation/ANNUITYCG.inc Seperate to model, don't change --- base/auxils/annuity_calculation/ANNUITYCG.inc | 23150 ---------------- 1 file changed, 23150 deletions(-) delete mode 100644 base/auxils/annuity_calculation/ANNUITYCG.inc diff --git a/base/auxils/annuity_calculation/ANNUITYCG.inc b/base/auxils/annuity_calculation/ANNUITYCG.inc deleted file mode 100644 index 6628d991..00000000 --- a/base/auxils/annuity_calculation/ANNUITYCG.inc +++ /dev/null @@ -1,23150 +0,0 @@ -*PARAMETER ANNUITYCG(CCC,GGG) CALCULATED WITH AUXILS - -ANNUITYCG('NORWAY','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('NORWAY','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('NORWAY','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','DE4-NW-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','EE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('NORWAY','EE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','EE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','EE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','ES-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('NORWAY','ES-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','ES-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','ES-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','ES-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('NORWAY','ES-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','ES-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','ES-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','FI-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('NORWAY','FI-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','FI-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','FI-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('NORWAY','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2020')=0.057830099134; -ANNUITYCG('NORWAY','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2030')=0.057830099134; -ANNUITYCG('NORWAY','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2040')=0.057830099134; -ANNUITYCG('NORWAY','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2050')=0.057830099134; -ANNUITYCG('NORWAY','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_NGAS_E-105_MS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_NGAS_E-106_MS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_NGAS_E-106_MS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_NGAS_E-106_MS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_NGAS_E-95_SS-10-KW_Y-2020')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_NGAS_E-96_SS-10-KW_Y-2030')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_NGAS_E-96_SS-10-KW_Y-2040')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_NGAS_E-97_SS-10-KW_Y-2050')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_NGASCCS_E-105_MS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_STRW_E-102_LS-6-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_STRW_E-102_LS-6-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_STRW_E-102_LS-6-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_STRW_E-102_LS-6-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_BO_WOODPEL_E-78_SS-10-KW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('NORWAY','GNR_BO_WOODPEL_E-85_SS-8-KW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('NORWAY','GNR_CC_NGAS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGAS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_CC_NGASCCS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_BGAS_BP_E-43_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_BGAS_BP_E-45_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_BGAS_BP_E-46_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_BGAS_BP_E-47_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_BGAS_CND_E-43_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_BGAS_CND_E-45_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_BGAS_CND_E-46_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_BGAS_CND_E-47_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_NGAS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_NGAS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_NGAS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_NGAS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_NGAS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_NGAS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_NGAS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('NORWAY','GNR_ENG_NGAS_CND_E-50_Y-2050')=0.064011962786; 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-ANNUITYCG('SWEDEN','GNR_CC_NGAS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGAS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGAS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGAS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_CC_NGASCCS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_BGAS_BP_E-43_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_BGAS_BP_E-45_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_BGAS_BP_E-46_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_BGAS_BP_E-47_Y-2050')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_BGAS_CND_E-43_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_BGAS_CND_E-45_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_BGAS_CND_E-46_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_BGAS_CND_E-47_Y-2050')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGAS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGAS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGAS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGAS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGAS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGAS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGAS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGAS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGASCCS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGASCCS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGASCCS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGASCCS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGASCCS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGASCCS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGASCCS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ENG_NGASCCS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_BAT-LEAD_E-82_Y-2020')=0.134492992698; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_BAT-LEAD_E-85_Y-2030')=0.100143727814; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2020')=0.073581750329; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2030')=0.073581750329; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2040')=0.073581750329; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2050')=0.073581750329; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2020')=0.073581750329; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2030')=0.073581750329; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2040')=0.073581750329; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2050')=0.073581750329; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_BAT-NAS_E-80_Y-2020')=0.082198522089; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_BAT-NAS_E-85_Y-2030')=0.065586831340; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_BAT-VRFB_E-78_Y-2020')=0.076138618402; -ANNUITYCG('SWEDEN','GNR_ES_ELEC_CAES_E-71_Y-2020')=0.046258849740; -ANNUITYCG('SWEDEN','GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_GT_NGAS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_GT_NGAS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('SWEDEN','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; 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-ANNUITYCG('LATVIA','ES-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','ES-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','ES-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','ES-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','ES-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','ES-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','ES-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','ES-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','FI-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','FI-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','FI-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','FI-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2020')=0.057830099134; -ANNUITYCG('LATVIA','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_NGAS_E-105_MS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_NGAS_E-106_MS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_NGAS_E-106_MS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_NGAS_E-106_MS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_NGAS_E-95_SS-10-KW_Y-2020')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_NGAS_E-96_SS-10-KW_Y-2030')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_NGAS_E-96_SS-10-KW_Y-2040')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_NGAS_E-97_SS-10-KW_Y-2050')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_NGASCCS_E-105_MS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_STRW_E-102_LS-6-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_STRW_E-102_LS-6-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_STRW_E-102_LS-6-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_STRW_E-102_LS-6-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-78_SS-10-KW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('LATVIA','GNR_BO_WOODPEL_E-85_SS-8-KW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('LATVIA','GNR_CC_NGAS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGAS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_CC_NGASCCS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_BGAS_BP_E-43_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_BGAS_BP_E-45_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_BGAS_BP_E-46_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_BGAS_BP_E-47_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_BGAS_CND_E-43_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_BGAS_CND_E-45_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_BGAS_CND_E-46_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_BGAS_CND_E-47_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGAS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGAS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGAS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGAS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGAS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGAS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGAS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGAS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ENG_NGASCCS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LEAD_E-82_Y-2020')=0.134492992698; -ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LEAD_E-85_Y-2030')=0.100143727814; -ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2020')=0.073581750329; -ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2030')=0.073581750329; -ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2040')=0.073581750329; -ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2050')=0.073581750329; -ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2020')=0.073581750329; -ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2030')=0.073581750329; -ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2040')=0.073581750329; -ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2050')=0.073581750329; -ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-NAS_E-80_Y-2020')=0.082198522089; -ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-NAS_E-85_Y-2030')=0.065586831340; -ANNUITYCG('LATVIA','GNR_ES_ELEC_BAT-VRFB_E-78_Y-2020')=0.076138618402; -ANNUITYCG('LATVIA','GNR_ES_ELEC_CAES_E-71_Y-2020')=0.046258849740; -ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-AIR_COP-340_SS-4-KW_Y-2030')=0.106552172686; -ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-AIR_COP-350_SS-4-KW_Y-2040')=0.106552172686; -ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-AIR_COP-360_SS-4-KW_Y-2050')=0.106552172686; -ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-AIR_COP-490_SS-3-KW_Y-2020')=0.106552172686; -ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-310_LS_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-330_LS_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-335_SS-4-KW_Y-2020')=0.078993328144; -ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-343_LS_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2030')=0.078993328144; -ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-360_LS_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_AIR-WTR_COP-365_SS-4-KW_Y-2050')=0.078993328144; -ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-1200_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-1800_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-400_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-480_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-500_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-600_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-600_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-740_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_COP-900_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_GROUND-WTR_COP-360_LS-4-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_GROUND-WTR_COP-370_SS-10-KW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_GROUND-WTR_COP-380_LS-4-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_GROUND-WTR_COP-380_SS-10-KW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_GROUND-WTR_COP-388_SS-10-KW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_GROUND-WTR_COP-395_LS-4-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_GROUND-WTR_COP-400_SS-10-KW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HP_ELEC_GROUND-WTR_COP-410_LS-4-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2020')=0.073581750329; -ANNUITYCG('LATVIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2030')=0.073581750329; -ANNUITYCG('LATVIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2040')=0.073581750329; -ANNUITYCG('LATVIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2050')=0.073581750329; -ANNUITYCG('LATVIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2020')=0.073581750329; -ANNUITYCG('LATVIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2030')=0.073581750329; -ANNUITYCG('LATVIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2040')=0.073581750329; -ANNUITYCG('LATVIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2050')=0.073581750329; -ANNUITYCG('LATVIA','GNR_HS_HEAT_WTR-TANK_LS_E-95_Y-2010')=0.050523489324; -ANNUITYCG('LATVIA','GNR_HS_HEAT_WTR-TANK_SS_E-95_Y-2010')=0.057830099134; -ANNUITYCG('LATVIA','GNR_IND-BO_NGAS_E-93_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_IND-BO_NGAS_E-94_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_IND-BO_NGAS_E-95_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_IND-BO_NGAS_E-96_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_IND-BO_NGASCCS_E-93_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_IND-BO_NGASCCS_E-94_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_IND-BO_NGASCCS_E-95_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_IND-BO_NGASCCS_E-96_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_IND-DF_ELEC_E-100_MS-5-MW_Y-2020')=0.103217228610; -ANNUITYCG('LATVIA','GNR_IND-DF_NGAS_E-100_MS-3-MW_Y-2020')=0.089941100371; -ANNUITYCG('LATVIA','GNR_IND-DF_NGASCCS_E-100_MS-3-MW_Y-2020')=0.089941100371; -ANNUITYCG('LATVIA','GNR_IND-DF_WOODCHI_E-100_MS-10-MW_Y-2020')=0.089941100371; -ANNUITYCG('LATVIA','GNR_OFF_HUB_2GW_Y-2020')=0.050523489324; -ANNUITYCG('LATVIA','GNR_OFF_HUB_2GW_Y-2030')=0.050523489324; -ANNUITYCG('LATVIA','GNR_OFF_HUB_2GW_Y-2040')=0.050523489324; -ANNUITYCG('LATVIA','GNR_OFF_HUB_2GW_Y-2050')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2020')=0.053577322368; -ANNUITYCG('LATVIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2030')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2040')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2050')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2020')=0.053577322368; -ANNUITYCG('LATVIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2030')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2040')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2050')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2020')=0.053577322368; -ANNUITYCG('LATVIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2030')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2040')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2050')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_MS-100-KW_Y-2020')=0.053577322368; -ANNUITYCG('LATVIA','GNR_PV_SUN_MS-100-KW_Y-2030')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_MS-100-KW_Y-2040')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_MS-100-KW_Y-2050')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_SS-6-KW_Y-2020')=0.053577322368; -ANNUITYCG('LATVIA','GNR_PV_SUN_SS-6-KW_Y-2030')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_SS-6-KW_Y-2040')=0.050523489324; -ANNUITYCG('LATVIA','GNR_PV_SUN_SS-6-KW_Y-2050')=0.050523489324; -ANNUITYCG('LATVIA','GNR_RES_WTR_PMP_E-80_Y-2020')=0.044201845123; -ANNUITYCG('LATVIA','GNR_SH_SUN_LS_Y-2020')=0.057830099134; -ANNUITYCG('LATVIA','GNR_SH_SUN_LS_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_SH_SUN_LS_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_SH_SUN_LS_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_SH_SUN_SS-4-KW_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_SH_SUN_SS-4-KW_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_SH_SUN_SS-4-KW_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_SH_SUN_SS-4-KW_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_ST_COAL_CND_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('LATVIA','GNR_ST_COAL_CND_E-52_LS-400-MW_Y-2030')=0.050523489324; -ANNUITYCG('LATVIA','GNR_ST_COAL_CND_E-53_LS-400-MW_Y-2040')=0.050523489324; -ANNUITYCG('LATVIA','GNR_ST_COAL_CND_E-54_LS-400-MW_Y-2050')=0.050523489324; -ANNUITYCG('LATVIA','GNR_ST_COAL_EXT_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('LATVIA','GNR_ST_COAL_EXT_E-52_LS-400-MW_Y-2030')=0.050523489324; -ANNUITYCG('LATVIA','GNR_ST_COAL_EXT_E-53_LS-400-MW_Y-2040')=0.050523489324; -ANNUITYCG('LATVIA','GNR_ST_COAL_EXT_E-54_LS-400-MW_Y-2050')=0.050523489324; -ANNUITYCG('LATVIA','GNR_ST_MSW_BP_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_BP_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_BP_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_BP_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_CND_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_CND_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_CND_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_CND_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_NGAS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; -ANNUITYCG('LATVIA','GNR_ST_NGAS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('LATVIA','GNR_ST_NGAS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('LATVIA','GNR_ST_NGASCCS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; -ANNUITYCG('LATVIA','GNR_ST_NGASCCS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('LATVIA','GNR_ST_NGASCCS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('LATVIA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT_WIND_ONS_SS-25-KW_Y-2020')=0.073581750329; -ANNUITYCG('LATVIA','GNR_WT_WIND_ONS_SS-25-KW_Y-2030')=0.073581750329; -ANNUITYCG('LATVIA','GNR_WT_WIND_ONS_SS-25-KW_Y-2040')=0.073581750329; -ANNUITYCG('LATVIA','GNR_WT_WIND_ONS_SS-25-KW_Y-2050')=0.073581750329; -ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','SE3-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','SE3-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','SE3-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','SE3-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','SE4-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','SE4-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','SE4-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','SE4-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','UK-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LATVIA','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_Y-2040')=0.055948589741; -ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_Y-2050')=0.053577322368; -ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_DH_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_DH_Y-2030')=0.057830099134; -ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_DH_Y-2040')=0.055948589741; -ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_DH_Y-2050')=0.053577322368; -ANNUITYCG('LATVIA','GNR_ELYS_ELEC_AEC_Y-2020_HUB')=0.064011962786; 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-ANNUITYCG('LATVIA','GNR_STEAM-REFORMING_E-70_Y-2020')=0.064011962786; -ANNUITYCG('LATVIA','GNR_STEAM-REFORMING-CCS_E-70_Y-2020')=0.064011962786; -ANNUITYCG('LITHUANIA','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LITHUANIA','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LITHUANIA','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LITHUANIA','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LITHUANIA','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LITHUANIA','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LITHUANIA','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LITHUANIA','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LITHUANIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LITHUANIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LITHUANIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LITHUANIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; 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-ANNUITYCG('LITHUANIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2040')=0.073581750329; -ANNUITYCG('LITHUANIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2050')=0.073581750329; -ANNUITYCG('LITHUANIA','GNR_ES_ELEC_BAT-NAS_E-80_Y-2020')=0.082198522089; -ANNUITYCG('LITHUANIA','GNR_ES_ELEC_BAT-NAS_E-85_Y-2030')=0.065586831340; -ANNUITYCG('LITHUANIA','GNR_ES_ELEC_BAT-VRFB_E-78_Y-2020')=0.076138618402; -ANNUITYCG('LITHUANIA','GNR_ES_ELEC_CAES_E-71_Y-2020')=0.046258849740; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGASCCS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGASCCS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; 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-ANNUITYCG('LITHUANIA','GNR_HP_ELEC_AIR-AIR_COP-350_SS-4-KW_Y-2040')=0.106552172686; -ANNUITYCG('LITHUANIA','GNR_HP_ELEC_AIR-AIR_COP-360_SS-4-KW_Y-2050')=0.106552172686; -ANNUITYCG('LITHUANIA','GNR_HP_ELEC_AIR-AIR_COP-490_SS-3-KW_Y-2020')=0.106552172686; -ANNUITYCG('LITHUANIA','GNR_HP_ELEC_AIR-WTR_COP-310_LS_Y-2020')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_HP_ELEC_AIR-WTR_COP-330_LS_Y-2030')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_HP_ELEC_AIR-WTR_COP-335_SS-4-KW_Y-2020')=0.078993328144; -ANNUITYCG('LITHUANIA','GNR_HP_ELEC_AIR-WTR_COP-343_LS_Y-2040')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2030')=0.078993328144; -ANNUITYCG('LITHUANIA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2040')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_HP_ELEC_AIR-WTR_COP-360_LS_Y-2050')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_HP_ELEC_AIR-WTR_COP-365_SS-4-KW_Y-2050')=0.078993328144; -ANNUITYCG('LITHUANIA','GNR_HP_ELEC_COP-1200_Y-2030')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_HP_ELEC_COP-1800_Y-2050')=0.064011962786; 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-ANNUITYCG('LITHUANIA','GNR_HP_ELEC_GROUND-WTR_COP-400_SS-10-KW_Y-2050')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_HP_ELEC_GROUND-WTR_COP-410_LS-4-MW_Y-2050')=0.064011962786; -ANNUITYCG('LITHUANIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2020')=0.073581750329; -ANNUITYCG('LITHUANIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2030')=0.073581750329; -ANNUITYCG('LITHUANIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2040')=0.073581750329; -ANNUITYCG('LITHUANIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2050')=0.073581750329; -ANNUITYCG('LITHUANIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2020')=0.073581750329; -ANNUITYCG('LITHUANIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2030')=0.073581750329; -ANNUITYCG('LITHUANIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2040')=0.073581750329; -ANNUITYCG('LITHUANIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2050')=0.073581750329; -ANNUITYCG('LITHUANIA','GNR_HS_HEAT_WTR-TANK_LS_E-95_Y-2010')=0.050523489324; -ANNUITYCG('LITHUANIA','GNR_HS_HEAT_WTR-TANK_SS_E-95_Y-2010')=0.057830099134; -ANNUITYCG('LITHUANIA','GNR_IND-BO_NGAS_E-93_MS-20-MW_Y-2020')=0.064011962786; 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-ANNUITYCG('POLAND','GNR_PV_SUN_LS-8-MW_RG2_Y-2030')=0.050523489324; -ANNUITYCG('POLAND','GNR_PV_SUN_LS-8-MW_RG2_Y-2040')=0.050523489324; -ANNUITYCG('POLAND','GNR_PV_SUN_LS-8-MW_RG2_Y-2050')=0.050523489324; -ANNUITYCG('POLAND','GNR_PV_SUN_LS-8-MW_RG3_Y-2020')=0.053577322368; -ANNUITYCG('POLAND','GNR_PV_SUN_LS-8-MW_RG3_Y-2030')=0.050523489324; -ANNUITYCG('POLAND','GNR_PV_SUN_LS-8-MW_RG3_Y-2040')=0.050523489324; -ANNUITYCG('POLAND','GNR_PV_SUN_LS-8-MW_RG3_Y-2050')=0.050523489324; -ANNUITYCG('POLAND','GNR_PV_SUN_MS-100-KW_Y-2020')=0.053577322368; -ANNUITYCG('POLAND','GNR_PV_SUN_MS-100-KW_Y-2030')=0.050523489324; -ANNUITYCG('POLAND','GNR_PV_SUN_MS-100-KW_Y-2040')=0.050523489324; -ANNUITYCG('POLAND','GNR_PV_SUN_MS-100-KW_Y-2050')=0.050523489324; -ANNUITYCG('POLAND','GNR_PV_SUN_SS-6-KW_Y-2020')=0.053577322368; -ANNUITYCG('POLAND','GNR_PV_SUN_SS-6-KW_Y-2030')=0.050523489324; -ANNUITYCG('POLAND','GNR_PV_SUN_SS-6-KW_Y-2040')=0.050523489324; -ANNUITYCG('POLAND','GNR_PV_SUN_SS-6-KW_Y-2050')=0.050523489324; -ANNUITYCG('POLAND','GNR_RES_WTR_PMP_E-80_Y-2020')=0.044201845123; -ANNUITYCG('POLAND','GNR_SH_SUN_LS_Y-2020')=0.057830099134; -ANNUITYCG('POLAND','GNR_SH_SUN_LS_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_SH_SUN_LS_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_SH_SUN_LS_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_SH_SUN_SS-4-KW_Y-2020')=0.064011962786; -ANNUITYCG('POLAND','GNR_SH_SUN_SS-4-KW_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_SH_SUN_SS-4-KW_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_SH_SUN_SS-4-KW_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_ST_COAL_CND_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('POLAND','GNR_ST_COAL_CND_E-52_LS-400-MW_Y-2030')=0.050523489324; -ANNUITYCG('POLAND','GNR_ST_COAL_CND_E-53_LS-400-MW_Y-2040')=0.050523489324; -ANNUITYCG('POLAND','GNR_ST_COAL_CND_E-54_LS-400-MW_Y-2050')=0.050523489324; -ANNUITYCG('POLAND','GNR_ST_COAL_EXT_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('POLAND','GNR_ST_COAL_EXT_E-52_LS-400-MW_Y-2030')=0.050523489324; 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-ANNUITYCG('POLAND','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_MSW_CND_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_MSW_CND_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_MSW_CND_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_MSW_CND_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; 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-ANNUITYCG('POLAND','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; 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-ANNUITYCG('POLAND','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('POLAND','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT_WIND_ONS_SS-25-KW_Y-2020')=0.073581750329; -ANNUITYCG('POLAND','GNR_WT_WIND_ONS_SS-25-KW_Y-2030')=0.073581750329; -ANNUITYCG('POLAND','GNR_WT_WIND_ONS_SS-25-KW_Y-2040')=0.073581750329; -ANNUITYCG('POLAND','GNR_WT_WIND_ONS_SS-25-KW_Y-2050')=0.073581750329; -ANNUITYCG('POLAND','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('POLAND','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('POLAND','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('POLAND','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('POLAND','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; 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-ANNUITYCG('PORTUGAL','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGAS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGAS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGAS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGAS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_HP_ELEC_AIR-AIR_COP-340_SS-4-KW_Y-2030')=0.106552172686; 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-ANNUITYCG('PORTUGAL','GNR_HP_ELEC_GROUND-WTR_COP-400_SS-10-KW_Y-2050')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_HP_ELEC_GROUND-WTR_COP-410_LS-4-MW_Y-2050')=0.064011962786; -ANNUITYCG('PORTUGAL','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2020')=0.073581750329; -ANNUITYCG('PORTUGAL','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2030')=0.073581750329; -ANNUITYCG('PORTUGAL','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2040')=0.073581750329; -ANNUITYCG('PORTUGAL','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2050')=0.073581750329; -ANNUITYCG('PORTUGAL','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2020')=0.073581750329; -ANNUITYCG('PORTUGAL','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2030')=0.073581750329; -ANNUITYCG('PORTUGAL','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2040')=0.073581750329; -ANNUITYCG('PORTUGAL','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2050')=0.073581750329; -ANNUITYCG('PORTUGAL','GNR_HS_HEAT_WTR-TANK_LS_E-95_Y-2010')=0.050523489324; -ANNUITYCG('PORTUGAL','GNR_HS_HEAT_WTR-TANK_SS_E-95_Y-2010')=0.057830099134; -ANNUITYCG('PORTUGAL','GNR_IND-BO_NGAS_E-93_MS-20-MW_Y-2020')=0.064011962786; 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-ANNUITYCG('PORTUGAL','GNR_IND-DF_NGASCCS_E-100_MS-3-MW_Y-2020')=0.089941100371; -ANNUITYCG('PORTUGAL','GNR_IND-DF_WOODCHI_E-100_MS-10-MW_Y-2020')=0.089941100371; -ANNUITYCG('PORTUGAL','GNR_OFF_HUB_2GW_Y-2020')=0.050523489324; -ANNUITYCG('PORTUGAL','GNR_OFF_HUB_2GW_Y-2030')=0.050523489324; -ANNUITYCG('PORTUGAL','GNR_OFF_HUB_2GW_Y-2040')=0.050523489324; -ANNUITYCG('PORTUGAL','GNR_OFF_HUB_2GW_Y-2050')=0.050523489324; -ANNUITYCG('PORTUGAL','GNR_PV_SUN_LS-8-MW_RG1_Y-2020')=0.053577322368; -ANNUITYCG('PORTUGAL','GNR_PV_SUN_LS-8-MW_RG1_Y-2030')=0.050523489324; -ANNUITYCG('PORTUGAL','GNR_PV_SUN_LS-8-MW_RG1_Y-2040')=0.050523489324; -ANNUITYCG('PORTUGAL','GNR_PV_SUN_LS-8-MW_RG1_Y-2050')=0.050523489324; -ANNUITYCG('PORTUGAL','GNR_PV_SUN_LS-8-MW_RG2_Y-2020')=0.053577322368; -ANNUITYCG('PORTUGAL','GNR_PV_SUN_LS-8-MW_RG2_Y-2030')=0.050523489324; -ANNUITYCG('PORTUGAL','GNR_PV_SUN_LS-8-MW_RG2_Y-2040')=0.050523489324; -ANNUITYCG('PORTUGAL','GNR_PV_SUN_LS-8-MW_RG2_Y-2050')=0.050523489324; -ANNUITYCG('PORTUGAL','GNR_PV_SUN_LS-8-MW_RG3_Y-2020')=0.053577322368; 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-ANNUITYCG('SLOVAKIA','GNR_CC_NGASCCS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_CC_NGASCCS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_CC_NGASCCS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_CC_NGASCCS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_CC_NGASCCS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_CC_NGASCCS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_BGAS_BP_E-43_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_BGAS_BP_E-45_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_BGAS_BP_E-46_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_BGAS_BP_E-47_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_BGAS_CND_E-43_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_BGAS_CND_E-45_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_BGAS_CND_E-46_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_BGAS_CND_E-47_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGAS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGAS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGAS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGAS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGAS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGAS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGAS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGAS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGASCCS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGASCCS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGASCCS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGASCCS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGASCCS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGASCCS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGASCCS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ENG_NGASCCS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_BAT-LEAD_E-82_Y-2020')=0.134492992698; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_BAT-LEAD_E-85_Y-2030')=0.100143727814; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2020')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2030')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2040')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2050')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2020')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2030')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2040')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2050')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_BAT-NAS_E-80_Y-2020')=0.082198522089; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_BAT-NAS_E-85_Y-2030')=0.065586831340; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_BAT-VRFB_E-78_Y-2020')=0.076138618402; -ANNUITYCG('SLOVAKIA','GNR_ES_ELEC_CAES_E-71_Y-2020')=0.046258849740; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_AIR-AIR_COP-340_SS-4-KW_Y-2030')=0.106552172686; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_AIR-AIR_COP-350_SS-4-KW_Y-2040')=0.106552172686; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_AIR-AIR_COP-360_SS-4-KW_Y-2050')=0.106552172686; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_AIR-AIR_COP-490_SS-3-KW_Y-2020')=0.106552172686; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_AIR-WTR_COP-310_LS_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_AIR-WTR_COP-330_LS_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_AIR-WTR_COP-335_SS-4-KW_Y-2020')=0.078993328144; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_AIR-WTR_COP-343_LS_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2030')=0.078993328144; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_AIR-WTR_COP-360_LS_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_AIR-WTR_COP-365_SS-4-KW_Y-2050')=0.078993328144; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_COP-1200_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_COP-1800_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_COP-400_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_COP-480_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_COP-500_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_COP-600_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_COP-600_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_COP-740_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_COP-900_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_GROUND-WTR_COP-360_LS-4-MW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_GROUND-WTR_COP-370_SS-10-KW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_GROUND-WTR_COP-380_LS-4-MW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_GROUND-WTR_COP-380_SS-10-KW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_GROUND-WTR_COP-388_SS-10-KW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_GROUND-WTR_COP-395_LS-4-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_GROUND-WTR_COP-400_SS-10-KW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HP_ELEC_GROUND-WTR_COP-410_LS-4-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2020')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2030')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2040')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2050')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2020')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2030')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2040')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2050')=0.073581750329; -ANNUITYCG('SLOVAKIA','GNR_HS_HEAT_WTR-TANK_LS_E-95_Y-2010')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_HS_HEAT_WTR-TANK_SS_E-95_Y-2010')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_IND-BO_NGAS_E-93_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_IND-BO_NGAS_E-94_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_IND-BO_NGAS_E-95_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_IND-BO_NGAS_E-96_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_IND-BO_NGASCCS_E-93_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_IND-BO_NGASCCS_E-94_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_IND-BO_NGASCCS_E-95_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_IND-BO_NGASCCS_E-96_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_IND-DF_ELEC_E-100_MS-5-MW_Y-2020')=0.103217228610; -ANNUITYCG('SLOVAKIA','GNR_IND-DF_NGAS_E-100_MS-3-MW_Y-2020')=0.089941100371; -ANNUITYCG('SLOVAKIA','GNR_IND-DF_NGASCCS_E-100_MS-3-MW_Y-2020')=0.089941100371; -ANNUITYCG('SLOVAKIA','GNR_IND-DF_WOODCHI_E-100_MS-10-MW_Y-2020')=0.089941100371; -ANNUITYCG('SLOVAKIA','GNR_OFF_HUB_2GW_Y-2020')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_OFF_HUB_2GW_Y-2030')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_OFF_HUB_2GW_Y-2040')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_OFF_HUB_2GW_Y-2050')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2020')=0.053577322368; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2030')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2040')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2050')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2020')=0.053577322368; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2030')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2040')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2050')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2020')=0.053577322368; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2030')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2040')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2050')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_MS-100-KW_Y-2020')=0.053577322368; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_MS-100-KW_Y-2030')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_MS-100-KW_Y-2040')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_MS-100-KW_Y-2050')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_SS-6-KW_Y-2020')=0.053577322368; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_SS-6-KW_Y-2030')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_SS-6-KW_Y-2040')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_PV_SUN_SS-6-KW_Y-2050')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_RES_WTR_PMP_E-80_Y-2020')=0.044201845123; -ANNUITYCG('SLOVAKIA','GNR_SH_SUN_LS_Y-2020')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_SH_SUN_LS_Y-2030')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_SH_SUN_LS_Y-2040')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_SH_SUN_LS_Y-2050')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_SH_SUN_SS-4-KW_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_SH_SUN_SS-4-KW_Y-2030')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_SH_SUN_SS-4-KW_Y-2040')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_SH_SUN_SS-4-KW_Y-2050')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_ST_COAL_CND_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_ST_COAL_CND_E-52_LS-400-MW_Y-2030')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_ST_COAL_CND_E-53_LS-400-MW_Y-2040')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_ST_COAL_CND_E-54_LS-400-MW_Y-2050')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_ST_COAL_EXT_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_ST_COAL_EXT_E-52_LS-400-MW_Y-2030')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_ST_COAL_EXT_E-53_LS-400-MW_Y-2040')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_ST_COAL_EXT_E-54_LS-400-MW_Y-2050')=0.050523489324; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_BP_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_BP_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_BP_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_BP_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_CND_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_CND_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_CND_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_CND_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_NGAS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_ST_NGAS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_ST_NGAS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_ST_NGASCCS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_ST_NGASCCS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_ST_NGASCCS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; 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-ANNUITYCG('SLOVAKIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('SLOVAKIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('SLOVAKIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('SLOVAKIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('SLOVAKIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('SLOVAKIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2030')=0.057830099134; 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-ANNUITYCG('SLOVAKIA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('SLOVAKIA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('SLOVAKIA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('SLOVAKIA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('SLOVAKIA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('SLOVAKIA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('SLOVAKIA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('SLOVAKIA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('SLOVAKIA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('SLOVAKIA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('SLOVAKIA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('SLOVAKIA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('SLOVAKIA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('SLOVAKIA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; 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-ANNUITYCG('SLOVENIA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2040')=0.057830099134; -ANNUITYCG('SLOVENIA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2050')=0.057830099134; -ANNUITYCG('SLOVENIA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('SLOVENIA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('SLOVENIA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('SLOVENIA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('SLOVENIA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('SLOVENIA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('SLOVENIA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('SLOVENIA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('SLOVENIA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('SLOVENIA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2030')=0.057830099134; -ANNUITYCG('SLOVENIA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2040')=0.057830099134; 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-ANNUITYCG('BULGARIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BULGARIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BULGARIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BULGARIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BULGARIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BULGARIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BULGARIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BULGARIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BULGARIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BULGARIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BULGARIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BULGARIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; 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-ANNUITYCG('LUXEMBOURG','FI-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LUXEMBOURG','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LUXEMBOURG','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LUXEMBOURG','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2020')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2030')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2040')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_NGAS_E-105_MS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_NGAS_E-106_MS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_NGAS_E-106_MS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_NGAS_E-106_MS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_NGAS_E-95_SS-10-KW_Y-2020')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_NGAS_E-96_SS-10-KW_Y-2030')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_NGAS_E-96_SS-10-KW_Y-2040')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_NGAS_E-97_SS-10-KW_Y-2050')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_NGASCCS_E-105_MS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_STRW_E-102_LS-6-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_STRW_E-102_LS-6-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_STRW_E-102_LS-6-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_STRW_E-102_LS-6-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_BO_WOODPEL_E-78_SS-10-KW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_BO_WOODPEL_E-85_SS-8-KW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGAS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_CC_NGASCCS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_BGAS_BP_E-43_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_BGAS_BP_E-45_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_BGAS_BP_E-46_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_BGAS_BP_E-47_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_BGAS_CND_E-43_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_BGAS_CND_E-45_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_BGAS_CND_E-46_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_BGAS_CND_E-47_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGAS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGAS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGAS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGAS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGAS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGAS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGAS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGAS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGASCCS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGASCCS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGASCCS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGASCCS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGASCCS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGASCCS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGASCCS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ENG_NGASCCS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_BAT-LEAD_E-82_Y-2020')=0.134492992698; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_BAT-LEAD_E-85_Y-2030')=0.100143727814; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2020')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2030')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2040')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2050')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2020')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2030')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2040')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2050')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_BAT-NAS_E-80_Y-2020')=0.082198522089; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_BAT-NAS_E-85_Y-2030')=0.065586831340; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_BAT-VRFB_E-78_Y-2020')=0.076138618402; -ANNUITYCG('LUXEMBOURG','GNR_ES_ELEC_CAES_E-71_Y-2020')=0.046258849740; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_AIR-AIR_COP-340_SS-4-KW_Y-2030')=0.106552172686; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_AIR-AIR_COP-350_SS-4-KW_Y-2040')=0.106552172686; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_AIR-AIR_COP-360_SS-4-KW_Y-2050')=0.106552172686; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_AIR-AIR_COP-490_SS-3-KW_Y-2020')=0.106552172686; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_AIR-WTR_COP-310_LS_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_AIR-WTR_COP-330_LS_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_AIR-WTR_COP-335_SS-4-KW_Y-2020')=0.078993328144; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_AIR-WTR_COP-343_LS_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2030')=0.078993328144; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_AIR-WTR_COP-360_LS_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_AIR-WTR_COP-365_SS-4-KW_Y-2050')=0.078993328144; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_COP-1200_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_COP-1800_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_COP-400_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_COP-480_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_COP-500_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_COP-600_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_COP-600_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_COP-740_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_COP-900_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_GROUND-WTR_COP-360_LS-4-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_GROUND-WTR_COP-370_SS-10-KW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_GROUND-WTR_COP-380_LS-4-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_GROUND-WTR_COP-380_SS-10-KW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_GROUND-WTR_COP-388_SS-10-KW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_GROUND-WTR_COP-395_LS-4-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_GROUND-WTR_COP-400_SS-10-KW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HP_ELEC_GROUND-WTR_COP-410_LS-4-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2020')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2030')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2040')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2050')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2020')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2030')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2040')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2050')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_HS_HEAT_WTR-TANK_LS_E-95_Y-2010')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_HS_HEAT_WTR-TANK_SS_E-95_Y-2010')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_IND-BO_NGAS_E-93_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_IND-BO_NGAS_E-94_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_IND-BO_NGAS_E-95_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_IND-BO_NGAS_E-96_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_IND-BO_NGASCCS_E-93_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_IND-BO_NGASCCS_E-94_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_IND-BO_NGASCCS_E-95_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_IND-BO_NGASCCS_E-96_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_IND-DF_ELEC_E-100_MS-5-MW_Y-2020')=0.103217228610; -ANNUITYCG('LUXEMBOURG','GNR_IND-DF_NGAS_E-100_MS-3-MW_Y-2020')=0.089941100371; -ANNUITYCG('LUXEMBOURG','GNR_IND-DF_NGASCCS_E-100_MS-3-MW_Y-2020')=0.089941100371; -ANNUITYCG('LUXEMBOURG','GNR_IND-DF_WOODCHI_E-100_MS-10-MW_Y-2020')=0.089941100371; -ANNUITYCG('LUXEMBOURG','GNR_OFF_HUB_2GW_Y-2020')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_OFF_HUB_2GW_Y-2030')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_OFF_HUB_2GW_Y-2040')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_OFF_HUB_2GW_Y-2050')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_LS-8-MW_RG1_Y-2020')=0.053577322368; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_LS-8-MW_RG1_Y-2030')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_LS-8-MW_RG1_Y-2040')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_LS-8-MW_RG1_Y-2050')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_LS-8-MW_RG2_Y-2020')=0.053577322368; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_LS-8-MW_RG2_Y-2030')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_LS-8-MW_RG2_Y-2040')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_LS-8-MW_RG2_Y-2050')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_LS-8-MW_RG3_Y-2020')=0.053577322368; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_LS-8-MW_RG3_Y-2030')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_LS-8-MW_RG3_Y-2040')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_LS-8-MW_RG3_Y-2050')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_MS-100-KW_Y-2020')=0.053577322368; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_MS-100-KW_Y-2030')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_MS-100-KW_Y-2040')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_MS-100-KW_Y-2050')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_SS-6-KW_Y-2020')=0.053577322368; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_SS-6-KW_Y-2030')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_SS-6-KW_Y-2040')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_PV_SUN_SS-6-KW_Y-2050')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_RES_WTR_PMP_E-80_Y-2020')=0.044201845123; -ANNUITYCG('LUXEMBOURG','GNR_SH_SUN_LS_Y-2020')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_SH_SUN_LS_Y-2030')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_SH_SUN_LS_Y-2040')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_SH_SUN_LS_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_SH_SUN_SS-4-KW_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_SH_SUN_SS-4-KW_Y-2030')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_SH_SUN_SS-4-KW_Y-2040')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_SH_SUN_SS-4-KW_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_CND_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_CND_E-52_LS-400-MW_Y-2030')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_CND_E-53_LS-400-MW_Y-2040')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_CND_E-54_LS-400-MW_Y-2050')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_EXT_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_EXT_E-52_LS-400-MW_Y-2030')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_EXT_E-53_LS-400-MW_Y-2040')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_ST_COAL_EXT_E-54_LS-400-MW_Y-2050')=0.050523489324; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_CND_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_CND_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_CND_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_CND_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_NGAS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_ST_NGAS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_ST_NGAS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_ST_NGASCCS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_ST_NGASCCS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_ST_NGASCCS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_ONS_SS-25-KW_Y-2020')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_ONS_SS-25-KW_Y-2030')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_ONS_SS-25-KW_Y-2040')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_WT_WIND_ONS_SS-25-KW_Y-2050')=0.073581750329; -ANNUITYCG('LUXEMBOURG','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('LUXEMBOURG','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('LUXEMBOURG','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('LUXEMBOURG','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('LUXEMBOURG','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2030')=0.057830099134; 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-ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-78_SS-10-KW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_BO_WOODPEL_E-85_SS-8-KW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGAS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_CC_NGASCCS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_BP_E-43_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_BP_E-45_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_BP_E-46_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_BP_E-47_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_CND_E-43_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_CND_E-45_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_CND_E-46_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_BGAS_CND_E-47_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGAS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ENG_NGASCCS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LEAD_E-82_Y-2020')=0.134492992698; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LEAD_E-85_Y-2030')=0.100143727814; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2020')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2030')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2040')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2050')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2020')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2030')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2040')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2050')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-NAS_E-80_Y-2020')=0.082198522089; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-NAS_E-85_Y-2030')=0.065586831340; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_BAT-VRFB_E-78_Y-2020')=0.076138618402; -ANNUITYCG('MONTENEGRO','GNR_ES_ELEC_CAES_E-71_Y-2020')=0.046258849740; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-AIR_COP-340_SS-4-KW_Y-2030')=0.106552172686; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-AIR_COP-350_SS-4-KW_Y-2040')=0.106552172686; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-AIR_COP-360_SS-4-KW_Y-2050')=0.106552172686; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-AIR_COP-490_SS-3-KW_Y-2020')=0.106552172686; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-310_LS_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-330_LS_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-335_SS-4-KW_Y-2020')=0.078993328144; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-343_LS_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2030')=0.078993328144; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-360_LS_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_AIR-WTR_COP-365_SS-4-KW_Y-2050')=0.078993328144; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-1200_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-1800_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-400_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-480_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-500_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-600_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-600_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-740_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_COP-900_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-360_LS-4-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-370_SS-10-KW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-380_LS-4-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-380_SS-10-KW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-388_SS-10-KW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-395_LS-4-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-400_SS-10-KW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HP_ELEC_GROUND-WTR_COP-410_LS-4-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2020')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2030')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2040')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2050')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2020')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2030')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2040')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2050')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_WTR-TANK_LS_E-95_Y-2010')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_HS_HEAT_WTR-TANK_SS_E-95_Y-2010')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_IND-BO_NGAS_E-93_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_IND-BO_NGAS_E-94_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_IND-BO_NGAS_E-95_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_IND-BO_NGAS_E-96_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_IND-BO_NGASCCS_E-93_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_IND-BO_NGASCCS_E-94_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_IND-BO_NGASCCS_E-95_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_IND-BO_NGASCCS_E-96_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_IND-DF_ELEC_E-100_MS-5-MW_Y-2020')=0.103217228610; -ANNUITYCG('MONTENEGRO','GNR_IND-DF_NGAS_E-100_MS-3-MW_Y-2020')=0.089941100371; -ANNUITYCG('MONTENEGRO','GNR_IND-DF_NGASCCS_E-100_MS-3-MW_Y-2020')=0.089941100371; -ANNUITYCG('MONTENEGRO','GNR_IND-DF_WOODCHI_E-100_MS-10-MW_Y-2020')=0.089941100371; -ANNUITYCG('MONTENEGRO','GNR_OFF_HUB_2GW_Y-2020')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_OFF_HUB_2GW_Y-2030')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_OFF_HUB_2GW_Y-2040')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_OFF_HUB_2GW_Y-2050')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG1_Y-2020')=0.053577322368; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG1_Y-2030')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG1_Y-2040')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG1_Y-2050')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG2_Y-2020')=0.053577322368; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG2_Y-2030')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG2_Y-2040')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG2_Y-2050')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG3_Y-2020')=0.053577322368; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG3_Y-2030')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG3_Y-2040')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_LS-8-MW_RG3_Y-2050')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_MS-100-KW_Y-2020')=0.053577322368; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_MS-100-KW_Y-2030')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_MS-100-KW_Y-2040')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_MS-100-KW_Y-2050')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_SS-6-KW_Y-2020')=0.053577322368; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_SS-6-KW_Y-2030')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_SS-6-KW_Y-2040')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_PV_SUN_SS-6-KW_Y-2050')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_RES_WTR_PMP_E-80_Y-2020')=0.044201845123; -ANNUITYCG('MONTENEGRO','GNR_SH_SUN_LS_Y-2020')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_SH_SUN_LS_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_SH_SUN_LS_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_SH_SUN_LS_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_SH_SUN_SS-4-KW_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_SH_SUN_SS-4-KW_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_SH_SUN_SS-4-KW_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_SH_SUN_SS-4-KW_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_ST_COAL_CND_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_ST_COAL_CND_E-52_LS-400-MW_Y-2030')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_ST_COAL_CND_E-53_LS-400-MW_Y-2040')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_ST_COAL_CND_E-54_LS-400-MW_Y-2050')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_ST_COAL_EXT_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_ST_COAL_EXT_E-52_LS-400-MW_Y-2030')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_ST_COAL_EXT_E-53_LS-400-MW_Y-2040')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_ST_COAL_EXT_E-54_LS-400-MW_Y-2050')=0.050523489324; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_NGAS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_ST_NGAS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_ST_NGAS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_ST_NGASCCS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_ST_NGASCCS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_ST_NGASCCS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_ONS_SS-25-KW_Y-2020')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_ONS_SS-25-KW_Y-2030')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_ONS_SS-25-KW_Y-2040')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_WT_WIND_ONS_SS-25-KW_Y-2050')=0.073581750329; -ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('MONTENEGRO','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('MONTENEGRO','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; 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-ANNUITYCG('NORTH_MACEDONIA','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2030')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2040')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2050')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_ELEC_E-99_LS-10-MW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_ELEC_E-99_MS-1-MW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2020')=0.064011962786; 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-ANNUITYCG('NORTH_MACEDONIA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_STRW_E-102_LS-6-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_STRW_E-102_LS-6-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_STRW_E-102_LS-6-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_STRW_E-102_LS-6-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_WOODPEL_E-78_SS-10-KW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_BO_WOODPEL_E-85_SS-8-KW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGAS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_CC_NGASCCS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_BGAS_BP_E-43_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_BGAS_BP_E-45_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_BGAS_BP_E-46_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_BGAS_BP_E-47_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_BGAS_CND_E-43_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_BGAS_CND_E-45_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_BGAS_CND_E-46_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_BGAS_CND_E-47_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGAS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGAS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGAS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGAS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGAS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGAS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGAS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGAS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGASCCS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGASCCS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGASCCS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGASCCS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGASCCS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGASCCS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGASCCS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ENG_NGASCCS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_BAT-LEAD_E-82_Y-2020')=0.134492992698; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_BAT-LEAD_E-85_Y-2030')=0.100143727814; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2020')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2030')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2040')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2050')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2020')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2030')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2040')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2050')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_BAT-NAS_E-80_Y-2020')=0.082198522089; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_BAT-NAS_E-85_Y-2030')=0.065586831340; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_BAT-VRFB_E-78_Y-2020')=0.076138618402; -ANNUITYCG('NORTH_MACEDONIA','GNR_ES_ELEC_CAES_E-71_Y-2020')=0.046258849740; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-AIR_COP-340_SS-4-KW_Y-2030')=0.106552172686; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-AIR_COP-350_SS-4-KW_Y-2040')=0.106552172686; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-AIR_COP-360_SS-4-KW_Y-2050')=0.106552172686; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-AIR_COP-490_SS-3-KW_Y-2020')=0.106552172686; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-310_LS_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-330_LS_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-335_SS-4-KW_Y-2020')=0.078993328144; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-343_LS_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2030')=0.078993328144; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-360_LS_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_AIR-WTR_COP-365_SS-4-KW_Y-2050')=0.078993328144; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-1200_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-1800_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-400_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-480_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-500_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-600_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-600_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-740_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_COP-900_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-360_LS-4-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-370_SS-10-KW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-380_LS-4-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-380_SS-10-KW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-388_SS-10-KW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-395_LS-4-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-400_SS-10-KW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HP_ELEC_GROUND-WTR_COP-410_LS-4-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2020')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2030')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2040')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2050')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2020')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2030')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2040')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2050')=0.073581750329; -ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_WTR-TANK_LS_E-95_Y-2010')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_HS_HEAT_WTR-TANK_SS_E-95_Y-2010')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGAS_E-93_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGAS_E-94_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGAS_E-95_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGAS_E-96_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGASCCS_E-93_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGASCCS_E-94_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGASCCS_E-95_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_NGASCCS_E-96_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-DF_ELEC_E-100_MS-5-MW_Y-2020')=0.103217228610; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-DF_NGAS_E-100_MS-3-MW_Y-2020')=0.089941100371; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-DF_NGASCCS_E-100_MS-3-MW_Y-2020')=0.089941100371; -ANNUITYCG('NORTH_MACEDONIA','GNR_IND-DF_WOODCHI_E-100_MS-10-MW_Y-2020')=0.089941100371; -ANNUITYCG('NORTH_MACEDONIA','GNR_OFF_HUB_2GW_Y-2020')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_OFF_HUB_2GW_Y-2030')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_OFF_HUB_2GW_Y-2040')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_OFF_HUB_2GW_Y-2050')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2020')=0.053577322368; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2030')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2040')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG1_Y-2050')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2020')=0.053577322368; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2030')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2040')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG2_Y-2050')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2020')=0.053577322368; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2030')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2040')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_LS-8-MW_RG3_Y-2050')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_MS-100-KW_Y-2020')=0.053577322368; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_MS-100-KW_Y-2030')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_MS-100-KW_Y-2040')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_MS-100-KW_Y-2050')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_SS-6-KW_Y-2020')=0.053577322368; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_SS-6-KW_Y-2030')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_SS-6-KW_Y-2040')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_PV_SUN_SS-6-KW_Y-2050')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_RES_WTR_PMP_E-80_Y-2020')=0.044201845123; -ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_LS_Y-2020')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_LS_Y-2030')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_LS_Y-2040')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_LS_Y-2050')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_SS-4-KW_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_SS-4-KW_Y-2030')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_SS-4-KW_Y-2040')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_SH_SUN_SS-4-KW_Y-2050')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_CND_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_CND_E-52_LS-400-MW_Y-2030')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_CND_E-53_LS-400-MW_Y-2040')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_CND_E-54_LS-400-MW_Y-2050')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_EXT_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_EXT_E-52_LS-400-MW_Y-2030')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_EXT_E-53_LS-400-MW_Y-2040')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_COAL_EXT_E-54_LS-400-MW_Y-2050')=0.050523489324; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_CND_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_CND_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_CND_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_CND_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_NGAS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_NGAS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_NGAS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_NGASCCS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_NGASCCS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_NGASCCS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('NORTH_MACEDONIA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2030')=0.057830099134; 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-ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_MSW_E-106_LS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-105_MS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-106_MS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-106_MS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-106_MS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-95_SS-10-KW_Y-2020')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-96_SS-10-KW_Y-2030')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-96_SS-10-KW_Y-2040')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGAS_E-97_SS-10-KW_Y-2050')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGASCCS_E-105_MS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_NGASCCS_E-106_MS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_STRW_E-102_LS-6-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_STRW_E-102_LS-6-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_STRW_E-102_LS-6-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_STRW_E-102_LS-6-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODCHI_E-115_LS-7-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-100_LS-6-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-78_SS-10-KW-FEED_Y-2020')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2030')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-80_SS-8-KW-FEED_Y-2040')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_BO_WOODPEL_E-85_SS-8-KW-FEED_Y-2050')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGAS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_BP_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_BP_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_BP_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_BP_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-51_SS-10-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-53_SS-10-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-54_SS-10-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-55_SS-10-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_CND_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_EXT_E-59_LS-100-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_EXT_E-61_LS-100-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_EXT_E-62_LS-100-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_CC_NGASCCS_EXT_E-63_LS-100-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_BP_E-43_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_BP_E-45_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_BP_E-46_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_BP_E-47_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_CND_E-43_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_CND_E-45_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_CND_E-46_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_BGAS_CND_E-47_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGAS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ENG_NGASCCS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LEAD_E-82_Y-2020')=0.134492992698; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LEAD_E-85_Y-2030')=0.100143727814; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2020')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2030')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2040')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2050')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2020')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2030')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2040')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2050')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-NAS_E-80_Y-2020')=0.082198522089; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-NAS_E-85_Y-2030')=0.065586831340; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_BAT-VRFB_E-78_Y-2020')=0.076138618402; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ES_ELEC_CAES_E-71_Y-2020')=0.046258849740; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGAS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_BP_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-43_LS-40-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_GT_NGASCCS_CND_E-44_LS-40-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-AIR_COP-340_SS-4-KW_Y-2030')=0.106552172686; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-AIR_COP-350_SS-4-KW_Y-2040')=0.106552172686; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-AIR_COP-360_SS-4-KW_Y-2050')=0.106552172686; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-AIR_COP-490_SS-3-KW_Y-2020')=0.106552172686; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-310_LS_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-330_LS_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-335_SS-4-KW_Y-2020')=0.078993328144; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-343_LS_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2030')=0.078993328144; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-345_SS-4-KW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-360_LS_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_AIR-WTR_COP-365_SS-4-KW_Y-2050')=0.078993328144; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-1200_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-1800_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-400_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-480_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-500_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-600_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-600_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-740_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_COP-900_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-360_LS-4-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-370_SS-10-KW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-380_LS-4-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-380_SS-10-KW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-388_SS-10-KW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-395_LS-4-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-400_SS-10-KW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HP_ELEC_GROUND-WTR_COP-410_LS-4-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2020')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2030')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2040')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-CEN_E-70_Y-2050')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2020')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2030')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2040')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_PIT_L-DEC_E-70_Y-2050')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_WTR-TANK_LS_E-95_Y-2010')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_HS_HEAT_WTR-TANK_SS_E-95_Y-2010')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGAS_E-93_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGAS_E-94_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGAS_E-95_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGAS_E-96_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGASCCS_E-93_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGASCCS_E-94_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGASCCS_E-95_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_NGASCCS_E-96_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-BO_WOODCHI_E-90_MS-20-MW_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-DF_ELEC_E-100_MS-5-MW_Y-2020')=0.103217228610; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-DF_NGAS_E-100_MS-3-MW_Y-2020')=0.089941100371; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-DF_NGASCCS_E-100_MS-3-MW_Y-2020')=0.089941100371; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_IND-DF_WOODCHI_E-100_MS-10-MW_Y-2020')=0.089941100371; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_OFF_HUB_2GW_Y-2020')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_OFF_HUB_2GW_Y-2030')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_OFF_HUB_2GW_Y-2040')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_OFF_HUB_2GW_Y-2050')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG1_Y-2020')=0.053577322368; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG1_Y-2030')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG1_Y-2040')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG1_Y-2050')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG2_Y-2020')=0.053577322368; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG2_Y-2030')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG2_Y-2040')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG2_Y-2050')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG3_Y-2020')=0.053577322368; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG3_Y-2030')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG3_Y-2040')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_LS-8-MW_RG3_Y-2050')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_MS-100-KW_Y-2020')=0.053577322368; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_MS-100-KW_Y-2030')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_MS-100-KW_Y-2040')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_MS-100-KW_Y-2050')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_SS-6-KW_Y-2020')=0.053577322368; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_SS-6-KW_Y-2030')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_SS-6-KW_Y-2040')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_PV_SUN_SS-6-KW_Y-2050')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_RES_WTR_PMP_E-80_Y-2020')=0.044201845123; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_LS_Y-2020')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_LS_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_LS_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_LS_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_SS-4-KW_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_SS-4-KW_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_SS-4-KW_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_SH_SUN_SS-4-KW_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_CND_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_CND_E-52_LS-400-MW_Y-2030')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_CND_E-53_LS-400-MW_Y-2040')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_CND_E-54_LS-400-MW_Y-2050')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_EXT_E-49_LS-400-MW_Y-2020')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_EXT_E-52_LS-400-MW_Y-2030')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_EXT_E-53_LS-400-MW_Y-2040')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_COAL_EXT_E-54_LS-400-MW_Y-2050')=0.050523489324; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_BP_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-23_LS-220-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-23_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-23_SS-35-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_LS-220-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-24_SS-35-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_MSW_CND_E-25_LS-220-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_NGAS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_NGAS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_NGAS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_NGASCCS_BP_E-7_MS-15-MW_Y-2020')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_NGASCCS_CND_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_NGASCCS_EXT_E-47_LS-400-MW_Y-2020')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_PEAT_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_BP_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-25_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_STRW_CND_E-31_LS-132-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_BP_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-16_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_LS-600-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODCHI_CND_E-29_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_BP_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-17_SS-20-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-30_MS-80-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2030')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2040')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ST_WOODPEL_CND_E-33_LS-800-MW-FEED_Y-2050')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG1_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_6-GW_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG2_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-20M_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_OFF_HUB_RG3_SD-30M_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_ONS_SS-25-KW_Y-2020')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_ONS_SS-25-KW_Y-2030')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_ONS_SS-25-KW_Y-2040')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT_WIND_ONS_SS-25-KW_Y-2050')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-LWST100_ONS_LS_L-RG3_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP198-HH150_ONS_LS_L-RG3_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_WT-SP277-HH100_ONS_LS_L-RG3_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','LV-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NL-OFF2_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO2-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO3-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO4-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','NO5-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PL-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','PT-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','HR-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','RO-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','BG-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GR-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','IE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE1-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE2-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE3-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE3-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE3-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE3-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE4-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE4-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE4-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','SE4-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF2_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF3_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF3_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF4_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF5_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','UK-OFF5_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ELYS_ELEC_AEC_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ELYS_ELEC_AEC_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ELYS_ELEC_AEC_Y-2040')=0.055948589741; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ELYS_ELEC_AEC_Y-2050')=0.053577322368; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ELYS_ELEC_AEC_DH_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ELYS_ELEC_AEC_DH_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ELYS_ELEC_AEC_DH_Y-2040')=0.055948589741; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ELYS_ELEC_AEC_DH_Y-2050')=0.053577322368; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ELYS_ELEC_AEC_Y-2020_HUB')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ELYS_ELEC_AEC_Y-2030_HUB')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ELYS_ELEC_AEC_Y-2040_HUB')=0.055948589741; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_ELYS_ELEC_AEC_Y-2050_HUB')=0.053577322368; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_FC_H2_SOFCC_Y-2020')=0.089941100371; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_FC_H2_SOFCC_Y-2030')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_FC_H2_SOFCC_Y-2040')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_FC_H2_SOFCC_Y-2050')=0.073581750329; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_H2S_H2-TNKC_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_H2S_H2-TNKC_Y-2030')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_H2S_H2-TNKC_Y-2040')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_H2S_H2-TNKC_Y-2050')=0.057830099134; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_H2S_H2-CAVERN_Y-2020')=0.040808000038; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_H2S_H2-CAVERN_Y-2030')=0.040808000038; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_H2S_H2-CAVERN_Y-2040')=0.040808000038; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_H2S_H2-CAVERN_Y-2050')=0.040808000038; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_METHANATION-DAC_E-60_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_STEAM-REFORMING_E-70_Y-2020')=0.064011962786; -ANNUITYCG('BOSNIA_AND_HERZEGOVINA','GNR_STEAM-REFORMING-CCS_E-70_Y-2020')=0.064011962786; -ANNUITYCG('SERBIA','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('SERBIA','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('SERBIA','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('SERBIA','BE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('SERBIA','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('SERBIA','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('SERBIA','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('SERBIA','DE4-NE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('SERBIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('SERBIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('SERBIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('SERBIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('SERBIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('SERBIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('SERBIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('SERBIA','DE4-NW-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('SERBIA','DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('SERBIA','DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('SERBIA','DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('SERBIA','DK1-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('SERBIA','DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('SERBIA','DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('SERBIA','DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('SERBIA','DK1-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('SERBIA','DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('SERBIA','DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('SERBIA','DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('SERBIA','DK2-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('SERBIA','DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('SERBIA','DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('SERBIA','DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('SERBIA','DK2-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('SERBIA','EE-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('SERBIA','EE-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('SERBIA','EE-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('SERBIA','EE-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('SERBIA','ES-OFF1_WT_WIND_OFF_L-RG1_Y-2020')=0.061238540616; -ANNUITYCG('SERBIA','ES-OFF1_WT_WIND_OFF_L-RG1_Y-2030')=0.057830099134; -ANNUITYCG('SERBIA','ES-OFF1_WT_WIND_OFF_L-RG1_Y-2040')=0.057830099134; -ANNUITYCG('SERBIA','ES-OFF1_WT_WIND_OFF_L-RG1_Y-2050')=0.057830099134; -ANNUITYCG('SERBIA','ES-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('SERBIA','ES-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('SERBIA','ES-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('SERBIA','ES-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('SERBIA','FI-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('SERBIA','FI-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('SERBIA','FI-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('SERBIA','FI-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('SERBIA','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2020')=0.061238540616; -ANNUITYCG('SERBIA','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2030')=0.057830099134; -ANNUITYCG('SERBIA','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2040')=0.057830099134; -ANNUITYCG('SERBIA','FR-OFF1_WT_WIND_OFF_L-RG2_Y-2050')=0.057830099134; -ANNUITYCG('SERBIA','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2020')=0.057830099134; -ANNUITYCG('SERBIA','GNR_BO_ELEC_E-100_SS-3-KW-FEED_Y-2030')=0.057830099134; 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-ANNUITYCG('MALTA','GNR_ENG_BGAS_BP_E-43_Y-2020')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_BGAS_BP_E-45_Y-2030')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_BGAS_BP_E-46_Y-2040')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_BGAS_BP_E-47_Y-2050')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_BGAS_CND_E-43_Y-2020')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_BGAS_CND_E-45_Y-2030')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_BGAS_CND_E-46_Y-2040')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_BGAS_CND_E-47_Y-2050')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGAS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGAS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGAS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGAS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGAS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGAS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGAS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGAS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGASCCS_BP_E-47_Y-2020')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGASCCS_BP_E-48_Y-2030')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGASCCS_BP_E-49_Y-2040')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGASCCS_BP_E-50_Y-2050')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGASCCS_CND_E-47_Y-2020')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGASCCS_CND_E-48_Y-2030')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGASCCS_CND_E-49_Y-2040')=0.064011962786; -ANNUITYCG('MALTA','GNR_ENG_NGASCCS_CND_E-50_Y-2050')=0.064011962786; -ANNUITYCG('MALTA','GNR_ES_ELEC_BAT-LEAD_E-82_Y-2020')=0.134492992698; -ANNUITYCG('MALTA','GNR_ES_ELEC_BAT-LEAD_E-85_Y-2030')=0.100143727814; -ANNUITYCG('MALTA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2020')=0.073581750329; -ANNUITYCG('MALTA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2030')=0.073581750329; -ANNUITYCG('MALTA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2040')=0.073581750329; -ANNUITYCG('MALTA','GNR_ES_ELEC_BAT-LITHIO-GRID_E-86_Y-2050')=0.073581750329; -ANNUITYCG('MALTA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2020')=0.073581750329; -ANNUITYCG('MALTA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2030')=0.073581750329; -ANNUITYCG('MALTA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2040')=0.073581750329; -ANNUITYCG('MALTA','GNR_ES_ELEC_BAT-LITHIO-PEAK_E-86_Y-2050')=0.073581750329; -ANNUITYCG('MALTA','GNR_ES_ELEC_BAT-NAS_E-80_Y-2020')=0.082198522089; -ANNUITYCG('MALTA','GNR_ES_ELEC_BAT-NAS_E-85_Y-2030')=0.065586831340; -ANNUITYCG('MALTA','GNR_ES_ELEC_BAT-VRFB_E-78_Y-2020')=0.076138618402; -ANNUITYCG('MALTA','GNR_ES_ELEC_CAES_E-71_Y-2020')=0.046258849740; -ANNUITYCG('MALTA','GNR_GT_NGAS_BP_E-37_SS-5-MW_Y-2020')=0.064011962786; -ANNUITYCG('MALTA','GNR_GT_NGAS_BP_E-39_SS-5-MW_Y-2030')=0.064011962786; -ANNUITYCG('MALTA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2040')=0.064011962786; -ANNUITYCG('MALTA','GNR_GT_NGAS_BP_E-40_SS-5-MW_Y-2050')=0.064011962786; -ANNUITYCG('MALTA','GNR_GT_NGAS_BP_E-42_LS-40-MW_Y-2020')=0.064011962786; -ANNUITYCG('MALTA','GNR_GT_NGAS_BP_E-43_LS-40-MW_Y-2030')=0.064011962786; 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-ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_DH_Y-2030')=0.057830099134; -ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_DH_Y-2040')=0.055948589741; -ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_DH_Y-2050')=0.053577322368; -ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_Y-2020_HUB')=0.064011962786; -ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_Y-2030_HUB')=0.057830099134; -ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_Y-2040_HUB')=0.055948589741; -ANNUITYCG('CYPRUS','GNR_ELYS_ELEC_AEC_Y-2050_HUB')=0.053577322368; -ANNUITYCG('CYPRUS','GNR_FC_H2_SOFCC_Y-2020')=0.089941100371; -ANNUITYCG('CYPRUS','GNR_FC_H2_SOFCC_Y-2030')=0.073581750329; -ANNUITYCG('CYPRUS','GNR_FC_H2_SOFCC_Y-2040')=0.073581750329; -ANNUITYCG('CYPRUS','GNR_FC_H2_SOFCC_Y-2050')=0.073581750329; -ANNUITYCG('CYPRUS','GNR_H2S_H2-TNKC_Y-2020')=0.064011962786; -ANNUITYCG('CYPRUS','GNR_H2S_H2-TNKC_Y-2030')=0.057830099134; -ANNUITYCG('CYPRUS','GNR_H2S_H2-TNKC_Y-2040')=0.057830099134; -ANNUITYCG('CYPRUS','GNR_H2S_H2-TNKC_Y-2050')=0.057830099134; -ANNUITYCG('CYPRUS','GNR_H2S_H2-CAVERN_Y-2020')=0.040808000038; -ANNUITYCG('CYPRUS','GNR_H2S_H2-CAVERN_Y-2030')=0.040808000038; -ANNUITYCG('CYPRUS','GNR_H2S_H2-CAVERN_Y-2040')=0.040808000038; -ANNUITYCG('CYPRUS','GNR_H2S_H2-CAVERN_Y-2050')=0.040808000038; -ANNUITYCG('CYPRUS','GNR_METHANATION-DAC_E-60_Y-2020')=0.064011962786; -ANNUITYCG('CYPRUS','GNR_STEAM-REFORMING_E-70_Y-2020')=0.064011962786; -ANNUITYCG('CYPRUS','GNR_STEAM-REFORMING-CCS_E-70_Y-2020')=0.064011962786; From 7a470ce1bd597e332aabcef5b062382e3388795f Mon Sep 17 00:00:00 2001 From: TheisUM Date: Tue, 9 Jul 2024 13:22:22 +0200 Subject: [PATCH 19/21] Delete base/auxils/annuity_calculation/ANNUITYCX.inc Don't change --- base/auxils/annuity_calculation/ANNUITYCX.inc | 38 ------------------- 1 file changed, 38 deletions(-) delete mode 100644 base/auxils/annuity_calculation/ANNUITYCX.inc diff --git a/base/auxils/annuity_calculation/ANNUITYCX.inc b/base/auxils/annuity_calculation/ANNUITYCX.inc deleted file mode 100644 index 6127c2c3..00000000 --- a/base/auxils/annuity_calculation/ANNUITYCX.inc +++ /dev/null @@ -1,38 +0,0 @@ -*PARAMETER ANNUITYCX(CCC) CALCULATED WITH AUXILS - -ANNUITYCX('NORWAY')=0.050523489324; -ANNUITYCX('DENMARK')=0.050523489324; -ANNUITYCX('FINLAND')=0.050523489324; -ANNUITYCX('GERMANY')=0.050523489324; -ANNUITYCX('NETHERLANDS')=0.050523489324; -ANNUITYCX('SWEDEN')=0.050523489324; -ANNUITYCX('UNITED_KINGDOM')=0.050523489324; -ANNUITYCX('ESTONIA')=0.050523489324; -ANNUITYCX('LATVIA')=0.050523489324; -ANNUITYCX('LITHUANIA')=0.050523489324; -ANNUITYCX('POLAND')=0.050523489324; -ANNUITYCX('BELGIUM')=0.050523489324; -ANNUITYCX('FRANCE')=0.050523489324; -ANNUITYCX('ITALY')=0.050523489324; -ANNUITYCX('SWITZERLAND')=0.050523489324; -ANNUITYCX('AUSTRIA')=0.050523489324; -ANNUITYCX('CZECH_REPUBLIC')=0.050523489324; -ANNUITYCX('SPAIN')=0.050523489324; -ANNUITYCX('PORTUGAL')=0.050523489324; -ANNUITYCX('SLOVAKIA')=0.050523489324; -ANNUITYCX('HUNGARY')=0.050523489324; -ANNUITYCX('SLOVENIA')=0.050523489324; -ANNUITYCX('CROATIA')=0.050523489324; -ANNUITYCX('ROMANIA')=0.050523489324; -ANNUITYCX('BULGARIA')=0.050523489324; -ANNUITYCX('GREECE')=0.050523489324; -ANNUITYCX('IRELAND')=0.050523489324; -ANNUITYCX('LUXEMBOURG')=0.050523489324; -ANNUITYCX('ALBANIA')=0.050523489324; -ANNUITYCX('MONTENEGRO')=0.050523489324; -ANNUITYCX('NORTH_MACEDONIA')=0.050523489324; -ANNUITYCX('BOSNIA_AND_HERZEGOVINA')=0.050523489324; -ANNUITYCX('SERBIA')=0.050523489324; -ANNUITYCX('TURKEY')=0.050523489324; -ANNUITYCX('MALTA')=0.050523489324; -ANNUITYCX('CYPRUS')=0.050523489324; From 8908eb38fc0c7a93247f8a6afc306c020cc8ce17 Mon Sep 17 00:00:00 2001 From: TheisUM Date: Tue, 9 Jul 2024 13:23:02 +0200 Subject: [PATCH 20/21] Delete base/auxils/annuity_calculation/HEATTRANS_ANNUITYCXH.inc don't change --- .../HEATTRANS_ANNUITYCXH.inc | 38 ------------------- 1 file changed, 38 deletions(-) delete mode 100644 base/auxils/annuity_calculation/HEATTRANS_ANNUITYCXH.inc diff --git a/base/auxils/annuity_calculation/HEATTRANS_ANNUITYCXH.inc b/base/auxils/annuity_calculation/HEATTRANS_ANNUITYCXH.inc deleted file mode 100644 index ce0e978c..00000000 --- a/base/auxils/annuity_calculation/HEATTRANS_ANNUITYCXH.inc +++ /dev/null @@ -1,38 +0,0 @@ -*PARAMETER ANNUITYCXH(CCC) CALCULATED WITH AUXILS - -ANNUITYCXH('NORWAY')=0.050523489324; -ANNUITYCXH('DENMARK')=0.050523489324; -ANNUITYCXH('FINLAND')=0.050523489324; -ANNUITYCXH('GERMANY')=0.050523489324; -ANNUITYCXH('NETHERLANDS')=0.050523489324; -ANNUITYCXH('SWEDEN')=0.050523489324; -ANNUITYCXH('UNITED_KINGDOM')=0.050523489324; -ANNUITYCXH('ESTONIA')=0.050523489324; -ANNUITYCXH('LATVIA')=0.050523489324; -ANNUITYCXH('LITHUANIA')=0.050523489324; -ANNUITYCXH('POLAND')=0.050523489324; -ANNUITYCXH('BELGIUM')=0.050523489324; -ANNUITYCXH('FRANCE')=0.050523489324; -ANNUITYCXH('ITALY')=0.050523489324; -ANNUITYCXH('SWITZERLAND')=0.050523489324; -ANNUITYCXH('AUSTRIA')=0.050523489324; -ANNUITYCXH('CZECH_REPUBLIC')=0.050523489324; -ANNUITYCXH('SPAIN')=0.050523489324; -ANNUITYCXH('PORTUGAL')=0.050523489324; -ANNUITYCXH('SLOVAKIA')=0.050523489324; -ANNUITYCXH('HUNGARY')=0.050523489324; -ANNUITYCXH('SLOVENIA')=0.050523489324; -ANNUITYCXH('CROATIA')=0.050523489324; -ANNUITYCXH('ROMANIA')=0.050523489324; -ANNUITYCXH('BULGARIA')=0.050523489324; -ANNUITYCXH('GREECE')=0.050523489324; -ANNUITYCXH('IRELAND')=0.050523489324; -ANNUITYCXH('LUXEMBOURG')=0.050523489324; -ANNUITYCXH('ALBANIA')=0.050523489324; -ANNUITYCXH('MONTENEGRO')=0.050523489324; -ANNUITYCXH('NORTH_MACEDONIA')=0.050523489324; -ANNUITYCXH('BOSNIA_AND_HERZEGOVINA')=0.050523489324; -ANNUITYCXH('SERBIA')=0.050523489324; -ANNUITYCXH('TURKEY')=0.050523489324; -ANNUITYCXH('MALTA')=0.050523489324; -ANNUITYCXH('CYPRUS')=0.050523489324; From 0fa69eb0d049803d3965102a814e341a2f6c9182 Mon Sep 17 00:00:00 2001 From: TheisUM Date: Tue, 9 Jul 2024 13:23:27 +0200 Subject: [PATCH 21/21] Delete base/auxils/annuity_calculation/HYDROGEN_ANNUITYCXH2.inc don't change --- .../HYDROGEN_ANNUITYCXH2.inc | 38 ------------------- 1 file changed, 38 deletions(-) delete mode 100644 base/auxils/annuity_calculation/HYDROGEN_ANNUITYCXH2.inc diff --git a/base/auxils/annuity_calculation/HYDROGEN_ANNUITYCXH2.inc b/base/auxils/annuity_calculation/HYDROGEN_ANNUITYCXH2.inc deleted file mode 100644 index f986dd26..00000000 --- a/base/auxils/annuity_calculation/HYDROGEN_ANNUITYCXH2.inc +++ /dev/null @@ -1,38 +0,0 @@ -*PARAMETER ANNUITYCXH2(CCC) CALCULATED WITH AUXILS - -ANNUITYCXH2('NORWAY')=0.046550200450; -ANNUITYCXH2('DENMARK')=0.046550200450; -ANNUITYCXH2('FINLAND')=0.046550200450; -ANNUITYCXH2('GERMANY')=0.046550200450; -ANNUITYCXH2('NETHERLANDS')=0.046550200450; -ANNUITYCXH2('SWEDEN')=0.046550200450; -ANNUITYCXH2('UNITED_KINGDOM')=0.046550200450; -ANNUITYCXH2('ESTONIA')=0.046550200450; -ANNUITYCXH2('LATVIA')=0.046550200450; -ANNUITYCXH2('LITHUANIA')=0.046550200450; -ANNUITYCXH2('POLAND')=0.046550200450; -ANNUITYCXH2('BELGIUM')=0.046550200450; -ANNUITYCXH2('FRANCE')=0.046550200450; -ANNUITYCXH2('ITALY')=0.046550200450; -ANNUITYCXH2('SWITZERLAND')=0.046550200450; -ANNUITYCXH2('AUSTRIA')=0.046550200450; -ANNUITYCXH2('CZECH_REPUBLIC')=0.046550200450; -ANNUITYCXH2('SPAIN')=0.046550200450; -ANNUITYCXH2('PORTUGAL')=0.046550200450; -ANNUITYCXH2('SLOVAKIA')=0.046550200450; -ANNUITYCXH2('HUNGARY')=0.046550200450; -ANNUITYCXH2('SLOVENIA')=0.046550200450; -ANNUITYCXH2('CROATIA')=0.046550200450; -ANNUITYCXH2('ROMANIA')=0.046550200450; -ANNUITYCXH2('BULGARIA')=0.046550200450; -ANNUITYCXH2('GREECE')=0.046550200450; -ANNUITYCXH2('IRELAND')=0.046550200450; -ANNUITYCXH2('LUXEMBOURG')=0.046550200450; -ANNUITYCXH2('ALBANIA')=0.046550200450; -ANNUITYCXH2('MONTENEGRO')=0.046550200450; -ANNUITYCXH2('NORTH_MACEDONIA')=0.046550200450; -ANNUITYCXH2('BOSNIA_AND_HERZEGOVINA')=0.046550200450; -ANNUITYCXH2('SERBIA')=0.046550200450; -ANNUITYCXH2('TURKEY')=0.046550200450; -ANNUITYCXH2('MALTA')=0.046550200450; -ANNUITYCXH2('CYPRUS')=0.046550200450;

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