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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
config: fcdc215d0bedfc57a14a8d537aa21449
tags: 645f666f9bcd5a90fca523b33c5a78b7
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3 changes: 3 additions & 0 deletions _sources/about.md.txt
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# About this code

This documentation is under construction
47 changes: 47 additions & 0 deletions _sources/autoapi/aggregate_inputs/index.rst.txt
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aggregate_inputs
================

.. py:module:: aggregate_inputs
.. autoapi-nested-parse::

Spatial Aggregation of Balmorel

Loads Balmorel input and aggregates them based on a gis file that includes cluster categories for each of the old region names

Created on 01.10.2024
@author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)



Functions
---------

.. autoapisummary::

aggregate_inputs.get_symbols_to_aggregate
aggregate_inputs.merge_RRR_names
aggregate_inputs.merge_IRRRI_names
aggregate_inputs.loop_and_replace_names
aggregate_inputs.aggregate_parameter
aggregate_inputs.aggregate_sets
aggregate_inputs.main


Module Contents
---------------

.. py:function:: get_symbols_to_aggregate(incfile_folder: str, exceptions: str, mean_aggfuncs: str, median_aggfuncs: str, zero_fillnas: str)
.. py:function:: merge_RRR_names(df: pandas.DataFrame, clustering: geopandas.GeoDataFrame)
.. py:function:: merge_IRRRI_names(df: pandas.DataFrame, clustering: geopandas.GeoDataFrame)
.. py:function:: loop_and_replace_names(df: pandas.DataFrame, clustering: geopandas.GeoDataFrame, old_column: str)
.. py:function:: aggregate_parameter(db: gams.GamsDatabase, symbol: str, clustering: geopandas.GeoDataFrame, aggfunc: str, unique_names: dict, fillna: Tuple[float, int, str] = 'EPS')
.. py:function:: aggregate_sets(db: gams.GamsDatabase, symbol: str, clustering: geopandas.GeoDataFrame)
.. py:function:: main(model_path: str, scenario: str, exceptions: str = '', mean_aggfuncs: str = '', median_aggfuncs: str = '', zero_fillnas: str = '', incfile_folder: str = 'Output')
44 changes: 44 additions & 0 deletions _sources/autoapi/biomass_transport/index.rst.txt
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biomass_transport
=================

.. py:module:: biomass_transport
.. autoapi-nested-parse::

TITLE

Description

Created on 11.10.2024
@author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)



Functions
---------

.. autoapisummary::

biomass_transport.CLI
biomass_transport.transport
biomass_transport.plot_style


Module Contents
---------------

.. py:function:: CLI(ctx, dark_style: bool, plot_ext: str)
Description of the CLI


.. py:function:: transport(transport_cost: float)
Producing transport costs for fuels

Args:
transport_cost (_type_): the cost of transport in €/GJ/km


.. py:function:: plot_style(ctx, fig: matplotlib.pyplot.figure, ax: matplotlib.pyplot.axes, name: str)
66 changes: 66 additions & 0 deletions _sources/autoapi/clustering/index.rst.txt
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clustering
==========

.. py:module:: clustering
.. autoapi-nested-parse::

TITLE

Description

Created on 11.09.2024
@author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)



Attributes
----------

.. autoapisummary::

clustering.cmap
clustering.columns


Functions
---------

.. autoapisummary::

clustering.truncate_colormap
clustering.correct_VRE_data
clustering.convert_municipal_code_to_name
clustering.collect_clusterdata
clustering.apply_filters
clustering.gather_data
clustering.cluster
clustering.new_geofile
clustering.main


Module Contents
---------------

.. py:data:: cmap
.. py:function:: truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100)
.. py:function:: correct_VRE_data(path_to_file, generation_name: str)
.. py:function:: convert_municipal_code_to_name(to_be_converted: pandas.DataFrame, column_to_convert: Tuple[str, int], pivot_table: bool = False, exclude_regions: list = ['Herlev', 'Christiansø'], muni_geofile_path: str = 'C:\\Users\\mberos\\gitRepos\\balmorel-preprocessing\\src\\Data\\Shapefiles\\Denmark\\Adm\\gadm36_DNK_2.shp')
.. py:function:: collect_clusterdata(plot_cf: bool = False)
.. py:function:: apply_filters(df: pandas.DataFrame, value_name: str, aggfunc: str = 'sum')
.. py:data:: columns
.. py:function:: gather_data(db: gams.GamsDatabase, cluster_params: list, aggfuncs: list)
.. py:function:: cluster(collected_data: pandas.DataFrame, n_clusters: int, use_connectivity: bool = True, manual_corrections: list = [['Roedovre', 'Frederiksberg', 1], ['Slagelse', 'Nyborg', 0]], linkage: str = 'Ward', connection_remark: str = 'connec. included + artifical', data_remark: str = 'all combined + xy coords')
.. py:function:: new_geofile(clustering: geopandas.GeoDataFrame, plot: bool = False)
.. py:function:: main(model_path: str, scenario: str, cluster_params: str, aggregation_functions: str, cluster_size: int, plot_style: str = 'report')
37 changes: 37 additions & 0 deletions _sources/autoapi/create_addon_files/index.rst.txt
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create_addon_files
==================

.. py:module:: create_addon_files
.. autoapi-nested-parse::

Create Empty Sets for Aggregated Model

The clustered model will include exogenous heat and electricity demands from addons in DE, DE_VAR_T, DH, DH_VAR_T etc.
Thus, this script is needed to 1) create empty files for INDUSTRY_DH, INDIVUSERS_DH etc.,
and 2) to create the INDUSTRY_INDUSTRY_AAA and INDIVUSERS_INDIVUSERS_AAA etc. files required for categorising sets.

Created on 04.10.2024
@author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)



Functions
---------

.. autoapisummary::

create_addon_files.create_empty_set_files
create_addon_files.create_category_files
create_addon_files.main


Module Contents
---------------

.. py:function:: create_empty_set_files(addons: list, empty_files: list)
.. py:function:: create_category_files(addons: list, clustering: geopandas.GeoDataFrame, category_files: list, **kwargs)
.. py:function:: main(addons: Tuple[list, str] = ['INDUSTRY', 'HYDROGEN', 'INDIVUSERS'], empty_files: Tuple[list, str] = ['DE', 'DE_VAR_T', 'DH', 'DH_VAR_T', 'CCCRRRAAA', 'RRRAAA', 'AAA', 'AGKN', 'DISLOSS_E_AG'])
30 changes: 30 additions & 0 deletions _sources/autoapi/create_conversion_dictionaries/index.rst.txt
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create_conversion_dictionaries
==============================

.. py:module:: create_conversion_dictionaries
.. autoapi-nested-parse::

Is used to convert the easy-to-read-for-non-balmorel-user sets and elements to Balmorel input



Attributes
----------

.. autoapisummary::

create_conversion_dictionaries.exo_elec_dem_conversion_dictionaries
create_conversion_dictionaries.exo_heat_dem_conversion_dictionaries
create_conversion_dictionaries.exo_grid_conversion_dictionaries


Module Contents
---------------

.. py:data:: exo_elec_dem_conversion_dictionaries
.. py:data:: exo_heat_dem_conversion_dictionaries
.. py:data:: exo_grid_conversion_dictionaries
65 changes: 65 additions & 0 deletions _sources/autoapi/exo_electricity_demand/index.rst.txt
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exo_electricity_demand
======================

.. py:module:: exo_electricity_demand
.. autoapi-nested-parse::

Create electricity demand profiles for Denmark
Use Energinet data for municipalities:
https://www.energidataservice.dk/tso-electricity/consumptionindustry
Convert LAU code to municipality name with:
LAU – NUTS 2021, EU-27 and EFTA / available candidate countries
https://ec.europa.eu/eurostat/web/nuts/local-administrative-units
Note: The dataset above also contains population

Created on 22.08.2024

@author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)



Attributes
----------

.. autoapisummary::

exo_electricity_demand.cmap
exo_electricity_demand.style
exo_electricity_demand.fc


Functions
---------

.. autoapisummary::

exo_electricity_demand.main


Module Contents
---------------

.. py:data:: cmap
.. py:data:: style
:value: 'report'


.. py:data:: fc
:value: 'white'


.. py:function:: main(conversion_file: str, el_dataset: str, show_difference: bool = False)
Main function to process and convert dataset names, and create .inc files.
Args:
conversion_file (str): Path to the file containing conversion mappings.
el_dataset (str): Path to the dataset file to be processed.
show_difference (bool, optional): Flag to indicate whether to print the
dataset before and after conversion.
Defaults to False.
Returns:
None


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exo_heat_demand
===============

.. py:module:: exo_heat_demand
.. autoapi-nested-parse::

Exogenous Heat Demands

Create exogenous heat demands using data from various places

Created on 20.09.2024
@author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)



Attributes
----------

.. autoapisummary::

exo_heat_demand.cmap
exo_heat_demand.style
exo_heat_demand.fc


Classes
-------

.. autoapisummary::

exo_heat_demand.DistrictHeatAAU


Functions
---------

.. autoapisummary::

exo_heat_demand.create_incfile
exo_heat_demand.create_DH
exo_heat_demand.create_INDUSTRY_DH
exo_heat_demand.create_INDIVUSERS_DH
exo_heat_demand.create_INDUSTRY_DH_VAR_T
exo_heat_demand.main


Module Contents
---------------

.. py:data:: cmap
.. py:data:: style
:value: 'report'


.. py:data:: fc
:value: 'white'


.. py:class:: DistrictHeatAAU
.. py:attribute:: district_heat
.. py:attribute:: industry
.. py:attribute:: annual_industry_demand
.. py:method:: combine_data(plot: bool = False)
.. py:function:: create_incfile(unique_processing)
The general wrapper for creating and saving .inc files,
because the creating of the IncFile class and saving it is the same every time.
The unique processing of the .body content differs, however.

Args:
unique_processing (func): The unique processing per case
**incfile_kwargs: Keyword arguments to pass to IncFile

.. py:function:: create_DH(incfile, new_dataset: xarray.Dataset)
.. py:function:: create_INDUSTRY_DH(incfile, new_dataset: xarray.Dataset)
.. py:function:: create_INDIVUSERS_DH(incfile, new_dataset: xarray.Dataset)
.. py:function:: create_INDUSTRY_DH_VAR_T(incfile, el_new_dataset: xarray.Dataset, A_suffix: str)
.. py:function:: main(show_difference: bool = False)
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