From a3d688ce81251b7fa46d9db3a8b95832d74856a3 Mon Sep 17 00:00:00 2001 From: Radhakrishna Date: Thu, 25 May 2023 21:46:28 -0400 Subject: [PATCH 001/126] Added basic price-taker framework --- idaes/apps/grid_integration/__init__.py | 2 + .../design_and_operation_models.py | 126 +++++++++ .../multiperiod/price_taker_model.py | 260 ++++++++++++++++++ .../multiperiod/tests/test_price_taker.py | 63 +++++ 4 files changed, 451 insertions(+) create mode 100644 idaes/apps/grid_integration/multiperiod/design_and_operation_models.py create mode 100644 idaes/apps/grid_integration/multiperiod/price_taker_model.py create mode 100644 idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py diff --git a/idaes/apps/grid_integration/__init__.py b/idaes/apps/grid_integration/__init__.py index 9c02a8b98a..a08eeb13d4 100644 --- a/idaes/apps/grid_integration/__init__.py +++ b/idaes/apps/grid_integration/__init__.py @@ -15,3 +15,5 @@ from .coordinator import DoubleLoopCoordinator from .forecaster import PlaceHolderForecaster from .multiperiod.multiperiod import MultiPeriodModel +from .multiperiod.price_taker_model import PriceTakerModel +from .multiperiod.design_and_operation_models import DesignModel, OperationModel diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py new file mode 100644 index 0000000000..ec5f19c684 --- /dev/null +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -0,0 +1,126 @@ +################################################################################# +# The Institute for the Design of Advanced Energy Systems Integrated Platform +# Framework (IDAES IP) was produced under the DOE Institute for the +# Design of Advanced Energy Systems (IDAES). +# +# Copyright (c) 2018-2023 by the software owners: The Regents of the +# University of California, through Lawrence Berkeley National Laboratory, +# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon +# University, West Virginia University Research Corporation, et al. +# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md +# for full copyright and license information. +################################################################################# + +from pyomo.environ import ( + Var, + Binary, + Expression, +) + +from idaes.core.base.process_base import declare_process_block_class +from idaes.models.unit_models import SkeletonUnitModelData +from pyomo.common.config import ConfigValue, In + + +@declare_process_block_class("DesignModel") +class DesignModelData(SkeletonUnitModelData): + """ + Class for containing design model ... + """ + CONFIG = SkeletonUnitModelData.CONFIG() + CONFIG.declare("model_func", ConfigValue( + doc="Function that builds the design model", + )) + CONFIG.declare("model_args", ConfigValue( + default={}, + doc="Dictionary containing arguements needed for model_func", + )) + CONFIG.declare("declare_cost_expr", ConfigValue( + default=True, + domain=In([True, False]), + doc="Should capex and fom attributes be defined?", + )) + + # noinspection PyAttributeOutsideInit + def build(self): + super().build() + + # Build the model + self.config.model_func(self, **self.config.model_args) + + cost_expr_list = { + "capex": "CAPEX of the unit/process", + "fom": "Annual Fixed O&M cost for the unit/process", + } + + if self.config.declare_cost_expr: + for ce in cost_expr_list.keys(): + if not hasattr(self, ce): + # TODO: Use IDAES warning + print(f"declare_cost_expr is set to True, but {ce} is not defined in {self.config.model_func.__name__}") + print(f"setting {ce} to zero in {self.name}") + setattr(self, ce, Expression(expr=0, doc=cost_expr_list[ce])) + + +@declare_process_block_class("OperationModel") +class OperationModelData(SkeletonUnitModelData): + """ + Class for containing design model ... + """ + CONFIG = SkeletonUnitModelData.CONFIG() + CONFIG.declare("model_func", ConfigValue( + doc="Function that builds the design model", + )) + CONFIG.declare("model_args", ConfigValue( + default={}, + doc="Dictionary containing arguements needed for model_func", + )) + CONFIG.declare("declare_cost_expr", ConfigValue( + default=True, + domain=In([True, False]), + doc="Should capex and fom attributes be defined?", + )) + CONFIG.declare("declare_op_vars", ConfigValue( + default=True, + domain=In([True, False]), + doc="Should op_mode, startup, shutdown vars be defined?", + )) + + # noinspection PyAttributeOutsideInit + def build(self): + super().build() + + # Build the model + if self.config.declare_op_vars: + self.op_mode = Var( + within=Binary, + doc="Binary var: 1 if the unit is operating, 0 otherwise", + ) + + self.startup = Var( + within=Binary, + doc="Binary var: 1 if the startup is initiated, 0 otherwise", + ) + + self.shutdown = Var( + within=Binary, + doc="Binary var: 1 if the shutdown is initiated, 0 otherwise", + ) + + self.config.model_func(self, **self.config.model_args) + + cost_expr_list = { + "non_fuel_vom": "Variable O&M cost excluding fuel/power", + "fuel_cost": "Cost of fuel/power required for the unit/process", + "elec_revenue": "Electricity revenue generated by the unit/process", + "carbon_price": "Carbon price associated with CO2 emissions", + } + + if self.config.declare_cost_expr: + for ce in cost_expr_list.keys(): + if not hasattr(self, ce): + # TODO: Use IDAES warning + print(f"declare_cost_expr is set to True, but {ce} is not defined in {self.config.model_func.__name__}") + print(f"setting {ce} to zero in {self.name}") + setattr(self, ce, Expression(expr=0, doc=cost_expr_list[ce])) + \ No newline at end of file diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py new file mode 100644 index 0000000000..a6d21b7912 --- /dev/null +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -0,0 +1,260 @@ +################################################################################# +# The Institute for the Design of Advanced Energy Systems Integrated Platform +# Framework (IDAES IP) was produced under the DOE Institute for the +# Design of Advanced Energy Systems (IDAES). +# +# Copyright (c) 2018-2023 by the software owners: The Regents of the +# University of California, through Lawrence Berkeley National Laboratory, +# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon +# University, West Virginia University Research Corporation, et al. +# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md +# for full copyright and license information. +################################################################################# + +import pandas as pd + +from pyomo.environ import ( + ConcreteModel, + Block, + Var, + RangeSet, + Objective, + Constraint, + NonNegativeReals, + Expression, + maximize, +) + +from idaes.apps.grid_integration import MultiPeriodModel +from idaes.apps.grid_integration.multiperiod.design_and_operation_models \ + import DesignModelData, OperationModelData + + +class PriceTakerModel(ConcreteModel): + + @staticmethod + def get_elbow_plot(): + """ + Generates metrics for determining the appropriate number of clusters needed for a + given price signal. Available metrics include elbow_plot, ... + + Args: + + Returns: + """ + pass + + @staticmethod + def cluster_lmp_data(data, n_clusters, horizon_length): + """ + Clusters the given price signal in n_clusters. This method supports k-means, k-meteiod,... + techniques for clustering. + + Args: + + Returns: + """ + lmp_data = {1: {1: 2, 2: 3, 3: 5}, 2: {1: 2, 2: 3, 3: 5}} + weights = {1: 45, 2: 56} + + return lmp_data, weights + + def append_lmp_data( + self, + filename, + column_name="price", + n_clusters=None, + horizon_length=None, + ): + full_data = pd.read_csv(filename) + + if isinstance(column_name, list) and n_clusters is not None: + # Multiple years and representative days + self.set_years = [int(y) for y in column_name] + self.set_days = RangeSet(n_clusters) + self._n_time_points = horizon_length if horizon_length is not None else 24 + self.set_time = RangeSet(self._n_time_points) + + self.LMP = {} + self.WEIGHTS = {} + + for year in column_name: + price_data = full_data[year].to_list() + lmp_data, weights = self.cluster_lmp_data(price_data, n_clusters, horizon_length) + y = int(year) + + for d in self.set_days: + for t in self.set_time: + self.LMP[t, d, y] = lmp_data[d][t] + self.WEIGHTS[d, y] = weights[d] + + return + + elif isinstance(column_name, list): + # Multiple years, use fullyear price signal for each year + self.set_years = [int(y) for y in column_name] + self.set_days = None + self._n_time_points = len(full_data) + self.set_time = RangeSet(self._n_time_points) + + self.LMP = {} + + for year in column_name: + price_data = full_data[year].to_list() + y = int(year) + + for t in self.set_time: + self.LMP[t, y] = price_data[t-1] + + return + + elif n_clusters is not None: + # Single price signal, use reprentative days + self.set_years = None + self.set_days = RangeSet(n_clusters) + self._n_time_points = horizon_length if horizon_length is not None else 24 + self.set_time = RangeSet(self._n_time_points) + + self.LMP = {} + self.WEIGHTS = {} + + price_data = full_data[year].to_list() + lmp_data, weights = self.cluster_lmp_data(price_data, n_clusters, horizon_length) + + for d in self.set_days: + for t in self.set_time: + self.LMP[t, d] = lmp_data[d][t] + self.WEIGHTS[d] = weights[d] + + return + + else: + # Single price signal, use full year's price signal + self.set_years = None + self.set_days = None + self._n_time_points = len(full_data) + self.set_time = RangeSet(self._n_time_points) + + price_data = full_data[column_name].to_list() + self.LMP = {t: price_data[t-1] for t in self.set_time} + + return + + def build_multiperiod_model(self, **kwargs): + + if not self.model_sets_available: + raise Exception( + "Model sets have not been defined. Run get_lmp_data to construct model sets" + ) + + self.mp_model = MultiPeriodModel( + n_time_points=self._n_time_points, + set_days=self.set_days, + set_years=self.set_years, + use_stochastic_build=True, + **kwargs, + ) + + def add_ramping_constraints(self, var, ramp_up_limit, ramp_down_limit): + """ + Adds ramping constraints of the form + -ramp_down_limit <= var(t) - var(t-1) <= ramp_up_limit on var + """ + pass + + def add_startup_shutdown(self): + """ + Adds startup/shutdown and minimum uptime/downtime constraints on + a given unit/process + """ + pass + + def build_hourly_cashflows(self): + period = self.mp_model.period + + for p in period: + non_fuel_vom = 0 + fuel_cost = 0 + elec_revenue = 0 + carbon_price = 0 + + for blk in period[p].component_data_objects(Block): + if isinstance(blk, OperationModelData): + non_fuel_vom += blk.non_fuel_vom + fuel_cost += blk.fuel_cost + elec_revenue += blk.elec_revenue + carbon_price += blk.carbon_price + + period[p].non_fuel_vom = Expression(expr=non_fuel_vom) + period[p].fuel_cost = Expression(expr=fuel_cost) + period[p].elec_revenue = Expression(expr=elec_revenue) + period[p].carbon_price = Expression(expr=carbon_price) + + period[p].net_cash_inflow = Expression( + expr=period[p].elec_revenue - period[p].non_fuel_vom + - period[p].fuel_cost - period[p].carbon_price + ) + + def build_cashflows( + self, + lifetime=30, + discount_rate=0.08, + corp_tax=0.2, + other_costs=0, + other_revenue=0, + objective="NPV" + ): + """ + Builds overall cashflow expressions and appends objective function + to the model + """ + + capex_expr = 0 + fom_expr = 0 + for blk in self.component_data_objects(Block): + if isinstance(blk, DesignModelData): + capex_expr += blk.capex + fom_expr += blk.fom + + self.CAPEX = Var(within=NonNegativeReals, doc="Total CAPEX") + self.capex_calculation = Constraint(expr=self.CAPEX == capex_expr) + + self.FOM = Var(within=NonNegativeReals, doc="Yearly Fixed O&M") + self.fom_calculation = Constraint(expr=self.FOM == fom_expr) + + self.DEPRECIATION = Var(within=NonNegativeReals, doc="Yearly depreciation") + self.dep_calculation = Constraint(expr=self.DEPRECIATION == self.CAPEX / lifetime) + + self.NET_CASH_INFLOW = Var(doc="Net cash inflow") + self.net_cash_inflow_calculation = Constraint( + expr=self.NET_CASH_INFLOW == + sum(self.mp_model[p].net_cash_inflow for p in self.mp_model) + ) + + self.CORP_TAX = Var(within=NonNegativeReals, doc="Corporate tax") + self.corp_tax_calculation = Constraint( + expr=self.CORP_TAX >= corp_tax * + (self.NET_CASH_INFLOW + other_revenue - other_costs - self.FOM - self.DEPRECIATION) + ) + + self.NET_PROFIT = Var(doc="Net profit after taxes") + self.net_profit_calculation = Constraint( + expr=self.NET_PROFIT == self.NET_CASH_INFLOW + other_revenue + - other_costs - self.FOM - self.CORP_TAX + ) + + constant_cf_factor = (1 - (1 + discount_rate) ** (- lifetime)) / discount_rate + self.NPV = Expression(expr=constant_cf_factor * self.NET_PROFIT - self.CAPEX) + self.Annualized_NPV = Expression( + expr=self.NET_PROFIT - (1 / constant_cf_factor) * self.CAPEX, + ) + + + if objective == "NPV": + self.obj = Objective(expr=self.NPV, sense=maximize) + + elif objective == "Annualized NPV": + self.obj = Objective(expr=self.Annualized_NPV, sense=maximize) + + elif objective == "Net Profit": + self.obj = Objective(expr=self.NET_PROFIT, sense=maximize) diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py new file mode 100644 index 0000000000..44b007a074 --- /dev/null +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -0,0 +1,63 @@ +################################################################################# +# The Institute for the Design of Advanced Energy Systems Integrated Platform +# Framework (IDAES IP) was produced under the DOE Institute for the +# Design of Advanced Energy Systems (IDAES). +# +# Copyright (c) 2018-2023 by the software owners: The Regents of the +# University of California, through Lawrence Berkeley National Laboratory, +# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon +# University, West Virginia University Research Corporation, et al. +# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md +# for full copyright and license information. +################################################################################# + + +from pyomo.environ import ( + Var, + Binary, + NonNegativeReals, + Constraint, + Expression, +) + +from idaes.apps.grid_integration import DesignModel, OperationModel + + +def dfc_design(m, params, capacity_range=(650, 900)): + _dfc_capacity = params["dfc_capacity"] + _ng_flow = params["ng_flow"] + _capex = params["capex"] + _fom_factor = params["fom_factor"] + + m.capacity = Var( + within=NonNegativeReals, + initialize=_dfc_capacity, + bounds=(0, capacity_range[1]), + doc="Capacity of the power plant [in MW]", + ) + + # Define a variable that informs whether the plant needs to built or not. + m.build_unit = Var( + within=Binary, + doc="1: Plant is built, 0: Plant is not built", + ) + + # Bound the capcity of the plant in the specified range + m.capacity_lb_con = Constraint(expr=m.capacity >= m.build_unit * capacity_range[0]) + m.capacity_ub_con = Constraint(expr=m.capacity <= m.build_unit * capacity_range[1]) + + # Compute the natural gas flowrate required at maximum capacity. + m.ng_flow = Expression( + expr=m.capacity * (_ng_flow / _dfc_capacity), + doc="Computes the natural flowrate required [in kg/s] at full load", + ) + + # Define an expression for CAPEX and FOM + m.capex = Expression( + expr=_capex * (m.capacity / _dfc_capacity), + doc="CAPEX of the power cycle [in 1000$]", + ) + m.fom = Expression( + expr=_fom_factor * m.capex, + doc="Fixed O&M cost [in 1000$/year]", + ) \ No newline at end of file From 67ce6a24d0a60542f1e7a0ff68a8ca5c617c0b21 Mon Sep 17 00:00:00 2001 From: adam-a-a Date: Thu, 8 Jun 2023 15:46:00 -0400 Subject: [PATCH 002/126] run black --- .../design_and_operation_models.py | 106 +++++++++++------- .../multiperiod/price_taker_model.py | 75 ++++++++----- .../multiperiod/tests/test_price_taker.py | 6 +- 3 files changed, 116 insertions(+), 71 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py index ec5f19c684..480b376b4f 100644 --- a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -27,19 +27,29 @@ class DesignModelData(SkeletonUnitModelData): """ Class for containing design model ... """ + CONFIG = SkeletonUnitModelData.CONFIG() - CONFIG.declare("model_func", ConfigValue( - doc="Function that builds the design model", - )) - CONFIG.declare("model_args", ConfigValue( - default={}, - doc="Dictionary containing arguements needed for model_func", - )) - CONFIG.declare("declare_cost_expr", ConfigValue( - default=True, - domain=In([True, False]), - doc="Should capex and fom attributes be defined?", - )) + CONFIG.declare( + "model_func", + ConfigValue( + doc="Function that builds the design model", + ), + ) + CONFIG.declare( + "model_args", + ConfigValue( + default={}, + doc="Dictionary containing arguements needed for model_func", + ), + ) + CONFIG.declare( + "declare_cost_expr", + ConfigValue( + default=True, + domain=In([True, False]), + doc="Should capex and fom attributes be defined?", + ), + ) # noinspection PyAttributeOutsideInit def build(self): @@ -49,15 +59,17 @@ def build(self): self.config.model_func(self, **self.config.model_args) cost_expr_list = { - "capex": "CAPEX of the unit/process", - "fom": "Annual Fixed O&M cost for the unit/process", + "capex": "CAPEX of the unit/process", + "fom": "Annual Fixed O&M cost for the unit/process", } if self.config.declare_cost_expr: for ce in cost_expr_list.keys(): - if not hasattr(self, ce): + if not hasattr(self, ce): # TODO: Use IDAES warning - print(f"declare_cost_expr is set to True, but {ce} is not defined in {self.config.model_func.__name__}") + print( + f"declare_cost_expr is set to True, but {ce} is not defined in {self.config.model_func.__name__}" + ) print(f"setting {ce} to zero in {self.name}") setattr(self, ce, Expression(expr=0, doc=cost_expr_list[ce])) @@ -67,24 +79,37 @@ class OperationModelData(SkeletonUnitModelData): """ Class for containing design model ... """ + CONFIG = SkeletonUnitModelData.CONFIG() - CONFIG.declare("model_func", ConfigValue( - doc="Function that builds the design model", - )) - CONFIG.declare("model_args", ConfigValue( - default={}, - doc="Dictionary containing arguements needed for model_func", - )) - CONFIG.declare("declare_cost_expr", ConfigValue( - default=True, - domain=In([True, False]), - doc="Should capex and fom attributes be defined?", - )) - CONFIG.declare("declare_op_vars", ConfigValue( - default=True, - domain=In([True, False]), - doc="Should op_mode, startup, shutdown vars be defined?", - )) + CONFIG.declare( + "model_func", + ConfigValue( + doc="Function that builds the design model", + ), + ) + CONFIG.declare( + "model_args", + ConfigValue( + default={}, + doc="Dictionary containing arguements needed for model_func", + ), + ) + CONFIG.declare( + "declare_cost_expr", + ConfigValue( + default=True, + domain=In([True, False]), + doc="Should capex and fom attributes be defined?", + ), + ) + CONFIG.declare( + "declare_op_vars", + ConfigValue( + default=True, + domain=In([True, False]), + doc="Should op_mode, startup, shutdown vars be defined?", + ), + ) # noinspection PyAttributeOutsideInit def build(self): @@ -93,7 +118,7 @@ def build(self): # Build the model if self.config.declare_op_vars: self.op_mode = Var( - within=Binary, + within=Binary, doc="Binary var: 1 if the unit is operating, 0 otherwise", ) @@ -110,17 +135,18 @@ def build(self): self.config.model_func(self, **self.config.model_args) cost_expr_list = { - "non_fuel_vom": "Variable O&M cost excluding fuel/power", - "fuel_cost": "Cost of fuel/power required for the unit/process", - "elec_revenue": "Electricity revenue generated by the unit/process", + "non_fuel_vom": "Variable O&M cost excluding fuel/power", + "fuel_cost": "Cost of fuel/power required for the unit/process", + "elec_revenue": "Electricity revenue generated by the unit/process", "carbon_price": "Carbon price associated with CO2 emissions", } if self.config.declare_cost_expr: for ce in cost_expr_list.keys(): - if not hasattr(self, ce): + if not hasattr(self, ce): # TODO: Use IDAES warning - print(f"declare_cost_expr is set to True, but {ce} is not defined in {self.config.model_func.__name__}") + print( + f"declare_cost_expr is set to True, but {ce} is not defined in {self.config.model_func.__name__}" + ) print(f"setting {ce} to zero in {self.name}") setattr(self, ce, Expression(expr=0, doc=cost_expr_list[ce])) - \ No newline at end of file diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index a6d21b7912..0fe2db1c50 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -26,12 +26,13 @@ ) from idaes.apps.grid_integration import MultiPeriodModel -from idaes.apps.grid_integration.multiperiod.design_and_operation_models \ - import DesignModelData, OperationModelData +from idaes.apps.grid_integration.multiperiod.design_and_operation_models import ( + DesignModelData, + OperationModelData, +) class PriceTakerModel(ConcreteModel): - @staticmethod def get_elbow_plot(): """ @@ -40,7 +41,7 @@ def get_elbow_plot(): Args: - Returns: + Returns: """ pass @@ -56,13 +57,13 @@ def cluster_lmp_data(data, n_clusters, horizon_length): """ lmp_data = {1: {1: 2, 2: 3, 3: 5}, 2: {1: 2, 2: 3, 3: 5}} weights = {1: 45, 2: 56} - + return lmp_data, weights def append_lmp_data( - self, - filename, - column_name="price", + self, + filename, + column_name="price", n_clusters=None, horizon_length=None, ): @@ -80,7 +81,9 @@ def append_lmp_data( for year in column_name: price_data = full_data[year].to_list() - lmp_data, weights = self.cluster_lmp_data(price_data, n_clusters, horizon_length) + lmp_data, weights = self.cluster_lmp_data( + price_data, n_clusters, horizon_length + ) y = int(year) for d in self.set_days: @@ -104,7 +107,7 @@ def append_lmp_data( y = int(year) for t in self.set_time: - self.LMP[t, y] = price_data[t-1] + self.LMP[t, y] = price_data[t - 1] return @@ -119,7 +122,9 @@ def append_lmp_data( self.WEIGHTS = {} price_data = full_data[year].to_list() - lmp_data, weights = self.cluster_lmp_data(price_data, n_clusters, horizon_length) + lmp_data, weights = self.cluster_lmp_data( + price_data, n_clusters, horizon_length + ) for d in self.set_days: for t in self.set_time: @@ -136,7 +141,7 @@ def append_lmp_data( self.set_time = RangeSet(self._n_time_points) price_data = full_data[column_name].to_list() - self.LMP = {t: price_data[t-1] for t in self.set_time} + self.LMP = {t: price_data[t - 1] for t in self.set_time} return @@ -164,7 +169,7 @@ def add_ramping_constraints(self, var, ramp_up_limit, ramp_down_limit): def add_startup_shutdown(self): """ - Adds startup/shutdown and minimum uptime/downtime constraints on + Adds startup/shutdown and minimum uptime/downtime constraints on a given unit/process """ pass @@ -189,20 +194,22 @@ def build_hourly_cashflows(self): period[p].fuel_cost = Expression(expr=fuel_cost) period[p].elec_revenue = Expression(expr=elec_revenue) period[p].carbon_price = Expression(expr=carbon_price) - + period[p].net_cash_inflow = Expression( - expr=period[p].elec_revenue - period[p].non_fuel_vom - - period[p].fuel_cost - period[p].carbon_price + expr=period[p].elec_revenue + - period[p].non_fuel_vom + - period[p].fuel_cost + - period[p].carbon_price ) def build_cashflows( - self, + self, lifetime=30, discount_rate=0.08, corp_tax=0.2, other_costs=0, other_revenue=0, - objective="NPV" + objective="NPV", ): """ Builds overall cashflow expressions and appends objective function @@ -223,33 +230,45 @@ def build_cashflows( self.fom_calculation = Constraint(expr=self.FOM == fom_expr) self.DEPRECIATION = Var(within=NonNegativeReals, doc="Yearly depreciation") - self.dep_calculation = Constraint(expr=self.DEPRECIATION == self.CAPEX / lifetime) + self.dep_calculation = Constraint( + expr=self.DEPRECIATION == self.CAPEX / lifetime + ) self.NET_CASH_INFLOW = Var(doc="Net cash inflow") self.net_cash_inflow_calculation = Constraint( - expr=self.NET_CASH_INFLOW == - sum(self.mp_model[p].net_cash_inflow for p in self.mp_model) + expr=self.NET_CASH_INFLOW + == sum(self.mp_model[p].net_cash_inflow for p in self.mp_model) ) - + self.CORP_TAX = Var(within=NonNegativeReals, doc="Corporate tax") self.corp_tax_calculation = Constraint( - expr=self.CORP_TAX >= corp_tax * - (self.NET_CASH_INFLOW + other_revenue - other_costs - self.FOM - self.DEPRECIATION) + expr=self.CORP_TAX + >= corp_tax + * ( + self.NET_CASH_INFLOW + + other_revenue + - other_costs + - self.FOM + - self.DEPRECIATION + ) ) self.NET_PROFIT = Var(doc="Net profit after taxes") self.net_profit_calculation = Constraint( - expr=self.NET_PROFIT == self.NET_CASH_INFLOW + other_revenue - - other_costs - self.FOM - self.CORP_TAX + expr=self.NET_PROFIT + == self.NET_CASH_INFLOW + + other_revenue + - other_costs + - self.FOM + - self.CORP_TAX ) - constant_cf_factor = (1 - (1 + discount_rate) ** (- lifetime)) / discount_rate + constant_cf_factor = (1 - (1 + discount_rate) ** (-lifetime)) / discount_rate self.NPV = Expression(expr=constant_cf_factor * self.NET_PROFIT - self.CAPEX) self.Annualized_NPV = Expression( expr=self.NET_PROFIT - (1 / constant_cf_factor) * self.CAPEX, ) - if objective == "NPV": self.obj = Objective(expr=self.NPV, sense=maximize) diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index 44b007a074..6de585b72b 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -36,7 +36,7 @@ def dfc_design(m, params, capacity_range=(650, 900)): doc="Capacity of the power plant [in MW]", ) - # Define a variable that informs whether the plant needs to built or not. + # Define a variable that informs whether the plant needs to built or not. m.build_unit = Var( within=Binary, doc="1: Plant is built, 0: Plant is not built", @@ -46,7 +46,7 @@ def dfc_design(m, params, capacity_range=(650, 900)): m.capacity_lb_con = Constraint(expr=m.capacity >= m.build_unit * capacity_range[0]) m.capacity_ub_con = Constraint(expr=m.capacity <= m.build_unit * capacity_range[1]) - # Compute the natural gas flowrate required at maximum capacity. + # Compute the natural gas flowrate required at maximum capacity. m.ng_flow = Expression( expr=m.capacity * (_ng_flow / _dfc_capacity), doc="Computes the natural flowrate required [in kg/s] at full load", @@ -60,4 +60,4 @@ def dfc_design(m, params, capacity_range=(650, 900)): m.fom = Expression( expr=_fom_factor * m.capex, doc="Fixed O&M cost [in 1000$/year]", - ) \ No newline at end of file + ) From e716c36a6b30fb55e9f92dbb54acee184e183695 Mon Sep 17 00:00:00 2001 From: adam-a-a Date: Thu, 8 Jun 2023 16:05:29 -0400 Subject: [PATCH 003/126] add in get_optimal_n_clusters as separate method. hand off to Marcus --- .../multiperiod/price_taker_model.py | 22 +++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 0fe2db1c50..0e642995c0 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -33,6 +33,27 @@ class PriceTakerModel(ConcreteModel): + @staticmethod + def get_optimal_n_clusters(data, kmin=None, kmax=None): + """ + Determines the appropriate number of clusters needed for a + given price signal. + + Args: + + Returns: + """ + if kmin is None: + kmin = 1 + if kmax is None: + # setting an arbitrary kmax. Maybe log warning that no kmax was set and default of 14 used? + kmax = 14 + k_values = range(kmin, kmax) + + # Leaving for Marcus + + pass + @staticmethod def get_elbow_plot(): """ @@ -43,6 +64,7 @@ def get_elbow_plot(): Returns: """ + # can use get_optimal_n_clusters here then plot pass @staticmethod From e3788fb3f00be64913116a1cd3559a0653a7dac2 Mon Sep 17 00:00:00 2001 From: MarcusHolly Date: Fri, 9 Jun 2023 14:05:04 -0400 Subject: [PATCH 004/126] Add methods for computing the optimal # of clusters and making an elbow plot --- .../multiperiod/price_taker_model.py | 122 ++++++++++++++++-- 1 file changed, 114 insertions(+), 8 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 0e642995c0..a536f06108 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -31,41 +31,147 @@ OperationModelData, ) +from sklearn.cluster import KMeans +from kneed import KneeLocator + +import matplotlib.pyplot as plt + +import logging + +_logger = logging.getLogger(__name__) + class PriceTakerModel(ConcreteModel): @staticmethod - def get_optimal_n_clusters(data, kmin=None, kmax=None): + def generate_daily_data(raw_data, day_list): + + daily_data = pd.DataFrame(columns=day_list) + + # Extracting data to populate empty dataframe + i = 0 + j = 24 + day = 1 + while j <= len(raw_data): + daily_data[day] = raw_data[i:j].reset_index(drop=True) + i = j + j = j + 24 + day = day + 1 + + return daily_data + + @staticmethod + def reconfigure_raw_data(self, raw_data): + """ + Reconfigures the raw price series data into a usable form + + Args: + raw_data: imported price series data + + Returns: + daily_data: reconfigured price series data + """ + # Get column headings + column_head = raw_data.columns.tolist() + # Remove the date/time column + scenarios = column_head[1:] + + # Creating an empty dataframe to store daily data for clustering + day_list = list(range(1, (len(raw_data) // 24) + 1)) + + # Generate daily data + for i in scenarios[0:1]: + daily_data = self.generate_daily_data( + raw_data=raw_data[i], day_list=day_list + ) + + return daily_data + + @staticmethod + def get_optimal_n_clusters(daily_data, kmin=None, kmax=None, sample_weight=None): """ Determines the appropriate number of clusters needed for a given price signal. Args: + daily_data: reconfigured price series data + kmin: minimum number of clusters + kmax: maximum number of clusters + sample_weight: applies a weight to each entry in daily_data Returns: + n_clusters: the optimal number of clusters for the given data """ if kmin is None: kmin = 1 if kmax is None: # setting an arbitrary kmax. Maybe log warning that no kmax was set and default of 14 used? kmax = 14 + + if sample_weight is not None: + if len(daily_data) != len(sample_weight): + _logger.error( + f"len(daily_data) != len(sample_weight).\n " + f"Ensure that the dimensions of the datasets match or set sample_weight to None." + ) + k_values = range(kmin, kmax) + inertia_values = [] - # Leaving for Marcus + # Compute the inertia (SSE) for k clusters + for k in k_values: + kmeans = KMeans(n_clusters=k).fit( + daily_data.transpose(), sample_weight=sample_weight + ) + inertia_values.append(kmeans.inertia_) - pass + # Identify the "elbow point" + elbow_point = KneeLocator( + k_values, inertia_values, curve="convex", direction="decreasing" + ) + n_clusters = elbow_point.knee + + print(f"Optimal # of clusters is: {n_clusters}") + + return n_clusters, inertia_values @staticmethod - def get_elbow_plot(): + def get_elbow_plot(self, daily_data, kmin=None, kmax=None, sample_weight=None): """ - Generates metrics for determining the appropriate number of clusters needed for a - given price signal. Available metrics include elbow_plot, ... + Determines the appropriate number of clusters needed for a + given price signal. Args: + daily_data: reconfigured price series data + kmin: minimum number of clusters + kmax: maximum number of clusters + sample_weight: applies a weight to each entry in daily_data Returns: + k_values: range of clusters from kmin to kmax (x-axis of elbow plot) + inertia_values: within-cluster sum-of-squares (y-axis of elbow plot) """ - # can use get_optimal_n_clusters here then plot - pass + if kmin is None: + kmin = 1 + if kmax is None: + # setting an arbitrary kmax. Maybe log warning that no kmax was set and default of 14 used? + kmax = 14 + + k_values = range(kmin, kmax) + n_clusters, inertia_values = self.get_optimal_n_clusters( + daily_data, kmin, kmax, sample_weight + ) + + plt.show() + plt.plot(k_values, inertia_values) + plt.axvline(x=n_clusters, color="red", linestyle="--", label="Elbow") + plt.xlabel("Number of clusters") + plt.ylabel("Inertia") + plt.title("Elbow Method") + plt.xlim(kmin, kmax) + plt.grid() + plt.show() + + return k_values, inertia_values @staticmethod def cluster_lmp_data(data, n_clusters, horizon_length): From 422d0bae4541c7698e3ebe79b50e6301421ecb6f Mon Sep 17 00:00:00 2001 From: adam-a-a Date: Mon, 12 Jun 2023 10:53:18 -0400 Subject: [PATCH 005/126] add fom to typos.toml in github/workflows to pass spellcheck --- .github/workflows/typos.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/typos.toml b/.github/workflows/typos.toml index 13bcee5705..b767deddfb 100644 --- a/.github/workflows/typos.toml +++ b/.github/workflows/typos.toml @@ -36,5 +36,8 @@ equil = "equil" astroid = "astroid" # delt == delta delt = "delt" +# FOM for fixed operating and maintenance for DISPATCHES +FOM = "FOM" +fom = "fom" # TEMPORARY - Remove after example update upadate = "upadate" From 2f36bbb0d8ab6a9e870744b714e4b98f2346dd37 Mon Sep 17 00:00:00 2001 From: adam-a-a Date: Mon, 12 Jun 2023 10:58:14 -0400 Subject: [PATCH 006/126] fix typo --- .../multiperiod/design_and_operation_models.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py index 480b376b4f..54826fbbc7 100644 --- a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -39,7 +39,7 @@ class DesignModelData(SkeletonUnitModelData): "model_args", ConfigValue( default={}, - doc="Dictionary containing arguements needed for model_func", + doc="Dictionary containing arguments needed for model_func", ), ) CONFIG.declare( @@ -91,7 +91,7 @@ class OperationModelData(SkeletonUnitModelData): "model_args", ConfigValue( default={}, - doc="Dictionary containing arguements needed for model_func", + doc="Dictionary containing arguments needed for model_func", ), ) CONFIG.declare( From 0aa103472d00014f51130523863e460c18c2cab7 Mon Sep 17 00:00:00 2001 From: MarcusHolly Date: Tue, 13 Jun 2023 14:40:39 -0400 Subject: [PATCH 007/126] Add tests for new functions (excel import failing) --- .../multiperiod/price_taker_model.py | 5 +- .../multiperiod/tests/FLECCS.xlsx | Bin 0 -> 1794331 bytes .../multiperiod/tests/test_price_taker.py | 79 ++++++++++++++++++ 3 files changed, 82 insertions(+), 2 deletions(-) create mode 100644 idaes/apps/grid_integration/multiperiod/tests/FLECCS.xlsx diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index a536f06108..57655877db 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -106,11 +106,12 @@ def get_optimal_n_clusters(daily_data, kmin=None, kmax=None, sample_weight=None) if kmax is None: # setting an arbitrary kmax. Maybe log warning that no kmax was set and default of 14 used? kmax = 14 + if kmin > kmax: + _logger.error(f"kmin:{kmin} needs to be less than kmax:{kmax}.") if sample_weight is not None: - if len(daily_data) != len(sample_weight): + if daily_data.shape != sample_weight.shape: _logger.error( - f"len(daily_data) != len(sample_weight).\n " f"Ensure that the dimensions of the datasets match or set sample_weight to None." ) diff --git a/idaes/apps/grid_integration/multiperiod/tests/FLECCS.xlsx b/idaes/apps/grid_integration/multiperiod/tests/FLECCS.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..826712d758bb4cabf71f1b61be8a7be3515aaa59 GIT binary patch literal 1794331 zcmeFX^Oq!F6en2j>aOatZQE9tZ5v&-)n(hZZL>>Rwr$(4&Hm2pIlE_P|ALwLA@ZDz z7m@efcR#oeMjPyc&;lA@=4Z9@SQ24%kLrw8$;B7Zc8xa~+is~1UdE3eSey&b; zDF&AazJNImNGX*PJ}h%EB0?P_$}NGZFH%OFjZTp^W{`ma&t}~NG3>*rz1mtkJf0W> zR(#%%`)aC3I<=yA2LnEPo9t{T^VRg_{6@v0OQ)Rem<)v>t)o#P3m?SRyy#2*8T&6W 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zWdrMu+MO|K5#M@bC?a(eVOwN%tik21q@UEFjmqEioe8H5N5V!FbL)GedM_+n;;9`h z^+ZxcyyJp*Xz_)TkUcMPa=YFaJ{^f@kV||hmicY`Umy6(WHLTbBuvOh^?<&){{SE|Yn%&aBMDHHBwtnNs<1)J>#iQ|xqhGgEe&oE~yS9tq zpJ^m*y|P(f(S6N$rV?NPw}2T&GnE(mM*o2NVSHOD}XaBD%A g!QcAwKEBpG2V!SC6Kp7Clg-5q0bQT(tN^b54QA+NG5`Po literal 0 HcmV?d00001 diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index 6de585b72b..8a0ceae65f 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -11,6 +11,8 @@ # for full copyright and license information. ################################################################################# +import os +import pandas as pd from pyomo.environ import ( Var, @@ -22,6 +24,83 @@ from idaes.apps.grid_integration import DesignModel, OperationModel +import idaes.logger as idaeslog + +from idaes.apps.grid_integration.multiperiod.price_taker_model import PriceTakerModel + +import matplotlib.pyplot as plt + + +def test_daily_data_size(): + m = PriceTakerModel + + file_path = os.path.join("FLECCS.xlsx") + file_name = "FLECCS.xlsx" + excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") + + # Generate price data for each hour of every day in the data + daily_data = m.reconfigure_raw_data(excel_data) + + # Check that there is a row for each hour + assert len(daily_data) == 24 + + # Check that there is a column for each day + assert all(len(row) == 365 for row in daily_data) + + +def test_determine_optimal_num_clusters(): + m = PriceTakerModel + + file_name = "FLECCS.xlsx" + file_path = os.path.join(os.getcwd(), file_name) + excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") + + daily_data = m.reconfigure_raw_data(excel_data) + n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data) + + assert n_clusters == 13 + + +def test_elbow_plot(): + m = PriceTakerModel + + file_name = "FLECCS.xlsx" + file_path = os.path.join(os.getcwd(), file_name) + excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") + + daily_data = m.reconfigure_raw_data(excel_data) + m.get_elbow_plot(daily_data) + + assert plt.gcf() is not None + + +def test_bad_inputs(caplog): + with caplog.at_level(idaeslog.WARNING): + m = PriceTakerModel + + file_name = "FLECCS.xlsx" + file_path = os.path.join(os.getcwd(), file_name) + excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") + + daily_data = m.reconfigure_raw_data(excel_data) + sample_weight = ["1", "2", "3"] + m.get_optimal_n_clusters(daily_data, sample_weight=sample_weight) + + assert ( + f"Ensure that the dimensions of the datasets match or set sample_weight to None." + in caplog.text + ) + + caplog.clear() + with caplog.at_level(idaeslog.WARNING): + m = PriceTakerModel + daily_data = m.reconfigure_raw_data(excel_data) + kmin = 10 + kmax = 1 + m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) + + assert f"kmin:{kmin} needs to be less than kmax:{kmax}." in caplog.text + def dfc_design(m, params, capacity_range=(650, 900)): _dfc_capacity = params["dfc_capacity"] From 9042e5c74adeaf6fabe7ac23ad5559d9e11a7e0f Mon Sep 17 00:00:00 2001 From: MarcusHolly Date: Tue, 13 Jun 2023 14:40:59 -0400 Subject: [PATCH 008/126] Update dependencies --- setup.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/setup.py b/setup.py index fd82f66646..9e0b241866 100644 --- a/setup.py +++ b/setup.py @@ -96,6 +96,8 @@ class ExtraDependencies: ] grid = [ "gridx-prescient>=2.2.1", # idaes.tests.prescient + "scikit-learn", + "kneed", ] coolprop = [ "coolprop", # idaes.generic_models.properties.general.coolprop From 2fde47f2107850f25f8acb0db85f5da21d632d62 Mon Sep 17 00:00:00 2001 From: MarcusHolly Date: Tue, 13 Jun 2023 15:17:15 -0400 Subject: [PATCH 009/126] Add pytest marks --- .../grid_integration/multiperiod/tests/test_price_taker.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index 8a0ceae65f..d56295cf05 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -11,6 +11,7 @@ # for full copyright and license information. ################################################################################# +import pytest import os import pandas as pd @@ -31,6 +32,7 @@ import matplotlib.pyplot as plt +@pytest.mark.unit def test_daily_data_size(): m = PriceTakerModel @@ -48,6 +50,7 @@ def test_daily_data_size(): assert all(len(row) == 365 for row in daily_data) +@pytest.mark.unit def test_determine_optimal_num_clusters(): m = PriceTakerModel @@ -61,6 +64,7 @@ def test_determine_optimal_num_clusters(): assert n_clusters == 13 +@pytest.mark.unit def test_elbow_plot(): m = PriceTakerModel @@ -74,6 +78,7 @@ def test_elbow_plot(): assert plt.gcf() is not None +@pytest.mark.unit def test_bad_inputs(caplog): with caplog.at_level(idaeslog.WARNING): m = PriceTakerModel From d0a6c13f1c6ef8561e95b04510502814ec827220 Mon Sep 17 00:00:00 2001 From: MarcusHolly Date: Thu, 15 Jun 2023 11:01:55 -0400 Subject: [PATCH 010/126] Fix typo where TimeStep data was being used instead of BaseCaseTax --- idaes/apps/grid_integration/multiperiod/price_taker_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 57655877db..07f79ed652 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -73,7 +73,7 @@ def reconfigure_raw_data(self, raw_data): # Get column headings column_head = raw_data.columns.tolist() # Remove the date/time column - scenarios = column_head[1:] + scenarios = column_head[2:] # Creating an empty dataframe to store daily data for clustering day_list = list(range(1, (len(raw_data) // 24) + 1)) From e5f6c07ed513e16886338e494d819bfd1c9bad87 Mon Sep 17 00:00:00 2001 From: MarcusHolly Date: Thu, 15 Jun 2023 11:05:15 -0400 Subject: [PATCH 011/126] Update test file --- .../apps/grid_integration/multiperiod/tests/test_price_taker.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index d56295cf05..10447bdba1 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -61,7 +61,7 @@ def test_determine_optimal_num_clusters(): daily_data = m.reconfigure_raw_data(excel_data) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data) - assert n_clusters == 13 + assert n_clusters == 14 @pytest.mark.unit From 56d93694d7314253dae3027023aa919cca398dc0 Mon Sep 17 00:00:00 2001 From: MarcusHolly Date: Thu, 15 Jun 2023 13:48:29 -0400 Subject: [PATCH 012/126] Add warning for when kmax is not set --- .../multiperiod/price_taker_model.py | 2 +- .../multiperiod/tests/test_price_taker.py | 26 ++++++++++++++----- 2 files changed, 21 insertions(+), 7 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 07f79ed652..ada4ad1357 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -104,8 +104,8 @@ def get_optimal_n_clusters(daily_data, kmin=None, kmax=None, sample_weight=None) if kmin is None: kmin = 1 if kmax is None: - # setting an arbitrary kmax. Maybe log warning that no kmax was set and default of 14 used? kmax = 14 + _logger.warning(f"{kmax} was not set - using a default value of 14.") if kmin > kmax: _logger.error(f"kmin:{kmin} needs to be less than kmax:{kmax}.") diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index 10447bdba1..7b7219f26d 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -79,17 +79,30 @@ def test_elbow_plot(): @pytest.mark.unit -def test_bad_inputs(caplog): +def test_logger_messages(caplog, kmin=None, kmax=None): + file_name = "FLECCS.xlsx" + file_path = os.path.join(os.getcwd(), file_name) + excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") + with caplog.at_level(idaeslog.WARNING): m = PriceTakerModel - file_name = "FLECCS.xlsx" - file_path = os.path.join(os.getcwd(), file_name) - excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") + daily_data = m.reconfigure_raw_data(excel_data) + m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) + + assert f"{kmax} was not set - using a default value of 14." in caplog.text + + caplog.clear() + with caplog.at_level(idaeslog.WARNING): + m = PriceTakerModel + kmin = 1 + kmax = 10 daily_data = m.reconfigure_raw_data(excel_data) sample_weight = ["1", "2", "3"] - m.get_optimal_n_clusters(daily_data, sample_weight=sample_weight) + m.get_optimal_n_clusters( + daily_data, kmin=kmin, kmax=kmax, sample_weight=sample_weight + ) assert ( f"Ensure that the dimensions of the datasets match or set sample_weight to None." @@ -99,9 +112,10 @@ def test_bad_inputs(caplog): caplog.clear() with caplog.at_level(idaeslog.WARNING): m = PriceTakerModel - daily_data = m.reconfigure_raw_data(excel_data) kmin = 10 kmax = 1 + + daily_data = m.reconfigure_raw_data(excel_data) m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) assert f"kmin:{kmin} needs to be less than kmax:{kmax}." in caplog.text From 9b5edd48bb7fbef75b8b690ddcf7566c855a52e6 Mon Sep 17 00:00:00 2001 From: MarcusHolly Date: Wed, 21 Jun 2023 09:58:55 -0400 Subject: [PATCH 013/126] Address Radha's comments and start adding testing (WIP) --- .../multiperiod/price_taker_model.py | 100 +- .../multiperiod/tests/FLECCS_princeton.csv | 8761 +++++++++++++++++ .../multiperiod/tests/test_price_taker.py | 225 +- pytest.ini | 3 +- 4 files changed, 8925 insertions(+), 164 deletions(-) create mode 100644 idaes/apps/grid_integration/multiperiod/tests/FLECCS_princeton.csv diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index ada4ad1357..bd91af24cf 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -12,6 +12,7 @@ ################################################################################# import pandas as pd +import numpy as np from pyomo.environ import ( ConcreteModel, @@ -42,24 +43,44 @@ class PriceTakerModel(ConcreteModel): - @staticmethod - def generate_daily_data(raw_data, day_list): + def __init__(self, seed=20, horizon_length=24): + self._seed = seed + self._horizon_length = horizon_length + + @property + def seed(self): + return self._seed + + @seed.setter + def seed(self, value): + self._seed = value + + @property + def horizon_length(self): + return self._horizon_length + + @horizon_length.setter + def horizon_length(self, value): + if value <= 0: + raise ValueError(f"horizon_length must be > 0, but {value} is provided.") + self._horizon_length = value + + def generate_daily_data(self, raw_data, day_list): daily_data = pd.DataFrame(columns=day_list) # Extracting data to populate empty dataframe i = 0 - j = 24 + j = self._horizon_length day = 1 while j <= len(raw_data): daily_data[day] = raw_data[i:j].reset_index(drop=True) i = j - j = j + 24 + j = j + self._horizon_length day = day + 1 return daily_data - @staticmethod def reconfigure_raw_data(self, raw_data): """ Reconfigures the raw price series data into a usable form @@ -76,7 +97,7 @@ def reconfigure_raw_data(self, raw_data): scenarios = column_head[2:] # Creating an empty dataframe to store daily data for clustering - day_list = list(range(1, (len(raw_data) // 24) + 1)) + day_list = list(range(1, (len(raw_data) // self._horizon_length) + 1)) # Generate daily data for i in scenarios[0:1]: @@ -86,8 +107,9 @@ def reconfigure_raw_data(self, raw_data): return daily_data - @staticmethod - def get_optimal_n_clusters(daily_data, kmin=None, kmax=None, sample_weight=None): + def get_optimal_n_clusters( + self, daily_data, kmin=None, kmax=None, sample_weight=None, plot=False + ): """ Determines the appropriate number of clusters needed for a given price signal. @@ -97,15 +119,16 @@ def get_optimal_n_clusters(daily_data, kmin=None, kmax=None, sample_weight=None) kmin: minimum number of clusters kmax: maximum number of clusters sample_weight: applies a weight to each entry in daily_data + plot: flag to determine if an elbow plot should be displayed Returns: n_clusters: the optimal number of clusters for the given data """ if kmin is None: - kmin = 1 + kmin = 4 if kmax is None: - kmax = 14 - _logger.warning(f"{kmax} was not set - using a default value of 14.") + kmax = 30 + _logger.warning(f"{kmax} was not set - using a default value of 30.") if kmin > kmax: _logger.error(f"kmin:{kmin} needs to be less than kmax:{kmax}.") @@ -118,6 +141,8 @@ def get_optimal_n_clusters(daily_data, kmin=None, kmax=None, sample_weight=None) k_values = range(kmin, kmax) inertia_values = [] + np.random.seed(self._seed) + # Compute the inertia (SSE) for k clusters for k in k_values: kmeans = KMeans(n_clusters=k).fit( @@ -133,46 +158,23 @@ def get_optimal_n_clusters(daily_data, kmin=None, kmax=None, sample_weight=None) print(f"Optimal # of clusters is: {n_clusters}") - return n_clusters, inertia_values - - @staticmethod - def get_elbow_plot(self, daily_data, kmin=None, kmax=None, sample_weight=None): - """ - Determines the appropriate number of clusters needed for a - given price signal. - - Args: - daily_data: reconfigured price series data - kmin: minimum number of clusters - kmax: maximum number of clusters - sample_weight: applies a weight to each entry in daily_data - - Returns: - k_values: range of clusters from kmin to kmax (x-axis of elbow plot) - inertia_values: within-cluster sum-of-squares (y-axis of elbow plot) - """ - if kmin is None: - kmin = 1 - if kmax is None: - # setting an arbitrary kmax. Maybe log warning that no kmax was set and default of 14 used? - kmax = 14 + if n_clusters + 2 >= kmax: + _logger.warning( + f"Optimal number of clusters is close to kmax: {kmax}. Consider increasing kmax." + ) - k_values = range(kmin, kmax) - n_clusters, inertia_values = self.get_optimal_n_clusters( - daily_data, kmin, kmax, sample_weight - ) + if plot == True: + plt.show() + plt.plot(k_values, inertia_values) + plt.axvline(x=n_clusters, color="red", linestyle="--", label="Elbow") + plt.xlabel("Number of clusters") + plt.ylabel("Inertia") + plt.title("Elbow Method") + plt.xlim(kmin, kmax) + plt.grid() + plt.show() - plt.show() - plt.plot(k_values, inertia_values) - plt.axvline(x=n_clusters, color="red", linestyle="--", label="Elbow") - plt.xlabel("Number of clusters") - plt.ylabel("Inertia") - plt.title("Elbow Method") - plt.xlim(kmin, kmax) - plt.grid() - plt.show() - - return k_values, inertia_values + return n_clusters, inertia_values @staticmethod def cluster_lmp_data(data, n_clusters, horizon_length): diff --git a/idaes/apps/grid_integration/multiperiod/tests/FLECCS_princeton.csv b/idaes/apps/grid_integration/multiperiod/tests/FLECCS_princeton.csv new file mode 100644 index 0000000000..6c037b23d4 --- /dev/null +++ b/idaes/apps/grid_integration/multiperiod/tests/FLECCS_princeton.csv @@ -0,0 +1,8761 @@ +Date Time,TimeStep,BaseCaseTax,HighWindTax,HighSolarTax,WinterNYTax +1/1/2030 0:00,1,9.19,0.00,46.68,54.20 +1/1/2030 1:00,2,46.68,0.00,46.68,55.98 +1/1/2030 2:00,3,46.68,0.00,62.25,139.62 +1/1/2030 3:00,4,54.20,9.19,62.25,59.15 +1/1/2030 4:00,5,54.20,9.19,62.25,59.15 +1/1/2030 5:00,6,54.20,9.19,62.25,55.98 +1/1/2030 6:00,7,54.20,43.32,62.25,54.20 +1/1/2030 7:00,8,54.20,46.68,62.25,55.98 +1/1/2030 8:00,9,54.20,46.68,62.25,55.98 +1/1/2030 9:00,10,69.24,46.68,98.08,55.98 +1/1/2030 10:00,11,46.68,46.68,46.68,54.20 +1/1/2030 11:00,12,46.68,46.68,46.68,9.19 +1/1/2030 12:00,13,46.68,46.68,46.68,9.19 +1/1/2030 13:00,14,46.68,46.68,9.19,9.19 +1/1/2030 14:00,15,46.68,46.68,9.19,9.19 +1/1/2030 15:00,16,46.68,46.68,1.18,32.64 +1/1/2030 16:00,17,46.68,46.68,0.00,54.20 +1/1/2030 17:00,18,46.68,46.68,18.39,54.20 +1/1/2030 18:00,19,46.68,46.68,46.68,54.20 +1/1/2030 19:00,20,54.20,46.68,54.20,61.84 +1/1/2030 20:00,21,54.20,46.68,54.20,83.25 +1/1/2030 21:00,22,54.20,46.68,54.20,83.25 +1/1/2030 22:00,23,54.20,46.68,54.20,221.45 +1/1/2030 23:00,24,54.20,46.68,54.20,83.25 +1/2/2030 0:00,25,54.20,46.68,54.20,70.13 +1/2/2030 0:59,26,54.20,46.68,54.20,71.85 +1/2/2030 1:59,27,57.76,46.68,62.25,73.57 +1/2/2030 2:59,28,62.25,46.68,67.04,70.13 +1/2/2030 3:59,29,71.20,54.20,67.04,58.02 +1/2/2030 4:59,30,62.25,54.20,67.04,55.98 +1/2/2030 5:59,31,62.25,54.20,67.04,55.98 +1/2/2030 6:59,32,62.25,54.20,62.25,55.98 +1/2/2030 7:59,33,62.25,54.20,66.52,55.98 +1/2/2030 8:59,34,62.64,54.20,88.26,55.98 +1/2/2030 9:59,35,54.20,54.20,46.68,54.20 +1/2/2030 10:59,36,46.68,57.76,46.68,52.59 +1/2/2030 11:59,37,46.68,57.76,46.68,9.19 +1/2/2030 12:59,38,46.68,102.28,46.68,9.19 +1/2/2030 13:59,39,46.68,103.47,25.33,9.19 +1/2/2030 14:59,40,46.68,57.76,9.19,9.19 +1/2/2030 15:59,41,46.68,57.76,9.19,52.59 +1/2/2030 16:59,42,46.68,57.76,46.68,54.20 +1/2/2030 17:59,43,46.68,50.65,46.68,54.20 +1/2/2030 18:59,44,46.68,46.68,46.68,55.98 +1/2/2030 19:59,45,46.68,46.68,46.68,55.98 +1/2/2030 20:59,46,46.68,9.19,46.68,55.98 +1/2/2030 21:59,47,46.68,9.19,46.68,55.98 +1/2/2030 22:59,48,46.68,46.68,46.68,55.98 +1/3/2030 0:00,49,54.20,46.68,54.20,55.98 +1/3/2030 0:59,50,55.98,54.20,57.76,70.13 +1/3/2030 1:59,51,62.25,57.76,62.25,156.45 +1/3/2030 2:59,52,91.54,54.20,124.90,70.13 +1/3/2030 3:59,53,62.25,46.68,62.25,55.98 +1/3/2030 4:59,54,62.25,46.68,62.25,55.98 +1/3/2030 5:59,55,55.98,46.68,62.25,55.98 +1/3/2030 6:59,56,55.98,46.68,62.25,55.98 +1/3/2030 7:59,57,55.98,46.68,55.98,55.98 +1/3/2030 8:59,58,55.98,46.68,55.98,54.20 +1/3/2030 9:59,59,54.20,46.68,46.68,54.20 +1/3/2030 10:59,60,46.68,46.68,46.68,34.25 +1/3/2030 11:59,61,46.68,57.76,46.68,9.19 +1/3/2030 12:59,62,46.68,57.76,46.68,9.19 +1/3/2030 13:59,63,46.68,127.08,46.68,9.19 +1/3/2030 14:59,64,46.68,57.76,46.68,9.19 +1/3/2030 15:59,65,46.68,57.76,46.65,52.59 +1/3/2030 16:59,66,46.68,54.20,46.68,54.20 +1/3/2030 17:59,67,46.68,46.68,46.68,54.20 +1/3/2030 18:59,68,46.68,43.32,46.68,54.20 +1/3/2030 19:59,69,46.68,0.00,46.68,54.20 +1/3/2030 20:59,70,46.68,0.00,46.68,54.20 +1/3/2030 21:59,71,46.68,0.00,46.68,55.98 +1/3/2030 22:59,72,46.68,41.14,46.68,55.98 +1/4/2030 0:00,73,54.20,46.68,55.98,70.13 +1/4/2030 0:59,74,55.98,49.22,60.60,70.13 +1/4/2030 1:59,75,151.11,75.13,95.71,204.36 +1/4/2030 2:59,76,62.25,57.76,62.25,55.98 +1/4/2030 3:59,77,55.98,46.68,62.25,55.98 +1/4/2030 4:59,78,55.98,46.68,55.98,55.98 +1/4/2030 5:59,79,55.98,46.68,55.98,55.98 +1/4/2030 6:59,80,55.98,46.68,55.98,55.98 +1/4/2030 7:59,81,55.98,46.68,55.98,55.98 +1/4/2030 8:59,82,55.98,46.68,55.98,55.98 +1/4/2030 9:59,83,54.20,46.68,46.68,54.20 +1/4/2030 10:59,84,46.68,158.31,46.68,52.59 +1/4/2030 11:59,85,46.68,54.20,46.68,9.19 +1/4/2030 12:59,86,46.68,54.20,46.68,9.19 +1/4/2030 13:59,87,46.68,54.20,20.69,9.19 +1/4/2030 14:59,88,46.68,54.20,9.19,9.19 +1/4/2030 15:59,89,46.68,54.20,9.19,52.59 +1/4/2030 16:59,90,46.68,54.20,13.73,54.20 +1/4/2030 17:59,91,46.68,46.68,46.68,55.98 +1/4/2030 18:59,92,54.20,46.68,54.20,70.13 +1/4/2030 19:59,93,54.20,46.68,54.20,83.25 +1/4/2030 20:59,94,54.20,46.68,54.20,83.25 +1/4/2030 21:59,95,54.20,46.68,54.20,83.25 +1/4/2030 22:59,96,55.98,54.20,55.98,221.45 +1/5/2030 0:00,97,62.25,57.76,77.90,83.25 +1/5/2030 0:59,98,73.56,64.20,78.92,83.25 +1/5/2030 1:59,99,62.25,57.76,93.11,83.25 +1/5/2030 2:59,100,62.25,55.99,62.25,83.25 +1/5/2030 3:59,101,55.98,54.20,62.25,58.02 +1/5/2030 4:59,102,55.98,54.20,55.98,55.98 +1/5/2030 5:59,103,55.98,54.20,55.98,55.98 +1/5/2030 6:59,104,55.98,46.68,55.98,55.98 +1/5/2030 7:59,105,55.98,46.68,55.98,55.98 +1/5/2030 8:59,106,55.98,46.68,61.72,55.98 +1/5/2030 9:59,107,54.20,46.68,46.68,54.20 +1/5/2030 10:59,108,46.68,54.20,46.68,41.98 +1/5/2030 11:59,109,46.68,61.44,46.68,41.98 +1/5/2030 12:59,110,46.68,57.76,46.68,9.19 +1/5/2030 13:59,111,46.68,57.76,27.44,9.19 +1/5/2030 14:59,112,46.68,54.98,9.19,9.19 +1/5/2030 15:59,113,46.68,54.20,9.19,41.98 +1/5/2030 16:59,114,46.68,54.20,44.57,54.20 +1/5/2030 17:59,115,46.68,46.68,46.68,54.20 +1/5/2030 18:59,116,46.68,43.32,46.68,55.98 +1/5/2030 19:59,117,46.68,0.00,46.68,55.98 +1/5/2030 20:59,118,46.68,18.39,46.68,55.98 +1/5/2030 21:59,119,9.19,0.00,46.68,55.98 +1/5/2030 22:59,120,46.68,0.00,46.68,55.98 +1/6/2030 0:00,121,46.68,0.00,46.68,55.98 +1/6/2030 0:59,122,54.20,43.32,54.20,55.98 +1/6/2030 1:59,123,62.25,46.68,79.95,138.00 +1/6/2030 2:59,124,88.67,46.68,97.96,55.98 +1/6/2030 3:59,125,62.25,46.68,62.25,55.98 +1/6/2030 4:59,126,60.95,46.68,62.25,55.98 +1/6/2030 5:59,127,62.25,46.68,62.25,55.98 +1/6/2030 6:59,128,62.25,46.68,62.25,55.98 +1/6/2030 7:59,129,62.25,46.68,62.25,55.98 +1/6/2030 8:59,130,62.25,46.68,62.25,55.98 +1/6/2030 9:59,131,46.68,46.68,46.68,54.20 +1/6/2030 10:59,132,46.68,46.68,46.68,19.98 +1/6/2030 11:59,133,46.68,46.68,46.68,9.19 +1/6/2030 12:59,134,46.68,46.68,12.73,9.19 +1/6/2030 13:59,135,46.68,46.68,9.19,9.19 +1/6/2030 14:59,136,46.68,46.68,9.19,9.19 +1/6/2030 15:59,137,46.68,46.68,0.00,54.20 +1/6/2030 16:59,138,46.68,46.68,18.39,55.98 +1/6/2030 17:59,139,46.68,46.68,46.68,54.20 +1/6/2030 18:59,140,46.68,43.32,46.68,55.98 +1/6/2030 19:59,141,46.68,0.00,46.68,55.98 +1/6/2030 20:59,142,46.68,0.00,46.68,55.98 +1/6/2030 21:59,143,9.19,0.00,46.68,54.20 +1/6/2030 22:59,144,46.68,0.00,46.68,54.20 +1/7/2030 0:00,145,46.68,0.00,46.68,55.98 +1/7/2030 0:59,146,46.68,46.68,46.68,55.98 +1/7/2030 1:59,147,54.20,46.68,62.25,152.06 +1/7/2030 2:59,148,62.25,54.20,85.27,55.98 +1/7/2030 3:59,149,62.25,54.20,62.68,55.98 +1/7/2030 4:59,150,56.30,54.20,62.68,55.98 +1/7/2030 5:59,151,58.39,54.20,62.68,55.98 +1/7/2030 6:59,152,54.20,54.20,62.68,55.98 +1/7/2030 7:59,153,62.25,54.20,62.68,55.98 +1/7/2030 8:59,154,62.25,54.20,79.52,55.98 +1/7/2030 9:59,155,46.68,54.20,46.68,55.98 +1/7/2030 10:59,156,46.68,55.98,46.68,28.60 +1/7/2030 11:59,157,46.68,58.84,46.68,9.19 +1/7/2030 12:59,158,46.68,153.21,46.68,9.19 +1/7/2030 13:59,159,46.68,58.84,9.19,9.19 +1/7/2030 14:59,160,46.68,55.98,9.19,9.19 +1/7/2030 15:59,161,46.68,54.20,9.19,52.42 +1/7/2030 16:59,162,46.68,54.20,46.68,55.98 +1/7/2030 17:59,163,46.68,54.20,46.68,54.20 +1/7/2030 18:59,164,46.68,46.68,55.54,55.98 +1/7/2030 19:59,165,46.68,46.68,51.55,55.98 +1/7/2030 20:59,166,46.68,44.13,46.68,55.98 +1/7/2030 21:59,167,46.68,9.19,46.68,55.98 +1/7/2030 22:59,168,46.68,9.19,46.68,55.98 +1/8/2030 0:00,169,46.68,44.13,46.68,55.98 +1/8/2030 0:59,170,46.68,46.68,46.68,55.98 +1/8/2030 1:59,171,54.20,54.20,62.25,210.13 +1/8/2030 2:59,172,99.37,58.84,67.35,71.85 +1/8/2030 3:59,173,62.25,59.47,67.35,55.98 +1/8/2030 4:59,174,62.25,59.16,62.41,55.98 +1/8/2030 5:59,175,62.25,58.84,62.41,55.98 +1/8/2030 6:59,176,62.25,59.79,62.41,55.98 +1/8/2030 7:59,177,62.25,58.84,67.35,55.98 +1/8/2030 8:59,178,70.20,54.20,88.91,55.98 +1/8/2030 9:59,179,54.20,58.84,46.68,54.20 +1/8/2030 10:59,180,54.20,58.84,46.68,54.20 +1/8/2030 11:59,181,46.68,64.75,46.68,54.20 +1/8/2030 12:59,182,46.68,66.75,46.68,41.98 +1/8/2030 13:59,183,46.68,58.84,27.44,9.19 +1/8/2030 14:59,184,46.68,54.20,9.19,41.98 +1/8/2030 15:59,185,46.68,54.20,9.19,54.20 +1/8/2030 16:59,186,46.68,46.68,46.68,54.20 +1/8/2030 17:59,187,46.68,44.13,44.57,41.98 +1/8/2030 18:59,188,46.68,0.00,46.68,54.20 +1/8/2030 19:59,189,9.19,0.00,46.68,54.20 +1/8/2030 20:59,190,9.19,0.00,46.68,54.20 +1/8/2030 21:59,191,17.81,0.00,46.68,55.98 +1/8/2030 22:59,192,46.68,0.00,46.68,55.98 +1/9/2030 0:00,193,46.68,44.13,46.68,55.98 +1/9/2030 0:59,194,54.20,46.68,54.20,55.98 +1/9/2030 1:59,195,57.76,58.84,62.25,136.57 +1/9/2030 2:59,196,116.98,58.84,102.21,55.98 +1/9/2030 3:59,197,62.25,58.84,62.41,55.98 +1/9/2030 4:59,198,62.25,54.20,62.41,55.98 +1/9/2030 5:59,199,62.25,46.68,62.41,55.98 +1/9/2030 6:59,200,62.25,54.20,62.41,54.20 +1/9/2030 7:59,201,62.25,52.40,62.41,54.20 +1/9/2030 8:59,202,62.25,46.68,72.51,54.20 +1/9/2030 9:59,203,54.20,46.68,54.20,54.20 +1/9/2030 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1:59,8595,53.22,51.75,53.22,56.73 +12/25/2030 2:59,8596,53.22,51.75,56.99,48.10 +12/25/2030 3:59,8597,53.22,39.91,61.22,48.10 +12/25/2030 4:59,8598,53.22,39.91,53.22,48.10 +12/25/2030 5:59,8599,53.22,39.91,53.22,48.10 +12/25/2030 6:59,8600,53.22,39.91,53.22,48.10 +12/25/2030 7:59,8601,50.31,39.91,53.22,48.10 +12/25/2030 8:59,8602,46.58,39.91,53.22,48.10 +12/25/2030 9:59,8603,39.91,39.91,39.91,48.10 +12/25/2030 10:59,8604,39.91,39.91,39.91,46.58 +12/25/2030 11:59,8605,39.91,39.91,39.91,42.55 +12/25/2030 12:59,8606,39.91,39.91,9.19,42.55 +12/25/2030 13:59,8607,39.91,39.91,9.19,42.55 +12/25/2030 14:59,8608,39.91,39.91,9.19,38.14 +12/25/2030 15:59,8609,39.91,39.91,39.91,46.58 +12/25/2030 16:59,8610,39.91,39.91,39.91,46.58 +12/25/2030 17:59,8611,39.91,39.91,39.91,48.10 +12/25/2030 18:59,8612,39.91,39.91,46.58,64.13 +12/25/2030 19:59,8613,39.91,39.91,39.91,71.42 +12/25/2030 20:59,8614,39.91,39.91,46.58,141.44 +12/25/2030 21:59,8615,39.91,39.91,46.58,135.83 +12/25/2030 22:59,8616,46.58,39.91,46.58,71.42 +12/26/2030 0:00,8617,48.10,46.58,48.10,64.13 +12/26/2030 0:59,8618,48.10,46.58,48.10,61.72 +12/26/2030 1:59,8619,48.10,48.10,53.22,48.10 +12/26/2030 2:59,8620,53.22,48.10,54.07,48.10 +12/26/2030 3:59,8621,53.22,48.10,54.93,48.10 +12/26/2030 4:59,8622,53.22,48.10,53.22,48.10 +12/26/2030 5:59,8623,53.22,48.10,53.22,48.10 +12/26/2030 6:59,8624,48.87,48.10,53.22,48.10 +12/26/2030 7:59,8625,51.04,48.10,53.22,48.10 +12/26/2030 8:59,8626,53.22,48.10,58.80,48.10 +12/26/2030 9:59,8627,53.22,51.75,48.10,61.72 +12/26/2030 10:59,8628,46.58,48.10,39.91,48.10 +12/26/2030 11:59,8629,46.58,88.21,39.91,48.10 +12/26/2030 12:59,8630,46.58,99.91,39.91,48.10 +12/26/2030 13:59,8631,39.91,51.75,39.91,46.58 +12/26/2030 14:59,8632,39.91,48.10,39.91,48.10 +12/26/2030 15:59,8633,39.91,48.10,39.91,48.10 +12/26/2030 16:59,8634,39.91,48.10,39.91,48.10 +12/26/2030 17:59,8635,39.91,46.58,39.91,48.10 +12/26/2030 18:59,8636,46.58,46.58,46.58,71.42 +12/26/2030 19:59,8637,46.58,39.91,46.58,234.70 +12/26/2030 20:59,8638,39.91,39.91,39.91,71.42 +12/26/2030 21:59,8639,39.91,39.91,39.91,71.42 +12/26/2030 22:59,8640,46.58,39.91,46.58,71.42 +12/27/2030 0:00,8641,46.58,39.91,46.58,61.72 +12/27/2030 0:59,8642,48.10,39.91,48.10,64.13 +12/27/2030 1:59,8643,53.22,46.58,59.32,61.72 +12/27/2030 2:59,8644,60.16,46.58,65.52,48.10 +12/27/2030 3:59,8645,53.22,46.58,53.22,48.10 +12/27/2030 4:59,8646,53.22,46.58,53.22,48.10 +12/27/2030 5:59,8647,52.00,39.91,53.22,48.10 +12/27/2030 6:59,8648,48.10,39.91,53.22,48.10 +12/27/2030 7:59,8649,48.10,39.91,53.22,48.10 +12/27/2030 8:59,8650,48.10,39.91,53.22,48.10 +12/27/2030 9:59,8651,46.58,46.58,46.58,48.10 +12/27/2030 10:59,8652,46.58,46.58,39.91,48.10 +12/27/2030 11:59,8653,46.58,48.10,39.91,46.58 +12/27/2030 12:59,8654,46.58,49.16,39.91,46.58 +12/27/2030 13:59,8655,39.91,48.63,39.91,46.58 +12/27/2030 14:59,8656,39.91,48.63,39.91,46.58 +12/27/2030 15:59,8657,39.91,51.20,39.91,48.10 +12/27/2030 16:59,8658,39.91,48.10,39.91,48.10 +12/27/2030 17:59,8659,39.91,46.58,39.91,48.10 +12/27/2030 18:59,8660,46.58,46.58,46.58,71.42 +12/27/2030 19:59,8661,46.58,39.91,46.58,764.25 +12/27/2030 20:59,8662,46.58,39.91,46.58,218.52 +12/27/2030 21:59,8663,46.58,46.58,46.58,664.72 +12/27/2030 22:59,8664,46.58,46.58,46.58,71.42 +12/28/2030 0:00,8665,48.10,48.10,48.10,71.42 +12/28/2030 0:59,8666,53.22,51.75,53.22,71.42 +12/28/2030 1:59,8667,84.25,51.75,70.46,66.16 +12/28/2030 2:59,8668,77.45,51.75,53.45,62.11 +12/28/2030 3:59,8669,53.22,48.10,53.22,51.83 +12/28/2030 4:59,8670,53.22,48.10,53.22,48.10 +12/28/2030 5:59,8671,53.22,48.10,53.22,48.10 +12/28/2030 6:59,8672,48.10,46.58,53.22,48.10 +12/28/2030 7:59,8673,48.10,48.05,53.22,48.10 +12/28/2030 8:59,8674,49.62,48.10,53.22,48.10 +12/28/2030 9:59,8675,46.58,48.10,39.91,46.58 +12/28/2030 10:59,8676,39.91,48.10,39.91,46.58 +12/28/2030 11:59,8677,39.91,48.10,39.91,9.19 +12/28/2030 12:59,8678,39.91,48.10,39.91,9.19 +12/28/2030 13:59,8679,39.91,48.10,9.19,9.19 +12/28/2030 14:59,8680,39.91,48.10,9.19,9.19 +12/28/2030 15:59,8681,39.91,46.58,0.00,9.19 +12/28/2030 16:59,8682,39.91,46.58,30.61,46.58 +12/28/2030 17:59,8683,39.91,39.91,39.91,46.58 +12/28/2030 18:59,8684,39.91,39.91,39.91,48.10 +12/28/2030 19:59,8685,39.91,39.91,39.91,62.11 +12/28/2030 20:59,8686,39.91,9.19,39.91,48.10 +12/28/2030 21:59,8687,39.91,0.00,39.91,48.10 +12/28/2030 22:59,8688,39.91,39.91,46.58,62.11 +12/29/2030 0:00,8689,46.58,39.91,48.10,194.15 +12/29/2030 0:59,8690,48.10,46.58,48.10,62.11 +12/29/2030 1:59,8691,53.22,46.58,63.27,62.11 +12/29/2030 2:59,8692,53.22,46.58,63.27,48.10 +12/29/2030 3:59,8693,53.22,46.58,63.27,48.10 +12/29/2030 4:59,8694,53.22,46.58,74.12,48.10 +12/29/2030 5:59,8695,48.10,46.58,53.22,48.10 +12/29/2030 6:59,8696,48.10,46.58,53.22,48.10 +12/29/2030 7:59,8697,48.10,46.58,53.22,48.10 +12/29/2030 8:59,8698,48.10,46.58,54.46,48.10 +12/29/2030 9:59,8699,46.58,46.58,39.91,46.58 +12/29/2030 10:59,8700,39.91,48.10,39.91,9.19 +12/29/2030 11:59,8701,39.91,51.75,39.91,9.19 +12/29/2030 12:59,8702,39.91,100.04,39.91,9.19 +12/29/2030 13:59,8703,39.91,88.08,24.71,9.19 +12/29/2030 14:59,8704,39.91,51.75,9.19,9.19 +12/29/2030 15:59,8705,39.91,48.10,9.19,46.58 +12/29/2030 16:59,8706,39.91,48.10,39.91,46.58 +12/29/2030 17:59,8707,39.91,46.58,39.91,48.10 +12/29/2030 18:59,8708,46.58,46.58,46.58,62.11 +12/29/2030 19:59,8709,46.58,39.91,46.58,210.77 +12/29/2030 20:59,8710,39.91,39.91,39.91,62.11 +12/29/2030 21:59,8711,39.91,39.91,39.91,49.97 +12/29/2030 22:59,8712,44.55,39.91,46.58,62.11 +12/30/2030 0:00,8713,46.58,39.91,48.10,72.73 +12/30/2030 0:59,8714,48.10,39.91,53.05,62.11 +12/30/2030 1:59,8715,48.10,39.91,53.22,62.11 +12/30/2030 2:59,8716,48.10,39.91,53.22,48.10 +12/30/2030 3:59,8717,48.10,39.91,53.22,48.10 +12/30/2030 4:59,8718,46.58,39.91,53.22,48.10 +12/30/2030 5:59,8719,46.58,38.81,48.10,46.58 +12/30/2030 6:59,8720,48.10,84.01,53.22,48.10 +12/30/2030 7:59,8721,46.58,0.00,48.10,48.10 +12/30/2030 8:59,8722,46.58,38.81,48.10,48.10 +12/30/2030 9:59,8723,46.58,39.91,46.58,48.10 +12/30/2030 10:59,8724,46.58,39.91,46.58,46.58 +12/30/2030 11:59,8725,46.58,39.91,39.91,46.58 +12/30/2030 12:59,8726,46.58,39.91,39.91,46.58 +12/30/2030 13:59,8727,46.58,39.91,39.91,46.58 +12/30/2030 14:59,8728,46.58,39.91,39.91,46.58 +12/30/2030 15:59,8729,39.91,39.91,39.91,48.10 +12/30/2030 16:59,8730,39.91,39.91,39.91,48.10 +12/30/2030 17:59,8731,46.58,39.91,39.91,48.10 +12/30/2030 18:59,8732,46.58,39.91,46.58,62.11 +12/30/2030 19:59,8733,39.91,38.81,39.91,193.36 +12/30/2030 20:59,8734,39.91,12.47,39.91,71.42 +12/30/2030 21:59,8735,46.58,38.81,46.58,71.42 +12/30/2030 22:59,8736,46.58,39.91,46.58,83.91 +12/31/2030 0:00,8737,48.10,39.91,48.10,71.42 +12/31/2030 0:59,8738,48.10,39.91,53.22,62.11 +12/31/2030 1:59,8739,53.22,39.91,53.22,49.97 +12/31/2030 2:59,8740,60.05,46.58,60.02,62.11 +12/31/2030 3:59,8741,53.22,46.58,57.03,62.11 +12/31/2030 4:59,8742,53.22,48.10,55.87,48.10 +12/31/2030 5:59,8743,53.22,48.10,55.87,48.10 +12/31/2030 6:59,8744,95.64,103.78,60.84,48.10 +12/31/2030 7:59,8745,53.22,48.10,53.22,48.10 +12/31/2030 8:59,8746,53.22,51.75,63.87,48.10 +12/31/2030 9:59,8747,48.10,51.75,46.58,46.58 +12/31/2030 10:59,8748,46.58,84.89,39.91,46.58 +12/31/2030 11:59,8749,39.91,46.58,39.91,0.00 +12/31/2030 12:59,8750,39.91,48.10,39.91,0.00 +12/31/2030 13:59,8751,39.91,48.10,9.19,0.00 +12/31/2030 14:59,8752,39.91,46.58,9.19,0.00 +12/31/2030 15:59,8753,39.91,46.58,9.19,35.59 +12/31/2030 16:59,8754,39.91,46.58,39.91,46.58 +12/31/2030 17:59,8755,39.91,47.34,39.91,46.58 +12/31/2030 18:59,8756,39.91,0.00,39.91,46.58 +12/31/2030 19:59,8757,39.91,0.00,39.91,46.58 +12/31/2030 20:59,8758,39.91,0.00,39.91,46.58 +12/31/2030 21:59,8759,39.91,0.00,39.91,46.58 +12/31/2030 22:59,8760,16.11,0.00,39.91,46.58 diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index 7b7219f26d..078855d0ee 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -11,6 +11,7 @@ # for full copyright and license information. ################################################################################# +from pathlib import Path import pytest import os import pandas as pd @@ -32,130 +33,128 @@ import matplotlib.pyplot as plt -@pytest.mark.unit -def test_daily_data_size(): - m = PriceTakerModel - - file_path = os.path.join("FLECCS.xlsx") - file_name = "FLECCS.xlsx" - excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") - - # Generate price data for each hour of every day in the data - daily_data = m.reconfigure_raw_data(excel_data) - - # Check that there is a row for each hour - assert len(daily_data) == 24 - - # Check that there is a column for each day - assert all(len(row) == 365 for row in daily_data) +@pytest.fixture +def excel_data(): + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS.xlsx" + data = pd.read_excel(file_path, sheet_name=1) + return data @pytest.mark.unit -def test_determine_optimal_num_clusters(): - m = PriceTakerModel - - file_name = "FLECCS.xlsx" - file_path = os.path.join(os.getcwd(), file_name) - excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") +def test_daily_data_size(excel_data): + m = PriceTakerModel() + # Generate price data for each hour of every day in the data daily_data = m.reconfigure_raw_data(excel_data) - n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data) + print(daily_data) - assert n_clusters == 14 + # Check that there is a row for each horizon length in a representative day + assert len(daily_data) == m.horizon_length @pytest.mark.unit -def test_elbow_plot(): - m = PriceTakerModel - - file_name = "FLECCS.xlsx" - file_path = os.path.join(os.getcwd(), file_name) - excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") +def test_determine_optimal_num_clusters(excel_data): + m = PriceTakerModel() daily_data = m.reconfigure_raw_data(excel_data) - m.get_elbow_plot(daily_data) + n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data) - assert plt.gcf() is not None + assert n_clusters == 10 -@pytest.mark.unit -def test_logger_messages(caplog, kmin=None, kmax=None): - file_name = "FLECCS.xlsx" - file_path = os.path.join(os.getcwd(), file_name) - excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") - - with caplog.at_level(idaeslog.WARNING): - m = PriceTakerModel - - daily_data = m.reconfigure_raw_data(excel_data) - m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) - - assert f"{kmax} was not set - using a default value of 14." in caplog.text - - caplog.clear() - with caplog.at_level(idaeslog.WARNING): - m = PriceTakerModel - kmin = 1 - kmax = 10 - - daily_data = m.reconfigure_raw_data(excel_data) - sample_weight = ["1", "2", "3"] - m.get_optimal_n_clusters( - daily_data, kmin=kmin, kmax=kmax, sample_weight=sample_weight - ) - - assert ( - f"Ensure that the dimensions of the datasets match or set sample_weight to None." - in caplog.text - ) - - caplog.clear() - with caplog.at_level(idaeslog.WARNING): - m = PriceTakerModel - kmin = 10 - kmax = 1 - - daily_data = m.reconfigure_raw_data(excel_data) - m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) - - assert f"kmin:{kmin} needs to be less than kmax:{kmax}." in caplog.text - - -def dfc_design(m, params, capacity_range=(650, 900)): - _dfc_capacity = params["dfc_capacity"] - _ng_flow = params["ng_flow"] - _capex = params["capex"] - _fom_factor = params["fom_factor"] - - m.capacity = Var( - within=NonNegativeReals, - initialize=_dfc_capacity, - bounds=(0, capacity_range[1]), - doc="Capacity of the power plant [in MW]", - ) - - # Define a variable that informs whether the plant needs to built or not. - m.build_unit = Var( - within=Binary, - doc="1: Plant is built, 0: Plant is not built", - ) - - # Bound the capcity of the plant in the specified range - m.capacity_lb_con = Constraint(expr=m.capacity >= m.build_unit * capacity_range[0]) - m.capacity_ub_con = Constraint(expr=m.capacity <= m.build_unit * capacity_range[1]) - - # Compute the natural gas flowrate required at maximum capacity. - m.ng_flow = Expression( - expr=m.capacity * (_ng_flow / _dfc_capacity), - doc="Computes the natural flowrate required [in kg/s] at full load", - ) - - # Define an expression for CAPEX and FOM - m.capex = Expression( - expr=_capex * (m.capacity / _dfc_capacity), - doc="CAPEX of the power cycle [in 1000$]", - ) - m.fom = Expression( - expr=_fom_factor * m.capex, - doc="Fixed O&M cost [in 1000$/year]", - ) +# @pytest.mark.unit +# def test_elbow_plot(): +# m = PriceTakerModel +# +# file_name = "FLECCS.xlsx" +# file_path = os.path.join(os.getcwd(), file_name) +# excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") +# +# daily_data = m.reconfigure_raw_data(excel_data) +# m.get_elbow_plot(daily_data) +# +# assert plt.gcf() is not None +# +# +# @pytest.mark.unit +# def test_logger_messages(caplog, kmin=None, kmax=None): +# file_name = "FLECCS.xlsx" +# file_path = os.path.join(os.getcwd(), file_name) +# excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") +# +# with caplog.at_level(idaeslog.WARNING): +# m = PriceTakerModel +# +# daily_data = m.reconfigure_raw_data(excel_data) +# m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) +# +# assert f"{kmax} was not set - using a default value of 14." in caplog.text +# +# caplog.clear() +# with caplog.at_level(idaeslog.WARNING): +# m = PriceTakerModel +# kmin = 1 +# kmax = 10 +# +# daily_data = m.reconfigure_raw_data(excel_data) +# sample_weight = ["1", "2", "3"] +# m.get_optimal_n_clusters( +# daily_data, kmin=kmin, kmax=kmax, sample_weight=sample_weight +# ) +# +# assert ( +# f"Ensure that the dimensions of the datasets match or set sample_weight to None." +# in caplog.text +# ) +# +# caplog.clear() +# with caplog.at_level(idaeslog.WARNING): +# m = PriceTakerModel +# kmin = 10 +# kmax = 1 +# +# daily_data = m.reconfigure_raw_data(excel_data) +# m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) +# +# assert f"kmin:{kmin} needs to be less than kmax:{kmax}." in caplog.text +# +# +# def dfc_design(m, params, capacity_range=(650, 900)): +# _dfc_capacity = params["dfc_capacity"] +# _ng_flow = params["ng_flow"] +# _capex = params["capex"] +# _fom_factor = params["fom_factor"] +# +# m.capacity = Var( +# within=NonNegativeReals, +# initialize=_dfc_capacity, +# bounds=(0, capacity_range[1]), +# doc="Capacity of the power plant [in MW]", +# ) +# +# # Define a variable that informs whether the plant needs to built or not. +# m.build_unit = Var( +# within=Binary, +# doc="1: Plant is built, 0: Plant is not built", +# ) +# +# # Bound the capcity of the plant in the specified range +# m.capacity_lb_con = Constraint(expr=m.capacity >= m.build_unit * capacity_range[0]) +# m.capacity_ub_con = Constraint(expr=m.capacity <= m.build_unit * capacity_range[1]) +# +# # Compute the natural gas flowrate required at maximum capacity. +# m.ng_flow = Expression( +# expr=m.capacity * (_ng_flow / _dfc_capacity), +# doc="Computes the natural flowrate required [in kg/s] at full load", +# ) +# +# # Define an expression for CAPEX and FOM +# m.capex = Expression( +# expr=_capex * (m.capacity / _dfc_capacity), +# doc="CAPEX of the power cycle [in 1000$]", +# ) +# m.fom = Expression( +# expr=_fom_factor * m.capex, +# doc="Fixed O&M cost [in 1000$/year]", +# ) diff --git a/pytest.ini b/pytest.ini index 1a53544a9a..6f31bb0711 100644 --- a/pytest.ini +++ b/pytest.ini @@ -1,6 +1,5 @@ [pytest] -addopts = --pyargs idaes - --durations=100 +addopts = --durations=100 -W ignore log_file = pytest.log log_file_date_format = %Y-%m-%dT%H:%M:%S From 948f40c30467716191d7a32ce2cb02b83797a88c Mon Sep 17 00:00:00 2001 From: MarcusHolly Date: Wed, 21 Jun 2023 21:09:02 -0400 Subject: [PATCH 014/126] Add more tests --- .../multiperiod/price_taker_model.py | 59 ++++-- .../multiperiod/tests/test_price_taker.py | 197 +++++++++--------- 2 files changed, 134 insertions(+), 122 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index bd91af24cf..08ae5d0433 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -44,6 +44,7 @@ class PriceTakerModel(ConcreteModel): def __init__(self, seed=20, horizon_length=24): + super().__init__() self._seed = seed self._horizon_length = horizon_length @@ -65,7 +66,7 @@ def horizon_length(self, value): raise ValueError(f"horizon_length must be > 0, but {value} is provided.") self._horizon_length = value - def generate_daily_data(self, raw_data, day_list): + def generate_daily_data(self, raw_data, raw_weight_data, day_list): daily_data = pd.DataFrame(columns=day_list) @@ -79,17 +80,32 @@ def generate_daily_data(self, raw_data, day_list): j = j + self._horizon_length day = day + 1 - return daily_data + # TODO: I'm not sure what the weight data would typically look like, so I have this simple check for now + # TODO: Should eventually add something to transform raw_weight_data into a usable format + if raw_weight_data is not None: + if daily_data.shape != raw_weight_data.shape: + raise AssertionError( + f"Ensure that the dimensions of the datasets match or remove raw_weight_data" + ) + else: + daily_weight_data = raw_weight_data + + else: + daily_weight_data = raw_weight_data + + return daily_data, daily_weight_data - def reconfigure_raw_data(self, raw_data): + def reconfigure_raw_data(self, raw_data, raw_weight_data=None): """ Reconfigures the raw price series data into a usable form Args: raw_data: imported price series data + raw_weight_data: imported weights for the price series data Returns: daily_data: reconfigured price series data + daily_weight_data: reconfigured weights for price series data """ # Get column headings column_head = raw_data.columns.tolist() @@ -101,14 +117,14 @@ def reconfigure_raw_data(self, raw_data): # Generate daily data for i in scenarios[0:1]: - daily_data = self.generate_daily_data( - raw_data=raw_data[i], day_list=day_list + daily_data, daily_weight_data = self.generate_daily_data( + raw_data=raw_data[i], raw_weight_data=raw_weight_data, day_list=day_list ) - return daily_data + return daily_data, daily_weight_data def get_optimal_n_clusters( - self, daily_data, kmin=None, kmax=None, sample_weight=None, plot=False + self, daily_data, daily_weight_data, kmin=None, kmax=None, plot=False ): """ Determines the appropriate number of clusters needed for a @@ -116,27 +132,20 @@ def get_optimal_n_clusters( Args: daily_data: reconfigured price series data + daily_weight_data: applies a weight to each entry in daily_data kmin: minimum number of clusters kmax: maximum number of clusters - sample_weight: applies a weight to each entry in daily_data plot: flag to determine if an elbow plot should be displayed Returns: n_clusters: the optimal number of clusters for the given data + inertia_values: within-cluster sum-of-squares """ if kmin is None: kmin = 4 if kmax is None: kmax = 30 - _logger.warning(f"{kmax} was not set - using a default value of 30.") - if kmin > kmax: - _logger.error(f"kmin:{kmin} needs to be less than kmax:{kmax}.") - - if sample_weight is not None: - if daily_data.shape != sample_weight.shape: - _logger.error( - f"Ensure that the dimensions of the datasets match or set sample_weight to None." - ) + _logger.warning(f"kmax was not set - using a default value of 30.") k_values = range(kmin, kmax) inertia_values = [] @@ -144,11 +153,17 @@ def get_optimal_n_clusters( np.random.seed(self._seed) # Compute the inertia (SSE) for k clusters - for k in k_values: - kmeans = KMeans(n_clusters=k).fit( - daily_data.transpose(), sample_weight=sample_weight - ) - inertia_values.append(kmeans.inertia_) + if daily_weight_data == None: + for k in k_values: + kmeans = KMeans(n_clusters=k).fit(daily_data.transpose()) + inertia_values.append(kmeans.inertia_) + + elif daily_data.shape == daily_weight_data.shape: + for k in k_values: + kmeans = KMeans(n_clusters=k).fit( + daily_data.transpose(), sample_weight=daily_weight_data.transpose() + ) + inertia_values.append(kmeans.inertia_) # Identify the "elbow point" elbow_point = KneeLocator( diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index 078855d0ee..df36e10122 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -13,7 +13,7 @@ from pathlib import Path import pytest -import os +import numpy as np import pandas as pd from pyomo.environ import ( @@ -46,8 +46,7 @@ def test_daily_data_size(excel_data): m = PriceTakerModel() # Generate price data for each hour of every day in the data - daily_data = m.reconfigure_raw_data(excel_data) - print(daily_data) + daily_data, daily_weight_data = m.reconfigure_raw_data(excel_data) # Check that there is a row for each horizon length in a representative day assert len(daily_data) == m.horizon_length @@ -57,104 +56,102 @@ def test_daily_data_size(excel_data): def test_determine_optimal_num_clusters(excel_data): m = PriceTakerModel() - daily_data = m.reconfigure_raw_data(excel_data) - n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data) + daily_data, daily_weight_data = m.reconfigure_raw_data(excel_data) + n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, daily_weight_data) assert n_clusters == 10 -# @pytest.mark.unit -# def test_elbow_plot(): -# m = PriceTakerModel -# -# file_name = "FLECCS.xlsx" -# file_path = os.path.join(os.getcwd(), file_name) -# excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") -# -# daily_data = m.reconfigure_raw_data(excel_data) -# m.get_elbow_plot(daily_data) -# -# assert plt.gcf() is not None -# -# -# @pytest.mark.unit -# def test_logger_messages(caplog, kmin=None, kmax=None): -# file_name = "FLECCS.xlsx" -# file_path = os.path.join(os.getcwd(), file_name) -# excel_data = pd.read_excel(file_path, sheet_name="2030 - Princeton") -# -# with caplog.at_level(idaeslog.WARNING): -# m = PriceTakerModel -# -# daily_data = m.reconfigure_raw_data(excel_data) -# m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) -# -# assert f"{kmax} was not set - using a default value of 14." in caplog.text -# -# caplog.clear() -# with caplog.at_level(idaeslog.WARNING): -# m = PriceTakerModel -# kmin = 1 -# kmax = 10 -# -# daily_data = m.reconfigure_raw_data(excel_data) -# sample_weight = ["1", "2", "3"] -# m.get_optimal_n_clusters( -# daily_data, kmin=kmin, kmax=kmax, sample_weight=sample_weight -# ) -# -# assert ( -# f"Ensure that the dimensions of the datasets match or set sample_weight to None." -# in caplog.text -# ) -# -# caplog.clear() -# with caplog.at_level(idaeslog.WARNING): -# m = PriceTakerModel -# kmin = 10 -# kmax = 1 -# -# daily_data = m.reconfigure_raw_data(excel_data) -# m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) -# -# assert f"kmin:{kmin} needs to be less than kmax:{kmax}." in caplog.text -# -# -# def dfc_design(m, params, capacity_range=(650, 900)): -# _dfc_capacity = params["dfc_capacity"] -# _ng_flow = params["ng_flow"] -# _capex = params["capex"] -# _fom_factor = params["fom_factor"] -# -# m.capacity = Var( -# within=NonNegativeReals, -# initialize=_dfc_capacity, -# bounds=(0, capacity_range[1]), -# doc="Capacity of the power plant [in MW]", -# ) -# -# # Define a variable that informs whether the plant needs to built or not. -# m.build_unit = Var( -# within=Binary, -# doc="1: Plant is built, 0: Plant is not built", -# ) -# -# # Bound the capcity of the plant in the specified range -# m.capacity_lb_con = Constraint(expr=m.capacity >= m.build_unit * capacity_range[0]) -# m.capacity_ub_con = Constraint(expr=m.capacity <= m.build_unit * capacity_range[1]) -# -# # Compute the natural gas flowrate required at maximum capacity. -# m.ng_flow = Expression( -# expr=m.capacity * (_ng_flow / _dfc_capacity), -# doc="Computes the natural flowrate required [in kg/s] at full load", -# ) -# -# # Define an expression for CAPEX and FOM -# m.capex = Expression( -# expr=_capex * (m.capacity / _dfc_capacity), -# doc="CAPEX of the power cycle [in 1000$]", -# ) -# m.fom = Expression( -# expr=_fom_factor * m.capex, -# doc="Fixed O&M cost [in 1000$/year]", -# ) +@pytest.mark.unit +def test_elbow_plot(excel_data): + m = PriceTakerModel() + + daily_data, daily_weight_data = m.reconfigure_raw_data(excel_data) + m.get_optimal_n_clusters(daily_data, daily_weight_data, plot=True) + + assert plt.gcf() is not None + + +@pytest.mark.unit +def test_logger_messages(excel_data, caplog): + with caplog.at_level(idaeslog.WARNING): + m = PriceTakerModel() + + daily_data, daily_weight_data = m.reconfigure_raw_data(excel_data) + m.get_optimal_n_clusters(daily_data, daily_weight_data) + + assert f"kmax was not set - using a default value of 30." in caplog.text + + # TODO: The below test is not working because our data doesn't ever seem to arrive at an n_clusters close to kmax + # caplog.clear() + # with caplog.at_level(idaeslog.WARNING): + # m = PriceTakerModel() + # kmin = 1 + # kmax = 7 + # + # daily_data, daily_weight_data = m.reconfigure_raw_data(excel_data) + # m.get_optimal_n_clusters(daily_data, daily_weight_data, kmin=kmin, kmax=kmax) + # + # assert f"Optimal number of clusters is close to kmax: {kmax}. Consider increasing kmax." in caplog.text + + value = 0 + with pytest.raises( + ValueError, + match=(f"horizon_length must be > 0, but {value} is provided."), + ): + m = PriceTakerModel() + m.horizon_length = value + + with pytest.raises( + AssertionError, + match=( + f"Ensure that the dimensions of the datasets match or remove raw_weight_data" + ), + ): + m = PriceTakerModel() + raw_weight_data = np.array([1, 2, 3]) + + daily_data, daily_weight_data = m.reconfigure_raw_data( + excel_data, raw_weight_data=raw_weight_data + ) + m.get_optimal_n_clusters(daily_data, daily_weight_data) + + +def dfc_design(m, params, capacity_range=(650, 900)): + _dfc_capacity = params["dfc_capacity"] + _ng_flow = params["ng_flow"] + _capex = params["capex"] + _fom_factor = params["fom_factor"] + + m.capacity = Var( + within=NonNegativeReals, + initialize=_dfc_capacity, + bounds=(0, capacity_range[1]), + doc="Capacity of the power plant [in MW]", + ) + + # Define a variable that informs whether the plant needs to built or not. + m.build_unit = Var( + within=Binary, + doc="1: Plant is built, 0: Plant is not built", + ) + + # Bound the capcity of the plant in the specified range + m.capacity_lb_con = Constraint(expr=m.capacity >= m.build_unit * capacity_range[0]) + m.capacity_ub_con = Constraint(expr=m.capacity <= m.build_unit * capacity_range[1]) + + # Compute the natural gas flowrate required at maximum capacity. + m.ng_flow = Expression( + expr=m.capacity * (_ng_flow / _dfc_capacity), + doc="Computes the natural flowrate required [in kg/s] at full load", + ) + + # Define an expression for CAPEX and FOM + m.capex = Expression( + expr=_capex * (m.capacity / _dfc_capacity), + doc="CAPEX of the power cycle [in 1000$]", + ) + m.fom = Expression( + expr=_fom_factor * m.capex, + doc="Fixed O&M cost [in 1000$/year]", + ) From 30fed63d074f902d20376d1f6fcc4774165ec178 Mon Sep 17 00:00:00 2001 From: MarcusHolly Date: Thu, 22 Jun 2023 15:40:46 -0400 Subject: [PATCH 015/126] Add cluster_lmp_data function --- .../multiperiod/price_taker_model.py | 84 ++++++++++--------- .../multiperiod/tests/test_price_taker.py | 48 ++++++----- 2 files changed, 69 insertions(+), 63 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 08ae5d0433..a0283d6f46 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -66,7 +66,7 @@ def horizon_length(self, value): raise ValueError(f"horizon_length must be > 0, but {value} is provided.") self._horizon_length = value - def generate_daily_data(self, raw_data, raw_weight_data, day_list): + def generate_daily_data(self, raw_data, day_list): daily_data = pd.DataFrame(columns=day_list) @@ -80,32 +80,17 @@ def generate_daily_data(self, raw_data, raw_weight_data, day_list): j = j + self._horizon_length day = day + 1 - # TODO: I'm not sure what the weight data would typically look like, so I have this simple check for now - # TODO: Should eventually add something to transform raw_weight_data into a usable format - if raw_weight_data is not None: - if daily_data.shape != raw_weight_data.shape: - raise AssertionError( - f"Ensure that the dimensions of the datasets match or remove raw_weight_data" - ) - else: - daily_weight_data = raw_weight_data - - else: - daily_weight_data = raw_weight_data - - return daily_data, daily_weight_data + return daily_data - def reconfigure_raw_data(self, raw_data, raw_weight_data=None): + def reconfigure_raw_data(self, raw_data): """ Reconfigures the raw price series data into a usable form Args: raw_data: imported price series data - raw_weight_data: imported weights for the price series data Returns: daily_data: reconfigured price series data - daily_weight_data: reconfigured weights for price series data """ # Get column headings column_head = raw_data.columns.tolist() @@ -117,22 +102,19 @@ def reconfigure_raw_data(self, raw_data, raw_weight_data=None): # Generate daily data for i in scenarios[0:1]: - daily_data, daily_weight_data = self.generate_daily_data( - raw_data=raw_data[i], raw_weight_data=raw_weight_data, day_list=day_list + daily_data = self.generate_daily_data( + raw_data=raw_data[i], day_list=day_list ) - return daily_data, daily_weight_data + return daily_data, scenarios - def get_optimal_n_clusters( - self, daily_data, daily_weight_data, kmin=None, kmax=None, plot=False - ): + def get_optimal_n_clusters(self, daily_data, kmin=None, kmax=None, plot=False): """ Determines the appropriate number of clusters needed for a given price signal. Args: daily_data: reconfigured price series data - daily_weight_data: applies a weight to each entry in daily_data kmin: minimum number of clusters kmax: maximum number of clusters plot: flag to determine if an elbow plot should be displayed @@ -153,17 +135,10 @@ def get_optimal_n_clusters( np.random.seed(self._seed) # Compute the inertia (SSE) for k clusters - if daily_weight_data == None: - for k in k_values: - kmeans = KMeans(n_clusters=k).fit(daily_data.transpose()) - inertia_values.append(kmeans.inertia_) - - elif daily_data.shape == daily_weight_data.shape: - for k in k_values: - kmeans = KMeans(n_clusters=k).fit( - daily_data.transpose(), sample_weight=daily_weight_data.transpose() - ) - inertia_values.append(kmeans.inertia_) + + for k in k_values: + kmeans = KMeans(n_clusters=k).fit(daily_data.transpose()) + inertia_values.append(kmeans.inertia_) # Identify the "elbow point" elbow_point = KneeLocator( @@ -191,8 +166,7 @@ def get_optimal_n_clusters( return n_clusters, inertia_values - @staticmethod - def cluster_lmp_data(data, n_clusters, horizon_length): + def cluster_lmp_data(self, daily_data, n_clusters, scenarios): """ Clusters the given price signal in n_clusters. This method supports k-means, k-meteiod,... techniques for clustering. @@ -200,9 +174,39 @@ def cluster_lmp_data(data, n_clusters, horizon_length): Args: Returns: + lmp_data = {1: {1: 2, 2: 3, 3: 5}, 2: {1: 2, 2: 3, 3: 5}} + weights = {1: 45, 2: 56} """ - lmp_data = {1: {1: 2, 2: 3, 3: 5}, 2: {1: 2, 2: 3, 3: 5}} - weights = {1: 45, 2: 56} + + # KMeans clustering with the optimal number of clusters + kmeans = KMeans(n_clusters=n_clusters).fit(daily_data.transpose()) + centroids = kmeans.cluster_centers_ + labels = kmeans.labels_ + + # Set any centroid values that are < 1e-4 to 0 to avoid noise + for d in range(n_clusters): + for t in range(24): + if centroids[d][t] < 1e-4: + centroids[d][t] = 0 + + n_clusters_list = range(0, n_clusters) + weights_counter = np.zeros(n_clusters) + + # Compute weight for each cluster by counting its occurrences in the dataset + for j in range(0, len(labels)): + for k in n_clusters_list: + if labels[j] == k: + weights_counter[k] += 1 + + # Create dicts for lmp data and the weight of each cluster + rep_days_data = {} + weights_data = {} + + for i in scenarios[0:1]: + rep_days_data[i] = pd.DataFrame(centroids.transpose()) + lmp_data = rep_days_data[i].to_dict() + weights_data[i] = pd.DataFrame(weights_counter) + weights = weights_data[i].to_dict() return lmp_data, weights diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index df36e10122..d6d008d931 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -46,7 +46,7 @@ def test_daily_data_size(excel_data): m = PriceTakerModel() # Generate price data for each hour of every day in the data - daily_data, daily_weight_data = m.reconfigure_raw_data(excel_data) + daily_data, scenarios = m.reconfigure_raw_data(excel_data) # Check that there is a row for each horizon length in a representative day assert len(daily_data) == m.horizon_length @@ -56,8 +56,8 @@ def test_daily_data_size(excel_data): def test_determine_optimal_num_clusters(excel_data): m = PriceTakerModel() - daily_data, daily_weight_data = m.reconfigure_raw_data(excel_data) - n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, daily_weight_data) + daily_data, scenarios = m.reconfigure_raw_data(excel_data) + n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data) assert n_clusters == 10 @@ -66,19 +66,35 @@ def test_determine_optimal_num_clusters(excel_data): def test_elbow_plot(excel_data): m = PriceTakerModel() - daily_data, daily_weight_data = m.reconfigure_raw_data(excel_data) - m.get_optimal_n_clusters(daily_data, daily_weight_data, plot=True) + daily_data, scenarios = m.reconfigure_raw_data(excel_data) + m.get_optimal_n_clusters(daily_data, plot=True) assert plt.gcf() is not None +@pytest.mark.unit +def test_cluster_lmp_data(excel_data): + m = PriceTakerModel() + + daily_data, scenarios = m.reconfigure_raw_data(excel_data) + n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data) + lmp_data, weights = m.cluster_lmp_data(daily_data, n_clusters, scenarios) + + sum_of_weights = 0 + for i in range(0, n_clusters): + sum_of_weights = sum_of_weights + weights[0][i] + assert sum_of_weights == 365 + + assert len(lmp_data) == n_clusters + + @pytest.mark.unit def test_logger_messages(excel_data, caplog): with caplog.at_level(idaeslog.WARNING): m = PriceTakerModel() - daily_data, daily_weight_data = m.reconfigure_raw_data(excel_data) - m.get_optimal_n_clusters(daily_data, daily_weight_data) + daily_data, scenarios = m.reconfigure_raw_data(excel_data) + m.get_optimal_n_clusters(daily_data) assert f"kmax was not set - using a default value of 30." in caplog.text @@ -89,8 +105,8 @@ def test_logger_messages(excel_data, caplog): # kmin = 1 # kmax = 7 # - # daily_data, daily_weight_data = m.reconfigure_raw_data(excel_data) - # m.get_optimal_n_clusters(daily_data, daily_weight_data, kmin=kmin, kmax=kmax) + # daily_data, scenarios = m.reconfigure_raw_data(excel_data) + # m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) # # assert f"Optimal number of clusters is close to kmax: {kmax}. Consider increasing kmax." in caplog.text @@ -102,20 +118,6 @@ def test_logger_messages(excel_data, caplog): m = PriceTakerModel() m.horizon_length = value - with pytest.raises( - AssertionError, - match=( - f"Ensure that the dimensions of the datasets match or remove raw_weight_data" - ), - ): - m = PriceTakerModel() - raw_weight_data = np.array([1, 2, 3]) - - daily_data, daily_weight_data = m.reconfigure_raw_data( - excel_data, raw_weight_data=raw_weight_data - ) - m.get_optimal_n_clusters(daily_data, daily_weight_data) - def dfc_design(m, params, capacity_range=(650, 900)): _dfc_capacity = params["dfc_capacity"] From fc43a3f4f18785b7a862db30fbfa433b58a4ce3b Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Thu, 3 Aug 2023 13:29:36 -0400 Subject: [PATCH 016/126] linearizing su_sd cosntraints and fixing the price traker model --- .../multiperiod/price_taker_model.py | 69 +- .../FLECCS_Price_Series_Data_01_20_2021.xlsx | Bin 0 -> 1793340 bytes .../flowsheets/direct_fired_cycle/__init__.py | 0 .../asu_startup_shutdown.py | 116 +++ .../default_model_parameters.py | 94 +++ .../direct_fired_cycle/dfc_flowsheet.py | 309 ++++++++ .../dfc_startup_shutdown.py | 258 +++++++ .../direct_fired_cycle/npv_optimization.py | 462 +++++++++++ .../direct_fired_cycle/run_script.py | 58 ++ .../direct_fired_cycle/unit_models.py | 730 ++++++++++++++++++ 10 files changed, 2066 insertions(+), 30 deletions(-) create mode 100644 idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/FLECCS_Price_Series_Data_01_20_2021.xlsx create mode 100644 idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/__init__.py create mode 100644 idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/asu_startup_shutdown.py create mode 100644 idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/default_model_parameters.py create mode 100644 idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_flowsheet.py create mode 100644 idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_startup_shutdown.py create mode 100644 idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/npv_optimization.py create mode 100644 idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/run_script.py create mode 100644 idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/unit_models.py diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index a0283d6f46..02a69af8ec 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -10,10 +10,10 @@ # All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md # for full copyright and license information. ################################################################################# - import pandas as pd import numpy as np - +from importlib import resources +from pathlib import Path from pyomo.environ import ( ConcreteModel, Block, @@ -66,7 +66,7 @@ def horizon_length(self, value): raise ValueError(f"horizon_length must be > 0, but {value} is provided.") self._horizon_length = value - def generate_daily_data(self, raw_data, day_list): + def generate_daily_data(self,raw_data,day_list): daily_data = pd.DataFrame(columns=day_list) @@ -79,7 +79,7 @@ def generate_daily_data(self, raw_data, day_list): i = j j = j + self._horizon_length day = day + 1 - + return daily_data def reconfigure_raw_data(self, raw_data): @@ -92,6 +92,7 @@ def reconfigure_raw_data(self, raw_data): Returns: daily_data: reconfigured price series data """ + # Get column headings column_head = raw_data.columns.tolist() # Remove the date/time column @@ -166,7 +167,7 @@ def get_optimal_n_clusters(self, daily_data, kmin=None, kmax=None, plot=False): return n_clusters, inertia_values - def cluster_lmp_data(self, daily_data, n_clusters, scenarios): + def cluster_lmp_data(self, raw_data, n_clusters, scenarios): """ Clusters the given price signal in n_clusters. This method supports k-means, k-meteiod,... techniques for clustering. @@ -177,7 +178,9 @@ def cluster_lmp_data(self, daily_data, n_clusters, scenarios): lmp_data = {1: {1: 2, 2: 3, 3: 5}, 2: {1: 2, 2: 3, 3: 5}} weights = {1: 45, 2: 56} """ - + # reconfiguring raw data + daily_data,scenarios = self.reconfigure_raw_data(raw_data) + # KMeans clustering with the optimal number of clusters kmeans = KMeans(n_clusters=n_clusters).fit(daily_data.transpose()) centroids = kmeans.cluster_centers_ @@ -202,27 +205,33 @@ def cluster_lmp_data(self, daily_data, n_clusters, scenarios): rep_days_data = {} weights_data = {} - for i in scenarios[0:1]: - rep_days_data[i] = pd.DataFrame(centroids.transpose()) - lmp_data = rep_days_data[i].to_dict() - weights_data[i] = pd.DataFrame(weights_counter) - weights = weights_data[i].to_dict() + + rep_days_data = pd.DataFrame(centroids.transpose(), columns = range(1,n_clusters+1)) + lmp_data = rep_days_data.to_dict() + weights_data = pd.DataFrame(weights_counter) + weights_data.index = np.arange(1, len(weights_data) + 1) + weights = weights_data.to_dict() return lmp_data, weights def append_lmp_data( self, - filename, + file_path, + file_name, + sheet = None, column_name="price", n_clusters=None, horizon_length=None, ): - full_data = pd.read_csv(filename) - + with resources.path(file_path, file_name) as p: + path_to_file = Path(p).resolve() + + full_data = pd.read_excel( path_to_file, sheet_name=[sheet])[sheet] + # editing the data if isinstance(column_name, list) and n_clusters is not None: # Multiple years and representative days self.set_years = [int(y) for y in column_name] - self.set_days = RangeSet(n_clusters) + self.set_days = RangeSet(1,n_clusters) self._n_time_points = horizon_length if horizon_length is not None else 24 self.set_time = RangeSet(self._n_time_points) @@ -230,7 +239,7 @@ def append_lmp_data( self.WEIGHTS = {} for year in column_name: - price_data = full_data[year].to_list() + price_data = full_data[year] lmp_data, weights = self.cluster_lmp_data( price_data, n_clusters, horizon_length ) @@ -239,7 +248,7 @@ def append_lmp_data( for d in self.set_days: for t in self.set_time: self.LMP[t, d, y] = lmp_data[d][t] - self.WEIGHTS[d, y] = weights[d] + self.WEIGHTS[d, y] = weights[0][d] return @@ -253,7 +262,7 @@ def append_lmp_data( self.LMP = {} for year in column_name: - price_data = full_data[year].to_list() + price_data = full_data[year] y = int(year) for t in self.set_time: @@ -264,23 +273,23 @@ def append_lmp_data( elif n_clusters is not None: # Single price signal, use reprentative days self.set_years = None - self.set_days = RangeSet(n_clusters) + self.set_days = RangeSet(1,n_clusters) self._n_time_points = horizon_length if horizon_length is not None else 24 - self.set_time = RangeSet(self._n_time_points) + self.set_time = RangeSet(self._n_time_points-1) self.LMP = {} self.WEIGHTS = {} - price_data = full_data[year].to_list() + price_data = full_data lmp_data, weights = self.cluster_lmp_data( price_data, n_clusters, horizon_length ) - + for d in self.set_days: for t in self.set_time: self.LMP[t, d] = lmp_data[d][t] - self.WEIGHTS[d] = weights[d] - + self.WEIGHTS[d] = weights[0][d] + return else: @@ -297,10 +306,10 @@ def append_lmp_data( def build_multiperiod_model(self, **kwargs): - if not self.model_sets_available: - raise Exception( - "Model sets have not been defined. Run get_lmp_data to construct model sets" - ) + # if not self.model_sets_available: + # raise Exception( + # "Model sets have not been defined. Run get_lmp_data to construct model sets" + # ) self.mp_model = MultiPeriodModel( n_time_points=self._n_time_points, @@ -387,8 +396,8 @@ def build_cashflows( self.NET_CASH_INFLOW = Var(doc="Net cash inflow") self.net_cash_inflow_calculation = Constraint( expr=self.NET_CASH_INFLOW - == sum(self.mp_model[p].net_cash_inflow for p in self.mp_model) - ) + == sum(self.mp_model.period[p].net_cash_inflow for p in self.mp_model.period) # added period block name to the net_cash_inflow callout + ) # added period block name to mp_model, to kthe len, and made a range list to loop over self.CORP_TAX = Var(within=NonNegativeReals, doc="Corporate tax") self.corp_tax_calculation = Constraint( diff --git a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/FLECCS_Price_Series_Data_01_20_2021.xlsx b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/FLECCS_Price_Series_Data_01_20_2021.xlsx new file mode 100644 index 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z`N^2uB#maVNtX5 Consumes 80% of the max_power, but does not produce any oxygen +t > 8 => Can operate anywhere between P_min and P_max + +Cycle shutdown: +t = 0 - 1 => Consumes 50% of the max_power and does not produce any oxygen +""" + +from pyomo.environ import Constraint + +# Startup time for the ASU +SU_TIME = 8 + + +def asu_startup_shutdown_constraints(m): + # The startup and shutdown constraints will be added in a separate block + # for eCach of the processes. + + # Create an alias for the parent block. + pb = m.parent_block() + set_period = pb.set_period + num_time_periods = len(set_period) + + # Cycle can operate at time t, only if one of the following two is true + # - Cycle was in operation at time t - 1 + # - Cycle startup was initiated at time t - 8 + @m.Constraint(set_period) + def cycle_operation(blk, t): + if t == 1: + # Constraint is not applicable at t = 1. Initial condition is left free + # But it can choose at most one of the three + return ( + pb.period[t].fs.asu.op_mode + pb.period[t].fs.asu.startup + + pb.period[t].fs.asu.shutdown <= 1 + ) + + # Slightly modify the constraint at t <= 8, since startup variable is not + # defined for t <= 0 + return ( + pb.period[t].fs.asu.op_mode <= pb.period[t - 1].fs.asu.op_mode + + (pb.period[t - SU_TIME].fs.asu.startup if t-SU_TIME > 0 else 0) + ) + + # After plant startup, the unit cannot produce oxygen in the next 8 hours, but + # consumes 80% of P_max power during that period + @m.Constraint(set_period, [j for j in range(1, SU_TIME)]) + def startup_con_1(blk, t, i): + if t + i > num_time_periods: + return Constraint.Skip + + return ( + pb.period[t + i].fs.asu.startup + pb.period[t + i].fs.asu.op_mode + + pb.period[t + i].fs.asu.shutdown <= 1 - pb.period[t].fs.asu.startup + ) + + + + @m.Constraint(set_period, [j for j in range(SU_TIME)]) + def startup_con_2(blk, t, i): + if t + i > num_time_periods: + return Constraint.Skip + + des_mdl = pb.parent_block() + return ( + pb.period[t + i].fs.asu.su_sd_power >= 0.8 * des_mdl.asu_design.max_power + - 0.8 * des_mdl.asu_design.max_power_ub * (1-pb.period[t].fs.asu.startup) + ) + + # ASU must operate at the ninth hour after startup is initiated + @m.Constraint(set_period) + def startup_con_3(blk, t): + if t + SU_TIME > num_time_periods: + return Constraint.Skip + + return pb.period[t + SU_TIME].fs.asu.op_mode >= pb.period[t].fs.asu.startup + + # Startup can be initiated at time t only if op_mode[t - 1] = 0 + @m.Constraint(set_period) + def startup_con_4(blk, t): + if t == 1: + return Constraint.Skip + + return pb.period[t].fs.asu.startup <= 1 - pb.period[t - 1].fs.asu.op_mode + + # Shutdown can be initiated at time t only if the ASU was operating at time t - 1 + @m.Constraint(set_period) + def shutdown_con_1(blk, t): + if t == 1: + return Constraint.Skip + + # I suspect this will always be satisfied by the optimal solution. Need to verify it though + return pb.period[t].fs.asu.shutdown <= pb.period[t - 1].fs.asu.op_mode + + # During the shutdown, power consumption is 50% of the P_max and O2 is not produced + @m.Constraint(set_period) + def shutdown_con_2(blk, t): + des_mdl = pb.parent_block() + return ( + pb.period[t].fs.asu.su_sd_power >= 0.5 * des_mdl.asu_design.max_power + - 0.5 * des_mdl.asu_design.max_power_ub * (1-pb.period[t].fs.asu.shutdown) + ) + + # If op_mode[t] = 1, and op_mode[t + 1] = 0, then shutdown must be initated at t + 1 + @m.Constraint(set_period) + def shutdown_con_3(blk, t): + if t + 1 > num_time_periods: + return Constraint.Skip + + return ( + pb.period[t].fs.asu.op_mode - pb.period[t + 1].fs.asu.op_mode <= pb.period[t + 1].fs.asu.shutdown + ) diff --git a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/default_model_parameters.py b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/default_model_parameters.py new file mode 100644 index 0000000000..d8f3ec21df --- /dev/null +++ b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/default_model_parameters.py @@ -0,0 +1,94 @@ + + +""" +This script contains parameter data + +In the current formulation, the fixed O&M cost is calculated as a fraction of +CAPEX, and it includes: TBD. e.g., insurance, labor, etc + +The variable O&M cost (non-fuel) is calculated as follows: +Non-fuel VOM includes: TBD. +Total VOM = 3339.92 + 18048.07 = 21387.99. Split the VOM for inidividual units +in the ratio of their CAPEX. 1128855 / (1128855 + 545522) = 0.6742. + +The VOM of the DFC cycle = 0.6742 * 21387.99 = 14419.78286 + ==> 14419.78286 / (0.85 * 8760) = 1.93658 [in $1000/hr] + This number will be scaled linearly with normalized DFC's capacity + +The VOM of the ASU unit = 0.3258 * 21387.99 = 6968.207 + ==> 6968.207 / (0.85 * 8760) = 0.93583 [in $1000/hr] + This number will be scaled linearly with normalized ASU's capacity + +The VOM of the NLU unit = 5404.96 + ==> 5404.96 / (0.85 * 8760) = 0.7259 [in $1000/hr] + This number will be scaled linearly with normalized NLU's capacity +""" + +# TODO: Contact Sandeep for reference. +# HHV of NG = 22499.17034 btu/lb = 0.0496 MMBtu/kg +NG_HHV = 0.0496 +HR_TO_SEC = 3600 +LBS_TO_KG = 0.453592 + +# Parameter data for DFC +# Calculating the FOM as 3.157% of the CAPEX. The percentage value is obtained from +# 35,641.27 (FOM) / 1,128,855 (CAPEX). The FOM is also assumed +# to vary linearly with the capacity. +DFC_PARAMS = { + "dfc_capacity" : 838.11322145, # [MW] Net power output + "ng_flow" : 28.87, # [kg/s] NG flow at full capacity + "o2_ng_ratio" : 3.785632948, # [-] Oxygen/NG flowratio + "capex" : 1128855, # [$1000] CAPEX of DFC + "fom_factor" : 0.03157, # [-] Multiplier for FOM + "op_curve_coeff": [1.2845, -0.2845], # Coefficients of performance curve + "co2_emission" : 3.06, # [kg/MWh] CO2 emissions + "vom" : 1.93658, # [$1000/hr] Non-fuel VOM +} + +# Parameter data for Monolithic ASU +MONO_ASU_PARAMS = { + "asu_capacity" : 109.2912232, # [kg/s] Maximum O2 flow + "power_requirement" : 162.1878617, # [MW] Power requirement at max capacity + "capex" : 545522, # [$1000] CAPEX of ASU + "fom_factor" : 0.03157, # [-] Multiplier for FOM + "op_curve_coeff" : [0.9625, 0.0375], # Coefficients of performance curve + "vom" : 0.93583, # [$1000/hr] Non-electricity VOM +} + +# Parameter data for ASU +ASU_PARAMS = { + "asu_capacity" : 27.3228058, # [kg/s] Maximum O2 flow + "power_requirement" : 40.546965425, # [MW] Power requirement at max capacity + "capex" : 136380.5, # [$1000] CAPEX of ASU + "fom_factor" : 0.03157, # [-] Multiplier for FOM + "op_curve_coeff" : [0.9625, 0.0375], # coefficients of performance curve + "vom" : 0.2339575, # [$1000/hr] Non-electricity VOM +} + +# Parameter data for NLU +NLU_PARAMS = { + "nlu_capacity" : 109.291223201103, # [kg/s] Maximum O2 flow + "power_requirement" : 143.8024, # [MW] Power requirement at max capacity + "capex" : 171189.6, # [$1000] CAPEX of NLU + "fom_factor" : 0.03157, # [-] Multiplier for FOM [-] + "vom" : 0.7259, # [$1000/hr] Non-electricity VOM +} + +# Parameter data for LOx tank +O2_TANK_PARAMS = { + "capex" : [0.98167214, 2779.90543786], + "fom_factor" : 0.03157, # [-] Multiplier for FOM + "min_holdup" : 0.1, # [-] Minimum holdup fraction + "power_requirement" : 1.47054, # [MW] Power requirement for storage + "o2_flow" : 112.0865459, # [kg/s] O2 flowrate for + "tank_constraint" : "periodic", # "Periodic" or "initial_holdup" +} + + +# Cashflow parameters +CASHFLOW_PARAMS = { + "plant_life" : 30, # [-] Plant lifetime in years + "discount_rate" : 0.075, # [-] Discount rate + "tax_rate" : 0.2, # [-] Corporate tax rate + "electricity_cost" : 5, # [$/MWh] Excess penalty for purchasing electricity +} diff --git a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_flowsheet.py b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_flowsheet.py new file mode 100644 index 0000000000..026c0bb73f --- /dev/null +++ b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_flowsheet.py @@ -0,0 +1,309 @@ +from pyomo.environ import ( + Var, + NonNegativeReals, + Constraint, + Expression, +) +from idaes.core import FlowsheetBlock +from idaes.models_extra.power_generation.flowsheets.direct_fired_cycle.unit_models import ( + DFCOperation, + MonoASUOperation, + NLUOperation, + OxygenTankOperation, +) +from idaes.models_extra.power_generation.flowsheets.direct_fired_cycle.default_model_parameters import NG_HHV, HR_TO_SEC + + +def build_dfc_flowsheet( + m, + dfc_design, + asu_design, +): + """ + Build a flowsheet with DFC and ASU units only. ASU provides all the oxygen required + for the power cycle. + """ + + m.fs = FlowsheetBlock(dynamic=False) + + # Append unit models + m.fs.dfc = DFCOperation(design_blk=dfc_design) + m.fs.asu = MonoASUOperation(design_blk=asu_design) + + # Declare new variables + m.fs.power_dfc_to_grid = Var(within=NonNegativeReals) + m.fs.power_dfc_to_asu = Var(within=NonNegativeReals) + m.fs.power_grid_to_asu = Var(within=NonNegativeReals) + + m.fs.oxygen_asu_to_dfc = Var(within=NonNegativeReals) + m.fs.oxygen_asu_to_vent = Var(within=NonNegativeReals) + + # Power balance across the power cycle + m.fs.dfc_power_balance = Constraint( + expr=m.fs.dfc.power == m.fs.power_dfc_to_grid + m.fs.power_dfc_to_asu, + ) + + # Power balance across the ASU + m.fs.asu_power_balance = Constraint( + expr=m.fs.asu.total_power == m.fs.power_dfc_to_asu + m.fs.power_grid_to_asu, + ) + + # Oxygen balance across the power cycle + m.fs.dfc_oxygen_balance = Constraint( + expr=m.fs.dfc.o2_flow == m.fs.oxygen_asu_to_dfc, + ) + + # Oxygen balance across the ASU + m.fs.asu_oxygen_balance = Constraint( + expr=m.fs.asu.o2_flow == m.fs.oxygen_asu_to_dfc + m.fs.oxygen_asu_to_vent, + ) + + +def build_dfc_flowsheet_with_lox( + m, + dfc_design, + asu_design, + tank_design, +): + """Build a flowsheet with DFC, ASU unit with a provision for LOx withdrawal, + and an oxygen storage tank. + """ + m.fs = FlowsheetBlock(dynamic=False) + + # Append unit models + m.fs.dfc = DFCOperation(design_blk=dfc_design) + m.fs.asu = MonoASUOperation(design_blk=asu_design) + m.fs.tank = OxygenTankOperation(design_blk=tank_design) + + # Declare new variables + m.fs.power_dfc_to_grid = Var(within=NonNegativeReals) + m.fs.power_dfc_to_asu = Var(within=NonNegativeReals) + m.fs.power_dfc_to_tank = Var(within=NonNegativeReals) + m.fs.power_grid_to_asu = Var(within=NonNegativeReals) + m.fs.power_grid_to_tank = Var(within=NonNegativeReals) + + m.fs.gox_fraction = Var( + within=NonNegativeReals, + bounds=(0.9, 1), + doc="Fraction of O2 withdrawn in gasoues state from the ASU", + ) + m.fs.oxygen_asu_to_dfc = Var(within=NonNegativeReals) + m.fs.oxygen_asu_to_vent = Var(within=NonNegativeReals) + + # Power balance across the power cycle + m.fs.dfc_power_balance = Constraint( + expr=m.fs.dfc.power == m.fs.power_dfc_to_grid + m.fs.power_dfc_to_asu + + m.fs.power_dfc_to_tank, + ) + + # Power balance across the ASU + m.fs.asu_power_balance = Constraint( + expr=2 * m.fs.asu.total_power - m.fs.gox_fraction * m.fs.asu.total_power == + m.fs.power_dfc_to_asu + m.fs.power_grid_to_asu, + ) + + # Power balance across the tank + m.fs.tank_power_balance = Constraint( + expr=m.fs.tank.power == m.fs.power_dfc_to_tank + m.fs.power_grid_to_tank, + ) + + # Oxygen balance across the power cycle + m.fs.dfc_oxygen_balance = Constraint( + expr=m.fs.dfc.o2_flow == m.fs.oxygen_asu_to_dfc + m.fs.tank.lox_out, + ) + + # Gasoues oxygen balance across the ASU + m.fs.gox_balance = Constraint( + expr=m.fs.asu.o2_flow * m.fs.gox_fraction == m.fs.oxygen_asu_to_dfc + + m.fs.oxygen_asu_to_vent, + ) + + # Liquid oxygen balance across the ASU + m.fs.lox_balance = Constraint( + expr=m.fs.asu.o2_flow - m.fs.asu.o2_flow * m.fs.gox_fraction == + m.fs.tank.lox_in, + ) + + +def build_dfc_flowsheet_with_nlu( + m, + dfc_design, + asu_design, + nlu_design, + tank_design +): + """ + Build a flowsheet with DFC, ASU, NLU and an oxygen storage tank. + """ + m.fs = FlowsheetBlock(dynamic=False) + + # Append unit models + m.fs.dfc = DFCOperation(design_blk=dfc_design) + m.fs.asu = MonoASUOperation(design_blk=asu_design) + m.fs.nlu = NLUOperation(design_blk=nlu_design) + m.fs.tank = OxygenTankOperation(design_blk=tank_design) + + # Declare new variables + m.fs.power_dfc_to_grid = Var(within=NonNegativeReals) + m.fs.power_dfc_to_asu = Var(within=NonNegativeReals) + m.fs.power_dfc_to_nlu = Var(within=NonNegativeReals) + m.fs.power_dfc_to_tank = Var(within=NonNegativeReals) + m.fs.power_grid_to_asu = Var(within=NonNegativeReals) + m.fs.power_grid_to_nlu = Var(within=NonNegativeReals) + m.fs.power_grid_to_tank = Var(within=NonNegativeReals) + + m.fs.oxygen_asu_to_dfc = Var(within=NonNegativeReals) + m.fs.oxygen_asu_to_vent = Var(within=NonNegativeReals) + + # Power balance across the power cycle + m.fs.dfc_power_balance = Constraint( + expr=m.fs.dfc.power == m.fs.power_dfc_to_grid + m.fs.power_dfc_to_asu + + m.fs.power_dfc_to_nlu + m.fs.power_dfc_to_tank, + ) + + # Power balance across the ASU + m.fs.asu_power_balance = Constraint( + expr=m.fs.asu.total_power == m.fs.power_dfc_to_asu + m.fs.power_grid_to_asu + ) + + # Power balance across the NLU + m.fs.nlu_power_balance = Constraint( + expr=m.fs.nlu.power == m.fs.power_dfc_to_nlu + m.fs.power_grid_to_nlu + ) + + # Power balance across the tank + m.fs.tank_power_balance = Constraint( + expr=m.fs.tank.power == m.fs.power_dfc_to_tank + m.fs.power_grid_to_tank + ) + + # Oxygen balance across the power cycle + m.fs.dfc_oxygen_balance = Constraint( + expr=m.fs.dfc.o2_flow == m.fs.oxygen_asu_to_dfc + m.fs.tank.lox_out, + ) + + # Oxygen balance across the ASU + m.fs.asu_oxygen_balance = Constraint( + expr=m.fs.asu.o2_flow == m.fs.oxygen_asu_to_dfc + m.fs.nlu.o2_flow + + m.fs.oxygen_asu_to_vent, + ) + + # Oxygen balance across the NLU + m.fs.nlu_oxygen_balance = Constraint( + expr=m.fs.nlu.o2_flow == m.fs.tank.lox_in, + ) + + +def append_op_costs_dfc( + m, + lmp, + penalty, + cost_ng, + carbon_price, +): + """Append cost and revenue expressions for each time step + + Args: + m: Object containing the flowsheet + lmp: LMP + penalty: To avoid degeneracy, we impose penalty for borrowing electricity + from the grid. (default: electricity borrowed from the grid costs $5/MWh + more than the LMP at that time instant) + cost_ng: Cost of natural gas [in $/MMBtu] + """ + + m.fs.electricity_revenue = Expression(expr=lmp * m.fs.power_dfc_to_grid) + + m.fs.electricity_cost = Expression( + expr=(lmp + penalty) * (m.fs.power_grid_to_asu + + (m.fs.power_grid_to_nlu if hasattr(m.fs, "nlu") else 0) + + (m.fs.power_grid_to_tank if hasattr(m.fs, "tank") else 0) + ), + doc="Net revenue generated by selling power to the grid [in $1000]", + ) + + m.fs.fuel_cost = Expression( + expr=cost_ng * NG_HHV * (HR_TO_SEC / 1000) * m.fs.dfc.total_ng_flow, + doc="Operational cost associated with fuel consumption [in $1000]", + ) + + # TODO: Add CO2 emissions for the startup and shutdown (likely negligible) + m.fs.co2_price = Expression( + expr=m.fs.dfc.co2_emissions * carbon_price / 1000, + doc="Carbon price per hour [in $1000]", + ) + + m.fs.net_cash_flow = Expression( + expr=m.fs.electricity_revenue - m.fs.electricity_cost + - m.fs.fuel_cost + - m.fs.dfc.non_fuel_vom - m.fs.asu.non_fuel_vom + - (m.fs.nlu.non_fuel_vom if hasattr(m.fs, "nlu") else 0) + - m.fs.co2_price, + ) + + +def append_cashflows(m, params): + plant_life = params["plant_life"] + tax_rate = params["tax_rate"] + discount_rate = params["discount_rate"] + + m.CAPEX = Var( + within=NonNegativeReals, + doc="Total CAPEX of the plant [in $1000]", + ) + m.FOM = Var( + within=NonNegativeReals, + doc="Total fixed O&M costs per year [in $1000]", + ) + m.DEPRECIATION = Var( + within=NonNegativeReals, + doc="Depreciation value per year [in $1000]" + ) + m.CORP_TAX = Var( + within=NonNegativeReals, + doc="Net corporate tax per year [in $1000]", + ) + m.NET_PROFIT = Var( + doc="Net profit per year [in $1000]", + ) + + m.capex_calculation = Constraint( + expr=m.CAPEX == m.dfc_design.capex + m.asu_design.capex + + (m.nlu_design.capex if hasattr(m, "nlu_design") else 0) + + (m.tank_design.capex if hasattr(m, "tank_design") else 0), + doc="Calculates the total CAPEX [in $1000]", + ) + m.fom_calculation = Constraint( + expr=m.FOM == m.dfc_design.fom + m.asu_design.fom + + (m.nlu_design.fom if hasattr(m, "nlu_design") else 0) + + (m.tank_design.fom if hasattr(m, "tank_design") else 0), + doc="Calculates the total FOM [in $1000]", + ) + m.depreciation_calculation = Constraint( + expr=m.DEPRECIATION == m.CAPEX / plant_life, + doc="Straight line depreciation with zero salvage value [in $1000]", + ) + + # Tax = max{0, Formula below}. We will relax it to Tax >= max{0, Formula below} + # The inequality will be binding for the optimal solution. + m.corp_tax_calculation = Constraint( + expr=m.CORP_TAX >= tax_rate * ( + sum(m.mp_model.period[t].fs.net_cash_flow for t in m.set_time) + - m.FOM - m.DEPRECIATION + ), + doc="Calculates the total corporate tax [in $1000]", + ) + + m.net_profit_calculation = Constraint( + expr=m.NET_PROFIT == + + sum(m.mp_model.period[t].fs.net_cash_flow for t in m.set_time) + - m.FOM - m.CORP_TAX, + doc="Calculate the net profit generated in a year [in $1000]", + ) + + constant_cf_factor = (1 - (1 + discount_rate) ** (- plant_life)) / discount_rate + + # NPV Calculation + m.npv = Expression( + expr=constant_cf_factor * m.NET_PROFIT - m.CAPEX, + doc="Calculates the net present value [in $1000]", + ) diff --git a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_startup_shutdown.py b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_startup_shutdown.py new file mode 100644 index 0000000000..3b343521ae --- /dev/null +++ b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_startup_shutdown.py @@ -0,0 +1,258 @@ +""" +Contains functions that implement startup and shutdown constraints for the power cycle. + +In this version, we implement the following and startup and shutdown procedure. + +Cycle startup: +t = 0 - 1 => Ramp up fuel from 0 to 5.8518 kg/s. Power production = 0 MW +t = 1 - 2 => Ramp up fuel from 5.8515 to 10.36 kg/s. Power production = 0 MW +t = 2 - 3 => Cycle operates at P_min, (20% of the maximum load) +t = 3 - 4 => Normal operation of the plant. Cycle could ramp up to P_max + +Cycle shutdown: +t = 0 - 1 => Cycle needs to ramp down to P_min +t = 1 - 2 => Cycle operates at P_min, (20% of the maximum load) +t = 2 - 3 => Ramp down fuel from 10.36 to 5.85 kg/s. Power production = 0 MW +t = 3 - 4 => Ramp down fuel from 5.8518 kg/s to 0. Power production = 0 MW +""" + +from pyomo.environ import Constraint + + +DFC_OFFLOAD = 0.2 + + +def dfc_startup_shutdown_constraints(m): + # The startup and shutdown constraints will be added in a separate block + # for eCach of the processes. + + # Create an alias for the parent block. + pb = m.parent_block() + set_period = pb.set_period + num_time_periods = len(set_period) + + # Cycle can operate at time t, only if one of the following two is true + # - Cycle was in operation at time t - 1 + # - Cycle startup was initiated at time t - 2 + @m.Constraint(set_period) + def cycle_operation(blk, t): + if t == 1: + # Constraint is not applicable at t = 1. Initial condition is left free + # But it can choose at most one of the three + return ( + pb.period[t].fs.dfc.op_mode + pb.period[t].fs.dfc.startup + + pb.period[t].fs.dfc.shutdown <= 1 + ) + + if t == 2: + # Slightly modify the constraint at t = 2, since startup variable is not + # defined at t = 0 + return pb.period[t].fs.dfc.op_mode == pb.period[t - 1].fs.dfc.op_mode + + return ( + pb.period[t].fs.dfc.op_mode <= pb.period[t - 1].fs.dfc.op_mode + + pb.period[t - 2].fs.dfc.startup + ) + + # If cycle startup is initiated at time t i.e., startup[t] = 1, then + # at t + 1, op_mode, startup and shutdown must be 0 + # at t + 2, op_mode must be 1 and power output must be P_min + # at t + 3, op_mode must be 1 + @m.Constraint(set_period) + def startup_con_1(blk, t): + if t + 1 > num_time_periods: + return Constraint.Skip + + # Also ensures that at most one of the three operations (operation, startup, shutdown) + # takes place at each time period + return ( + pb.period[t + 1].fs.dfc.startup + pb.period[t + 1].fs.dfc.op_mode + + pb.period[t + 1].fs.dfc.shutdown <= 1 - pb.period[t].fs.dfc.startup + ) + + @m.Constraint(set_period) + def startup_con_2(blk, t): + if t + 2 > num_time_periods: + return Constraint.Skip + + return pb.period[t].fs.dfc.startup <= pb.period[t + 2].fs.dfc.op_mode + + @m.Constraint(set_period) + def startup_con_3(blk, t): + if t + 3 > num_time_periods: + return Constraint.Skip + + return pb.period[t].fs.dfc.startup <= pb.period[t + 3].fs.dfc.op_mode + + # Ensure that the power output is P_min at t + 2 + # old NL constraint linearized + # pb.period[t + 2].fs.dfc.power - DFC_OFFLOAD * des_mdl.dfc_design.capacity <= + # (1 - DFC_OFFLOAD) * des_mdl.dfc_design.capacity * (1 - pb.period[t].fs.dfc.startup) + @m.Constraint(set_period) + def startup_con_4(blk, t): + if t + 2 > num_time_periods: + return Constraint.Skip + + # Design models are contained in the parent block of pb + des_mdl = pb.parent_block() + + return ( + pb.period[t + 2].fs.dfc.power <= des_mdl.dfc_design.capacity + (DFC_OFFLOAD - 1) + * des_mdl.dfc_design.capacity_ub * pb.period[t].fs.dfc.startup + ) + + @m.Constraint(set_period) + def shutdown_con_4_1(blk, t): + if t + 2 > num_time_periods: + return Constraint.Skip + + des_mdl = pb.parent_block() + + return ( + pb.period[t + 2].fs.dfc.power <= (des_mdl.dfc_design.capacity * DFC_OFFLOAD + - des_mdl.dfc_design.capacity_lb + des_mdl.dfc_design.capacity_lb * + pb.period[t].fs.dfc.startup)/(DFC_OFFLOAD -1) + ) + # Fuel requirement during the first hour of the startup + # FIXME: The fuel consumption is for a fixed capacity of 838 MW. This needs to be modified + # if the design the DFC is a variable + @m.Constraint(set_period) + def startup_con_5(blk, t): + # During the first hour, the fuel flowrate ramps up from 0 to 5.8518 kg/s. + # In the model, we work with the average fuel flowrate: (0 + 5.8518) /2 = 2.9529 + return pb.period[t].fs.dfc.su_sd_ng_flow >= 2.9529 * pb.period[t].fs.dfc.startup + + @m.Constraint(set_period) + def startup_con_6(blk, t): + # During the second hour, the fuel flowrate ramps up from 5.8518 to 10.889 kg/s. + # In the model, we work with the average fuel flowrate: (5.8518 + 10.889) / 2 = 8.3704 + # 10.889 kg/s is chosen instead of 10.36 to be consistent with the surrogate model + if t + 1 > num_time_periods: + return Constraint.Skip + + return pb.period[t + 1].fs.dfc.su_sd_ng_flow >= 8.3704 * pb.period[t].fs.dfc.startup + + # TODO: I suspect this constraint is implied by previous constraints, but I have not verified + # it. So, I'm adding this constraint for now. If we can show that it is implied, we can remove + # it later. Even if it is not implied, it is less likely that we will find a solution violating this + # constraint. So, we can add it as a lazy constraint, if pyomo supports one. + # Cycle must not be operating at time t - 1, if startup is initiated at time t + @m.Constraint(set_period) + def startup_con_7(blk, t): + if t <= 1: + return Constraint.Skip + + return pb.period[t].fs.dfc.startup <= 1 - pb.period[t - 1].fs.dfc.op_mode + + # If cycle shutdown is initiated at time t, i.e., shutdown[t] = 1, then + # at time t - 2, op_mode must be 1 and power produced must be P_min + # at time t - 1, op_mode must be 1 and power produced must be P_min + # at time t, shutdown is initiated so power must be zero + # at time t + 1, op_mode, startup and shutdown must be zero + @m.Constraint(set_period) + def shutdown_con_1(blk, t): + if t <= 2: + return Constraint.Skip + + return pb.period[t - 2].fs.dfc.op_mode >= pb.period[t].fs.dfc.shutdown + + @m.Constraint(set_period) + def shutdown_con_2(blk, t): + if t <= 1: + return Constraint.Skip + + return pb.period[t - 1].fs.dfc.op_mode >= pb.period[t].fs.dfc.shutdown + + @m.Constraint(set_period) + def shutdown_con_3(blk, t): + if t + 1 > num_time_periods: + return Constraint.Skip + + return ( + pb.period[t + 1].fs.dfc.startup + pb.period[t + 1].fs.dfc.op_mode + + pb.period[t + 1].fs.dfc.shutdown <= 1 - pb.period[t].fs.dfc.shutdown + ) + + # Ensure that the power output is P_min at t - 2 + # old cosntraint linearized + # pb.period[t - 2].fs.dfc.power - DFC_OFFLOAD * des_mdl.dfc_design.capacity <= + # (1 - DFC_OFFLOAD) * des_mdl.dfc_design.capacity * (1 - pb.period[t].fs.dfc.shutdown) + @m.Constraint(set_period) + def shutdown_con_4(blk, t): + if t <= 2: + return Constraint.Skip + + des_mdl = pb.parent_block() + + return ( + pb.period[t - 2].fs.dfc.power <= des_mdl.dfc_design.capacity + (DFC_OFFLOAD - 1) + * des_mdl.dfc_design.capacity_ub * pb.period[t].fs.dfc.shutdown + ) + @m.Constraint(set_period) + def shutdown_con_4_1(blk, t): + if t <= 2: + return Constraint.Skip + + des_mdl = pb.parent_block() + + return ( + pb.period[t - 2].fs.dfc.power <= (des_mdl.dfc_design.capacity * DFC_OFFLOAD + - des_mdl.dfc_design.capacity_lb + des_mdl.dfc_design.capacity_lb * + pb.period[t].fs.dfc.shutdown)/(DFC_OFFLOAD -1) + ) + # Ensure that the power output is P_min at t - 1 + + # pb.period[t - 1].fs.dfc.power - DFC_OFFLOAD * des_mdl.dfc_design.capacity <= + # (1 - DFC_OFFLOAD) * des_mdl.dfc_design.capacity * (1 - pb.period[t].fs.dfc.shutdown) + + @m.Constraint(set_period) + def shutdown_con_5(blk, t): + if t <= 1: + return Constraint.Skip + + des_mdl = pb.parent_block() + + return ( + pb.period[t - 1].fs.dfc.power <= des_mdl.dfc_design.capacity + (DFC_OFFLOAD - 1) + * des_mdl.dfc_design.capacity_ub * pb.period[t].fs.dfc.shutdown + ) + + + @m.Constraint(set_period) + def shutdown_con_5_1(blk, t): + if t <= 2: + return Constraint.Skip + + des_mdl = pb.parent_block() + + return ( + pb.period[t - 1].fs.dfc.power <= (des_mdl.dfc_design.capacity * DFC_OFFLOAD + - des_mdl.dfc_design.capacity_lb + des_mdl.dfc_design.capacity_lb * + pb.period[t].fs.dfc.shutdown)/(DFC_OFFLOAD -1) + ) + # When shutdown is initiated, the fuel flowrate is ramped down from 10.889 to 5.8515 + # In the model, we use the average value: (10.889 + 5.8515) / 2 = 8.3704 + @m.Constraint(set_period) + def shutdown_con_6(blk, t): + return pb.period[t].fs.dfc.su_sd_ng_flow >= 8.3704 * pb.period[t].fs.dfc.shutdown + + # During the second hour, the fuel flowrate is ramped down from 5.8515 to 0 + # In the model, we use the average value: (5.8515 + 0) / 2 = 2.9529 + @m.Constraint(set_period) + def shutdown_con_7(blk, t): + if t + 1 > num_time_periods: + return Constraint.Skip + + return pb.period[t + 1].fs.dfc.su_sd_ng_flow >= 2.9529 * pb.period[t].fs.dfc.shutdown + + # If the plant was in operation at time t and *not* in operation at t + 1, then plant shutdown + # must have been initiated at time t + 1 + @m.Constraint(set_period) + def shutdown_con_8(blk, t): + if t + 1 > num_time_periods: + return Constraint.Skip + + return ( + pb.period[t].fs.dfc.op_mode - pb.period[t + 1].fs.dfc.op_mode <= + pb.period[t + 1].fs.dfc.shutdown + ) diff --git a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/npv_optimization.py b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/npv_optimization.py new file mode 100644 index 0000000000..77e10705a5 --- /dev/null +++ b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/npv_optimization.py @@ -0,0 +1,462 @@ +import os +import pandas as pd +import json + +from pyomo.environ import ( + ConcreteModel, + Var, + NonNegativeReals, + Constraint, + Expression, + Block, + Objective, + maximize, + SolverFactory, + value, +) +from idaes.apps.grid_integration import MultiPeriodModel + +from idaes.models_extra.power_generation.flowsheets.direct_fired_cycle.unit_models import ( + get_lmp_data, + DFCDesign, + MonoASUDesign, + NLUDesign, + OxygenTankDesign, +) + +from idaes.models_extra.power_generation.flowsheets.direct_fired_cycle.dfc_flowsheet import ( + build_dfc_flowsheet, + build_dfc_flowsheet_with_lox, + build_dfc_flowsheet_with_nlu, + append_op_costs_dfc, + append_cashflows, +) + +from idaes.models_extra.power_generation.flowsheets.direct_fired_cycle.dfc_startup_shutdown import dfc_startup_shutdown_constraints +from idaes.models_extra.power_generation.flowsheets.direct_fired_cycle.asu_startup_shutdown import asu_startup_shutdown_constraints +import idaes.models_extra.power_generation.flowsheets.direct_fired_cycle. \ + default_model_parameters as dmp + + +# Source: DOE ARPA - E +NG_PRICE_DATA = { + "NREL": { + 100: { + "CAISO": 2.26, + "ERCOT": 2.64, + "MISO-W": 1.69, + "NYISO": 1.12, + "PJM-W": 1.42 + }, + 150: { + "CAISO": 2.26, + "ERCOT": 2.64, + "MISO-W": 2.01, + "NYISO": 1.14, + "PJM-W": 1.43 + } + }, + "Princeton": 2.94, + "New_Princeton": 2.94 +} + + +def _write_results( + m, + sol, + lox_withdrawal=False, + includes_nlu=False, + filename="results", +): + _filename = filename + ".xlsx" + + set_flowsheets = m.mp_model.period[:].fs + + results = { + "LMP [$/MWh]": [m.LMP[t] * 1000 for t in m.set_time], + "DFC_Schedule": [value(fs.dfc.op_mode) for fs in set_flowsheets], + "DFC_Startup": [value(fs.dfc.startup) for fs in set_flowsheets], + "DFC_Shutdown": [value(fs.dfc.shutdown) for fs in set_flowsheets], + "DFC_Power": [value(fs.dfc.power) for fs in set_flowsheets], + "DFC_Ng_Flow": [value(fs.dfc.total_ng_flow) for fs in set_flowsheets], + "DFC_O2_Flow": [value(fs.dfc.o2_flow) for fs in set_flowsheets], + + "ASU_Schedule": [value(fs.asu.op_mode) for fs in set_flowsheets], + "ASU_Startup": [value(fs.asu.startup) for fs in set_flowsheets], + "ASU_Shutdown": [value(fs.asu.shutdown) for fs in set_flowsheets], + "ASU_Power": [value(fs.asu.total_power) for fs in set_flowsheets], + "ASU_O2_Flow": [value(fs.asu.o2_flow) for fs in set_flowsheets], + + "Power_to_grid": [value(fs.power_dfc_to_grid) for fs in set_flowsheets], + "Power_grid_to_asu": [value(fs.power_grid_to_asu) for fs in set_flowsheets], + "Power_dfc_to_asu": [value(fs.power_dfc_to_asu) for fs in set_flowsheets], + + "Oxygen_asu_to_dfc": [value(fs.oxygen_asu_to_dfc) for fs in set_flowsheets], + "Oxygen_asu_to_vent": [value(fs.oxygen_asu_to_vent) for fs in set_flowsheets], + } + + if includes_nlu: + results["NLU_Schedule"] = [value(fs.nlu.op_mode) for fs in set_flowsheets] + results["NLU_O2_Flow"] = [value(fs.nlu.o2_flow) for fs in set_flowsheets] + results["NLU_Power"] = [value(fs.nlu.power) for fs in set_flowsheets] + + results["Tank_init_holdup"] = [value(fs.tank.initial_holdup) for fs in set_flowsheets] + results["Tank_final_holdup"] = [value(fs.tank.final_holdup) for fs in set_flowsheets] + results["Tank_lox_in"] = [value(fs.tank.lox_in) for fs in set_flowsheets] + results["Tank_lox_out"] = [value(fs.tank.lox_out) for fs in set_flowsheets] + + results["Power_dfc_to_nlu"] = [value(fs.power_dfc_to_nlu) for fs in set_flowsheets] + results["Power_dfc_to_tank"] = [value(fs.power_dfc_to_tank) for fs in set_flowsheets] + results["Power_grid_to_nlu"] = [value(fs.power_grid_to_nlu) for fs in set_flowsheets] + results["Power_grid_to_tank"] = [value(fs.power_grid_to_tank) for fs in set_flowsheets] + + if lox_withdrawal: + results["GOx_fraction"] = [value(fs.gox_fraction) for fs in set_flowsheets] + + results["Tank_init_holdup"] = [value(fs.tank.initial_holdup) for fs in set_flowsheets] + results["Tank_final_holdup"] = [value(fs.tank.final_holdup) for fs in set_flowsheets] + results["Tank_lox_in"] = [value(fs.tank.lox_in) for fs in set_flowsheets] + results["Tank_lox_out"] = [value(fs.tank.lox_out) for fs in set_flowsheets] + + results["Power_dfc_to_tank"] = [value(fs.power_dfc_to_tank) for fs in set_flowsheets] + results["Power_grid_to_tank"] = [value(fs.power_grid_to_tank) for fs in set_flowsheets] + + results_df = pd.DataFrame(results) + results_df.to_excel(_filename) + + lower_bnd = sol["Problem"][0]["Lower bound"] + upper_bnd = sol["Problem"][0]["Upper bound"] + + elec_rev = [value(fs.electricity_revenue) for fs in set_flowsheets] + elec_cost = [value(fs.electricity_cost) for fs in set_flowsheets] + fuel_cost = [value(fs.fuel_cost) for fs in set_flowsheets] + co2_price = [value(fs.co2_price) for fs in set_flowsheets] + dfc_vom = [value(fs.dfc.non_fuel_vom) for fs in set_flowsheets] + asu_vom = [value(fs.asu.non_fuel_vom) for fs in set_flowsheets] + if includes_nlu: + nlu_vom = [value(fs.nlu.non_fuel_vom) for fs in set_flowsheets] + else: + nlu_vom = [0] + + solution = { + "Lower bound": lower_bnd, + "Upper bound": upper_bnd, + "Gap": ((upper_bnd - lower_bnd) / lower_bnd) * 100, + #"Wall time": sol["Solver"][0]["Wall time"], gurobi atrribute, cant call when using cplex + "Status": sol["Solver"][0]["Status"], + "Termination message": sol["Solver"][0]["Termination message"], + + "NPV": value(m.obj) / 1000, + "DFC_Capacity": m.dfc_design.capacity.value, + "ASU_Capacity": m.asu_design.max_o2_flow.value, + "NLU_Capacity": (m.nlu_design.max_o2_flow.value if hasattr(m, "nlu_design") else "N/A"), + "Tank_Capacity": (m.tank_design.tank_capacity.value + if hasattr(m, "tank_design") else "N/A"), + "DFC Startups": sum(results["DFC_Startup"]), + "DFC Shutdowns": sum(results["DFC_Shutdown"]), + "ASU Startups": sum(results["ASU_Startup"]), + "ASU Shutdowns": sum(results["ASU_Shutdown"]), + "Total Power Produced": sum(results["DFC_Power"]), + "Total Power Sold": sum(results["Power_to_grid"]), + "DFC CAPEX": value(m.dfc_design.capex) / 1e3, + "ASU CAPEX": value(m.asu_design.capex) / 1e3, + "NLU CAPEX": value(m.nlu_design.capex) / 1e3 if hasattr(m, "nlu_Design") else 0, + "TANK CAPEX": value(m.tank_design.capex) / 1e3 if hasattr(m, "tank_design") else 0, + "Total CAPEX": m.CAPEX.value / 1e3, + "Total FOM": m.FOM.value / 1e3, + "Total Revenue": sum(elec_rev) / 1e3, + "Total Elec Cost": sum(elec_cost) / 1e3, + "Total Fuel Cost": sum(fuel_cost) / 1e3, + "Total CO2 Price": sum(co2_price) / 1e3, + "Total VOM": (sum(dfc_vom) + sum(asu_vom) + sum(nlu_vom)) / 1e3, + "Total Corp. tax": m.CORP_TAX.value / 1e3, + "Total Net Profit": m.NET_PROFIT.value / 1e3, + + "Num Vars": sol["Problem"][0]["Number of variables"], + "Num Bin Vars": sol["Problem"][0]["Number of binary variables"], + "Num constraints": sol["Problem"][0]["Number of constraints"], + } + + with open(filename + ".json", "w") as fp: + json.dump(solution, fp, indent=4) + + +def get_linking_var_pairs(m1, m2): + return [(m1.fs.tank.final_holdup, m2.fs.tank.initial_holdup)] + + +def npv_model_dfc_asu( + dataset="NREL", + location="PJM-W", + carbon_tax=150, + dfc_params=dmp.DFC_PARAMS, + asu_params=dmp.ASU_PARAMS, + cost_params=dmp.CASHFLOW_PARAMS, + solver=None, + folder="", +): + if dataset == "NREL": + cost_ng = NG_PRICE_DATA[dataset][carbon_tax][location] + else: + cost_ng = NG_PRICE_DATA[dataset] + + penalty = cost_params["electricity_cost"] / 1e3 + + m = ConcreteModel() + get_lmp_data(m, dataset=dataset, location=location, carbon_tax=carbon_tax) + + m.dfc_design = DFCDesign(model_params=dfc_params) + m.asu_design = MonoASUDesign(model_params=asu_params) + + # Currently, we are not varying the capacity of the power cycle, so fixing the value + # of capacity. Also, we want the power cycle to be built, so fixing build_dfc. + m.dfc_design.build_dfc.fix(1) + m.dfc_design.capacity.fix() + + m.mp_model = MultiPeriodModel( + n_time_points=365 * 24, + process_model_func=build_dfc_flowsheet, + linking_variable_func=None, + use_stochastic_build=True, + flowsheet_options={ + "dfc_design": m.dfc_design, + "asu_design": m.asu_design, + }, + ) + + # Append cashflows at each hour + for t in m.set_time: + append_op_costs_dfc( + m=m.mp_model.period[t], + lmp=m.LMP[t], + penalty=penalty, + cost_ng=cost_ng, + carbon_price=carbon_tax / 1e3, + ) + + # Append startup and shutdown constraints for the power cycle + m.mp_model.dfc_su_sd = Block(rule=dfc_startup_shutdown_constraints) + + # Append startup and shutdown constraints for the ASU unit + m.mp_model.asu_su_sd = Block(rule=asu_startup_shutdown_constraints) + + # Append the overall cashflows + append_cashflows(m, cost_params) + + # Declare the objective function + m.obj = Objective(expr=m.npv, sense=maximize) + + # Use Gurobi solver + if solver is None: + solver = SolverFactory("gams") + #solver.options["threads"] = 10 + #Gurobi specific options + # solver.options['MIPGap'] = 0.01 + # solver.options['TimeLimit'] = 7500 + # solver.options['OutputFlag'] = 1 + + sol = solver.solve(m,solver = "cplex", tee=True) + + _filename = folder + dataset + "_" + location + "_" + str(carbon_tax) + _write_results(m, sol, filename=_filename) + + return m, sol + + +def npv_model_dfc_asu_with_lox( + dataset="NREL", + location="PJM-W", + carbon_tax=150, + dfc_params=dmp.DFC_PARAMS, + asu_params=dmp.ASU_PARAMS, + tank_params=dmp.O2_TANK_PARAMS, + cost_params=dmp.CASHFLOW_PARAMS, + solver=None, + folder="", +): + if dataset == "NREL": + cost_ng = NG_PRICE_DATA[dataset][carbon_tax][location] + else: + cost_ng = NG_PRICE_DATA[dataset] + + penalty = cost_params["electricity_cost"] / 1e3 + + m = ConcreteModel() + get_lmp_data(m, dataset=dataset, location=location, carbon_tax=carbon_tax) + + m.dfc_design = DFCDesign(model_params=dfc_params) + m.asu_design = MonoASUDesign( + o2_flow_range=(10, 130), model_params=asu_params, + ) + m.tank_design = OxygenTankDesign( + tank_size_range=(10, 400000), model_params=tank_params, + ) + + # Currently, we are not varying the capacity of the power cycle, so fixing the value + # of capacity. Also, we want the power cycle to be built, so fixing build_dfc. + m.dfc_design.build_dfc.fix(1) + m.dfc_design.capacity.fix() + + m.mp_model = MultiPeriodModel( + n_time_points=365 * 24, + process_model_func=build_dfc_flowsheet_with_lox, + linking_variable_func=get_linking_var_pairs, + use_stochastic_build=True, + flowsheet_options={ + "dfc_design": m.dfc_design, + "asu_design": m.asu_design, + "tank_design": m.tank_design, + }, + ) + + # Append cashflows at each hour + for t in m.set_time: + append_op_costs_dfc( + m=m.mp_model.period[t], + lmp=m.LMP[t], + penalty=penalty, + cost_ng=cost_ng, + carbon_price=carbon_tax / 1e3, + ) + + # Append startup and shutdown constraints for the power cycle + m.mp_model.dfc_su_sd = Block(rule=dfc_startup_shutdown_constraints) + + # Append startup and shutdown constraints for the ASU unit + m.mp_model.asu_su_sd = Block(rule=asu_startup_shutdown_constraints) + + # Set the initial holdup of the tank + if tank_params["tank_constraint"] == "initial_holdup": + m.mp_model.initial_tank_level = Constraint( + expr=m.mp_model.period[1].fs.tank.initial_holdup == + tank_params["min_holdup"] * m.tank_design.tank_capacity + ) + + elif tank_params["tank_constraint"] == "periodic": + m.mp_model.periodic_constraint = Constraint( + expr=m.mp_model.period[1].fs.tank.initial_holdup == + m.mp_model.period[8760].fs.tank.holdup + ) + + # Append the overall cashflows + append_cashflows(m, cost_params) + + # Declare the objective function + m.obj = Objective(expr=m.npv, sense=maximize) + + # Use Gurobi solver + if solver is None: + solver = SolverFactory("gurobi") + solver.options['NonConvex'] = 2 + solver.options['MIPGap'] = 0.01 + solver.options['TimeLimit'] = 7500 + solver.options['OutputFlag'] = 1 + + sol = solver.solve(m, tee=True) + + _filename = folder + dataset + "_" + location + "_" + str(carbon_tax) + _write_results(m, sol, filename=_filename, lox_withdrawal=True) + + return m, sol + + +def npv_model_dfc_asu_nlu( + dataset="NREL", + location="PJM-W", + carbon_tax=150, + dfc_params=dmp.DFC_PARAMS, + asu_params=dmp.ASU_PARAMS, + nlu_params=dmp.NLU_PARAMS, + tank_params=dmp.O2_TANK_PARAMS, + cost_params=dmp.CASHFLOW_PARAMS, + solver=None, + folder="", +): + + penalty = cost_params["electricity_cost"] / 1e3 + + if dataset == "NREL": + cost_ng = NG_PRICE_DATA[dataset][carbon_tax][location] + else: + cost_ng = NG_PRICE_DATA[dataset] + + m = ConcreteModel() + get_lmp_data(m, dataset=dataset, location=location, carbon_tax=carbon_tax) + + m.dfc_design = DFCDesign(model_params=dfc_params) + m.asu_design = MonoASUDesign( + o2_flow_range=(10, 130), model_params=asu_params, + ) + m.nlu_design = NLUDesign( + o2_flow_range=(10, 130), model_params=nlu_params, + ) + m.tank_design = OxygenTankDesign( + tank_size_range=(10, 400000), model_params=tank_params, + ) + + # Currently, we are not varying the capacity of the power cycle, so fixing the value + # of capacity. Also, we want the power cycle to be built, so fixing build_dfc. + m.dfc_design.build_dfc.fix(1) + m.dfc_design.capacity.fix() + + m.mp_model = MultiPeriodModel( + n_time_points=365 * 24, + process_model_func=build_dfc_flowsheet_with_nlu, + linking_variable_func=get_linking_var_pairs, + use_stochastic_build=True, + flowsheet_options={ + "dfc_design": m.dfc_design, + "asu_design": m.asu_design, + "nlu_design": m.nlu_design, + "tank_design": m.tank_design, + }, + ) + + # Append cashflows at each hour + for t in m.set_time: + append_op_costs_dfc( + m=m.mp_model.period[t], + lmp=m.LMP[t], + penalty=penalty, + cost_ng=cost_ng, + carbon_price=carbon_tax / 1e3, + ) + + # Append startup and shutdown constraints for the power cycle + m.mp_model.dfc_su_sd = Block(rule=dfc_startup_shutdown_constraints) + + # Append startup and shutdown constraints for the ASU unit + m.mp_model.asu_su_sd = Block(rule=asu_startup_shutdown_constraints) + + # Set the initial holdup of the tank + if tank_params["tank_constraint"] == "initial_holdup": + m.mp_model.initial_tank_level = Constraint( + expr=m.mp_model.period[1].fs.tank.initial_holdup == + tank_params["min_holdup"] * m.tank_design.tank_capacity + ) + + elif tank_params["tank_constraint"] == "periodic": + m.mp_model.periodic_constraint = Constraint( + expr=m.mp_model.period[1].fs.tank.initial_holdup == + m.mp_model.period[8760].fs.tank.holdup + ) + + # Append the overall cashflows + append_cashflows(m, cost_params) + + # Declare the objective function + m.obj = Objective(expr=m.npv, sense=maximize) + + # Use Gurobi solver + if solver is None: + solver = SolverFactory("gurobi") + solver.options['MIPGap'] = 0.01 + solver.options['TimeLimit'] = 7200 + solver.options['OutputFlag'] = 1 + + sol = solver.solve(m, tee=True) + + _filename = folder + dataset + "_" + location + "_" + str(carbon_tax) + _write_results(m, sol, filename=_filename, includes_nlu=True) + + return m, sol diff --git a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/run_script.py b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/run_script.py new file mode 100644 index 0000000000..2a4bfa52ab --- /dev/null +++ b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/run_script.py @@ -0,0 +1,58 @@ +import os +import copy +from idaes.power_generation.flowsheets.direct_fired_cycle.npv_optimization import ( + npv_model_dfc_asu, + npv_model_dfc_asu_with_lox, + npv_model_dfc_asu_nlu, +) +import idaes.power_generation.flowsheets.direct_fired_cycle. \ + default_model_parameters as dmp + +DFC_PARAMS = dmp.DFC_PARAMS +ASU_PARAMS = dmp.ASU_PARAMS +TANK_PARAMS = dmp.O2_TANK_PARAMS +NLU_PARAMS = dmp.NLU_PARAMS +CASHFLOW_PARAMS = dmp.CASHFLOW_PARAMS + + +# for tax in [100, 150]: +# for region in ["CAISO", "ERCOT", "MISO-W", "NYISO", "PJM-W"]: +# m, sol = npv_model_dfc_asu(location=region, carbon_tax=tax) +# # m, sol = npv_model_dfc_asu_with_lox(location=region, carbon_tax=tax) +# # m, sol = npv_model_dfc_asu_nlu(location=region, carbon_tax=tax) + +# for tax in [100]: +# for region in ["CAISO"]: +# m, sol = npv_model_dfc_asu(location=region, carbon_tax=tax) + +# for tax, region in [(100, "CAISO"), (150, "ERCOT"), (150, "PJM-W")]: +# print("*" * 80) +# print("Running ", region, tax) +# print("*" * 80) +# m, sol = npv_model_dfc_asu_nlu(location=region, carbon_tax=tax) + + +for tax, region in [(100, "CAISO")]: + print("*" * 80) + print("Running ", region, tax) + print("*" * 80) + + dfc_params = copy.deepcopy(DFC_PARAMS) + asu_params = copy.deepcopy(ASU_PARAMS) + cost_params = copy.deepcopy(CASHFLOW_PARAMS) + + # Modify any default parameters if needed + # dfc_params["capex"] /= 2 + + cwd = os.getcwd() + if not os.path.exists(cwd + "\\results"): + os.mkdir(os.path.join(cwd, "results")) + + m, sol = npv_model_dfc_asu( + location=region, + carbon_tax=tax, + dfc_params=dfc_params, + asu_params=asu_params, + cost_params=cost_params, + folder="results/", + ) diff --git a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/unit_models.py b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/unit_models.py new file mode 100644 index 0000000000..f6f781c231 --- /dev/null +++ b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/unit_models.py @@ -0,0 +1,730 @@ +from importlib import resources +from pathlib import Path +import pandas as pd + +from pyomo.environ import ( + Var, + NonNegativeReals, + Binary, + Param, + RangeSet, + Constraint, + Expression, +) + +from idaes.core.base.process_base import declare_process_block_class +from idaes.models.unit_models import SkeletonUnitModelData +from pyomo.common.config import ConfigValue +import idaes.models_extra.power_generation.flowsheets.direct_fired_cycle. \ + default_model_parameters as dmp + + +def get_lmp_data( + m, + dataset="NREL", + location="CAISO", + carbon_tax=100, + princeton_case="BaseCaseTax", +): + """ + This function reads and appends LMP data to the model. + """ + with resources.path( + "idaes.models_extra.power_generation.flowsheets.direct_fired_cycle", + "FLECCS_Price_Series_Data_01_20_2021.xlsx" + ) as p: + path_to_file = Path(p).resolve() + + if dataset == "NREL": + raw_data = pd.read_excel( + path_to_file, + sheet_name="2035 - NREL", + ) + column_name = 'MiNg_$' + str(carbon_tax) + '_' + location + # We will take the price signal for day 365 same as that for day 364 + num_days = 365 + + elif dataset == "Princeton": + raw_data = pd.read_excel( + path_to_file, + sheet_name="2030 - Princeton", + ) + column_name = princeton_case + num_days = 365 + + m.set_time = RangeSet(num_days * 24) + price_all = raw_data[column_name].tolist() + + # Set prices lower than $0.001/MWh to zero to avoid numerical issues + price_all = [i if i > 1e-3 else 0 for i in price_all] + + # LMP is divided by 1000 to formulate the objective in thousands of dollars. + # This helps in keeping the scale of the CAPEX in O(1e6), otherwise it would be O(1e9). + m.LMP = Param( + m.set_time, + initialize={t + 1: lmp / 1000 for (t, lmp) in enumerate(price_all)}, + doc="Locational Marginal Prices [in 1000$/MWh]" + ) + + +@declare_process_block_class("DFCDesign") +class DFCDesignData(SkeletonUnitModelData): + """ + This class contains variables/parameters related to the design of the direct fired cycle + """ + CONFIG = SkeletonUnitModelData.CONFIG() + CONFIG.declare("capacity_range", ConfigValue( + default=(500, 850), + doc="Range for the capacity of the DFC [in MW]", + )) + CONFIG.declare("model_params", ConfigValue( + default=dmp.DFC_PARAMS, + doc="Dictionary containing model parameters", + )) + + # noinspection PyAttributeOutsideInit + def build(self): + super().build() + + params = self.config.model_params + _dfc_capacity = params["dfc_capacity"] + _ng_flow = params["ng_flow"] + _o2_ng_ratio = params["o2_ng_ratio"] + _capex = params["capex"] + _fom_factor = params["fom_factor"] + + self.capacity = Var( + within=NonNegativeReals, + initialize=_dfc_capacity, + bounds=(0, self.config.capacity_range[1]), + doc="Capacity of the power plant [in MW]", + ) + + # Define a variable that informs whether the plant needs to built or not. + self.build_dfc = Var( + within=Binary, + doc="1: Plant is built, 0: Plant is not built", + ) + + # Bound the capcity of the plant in the specified range + capacity_range = self.config.capacity_range + self.capacity_lb_con = Constraint(expr=self.capacity >= self.build_dfc * capacity_range[0]) + self.capacity_ub_con = Constraint(expr=self.capacity <= self.build_dfc * capacity_range[1]) + + # Compute the natural gas flowrate required at maximum capacity. + # FIXME: Assuming a linear relation for now. Update the equation when we have more data + self.ng_flow = Expression( + expr=self.capacity * (_ng_flow / _dfc_capacity), + doc="Computes the natural flowrate required [in kg/s] at full load", + ) + self.capacity_ub = Expression( + expr=self.config.capacity_range[1], + doc="Upper bound on the capacity of the dfc", + ) + + self.capacity_lb = Expression( + expr=self.config.capacity_range[0], + doc="Upper bound on the capacity of the dfc", + ) + + self.o2_flow = Expression( + expr=_o2_ng_ratio * self.ng_flow, + doc="Flowrate of oxygen [in kg/s] required at full load", + ) + + # Assumuing that the capex varies linearly with capacity + self.capex = Expression( + expr=_capex * (self.capacity / _dfc_capacity), + doc="CAPEX of the power cycle [in 1000$]", + ) + + # Calculating the FOM as 3.157% of the CAPEX. The percentage value is obtained from + # 35,641.27 (FOM) / 1,128,855 (CAPEX). The FOM is also assumed to vary linearly with the capacity. + self.fom = Expression( + expr=_fom_factor * self.capex, + doc="Fixed O&M cost [in 1000$/year]", + ) + + +@declare_process_block_class("DFCOperation") +class DFCOperationData(SkeletonUnitModelData): + """ + This class contains the model for the operation of the power cycle + """ + + CONFIG = SkeletonUnitModelData.CONFIG() + CONFIG.declare("design_blk", ConfigValue( + doc="Pointer to the object containing the DFC design information", + )) + CONFIG.declare("operating_range", ConfigValue( + default=(0.2, 1), + doc="Off-design operating range. Default: (0.2 * full load, full load)", + )) + + # noinspection PyAttributeOutsideInit + def build(self): + super().build() + + design_blk = self.config.design_blk + + # Declare variables + self.power = Var( + within=NonNegativeReals, + doc="Total power generated [in MW]", + ) + self.ng_flow = Var( + within=NonNegativeReals, + doc="Natural gas flowrate [in kg/s]", + ) + self.op_mode = Var( + within=Binary, + doc="1: In Operation, 0: Shutdown", + ) + self.startup = Var( + within=Binary, + doc="1: Plant is startup at this hour, 0: Otherwise", + ) + self.shutdown = Var( + within=Binary, + doc="1: Plant is shutdown at this hour, 0: Otherwise", + ) + + self.op_mode_capacity = Var( + within=NonNegativeReals, + doc="Auxiliary variable for the product of op_mode and capacity", + ) + self.su_sd_dfc_aux_1 = Var( + within=NonNegativeReals, + doc="Auxiliary variable for the product of capacity and startup & shutdown binaries", + ) + self.su_sd_dfc_aux_2 = Var( + within=NonNegativeReals, + doc="Auxiliary variable for the product of capacity and startup & shutdown binaries", + ) + self.su_sd_dfc_aux_3 = Var( + within=NonNegativeReals, + doc="Auxiliary variable for the product of capacity and startup & shutdown binaries", + ) + self.su_sd_dfc_aux_4 = Var( + within=NonNegativeReals, + doc="Auxiliary variable for the product of capacity and startup & shutdown binaries", + ) + # Linear relaxation of the product op_mode * capacity + self.mccor_conv = Constraint( + expr=self.op_mode_capacity >= design_blk.capacity + + design_blk.capacity.ub * self.op_mode - design_blk.capacity.ub, + ) + self.mccor_conc_1 = Constraint( + expr=self.op_mode_capacity <= design_blk.capacity, + ) + self.mccor_conc_2 = Constraint( + expr=self.op_mode_capacity <= self.op_mode * design_blk.capacity.ub, + ) + + # Power production as a function of natural gas flowrate + # We construct a surrogate model of the form (1 - norm_power) = m * (1 - norm_ng_flow). + # This way, the natural gas requirement is exact at full load. + # Rearranging the equation yields norm_power = m * norm_ng_flow + 1-m + # Next, we replace norm_power = power / capacity and norm_ng_flow = ng_flow / max_ng_flow, + # substitute max_ng_flow in terms of capacity and rearrange the equation. + + params = design_blk.config.model_params + _dfc_capacity = params["dfc_capacity"] + _ng_flow = params["ng_flow"] + _perf_curve = params["op_curve_coeff"] + _o2_ng_ratio = params["o2_ng_ratio"] + _vom = params["vom"] + _co2_emission = params["co2_emission"] + + self.power_production = Constraint( + expr=self.power == (_perf_curve[0] / (_ng_flow / _dfc_capacity)) * self.ng_flow + + _perf_curve[1] * self.op_mode_capacity, + ) + + # Ensure that power production is within P_min and P_max + operating_range = self.config.operating_range + self.power_production_lb = Constraint( + expr=operating_range[0] * self.op_mode_capacity <= self.power, + ) + + self.power_production_ub = Constraint( + expr=self.power <= operating_range[1] * self.op_mode_capacity, + ) + + # Declare a variable to track NG requirement for startup and shutdown + self.su_sd_ng_flow = Var( + within=NonNegativeReals, + doc="Fuel requirement for startup/shutdown [in kg/s]", + ) + + self.total_ng_flow = Expression( + expr=self.ng_flow + self.su_sd_ng_flow, + doc="Total NG flow rate requirement [in kg/s]", + ) + + self.o2_flow = Expression( + expr=_o2_ng_ratio * self.total_ng_flow, + doc="Flowrate of oxygen required [in kg/s]", + ) + + self.non_fuel_vom = Expression( + expr=_vom * (self.power / _dfc_capacity), + doc="Non-fuel VOM cost [in $1000/hr]", + ) + + self.co2_emissions = Expression( + expr=_co2_emission * self.power, + doc="Net CO2 emissions [in kg/hr]", + ) + + +@declare_process_block_class("MonoASUDesign") +class MonoASUDesignData(SkeletonUnitModelData): + """ + This class builds the model for the design of the ASU. Here, we treat the + system of ASUs as a monolithic ASU. + """ + + CONFIG = SkeletonUnitModelData.CONFIG() + CONFIG.declare("o2_flow_range", ConfigValue( + default=(80, 130), + doc="Range for the size of the ASU in terms of O2 flow rate [in kg/s]", + )) + CONFIG.declare("model_params", ConfigValue( + default=dmp.MONO_ASU_PARAMS, + doc="Dictionary containing model parameters", + )) + + # noinspection PyAttributeOutsideInit + def build(self): + super().build() + + params = self.config.model_params + _asu_capacity = params["asu_capacity"] + _power_req = params["power_requirement"] + _capex = params["capex"] + _fom_factor = params["fom_factor"] + + self.max_o2_flow = Var( + within=NonNegativeReals, + initialize=_asu_capacity, + bounds=(0, self.config.o2_flow_range[1]), + doc="Maximum flowrate of O2 the ASU can produce [in kg/s]", + ) + self.build_asu = Var( + within=Binary, + doc="1: ASU is built, 0: ASU is not built", + ) + + # Bound the capcity of the plant in the specified range + o2_flow_range = self.config.o2_flow_range + self.o2_flow_lb_con = Constraint(expr=self.max_o2_flow >= self.build_asu * o2_flow_range[0]) + self.o2_flow_ub_con = Constraint(expr=self.max_o2_flow <= self.build_asu * o2_flow_range[1]) + + # Relation between the flowrate and the power requirement + self.max_power = Expression( + expr=self.max_o2_flow * (_power_req / _asu_capacity), + doc="Power requirement at maximum capacity [in MW]", + ) + + self.max_power_ub = Expression( + expr=self.config.o2_flow_range[1]* (_power_req / _asu_capacity), + doc="Upper bound for the power requirement at maximum capacity [in MW]", + ) + + # Assuming that the capex of the ASU varies linearly with size + self.capex = Expression( + expr=_capex * (self.max_o2_flow / _asu_capacity), + doc="CAPEX of the ASU unit [in 1000$]", + ) + + # Calculating the FOM as 3.157% of the CAPEX. The percentage value is obtained from + # 17223.73 (FOM) / 545,522 (CAPEX). The FOM is also assumed to vary linearly with the capacity. + self.fom = Expression( + expr=_fom_factor * self.capex, + doc="Fixed O&M cost [in $1000]", + ) + + +@declare_process_block_class("MonoASUOperation") +class MonoASUOperationData(SkeletonUnitModelData): + """ + This class contains the model for the operation of the monolithic ASU + """ + + CONFIG = SkeletonUnitModelData.CONFIG() + CONFIG.declare("design_blk", ConfigValue( + doc="Pointer to the object containing the ASU design information", + )) + CONFIG.declare("operating_range", ConfigValue( + default=(0.3, 1), + doc="Off-design operating range. Default: (0.3 * full load, full load)", + )) + + # noinspection PyAttributeOutsideInit + def build(self): + super().build() + + design_blk = self.config.design_blk + + # Declare variables + self.power = Var( + within=NonNegativeReals, + doc="Power required to produce oxygen [in MW]", + ) + self.o2_flow = Var( + within=NonNegativeReals, + doc="Flowrate of oxygen produced [in kg/s]", + ) + self.op_mode = Var( + within=Binary, + doc="1: In Operation, 0: Shutdown", + ) + self.startup = Var( + within=Binary, + doc="1: ASU is startup at this hour, 0: Otherwise", + ) + self.shutdown = Var( + within=Binary, + doc="1: ASU is shutdown at this hour, 0: Otherwise", + ) + self.op_mode_o2_flow = Var( + within=NonNegativeReals, + doc="Auxiliary variable for linearizing the product of op_mode and max_o2_flow" + ) + self.su_sd_asu_aux_1 = Var( + within=NonNegativeReals, + doc="Auxiliary variable for linearizing the product of startup/stutdown and max_power" + ) + self.su_sd_asu_aux_2 = Var( + within=NonNegativeReals, + doc="Auxiliary variable for linearizing the product of startup/stutdown and max_power" + ) + self.su_sd_asu_aux_3 = Var( + within=NonNegativeReals, + doc="Auxiliary variable for linearizing the product of startup/stutdown and max_power" + ) + # Linear relaxation of op_mode * max_o2_flow + self.mccor_conv = Constraint( + expr=self.op_mode_o2_flow >= design_blk.max_o2_flow + + self.op_mode * design_blk.max_o2_flow.ub - design_blk.max_o2_flow.ub, + ) + self.mccor_conc_1 = Constraint( + expr=self.op_mode_o2_flow <= design_blk.max_o2_flow, + ) + self.mccor_conc_2 = Constraint( + expr=self.op_mode_o2_flow <= self.op_mode * design_blk.max_o2_flow.ub, + ) + + # Power production as a function of natural gas flowrate + # We construct a surrogate model of the form (1 - norm_power) = m * (1 - norm_o2_flow). + # This way, the power requirement is exact at full capacity. + # Rearranging the equation yields norm_power = m * norm_o2_flow + 1-m + + params = design_blk.config.model_params + _asu_capacity = params["asu_capacity"] + _power_req = params["power_requirement"] + _perf_curve = params["op_curve_coeff"] + _vom = params["vom"] + + self.power_requirement = Constraint( + expr=(1 / (_power_req / _asu_capacity)) * self.power == + _perf_curve[0] * self.o2_flow + _perf_curve[1] * self.op_mode_o2_flow, + ) + + # Ensure that the normalized oxygen flowrate is within the admissible operating range + operating_range = self.config.operating_range + self.o2_flow_lb = Constraint( + expr=operating_range[0] * self.op_mode_o2_flow <= self.o2_flow, + ) + + self.o2_flow_ub = Constraint( + expr=self.o2_flow <= operating_range[1] * self.op_mode_o2_flow, + ) + + self.su_sd_power = Var( + within=NonNegativeReals, + doc="Power requirement during startup and shutdown [in MW]", + ) + + self.total_power = Expression( + expr=self.power + self.su_sd_power, + doc="Total power required by the ASU [in MW]", + ) + + self.non_fuel_vom = Expression( + expr=_vom * (self.o2_flow / _asu_capacity), + doc="Non-electricity VOM cost [in $1000/hr]", + ) + + +@declare_process_block_class("NLUDesign") +class NLUDesignData(SkeletonUnitModelData): + """ + This class builds the model for the design of the NLU. + """ + + CONFIG = SkeletonUnitModelData.CONFIG() + CONFIG.declare("o2_flow_range", ConfigValue( + default=(50, 130), + doc="Range for the size of the NLU in terms of O2 flow rate [in kg/s]", + )) + CONFIG.declare("model_params", ConfigValue( + default=dmp.NLU_PARAMS, + doc="Dictionary containing NLU model parameters", + )) + + # noinspection PyAttributeOutsideInit + def build(self): + super().build() + + params = self.config.model_params + _nlu_capacity = params["nlu_capacity"] + _power_req = params["power_requirement"] + _capex = params["capex"] + _fom_factor = params["fom_factor"] + + self.max_o2_flow = Var( + within=NonNegativeReals, + initialize=_nlu_capacity, + bounds=(0, self.config.o2_flow_range[1]), + doc="Maximum flowrate of O2 the NLU can liquefy [in kg/s]", + ) + self.build_nlu = Var( + within=Binary, + doc="1: NLU is built, 0: NLU is not built", + ) + + # Bound the capcity of the plant in the specified range + o2_flow_range = self.config.o2_flow_range + self.o2_flow_lb_con = Constraint(expr=self.max_o2_flow >= self.build_nlu * o2_flow_range[0]) + self.o2_flow_ub_con = Constraint(expr=self.max_o2_flow <= self.build_nlu * o2_flow_range[1]) + + # Relation between the flowrate and the power requirement + self.max_power = Expression( + expr=self.max_o2_flow * (_power_req / _nlu_capacity), + doc="Power requirement at maximum capacity [in MW]", + ) + + # Assuming that the capex of the NLU varies linearly with size + # Obtained from $42,797.4 * 4 = $171,189.6 + self.capex = Expression( + expr=_capex * (self.max_o2_flow / _nlu_capacity), + doc="CAPEX of the ASU unit [in 1000$]", + ) + + # Assuming that the FOM of NLU is 3.157% of CAPEX. This number is calculated as + # 41,715 (FOM) / 171,189 (CAPEX) + self.fom = Expression( + expr=_fom_factor * self.capex, + doc="Fixed O&M of NLU [in $1000]", + ) + + +@declare_process_block_class("NLUOperation") +class NLUOperationData(SkeletonUnitModelData): + """ + This class contains the model for the operation of the NLU + """ + # NOTE: The NLU model can be simplified by not defining the design variables. + # But we modeled it in this manner to keep it consistent with the rest of the models. + # If the solver finds it difficult to solve, then we'll switch to the simpler model. + + CONFIG = SkeletonUnitModelData.CONFIG() + CONFIG.declare("design_blk", ConfigValue( + doc="Pointer to the object containing the NLU design information", + )) + CONFIG.declare("operating_range", ConfigValue( + default=(0.3, 1), + doc="Off-design operating range. Default: (0.3 * full load, full load)", + )) + + # noinspection PyAttributeOutsideInit + def build(self): + super().build() + + design_blk = self.config.design_blk + params = design_blk.config.model_params + _nlu_capacity = params["nlu_capacity"] + _power_req = params["power_requirement"] + _vom = params["vom"] + + # Declare variables + self.power = Var( + within=NonNegativeReals, + doc="Power required to liquify oxygen [in MW]", + ) + self.o2_flow = Var( + within=NonNegativeReals, + doc="Flowrate of liquified oxygen [in kg/s]", + ) + + # Note: Assuming that the NLU does not have any startup/shutdown constraints. + # If it does, we need to define startup and shutdown binary variables like before. + self.op_mode = Var( + within=Binary, + doc="1: In Operation, 0: Shutdown", + ) + self.op_mode_o2_flow = Var( + within=NonNegativeReals, + doc="Auxiliary variable for linearizing op_mode and max_o2_flow", + ) + self.mccor_conv = Constraint( + expr=self.op_mode_o2_flow >= design_blk.max_o2_flow + + self.op_mode * design_blk.max_o2_flow.ub - design_blk.max_o2_flow.ub, + ) + self.mccor_conc_1 = Constraint( + expr=self.op_mode_o2_flow <= design_blk.max_o2_flow, + ) + self.mccor_conc_2 = Constraint( + expr=self.op_mode_o2_flow <= self.op_mode * design_blk.max_o2_flow.ub + ) + + # Power production as a function of natural gas flowrate + self.power_requirement = Constraint( + expr=self.power == self.o2_flow * (_power_req / _nlu_capacity), + ) + + # Ensure that the normalized oxygen flowrate is within the operating_range + operating_range = self.config.operating_range + self.o2_flow_lb = Constraint( + expr=operating_range[0] * self.op_mode_o2_flow <= self.o2_flow, + ) + + self.o2_flow_ub = Constraint( + expr=self.o2_flow <= operating_range[1] * self.op_mode_o2_flow, + ) + + self.non_fuel_vom = Expression( + expr=_vom * (self.o2_flow / _nlu_capacity), + doc="Non-electricity VOM cost [in $1000/hr]", + ) + + +@declare_process_block_class("OxygenTankDesign") +class OxygenTankDesignData(SkeletonUnitModelData): + """ + This class builds the model for the design of the tank. + """ + + CONFIG = SkeletonUnitModelData.CONFIG() + CONFIG.declare("tank_size_range", ConfigValue( + default=(2000, 400000), + doc="Range for the tank capacity [in tons]", + )) + CONFIG.declare("model_params", ConfigValue( + default=dmp.O2_TANK_PARAMS, + doc="Dictionary containing model parameters" + )) + + # noinspection PyAttributeOutsideInit + def build(self): + super().build() + + params = self.config.model_params + _fom_factor = params["fom_factor"] + _capex = params["capex"] + + self.tank_capacity = Var( + within=NonNegativeReals, + initialize=4000, + bounds=(0, self.config.tank_size_range[1]), + doc="Maximum amount of oxygen the tank can hold [in tons]", + ) + + self.build_tank = Var( + within=Binary, + doc="1: The storage tank is built, 0: Otherwise", + ) + + # Bound the capcity of the tank in the specified range + tank_size_range = self.config.tank_size_range + self.tank_capacity_lb_con = Constraint( + expr=self.tank_capacity >= self.build_tank * tank_size_range[0] + ) + self.tank_capacity_ub_con = Constraint( + expr=self.tank_capacity <= self.build_tank * tank_size_range[1] + ) + + self.capex = Expression( + expr=_capex[0] * self.tank_capacity + _capex[1] * self.build_tank, + doc="CAPEX of the oxygen storage tank [in 1000$]", + ) + + self.fom = Expression( + expr=_fom_factor * self.capex, + doc="FOM of the tank [in $1000]", + ) + + +@declare_process_block_class("OxygenTankOperation") +class OxygenTankOperationData(SkeletonUnitModelData): + """ + This class contains the model for the operation of the oxygen tank + """ + + CONFIG = SkeletonUnitModelData.CONFIG() + CONFIG.declare("design_blk", ConfigValue( + doc="Pointer to the object containing the tank design information", + )) + CONFIG.declare("o2_flow_range", ConfigValue( + default=(0, 130), + doc="Range for the inlet flowrate of O2 [in kg/s]", + )) + + # noinspection PyAttributeOutsideInit + def build(self): + super().build() + + design_blk = self.config.design_blk + params = design_blk.config.model_params + _min_holdup = params["min_holdup"] + _power_req = params["power_requirement"] + _o2_flow = params["o2_flow"] + + self.initial_holdup = Var( + within=NonNegativeReals, + doc="Initial holdup of the tank [in tons]", + ) + self.final_holdup = Var( + within=NonNegativeReals, + doc="Final holdup of the tank [in tons]", + ) + self.lox_in = Var( + within=NonNegativeReals, + doc="Flowrate of LOX entering the tank [in kg/s]", + ) + self.lox_out = Var( + within=NonNegativeReals, + doc="Flowrate of LOX leaving the tank [in kg/s]", + ) + + # LOX cannot be stored/withdrawn if the tank is not built + o2_flow_range = self.config.o2_flow_range + self.lox_in_ub_con = Constraint( + expr=self.lox_in <= design_blk.build_tank * o2_flow_range[1] + ) + self.lox_out_ub_con = Constraint( + expr=self.lox_out <= design_blk.build_tank * o2_flow_range[1] + ) + + # Holdup must be greater than 10% of the tank capacity at all times + self.holdup_constraint = Constraint( + expr=self.final_holdup >= _min_holdup * design_blk.tank_capacity, + ) + + # Holdup cannot exceed the capacity of the tank + self.holdup_ub = Constraint( + expr=self.final_holdup <= design_blk.tank_capacity + ) + + # Mass balance over the period of one hour: + self.mass_balance = Constraint( + expr=self.final_holdup - self.initial_holdup == (self.lox_in - self.lox_out) * (3600 / 1000), + ) + + # Power requirement for discharging + self.power = Expression( + expr=_power_req * (self.lox_out / _o2_flow), + doc="Power requirement for discharging LOX [in MW]", + ) From 417050162f162266fd64e23c15db8f1d5de243e0 Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Thu, 3 Aug 2023 15:07:51 -0400 Subject: [PATCH 017/126] Removing files that were mistakenly saved on this branch --- .../FLECCS_Price_Series_Data_01_20_2021.xlsx | Bin 1793340 -> 0 bytes .../flowsheets/direct_fired_cycle/__init__.py | 0 .../asu_startup_shutdown.py 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z`N^2uB#maVNtX5 Consumes 80% of the max_power, but does not produce any oxygen -t > 8 => Can operate anywhere between P_min and P_max - -Cycle shutdown: -t = 0 - 1 => Consumes 50% of the max_power and does not produce any oxygen -""" - -from pyomo.environ import Constraint - -# Startup time for the ASU -SU_TIME = 8 - - -def asu_startup_shutdown_constraints(m): - # The startup and shutdown constraints will be added in a separate block - # for eCach of the processes. - - # Create an alias for the parent block. - pb = m.parent_block() - set_period = pb.set_period - num_time_periods = len(set_period) - - # Cycle can operate at time t, only if one of the following two is true - # - Cycle was in operation at time t - 1 - # - Cycle startup was initiated at time t - 8 - @m.Constraint(set_period) - def cycle_operation(blk, t): - if t == 1: - # Constraint is not applicable at t = 1. Initial condition is left free - # But it can choose at most one of the three - return ( - pb.period[t].fs.asu.op_mode + pb.period[t].fs.asu.startup + - pb.period[t].fs.asu.shutdown <= 1 - ) - - # Slightly modify the constraint at t <= 8, since startup variable is not - # defined for t <= 0 - return ( - pb.period[t].fs.asu.op_mode <= pb.period[t - 1].fs.asu.op_mode + - (pb.period[t - SU_TIME].fs.asu.startup if t-SU_TIME > 0 else 0) - ) - - # After plant startup, the unit cannot produce oxygen in the next 8 hours, but - # consumes 80% of P_max power during that period - @m.Constraint(set_period, [j for j in range(1, SU_TIME)]) - def startup_con_1(blk, t, i): - if t + i > num_time_periods: - return Constraint.Skip - - return ( - pb.period[t + i].fs.asu.startup + pb.period[t + i].fs.asu.op_mode + - pb.period[t + i].fs.asu.shutdown <= 1 - pb.period[t].fs.asu.startup - ) - - - - @m.Constraint(set_period, [j for j in range(SU_TIME)]) - def startup_con_2(blk, t, i): - if t + i > num_time_periods: - return Constraint.Skip - - des_mdl = pb.parent_block() - return ( - pb.period[t + i].fs.asu.su_sd_power >= 0.8 * des_mdl.asu_design.max_power - - 0.8 * des_mdl.asu_design.max_power_ub * (1-pb.period[t].fs.asu.startup) - ) - - # ASU must operate at the ninth hour after startup is initiated - @m.Constraint(set_period) - def startup_con_3(blk, t): - if t + SU_TIME > num_time_periods: - return Constraint.Skip - - return pb.period[t + SU_TIME].fs.asu.op_mode >= pb.period[t].fs.asu.startup - - # Startup can be initiated at time t only if op_mode[t - 1] = 0 - @m.Constraint(set_period) - def startup_con_4(blk, t): - if t == 1: - return Constraint.Skip - - return pb.period[t].fs.asu.startup <= 1 - pb.period[t - 1].fs.asu.op_mode - - # Shutdown can be initiated at time t only if the ASU was operating at time t - 1 - @m.Constraint(set_period) - def shutdown_con_1(blk, t): - if t == 1: - return Constraint.Skip - - # I suspect this will always be satisfied by the optimal solution. Need to verify it though - return pb.period[t].fs.asu.shutdown <= pb.period[t - 1].fs.asu.op_mode - - # During the shutdown, power consumption is 50% of the P_max and O2 is not produced - @m.Constraint(set_period) - def shutdown_con_2(blk, t): - des_mdl = pb.parent_block() - return ( - pb.period[t].fs.asu.su_sd_power >= 0.5 * des_mdl.asu_design.max_power - - 0.5 * des_mdl.asu_design.max_power_ub * (1-pb.period[t].fs.asu.shutdown) - ) - - # If op_mode[t] = 1, and op_mode[t + 1] = 0, then shutdown must be initated at t + 1 - @m.Constraint(set_period) - def shutdown_con_3(blk, t): - if t + 1 > num_time_periods: - return Constraint.Skip - - return ( - pb.period[t].fs.asu.op_mode - pb.period[t + 1].fs.asu.op_mode <= pb.period[t + 1].fs.asu.shutdown - ) diff --git a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/default_model_parameters.py b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/default_model_parameters.py deleted file mode 100644 index d8f3ec21df..0000000000 --- a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/default_model_parameters.py +++ /dev/null @@ -1,94 +0,0 @@ - - -""" -This script contains parameter data - -In the current formulation, the fixed O&M cost is calculated as a fraction of -CAPEX, and it includes: TBD. e.g., insurance, labor, etc - -The variable O&M cost (non-fuel) is calculated as follows: -Non-fuel VOM includes: TBD. -Total VOM = 3339.92 + 18048.07 = 21387.99. Split the VOM for inidividual units -in the ratio of their CAPEX. 1128855 / (1128855 + 545522) = 0.6742. - -The VOM of the DFC cycle = 0.6742 * 21387.99 = 14419.78286 - ==> 14419.78286 / (0.85 * 8760) = 1.93658 [in $1000/hr] - This number will be scaled linearly with normalized DFC's capacity - -The VOM of the ASU unit = 0.3258 * 21387.99 = 6968.207 - ==> 6968.207 / (0.85 * 8760) = 0.93583 [in $1000/hr] - This number will be scaled linearly with normalized ASU's capacity - -The VOM of the NLU unit = 5404.96 - ==> 5404.96 / (0.85 * 8760) = 0.7259 [in $1000/hr] - This number will be scaled linearly with normalized NLU's capacity -""" - -# TODO: Contact Sandeep for reference. -# HHV of NG = 22499.17034 btu/lb = 0.0496 MMBtu/kg -NG_HHV = 0.0496 -HR_TO_SEC = 3600 -LBS_TO_KG = 0.453592 - -# Parameter data for DFC -# Calculating the FOM as 3.157% of the CAPEX. The percentage value is obtained from -# 35,641.27 (FOM) / 1,128,855 (CAPEX). The FOM is also assumed -# to vary linearly with the capacity. -DFC_PARAMS = { - "dfc_capacity" : 838.11322145, # [MW] Net power output - "ng_flow" : 28.87, # [kg/s] NG flow at full capacity - "o2_ng_ratio" : 3.785632948, # [-] Oxygen/NG flowratio - "capex" : 1128855, # [$1000] CAPEX of DFC - "fom_factor" : 0.03157, # [-] Multiplier for FOM - "op_curve_coeff": [1.2845, -0.2845], # Coefficients of performance curve - "co2_emission" : 3.06, # [kg/MWh] CO2 emissions - "vom" : 1.93658, # [$1000/hr] Non-fuel VOM -} - -# Parameter data for Monolithic ASU -MONO_ASU_PARAMS = { - "asu_capacity" : 109.2912232, # [kg/s] Maximum O2 flow - "power_requirement" : 162.1878617, # [MW] Power requirement at max capacity - "capex" : 545522, # [$1000] CAPEX of ASU - "fom_factor" : 0.03157, # [-] Multiplier for FOM - "op_curve_coeff" : [0.9625, 0.0375], # Coefficients of performance curve - "vom" : 0.93583, # [$1000/hr] Non-electricity VOM -} - -# Parameter data for ASU -ASU_PARAMS = { - "asu_capacity" : 27.3228058, # [kg/s] Maximum O2 flow - "power_requirement" : 40.546965425, # [MW] Power requirement at max capacity - "capex" : 136380.5, # [$1000] CAPEX of ASU - "fom_factor" : 0.03157, # [-] Multiplier for FOM - "op_curve_coeff" : [0.9625, 0.0375], # coefficients of performance curve - "vom" : 0.2339575, # [$1000/hr] Non-electricity VOM -} - -# Parameter data for NLU -NLU_PARAMS = { - "nlu_capacity" : 109.291223201103, # [kg/s] Maximum O2 flow - "power_requirement" : 143.8024, # [MW] Power requirement at max capacity - "capex" : 171189.6, # [$1000] CAPEX of NLU - "fom_factor" : 0.03157, # [-] Multiplier for FOM [-] - "vom" : 0.7259, # [$1000/hr] Non-electricity VOM -} - -# Parameter data for LOx tank -O2_TANK_PARAMS = { - "capex" : [0.98167214, 2779.90543786], - "fom_factor" : 0.03157, # [-] Multiplier for FOM - "min_holdup" : 0.1, # [-] Minimum holdup fraction - "power_requirement" : 1.47054, # [MW] Power requirement for storage - "o2_flow" : 112.0865459, # [kg/s] O2 flowrate for - "tank_constraint" : "periodic", # "Periodic" or "initial_holdup" -} - - -# Cashflow parameters -CASHFLOW_PARAMS = { - "plant_life" : 30, # [-] Plant lifetime in years - "discount_rate" : 0.075, # [-] Discount rate - "tax_rate" : 0.2, # [-] Corporate tax rate - "electricity_cost" : 5, # [$/MWh] Excess penalty for purchasing electricity -} diff --git a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_flowsheet.py b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_flowsheet.py deleted file mode 100644 index 026c0bb73f..0000000000 --- a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_flowsheet.py +++ /dev/null @@ -1,309 +0,0 @@ -from pyomo.environ import ( - Var, - NonNegativeReals, - Constraint, - Expression, -) -from idaes.core import FlowsheetBlock -from idaes.models_extra.power_generation.flowsheets.direct_fired_cycle.unit_models import ( - DFCOperation, - MonoASUOperation, - NLUOperation, - OxygenTankOperation, -) -from idaes.models_extra.power_generation.flowsheets.direct_fired_cycle.default_model_parameters import NG_HHV, HR_TO_SEC - - -def build_dfc_flowsheet( - m, - dfc_design, - asu_design, -): - """ - Build a flowsheet with DFC and ASU units only. ASU provides all the oxygen required - for the power cycle. - """ - - m.fs = FlowsheetBlock(dynamic=False) - - # Append unit models - m.fs.dfc = DFCOperation(design_blk=dfc_design) - m.fs.asu = MonoASUOperation(design_blk=asu_design) - - # Declare new variables - m.fs.power_dfc_to_grid = Var(within=NonNegativeReals) - m.fs.power_dfc_to_asu = Var(within=NonNegativeReals) - m.fs.power_grid_to_asu = Var(within=NonNegativeReals) - - m.fs.oxygen_asu_to_dfc = Var(within=NonNegativeReals) - m.fs.oxygen_asu_to_vent = Var(within=NonNegativeReals) - - # Power balance across the power cycle - m.fs.dfc_power_balance = Constraint( - expr=m.fs.dfc.power == m.fs.power_dfc_to_grid + m.fs.power_dfc_to_asu, - ) - - # Power balance across the ASU - m.fs.asu_power_balance = Constraint( - expr=m.fs.asu.total_power == m.fs.power_dfc_to_asu + m.fs.power_grid_to_asu, - ) - - # Oxygen balance across the power cycle - m.fs.dfc_oxygen_balance = Constraint( - expr=m.fs.dfc.o2_flow == m.fs.oxygen_asu_to_dfc, - ) - - # Oxygen balance across the ASU - m.fs.asu_oxygen_balance = Constraint( - expr=m.fs.asu.o2_flow == m.fs.oxygen_asu_to_dfc + m.fs.oxygen_asu_to_vent, - ) - - -def build_dfc_flowsheet_with_lox( - m, - dfc_design, - asu_design, - tank_design, -): - """Build a flowsheet with DFC, ASU unit with a provision for LOx withdrawal, - and an oxygen storage tank. - """ - m.fs = FlowsheetBlock(dynamic=False) - - # Append unit models - m.fs.dfc = DFCOperation(design_blk=dfc_design) - m.fs.asu = MonoASUOperation(design_blk=asu_design) - m.fs.tank = OxygenTankOperation(design_blk=tank_design) - - # Declare new variables - m.fs.power_dfc_to_grid = Var(within=NonNegativeReals) - m.fs.power_dfc_to_asu = Var(within=NonNegativeReals) - m.fs.power_dfc_to_tank = Var(within=NonNegativeReals) - m.fs.power_grid_to_asu = Var(within=NonNegativeReals) - m.fs.power_grid_to_tank = Var(within=NonNegativeReals) - - m.fs.gox_fraction = Var( - within=NonNegativeReals, - bounds=(0.9, 1), - doc="Fraction of O2 withdrawn in gasoues state from the ASU", - ) - m.fs.oxygen_asu_to_dfc = Var(within=NonNegativeReals) - m.fs.oxygen_asu_to_vent = Var(within=NonNegativeReals) - - # Power balance across the power cycle - m.fs.dfc_power_balance = Constraint( - expr=m.fs.dfc.power == m.fs.power_dfc_to_grid + m.fs.power_dfc_to_asu + - m.fs.power_dfc_to_tank, - ) - - # Power balance across the ASU - m.fs.asu_power_balance = Constraint( - expr=2 * m.fs.asu.total_power - m.fs.gox_fraction * m.fs.asu.total_power == - m.fs.power_dfc_to_asu + m.fs.power_grid_to_asu, - ) - - # Power balance across the tank - m.fs.tank_power_balance = Constraint( - expr=m.fs.tank.power == m.fs.power_dfc_to_tank + m.fs.power_grid_to_tank, - ) - - # Oxygen balance across the power cycle - m.fs.dfc_oxygen_balance = Constraint( - expr=m.fs.dfc.o2_flow == m.fs.oxygen_asu_to_dfc + m.fs.tank.lox_out, - ) - - # Gasoues oxygen balance across the ASU - m.fs.gox_balance = Constraint( - expr=m.fs.asu.o2_flow * m.fs.gox_fraction == m.fs.oxygen_asu_to_dfc + - m.fs.oxygen_asu_to_vent, - ) - - # Liquid oxygen balance across the ASU - m.fs.lox_balance = Constraint( - expr=m.fs.asu.o2_flow - m.fs.asu.o2_flow * m.fs.gox_fraction == - m.fs.tank.lox_in, - ) - - -def build_dfc_flowsheet_with_nlu( - m, - dfc_design, - asu_design, - nlu_design, - tank_design -): - """ - Build a flowsheet with DFC, ASU, NLU and an oxygen storage tank. - """ - m.fs = FlowsheetBlock(dynamic=False) - - # Append unit models - m.fs.dfc = DFCOperation(design_blk=dfc_design) - m.fs.asu = MonoASUOperation(design_blk=asu_design) - m.fs.nlu = NLUOperation(design_blk=nlu_design) - m.fs.tank = OxygenTankOperation(design_blk=tank_design) - - # Declare new variables - m.fs.power_dfc_to_grid = Var(within=NonNegativeReals) - m.fs.power_dfc_to_asu = Var(within=NonNegativeReals) - m.fs.power_dfc_to_nlu = Var(within=NonNegativeReals) - m.fs.power_dfc_to_tank = Var(within=NonNegativeReals) - m.fs.power_grid_to_asu = Var(within=NonNegativeReals) - m.fs.power_grid_to_nlu = Var(within=NonNegativeReals) - m.fs.power_grid_to_tank = Var(within=NonNegativeReals) - - m.fs.oxygen_asu_to_dfc = Var(within=NonNegativeReals) - m.fs.oxygen_asu_to_vent = Var(within=NonNegativeReals) - - # Power balance across the power cycle - m.fs.dfc_power_balance = Constraint( - expr=m.fs.dfc.power == m.fs.power_dfc_to_grid + m.fs.power_dfc_to_asu + - m.fs.power_dfc_to_nlu + m.fs.power_dfc_to_tank, - ) - - # Power balance across the ASU - m.fs.asu_power_balance = Constraint( - expr=m.fs.asu.total_power == m.fs.power_dfc_to_asu + m.fs.power_grid_to_asu - ) - - # Power balance across the NLU - m.fs.nlu_power_balance = Constraint( - expr=m.fs.nlu.power == m.fs.power_dfc_to_nlu + m.fs.power_grid_to_nlu - ) - - # Power balance across the tank - m.fs.tank_power_balance = Constraint( - expr=m.fs.tank.power == m.fs.power_dfc_to_tank + m.fs.power_grid_to_tank - ) - - # Oxygen balance across the power cycle - m.fs.dfc_oxygen_balance = Constraint( - expr=m.fs.dfc.o2_flow == m.fs.oxygen_asu_to_dfc + m.fs.tank.lox_out, - ) - - # Oxygen balance across the ASU - m.fs.asu_oxygen_balance = Constraint( - expr=m.fs.asu.o2_flow == m.fs.oxygen_asu_to_dfc + m.fs.nlu.o2_flow + - m.fs.oxygen_asu_to_vent, - ) - - # Oxygen balance across the NLU - m.fs.nlu_oxygen_balance = Constraint( - expr=m.fs.nlu.o2_flow == m.fs.tank.lox_in, - ) - - -def append_op_costs_dfc( - m, - lmp, - penalty, - cost_ng, - carbon_price, -): - """Append cost and revenue expressions for each time step - - Args: - m: Object containing the flowsheet - lmp: LMP - penalty: To avoid degeneracy, we impose penalty for borrowing electricity - from the grid. (default: electricity borrowed from the grid costs $5/MWh - more than the LMP at that time instant) - cost_ng: Cost of natural gas [in $/MMBtu] - """ - - m.fs.electricity_revenue = Expression(expr=lmp * m.fs.power_dfc_to_grid) - - m.fs.electricity_cost = Expression( - expr=(lmp + penalty) * (m.fs.power_grid_to_asu - + (m.fs.power_grid_to_nlu if hasattr(m.fs, "nlu") else 0) - + (m.fs.power_grid_to_tank if hasattr(m.fs, "tank") else 0) - ), - doc="Net revenue generated by selling power to the grid [in $1000]", - ) - - m.fs.fuel_cost = Expression( - expr=cost_ng * NG_HHV * (HR_TO_SEC / 1000) * m.fs.dfc.total_ng_flow, - doc="Operational cost associated with fuel consumption [in $1000]", - ) - - # TODO: Add CO2 emissions for the startup and shutdown (likely negligible) - m.fs.co2_price = Expression( - expr=m.fs.dfc.co2_emissions * carbon_price / 1000, - doc="Carbon price per hour [in $1000]", - ) - - m.fs.net_cash_flow = Expression( - expr=m.fs.electricity_revenue - m.fs.electricity_cost - - m.fs.fuel_cost - - m.fs.dfc.non_fuel_vom - m.fs.asu.non_fuel_vom - - (m.fs.nlu.non_fuel_vom if hasattr(m.fs, "nlu") else 0) - - m.fs.co2_price, - ) - - -def append_cashflows(m, params): - plant_life = params["plant_life"] - tax_rate = params["tax_rate"] - discount_rate = params["discount_rate"] - - m.CAPEX = Var( - within=NonNegativeReals, - doc="Total CAPEX of the plant [in $1000]", - ) - m.FOM = Var( - within=NonNegativeReals, - doc="Total fixed O&M costs per year [in $1000]", - ) - m.DEPRECIATION = Var( - within=NonNegativeReals, - doc="Depreciation value per year [in $1000]" - ) - m.CORP_TAX = Var( - within=NonNegativeReals, - doc="Net corporate tax per year [in $1000]", - ) - m.NET_PROFIT = Var( - doc="Net profit per year [in $1000]", - ) - - m.capex_calculation = Constraint( - expr=m.CAPEX == m.dfc_design.capex + m.asu_design.capex + - (m.nlu_design.capex if hasattr(m, "nlu_design") else 0) + - (m.tank_design.capex if hasattr(m, "tank_design") else 0), - doc="Calculates the total CAPEX [in $1000]", - ) - m.fom_calculation = Constraint( - expr=m.FOM == m.dfc_design.fom + m.asu_design.fom + - (m.nlu_design.fom if hasattr(m, "nlu_design") else 0) + - (m.tank_design.fom if hasattr(m, "tank_design") else 0), - doc="Calculates the total FOM [in $1000]", - ) - m.depreciation_calculation = Constraint( - expr=m.DEPRECIATION == m.CAPEX / plant_life, - doc="Straight line depreciation with zero salvage value [in $1000]", - ) - - # Tax = max{0, Formula below}. We will relax it to Tax >= max{0, Formula below} - # The inequality will be binding for the optimal solution. - m.corp_tax_calculation = Constraint( - expr=m.CORP_TAX >= tax_rate * ( - sum(m.mp_model.period[t].fs.net_cash_flow for t in m.set_time) - - m.FOM - m.DEPRECIATION - ), - doc="Calculates the total corporate tax [in $1000]", - ) - - m.net_profit_calculation = Constraint( - expr=m.NET_PROFIT == - + sum(m.mp_model.period[t].fs.net_cash_flow for t in m.set_time) - - m.FOM - m.CORP_TAX, - doc="Calculate the net profit generated in a year [in $1000]", - ) - - constant_cf_factor = (1 - (1 + discount_rate) ** (- plant_life)) / discount_rate - - # NPV Calculation - m.npv = Expression( - expr=constant_cf_factor * m.NET_PROFIT - m.CAPEX, - doc="Calculates the net present value [in $1000]", - ) diff --git a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_startup_shutdown.py b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_startup_shutdown.py deleted file mode 100644 index 3b343521ae..0000000000 --- a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/dfc_startup_shutdown.py +++ /dev/null @@ -1,258 +0,0 @@ -""" -Contains functions that implement startup and shutdown constraints for the power cycle. - -In this version, we implement the following and startup and shutdown procedure. - -Cycle startup: -t = 0 - 1 => Ramp up fuel from 0 to 5.8518 kg/s. Power production = 0 MW -t = 1 - 2 => Ramp up fuel from 5.8515 to 10.36 kg/s. Power production = 0 MW -t = 2 - 3 => Cycle operates at P_min, (20% of the maximum load) -t = 3 - 4 => Normal operation of the plant. Cycle could ramp up to P_max - -Cycle shutdown: -t = 0 - 1 => Cycle needs to ramp down to P_min -t = 1 - 2 => Cycle operates at P_min, (20% of the maximum load) -t = 2 - 3 => Ramp down fuel from 10.36 to 5.85 kg/s. Power production = 0 MW -t = 3 - 4 => Ramp down fuel from 5.8518 kg/s to 0. Power production = 0 MW -""" - -from pyomo.environ import Constraint - - -DFC_OFFLOAD = 0.2 - - -def dfc_startup_shutdown_constraints(m): - # The startup and shutdown constraints will be added in a separate block - # for eCach of the processes. - - # Create an alias for the parent block. - pb = m.parent_block() - set_period = pb.set_period - num_time_periods = len(set_period) - - # Cycle can operate at time t, only if one of the following two is true - # - Cycle was in operation at time t - 1 - # - Cycle startup was initiated at time t - 2 - @m.Constraint(set_period) - def cycle_operation(blk, t): - if t == 1: - # Constraint is not applicable at t = 1. Initial condition is left free - # But it can choose at most one of the three - return ( - pb.period[t].fs.dfc.op_mode + pb.period[t].fs.dfc.startup + - pb.period[t].fs.dfc.shutdown <= 1 - ) - - if t == 2: - # Slightly modify the constraint at t = 2, since startup variable is not - # defined at t = 0 - return pb.period[t].fs.dfc.op_mode == pb.period[t - 1].fs.dfc.op_mode - - return ( - pb.period[t].fs.dfc.op_mode <= pb.period[t - 1].fs.dfc.op_mode + - pb.period[t - 2].fs.dfc.startup - ) - - # If cycle startup is initiated at time t i.e., startup[t] = 1, then - # at t + 1, op_mode, startup and shutdown must be 0 - # at t + 2, op_mode must be 1 and power output must be P_min - # at t + 3, op_mode must be 1 - @m.Constraint(set_period) - def startup_con_1(blk, t): - if t + 1 > num_time_periods: - return Constraint.Skip - - # Also ensures that at most one of the three operations (operation, startup, shutdown) - # takes place at each time period - return ( - pb.period[t + 1].fs.dfc.startup + pb.period[t + 1].fs.dfc.op_mode + - pb.period[t + 1].fs.dfc.shutdown <= 1 - pb.period[t].fs.dfc.startup - ) - - @m.Constraint(set_period) - def startup_con_2(blk, t): - if t + 2 > num_time_periods: - return Constraint.Skip - - return pb.period[t].fs.dfc.startup <= pb.period[t + 2].fs.dfc.op_mode - - @m.Constraint(set_period) - def startup_con_3(blk, t): - if t + 3 > num_time_periods: - return Constraint.Skip - - return pb.period[t].fs.dfc.startup <= pb.period[t + 3].fs.dfc.op_mode - - # Ensure that the power output is P_min at t + 2 - # old NL constraint linearized - # pb.period[t + 2].fs.dfc.power - DFC_OFFLOAD * des_mdl.dfc_design.capacity <= - # (1 - DFC_OFFLOAD) * des_mdl.dfc_design.capacity * (1 - pb.period[t].fs.dfc.startup) - @m.Constraint(set_period) - def startup_con_4(blk, t): - if t + 2 > num_time_periods: - return Constraint.Skip - - # Design models are contained in the parent block of pb - des_mdl = pb.parent_block() - - return ( - pb.period[t + 2].fs.dfc.power <= des_mdl.dfc_design.capacity + (DFC_OFFLOAD - 1) - * des_mdl.dfc_design.capacity_ub * pb.period[t].fs.dfc.startup - ) - - @m.Constraint(set_period) - def shutdown_con_4_1(blk, t): - if t + 2 > num_time_periods: - return Constraint.Skip - - des_mdl = pb.parent_block() - - return ( - pb.period[t + 2].fs.dfc.power <= (des_mdl.dfc_design.capacity * DFC_OFFLOAD - - des_mdl.dfc_design.capacity_lb + des_mdl.dfc_design.capacity_lb * - pb.period[t].fs.dfc.startup)/(DFC_OFFLOAD -1) - ) - # Fuel requirement during the first hour of the startup - # FIXME: The fuel consumption is for a fixed capacity of 838 MW. This needs to be modified - # if the design the DFC is a variable - @m.Constraint(set_period) - def startup_con_5(blk, t): - # During the first hour, the fuel flowrate ramps up from 0 to 5.8518 kg/s. - # In the model, we work with the average fuel flowrate: (0 + 5.8518) /2 = 2.9529 - return pb.period[t].fs.dfc.su_sd_ng_flow >= 2.9529 * pb.period[t].fs.dfc.startup - - @m.Constraint(set_period) - def startup_con_6(blk, t): - # During the second hour, the fuel flowrate ramps up from 5.8518 to 10.889 kg/s. - # In the model, we work with the average fuel flowrate: (5.8518 + 10.889) / 2 = 8.3704 - # 10.889 kg/s is chosen instead of 10.36 to be consistent with the surrogate model - if t + 1 > num_time_periods: - return Constraint.Skip - - return pb.period[t + 1].fs.dfc.su_sd_ng_flow >= 8.3704 * pb.period[t].fs.dfc.startup - - # TODO: I suspect this constraint is implied by previous constraints, but I have not verified - # it. So, I'm adding this constraint for now. If we can show that it is implied, we can remove - # it later. Even if it is not implied, it is less likely that we will find a solution violating this - # constraint. So, we can add it as a lazy constraint, if pyomo supports one. - # Cycle must not be operating at time t - 1, if startup is initiated at time t - @m.Constraint(set_period) - def startup_con_7(blk, t): - if t <= 1: - return Constraint.Skip - - return pb.period[t].fs.dfc.startup <= 1 - pb.period[t - 1].fs.dfc.op_mode - - # If cycle shutdown is initiated at time t, i.e., shutdown[t] = 1, then - # at time t - 2, op_mode must be 1 and power produced must be P_min - # at time t - 1, op_mode must be 1 and power produced must be P_min - # at time t, shutdown is initiated so power must be zero - # at time t + 1, op_mode, startup and shutdown must be zero - @m.Constraint(set_period) - def shutdown_con_1(blk, t): - if t <= 2: - return Constraint.Skip - - return pb.period[t - 2].fs.dfc.op_mode >= pb.period[t].fs.dfc.shutdown - - @m.Constraint(set_period) - def shutdown_con_2(blk, t): - if t <= 1: - return Constraint.Skip - - return pb.period[t - 1].fs.dfc.op_mode >= pb.period[t].fs.dfc.shutdown - - @m.Constraint(set_period) - def shutdown_con_3(blk, t): - if t + 1 > num_time_periods: - return Constraint.Skip - - return ( - pb.period[t + 1].fs.dfc.startup + pb.period[t + 1].fs.dfc.op_mode + - pb.period[t + 1].fs.dfc.shutdown <= 1 - pb.period[t].fs.dfc.shutdown - ) - - # Ensure that the power output is P_min at t - 2 - # old cosntraint linearized - # pb.period[t - 2].fs.dfc.power - DFC_OFFLOAD * des_mdl.dfc_design.capacity <= - # (1 - DFC_OFFLOAD) * des_mdl.dfc_design.capacity * (1 - pb.period[t].fs.dfc.shutdown) - @m.Constraint(set_period) - def shutdown_con_4(blk, t): - if t <= 2: - return Constraint.Skip - - des_mdl = pb.parent_block() - - return ( - pb.period[t - 2].fs.dfc.power <= des_mdl.dfc_design.capacity + (DFC_OFFLOAD - 1) - * des_mdl.dfc_design.capacity_ub * pb.period[t].fs.dfc.shutdown - ) - @m.Constraint(set_period) - def shutdown_con_4_1(blk, t): - if t <= 2: - return Constraint.Skip - - des_mdl = pb.parent_block() - - return ( - pb.period[t - 2].fs.dfc.power <= (des_mdl.dfc_design.capacity * DFC_OFFLOAD - - des_mdl.dfc_design.capacity_lb + des_mdl.dfc_design.capacity_lb * - pb.period[t].fs.dfc.shutdown)/(DFC_OFFLOAD -1) - ) - # Ensure that the power output is P_min at t - 1 - - # pb.period[t - 1].fs.dfc.power - DFC_OFFLOAD * des_mdl.dfc_design.capacity <= - # (1 - DFC_OFFLOAD) * des_mdl.dfc_design.capacity * (1 - pb.period[t].fs.dfc.shutdown) - - @m.Constraint(set_period) - def shutdown_con_5(blk, t): - if t <= 1: - return Constraint.Skip - - des_mdl = pb.parent_block() - - return ( - pb.period[t - 1].fs.dfc.power <= des_mdl.dfc_design.capacity + (DFC_OFFLOAD - 1) - * des_mdl.dfc_design.capacity_ub * pb.period[t].fs.dfc.shutdown - ) - - - @m.Constraint(set_period) - def shutdown_con_5_1(blk, t): - if t <= 2: - return Constraint.Skip - - des_mdl = pb.parent_block() - - return ( - pb.period[t - 1].fs.dfc.power <= (des_mdl.dfc_design.capacity * DFC_OFFLOAD - - des_mdl.dfc_design.capacity_lb + des_mdl.dfc_design.capacity_lb * - pb.period[t].fs.dfc.shutdown)/(DFC_OFFLOAD -1) - ) - # When shutdown is initiated, the fuel flowrate is ramped down from 10.889 to 5.8515 - # In the model, we use the average value: (10.889 + 5.8515) / 2 = 8.3704 - @m.Constraint(set_period) - def shutdown_con_6(blk, t): - return pb.period[t].fs.dfc.su_sd_ng_flow >= 8.3704 * pb.period[t].fs.dfc.shutdown - - # During the second hour, the fuel flowrate is ramped down from 5.8515 to 0 - # In the model, we use the average value: (5.8515 + 0) / 2 = 2.9529 - @m.Constraint(set_period) - def shutdown_con_7(blk, t): - if t + 1 > num_time_periods: - return Constraint.Skip - - return pb.period[t + 1].fs.dfc.su_sd_ng_flow >= 2.9529 * pb.period[t].fs.dfc.shutdown - - # If the plant was in operation at time t and *not* in operation at t + 1, then plant shutdown - # must have been initiated at time t + 1 - @m.Constraint(set_period) - def shutdown_con_8(blk, t): - if t + 1 > num_time_periods: - return Constraint.Skip - - return ( - pb.period[t].fs.dfc.op_mode - pb.period[t + 1].fs.dfc.op_mode <= - pb.period[t + 1].fs.dfc.shutdown - ) diff --git a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/npv_optimization.py b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/npv_optimization.py deleted file mode 100644 index 77e10705a5..0000000000 --- a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/npv_optimization.py +++ /dev/null @@ -1,462 +0,0 @@ -import os -import pandas as pd -import json - -from pyomo.environ import ( - ConcreteModel, - Var, - NonNegativeReals, - Constraint, - Expression, - Block, - Objective, - maximize, - SolverFactory, - value, -) -from idaes.apps.grid_integration import MultiPeriodModel - -from idaes.models_extra.power_generation.flowsheets.direct_fired_cycle.unit_models import ( - get_lmp_data, - DFCDesign, - MonoASUDesign, - NLUDesign, - OxygenTankDesign, -) - -from idaes.models_extra.power_generation.flowsheets.direct_fired_cycle.dfc_flowsheet import ( - build_dfc_flowsheet, - build_dfc_flowsheet_with_lox, - build_dfc_flowsheet_with_nlu, - append_op_costs_dfc, - append_cashflows, -) - -from idaes.models_extra.power_generation.flowsheets.direct_fired_cycle.dfc_startup_shutdown import dfc_startup_shutdown_constraints -from idaes.models_extra.power_generation.flowsheets.direct_fired_cycle.asu_startup_shutdown import asu_startup_shutdown_constraints -import idaes.models_extra.power_generation.flowsheets.direct_fired_cycle. \ - default_model_parameters as dmp - - -# Source: DOE ARPA - E -NG_PRICE_DATA = { - "NREL": { - 100: { - "CAISO": 2.26, - "ERCOT": 2.64, - "MISO-W": 1.69, - "NYISO": 1.12, - "PJM-W": 1.42 - }, - 150: { - "CAISO": 2.26, - "ERCOT": 2.64, - "MISO-W": 2.01, - "NYISO": 1.14, - "PJM-W": 1.43 - } - }, - "Princeton": 2.94, - "New_Princeton": 2.94 -} - - -def _write_results( - m, - sol, - lox_withdrawal=False, - includes_nlu=False, - filename="results", -): - _filename = filename + ".xlsx" - - set_flowsheets = m.mp_model.period[:].fs - - results = { - "LMP [$/MWh]": [m.LMP[t] * 1000 for t in m.set_time], - "DFC_Schedule": [value(fs.dfc.op_mode) for fs in set_flowsheets], - "DFC_Startup": [value(fs.dfc.startup) for fs in set_flowsheets], - "DFC_Shutdown": [value(fs.dfc.shutdown) for fs in set_flowsheets], - "DFC_Power": [value(fs.dfc.power) for fs in set_flowsheets], - "DFC_Ng_Flow": [value(fs.dfc.total_ng_flow) for fs in set_flowsheets], - "DFC_O2_Flow": [value(fs.dfc.o2_flow) for fs in set_flowsheets], - - "ASU_Schedule": [value(fs.asu.op_mode) for fs in set_flowsheets], - "ASU_Startup": [value(fs.asu.startup) for fs in set_flowsheets], - "ASU_Shutdown": [value(fs.asu.shutdown) for fs in set_flowsheets], - "ASU_Power": [value(fs.asu.total_power) for fs in set_flowsheets], - "ASU_O2_Flow": [value(fs.asu.o2_flow) for fs in set_flowsheets], - - "Power_to_grid": [value(fs.power_dfc_to_grid) for fs in set_flowsheets], - "Power_grid_to_asu": [value(fs.power_grid_to_asu) for fs in set_flowsheets], - "Power_dfc_to_asu": [value(fs.power_dfc_to_asu) for fs in set_flowsheets], - - "Oxygen_asu_to_dfc": [value(fs.oxygen_asu_to_dfc) for fs in set_flowsheets], - "Oxygen_asu_to_vent": [value(fs.oxygen_asu_to_vent) for fs in set_flowsheets], - } - - if includes_nlu: - results["NLU_Schedule"] = [value(fs.nlu.op_mode) for fs in set_flowsheets] - results["NLU_O2_Flow"] = [value(fs.nlu.o2_flow) for fs in set_flowsheets] - results["NLU_Power"] = [value(fs.nlu.power) for fs in set_flowsheets] - - results["Tank_init_holdup"] = [value(fs.tank.initial_holdup) for fs in set_flowsheets] - results["Tank_final_holdup"] = [value(fs.tank.final_holdup) for fs in set_flowsheets] - results["Tank_lox_in"] = [value(fs.tank.lox_in) for fs in set_flowsheets] - results["Tank_lox_out"] = [value(fs.tank.lox_out) for fs in set_flowsheets] - - results["Power_dfc_to_nlu"] = [value(fs.power_dfc_to_nlu) for fs in set_flowsheets] - results["Power_dfc_to_tank"] = [value(fs.power_dfc_to_tank) for fs in set_flowsheets] - results["Power_grid_to_nlu"] = [value(fs.power_grid_to_nlu) for fs in set_flowsheets] - results["Power_grid_to_tank"] = [value(fs.power_grid_to_tank) for fs in set_flowsheets] - - if lox_withdrawal: - results["GOx_fraction"] = [value(fs.gox_fraction) for fs in set_flowsheets] - - results["Tank_init_holdup"] = [value(fs.tank.initial_holdup) for fs in set_flowsheets] - results["Tank_final_holdup"] = [value(fs.tank.final_holdup) for fs in set_flowsheets] - results["Tank_lox_in"] = [value(fs.tank.lox_in) for fs in set_flowsheets] - results["Tank_lox_out"] = [value(fs.tank.lox_out) for fs in set_flowsheets] - - results["Power_dfc_to_tank"] = [value(fs.power_dfc_to_tank) for fs in set_flowsheets] - results["Power_grid_to_tank"] = [value(fs.power_grid_to_tank) for fs in set_flowsheets] - - results_df = pd.DataFrame(results) - results_df.to_excel(_filename) - - lower_bnd = sol["Problem"][0]["Lower bound"] - upper_bnd = sol["Problem"][0]["Upper bound"] - - elec_rev = [value(fs.electricity_revenue) for fs in set_flowsheets] - elec_cost = [value(fs.electricity_cost) for fs in set_flowsheets] - fuel_cost = [value(fs.fuel_cost) for fs in set_flowsheets] - co2_price = [value(fs.co2_price) for fs in set_flowsheets] - dfc_vom = [value(fs.dfc.non_fuel_vom) for fs in set_flowsheets] - asu_vom = [value(fs.asu.non_fuel_vom) for fs in set_flowsheets] - if includes_nlu: - nlu_vom = [value(fs.nlu.non_fuel_vom) for fs in set_flowsheets] - else: - nlu_vom = [0] - - solution = { - "Lower bound": lower_bnd, - "Upper bound": upper_bnd, - "Gap": ((upper_bnd - lower_bnd) / lower_bnd) * 100, - #"Wall time": sol["Solver"][0]["Wall time"], gurobi atrribute, cant call when using cplex - "Status": sol["Solver"][0]["Status"], - "Termination message": sol["Solver"][0]["Termination message"], - - "NPV": value(m.obj) / 1000, - "DFC_Capacity": m.dfc_design.capacity.value, - "ASU_Capacity": m.asu_design.max_o2_flow.value, - "NLU_Capacity": (m.nlu_design.max_o2_flow.value if hasattr(m, "nlu_design") else "N/A"), - "Tank_Capacity": (m.tank_design.tank_capacity.value - if hasattr(m, "tank_design") else "N/A"), - "DFC Startups": sum(results["DFC_Startup"]), - "DFC Shutdowns": sum(results["DFC_Shutdown"]), - "ASU Startups": sum(results["ASU_Startup"]), - "ASU Shutdowns": sum(results["ASU_Shutdown"]), - "Total Power Produced": sum(results["DFC_Power"]), - "Total Power Sold": sum(results["Power_to_grid"]), - "DFC CAPEX": value(m.dfc_design.capex) / 1e3, - "ASU CAPEX": value(m.asu_design.capex) / 1e3, - "NLU CAPEX": value(m.nlu_design.capex) / 1e3 if hasattr(m, "nlu_Design") else 0, - "TANK CAPEX": value(m.tank_design.capex) / 1e3 if hasattr(m, "tank_design") else 0, - "Total CAPEX": m.CAPEX.value / 1e3, - "Total FOM": m.FOM.value / 1e3, - "Total Revenue": sum(elec_rev) / 1e3, - "Total Elec Cost": sum(elec_cost) / 1e3, - "Total Fuel Cost": sum(fuel_cost) / 1e3, - "Total CO2 Price": sum(co2_price) / 1e3, - "Total VOM": (sum(dfc_vom) + sum(asu_vom) + sum(nlu_vom)) / 1e3, - "Total Corp. tax": m.CORP_TAX.value / 1e3, - "Total Net Profit": m.NET_PROFIT.value / 1e3, - - "Num Vars": sol["Problem"][0]["Number of variables"], - "Num Bin Vars": sol["Problem"][0]["Number of binary variables"], - "Num constraints": sol["Problem"][0]["Number of constraints"], - } - - with open(filename + ".json", "w") as fp: - json.dump(solution, fp, indent=4) - - -def get_linking_var_pairs(m1, m2): - return [(m1.fs.tank.final_holdup, m2.fs.tank.initial_holdup)] - - -def npv_model_dfc_asu( - dataset="NREL", - location="PJM-W", - carbon_tax=150, - dfc_params=dmp.DFC_PARAMS, - asu_params=dmp.ASU_PARAMS, - cost_params=dmp.CASHFLOW_PARAMS, - solver=None, - folder="", -): - if dataset == "NREL": - cost_ng = NG_PRICE_DATA[dataset][carbon_tax][location] - else: - cost_ng = NG_PRICE_DATA[dataset] - - penalty = cost_params["electricity_cost"] / 1e3 - - m = ConcreteModel() - get_lmp_data(m, dataset=dataset, location=location, carbon_tax=carbon_tax) - - m.dfc_design = DFCDesign(model_params=dfc_params) - m.asu_design = MonoASUDesign(model_params=asu_params) - - # Currently, we are not varying the capacity of the power cycle, so fixing the value - # of capacity. Also, we want the power cycle to be built, so fixing build_dfc. - m.dfc_design.build_dfc.fix(1) - m.dfc_design.capacity.fix() - - m.mp_model = MultiPeriodModel( - n_time_points=365 * 24, - process_model_func=build_dfc_flowsheet, - linking_variable_func=None, - use_stochastic_build=True, - flowsheet_options={ - "dfc_design": m.dfc_design, - "asu_design": m.asu_design, - }, - ) - - # Append cashflows at each hour - for t in m.set_time: - append_op_costs_dfc( - m=m.mp_model.period[t], - lmp=m.LMP[t], - penalty=penalty, - cost_ng=cost_ng, - carbon_price=carbon_tax / 1e3, - ) - - # Append startup and shutdown constraints for the power cycle - m.mp_model.dfc_su_sd = Block(rule=dfc_startup_shutdown_constraints) - - # Append startup and shutdown constraints for the ASU unit - m.mp_model.asu_su_sd = Block(rule=asu_startup_shutdown_constraints) - - # Append the overall cashflows - append_cashflows(m, cost_params) - - # Declare the objective function - m.obj = Objective(expr=m.npv, sense=maximize) - - # Use Gurobi solver - if solver is None: - solver = SolverFactory("gams") - #solver.options["threads"] = 10 - #Gurobi specific options - # solver.options['MIPGap'] = 0.01 - # solver.options['TimeLimit'] = 7500 - # solver.options['OutputFlag'] = 1 - - sol = solver.solve(m,solver = "cplex", tee=True) - - _filename = folder + dataset + "_" + location + "_" + str(carbon_tax) - _write_results(m, sol, filename=_filename) - - return m, sol - - -def npv_model_dfc_asu_with_lox( - dataset="NREL", - location="PJM-W", - carbon_tax=150, - dfc_params=dmp.DFC_PARAMS, - asu_params=dmp.ASU_PARAMS, - tank_params=dmp.O2_TANK_PARAMS, - cost_params=dmp.CASHFLOW_PARAMS, - solver=None, - folder="", -): - if dataset == "NREL": - cost_ng = NG_PRICE_DATA[dataset][carbon_tax][location] - else: - cost_ng = NG_PRICE_DATA[dataset] - - penalty = cost_params["electricity_cost"] / 1e3 - - m = ConcreteModel() - get_lmp_data(m, dataset=dataset, location=location, carbon_tax=carbon_tax) - - m.dfc_design = DFCDesign(model_params=dfc_params) - m.asu_design = MonoASUDesign( - o2_flow_range=(10, 130), model_params=asu_params, - ) - m.tank_design = OxygenTankDesign( - tank_size_range=(10, 400000), model_params=tank_params, - ) - - # Currently, we are not varying the capacity of the power cycle, so fixing the value - # of capacity. Also, we want the power cycle to be built, so fixing build_dfc. - m.dfc_design.build_dfc.fix(1) - m.dfc_design.capacity.fix() - - m.mp_model = MultiPeriodModel( - n_time_points=365 * 24, - process_model_func=build_dfc_flowsheet_with_lox, - linking_variable_func=get_linking_var_pairs, - use_stochastic_build=True, - flowsheet_options={ - "dfc_design": m.dfc_design, - "asu_design": m.asu_design, - "tank_design": m.tank_design, - }, - ) - - # Append cashflows at each hour - for t in m.set_time: - append_op_costs_dfc( - m=m.mp_model.period[t], - lmp=m.LMP[t], - penalty=penalty, - cost_ng=cost_ng, - carbon_price=carbon_tax / 1e3, - ) - - # Append startup and shutdown constraints for the power cycle - m.mp_model.dfc_su_sd = Block(rule=dfc_startup_shutdown_constraints) - - # Append startup and shutdown constraints for the ASU unit - m.mp_model.asu_su_sd = Block(rule=asu_startup_shutdown_constraints) - - # Set the initial holdup of the tank - if tank_params["tank_constraint"] == "initial_holdup": - m.mp_model.initial_tank_level = Constraint( - expr=m.mp_model.period[1].fs.tank.initial_holdup == - tank_params["min_holdup"] * m.tank_design.tank_capacity - ) - - elif tank_params["tank_constraint"] == "periodic": - m.mp_model.periodic_constraint = Constraint( - expr=m.mp_model.period[1].fs.tank.initial_holdup == - m.mp_model.period[8760].fs.tank.holdup - ) - - # Append the overall cashflows - append_cashflows(m, cost_params) - - # Declare the objective function - m.obj = Objective(expr=m.npv, sense=maximize) - - # Use Gurobi solver - if solver is None: - solver = SolverFactory("gurobi") - solver.options['NonConvex'] = 2 - solver.options['MIPGap'] = 0.01 - solver.options['TimeLimit'] = 7500 - solver.options['OutputFlag'] = 1 - - sol = solver.solve(m, tee=True) - - _filename = folder + dataset + "_" + location + "_" + str(carbon_tax) - _write_results(m, sol, filename=_filename, lox_withdrawal=True) - - return m, sol - - -def npv_model_dfc_asu_nlu( - dataset="NREL", - location="PJM-W", - carbon_tax=150, - dfc_params=dmp.DFC_PARAMS, - asu_params=dmp.ASU_PARAMS, - nlu_params=dmp.NLU_PARAMS, - tank_params=dmp.O2_TANK_PARAMS, - cost_params=dmp.CASHFLOW_PARAMS, - solver=None, - folder="", -): - - penalty = cost_params["electricity_cost"] / 1e3 - - if dataset == "NREL": - cost_ng = NG_PRICE_DATA[dataset][carbon_tax][location] - else: - cost_ng = NG_PRICE_DATA[dataset] - - m = ConcreteModel() - get_lmp_data(m, dataset=dataset, location=location, carbon_tax=carbon_tax) - - m.dfc_design = DFCDesign(model_params=dfc_params) - m.asu_design = MonoASUDesign( - o2_flow_range=(10, 130), model_params=asu_params, - ) - m.nlu_design = NLUDesign( - o2_flow_range=(10, 130), model_params=nlu_params, - ) - m.tank_design = OxygenTankDesign( - tank_size_range=(10, 400000), model_params=tank_params, - ) - - # Currently, we are not varying the capacity of the power cycle, so fixing the value - # of capacity. Also, we want the power cycle to be built, so fixing build_dfc. - m.dfc_design.build_dfc.fix(1) - m.dfc_design.capacity.fix() - - m.mp_model = MultiPeriodModel( - n_time_points=365 * 24, - process_model_func=build_dfc_flowsheet_with_nlu, - linking_variable_func=get_linking_var_pairs, - use_stochastic_build=True, - flowsheet_options={ - "dfc_design": m.dfc_design, - "asu_design": m.asu_design, - "nlu_design": m.nlu_design, - "tank_design": m.tank_design, - }, - ) - - # Append cashflows at each hour - for t in m.set_time: - append_op_costs_dfc( - m=m.mp_model.period[t], - lmp=m.LMP[t], - penalty=penalty, - cost_ng=cost_ng, - carbon_price=carbon_tax / 1e3, - ) - - # Append startup and shutdown constraints for the power cycle - m.mp_model.dfc_su_sd = Block(rule=dfc_startup_shutdown_constraints) - - # Append startup and shutdown constraints for the ASU unit - m.mp_model.asu_su_sd = Block(rule=asu_startup_shutdown_constraints) - - # Set the initial holdup of the tank - if tank_params["tank_constraint"] == "initial_holdup": - m.mp_model.initial_tank_level = Constraint( - expr=m.mp_model.period[1].fs.tank.initial_holdup == - tank_params["min_holdup"] * m.tank_design.tank_capacity - ) - - elif tank_params["tank_constraint"] == "periodic": - m.mp_model.periodic_constraint = Constraint( - expr=m.mp_model.period[1].fs.tank.initial_holdup == - m.mp_model.period[8760].fs.tank.holdup - ) - - # Append the overall cashflows - append_cashflows(m, cost_params) - - # Declare the objective function - m.obj = Objective(expr=m.npv, sense=maximize) - - # Use Gurobi solver - if solver is None: - solver = SolverFactory("gurobi") - solver.options['MIPGap'] = 0.01 - solver.options['TimeLimit'] = 7200 - solver.options['OutputFlag'] = 1 - - sol = solver.solve(m, tee=True) - - _filename = folder + dataset + "_" + location + "_" + str(carbon_tax) - _write_results(m, sol, filename=_filename, includes_nlu=True) - - return m, sol diff --git a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/run_script.py b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/run_script.py deleted file mode 100644 index 2a4bfa52ab..0000000000 --- a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/run_script.py +++ /dev/null @@ -1,58 +0,0 @@ -import os -import copy -from idaes.power_generation.flowsheets.direct_fired_cycle.npv_optimization import ( - npv_model_dfc_asu, - npv_model_dfc_asu_with_lox, - npv_model_dfc_asu_nlu, -) -import idaes.power_generation.flowsheets.direct_fired_cycle. \ - default_model_parameters as dmp - -DFC_PARAMS = dmp.DFC_PARAMS -ASU_PARAMS = dmp.ASU_PARAMS -TANK_PARAMS = dmp.O2_TANK_PARAMS -NLU_PARAMS = dmp.NLU_PARAMS -CASHFLOW_PARAMS = dmp.CASHFLOW_PARAMS - - -# for tax in [100, 150]: -# for region in ["CAISO", "ERCOT", "MISO-W", "NYISO", "PJM-W"]: -# m, sol = npv_model_dfc_asu(location=region, carbon_tax=tax) -# # m, sol = npv_model_dfc_asu_with_lox(location=region, carbon_tax=tax) -# # m, sol = npv_model_dfc_asu_nlu(location=region, carbon_tax=tax) - -# for tax in [100]: -# for region in ["CAISO"]: -# m, sol = npv_model_dfc_asu(location=region, carbon_tax=tax) - -# for tax, region in [(100, "CAISO"), (150, "ERCOT"), (150, "PJM-W")]: -# print("*" * 80) -# print("Running ", region, tax) -# print("*" * 80) -# m, sol = npv_model_dfc_asu_nlu(location=region, carbon_tax=tax) - - -for tax, region in [(100, "CAISO")]: - print("*" * 80) - print("Running ", region, tax) - print("*" * 80) - - dfc_params = copy.deepcopy(DFC_PARAMS) - asu_params = copy.deepcopy(ASU_PARAMS) - cost_params = copy.deepcopy(CASHFLOW_PARAMS) - - # Modify any default parameters if needed - # dfc_params["capex"] /= 2 - - cwd = os.getcwd() - if not os.path.exists(cwd + "\\results"): - os.mkdir(os.path.join(cwd, "results")) - - m, sol = npv_model_dfc_asu( - location=region, - carbon_tax=tax, - dfc_params=dfc_params, - asu_params=asu_params, - cost_params=cost_params, - folder="results/", - ) diff --git a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/unit_models.py b/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/unit_models.py deleted file mode 100644 index f6f781c231..0000000000 --- a/idaes/models_extra/power_generation/flowsheets/direct_fired_cycle/unit_models.py +++ /dev/null @@ -1,730 +0,0 @@ -from importlib import resources -from pathlib import Path -import pandas as pd - -from pyomo.environ import ( - Var, - NonNegativeReals, - Binary, - Param, - RangeSet, - Constraint, - Expression, -) - -from idaes.core.base.process_base import declare_process_block_class -from idaes.models.unit_models import SkeletonUnitModelData -from pyomo.common.config import ConfigValue -import idaes.models_extra.power_generation.flowsheets.direct_fired_cycle. \ - default_model_parameters as dmp - - -def get_lmp_data( - m, - dataset="NREL", - location="CAISO", - carbon_tax=100, - princeton_case="BaseCaseTax", -): - """ - This function reads and appends LMP data to the model. - """ - with resources.path( - "idaes.models_extra.power_generation.flowsheets.direct_fired_cycle", - "FLECCS_Price_Series_Data_01_20_2021.xlsx" - ) as p: - path_to_file = Path(p).resolve() - - if dataset == "NREL": - raw_data = pd.read_excel( - path_to_file, - sheet_name="2035 - NREL", - ) - column_name = 'MiNg_$' + str(carbon_tax) + '_' + location - # We will take the price signal for day 365 same as that for day 364 - num_days = 365 - - elif dataset == "Princeton": - raw_data = pd.read_excel( - path_to_file, - sheet_name="2030 - Princeton", - ) - column_name = princeton_case - num_days = 365 - - m.set_time = RangeSet(num_days * 24) - price_all = raw_data[column_name].tolist() - - # Set prices lower than $0.001/MWh to zero to avoid numerical issues - price_all = [i if i > 1e-3 else 0 for i in price_all] - - # LMP is divided by 1000 to formulate the objective in thousands of dollars. - # This helps in keeping the scale of the CAPEX in O(1e6), otherwise it would be O(1e9). - m.LMP = Param( - m.set_time, - initialize={t + 1: lmp / 1000 for (t, lmp) in enumerate(price_all)}, - doc="Locational Marginal Prices [in 1000$/MWh]" - ) - - -@declare_process_block_class("DFCDesign") -class DFCDesignData(SkeletonUnitModelData): - """ - This class contains variables/parameters related to the design of the direct fired cycle - """ - CONFIG = SkeletonUnitModelData.CONFIG() - CONFIG.declare("capacity_range", ConfigValue( - default=(500, 850), - doc="Range for the capacity of the DFC [in MW]", - )) - CONFIG.declare("model_params", ConfigValue( - default=dmp.DFC_PARAMS, - doc="Dictionary containing model parameters", - )) - - # noinspection PyAttributeOutsideInit - def build(self): - super().build() - - params = self.config.model_params - _dfc_capacity = params["dfc_capacity"] - _ng_flow = params["ng_flow"] - _o2_ng_ratio = params["o2_ng_ratio"] - _capex = params["capex"] - _fom_factor = params["fom_factor"] - - self.capacity = Var( - within=NonNegativeReals, - initialize=_dfc_capacity, - bounds=(0, self.config.capacity_range[1]), - doc="Capacity of the power plant [in MW]", - ) - - # Define a variable that informs whether the plant needs to built or not. - self.build_dfc = Var( - within=Binary, - doc="1: Plant is built, 0: Plant is not built", - ) - - # Bound the capcity of the plant in the specified range - capacity_range = self.config.capacity_range - self.capacity_lb_con = Constraint(expr=self.capacity >= self.build_dfc * capacity_range[0]) - self.capacity_ub_con = Constraint(expr=self.capacity <= self.build_dfc * capacity_range[1]) - - # Compute the natural gas flowrate required at maximum capacity. - # FIXME: Assuming a linear relation for now. Update the equation when we have more data - self.ng_flow = Expression( - expr=self.capacity * (_ng_flow / _dfc_capacity), - doc="Computes the natural flowrate required [in kg/s] at full load", - ) - self.capacity_ub = Expression( - expr=self.config.capacity_range[1], - doc="Upper bound on the capacity of the dfc", - ) - - self.capacity_lb = Expression( - expr=self.config.capacity_range[0], - doc="Upper bound on the capacity of the dfc", - ) - - self.o2_flow = Expression( - expr=_o2_ng_ratio * self.ng_flow, - doc="Flowrate of oxygen [in kg/s] required at full load", - ) - - # Assumuing that the capex varies linearly with capacity - self.capex = Expression( - expr=_capex * (self.capacity / _dfc_capacity), - doc="CAPEX of the power cycle [in 1000$]", - ) - - # Calculating the FOM as 3.157% of the CAPEX. The percentage value is obtained from - # 35,641.27 (FOM) / 1,128,855 (CAPEX). The FOM is also assumed to vary linearly with the capacity. - self.fom = Expression( - expr=_fom_factor * self.capex, - doc="Fixed O&M cost [in 1000$/year]", - ) - - -@declare_process_block_class("DFCOperation") -class DFCOperationData(SkeletonUnitModelData): - """ - This class contains the model for the operation of the power cycle - """ - - CONFIG = SkeletonUnitModelData.CONFIG() - CONFIG.declare("design_blk", ConfigValue( - doc="Pointer to the object containing the DFC design information", - )) - CONFIG.declare("operating_range", ConfigValue( - default=(0.2, 1), - doc="Off-design operating range. Default: (0.2 * full load, full load)", - )) - - # noinspection PyAttributeOutsideInit - def build(self): - super().build() - - design_blk = self.config.design_blk - - # Declare variables - self.power = Var( - within=NonNegativeReals, - doc="Total power generated [in MW]", - ) - self.ng_flow = Var( - within=NonNegativeReals, - doc="Natural gas flowrate [in kg/s]", - ) - self.op_mode = Var( - within=Binary, - doc="1: In Operation, 0: Shutdown", - ) - self.startup = Var( - within=Binary, - doc="1: Plant is startup at this hour, 0: Otherwise", - ) - self.shutdown = Var( - within=Binary, - doc="1: Plant is shutdown at this hour, 0: Otherwise", - ) - - self.op_mode_capacity = Var( - within=NonNegativeReals, - doc="Auxiliary variable for the product of op_mode and capacity", - ) - self.su_sd_dfc_aux_1 = Var( - within=NonNegativeReals, - doc="Auxiliary variable for the product of capacity and startup & shutdown binaries", - ) - self.su_sd_dfc_aux_2 = Var( - within=NonNegativeReals, - doc="Auxiliary variable for the product of capacity and startup & shutdown binaries", - ) - self.su_sd_dfc_aux_3 = Var( - within=NonNegativeReals, - doc="Auxiliary variable for the product of capacity and startup & shutdown binaries", - ) - self.su_sd_dfc_aux_4 = Var( - within=NonNegativeReals, - doc="Auxiliary variable for the product of capacity and startup & shutdown binaries", - ) - # Linear relaxation of the product op_mode * capacity - self.mccor_conv = Constraint( - expr=self.op_mode_capacity >= design_blk.capacity + - design_blk.capacity.ub * self.op_mode - design_blk.capacity.ub, - ) - self.mccor_conc_1 = Constraint( - expr=self.op_mode_capacity <= design_blk.capacity, - ) - self.mccor_conc_2 = Constraint( - expr=self.op_mode_capacity <= self.op_mode * design_blk.capacity.ub, - ) - - # Power production as a function of natural gas flowrate - # We construct a surrogate model of the form (1 - norm_power) = m * (1 - norm_ng_flow). - # This way, the natural gas requirement is exact at full load. - # Rearranging the equation yields norm_power = m * norm_ng_flow + 1-m - # Next, we replace norm_power = power / capacity and norm_ng_flow = ng_flow / max_ng_flow, - # substitute max_ng_flow in terms of capacity and rearrange the equation. - - params = design_blk.config.model_params - _dfc_capacity = params["dfc_capacity"] - _ng_flow = params["ng_flow"] - _perf_curve = params["op_curve_coeff"] - _o2_ng_ratio = params["o2_ng_ratio"] - _vom = params["vom"] - _co2_emission = params["co2_emission"] - - self.power_production = Constraint( - expr=self.power == (_perf_curve[0] / (_ng_flow / _dfc_capacity)) * self.ng_flow + - _perf_curve[1] * self.op_mode_capacity, - ) - - # Ensure that power production is within P_min and P_max - operating_range = self.config.operating_range - self.power_production_lb = Constraint( - expr=operating_range[0] * self.op_mode_capacity <= self.power, - ) - - self.power_production_ub = Constraint( - expr=self.power <= operating_range[1] * self.op_mode_capacity, - ) - - # Declare a variable to track NG requirement for startup and shutdown - self.su_sd_ng_flow = Var( - within=NonNegativeReals, - doc="Fuel requirement for startup/shutdown [in kg/s]", - ) - - self.total_ng_flow = Expression( - expr=self.ng_flow + self.su_sd_ng_flow, - doc="Total NG flow rate requirement [in kg/s]", - ) - - self.o2_flow = Expression( - expr=_o2_ng_ratio * self.total_ng_flow, - doc="Flowrate of oxygen required [in kg/s]", - ) - - self.non_fuel_vom = Expression( - expr=_vom * (self.power / _dfc_capacity), - doc="Non-fuel VOM cost [in $1000/hr]", - ) - - self.co2_emissions = Expression( - expr=_co2_emission * self.power, - doc="Net CO2 emissions [in kg/hr]", - ) - - -@declare_process_block_class("MonoASUDesign") -class MonoASUDesignData(SkeletonUnitModelData): - """ - This class builds the model for the design of the ASU. Here, we treat the - system of ASUs as a monolithic ASU. - """ - - CONFIG = SkeletonUnitModelData.CONFIG() - CONFIG.declare("o2_flow_range", ConfigValue( - default=(80, 130), - doc="Range for the size of the ASU in terms of O2 flow rate [in kg/s]", - )) - CONFIG.declare("model_params", ConfigValue( - default=dmp.MONO_ASU_PARAMS, - doc="Dictionary containing model parameters", - )) - - # noinspection PyAttributeOutsideInit - def build(self): - super().build() - - params = self.config.model_params - _asu_capacity = params["asu_capacity"] - _power_req = params["power_requirement"] - _capex = params["capex"] - _fom_factor = params["fom_factor"] - - self.max_o2_flow = Var( - within=NonNegativeReals, - initialize=_asu_capacity, - bounds=(0, self.config.o2_flow_range[1]), - doc="Maximum flowrate of O2 the ASU can produce [in kg/s]", - ) - self.build_asu = Var( - within=Binary, - doc="1: ASU is built, 0: ASU is not built", - ) - - # Bound the capcity of the plant in the specified range - o2_flow_range = self.config.o2_flow_range - self.o2_flow_lb_con = Constraint(expr=self.max_o2_flow >= self.build_asu * o2_flow_range[0]) - self.o2_flow_ub_con = Constraint(expr=self.max_o2_flow <= self.build_asu * o2_flow_range[1]) - - # Relation between the flowrate and the power requirement - self.max_power = Expression( - expr=self.max_o2_flow * (_power_req / _asu_capacity), - doc="Power requirement at maximum capacity [in MW]", - ) - - self.max_power_ub = Expression( - expr=self.config.o2_flow_range[1]* (_power_req / _asu_capacity), - doc="Upper bound for the power requirement at maximum capacity [in MW]", - ) - - # Assuming that the capex of the ASU varies linearly with size - self.capex = Expression( - expr=_capex * (self.max_o2_flow / _asu_capacity), - doc="CAPEX of the ASU unit [in 1000$]", - ) - - # Calculating the FOM as 3.157% of the CAPEX. The percentage value is obtained from - # 17223.73 (FOM) / 545,522 (CAPEX). The FOM is also assumed to vary linearly with the capacity. - self.fom = Expression( - expr=_fom_factor * self.capex, - doc="Fixed O&M cost [in $1000]", - ) - - -@declare_process_block_class("MonoASUOperation") -class MonoASUOperationData(SkeletonUnitModelData): - """ - This class contains the model for the operation of the monolithic ASU - """ - - CONFIG = SkeletonUnitModelData.CONFIG() - CONFIG.declare("design_blk", ConfigValue( - doc="Pointer to the object containing the ASU design information", - )) - CONFIG.declare("operating_range", ConfigValue( - default=(0.3, 1), - doc="Off-design operating range. Default: (0.3 * full load, full load)", - )) - - # noinspection PyAttributeOutsideInit - def build(self): - super().build() - - design_blk = self.config.design_blk - - # Declare variables - self.power = Var( - within=NonNegativeReals, - doc="Power required to produce oxygen [in MW]", - ) - self.o2_flow = Var( - within=NonNegativeReals, - doc="Flowrate of oxygen produced [in kg/s]", - ) - self.op_mode = Var( - within=Binary, - doc="1: In Operation, 0: Shutdown", - ) - self.startup = Var( - within=Binary, - doc="1: ASU is startup at this hour, 0: Otherwise", - ) - self.shutdown = Var( - within=Binary, - doc="1: ASU is shutdown at this hour, 0: Otherwise", - ) - self.op_mode_o2_flow = Var( - within=NonNegativeReals, - doc="Auxiliary variable for linearizing the product of op_mode and max_o2_flow" - ) - self.su_sd_asu_aux_1 = Var( - within=NonNegativeReals, - doc="Auxiliary variable for linearizing the product of startup/stutdown and max_power" - ) - self.su_sd_asu_aux_2 = Var( - within=NonNegativeReals, - doc="Auxiliary variable for linearizing the product of startup/stutdown and max_power" - ) - self.su_sd_asu_aux_3 = Var( - within=NonNegativeReals, - doc="Auxiliary variable for linearizing the product of startup/stutdown and max_power" - ) - # Linear relaxation of op_mode * max_o2_flow - self.mccor_conv = Constraint( - expr=self.op_mode_o2_flow >= design_blk.max_o2_flow + - self.op_mode * design_blk.max_o2_flow.ub - design_blk.max_o2_flow.ub, - ) - self.mccor_conc_1 = Constraint( - expr=self.op_mode_o2_flow <= design_blk.max_o2_flow, - ) - self.mccor_conc_2 = Constraint( - expr=self.op_mode_o2_flow <= self.op_mode * design_blk.max_o2_flow.ub, - ) - - # Power production as a function of natural gas flowrate - # We construct a surrogate model of the form (1 - norm_power) = m * (1 - norm_o2_flow). - # This way, the power requirement is exact at full capacity. - # Rearranging the equation yields norm_power = m * norm_o2_flow + 1-m - - params = design_blk.config.model_params - _asu_capacity = params["asu_capacity"] - _power_req = params["power_requirement"] - _perf_curve = params["op_curve_coeff"] - _vom = params["vom"] - - self.power_requirement = Constraint( - expr=(1 / (_power_req / _asu_capacity)) * self.power == - _perf_curve[0] * self.o2_flow + _perf_curve[1] * self.op_mode_o2_flow, - ) - - # Ensure that the normalized oxygen flowrate is within the admissible operating range - operating_range = self.config.operating_range - self.o2_flow_lb = Constraint( - expr=operating_range[0] * self.op_mode_o2_flow <= self.o2_flow, - ) - - self.o2_flow_ub = Constraint( - expr=self.o2_flow <= operating_range[1] * self.op_mode_o2_flow, - ) - - self.su_sd_power = Var( - within=NonNegativeReals, - doc="Power requirement during startup and shutdown [in MW]", - ) - - self.total_power = Expression( - expr=self.power + self.su_sd_power, - doc="Total power required by the ASU [in MW]", - ) - - self.non_fuel_vom = Expression( - expr=_vom * (self.o2_flow / _asu_capacity), - doc="Non-electricity VOM cost [in $1000/hr]", - ) - - -@declare_process_block_class("NLUDesign") -class NLUDesignData(SkeletonUnitModelData): - """ - This class builds the model for the design of the NLU. - """ - - CONFIG = SkeletonUnitModelData.CONFIG() - CONFIG.declare("o2_flow_range", ConfigValue( - default=(50, 130), - doc="Range for the size of the NLU in terms of O2 flow rate [in kg/s]", - )) - CONFIG.declare("model_params", ConfigValue( - default=dmp.NLU_PARAMS, - doc="Dictionary containing NLU model parameters", - )) - - # noinspection PyAttributeOutsideInit - def build(self): - super().build() - - params = self.config.model_params - _nlu_capacity = params["nlu_capacity"] - _power_req = params["power_requirement"] - _capex = params["capex"] - _fom_factor = params["fom_factor"] - - self.max_o2_flow = Var( - within=NonNegativeReals, - initialize=_nlu_capacity, - bounds=(0, self.config.o2_flow_range[1]), - doc="Maximum flowrate of O2 the NLU can liquefy [in kg/s]", - ) - self.build_nlu = Var( - within=Binary, - doc="1: NLU is built, 0: NLU is not built", - ) - - # Bound the capcity of the plant in the specified range - o2_flow_range = self.config.o2_flow_range - self.o2_flow_lb_con = Constraint(expr=self.max_o2_flow >= self.build_nlu * o2_flow_range[0]) - self.o2_flow_ub_con = Constraint(expr=self.max_o2_flow <= self.build_nlu * o2_flow_range[1]) - - # Relation between the flowrate and the power requirement - self.max_power = Expression( - expr=self.max_o2_flow * (_power_req / _nlu_capacity), - doc="Power requirement at maximum capacity [in MW]", - ) - - # Assuming that the capex of the NLU varies linearly with size - # Obtained from $42,797.4 * 4 = $171,189.6 - self.capex = Expression( - expr=_capex * (self.max_o2_flow / _nlu_capacity), - doc="CAPEX of the ASU unit [in 1000$]", - ) - - # Assuming that the FOM of NLU is 3.157% of CAPEX. This number is calculated as - # 41,715 (FOM) / 171,189 (CAPEX) - self.fom = Expression( - expr=_fom_factor * self.capex, - doc="Fixed O&M of NLU [in $1000]", - ) - - -@declare_process_block_class("NLUOperation") -class NLUOperationData(SkeletonUnitModelData): - """ - This class contains the model for the operation of the NLU - """ - # NOTE: The NLU model can be simplified by not defining the design variables. - # But we modeled it in this manner to keep it consistent with the rest of the models. - # If the solver finds it difficult to solve, then we'll switch to the simpler model. - - CONFIG = SkeletonUnitModelData.CONFIG() - CONFIG.declare("design_blk", ConfigValue( - doc="Pointer to the object containing the NLU design information", - )) - CONFIG.declare("operating_range", ConfigValue( - default=(0.3, 1), - doc="Off-design operating range. Default: (0.3 * full load, full load)", - )) - - # noinspection PyAttributeOutsideInit - def build(self): - super().build() - - design_blk = self.config.design_blk - params = design_blk.config.model_params - _nlu_capacity = params["nlu_capacity"] - _power_req = params["power_requirement"] - _vom = params["vom"] - - # Declare variables - self.power = Var( - within=NonNegativeReals, - doc="Power required to liquify oxygen [in MW]", - ) - self.o2_flow = Var( - within=NonNegativeReals, - doc="Flowrate of liquified oxygen [in kg/s]", - ) - - # Note: Assuming that the NLU does not have any startup/shutdown constraints. - # If it does, we need to define startup and shutdown binary variables like before. - self.op_mode = Var( - within=Binary, - doc="1: In Operation, 0: Shutdown", - ) - self.op_mode_o2_flow = Var( - within=NonNegativeReals, - doc="Auxiliary variable for linearizing op_mode and max_o2_flow", - ) - self.mccor_conv = Constraint( - expr=self.op_mode_o2_flow >= design_blk.max_o2_flow - + self.op_mode * design_blk.max_o2_flow.ub - design_blk.max_o2_flow.ub, - ) - self.mccor_conc_1 = Constraint( - expr=self.op_mode_o2_flow <= design_blk.max_o2_flow, - ) - self.mccor_conc_2 = Constraint( - expr=self.op_mode_o2_flow <= self.op_mode * design_blk.max_o2_flow.ub - ) - - # Power production as a function of natural gas flowrate - self.power_requirement = Constraint( - expr=self.power == self.o2_flow * (_power_req / _nlu_capacity), - ) - - # Ensure that the normalized oxygen flowrate is within the operating_range - operating_range = self.config.operating_range - self.o2_flow_lb = Constraint( - expr=operating_range[0] * self.op_mode_o2_flow <= self.o2_flow, - ) - - self.o2_flow_ub = Constraint( - expr=self.o2_flow <= operating_range[1] * self.op_mode_o2_flow, - ) - - self.non_fuel_vom = Expression( - expr=_vom * (self.o2_flow / _nlu_capacity), - doc="Non-electricity VOM cost [in $1000/hr]", - ) - - -@declare_process_block_class("OxygenTankDesign") -class OxygenTankDesignData(SkeletonUnitModelData): - """ - This class builds the model for the design of the tank. - """ - - CONFIG = SkeletonUnitModelData.CONFIG() - CONFIG.declare("tank_size_range", ConfigValue( - default=(2000, 400000), - doc="Range for the tank capacity [in tons]", - )) - CONFIG.declare("model_params", ConfigValue( - default=dmp.O2_TANK_PARAMS, - doc="Dictionary containing model parameters" - )) - - # noinspection PyAttributeOutsideInit - def build(self): - super().build() - - params = self.config.model_params - _fom_factor = params["fom_factor"] - _capex = params["capex"] - - self.tank_capacity = Var( - within=NonNegativeReals, - initialize=4000, - bounds=(0, self.config.tank_size_range[1]), - doc="Maximum amount of oxygen the tank can hold [in tons]", - ) - - self.build_tank = Var( - within=Binary, - doc="1: The storage tank is built, 0: Otherwise", - ) - - # Bound the capcity of the tank in the specified range - tank_size_range = self.config.tank_size_range - self.tank_capacity_lb_con = Constraint( - expr=self.tank_capacity >= self.build_tank * tank_size_range[0] - ) - self.tank_capacity_ub_con = Constraint( - expr=self.tank_capacity <= self.build_tank * tank_size_range[1] - ) - - self.capex = Expression( - expr=_capex[0] * self.tank_capacity + _capex[1] * self.build_tank, - doc="CAPEX of the oxygen storage tank [in 1000$]", - ) - - self.fom = Expression( - expr=_fom_factor * self.capex, - doc="FOM of the tank [in $1000]", - ) - - -@declare_process_block_class("OxygenTankOperation") -class OxygenTankOperationData(SkeletonUnitModelData): - """ - This class contains the model for the operation of the oxygen tank - """ - - CONFIG = SkeletonUnitModelData.CONFIG() - CONFIG.declare("design_blk", ConfigValue( - doc="Pointer to the object containing the tank design information", - )) - CONFIG.declare("o2_flow_range", ConfigValue( - default=(0, 130), - doc="Range for the inlet flowrate of O2 [in kg/s]", - )) - - # noinspection PyAttributeOutsideInit - def build(self): - super().build() - - design_blk = self.config.design_blk - params = design_blk.config.model_params - _min_holdup = params["min_holdup"] - _power_req = params["power_requirement"] - _o2_flow = params["o2_flow"] - - self.initial_holdup = Var( - within=NonNegativeReals, - doc="Initial holdup of the tank [in tons]", - ) - self.final_holdup = Var( - within=NonNegativeReals, - doc="Final holdup of the tank [in tons]", - ) - self.lox_in = Var( - within=NonNegativeReals, - doc="Flowrate of LOX entering the tank [in kg/s]", - ) - self.lox_out = Var( - within=NonNegativeReals, - doc="Flowrate of LOX leaving the tank [in kg/s]", - ) - - # LOX cannot be stored/withdrawn if the tank is not built - o2_flow_range = self.config.o2_flow_range - self.lox_in_ub_con = Constraint( - expr=self.lox_in <= design_blk.build_tank * o2_flow_range[1] - ) - self.lox_out_ub_con = Constraint( - expr=self.lox_out <= design_blk.build_tank * o2_flow_range[1] - ) - - # Holdup must be greater than 10% of the tank capacity at all times - self.holdup_constraint = Constraint( - expr=self.final_holdup >= _min_holdup * design_blk.tank_capacity, - ) - - # Holdup cannot exceed the capacity of the tank - self.holdup_ub = Constraint( - expr=self.final_holdup <= design_blk.tank_capacity - ) - - # Mass balance over the period of one hour: - self.mass_balance = Constraint( - expr=self.final_holdup - self.initial_holdup == (self.lox_in - self.lox_out) * (3600 / 1000), - ) - - # Power requirement for discharging - self.power = Expression( - expr=_power_req * (self.lox_out / _o2_flow), - doc="Power requirement for discharging LOX [in MW]", - ) From 1d87aeadbf6f03ae47bc5a029e253127011ed6d3 Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Fri, 11 Aug 2023 13:56:09 -0400 Subject: [PATCH 018/126] modified startup and shutdown constraint function --- .../apps/grid_integration/multiperiod/price_taker_model.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 02a69af8ec..452246b1e8 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -324,13 +324,18 @@ def add_ramping_constraints(self, var, ramp_up_limit, ramp_down_limit): Adds ramping constraints of the form -ramp_down_limit <= var(t) - var(t-1) <= ramp_up_limit on var """ + + pass - def add_startup_shutdown(self): + def add_startup_shutdown(self,UT = 1,DT =1): """ Adds startup/shutdown and minimum uptime/downtime constraints on a given unit/process """ + + @Constraint(self) + def start_up(t) pass def build_hourly_cashflows(self): From 4710a10805cce96d36f72ad50fecc8b37ec05f4b Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Fri, 11 Aug 2023 16:59:15 -0400 Subject: [PATCH 019/126] add set_period to startup and shutdown func --- .../grid_integration/multiperiod/price_taker_model.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 452246b1e8..836c846376 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -333,9 +333,15 @@ def add_startup_shutdown(self,UT = 1,DT =1): Adds startup/shutdown and minimum uptime/downtime constraints on a given unit/process """ + set_period = self.mp_model.set_period + num_time_periods = len(set_period) + + + set_period_list = set_period.tolist() + self.st_con_range = RangeSet(set_period_list[UT:]) + self.sd_con_range = RangeSet(set_period_list[DT:]) + - @Constraint(self) - def start_up(t) pass def build_hourly_cashflows(self): From be681549970ad077528801c928790dbab4d2139c Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Fri, 18 Aug 2023 10:42:37 -0400 Subject: [PATCH 020/126] developed a function to add startup/shutdown constraints --- .../multiperiod/price_taker_model.py | 36 +++++++++++++++---- 1 file changed, 29 insertions(+), 7 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 836c846376..26dc50c37f 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -328,21 +328,43 @@ def add_ramping_constraints(self, var, ramp_up_limit, ramp_down_limit): pass - def add_startup_shutdown(self,UT = 1,DT =1): + def add_startup_shutdown(self,UT = 4,DT =4): """ Adds startup/shutdown and minimum uptime/downtime constraints on a given unit/process """ - set_period = self.mp_model.set_period - num_time_periods = len(set_period) - set_period_list = set_period.tolist() - self.st_con_range = RangeSet(set_period_list[UT:]) - self.sd_con_range = RangeSet(set_period_list[DT:]) + period = self.mp_model.period + self.range_time_periods_SU = RangeSet(UT,len(period)-1) + self.range_time_periods_SD = RangeSet(DT,len(period)-1) + range_time_periods = [] + + for p in period: + range_time_periods.append(p) + op_mode = 0 + startup = 0 + for blk in period[p].component_data_objects(Block): + if isinstance(blk, OperationModelData): + op_mode += blk.op_mode + startup += blk.startup + period[p].op_mode = Expression(expr = op_mode) + period[p].startup = Expression(expr = startup) + + + def startup_con1_rule(self,t): + return sum(period[range_time_periods[i]].startup for i in range(t-UT+1,t)) <= period[range_time_periods[t]].op_mode + + self.mp_model.startup_con1 = Constraint(self.range_time_periods_SU, rule = startup_con1_rule) + + def stutdown_con1_rule(self,t): + return ( sum(period[range_time_periods[i]].startup for i in range(t - DT, t + 1)) <= + 1 - period[range_time_periods[t - DT]].op_mode ) + + self.mp_model.stutdown_con1 = Constraint(self.range_time_periods_SD, rule = stutdown_con1_rule) - pass + def build_hourly_cashflows(self): period = self.mp_model.period From 66db0d8e864300cc23b9e7ccd17bb270ca238e8f Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Tue, 22 Aug 2023 13:04:42 -0400 Subject: [PATCH 021/126] Constructed a function that adds startup and shutdown constraints --- .../multiperiod/price_taker_model.py | 76 ++++++++++++++++++- 1 file changed, 72 insertions(+), 4 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 26dc50c37f..6f62942de2 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -318,20 +318,88 @@ def build_multiperiod_model(self, **kwargs): use_stochastic_build=True, **kwargs, ) - - def add_ramping_constraints(self, var, ramp_up_limit, ramp_down_limit): + #TODO: (1) Need to determine whether the minimum opterating power can be a parameter + # or whether it largly depends on the capacity of the plant. + # (2) Need to determine if the start up and shutdown limits can be modeled as bulk amounts vs + # a percentage of capacity + # (3) How to model/code the cosntraints in without knowing what variable + # these constraints will be applied to. + def add_ramping_constraints( + self, + startup_limit = 100, + shutdown_limit = 100, + ramp_up_limit = 110, + ramp_down_limit = 110, + minimum_opt_limit = 0, + ): """ Adds ramping constraints of the form -ramp_down_limit <= var(t) - var(t-1) <= ramp_up_limit on var - """ + + + Arguments: + startup_limit: The rate on the power when starting up (Mw/h) + shutdown_limit: The rate on the pwoer when shuting down (Mw/h) + ramp_up_limit: The ramp up rate for the system ( Mw/h) + ramp_down_limit = The ramp down rate for the system (Mw/h) + minimum_opt_limit: The minimum power the system is able to operate at at anytime t (Mw/h) + Assumptions/relationship: + Max_operating_power >= ramp_up_limit >= startup_limit >= minimum_opt_limit > 0 + Max_operating_power >= ramp_down_limit >= shutdown_limit >= minimum_opt_limit > 0 + """ + period = self.mp_model.period + range_time_periods = [] + self.ramp_up_down_range= RangeSet(2,len(period)-1) + + if period[1,1].find_component('op_mode') == None: + for p in period: + range_time_periods.append(p) + op_mode = 0 + startup = 0 + + for blk in period[p].component_data_objects(Block): + if isinstance(blk, OperationModelData): + op_mode += blk.op_mode + startup += blk.startup + + period[p].op_mode = Expression(expr = op_mode) + period[p].startup = Expression(expr = startup) + + + def ramp_up_con_rule(self,t): + return ( + period[range_time_periods[t]].opt_power - period[range_time_periods[t-1]].opt_power <= + startup_limit * period[range_time_periods[t]].startup - minimum_opt_limit * period[range_time_periods[t-1]].startup + + ramp_up_limit * period[range_time_periods[t]].op_mode + + minimum_opt_limit*(period[range_time_periods[t]].op_mode - period[range_time_periods[t-1]].op_mode) + ) + + self.mp_model.ramp_up_con = Constraint(self.ramp_up_down_range, rule = ramp_up_con_rule ) + + def ramp_down_con_rule(self,t) : + return ( + period[range_time_periods[t-1]].opt_power- period[range_time_periods[t]].opt_power <= + shutdown_limit * (period[range_time_periods[t-1]].startup + + period[range_time_periods[t-1]].op_mode - period[range_time_periods[t]].op_mode) + - minimum_opt_limit * period[range_time_periods[t]].startup + + ramp_down_limit * period[range_time_periods[t]].op_mode + ) - pass + self.mp_model.ramp_down_con = Constraint(self.ramp_up_down_range, rule = ramp_down_con_rule ) def add_startup_shutdown(self,UT = 4,DT =4): """ Adds startup/shutdown and minimum uptime/downtime constraints on a given unit/process + + + Arguments: + UT: Time required for the system to start up fully (hr) + DT: Time required for the system to shutdown fully (hr) + + Assumption: + UT >= 1 & DT >= 1 """ From b39f4d93c04ecaf8a29a9f658f5663c86d7f7efa Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Wed, 30 Aug 2023 14:17:25 -0400 Subject: [PATCH 022/126] Added deepgetattr function and had the ramp-up/down and start-up/down functions to add constraints through pyomo blocks --- .../multiperiod/price_taker_model.py | 189 +++++++++++------- 1 file changed, 112 insertions(+), 77 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 6f62942de2..750bd84d6c 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -12,6 +12,9 @@ ################################################################################# import pandas as pd import numpy as np +from functools import reduce +from operator import attrgetter + from importlib import resources from pathlib import Path from pyomo.environ import ( @@ -42,6 +45,10 @@ _logger = logging.getLogger(__name__) +def deepgetattr(obj, attr): + return reduce(getattr, attr.split('.'), obj) + + class PriceTakerModel(ConcreteModel): def __init__(self, seed=20, horizon_length=24): super().__init__() @@ -325,12 +332,14 @@ def build_multiperiod_model(self, **kwargs): # (3) How to model/code the cosntraints in without knowing what variable # these constraints will be applied to. def add_ramping_constraints( - self, + self, + op_blk, + var, + var_opt_lb, startup_limit = 100, shutdown_limit = 100, ramp_up_limit = 110, ramp_down_limit = 110, - minimum_opt_limit = 0, ): """ Adds ramping constraints of the form @@ -338,99 +347,125 @@ def add_ramping_constraints( Arguments: - startup_limit: The rate on the power when starting up (Mw/h) - shutdown_limit: The rate on the pwoer when shuting down (Mw/h) - ramp_up_limit: The ramp up rate for the system ( Mw/h) - ramp_down_limit = The ramp down rate for the system (Mw/h) - minimum_opt_limit: The minimum power the system is able to operate at at anytime t (Mw/h) + op_blk: The name of the operation model block, ex: ( "fs.op_name") + var: Name of the variable the ramping cosntraints will be applied to, ex: "total_power" + var_opt_lb: The name of the expression, or numerical value for the lower bound on the specified variable, + ex: "total_power_lower_bound" or 100 + startup_limit: The rate at which the specified variable changes as the system starts up + ex: 100 + shutdown_limit: The rate at which the specified varible changes as the system shutsdown + ex: 100 + ramp_up_limit: The rate at which the specified variable increases during operation + ex: 100 + ramp_down_limit = The rate at which the specified variable can decrease during operation + ex: 100 + Assumptions/relationship: - Max_operating_power >= ramp_up_limit >= startup_limit >= minimum_opt_limit > 0 - Max_operating_power >= ramp_down_limit >= shutdown_limit >= minimum_opt_limit > 0 + total_power_upper_bound >= ramp_up_limit >= startup_limit >= total_power_lower_bound > 0 + total_power_upper_bound >= ramp_down_limit >= shutdown_limit >= total_power_lower_bound > 0 """ - period = self.mp_model.period - range_time_periods = [] - self.ramp_up_down_range= RangeSet(2,len(period)-1) - - if period[1,1].find_component('op_mode') == None: - for p in period: - range_time_periods.append(p) - op_mode = 0 - startup = 0 - - for blk in period[p].component_data_objects(Block): - if isinstance(blk, OperationModelData): - op_mode += blk.op_mode - startup += blk.startup - - period[p].op_mode = Expression(expr = op_mode) - period[p].startup = Expression(expr = startup) - - - def ramp_up_con_rule(self,t): - return ( - period[range_time_periods[t]].opt_power - period[range_time_periods[t-1]].opt_power <= - startup_limit * period[range_time_periods[t]].startup - minimum_opt_limit * period[range_time_periods[t-1]].startup - + ramp_up_limit * period[range_time_periods[t]].op_mode - + minimum_opt_limit*(period[range_time_periods[t]].op_mode - period[range_time_periods[t-1]].op_mode) - ) - self.mp_model.ramp_up_con = Constraint(self.ramp_up_down_range, rule = ramp_up_con_rule ) + self.range_time_steps = RangeSet(len(self.mp_model.set_period)) + start_up = {t: deepgetattr(self.mp_model.period[t], op_blk + ".startup") for t in self.mp_model.period} + op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".op_mode") for t in self.mp_model.period} + shut_down = {t: deepgetattr(self.mp_model.period[t], op_blk + ".shutdown") for t in self.mp_model.period} + var = {t: deepgetattr(self.mp_model.period[t], op_blk + "." + var) for t in self.mp_model.period} + if isinstance(var_opt_lb,str): + var_opt_lb_ = {t: deepgetattr(self.mp_model.period[t],op_blk + "." + var_opt_lb) for t in self.mp_model.period} + else: + var_opt_lb_ = var_opt_lb - def ramp_down_con_rule(self,t) : - return ( - period[range_time_periods[t-1]].opt_power- period[range_time_periods[t]].opt_power <= - shutdown_limit * (period[range_time_periods[t-1]].startup + - period[range_time_periods[t-1]].op_mode - period[range_time_periods[t]].op_mode) - - minimum_opt_limit * period[range_time_periods[t]].startup - + ramp_down_limit * period[range_time_periods[t]].op_mode - ) + blk_name = op_blk.split(".")[-1] + "_rampup_rampdown" + setattr(self.mp_model, blk_name, Block()) + blk = getattr(self.mp_model,blk_name) + + if isinstance(var_opt_lb,str): + @blk.Constraint(self.range_time_steps) + def ramp_up_con(b,t): + if t == 1: + return Constraint.Skip + return ( + var[self.mp_model.set_period[t]] - var[self.mp_model.set_period[t-1]] <= + startup_limit * start_up[self.mp_model.set_period[t]] - var_opt_lb_[self.mp_model.set_period[t]] * start_up[self.mp_model.set_period[t-1]] + + ramp_up_limit * op_mode[self.mp_model.set_period[t]] + + var_opt_lb_[self.mp_model.set_period[t]]*(op_mode[self.mp_model.set_period[t]] - op_mode[self.mp_model.set_period[t-1]]) + ) - self.mp_model.ramp_down_con = Constraint(self.ramp_up_down_range, rule = ramp_down_con_rule ) + @blk.Constraint(self.range_time_steps) + def ramp_down_con(b,t): + if t == 1: + return Constraint.Skip + return ( + var[self.mp_model.set_period[t-1]]- var[self.mp_model.set_period[t]] <= + shutdown_limit * (start_up[self.mp_model.set_period[t-1]] + + op_mode[self.mp_model.set_period[t-1]] - op_mode[self.mp_model.set_period[t]]) + - var_opt_lb_[self.mp_model.set_period[t]] * start_up[self.mp_model.set_period[t]] + + ramp_down_limit * op_mode[self.mp_model.set_period[t]] + ) + else: + @blk.Constraint(self.range_time_steps) + def ramp_up_con(b,t): + if t == 1: + return Constraint.Skip + return ( + var[self.mp_model.set_period[t]] - var[self.mp_model.set_period[t-1]] <= + startup_limit * start_up[self.mp_model.set_period[t]] - var_opt_lb_* start_up[self.mp_model.set_period[t-1]] + + ramp_up_limit * op_mode[self.mp_model.set_period[t]] + + var_opt_lb_*(op_mode[self.mp_model.set_period[t]] - op_mode[self.mp_model.set_period[t-1]]) + ) - def add_startup_shutdown(self,UT = 4,DT =4): + @blk.Constraint(self.range_time_steps) + def ramp_down_con(b,t): + if t == 1: + return Constraint.Skip + return ( + var[self.mp_model.set_period[t-1]]- var[self.mp_model.set_period[t]] <= + shutdown_limit * (start_up[self.mp_model.set_period[t-1]] + + op_mode[self.mp_model.set_period[t-1]] - op_mode[self.mp_model.set_period[t]]) + - var_opt_lb_* start_up[self.mp_model.set_period[t]] + + ramp_down_limit * op_mode[self.mp_model.set_period[t]] + ) + + + def add_startup_shutdown(self, op_blk, up_time = 1, down_time =1): """ Adds startup/shutdown and minimum uptime/downtime constraints on a given unit/process Arguments: - UT: Time required for the system to start up fully (hr) - DT: Time required for the system to shutdown fully (hr) - + op_blk: op_blk: The name of the operation model block, ex: ( "fs.op_name") + up_time: Time required for the system to start up fully + ex: 4 + down_time: Time required for the system to shutdown fully + ex: 4 Assumption: - UT >= 1 & DT >= 1 + up_time >= 1 & down_time >= 1 """ - period = self.mp_model.period - self.range_time_periods_SU = RangeSet(UT,len(period)-1) - self.range_time_periods_SD = RangeSet(DT,len(period)-1) - range_time_periods = [] - - for p in period: - range_time_periods.append(p) - op_mode = 0 - startup = 0 - - for blk in period[p].component_data_objects(Block): - if isinstance(blk, OperationModelData): - op_mode += blk.op_mode - startup += blk.startup - period[p].op_mode = Expression(expr = op_mode) - period[p].startup = Expression(expr = startup) - - - def startup_con1_rule(self,t): - return sum(period[range_time_periods[i]].startup for i in range(t-UT+1,t)) <= period[range_time_periods[t]].op_mode - - self.mp_model.startup_con1 = Constraint(self.range_time_periods_SU, rule = startup_con1_rule) - - def stutdown_con1_rule(self,t): - return ( sum(period[range_time_periods[i]].startup for i in range(t - DT, t + 1)) <= - 1 - period[range_time_periods[t - DT]].op_mode ) + start_up = {t: deepgetattr(self.mp_model.period[t], op_blk + ".startup") for t in self.mp_model.period} + op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".op_mode") for t in self.mp_model.period} + self.range_time_steps = RangeSet(len(self.mp_model.set_period)) + number_time_steps = len(self.mp_model.set_period) + + blk_name = op_blk.split(".")[-1] + "_startup_shutdown" + setattr(self.mp_model, blk_name, Block()) + blk = getattr(self.mp_model,blk_name) + + @blk.Constraint(self.range_time_steps) + def minimum_up_time_con(b,t): + if t < up_time or t >= number_time_steps: + return Constraint.Skip + return sum(start_up[self.mp_model.set_period[i]] for i in range(t-up_time+2,t+1)) <= op_mode[self.mp_model.set_period[t+1]] - self.mp_model.stutdown_con1 = Constraint(self.range_time_periods_SD, rule = stutdown_con1_rule) + @blk.Constraint(self.range_time_steps) + def minimum_down_time_con(b, t): + if t < down_time or t >= number_time_steps: + return Constraint.Skip + return (sum(start_up[self.mp_model.set_period[i]] for i in range(t - down_time + 1, t + 2)) <= + 1 - op_mode[self.mp_model.set_period[t - down_time+1]]) From 799ed0bfa6cc055ae5ee238de3f57c1e2d12ea0f Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Thu, 14 Sep 2023 14:02:15 -0400 Subject: [PATCH 023/126] linearized ramping constraints --- .../multiperiod/price_taker_model.py | 137 +++++++++++------- 1 file changed, 81 insertions(+), 56 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 750bd84d6c..2f8df607f2 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -334,12 +334,13 @@ def build_multiperiod_model(self, **kwargs): def add_ramping_constraints( self, op_blk, + design_blk, var, - var_opt_lb, - startup_limit = 100, - shutdown_limit = 100, - ramp_up_limit = 110, - ramp_down_limit = 110, + op_range_lb_percentage = 0.2, + startup_limit_percentage = 0.5, + shutdown_limit_percentage = 0.5, + ramp_up_limit = 0.8, + ramp_down_limit = 0.8, ): """ Adds ramping constraints of the form @@ -348,87 +349,103 @@ def add_ramping_constraints( Arguments: op_blk: The name of the operation model block, ex: ( "fs.op_name") - var: Name of the variable the ramping cosntraints will be applied to, ex: "total_power" - var_opt_lb: The name of the expression, or numerical value for the lower bound on the specified variable, - ex: "total_power_lower_bound" or 100 - startup_limit: The rate at which the specified variable changes as the system starts up - ex: 100 - shutdown_limit: The rate at which the specified varible changes as the system shutsdown - ex: 100 - ramp_up_limit: The rate at which the specified variable increases during operation - ex: 100 - ramp_down_limit = The rate at which the specified variable can decrease during operation - ex: 100 + design_blk: The name of the design model block, ex: ("m.design_name") + var: Name of the variable the ramping cosntraints will be applied to, ex: ("total_power") + op_range_lb_percentage: The percetage of the capacity that represents the lower operating bound (%) + startup_limit_percentage: The percentage of the capacity that variable var can increase at + during startup (%) + shutdown_limit_percentage: The percentage of the capacity that variable var can decrease at + during shutdown (%) + ramp_up_limit: The rate at which variable var can increases during operation, + a percentage of the capacity (%) + ramp_down_limit: = The rate at which variable var can decrease during operation, + a percentage of the capacity (%) Assumptions/relationship: + example: total_power_upper_bound >= ramp_up_limit >= startup_limit >= total_power_lower_bound > 0 total_power_upper_bound >= ramp_down_limit >= shutdown_limit >= total_power_lower_bound > 0 """ - + # Importing in the nessisary variables self.range_time_steps = RangeSet(len(self.mp_model.set_period)) start_up = {t: deepgetattr(self.mp_model.period[t], op_blk + ".startup") for t in self.mp_model.period} op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".op_mode") for t in self.mp_model.period} shut_down = {t: deepgetattr(self.mp_model.period[t], op_blk + ".shutdown") for t in self.mp_model.period} + aux_shutdown= {t: deepgetattr(self.mp_model.period[t], op_blk + ".aux_shutdown") for t in self.mp_model.period} + aux_startup= {t: deepgetattr(self.mp_model.period[t], op_blk + ".aux_startup") for t in self.mp_model.period} + aux_op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".aux_op_mode") for t in self.mp_model.period} + capacity = deepgetattr(self,design_blk + ".capacity" ) + capacity_ub = deepgetattr(self,design_blk + ".capacity.ub" ) var = {t: deepgetattr(self.mp_model.period[t], op_blk + "." + var) for t in self.mp_model.period} - if isinstance(var_opt_lb,str): - var_opt_lb_ = {t: deepgetattr(self.mp_model.period[t],op_blk + "." + var_opt_lb) for t in self.mp_model.period} - else: - var_opt_lb_ = var_opt_lb + #Creating the pyomo block blk_name = op_blk.split(".")[-1] + "_rampup_rampdown" setattr(self.mp_model, blk_name, Block()) blk = getattr(self.mp_model,blk_name) - if isinstance(var_opt_lb,str): - @blk.Constraint(self.range_time_steps) - def ramp_up_con(b,t): - if t == 1: - return Constraint.Skip - return ( - var[self.mp_model.set_period[t]] - var[self.mp_model.set_period[t-1]] <= - startup_limit * start_up[self.mp_model.set_period[t]] - var_opt_lb_[self.mp_model.set_period[t]] * start_up[self.mp_model.set_period[t-1]] - + ramp_up_limit * op_mode[self.mp_model.set_period[t]] - + var_opt_lb_[self.mp_model.set_period[t]]*(op_mode[self.mp_model.set_period[t]] - op_mode[self.mp_model.set_period[t-1]]) - ) + #Linearized constraints to conenct auxiliary vaiables to the design capacity variable + @blk.Constraint(self.range_time_steps) + def startup_mccor1(b,t): + return (aux_startup[self.mp_model.set_period[t]] >= capacity + start_up[self.mp_model.set_period[t]]*capacity_ub + - capacity_ub + ) + + @blk.Constraint(self.range_time_steps) + def startup_mccor2(b,t): + return (aux_startup[self.mp_model.set_period[t]] <= start_up[self.mp_model.set_period[t]]*capacity_ub) + - @blk.Constraint(self.range_time_steps) - def ramp_down_con(b,t): - if t == 1: - return Constraint.Skip - return ( - var[self.mp_model.set_period[t-1]]- var[self.mp_model.set_period[t]] <= - shutdown_limit * (start_up[self.mp_model.set_period[t-1]] + - op_mode[self.mp_model.set_period[t-1]] - op_mode[self.mp_model.set_period[t]]) - - var_opt_lb_[self.mp_model.set_period[t]] * start_up[self.mp_model.set_period[t]] - + ramp_down_limit * op_mode[self.mp_model.set_period[t]] - ) - else: - @blk.Constraint(self.range_time_steps) - def ramp_up_con(b,t): + @blk.Constraint(self.range_time_steps) + def shutdown_mccor1(b,t): + return (aux_shutdown[self.mp_model.set_period[t]] >= capacity + shut_down[self.mp_model.set_period[t]]*capacity_ub + - capacity_ub + ) + + @blk.Constraint(self.range_time_steps) + def shutdown_mccor2(b,t): + return (aux_shutdown[self.mp_model.set_period[t]] <= shut_down[self.mp_model.set_period[t]]*capacity_ub) + + + @blk.Constraint(self.range_time_steps) + def op_mode_mccor1(b,t): + return (aux_op_mode[self.mp_model.set_period[t]] >= capacity + op_mode[self.mp_model.set_period[t]]*capacity_ub + - capacity_ub + ) + + @blk.Constraint(self.range_time_steps) + def op_mode_mccor2(b,t): + return (aux_op_mode[self.mp_model.set_period[t]] <= op_mode[self.mp_model.set_period[t]]*capacity_ub) + + + # The linearized ramping constraints + @blk.Constraint(self.range_time_steps) + def ramp_up_con(b,t): if t == 1: return Constraint.Skip return ( var[self.mp_model.set_period[t]] - var[self.mp_model.set_period[t-1]] <= - startup_limit * start_up[self.mp_model.set_period[t]] - var_opt_lb_* start_up[self.mp_model.set_period[t-1]] - + ramp_up_limit * op_mode[self.mp_model.set_period[t]] - + var_opt_lb_*(op_mode[self.mp_model.set_period[t]] - op_mode[self.mp_model.set_period[t-1]]) + startup_limit_percentage * aux_startup[self.mp_model.set_period[t]] + - op_range_lb_percentage * aux_startup[self.mp_model.set_period[t-1]] + + (ramp_up_limit - op_range_lb_percentage) * aux_op_mode[self.mp_model.set_period[t]] + - op_range_lb_percentage * aux_op_mode[self.mp_model.set_period[t-1]] ) - @blk.Constraint(self.range_time_steps) - def ramp_down_con(b,t): + @blk.Constraint(self.range_time_steps) + def ramp_down_con(b,t): if t == 1: return Constraint.Skip return ( var[self.mp_model.set_period[t-1]]- var[self.mp_model.set_period[t]] <= - shutdown_limit * (start_up[self.mp_model.set_period[t-1]] + - op_mode[self.mp_model.set_period[t-1]] - op_mode[self.mp_model.set_period[t]]) - - var_opt_lb_* start_up[self.mp_model.set_period[t]] - + ramp_down_limit * op_mode[self.mp_model.set_period[t]] + (shutdown_limit_percentage - op_range_lb_percentage) * aux_shutdown[self.mp_model.set_period[t]] + - op_range_lb_percentage * aux_startup[self.mp_model.set_period[t]] + + op_range_lb_percentage * aux_startup[self.mp_model.set_period[t-1]] + + (ramp_down_limit - op_range_lb_percentage) * aux_op_mode[self.mp_model.set_period[t]] + + op_range_lb_percentage * aux_op_mode[self.mp_model.set_period[t-1]] ) - def add_startup_shutdown(self, op_blk, up_time = 1, down_time =1): + def add_startup_shutdown(self, op_blk,design_blk, build_binary_var, up_time = 1, down_time =1): """ Adds startup/shutdown and minimum uptime/downtime constraints on a given unit/process @@ -447,6 +464,8 @@ def add_startup_shutdown(self, op_blk, up_time = 1, down_time =1): start_up = {t: deepgetattr(self.mp_model.period[t], op_blk + ".startup") for t in self.mp_model.period} op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".op_mode") for t in self.mp_model.period} + shut_down = {t: deepgetattr(self.mp_model.period[t], op_blk + ".shutdown") for t in self.mp_model.period} + build = deepgetattr(self,design_blk + "." + build_binary_var) self.range_time_steps = RangeSet(len(self.mp_model.set_period)) number_time_steps = len(self.mp_model.set_period) @@ -454,6 +473,12 @@ def add_startup_shutdown(self, op_blk, up_time = 1, down_time =1): setattr(self.mp_model, blk_name, Block()) blk = getattr(self.mp_model,blk_name) + @blk.Constraint(self.range_time_steps) + def design_op_relationship(b,t): + return (start_up[self.mp_model.set_period[t]] + shut_down[self.mp_model.set_period[t]] + + op_mode[self.mp_model.set_period[t]] <= build + ) + @blk.Constraint(self.range_time_steps) def minimum_up_time_con(b,t): if t < up_time or t >= number_time_steps: From 7625d0757380b9a03d1233e4c7ac95dc7277aff0 Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Thu, 14 Sep 2023 14:02:50 -0400 Subject: [PATCH 024/126] Added auxiliary variables --- .../multiperiod/design_and_operation_models.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py index 54826fbbc7..582d19690f 100644 --- a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -15,6 +15,7 @@ Var, Binary, Expression, + NonNegativeReals, ) from idaes.core.base.process_base import declare_process_block_class @@ -131,6 +132,21 @@ def build(self): within=Binary, doc="Binary var: 1 if the shutdown is initiated, 0 otherwise", ) + self.aux_startup = Var( + within=NonNegativeReals, + doc= "Auxiliary variable for the product of startup and capacity" + ) + + self.aux_shutdown = Var( + within=NonNegativeReals, + doc= "Auxiliary variable for the product of shutdown and capacity" + ) + + self.aux_op_mode= Var( + within=NonNegativeReals, + doc="Auxiliary variable for the product of op_mode and capacity" + ) + self.config.model_func(self, **self.config.model_args) From 0c2c461c6a28af20efb1a27eb7331204cda6a027 Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Mon, 18 Sep 2023 13:19:42 -0400 Subject: [PATCH 025/126] added a third mccor constraint for each aux var and created a capacity argument for ramping func --- .../multiperiod/price_taker_model.py | 24 ++++++++++++++++--- 1 file changed, 21 insertions(+), 3 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 2f8df607f2..c6c2282499 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -336,6 +336,7 @@ def add_ramping_constraints( op_blk, design_blk, var, + design_variable, op_range_lb_percentage = 0.2, startup_limit_percentage = 0.5, shutdown_limit_percentage = 0.5, @@ -375,8 +376,8 @@ def add_ramping_constraints( aux_shutdown= {t: deepgetattr(self.mp_model.period[t], op_blk + ".aux_shutdown") for t in self.mp_model.period} aux_startup= {t: deepgetattr(self.mp_model.period[t], op_blk + ".aux_startup") for t in self.mp_model.period} aux_op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".aux_op_mode") for t in self.mp_model.period} - capacity = deepgetattr(self,design_blk + ".capacity" ) - capacity_ub = deepgetattr(self,design_blk + ".capacity.ub" ) + capacity = deepgetattr(self,design_blk + "." + design_variable ) + capacity_ub = deepgetattr(self,design_blk + "." + design_variable + ".ub" ) var = {t: deepgetattr(self.mp_model.period[t], op_blk + "." + var) for t in self.mp_model.period} #Creating the pyomo block @@ -396,6 +397,12 @@ def startup_mccor2(b,t): return (aux_startup[self.mp_model.set_period[t]] <= start_up[self.mp_model.set_period[t]]*capacity_ub) + @blk.Constraint(self.range_time_steps) + def startup_mccor3(b,t): + return (aux_startup[self.mp_model.set_period[t]] <= capacity + ) + + @blk.Constraint(self.range_time_steps) def shutdown_mccor1(b,t): return (aux_shutdown[self.mp_model.set_period[t]] >= capacity + shut_down[self.mp_model.set_period[t]]*capacity_ub @@ -406,7 +413,13 @@ def shutdown_mccor1(b,t): def shutdown_mccor2(b,t): return (aux_shutdown[self.mp_model.set_period[t]] <= shut_down[self.mp_model.set_period[t]]*capacity_ub) - + + @blk.Constraint(self.range_time_steps) + def shutdown_mccor3(b,t): + return (aux_shutdown[self.mp_model.set_period[t]] <= capacity + ) + + @blk.Constraint(self.range_time_steps) def op_mode_mccor1(b,t): return (aux_op_mode[self.mp_model.set_period[t]] >= capacity + op_mode[self.mp_model.set_period[t]]*capacity_ub @@ -418,6 +431,11 @@ def op_mode_mccor2(b,t): return (aux_op_mode[self.mp_model.set_period[t]] <= op_mode[self.mp_model.set_period[t]]*capacity_ub) + @blk.Constraint(self.range_time_steps) + def op_mode_mccor3(b,t): + return (aux_op_mode[self.mp_model.set_period[t]] <= capacity + ) + # The linearized ramping constraints @blk.Constraint(self.range_time_steps) def ramp_up_con(b,t): From 16710a6a01d85aedb944b8ef0bd8255dcf7834a3 Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Tue, 19 Sep 2023 11:42:30 -0400 Subject: [PATCH 026/126] changed startup_shutdown constraints to have the shutdown binary var --- .../multiperiod/price_taker_model.py | 20 ++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index c6c2282499..bf1b123d88 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -254,7 +254,7 @@ def append_lmp_data( for d in self.set_days: for t in self.set_time: - self.LMP[t, d, y] = lmp_data[d][t] + self.LMP[t, d, y] = lmp_data[d][t-1] self.WEIGHTS[d, y] = weights[0][d] return @@ -282,7 +282,7 @@ def append_lmp_data( self.set_years = None self.set_days = RangeSet(1,n_clusters) self._n_time_points = horizon_length if horizon_length is not None else 24 - self.set_time = RangeSet(self._n_time_points-1) + self.set_time = RangeSet(self._n_time_points) self.LMP = {} self.WEIGHTS = {} @@ -294,7 +294,7 @@ def append_lmp_data( for d in self.set_days: for t in self.set_time: - self.LMP[t, d] = lmp_data[d][t] + self.LMP[t, d] = lmp_data[d][t-1] self.WEIGHTS[d] = weights[0][d] return @@ -492,7 +492,7 @@ def add_startup_shutdown(self, op_blk,design_blk, build_binary_var, up_time = 1, blk = getattr(self.mp_model,blk_name) @blk.Constraint(self.range_time_steps) - def design_op_relationship(b,t): + def design_op_relationship_con(b,t): return (start_up[self.mp_model.set_period[t]] + shut_down[self.mp_model.set_period[t]] + op_mode[self.mp_model.set_period[t]] <= build ) @@ -507,10 +507,16 @@ def minimum_up_time_con(b,t): def minimum_down_time_con(b, t): if t < down_time or t >= number_time_steps: return Constraint.Skip - return (sum(start_up[self.mp_model.set_period[i]] for i in range(t - down_time + 1, t + 2)) <= - 1 - op_mode[self.mp_model.set_period[t - down_time+1]]) + return (sum(shut_down[self.mp_model.set_period[i]] for i in range(t - down_time + 1, t + 2)) <= + 1 - op_mode[self.mp_model.set_period[t+1]]) - + @blk.Constraint(self.range_time_steps) + def Binary_relationhsip_con(b, t): + if t >= number_time_steps: + return Constraint.Skip + return (op_mode[self.mp_model.set_period[t+1]] - op_mode[self.mp_model.set_period[t]] == + start_up[self.mp_model.set_period[t]] - shut_down[self.mp_model.set_period[t+1]] + ) def build_hourly_cashflows(self): period = self.mp_model.period From 4518240785cad3fc8ca8862f648074cd30b6354f Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Tue, 19 Sep 2023 14:03:53 -0400 Subject: [PATCH 027/126] changed rule for min_start_up constraint --- idaes/apps/grid_integration/multiperiod/price_taker_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index bf1b123d88..ad5aa883d6 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -499,7 +499,7 @@ def design_op_relationship_con(b,t): @blk.Constraint(self.range_time_steps) def minimum_up_time_con(b,t): - if t < up_time or t >= number_time_steps: + if t < up_time-1 or t == 1 or t >= number_time_steps: return Constraint.Skip return sum(start_up[self.mp_model.set_period[i]] for i in range(t-up_time+2,t+1)) <= op_mode[self.mp_model.set_period[t+1]] From 35ddeadac82083575b3915f1f9bc721c48cbcb37 Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Wed, 20 Sep 2023 12:01:34 -0400 Subject: [PATCH 028/126] fixed startup_shutdown function constraints, changed default values for ramping function --- .../multiperiod/price_taker_model.py | 27 ++++++++++--------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index ad5aa883d6..a150d5e785 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -337,11 +337,11 @@ def add_ramping_constraints( design_blk, var, design_variable, - op_range_lb_percentage = 0.2, - startup_limit_percentage = 0.5, - shutdown_limit_percentage = 0.5, - ramp_up_limit = 0.8, - ramp_down_limit = 0.8, + op_range_lb_percentage = 0.6, + startup_limit_percentage = 0.7, + shutdown_limit_percentage = 0.7, + ramp_up_limit = 0.7, + ramp_down_limit = 0.7, ): """ Adds ramping constraints of the form @@ -499,23 +499,24 @@ def design_op_relationship_con(b,t): @blk.Constraint(self.range_time_steps) def minimum_up_time_con(b,t): - if t < up_time-1 or t == 1 or t >= number_time_steps: + if t == 1 or t < up_time or t > number_time_steps: return Constraint.Skip - return sum(start_up[self.mp_model.set_period[i]] for i in range(t-up_time+2,t+1)) <= op_mode[self.mp_model.set_period[t+1]] + else: + return sum(start_up[self.mp_model.set_period[i]] for i in range(t-up_time+1,t+1)) <= op_mode[self.mp_model.set_period[t]] @blk.Constraint(self.range_time_steps) def minimum_down_time_con(b, t): - if t < down_time or t >= number_time_steps: + if t <= down_time or t == 1 or t > number_time_steps: return Constraint.Skip - return (sum(shut_down[self.mp_model.set_period[i]] for i in range(t - down_time + 1, t + 2)) <= - 1 - op_mode[self.mp_model.set_period[t+1]]) + return (sum(shut_down[self.mp_model.set_period[i]] for i in range(t - down_time , t + 1)) <= + 1 - op_mode[self.mp_model.set_period[t]]) @blk.Constraint(self.range_time_steps) def Binary_relationhsip_con(b, t): - if t >= number_time_steps: + if t > number_time_steps or t == 1: return Constraint.Skip - return (op_mode[self.mp_model.set_period[t+1]] - op_mode[self.mp_model.set_period[t]] == - start_up[self.mp_model.set_period[t]] - shut_down[self.mp_model.set_period[t+1]] + return (op_mode[self.mp_model.set_period[t]] - op_mode[self.mp_model.set_period[t-1]] == + start_up[self.mp_model.set_period[t-1]] - shut_down[self.mp_model.set_period[t]] ) def build_hourly_cashflows(self): From fd15af9c2c4566fbca9a200485a6277659434746 Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Thu, 21 Sep 2023 10:41:13 -0400 Subject: [PATCH 029/126] added new method for adding aux variables --- .../multiperiod/design_and_operation_models.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py index 582d19690f..0e00d17fa4 100644 --- a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -166,3 +166,14 @@ def build(self): ) print(f"setting {ce} to zero in {self.name}") setattr(self, ce, Expression(expr=0, doc=cost_expr_list[ce])) + if self.config.capacity_var is not None: + self._add_capacity_aux_vars() + + def _add_capacity_aux_vars(self): + v1 = self.config.capacity_var.name.split(".")[-1] + "_op_mode" + v2 = self.config.capacity_var.name.split(".")[-1] + "_startup" + v3 = self.config.capacity_var.name.split(".")[-1] + "_shutdown" + + setattr(self,Var(),v1) + setattr(self,Var(),v2) + setattr(self,Var(),v3) \ No newline at end of file From b84ea4312d82f5029407716922826497f99c7767 Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Thu, 21 Sep 2023 10:43:26 -0400 Subject: [PATCH 030/126] removed aux variable delcaration and added a new config option to opt blk --- .../design_and_operation_models.py | 25 +++++++------------ 1 file changed, 9 insertions(+), 16 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py index 0e00d17fa4..b274a216b7 100644 --- a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -112,6 +112,14 @@ class OperationModelData(SkeletonUnitModelData): ), ) + CONFIG.declare( + "capacity_var", + ConfigValue( + default = None, + doc = "capacity variable" + + ), + ) # noinspection PyAttributeOutsideInit def build(self): super().build() @@ -132,22 +140,7 @@ def build(self): within=Binary, doc="Binary var: 1 if the shutdown is initiated, 0 otherwise", ) - self.aux_startup = Var( - within=NonNegativeReals, - doc= "Auxiliary variable for the product of startup and capacity" - ) - - self.aux_shutdown = Var( - within=NonNegativeReals, - doc= "Auxiliary variable for the product of shutdown and capacity" - ) - - self.aux_op_mode= Var( - within=NonNegativeReals, - doc="Auxiliary variable for the product of op_mode and capacity" - ) - - + self.config.model_func(self, **self.config.model_args) cost_expr_list = { From 15ea64122434d95a10970cf151a470c6d2bc1d2f Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Thu, 21 Sep 2023 10:46:30 -0400 Subject: [PATCH 031/126] added comments --- .../grid_integration/multiperiod/price_taker_model.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index a150d5e785..3c7765b676 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -368,6 +368,17 @@ def add_ramping_constraints( total_power_upper_bound >= ramp_up_limit >= startup_limit >= total_power_lower_bound > 0 total_power_upper_bound >= ramp_down_limit >= shutdown_limit >= total_power_lower_bound > 0 """ + + + # if constraint = "linear": + # if capacity_var is either a Parameter or ocsntant.... + #act_startup_rate = capacity* startup_rate* startup + # else: + # if hasattr(m,capacity_startup) == False: + # op_blk._add_capacity_aux_var + # else: + # act_startup_rate = capacity_startup * startup_rate + # The linearized ramping constraints # Importing in the nessisary variables self.range_time_steps = RangeSet(len(self.mp_model.set_period)) start_up = {t: deepgetattr(self.mp_model.period[t], op_blk + ".startup") for t in self.mp_model.period} From 53e427e9d781096c14e2517919d4f30f263621f1 Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Thu, 21 Sep 2023 11:25:43 -0400 Subject: [PATCH 032/126] small addition --- .../design_and_operation_models.py | 38 +++++++++++++++---- 1 file changed, 31 insertions(+), 7 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py index b274a216b7..4e38a85130 100644 --- a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -52,10 +52,23 @@ class DesignModelData(SkeletonUnitModelData): ), ) + CONFIG.declare( + "declare_build_vars", + ConfigValue( + default=True, + domain=In([True, False]), + doc="Should build_unit be declared/defined?", + ), + ) + # noinspection PyAttributeOutsideInit def build(self): super().build() - + if self.config.declare_op_vars: + self.build_unit = Var( + within=Binary, + doc="Binary var: 1 if we choose to build the unit, 0 otherwise", + ) # Build the model self.config.model_func(self, **self.config.model_args) @@ -95,6 +108,14 @@ class OperationModelData(SkeletonUnitModelData): doc="Dictionary containing arguments needed for model_func", ), ) + + CONFIG.declare( + "design_blk", + ConfigValue( + default={}, + doc="Dictionary containing arguments needed for model_func", + ), + ) CONFIG.declare( "declare_cost_expr", ConfigValue( @@ -163,10 +184,13 @@ def build(self): self._add_capacity_aux_vars() def _add_capacity_aux_vars(self): - v1 = self.config.capacity_var.name.split(".")[-1] + "_op_mode" - v2 = self.config.capacity_var.name.split(".")[-1] + "_startup" - v3 = self.config.capacity_var.name.split(".")[-1] + "_shutdown" + aux_op_mode = self.config.capacity_var.name.split(".")[-1] + "_op_mode" + aux_startup = self.config.capacity_var.name.split(".")[-1] + "_startup" + aux_shutdown = self.config.capacity_var.name.split(".")[-1] + "_shutdown" + + setattr(self,Var(),aux_op_mode) + setattr(self,Var(),aux_startup) + setattr(self,Var(),aux_shutdown) + for b in self.config.model_args: + if isinstance(b,) - setattr(self,Var(),v1) - setattr(self,Var(),v2) - setattr(self,Var(),v3) \ No newline at end of file From 14ee5447c4fe50697006d2c0f7f5c49b12d4162c Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Thu, 21 Sep 2023 12:05:40 -0400 Subject: [PATCH 033/126] added design_blk --- .../multiperiod/design_and_operation_models.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py index 4e38a85130..4280f8e13d 100644 --- a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -192,5 +192,7 @@ def _add_capacity_aux_vars(self): setattr(self,Var(),aux_startup) setattr(self,Var(),aux_shutdown) for b in self.config.model_args: - if isinstance(b,) + if isinstance(b,DesignModelData): + design_blk = b + From 4e10801c5ead3e1fb0c8bab4a1a0209a2606432e Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Thu, 21 Sep 2023 12:32:09 -0400 Subject: [PATCH 034/126] added deepgetattr and constraint for the add capacity aux var func --- .../multiperiod/design_and_operation_models.py | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py index 4280f8e13d..30fa4ede8b 100644 --- a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -16,13 +16,15 @@ Binary, Expression, NonNegativeReals, + Constraint, ) - +from functools import reduce from idaes.core.base.process_base import declare_process_block_class from idaes.models.unit_models import SkeletonUnitModelData from pyomo.common.config import ConfigValue, In - +def deepgetattr(obj, attr): + return reduce(getattr, attr.split('.'), obj) @declare_process_block_class("DesignModel") class DesignModelData(SkeletonUnitModelData): """ @@ -187,7 +189,7 @@ def _add_capacity_aux_vars(self): aux_op_mode = self.config.capacity_var.name.split(".")[-1] + "_op_mode" aux_startup = self.config.capacity_var.name.split(".")[-1] + "_startup" aux_shutdown = self.config.capacity_var.name.split(".")[-1] + "_shutdown" - + setattr(self,Var(),aux_op_mode) setattr(self,Var(),aux_startup) setattr(self,Var(),aux_shutdown) @@ -195,4 +197,13 @@ def _add_capacity_aux_vars(self): if isinstance(b,DesignModelData): design_blk = b + capacity = deepgetattr(design_blk,self.config.capacity_var.name) + capacity_ub = deepgetattr(design_blk,self.config.capacity_var.name + ".ub") + capacity_lb = deepgetattr(design_blk,self.config.capacity_var.name + ".lb") + + self.Constraint( + expr = (design_blk.build_unit - self.op_mode - self.startup - self.shutdown)*capacity_lb <= + capacity - aux_op_mode - aux_startup - aux_shutdown + ) + From d68d8fd84d2e4a1d85fb2254f41ac27a67b9e30d Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Fri, 22 Sep 2023 16:56:26 -0400 Subject: [PATCH 035/126] finished the new method and ramping function --- .../design_and_operation_models.py | 77 +++++++-- .../multiperiod/price_taker_model.py | 156 +++++++----------- 2 files changed, 119 insertions(+), 114 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py index 30fa4ede8b..e6a22e0552 100644 --- a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -66,7 +66,9 @@ class DesignModelData(SkeletonUnitModelData): # noinspection PyAttributeOutsideInit def build(self): super().build() - if self.config.declare_op_vars: + + + if self.config.declare_build_vars: self.build_unit = Var( within=Binary, doc="Binary var: 1 if we choose to build the unit, 0 otherwise", @@ -182,28 +184,67 @@ def build(self): ) print(f"setting {ce} to zero in {self.name}") setattr(self, ce, Expression(expr=0, doc=cost_expr_list[ce])) - if self.config.capacity_var is not None: - self._add_capacity_aux_vars() + # if isisntance(self.config.capacity_var, str): + # if isinstance(self.config.capacity_var,) + # self._add_capacity_aux_vars() def _add_capacity_aux_vars(self): - aux_op_mode = self.config.capacity_var.name.split(".")[-1] + "_op_mode" - aux_startup = self.config.capacity_var.name.split(".")[-1] + "_startup" - aux_shutdown = self.config.capacity_var.name.split(".")[-1] + "_shutdown" + aux_op_mode = self.config.capacity_var.split(".")[-1] + "_op_mode" + aux_startup = self.config.capacity_var.split(".")[-1] + "_startup" + aux_shutdown = self.config.capacity_var.split(".")[-1] + "_shutdown" - setattr(self,Var(),aux_op_mode) - setattr(self,Var(),aux_startup) - setattr(self,Var(),aux_shutdown) - for b in self.config.model_args: - if isinstance(b,DesignModelData): - design_blk = b + setattr(self,aux_op_mode, Var()) + setattr(self,aux_startup,Var()) + setattr(self,aux_shutdown, Var()) - capacity = deepgetattr(design_blk,self.config.capacity_var.name) - capacity_ub = deepgetattr(design_blk,self.config.capacity_var.name + ".ub") - capacity_lb = deepgetattr(design_blk,self.config.capacity_var.name + ".lb") + aux_var_op_mode = getattr(self,aux_op_mode) + aux_var_startup = getattr(self,aux_startup) + aux_var_shutdown = getattr(self,aux_shutdown) + for blk in self.config.model_args: + if isinstance(self.config.model_args[blk],DesignModelData): + design_blk = self.config.model_args[blk] - self.Constraint( + capacity = deepgetattr(design_blk,self.config.capacity_var) + capacity_ub = deepgetattr(design_blk,self.config.capacity_var + ".ub") + capacity_lb = deepgetattr(design_blk,self.config.capacity_var + ".lb") + + setattr(self, self.config.capacity_var.split(".")[-1] + "_linearization_con1", + Constraint( expr = (design_blk.build_unit - self.op_mode - self.startup - self.shutdown)*capacity_lb <= - capacity - aux_op_mode - aux_startup - aux_shutdown - ) + capacity - aux_var_op_mode - aux_var_startup - aux_var_shutdown + )) + + setattr(self, self.config.capacity_var.split(".")[-1] + "_linearization_con2", + Constraint( + expr = (design_blk.build_unit - self.op_mode - self.startup - self.shutdown)*capacity_ub >= + capacity - aux_var_op_mode - aux_var_startup - aux_var_shutdown + )) + + setattr(self,self.config.capacity_var.split(".")[-1] + "_linearization_con3", + Constraint( + expr = ( self.op_mode*capacity_lb <= aux_var_op_mode ))) + + setattr(self, self.config.capacity_var.split(".")[-1] + "_linearization_con4", + Constraint( + expr = ( self.op_mode*capacity_ub >= aux_var_op_mode ))) + + setattr(self, self.config.capacity_var.split(".")[-1] + "_linearization_con5", + Constraint( + expr = ( self.startup*capacity_lb <= aux_var_startup ))) + + setattr(self,self.config.capacity_var.split(".")[-1] + "_linearization_con6", + Constraint( + expr = ( self.startup*capacity_ub >= aux_var_startup ))) + + setattr(self, self.config.capacity_var.split(".")[-1] + "_linearization_con7", + Constraint( + expr = ( self.shutdown*capacity_lb <= aux_var_shutdown))) + + setattr(self, self.config.capacity_var.split(".")[-1] + "_linearization_con8", + Constraint( + expr = ( self.shutdown*capacity_ub >= aux_var_shutdown ))) + + + diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 3c7765b676..47df771303 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -27,6 +27,7 @@ NonNegativeReals, Expression, maximize, + Param, ) from idaes.apps.grid_integration import MultiPeriodModel @@ -335,13 +336,15 @@ def add_ramping_constraints( self, op_blk, design_blk, - var, - design_variable, - op_range_lb_percentage = 0.6, - startup_limit_percentage = 0.7, - shutdown_limit_percentage = 0.7, - ramp_up_limit = 0.7, - ramp_down_limit = 0.7, + capacity_var, + ramping_var, + constraint_type, + linearization = True, + op_range_lb = 0.6, + startup_rate = 0.7, + shutdown_rate= 0.7, + ramp_up_rate = 0.7, + ramp_down_rate = 0.7, ): """ Adds ramping constraints of the form @@ -368,110 +371,71 @@ def add_ramping_constraints( total_power_upper_bound >= ramp_up_limit >= startup_limit >= total_power_lower_bound > 0 total_power_upper_bound >= ramp_down_limit >= shutdown_limit >= total_power_lower_bound > 0 """ - - - # if constraint = "linear": - # if capacity_var is either a Parameter or ocsntant.... - #act_startup_rate = capacity* startup_rate* startup - # else: - # if hasattr(m,capacity_startup) == False: - # op_blk._add_capacity_aux_var - # else: - # act_startup_rate = capacity_startup * startup_rate - # The linearized ramping constraints - # Importing in the nessisary variables - self.range_time_steps = RangeSet(len(self.mp_model.set_period)) start_up = {t: deepgetattr(self.mp_model.period[t], op_blk + ".startup") for t in self.mp_model.period} op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".op_mode") for t in self.mp_model.period} shut_down = {t: deepgetattr(self.mp_model.period[t], op_blk + ".shutdown") for t in self.mp_model.period} - aux_shutdown= {t: deepgetattr(self.mp_model.period[t], op_blk + ".aux_shutdown") for t in self.mp_model.period} - aux_startup= {t: deepgetattr(self.mp_model.period[t], op_blk + ".aux_startup") for t in self.mp_model.period} - aux_op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".aux_op_mode") for t in self.mp_model.period} - capacity = deepgetattr(self,design_blk + "." + design_variable ) - capacity_ub = deepgetattr(self,design_blk + "." + design_variable + ".ub" ) - var = {t: deepgetattr(self.mp_model.period[t], op_blk + "." + var) for t in self.mp_model.period} + var_ramping = {t: deepgetattr(self.mp_model.period[t], op_blk + "." + ramping_var) for t in self.mp_model.period} + if constraint_type == "linear": + if isinstance(capacity_var,str): + var_capacity = deepgetattr(self, design_blk + "." + capacity_var) + act_startup_rate = {t: var_capacity * start_up[t] for t in self.mp_model.period} + act_shutdown_rate = {t:var_capacity * shut_down[t] for t in self.mp_model.period} + act_op_mode_rate = {t:var_capacity * op_mode[t] for t in self.mp_model.period} + if isinstance(capacity_var, (int,float)): + act_startup_rate = {t: capacity_var * start_up[t] for t in self.mp_model.period} + act_shutdown_rate = {t:capacity_var * shut_down[t]for t in self.mp_model.period} + act_op_mode_rate = {t:capacity_var * op_mode[t] for t in self.mp_model.period} + elif constraint_type == "nonlinear": + if linearization == True: + # if hasattr(self,"capacity_startup") == False: + # blk = deepgetattr(self.mp_model.period[t],op_blk) + # blk._add_capacity_aux_vars() + + act_startup_rate= {t: deepgetattr(self.mp_model.period[t], op_blk + "." + capacity_var + "_startup") for t in self.mp_model.period} + act_shutdown_rate= {t: deepgetattr(self.mp_model.period[t], op_blk + "." + capacity_var + "_shutdown") for t in self.mp_model.period} + act_op_mode_rate = {t: deepgetattr(self.mp_model.period[t], op_blk + "." + capacity_var + "_op_mode") for t in self.mp_model.period} + elif linearization == False: + var_capacity = deepgetattr(self, design_blk + "." + capacity_var) + act_startup_rate = {t: var_capacity * start_up[t] for t in self.mp_model.period} + act_shutdown_rate = {t:var_capacity * shut_down[t] for t in self.mp_model.period} + act_op_mode_rate = {t:var_capacity * op_mode[t] for t in self.mp_model.period} + + + + + # Importing in the nessisary variables + self.range_time_steps = RangeSet(len(self.mp_model.set_period)) #Creating the pyomo block blk_name = op_blk.split(".")[-1] + "_rampup_rampdown" setattr(self.mp_model, blk_name, Block()) blk = getattr(self.mp_model,blk_name) - - #Linearized constraints to conenct auxiliary vaiables to the design capacity variable - @blk.Constraint(self.range_time_steps) - def startup_mccor1(b,t): - return (aux_startup[self.mp_model.set_period[t]] >= capacity + start_up[self.mp_model.set_period[t]]*capacity_ub - - capacity_ub - ) - - @blk.Constraint(self.range_time_steps) - def startup_mccor2(b,t): - return (aux_startup[self.mp_model.set_period[t]] <= start_up[self.mp_model.set_period[t]]*capacity_ub) - - - @blk.Constraint(self.range_time_steps) - def startup_mccor3(b,t): - return (aux_startup[self.mp_model.set_period[t]] <= capacity - ) - - - @blk.Constraint(self.range_time_steps) - def shutdown_mccor1(b,t): - return (aux_shutdown[self.mp_model.set_period[t]] >= capacity + shut_down[self.mp_model.set_period[t]]*capacity_ub - - capacity_ub - ) - - @blk.Constraint(self.range_time_steps) - def shutdown_mccor2(b,t): - return (aux_shutdown[self.mp_model.set_period[t]] <= shut_down[self.mp_model.set_period[t]]*capacity_ub) - - - @blk.Constraint(self.range_time_steps) - def shutdown_mccor3(b,t): - return (aux_shutdown[self.mp_model.set_period[t]] <= capacity - ) - - - @blk.Constraint(self.range_time_steps) - def op_mode_mccor1(b,t): - return (aux_op_mode[self.mp_model.set_period[t]] >= capacity + op_mode[self.mp_model.set_period[t]]*capacity_ub - - capacity_ub - ) - - @blk.Constraint(self.range_time_steps) - def op_mode_mccor2(b,t): - return (aux_op_mode[self.mp_model.set_period[t]] <= op_mode[self.mp_model.set_period[t]]*capacity_ub) - - - @blk.Constraint(self.range_time_steps) - def op_mode_mccor3(b,t): - return (aux_op_mode[self.mp_model.set_period[t]] <= capacity - ) - # The linearized ramping constraints @blk.Constraint(self.range_time_steps) def ramp_up_con(b,t): - if t == 1: - return Constraint.Skip - return ( - var[self.mp_model.set_period[t]] - var[self.mp_model.set_period[t-1]] <= - startup_limit_percentage * aux_startup[self.mp_model.set_period[t]] - - op_range_lb_percentage * aux_startup[self.mp_model.set_period[t-1]] - + (ramp_up_limit - op_range_lb_percentage) * aux_op_mode[self.mp_model.set_period[t]] - - op_range_lb_percentage * aux_op_mode[self.mp_model.set_period[t-1]] - ) + if t == 1: + return Constraint.Skip + else: + return (var_ramping[self.mp_model.set_period[t]] - var_ramping[self.mp_model.set_period[t-1]] <= + startup_rate * act_startup_rate[self.mp_model.set_period[t]] + - op_range_lb * act_startup_rate[self.mp_model.set_period[t-1]] + + (ramp_up_rate - op_range_lb) * act_op_mode_rate[self.mp_model.set_period[t]] + - op_range_lb * act_op_mode_rate[self.mp_model.set_period[t-1]] + ) @blk.Constraint(self.range_time_steps) def ramp_down_con(b,t): - if t == 1: - return Constraint.Skip + if t == 1: + return Constraint.Skip + else: return ( - var[self.mp_model.set_period[t-1]]- var[self.mp_model.set_period[t]] <= - (shutdown_limit_percentage - op_range_lb_percentage) * aux_shutdown[self.mp_model.set_period[t]] - - op_range_lb_percentage * aux_startup[self.mp_model.set_period[t]] - + op_range_lb_percentage * aux_startup[self.mp_model.set_period[t-1]] - + (ramp_down_limit - op_range_lb_percentage) * aux_op_mode[self.mp_model.set_period[t]] - + op_range_lb_percentage * aux_op_mode[self.mp_model.set_period[t-1]] - ) + var_ramping[self.mp_model.set_period[t-1]]- var_ramping[self.mp_model.set_period[t]] <= + (shutdown_rate - op_range_lb) * act_shutdown_rate[self.mp_model.set_period[t]] + - op_range_lb * act_startup_rate[self.mp_model.set_period[t]] + + op_range_lb * act_startup_rate[self.mp_model.set_period[t-1]] + + (ramp_down_rate - op_range_lb) * act_op_mode_rate[self.mp_model.set_period[t]] + + op_range_lb * act_op_mode_rate[self.mp_model.set_period[t-1]] + ) def add_startup_shutdown(self, op_blk,design_blk, build_binary_var, up_time = 1, down_time =1): From 6cd966e1c324c694faca377e5cc6931865660a0a Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Wed, 4 Oct 2023 11:07:13 -0400 Subject: [PATCH 036/126] Allowed _add_capacity_aux_vars to be cosntructed when the build function gets called --- .../multiperiod/design_and_operation_models.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py index e6a22e0552..6f4ecd61a5 100644 --- a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -184,9 +184,8 @@ def build(self): ) print(f"setting {ce} to zero in {self.name}") setattr(self, ce, Expression(expr=0, doc=cost_expr_list[ce])) - # if isisntance(self.config.capacity_var, str): - # if isinstance(self.config.capacity_var,) - # self._add_capacity_aux_vars() + if isinstance(self.config.capacity_var, str): + self._add_capacity_aux_vars() def _add_capacity_aux_vars(self): aux_op_mode = self.config.capacity_var.split(".")[-1] + "_op_mode" From f7e3245ed1508a6574f151e821375cbae26021a9 Mon Sep 17 00:00:00 2001 From: Tyler Jaffe Date: Wed, 4 Oct 2023 11:08:18 -0400 Subject: [PATCH 037/126] added a for loop that calls on the _add_capacity_aux_vars function t times --- .../apps/grid_integration/multiperiod/price_taker_model.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 47df771303..f6439bb196 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -387,9 +387,10 @@ def add_ramping_constraints( act_op_mode_rate = {t:capacity_var * op_mode[t] for t in self.mp_model.period} elif constraint_type == "nonlinear": if linearization == True: - # if hasattr(self,"capacity_startup") == False: - # blk = deepgetattr(self.mp_model.period[t],op_blk) - # blk._add_capacity_aux_vars() + if hasattr(self,"capacity_startup") == False: + for t in self.mp_model.period: + blk = deepgetattr(self.mp_model.period[t],op_blk) + blk._add_capacity_aux_vars() act_startup_rate= {t: deepgetattr(self.mp_model.period[t], op_blk + "." + capacity_var + "_startup") for t in self.mp_model.period} act_shutdown_rate= {t: deepgetattr(self.mp_model.period[t], op_blk + "." + capacity_var + "_shutdown") for t in self.mp_model.period} From 56f80999fb28ba4b5a81b3ed1ae10784a78eb0e7 Mon Sep 17 00:00:00 2001 From: Daniel Laky Date: Tue, 31 Oct 2023 21:28:57 -0400 Subject: [PATCH 038/126] Updating PT model for SOFC case study --- .../multiperiod/price_taker_model.py | 37 +++++++++++++------ 1 file changed, 25 insertions(+), 12 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index f6439bb196..19228a56aa 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -14,6 +14,7 @@ import numpy as np from functools import reduce from operator import attrgetter +import os.path from importlib import resources from pathlib import Path @@ -231,10 +232,17 @@ def append_lmp_data( n_clusters=None, horizon_length=None, ): - with resources.path(file_path, file_name) as p: - path_to_file = Path(p).resolve() - - full_data = pd.read_excel( path_to_file, sheet_name=[sheet])[sheet] + # with resources.path(file_path, file_name) as p: + # path_to_file = Path(p).resolve() + if os.path.exists(file_path): + path_to_file = file_path + else: + raise ValueError() + + if '.xls' in path_to_file[-5:]: + full_data = pd.read_excel( path_to_file, sheet_name=[sheet])[sheet] + elif '.csv' in path_to_file[-5:]: + full_data = pd.read_csv(path_to_file, ) # editing the data if isinstance(column_name, list) and n_clusters is not None: # Multiple years and representative days @@ -304,10 +312,11 @@ def append_lmp_data( # Single price signal, use full year's price signal self.set_years = None self.set_days = None - self._n_time_points = len(full_data) + # self._n_time_points = len(full_data) + self._n_time_points = 48 self.set_time = RangeSet(self._n_time_points) - price_data = full_data[column_name].to_list() + price_data = full_data[column_name].to_list()[:48] self.LMP = {t: price_data[t - 1] for t in self.set_time} return @@ -459,7 +468,10 @@ def add_startup_shutdown(self, op_blk,design_blk, build_binary_var, up_time = 1, start_up = {t: deepgetattr(self.mp_model.period[t], op_blk + ".startup") for t in self.mp_model.period} op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".op_mode") for t in self.mp_model.period} shut_down = {t: deepgetattr(self.mp_model.period[t], op_blk + ".shutdown") for t in self.mp_model.period} - build = deepgetattr(self,design_blk + "." + build_binary_var) + + if design_blk is not None: + build = deepgetattr(self,design_blk + "." + build_binary_var) + self.range_time_steps = RangeSet(len(self.mp_model.set_period)) number_time_steps = len(self.mp_model.set_period) @@ -467,11 +479,12 @@ def add_startup_shutdown(self, op_blk,design_blk, build_binary_var, up_time = 1, setattr(self.mp_model, blk_name, Block()) blk = getattr(self.mp_model,blk_name) - @blk.Constraint(self.range_time_steps) - def design_op_relationship_con(b,t): - return (start_up[self.mp_model.set_period[t]] + shut_down[self.mp_model.set_period[t]] + - op_mode[self.mp_model.set_period[t]] <= build - ) + if design_blk is not None: + @blk.Constraint(self.range_time_steps) + def design_op_relationship_con(b, t): + return (start_up[self.mp_model.set_period[t]] + shut_down[self.mp_model.set_period[t]] + + op_mode[self.mp_model.set_period[t]] <= build + ) @blk.Constraint(self.range_time_steps) def minimum_up_time_con(b,t): From 48d4d981ed03c046e89e9d1e3f512ddbbee85494 Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Thu, 15 Feb 2024 11:46:27 -0500 Subject: [PATCH 039/126] Updated price_taker_model.py doc Removed some unused variables. Beautified some of the code. Made all documentation for functions consistent and more useful. Added a few ToDo's for new functionality to be added to the class. --- .../multiperiod/price_taker_model.py | 216 +++++++++++++----- 1 file changed, 159 insertions(+), 57 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 19228a56aa..6c7456126b 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -100,6 +100,7 @@ def reconfigure_raw_data(self, raw_data): Returns: daily_data: reconfigured price series data + scenarios: ??? Add description ??? """ # Get column headings @@ -118,19 +119,25 @@ def reconfigure_raw_data(self, raw_data): return daily_data, scenarios - def get_optimal_n_clusters(self, daily_data, kmin=None, kmax=None, plot=False): + def get_optimal_n_clusters( + self, + daily_data, + kmin=None, + kmax=None, + plot=False, + ): """ Determines the appropriate number of clusters needed for a given price signal. Args: daily_data: reconfigured price series data - kmin: minimum number of clusters - kmax: maximum number of clusters - plot: flag to determine if an elbow plot should be displayed + kmin: minimum number of clusters + kmax: maximum number of clusters + plot: flag to determine if an elbow plot should be displayed Returns: - n_clusters: the optimal number of clusters for the given data + n_clusters: the optimal number of clusters for the given data inertia_values: within-cluster sum-of-squares """ if kmin is None: @@ -176,19 +183,26 @@ def get_optimal_n_clusters(self, daily_data, kmin=None, kmax=None, plot=False): return n_clusters, inertia_values - def cluster_lmp_data(self, raw_data, n_clusters, scenarios): + def cluster_lmp_data(self, raw_data, n_clusters): """ Clusters the given price signal in n_clusters. This method supports k-means, k-meteiod,... techniques for clustering. Args: + raw_data: imported price series data + n_clusters: number of clusters desired for the data (representative days) Returns: - lmp_data = {1: {1: 2, 2: 3, 3: 5}, 2: {1: 2, 2: 3, 3: 5}} - weights = {1: 45, 2: 56} + lmp_data: dict of representative day LMP data, indices are indexed + by integers starting at 1 (ex: {1: {1: 4, 2: 3, 3: 5}, + 2: {1: 1, 2: 7, 3: 3}}) + Format: {day: {time_period: LMP}} + weights: dict of weights for each representative day, indexed the + same way as lmp_data (ex: {1: 45, 2: 56}) + Format: {day: weight} """ # reconfiguring raw data - daily_data,scenarios = self.reconfigure_raw_data(raw_data) + daily_data, scenarios = self.reconfigure_raw_data(raw_data) # KMeans clustering with the optimal number of clusters kmeans = KMeans(n_clusters=n_clusters).fit(daily_data.transpose()) @@ -226,14 +240,31 @@ def cluster_lmp_data(self, raw_data, n_clusters, scenarios): def append_lmp_data( self, file_path, - file_name, - sheet = None, - column_name="price", + sheet=None, + column_name=None, n_clusters=None, horizon_length=None, ): - # with resources.path(file_path, file_name) as p: - # path_to_file = Path(p).resolve() + """ + This function appends LMP data to the PriceTakerModel using single + or multiple year signals, as well as using full price data or + clustering to use representative days. + + Args: + file_path: path to the file containing LMP data + sheet: if the file is in axcel file, the sheet name + where the LMP data is located - (default: None) + column_name: list of integer year names if multiple years of + data are to be used, None otherwise - (default: None) + n_clusters: number of clusters for the data (representative days) + None if representative days not used - (default: None) + horizon_length: if a value is given, this will be used to set the + horizon_length attribute of the PriceTakerModel. + (default: None --> use existing horizon_length (default: 24)) + + Returns: + + """ if os.path.exists(file_path): path_to_file = file_path else: @@ -243,12 +274,16 @@ def append_lmp_data( full_data = pd.read_excel( path_to_file, sheet_name=[sheet])[sheet] elif '.csv' in path_to_file[-5:]: full_data = pd.read_csv(path_to_file, ) + + if horizon_length is not None: + self.horizon_length(horizon_length) + # editing the data if isinstance(column_name, list) and n_clusters is not None: # Multiple years and representative days self.set_years = [int(y) for y in column_name] self.set_days = RangeSet(1,n_clusters) - self._n_time_points = horizon_length if horizon_length is not None else 24 + self._n_time_points = self.horizon_length if self.horizon_length is not None else 24 self.set_time = RangeSet(self._n_time_points) self.LMP = {} @@ -257,7 +292,7 @@ def append_lmp_data( for year in column_name: price_data = full_data[year] lmp_data, weights = self.cluster_lmp_data( - price_data, n_clusters, horizon_length + price_data, n_clusters ) y = int(year) @@ -290,7 +325,7 @@ def append_lmp_data( # Single price signal, use reprentative days self.set_years = None self.set_days = RangeSet(1,n_clusters) - self._n_time_points = horizon_length if horizon_length is not None else 24 + self._n_time_points = self.horizon_length if self.horizon_length is not None else 24 self.set_time = RangeSet(self._n_time_points) self.LMP = {} @@ -298,7 +333,7 @@ def append_lmp_data( price_data = full_data lmp_data, weights = self.cluster_lmp_data( - price_data, n_clusters, horizon_length + price_data, n_clusters ) for d in self.set_days: @@ -354,31 +389,39 @@ def add_ramping_constraints( shutdown_rate= 0.7, ramp_up_rate = 0.7, ramp_down_rate = 0.7, - ): + ): """ - Adds ramping constraints of the form - -ramp_down_limit <= var(t) - var(t-1) <= ramp_up_limit on var + Adds ramping constraints of the form: + -ramp_down_limit <= var(t) - var(t-1) <= ramp_up_limit on var - Arguments: - op_blk: The name of the operation model block, ex: ( "fs.op_name") - design_blk: The name of the design model block, ex: ("m.design_name") - var: Name of the variable the ramping cosntraints will be applied to, ex: ("total_power") - op_range_lb_percentage: The percetage of the capacity that represents the lower operating bound (%) - startup_limit_percentage: The percentage of the capacity that variable var can increase at - during startup (%) - shutdown_limit_percentage: The percentage of the capacity that variable var can decrease at - during shutdown (%) - ramp_up_limit: The rate at which variable var can increases during operation, - a percentage of the capacity (%) - ramp_down_limit: = The rate at which variable var can decrease during operation, - a percentage of the capacity (%) + Args: + op_blk: String of the name of the operation model block, ex: ("fs.op_name") + design_blk: String of the name of the design model block, ex: ("m.design_name") + capacity_var: String of the name of the entity on the model the ramping constraints + will be applied to, ex: ("total_power") OR integer/float value of the + capacity value + ramping_var: String of the name of the variable that the ramping constraints will + be applied to + constraint_type: String to choose between linear and nonlinear constraints. Valid + inputs are in: ["linear", "nonlinear"] + linearization: Boolean indicating whether linearization is used when constraint_type is + "nonlinear", True to use linearization, False otherwise + op_range_lb: The fraction of the capacity that represents the lower operating bound + (between 0 and 1) + startup_rate: The fraction of the capacity that variable ramping_var can increase at + during startup (between 0 and 1) + shutdown_rate: The fraction of the capacity that variable ramping_var can decrease at + during shutdown (between 0 and 1) + ramp_up_rate: The fraction of the capacity that variable ramping_var can increase at + during operation (between 0 and 1) + ramp_down_rate: The fraction of the capacity that variable ramping_var can decrease at + during operation (between 0 and 1) Assumptions/relationship: - example: - total_power_upper_bound >= ramp_up_limit >= startup_limit >= total_power_lower_bound > 0 - total_power_upper_bound >= ramp_down_limit >= shutdown_limit >= total_power_lower_bound > 0 + total_power_upper_bound >= ramp_up_limit >= startup_limit >= total_power_lower_bound > 0 + total_power_upper_bound >= ramp_down_limit >= shutdown_limit >= total_power_lower_bound > 0 """ start_up = {t: deepgetattr(self.mp_model.period[t], op_blk + ".startup") for t in self.mp_model.period} op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".op_mode") for t in self.mp_model.period} @@ -388,15 +431,15 @@ def add_ramping_constraints( if isinstance(capacity_var,str): var_capacity = deepgetattr(self, design_blk + "." + capacity_var) act_startup_rate = {t: var_capacity * start_up[t] for t in self.mp_model.period} - act_shutdown_rate = {t:var_capacity * shut_down[t] for t in self.mp_model.period} - act_op_mode_rate = {t:var_capacity * op_mode[t] for t in self.mp_model.period} + act_shutdown_rate = {t: var_capacity * shut_down[t] for t in self.mp_model.period} + act_op_mode_rate = {t: var_capacity * op_mode[t] for t in self.mp_model.period} if isinstance(capacity_var, (int,float)): act_startup_rate = {t: capacity_var * start_up[t] for t in self.mp_model.period} - act_shutdown_rate = {t:capacity_var * shut_down[t]for t in self.mp_model.period} - act_op_mode_rate = {t:capacity_var * op_mode[t] for t in self.mp_model.period} + act_shutdown_rate = {t: capacity_var * shut_down[t] for t in self.mp_model.period} + act_op_mode_rate = {t: capacity_var * op_mode[t] for t in self.mp_model.period} elif constraint_type == "nonlinear": if linearization == True: - if hasattr(self,"capacity_startup") == False: + if hasattr(self, "capacity_startup") == False: for t in self.mp_model.period: blk = deepgetattr(self.mp_model.period[t],op_blk) blk._add_capacity_aux_vars() @@ -407,8 +450,8 @@ def add_ramping_constraints( elif linearization == False: var_capacity = deepgetattr(self, design_blk + "." + capacity_var) act_startup_rate = {t: var_capacity * start_up[t] for t in self.mp_model.period} - act_shutdown_rate = {t:var_capacity * shut_down[t] for t in self.mp_model.period} - act_op_mode_rate = {t:var_capacity * op_mode[t] for t in self.mp_model.period} + act_shutdown_rate = {t: var_capacity * shut_down[t] for t in self.mp_model.period} + act_op_mode_rate = {t: var_capacity * op_mode[t] for t in self.mp_model.period} @@ -448,23 +491,39 @@ def ramp_down_con(b,t): ) - def add_startup_shutdown(self, op_blk,design_blk, build_binary_var, up_time = 1, down_time =1): + def add_startup_shutdown( + self, + op_blk, + design_blk, + build_binary_var, + use_min_ud_time=True, + up_time=1, + down_time=1, + ): """ Adds startup/shutdown and minimum uptime/downtime constraints on a given unit/process - Arguments: - op_blk: op_blk: The name of the operation model block, ex: ( "fs.op_name") - up_time: Time required for the system to start up fully - ex: 4 - down_time: Time required for the system to shutdown fully - ex: 4 + Args: + op_blk: String of the name of the operation model block, ex: ("fs.op_name") + design_blk: String of the name of the design model block, ex: ("m.design_name") + build_binary_var: String of the name of the binary variable which indicates if we + should build (1) or not build (0) the design corresponding to the + 'design_blk' referenced above + use_min_ud_time: Boolean that can bypass constructing minimum up and downtime + constraints. (default: True --> constraints are constructed) + up_time: Time required for the system to start up fully + ex: 4 + down_time: Time required for the system to shutdown fully + ex: 4 + + Returns: + Assumption: - up_time >= 1 & down_time >= 1 + up_time >= 1 & down_time >= 1 """ - - + start_up = {t: deepgetattr(self.mp_model.period[t], op_blk + ".startup") for t in self.mp_model.period} op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".op_mode") for t in self.mp_model.period} shut_down = {t: deepgetattr(self.mp_model.period[t], op_blk + ".shutdown") for t in self.mp_model.period} @@ -508,7 +567,35 @@ def Binary_relationhsip_con(b, t): start_up[self.mp_model.set_period[t-1]] - shut_down[self.mp_model.set_period[t]] ) - def build_hourly_cashflows(self): + + def build_hourly_cashflows(self, revenue_streams=['elec_revenue',], additional_costs=None): + """ + Adds an expression for the net cash inflow for each operational + block. This is the new cash inflow for each time period of the + PriceTakerModel. Default costs for each model should include + 'non_fuel_vom' (non-fuel variable operating costs), 'fuel_cost' + (cost of fuel), and 'carbon_price' (cost associated with producing + carbon; i.e., a carbon tax). The net cash inflow is caluclated as: + + Sum(revenue streams) - Sum(costs) + + for every time period in the PriceTakerModel's MultiPeriodModel + + Args: + revenue_streams: List of strings representing the names of the + revenue streams coming from the model. + (default: ['elec_revenue',]) + Coproduction example: ['elec_revenue', + 'H2_revenue', ] + additional_costs: List of strings representing the names of the + costs associated with operating at a time period. + (default: None) + example: ['hourly_fixed_cost', + '',] + + Returns: + + """ period = self.mp_model.period for p in period: @@ -517,6 +604,8 @@ def build_hourly_cashflows(self): elec_revenue = 0 carbon_price = 0 + # ToDo: Add multiple revenue streams to the model + for blk in period[p].component_data_objects(Block): if isinstance(blk, OperationModelData): non_fuel_vom += blk.non_fuel_vom @@ -546,8 +635,21 @@ def build_cashflows( objective="NPV", ): """ - Builds overall cashflow expressions and appends objective function - to the model + Builds overall cashflow expressions and appends the objective function + in terms of cashflows to the PriceTakerModel + + Args: + lifetime: Number of years (lifetime) to evaluate the equipment + discount_rate: Fractional rate of discount used in NPV calculations. + Must be between 0 and 1. + corp_tax: Fractional value of corporate tax used in NPV calculations. + ??? What are the restrictions on tax ??? + other_costs: Additional costs that are not captured + other_revenue: + objective: + + Returns: + """ capex_expr = 0 From fde3f31eacde8a456eb4a5fae66665a84e7c65be Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Thu, 15 Feb 2024 12:24:45 -0500 Subject: [PATCH 040/126] Updated init of PriceTakerModel Updated the formatting in the initialization of the PriceTakerModel. Added a check to the seed setter for integer inputs. Also added a check to the horizon_length setter for integer inputs. --- .../grid_integration/multiperiod/price_taker_model.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 6c7456126b..b80dac3769 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -54,8 +54,8 @@ def deepgetattr(obj, attr): class PriceTakerModel(ConcreteModel): def __init__(self, seed=20, horizon_length=24): super().__init__() - self._seed = seed - self._horizon_length = horizon_length + self.seed = seed + self.horizon_length = horizon_length @property def seed(self): @@ -63,6 +63,8 @@ def seed(self): @seed.setter def seed(self, value): + if not isinstance(value, int): + raise ValueError(f"seed must be an integer, but {value} is not an integer") self._seed = value @property @@ -73,6 +75,8 @@ def horizon_length(self): def horizon_length(self, value): if value <= 0: raise ValueError(f"horizon_length must be > 0, but {value} is provided.") + if not isinstance(value, int): + raise ValueError(f"horizon_length must be an integer, but {value} is not an integer") self._horizon_length = value def generate_daily_data(self,raw_data,day_list): @@ -605,6 +609,7 @@ def build_hourly_cashflows(self, revenue_streams=['elec_revenue',], additional_c carbon_price = 0 # ToDo: Add multiple revenue streams to the model + # ToDo: Add additional costs for time period for blk in period[p].component_data_objects(Block): if isinstance(blk, OperationModelData): From 5bd0d5b94d084ab3b55b1f050e1ea0ac15883b67 Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Thu, 15 Feb 2024 12:30:02 -0500 Subject: [PATCH 041/126] Added missing documentation Added function description and Args/Returns statements for the 'generate_daily_data' function. --- .../grid_integration/multiperiod/price_taker_model.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index b80dac3769..9a3c76e065 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -80,6 +80,17 @@ def horizon_length(self, value): self._horizon_length = value def generate_daily_data(self,raw_data,day_list): + """ + Function used to generate the daily data in a usable format + from the raw data provided. + + Args: + raw_data: LMP data in raw format + day_list: List of days to be used from the raw data + + Returns: + daily_data: Correctly formatted LMP data for later use + """ daily_data = pd.DataFrame(columns=day_list) From 0df8b453e8b36a2ebca118a947fb98f95db1dc92 Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Thu, 15 Feb 2024 12:45:28 -0500 Subject: [PATCH 042/126] Corrected constraint expressions Corrected the mathematical expressions for the constraints for ramping rates and startup/shutdown expressions. Namely, the expressions now appropriately constrain power ramp rates under all scenarios. Also, design decisions to build and it's impact on whether an equipment can turn on or not has been fixed. Minimum downtime constraint was also fixed. Also, I added a check to make sure that the up_time and down_time inputs are valid under the assumptions. --- .../multiperiod/price_taker_model.py | 65 ++++++++++--------- 1 file changed, 35 insertions(+), 30 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 9a3c76e065..7b60b10d29 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -485,10 +485,8 @@ def ramp_up_con(b,t): return Constraint.Skip else: return (var_ramping[self.mp_model.set_period[t]] - var_ramping[self.mp_model.set_period[t-1]] <= - startup_rate * act_startup_rate[self.mp_model.set_period[t]] - - op_range_lb * act_startup_rate[self.mp_model.set_period[t-1]] - + (ramp_up_rate - op_range_lb) * act_op_mode_rate[self.mp_model.set_period[t]] - - op_range_lb * act_op_mode_rate[self.mp_model.set_period[t-1]] + (startup_rate - ramp_up_rate) * act_startup_rate[self.mp_model.set_period[t]] + + ramp_up_rate * act_op_mode_rate[self.mp_model.set_period[t]] ) @blk.Constraint(self.range_time_steps) @@ -498,11 +496,8 @@ def ramp_down_con(b,t): else: return ( var_ramping[self.mp_model.set_period[t-1]]- var_ramping[self.mp_model.set_period[t]] <= - (shutdown_rate - op_range_lb) * act_shutdown_rate[self.mp_model.set_period[t]] - - op_range_lb * act_startup_rate[self.mp_model.set_period[t]] - + op_range_lb * act_startup_rate[self.mp_model.set_period[t-1]] - + (ramp_down_rate - op_range_lb) * act_op_mode_rate[self.mp_model.set_period[t]] - + op_range_lb * act_op_mode_rate[self.mp_model.set_period[t-1]] + shutdown_rate * act_shutdown_rate[self.mp_model.set_period[t]] + + ramp_down_rate * act_op_mode_rate[self.mp_model.set_period[t]] ) @@ -511,7 +506,7 @@ def add_startup_shutdown( op_blk, design_blk, build_binary_var, - use_min_ud_time=True, + use_min_time=True, up_time=1, down_time=1, ): @@ -526,7 +521,7 @@ def add_startup_shutdown( build_binary_var: String of the name of the binary variable which indicates if we should build (1) or not build (0) the design corresponding to the 'design_blk' referenced above - use_min_ud_time: Boolean that can bypass constructing minimum up and downtime + use_min_time: Boolean that can bypass constructing minimum up and downtime constraints. (default: True --> constraints are constructed) up_time: Time required for the system to start up fully ex: 4 @@ -538,7 +533,18 @@ def add_startup_shutdown( Assumption: up_time >= 1 & down_time >= 1 """ - + # Check up_time and down_time for validity + if not isinstance(up_time, int): + raise ValueError(f"up_time must be an integer, but {value} is not an integer") + if up_time < 1: + raise ValueError(f"up_time must be >= 1, but {value} is not") + if not isinstance(down_time, int): + raise ValueError(f"down_time must be an integer, but {value} is not an integer") + if down_time < 1: + raise ValueError(f"down_time must be >= 1, but {value} is not") + + + start_up = {t: deepgetattr(self.mp_model.period[t], op_blk + ".startup") for t in self.mp_model.period} op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".op_mode") for t in self.mp_model.period} shut_down = {t: deepgetattr(self.mp_model.period[t], op_blk + ".shutdown") for t in self.mp_model.period} @@ -556,32 +562,31 @@ def add_startup_shutdown( if design_blk is not None: @blk.Constraint(self.range_time_steps) def design_op_relationship_con(b, t): - return (start_up[self.mp_model.set_period[t]] + shut_down[self.mp_model.set_period[t]] + - op_mode[self.mp_model.set_period[t]] <= build - ) - - @blk.Constraint(self.range_time_steps) - def minimum_up_time_con(b,t): - if t == 1 or t < up_time or t > number_time_steps: - return Constraint.Skip - else: - return sum(start_up[self.mp_model.set_period[i]] for i in range(t-up_time+1,t+1)) <= op_mode[self.mp_model.set_period[t]] + return (op_mode[self.mp_model.set_period[t]] <= build) - @blk.Constraint(self.range_time_steps) - def minimum_down_time_con(b, t): - if t <= down_time or t == 1 or t > number_time_steps: - return Constraint.Skip - return (sum(shut_down[self.mp_model.set_period[i]] for i in range(t - down_time , t + 1)) <= - 1 - op_mode[self.mp_model.set_period[t]]) - @blk.Constraint(self.range_time_steps) def Binary_relationhsip_con(b, t): - if t > number_time_steps or t == 1: + if t == 1 or t > number_time_steps: return Constraint.Skip return (op_mode[self.mp_model.set_period[t]] - op_mode[self.mp_model.set_period[t-1]] == start_up[self.mp_model.set_period[t-1]] - shut_down[self.mp_model.set_period[t]] ) + if use_min_time: + @blk.Constraint(self.range_time_steps) + def minimum_up_time_con(b,t): + if t == 1 or t < up_time or t > number_time_steps: + return Constraint.Skip + else: + return sum(start_up[self.mp_model.set_period[i]] for i in range(t-up_time+1, t+1)) <= op_mode[self.mp_model.set_period[t]] + + @blk.Constraint(self.range_time_steps) + def minimum_down_time_con(b, t): + if t < down_time or t == 1 or t > number_time_steps: + return Constraint.Skip + return (sum(shut_down[self.mp_model.set_period[i]] for i in range(t-down_time+1, t+1)) <= + 1 - op_mode[self.mp_model.set_period[t]]) + def build_hourly_cashflows(self, revenue_streams=['elec_revenue',], additional_costs=None): """ From fd595b4c4ee418480f26736e1d61b296d638a86d Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Thu, 15 Feb 2024 13:46:19 -0500 Subject: [PATCH 043/126] Added tests for input checking Added tests to check the seed, horizon_length, and minimum up_time/down_time inputs. All tests pass. As a note, the tests for clustering the LMP data need to change as they are failing. Parsed data is passed instead of raw data. I am looking into this. --- .../multiperiod/price_taker_model.py | 8 +-- .../multiperiod/tests/test_price_taker.py | 58 ++++++++++++++++++- 2 files changed, 61 insertions(+), 5 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 7b60b10d29..2f8f8906d3 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -535,13 +535,13 @@ def add_startup_shutdown( """ # Check up_time and down_time for validity if not isinstance(up_time, int): - raise ValueError(f"up_time must be an integer, but {value} is not an integer") + raise ValueError(f"up_time must be an integer, but {up_time} is not an integer") if up_time < 1: - raise ValueError(f"up_time must be >= 1, but {value} is not") + raise ValueError(f"up_time must be >= 1, but {up_time} is not") if not isinstance(down_time, int): - raise ValueError(f"down_time must be an integer, but {value} is not an integer") + raise ValueError(f"down_time must be an integer, but {down_time} is not an integer") if down_time < 1: - raise ValueError(f"down_time must be >= 1, but {value} is not") + raise ValueError(f"down_time must be >= 1, but {down_time} is not") diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index d6d008d931..cab1d447a3 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -78,7 +78,8 @@ def test_cluster_lmp_data(excel_data): daily_data, scenarios = m.reconfigure_raw_data(excel_data) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data) - lmp_data, weights = m.cluster_lmp_data(daily_data, n_clusters, scenarios) + # lmp_data, weights = m.cluster_lmp_data(daily_data, n_clusters) + lmp_data, weights = m.cluster_lmp_data(excel_data, n_clusters) sum_of_weights = 0 for i in range(0, n_clusters): @@ -110,6 +111,7 @@ def test_logger_messages(excel_data, caplog): # # assert f"Optimal number of clusters is close to kmax: {kmax}. Consider increasing kmax." in caplog.text + # Testing horizon_length errors value = 0 with pytest.raises( ValueError, @@ -117,6 +119,60 @@ def test_logger_messages(excel_data, caplog): ): m = PriceTakerModel() m.horizon_length = value + value = 12.34 + with pytest.raises( + ValueError, + match=(f"horizon_length must be an integer, but {value} is not an integer"), + ): + m = PriceTakerModel() + m.horizon_length = value + + # Testing seed errors + value = 12.34 + with pytest.raises( + ValueError, + match=(f"seed must be an integer, but {value} is not an integer"), + ): + m = PriceTakerModel() + m.seed = value + + # Testing up_time and down_time errors + des = DesignModel() + oper = OperationModel() + build_bin_var = 'build' + use_min_time=True + up_time = [10, -5, 2.2] + down_time = [10, -5, 2.2] + with pytest.raises( + ValueError, + match=(f"up_time must be an integer, but {up_time[2]} is not an integer"), + ): + m = PriceTakerModel() + m.add_startup_shutdown(des, oper, build_bin_var, use_min_time, up_time[2], down_time[0]) + + with pytest.raises( + ValueError, + match=(f"down_time must be an integer, but {down_time[2]} is not an integer"), + ): + m = PriceTakerModel() + m.add_startup_shutdown(des, oper, build_bin_var, use_min_time, up_time[0], down_time[2]) + + with pytest.raises( + ValueError, + match=(f"up_time must be >= 1, but {up_time[1]} is not"), + ): + m = PriceTakerModel() + m.add_startup_shutdown(des, oper, build_bin_var, use_min_time, up_time[1], down_time[0]) + + with pytest.raises( + ValueError, + match=(f"down_time must be >= 1, but {down_time[1]} is not"), + ): + m = PriceTakerModel() + m.add_startup_shutdown(des, oper, build_bin_var, use_min_time, up_time[0], down_time[1]) + + + def dfc_design(m, params, capacity_range=(650, 900)): From a54c3acff79de3e4aa99aa7cd228259475ce8c56 Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Thu, 15 Feb 2024 14:45:33 -0500 Subject: [PATCH 044/126] Updated checks for get_optimal_clusters Added validity checks to make sure kmin and kmax >= 0 and are integer values. Also, kmin < kmax. Also, added tests to ensure that each edge case is appropriately handled. --- .../multiperiod/price_taker_model.py | 14 +++++- .../multiperiod/tests/test_price_taker.py | 49 +++++++++++++++++++ 2 files changed, 62 insertions(+), 1 deletion(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 2f8f8906d3..8dc3b6b037 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -155,12 +155,24 @@ def get_optimal_n_clusters( n_clusters: the optimal number of clusters for the given data inertia_values: within-cluster sum-of-squares """ + # Check kmin and kmax for validity if kmin is None: kmin = 4 if kmax is None: kmax = 30 _logger.warning(f"kmax was not set - using a default value of 30.") - + + if not isinstance(kmin, int): + raise ValueError(f"kmin must be an integer, but {kmin} is not an integer") + if not isinstance(kmax, int): + raise ValueError(f"kmax must be an integer, but {kmax} is not an integer") + if kmin < 1: + raise ValueError(f"kmin must be > 0, but {kmin} is provided.") + if kmax < 1: + raise ValueError(f"kmax must be > 0, but {kmax} is provided.") + if kmin >= kmax: + raise ValueError(f"kmin must be less than kmax, but {kmin} >= {kmax}") + k_values = range(kmin, kmax) inertia_values = [] diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index cab1d447a3..11beaf20dc 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -170,6 +170,55 @@ def test_logger_messages(excel_data, caplog): ): m = PriceTakerModel() m.add_startup_shutdown(des, oper, build_bin_var, use_min_time, up_time[0], down_time[1]) + + kmin = [-5, 10.2, 9] + kmax = [-5, 10.2, 8] + with pytest.raises( + ValueError, + match=(f"kmin must be an integer, but {kmin[1]} is not an integer"), + ): + m = PriceTakerModel() + + daily_data, scenarios = m.reconfigure_raw_data(excel_data) + n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin[1], kmax=kmax[2]) + + with pytest.raises( + ValueError, + match=(f"kmax must be an integer, but {kmax[1]} is not an integer"), + ): + m = PriceTakerModel() + + daily_data, scenarios = m.reconfigure_raw_data(excel_data) + n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin[2], kmax=kmax[1]) + + with pytest.raises( + ValueError, + match=(f"kmin must be > 0, but {kmin[0]} is provided."), + ): + m = PriceTakerModel() + + daily_data, scenarios = m.reconfigure_raw_data(excel_data) + n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin[0], kmax=kmax[2]) + + + with pytest.raises( + ValueError, + match=(f"kmax must be > 0, but {kmax[0]} is provided."), + ): + m = PriceTakerModel() + + daily_data, scenarios = m.reconfigure_raw_data(excel_data) + n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin[2], kmax=kmax[0]) + m.cluster_lmp_data(excel_data, n_clusters) + + with pytest.raises( + ValueError, + match=(f"kmin must be less than kmax, but {kmin[2]} >= {kmax[2]}"), + ): + m = PriceTakerModel() + + daily_data, scenarios = m.reconfigure_raw_data(excel_data) + n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin[2], kmax=kmax[2]) From 70cbaba6b911bd037f2e13fbd3befbf42d10b5bf Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Thu, 15 Feb 2024 14:47:46 -0500 Subject: [PATCH 045/126] Fixed typos in test_price_taker.py Fixed a few typos and made language more clear. --- .../grid_integration/multiperiod/tests/test_price_taker.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index 11beaf20dc..77fd4151b0 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -111,7 +111,7 @@ def test_logger_messages(excel_data, caplog): # # assert f"Optimal number of clusters is close to kmax: {kmax}. Consider increasing kmax." in caplog.text - # Testing horizon_length errors + # Testing horizon_length input value errors value = 0 with pytest.raises( ValueError, @@ -136,7 +136,7 @@ def test_logger_messages(excel_data, caplog): m = PriceTakerModel() m.seed = value - # Testing up_time and down_time errors + # Testing up_time and down_time input value errors des = DesignModel() oper = OperationModel() build_bin_var = 'build' @@ -171,6 +171,7 @@ def test_logger_messages(excel_data, caplog): m = PriceTakerModel() m.add_startup_shutdown(des, oper, build_bin_var, use_min_time, up_time[0], down_time[1]) + # Testing kmin and kmax input value errors kmin = [-5, 10.2, 9] kmax = [-5, 10.2, 8] with pytest.raises( From 85b7bd0bc4281f9c953df49df95104822221d1f0 Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Thu, 15 Feb 2024 15:38:55 -0500 Subject: [PATCH 046/126] Added input checks on add_ramping_constraints Added checks that the ramping rates as inputs are between 0 and 1 (fractions of the maximum capacity). Also added tests for each of these scenarios. --- .../multiperiod/price_taker_model.py | 36 ++++++++--- .../multiperiod/tests/test_price_taker.py | 61 +++++++++++++++++++ 2 files changed, 87 insertions(+), 10 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 8dc3b6b037..390a11a6fb 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -434,22 +434,38 @@ def add_ramping_constraints( inputs are in: ["linear", "nonlinear"] linearization: Boolean indicating whether linearization is used when constraint_type is "nonlinear", True to use linearization, False otherwise - op_range_lb: The fraction of the capacity that represents the lower operating bound - (between 0 and 1) - startup_rate: The fraction of the capacity that variable ramping_var can increase at - during startup (between 0 and 1) - shutdown_rate: The fraction of the capacity that variable ramping_var can decrease at - during shutdown (between 0 and 1) - ramp_up_rate: The fraction of the capacity that variable ramping_var can increase at - during operation (between 0 and 1) - ramp_down_rate: The fraction of the capacity that variable ramping_var can decrease at - during operation (between 0 and 1) + op_range_lb: The fraction of the maximum capacity that represents the lower operating + bound (between 0 and 1) + startup_rate: The fraction of the maximum capacity that variable ramping_var can + increase during startup (between 0 and 1) + shutdown_rate: The fraction of the maximum capacity that variable ramping_var can + decrease during shutdown (between 0 and 1) + ramp_up_rate: The fraction of the maximum capacity that variable ramping_var can + increase during operation (between 0 and 1) + ramp_down_rate: The fraction of the maximum capacity that variable ramping_var can + decrease during operation (between 0 and 1) Assumptions/relationship: total_power_upper_bound >= ramp_up_limit >= startup_limit >= total_power_lower_bound > 0 total_power_upper_bound >= ramp_down_limit >= shutdown_limit >= total_power_lower_bound > 0 """ + # Checking that all ramping rates are between 0 and 1 and that the + # lower bound for operation is less than the startup/shutdown ramps + if startup_rate <= 0 or startup_rate > 1: + raise ValueError(f"startup_rate fraction must be between 0 and 1, but {startup_rate} is not.") + if shutdown_rate <= 0 or shutdown_rate > 1: + raise ValueError(f"shutdown_rate fraction must be between 0 and 1, but {shutdown_rate} is not.") + if ramp_up_rate <= 0 or ramp_up_rate > 1: + raise ValueError(f"ramp_up_rate fraction must be between 0 and 1, but {ramp_up_rate} is not.") + if ramp_down_rate <= 0 or ramp_down_rate > 1: + raise ValueError(f"ramp_down_rate fraction must be between 0 and 1, but {ramp_down_rate} is not.") + if op_range_lb < 0 or op_range_lb > 1: + raise ValueError(f"op_range_lb fraction must be between 0 and 1, but {op_range_lb} is not.") + if op_range_lb >= shutdown_rate: + raise ValueError(f"op_range_lb fraction must be <= shut_down_rate, otherwise the system cannot reach the off state.") + + start_up = {t: deepgetattr(self.mp_model.period[t], op_blk + ".startup") for t in self.mp_model.period} op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".op_mode") for t in self.mp_model.period} shut_down = {t: deepgetattr(self.mp_model.period[t], op_blk + ".shutdown") for t in self.mp_model.period} diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index 77fd4151b0..f53a6646c3 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -220,6 +220,67 @@ def test_logger_messages(excel_data, caplog): daily_data, scenarios = m.reconfigure_raw_data(excel_data) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin[2], kmax=kmax[2]) + + # Testing ramping rate input value errors + des = DesignModel() + oper = OperationModel() + ramping_var = 'power' + capac_var = 'power_max' + constraint_type = 'linear' + linearization = False + op_range_lb = [-0.1, 0.5, 1.0] + su_rate = [-0.1, 0.5] + sd_rate = [-0.1, 0.5] + op_ru_rate = [-0.1, 0.5] + op_rd_rate = [-0.1, 0.5] + with pytest.raises( + ValueError, + match=(f"startup_rate fraction must be between 0 and 1, but {su_rate[0]} is not."), + ): + m = PriceTakerModel() + m.add_ramping_constraints(des, oper, capac_var, ramping_var, constraint_type, linearization, + op_range_lb[1], su_rate[0], sd_rate[1], op_ru_rate[1], op_rd_rate[1]) + + with pytest.raises( + ValueError, + match=(f"shutdown_rate fraction must be between 0 and 1, but {sd_rate[0]} is not."), + ): + m = PriceTakerModel() + m.add_ramping_constraints(des, oper, capac_var, ramping_var, constraint_type, linearization, + op_range_lb[1], su_rate[1], sd_rate[0], op_ru_rate[1], op_rd_rate[1]) + + with pytest.raises( + ValueError, + match=(f"ramp_up_rate fraction must be between 0 and 1, but {op_ru_rate[0]} is not."), + ): + m = PriceTakerModel() + m.add_ramping_constraints(des, oper, capac_var, ramping_var, constraint_type, linearization, + op_range_lb[1], su_rate[1], sd_rate[1], op_ru_rate[0], op_rd_rate[1]) + + with pytest.raises( + ValueError, + match=(f"ramp_down_rate fraction must be between 0 and 1, but {op_rd_rate[0]} is not."), + ): + m = PriceTakerModel() + m.add_ramping_constraints(des, oper, capac_var, ramping_var, constraint_type, linearization, + op_range_lb[1], su_rate[1], sd_rate[1], op_ru_rate[1], op_rd_rate[0]) + + with pytest.raises( + ValueError, + match=(f"op_range_lb fraction must be between 0 and 1, but {op_range_lb[0]} is not."), + ): + m = PriceTakerModel() + m.add_ramping_constraints(des, oper, capac_var, ramping_var, constraint_type, linearization, + op_range_lb[0], su_rate[1], sd_rate[1], op_ru_rate[1], op_rd_rate[1]) + + with pytest.raises( + ValueError, + match=(f"op_range_lb fraction must be <= shut_down_rate, otherwise the system cannot reach the off state."), + ): + m = PriceTakerModel() + m.add_ramping_constraints(des, oper, capac_var, ramping_var, constraint_type, linearization, + op_range_lb[2], su_rate[1], sd_rate[1], op_ru_rate[1], op_rd_rate[1]) + From 9851b88e52baf2b08849a0f2297e869d57e5ed82 Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Thu, 15 Feb 2024 16:00:05 -0500 Subject: [PATCH 047/126] Fixed broken test Code used 1-based indexing but test used 0-based. Fixed the mistmatch. --- .../apps/grid_integration/multiperiod/tests/test_price_taker.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index f53a6646c3..b734169436 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -82,7 +82,7 @@ def test_cluster_lmp_data(excel_data): lmp_data, weights = m.cluster_lmp_data(excel_data, n_clusters) sum_of_weights = 0 - for i in range(0, n_clusters): + for i in range(1, n_clusters + 1): sum_of_weights = sum_of_weights + weights[0][i] assert sum_of_weights == 365 From 6ede14e0b0b5f45ef10eee58fb05b05bfc01d8ad Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Fri, 16 Feb 2024 11:21:38 -0500 Subject: [PATCH 048/126] Added automated LMP population Added an attribute to the CONFIG of OperationModelData that will be True by default. The attribute, append_lmp_data, will work with building the multiperiod_model so that LMP data will automatically be populated. Old way required a dummy value for m.LMP at the operational level and the user updated LMP manually for each time period. Also removed some hard-coded values in the append_lmp_data() function. --- .../design_and_operation_models.py | 20 +++++++++-- .../multiperiod/price_taker_model.py | 34 +++++++++++++++++-- 2 files changed, 49 insertions(+), 5 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py index 6f4ecd61a5..34bcdb8851 100644 --- a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -13,6 +13,7 @@ from pyomo.environ import ( Var, + Param, Binary, Expression, NonNegativeReals, @@ -136,12 +137,20 @@ class OperationModelData(SkeletonUnitModelData): doc="Should op_mode, startup, shutdown vars be defined?", ), ) + CONFIG.declare( + "append_lmp_data", + ConfigValue( + default=True, + domain=In([True, False]), + doc="Should LMP data automatically be appended to the model?" + ) + ) CONFIG.declare( "capacity_var", ConfigValue( - default = None, - doc = "capacity variable" + default=None, + doc="capacity variable" ), ) @@ -165,6 +174,13 @@ def build(self): within=Binary, doc="Binary var: 1 if the shutdown is initiated, 0 otherwise", ) + + if self.config.append_lmp_data: + self.LMP = Param( + initialize=1, + mutable=True, + doc="Parameter: Will be updated to LMP value at given time" + ) self.config.model_func(self, **self.config.model_args) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 390a11a6fb..a691ca3d1d 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -374,11 +374,10 @@ def append_lmp_data( # Single price signal, use full year's price signal self.set_years = None self.set_days = None - # self._n_time_points = len(full_data) - self._n_time_points = 48 + self._n_time_points = len(full_data) self.set_time = RangeSet(self._n_time_points) - price_data = full_data[column_name].to_list()[:48] + price_data = full_data[column_name].to_list() self.LMP = {t: price_data[t - 1] for t in self.set_time} return @@ -397,6 +396,35 @@ def build_multiperiod_model(self, **kwargs): use_stochastic_build=True, **kwargs, ) + + # If append_lmp_data is automatic, need to append the LMP data. + # Check if LMP has already been defined with the append_lmp_data + # function above + LMP_exists = False + try: + _ = self.LMP + LMP_exists = True + except: + LMP_exists = False + + # Iterate through model to append LMP data if it's been defined + # and the model says it should be (default) + period = self.mp_model.period + for p in period: + for blk in period[p].component_data_objects(Block): + if isinstance(blk, OperationModelData): + if blk.config.append_lmp_data: + if not LMP_exists: + raise ValueError(f"OperationModelData has been defined to automatically " + + f"populate LMP data. However, m.LMP does not exist. " + + f"Please run append_lmp_data() first or set the " + + f"append_lmp_data attribute to False when configuring " + + f"your OperationModelData object.") + blk.LMP = self.LMP[p] + + + + #TODO: (1) Need to determine whether the minimum opterating power can be a parameter # or whether it largly depends on the capacity of the plant. # (2) Need to determine if the start up and shutdown limits can be modeled as bulk amounts vs From d31b272c7d59846caa2701b5b17ec69079c62e4c Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Fri, 16 Feb 2024 12:04:03 -0500 Subject: [PATCH 049/126] Fixed getitem issues with Sets Updated code to be more modern and follow new Pyomo paradigms. Removed warnings for constructing both the ramping rate and startup/shutdown/min uptime/min downtime constraints. Also edited a few lines to beautify. --- .../multiperiod/price_taker_model.py | 38 ++++++++++--------- 1 file changed, 20 insertions(+), 18 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index a691ca3d1d..c55db06060 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -438,12 +438,12 @@ def add_ramping_constraints( capacity_var, ramping_var, constraint_type, - linearization = True, - op_range_lb = 0.6, - startup_rate = 0.7, - shutdown_rate= 0.7, - ramp_up_rate = 0.7, - ramp_down_rate = 0.7, + linearization=True, + op_range_lb=0.6, + startup_rate=0.7, + shutdown_rate=0.7, + ramp_up_rate=0.7, + ramp_down_rate=0.7, ): """ Adds ramping constraints of the form: @@ -498,12 +498,14 @@ def add_ramping_constraints( op_mode = {t: deepgetattr(self.mp_model.period[t], op_blk + ".op_mode") for t in self.mp_model.period} shut_down = {t: deepgetattr(self.mp_model.period[t], op_blk + ".shutdown") for t in self.mp_model.period} var_ramping = {t: deepgetattr(self.mp_model.period[t], op_blk + "." + ramping_var) for t in self.mp_model.period} + if constraint_type == "linear": if isinstance(capacity_var,str): var_capacity = deepgetattr(self, design_blk + "." + capacity_var) act_startup_rate = {t: var_capacity * start_up[t] for t in self.mp_model.period} act_shutdown_rate = {t: var_capacity * shut_down[t] for t in self.mp_model.period} act_op_mode_rate = {t: var_capacity * op_mode[t] for t in self.mp_model.period} + if isinstance(capacity_var, (int,float)): act_startup_rate = {t: capacity_var * start_up[t] for t in self.mp_model.period} act_shutdown_rate = {t: capacity_var * shut_down[t] for t in self.mp_model.period} @@ -540,9 +542,9 @@ def ramp_up_con(b,t): if t == 1: return Constraint.Skip else: - return (var_ramping[self.mp_model.set_period[t]] - var_ramping[self.mp_model.set_period[t-1]] <= - (startup_rate - ramp_up_rate) * act_startup_rate[self.mp_model.set_period[t]] - + ramp_up_rate * act_op_mode_rate[self.mp_model.set_period[t]] + return (var_ramping[self.mp_model.set_period.at(t)] - var_ramping[self.mp_model.set_period.at(t-1)] <= + (startup_rate - ramp_up_rate) * act_startup_rate[self.mp_model.set_period.at(t)] + + ramp_up_rate * act_op_mode_rate[self.mp_model.set_period.at(t)] ) @blk.Constraint(self.range_time_steps) @@ -551,9 +553,9 @@ def ramp_down_con(b,t): return Constraint.Skip else: return ( - var_ramping[self.mp_model.set_period[t-1]]- var_ramping[self.mp_model.set_period[t]] <= - shutdown_rate * act_shutdown_rate[self.mp_model.set_period[t]] - + ramp_down_rate * act_op_mode_rate[self.mp_model.set_period[t]] + var_ramping[self.mp_model.set_period.at(t-1)]- var_ramping[self.mp_model.set_period.at(t)] <= + shutdown_rate * act_shutdown_rate[self.mp_model.set_period.at(t)] + + ramp_down_rate * act_op_mode_rate[self.mp_model.set_period.at(t)] ) @@ -618,14 +620,14 @@ def add_startup_shutdown( if design_blk is not None: @blk.Constraint(self.range_time_steps) def design_op_relationship_con(b, t): - return (op_mode[self.mp_model.set_period[t]] <= build) + return (op_mode[self.mp_model.set_period.at(t)] <= build) @blk.Constraint(self.range_time_steps) def Binary_relationhsip_con(b, t): if t == 1 or t > number_time_steps: return Constraint.Skip - return (op_mode[self.mp_model.set_period[t]] - op_mode[self.mp_model.set_period[t-1]] == - start_up[self.mp_model.set_period[t-1]] - shut_down[self.mp_model.set_period[t]] + return (op_mode[self.mp_model.set_period.at(t)] - op_mode[self.mp_model.set_period.at(t-1)] == + start_up[self.mp_model.set_period.at(t-1)] - shut_down[self.mp_model.set_period.at(t)] ) if use_min_time: @@ -634,14 +636,14 @@ def minimum_up_time_con(b,t): if t == 1 or t < up_time or t > number_time_steps: return Constraint.Skip else: - return sum(start_up[self.mp_model.set_period[i]] for i in range(t-up_time+1, t+1)) <= op_mode[self.mp_model.set_period[t]] + return sum(start_up[self.mp_model.set_period.at(i)] for i in range(t-up_time+1, t+1)) <= op_mode[self.mp_model.set_period.at(t)] @blk.Constraint(self.range_time_steps) def minimum_down_time_con(b, t): if t < down_time or t == 1 or t > number_time_steps: return Constraint.Skip - return (sum(shut_down[self.mp_model.set_period[i]] for i in range(t-down_time+1, t+1)) <= - 1 - op_mode[self.mp_model.set_period[t]]) + return (sum(shut_down[self.mp_model.set_period.at(i)] for i in range(t-down_time+1, t+1)) <= + 1 - op_mode[self.mp_model.set_period.at(t)]) def build_hourly_cashflows(self, revenue_streams=['elec_revenue',], additional_costs=None): From 769ba7bfd2cd19e6708bb348dcacf97e4aefc6e9 Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Fri, 16 Feb 2024 12:19:24 -0500 Subject: [PATCH 050/126] Removed redundant set definition The set 'range_time_steps' was defined in both the ramping rate and startup/shutdown function. Also, wrote the try/except statement with the hasattr() function instead since it is one line and much neater. --- .../multiperiod/price_taker_model.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index c55db06060..bb9cb0e497 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -400,12 +400,7 @@ def build_multiperiod_model(self, **kwargs): # If append_lmp_data is automatic, need to append the LMP data. # Check if LMP has already been defined with the append_lmp_data # function above - LMP_exists = False - try: - _ = self.LMP - LMP_exists = True - except: - LMP_exists = False + LMP_exists = hasattr(self, 'LMP') # Iterate through model to append LMP data if it's been defined # and the model says it should be (default) @@ -529,8 +524,9 @@ def add_ramping_constraints( - # Importing in the nessisary variables - self.range_time_steps = RangeSet(len(self.mp_model.set_period)) + # Importing in the necessary variables + if not hasattr(self, 'range_time_steps'): + self.range_time_steps = RangeSet(len(self.mp_model.set_period)) #Creating the pyomo block blk_name = op_blk.split(".")[-1] + "_rampup_rampdown" @@ -610,7 +606,8 @@ def add_startup_shutdown( if design_blk is not None: build = deepgetattr(self,design_blk + "." + build_binary_var) - self.range_time_steps = RangeSet(len(self.mp_model.set_period)) + if not hasattr(self, 'range_time_steps'): + self.range_time_steps = RangeSet(len(self.mp_model.set_period)) number_time_steps = len(self.mp_model.set_period) blk_name = op_blk.split(".")[-1] + "_startup_shutdown" From 0bba4d2cf46a615a22fba7f367c6a81781c7a161 Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Fri, 16 Feb 2024 15:05:24 -0500 Subject: [PATCH 051/126] Added n_clusters check in append_lmp_data Added a check to make sure that n_clusters is feasible. Also added tests to capture edge cases. --- .../multiperiod/price_taker_model.py | 26 +++++++++++++----- .../multiperiod/tests/test_price_taker.py | 27 ++++++++++++++++++- 2 files changed, 45 insertions(+), 8 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index bb9cb0e497..8c335fedd8 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -279,7 +279,7 @@ def append_lmp_data( Args: file_path: path to the file containing LMP data - sheet: if the file is in axcel file, the sheet name + sheet: if the file is an excel file, the sheet name where the LMP data is located - (default: None) column_name: list of integer year names if multiple years of data are to be used, None otherwise - (default: None) @@ -295,7 +295,7 @@ def append_lmp_data( if os.path.exists(file_path): path_to_file = file_path else: - raise ValueError() + raise ValueError(f"The file path {file_path} does not exist. Please check your file path.") if '.xls' in path_to_file[-5:]: full_data = pd.read_excel( path_to_file, sheet_name=[sheet])[sheet] @@ -303,7 +303,13 @@ def append_lmp_data( full_data = pd.read_csv(path_to_file, ) if horizon_length is not None: - self.horizon_length(horizon_length) + self.horizon_length = horizon_length + + if n_clusters is not None: + if not isinstance(n_clusters, int): + raise ValueError(f"n_clusters must be an integer, but {n_clusters} is not an integer") + if n_clusters < 1: + raise ValueError(f"n_clusters must be > 0, but {n_clusters} is provided.") # editing the data if isinstance(column_name, list) and n_clusters is not None: @@ -643,7 +649,7 @@ def minimum_down_time_con(b, t): 1 - op_mode[self.mp_model.set_period.at(t)]) - def build_hourly_cashflows(self, revenue_streams=['elec_revenue',], additional_costs=None): + def build_hourly_cashflows(self, revenue_streams=['elec_revenue',], costs=None): """ Adds an expression for the net cash inflow for each operational block. This is the new cash inflow for each time period of the @@ -666,7 +672,7 @@ def build_hourly_cashflows(self, revenue_streams=['elec_revenue',], additional_c costs associated with operating at a time period. (default: None) example: ['hourly_fixed_cost', - '',] + 'electricity_cost',] Returns: @@ -674,13 +680,19 @@ def build_hourly_cashflows(self, revenue_streams=['elec_revenue',], additional_c period = self.mp_model.period for p in period: + for cost_name in costs: + setattr(self.mp_model.period[p], cost_name, 0) + + for revenue_name in revenue_streams: + setattr(self.mp_model.period[p], revenue_name, 0) + non_fuel_vom = 0 fuel_cost = 0 elec_revenue = 0 carbon_price = 0 - # ToDo: Add multiple revenue streams to the model - # ToDo: Add additional costs for time period + # ToDo: Add multiple revenue streams to the model (full user control) + # ToDo: Make costs a list as well (full user control) for blk in period[p].component_data_objects(Block): if isinstance(blk, OperationModelData): diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index b734169436..b5250ef993 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -78,7 +78,6 @@ def test_cluster_lmp_data(excel_data): daily_data, scenarios = m.reconfigure_raw_data(excel_data) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data) - # lmp_data, weights = m.cluster_lmp_data(daily_data, n_clusters) lmp_data, weights = m.cluster_lmp_data(excel_data, n_clusters) sum_of_weights = 0 @@ -280,6 +279,32 @@ def test_logger_messages(excel_data, caplog): m = PriceTakerModel() m.add_ramping_constraints(des, oper, capac_var, ramping_var, constraint_type, linearization, op_range_lb[2], su_rate[1], sd_rate[1], op_ru_rate[1], op_rd_rate[1]) + + # Testing raised errors in append_lmp_data + file_path = "FLECCS.xlsx" + n_clusters = [-5, 1.7, 10] + with pytest.raises( + ValueError, + match=(f"n_clusters must be an integer, but {n_clusters[1]} is not an integer"), + ): + m = PriceTakerModel() + m.append_lmp_data(file_path, sheet='2035 - NREL', column_name='MiNg_$100_CAISO', n_clusters=n_clusters[1], horizon_length=24,) + + with pytest.raises( + ValueError, + match=(f"n_clusters must be > 0, but {n_clusters[0]} is provided."), + ): + m = PriceTakerModel() + m.append_lmp_data(file_path, sheet='2035 - NREL', column_name='MiNg_$100_CAISO', n_clusters=n_clusters[0], horizon_length=24,) + + file_path = 'trash' + with pytest.raises( + ValueError, + match=(f"The file path {file_path} does not exist. Please check your file path."), + ): + m = PriceTakerModel() + m.append_lmp_data(file_path, sheet='2035 - NREL', column_name='MiNg_$100_CAISO', n_clusters=n_clusters[2], horizon_length=24,) + From b06d467f3aca4266b9da8a69729d7071ae556b9d Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Tue, 20 Feb 2024 09:53:22 -0500 Subject: [PATCH 052/126] Reorganizing unit logging messages tests Disaggregated the unit tests for logger messages. Should make it easier to debug later. --- .../multiperiod/tests/test_price_taker.py | 70 ++++++++++++------- 1 file changed, 46 insertions(+), 24 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index b5250ef993..6768f00b68 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -89,7 +89,7 @@ def test_cluster_lmp_data(excel_data): @pytest.mark.unit -def test_logger_messages(excel_data, caplog): +def test_init_logger_messages(excel_data, caplog): with caplog.at_level(idaeslog.WARNING): m = PriceTakerModel() @@ -98,18 +98,6 @@ def test_logger_messages(excel_data, caplog): assert f"kmax was not set - using a default value of 30." in caplog.text - # TODO: The below test is not working because our data doesn't ever seem to arrive at an n_clusters close to kmax - # caplog.clear() - # with caplog.at_level(idaeslog.WARNING): - # m = PriceTakerModel() - # kmin = 1 - # kmax = 7 - # - # daily_data, scenarios = m.reconfigure_raw_data(excel_data) - # m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) - # - # assert f"Optimal number of clusters is close to kmax: {kmax}. Consider increasing kmax." in caplog.text - # Testing horizon_length input value errors value = 0 with pytest.raises( @@ -134,8 +122,10 @@ def test_logger_messages(excel_data, caplog): ): m = PriceTakerModel() m.seed = value - - # Testing up_time and down_time input value errors + + +@pytest.mark.unit +def test_min_up_down_time_logger_messages(excel_data): des = DesignModel() oper = OperationModel() build_bin_var = 'build' @@ -169,8 +159,10 @@ def test_logger_messages(excel_data, caplog): ): m = PriceTakerModel() m.add_startup_shutdown(des, oper, build_bin_var, use_min_time, up_time[0], down_time[1]) - - # Testing kmin and kmax input value errors + + +@pytest.mark.unit +def test_optimal_clusters_logger_messages(excel_data): kmin = [-5, 10.2, 9] kmax = [-5, 10.2, 8] with pytest.raises( @@ -220,7 +212,21 @@ def test_logger_messages(excel_data, caplog): daily_data, scenarios = m.reconfigure_raw_data(excel_data) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin[2], kmax=kmax[2]) - # Testing ramping rate input value errors + # TODO: The below test is not working because our data doesn't ever seem to arrive at an n_clusters close to kmax + # caplog.clear() + # with caplog.at_level(idaeslog.WARNING): + # m = PriceTakerModel() + # kmin = 1 + # kmax = 7 + # + # daily_data, scenarios = m.reconfigure_raw_data(excel_data) + # m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) + # + # assert f"Optimal number of clusters is close to kmax: {kmax}. Consider increasing kmax." in caplog.text + + +@pytest.mark.unit +def test_ramping_constraint_logger_messages(excel_data): des = DesignModel() oper = OperationModel() ramping_var = 'power' @@ -279,8 +285,10 @@ def test_logger_messages(excel_data, caplog): m = PriceTakerModel() m.add_ramping_constraints(des, oper, capac_var, ramping_var, constraint_type, linearization, op_range_lb[2], su_rate[1], sd_rate[1], op_ru_rate[1], op_rd_rate[1]) - - # Testing raised errors in append_lmp_data + + +@pytest.mark.unit +def test_append_lmp_data_logger_messages(excel_data): file_path = "FLECCS.xlsx" n_clusters = [-5, 1.7, 10] with pytest.raises( @@ -306,10 +314,24 @@ def test_logger_messages(excel_data, caplog): m.append_lmp_data(file_path, sheet='2035 - NREL', column_name='MiNg_$100_CAISO', n_clusters=n_clusters[2], horizon_length=24,) - - - - +# @pytest.mark.unit +# def test_deepgetattr_logger_messages(excel_data): +# m = PriceTakerModel() +# des = DesignModel() +# oper = OperationModel() +# ramping_var = 'power' +# capac_var = 'power_max' +# constraint_type = 'linear' +# linearization = False +# op_range_lb = [-0.1, 0.5, 1.0] +# su_rate = [-0.1, 0.5] +# sd_rate = [-0.1, 0.5] +# op_ru_rate = [-0.1, 0.5] +# op_rd_rate = [-0.1, 0.5] +# with pytest.raises( +# ValueError, +# match() +# ): def dfc_design(m, params, capacity_range=(650, 900)): _dfc_capacity = params["dfc_capacity"] From 0cf82bf0146b05cc26d05a902499b1d4aeb57e3a Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Wed, 21 Feb 2024 08:46:18 -0500 Subject: [PATCH 053/126] Moved deepgetattr to import from design_and_operation_models.py Removed duplicate deepgetattr function and added it as an import to the __init__.py file in the idaes.apps.grid_integration tree. Also, added a test for deepgetattr's error message. --- idaes/apps/grid_integration/__init__.py | 2 +- .../design_and_operation_models.py | 8 ++++- .../multiperiod/price_taker_model.py | 10 +++--- .../multiperiod/tests/test_price_taker.py | 33 ++++++++----------- 4 files changed, 28 insertions(+), 25 deletions(-) diff --git a/idaes/apps/grid_integration/__init__.py b/idaes/apps/grid_integration/__init__.py index a08eeb13d4..9a5214be69 100644 --- a/idaes/apps/grid_integration/__init__.py +++ b/idaes/apps/grid_integration/__init__.py @@ -16,4 +16,4 @@ from .forecaster import PlaceHolderForecaster from .multiperiod.multiperiod import MultiPeriodModel from .multiperiod.price_taker_model import PriceTakerModel -from .multiperiod.design_and_operation_models import DesignModel, OperationModel +from .multiperiod.design_and_operation_models import DesignModel, OperationModel, deepgetattr diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py index 34bcdb8851..363ab5d3ee 100644 --- a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -24,8 +24,14 @@ from idaes.models.unit_models import SkeletonUnitModelData from pyomo.common.config import ConfigValue, In + def deepgetattr(obj, attr): - return reduce(getattr, attr.split('.'), obj) + try: + return reduce(getattr, attr.split('.'), obj) + except: + raise ValueError(f"The attribute `{attr}` does not exist on the {obj.__class__.__name__} instance passed. Make sure that `{attr}` is defined for that object.") + + @declare_process_block_class("DesignModel") class DesignModelData(SkeletonUnitModelData): """ diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 8c335fedd8..3da7c44d5d 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -35,6 +35,7 @@ from idaes.apps.grid_integration.multiperiod.design_and_operation_models import ( DesignModelData, OperationModelData, + deepgetattr, ) from sklearn.cluster import KMeans @@ -47,10 +48,6 @@ _logger = logging.getLogger(__name__) -def deepgetattr(obj, attr): - return reduce(getattr, attr.split('.'), obj) - - class PriceTakerModel(ConcreteModel): def __init__(self, seed=20, horizon_length=24): super().__init__() @@ -693,6 +690,8 @@ def build_hourly_cashflows(self, revenue_streams=['elec_revenue',], costs=None): # ToDo: Add multiple revenue streams to the model (full user control) # ToDo: Make costs a list as well (full user control) + # ToDo: Add check for whether or not OperationModelData blocks were found + # (Should throw warning if none were found.) for blk in period[p].component_data_objects(Block): if isinstance(blk, OperationModelData): @@ -742,6 +741,9 @@ def build_cashflows( capex_expr = 0 fom_expr = 0 + + # ToDo: Add check for whether or not DesignModelData blocks were found + # (Should throw warning if none were found.) for blk in self.component_data_objects(Block): if isinstance(blk, DesignModelData): capex_expr += blk.capex diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index 6768f00b68..0a32df7d5a 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -24,7 +24,7 @@ Expression, ) -from idaes.apps.grid_integration import DesignModel, OperationModel +from idaes.apps.grid_integration import DesignModel, OperationModel, deepgetattr import idaes.logger as idaeslog @@ -314,24 +314,19 @@ def test_append_lmp_data_logger_messages(excel_data): m.append_lmp_data(file_path, sheet='2035 - NREL', column_name='MiNg_$100_CAISO', n_clusters=n_clusters[2], horizon_length=24,) -# @pytest.mark.unit -# def test_deepgetattr_logger_messages(excel_data): -# m = PriceTakerModel() -# des = DesignModel() -# oper = OperationModel() -# ramping_var = 'power' -# capac_var = 'power_max' -# constraint_type = 'linear' -# linearization = False -# op_range_lb = [-0.1, 0.5, 1.0] -# su_rate = [-0.1, 0.5] -# sd_rate = [-0.1, 0.5] -# op_ru_rate = [-0.1, 0.5] -# op_rd_rate = [-0.1, 0.5] -# with pytest.raises( -# ValueError, -# match() -# ): +@pytest.mark.unit +def test_deepgetattr_logger_messages(excel_data): + class Data: + def __init__(self, value): + self.value = value + + A = Data(5) + attr = 'valu' + with pytest.raises( + ValueError, + match=(f"The attribute `{attr}` does not exist on the {A.__class__.__name__} instance passed. Make sure that `{attr}` is defined for that object."), + ): + deepgetattr(A, attr) def dfc_design(m, params, capacity_range=(650, 900)): _dfc_capacity = params["dfc_capacity"] From 6af8c9ed0ad6ebcdf467266281688319b469e385 Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Wed, 21 Feb 2024 15:05:11 -0500 Subject: [PATCH 054/126] Added checks and tests Added a check and test for n_clusters being a valid value for cluster_lmp_data. Also added a check to see if the horizon length exceed the raw_data length, meaning an empty data frame would be returned. --- .../multiperiod/price_taker_model.py | 13 +++++++- .../multiperiod/tests/test_price_taker.py | 32 +++++++++++++++++++ 2 files changed, 44 insertions(+), 1 deletion(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 3da7c44d5d..984b3eed69 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -95,6 +95,10 @@ def generate_daily_data(self,raw_data,day_list): i = 0 j = self._horizon_length day = 1 + + if j > len(raw_data): + raise ValueError(f"tried to generate daily data, but horizon length of {self._horizon_length} exceeds raw_data length of {len(raw_data)}") + while j <= len(raw_data): daily_data[day] = raw_data[i:j].reset_index(drop=True) i = j @@ -205,7 +209,7 @@ def get_optimal_n_clusters( plt.grid() plt.show() - return n_clusters, inertia_values + return int(n_clusters), inertia_values def cluster_lmp_data(self, raw_data, n_clusters): """ @@ -225,6 +229,13 @@ def cluster_lmp_data(self, raw_data, n_clusters): same way as lmp_data (ex: {1: 45, 2: 56}) Format: {day: weight} """ + # testing if n_integers is valid + if n_clusters is not None: + if not isinstance(n_clusters, int): + raise ValueError(f"n_clusters must be an integer, but {n_clusters} is not an integer") + if n_clusters < 1: + raise ValueError(f"n_clusters must be > 0, but {n_clusters} is provided.") + # reconfiguring raw data daily_data, scenarios = self.reconfigure_raw_data(raw_data) diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index 0a32df7d5a..40636059de 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -54,6 +54,9 @@ def test_daily_data_size(excel_data): @pytest.mark.unit def test_determine_optimal_num_clusters(excel_data): + # Test fails because the optimal number of clusters is different + # based on which version of scikit-learn and kneed you have. + # Older versions get n_clusters = 10, Newer versions n_clusters = 11 m = PriceTakerModel() daily_data, scenarios = m.reconfigure_raw_data(excel_data) @@ -313,6 +316,23 @@ def test_append_lmp_data_logger_messages(excel_data): m = PriceTakerModel() m.append_lmp_data(file_path, sheet='2035 - NREL', column_name='MiNg_$100_CAISO', n_clusters=n_clusters[2], horizon_length=24,) +@pytest.mark.unit +def test_cluster_lmp_data_logger_messages(excel_data): + n_clusters = [-5, 1.7, 10] + with pytest.raises( + ValueError, + match=(f"n_clusters must be an integer, but {n_clusters[1]} is not an integer"), + ): + m = PriceTakerModel() + _, _ = m.cluster_lmp_data(excel_data, n_clusters[1]) + + with pytest.raises( + ValueError, + match=(f"n_clusters must be > 0, but {n_clusters[0]} is provided."), + ): + m = PriceTakerModel() + _, _ = m.cluster_lmp_data(excel_data, n_clusters[0]) + @pytest.mark.unit def test_deepgetattr_logger_messages(excel_data): @@ -328,6 +348,18 @@ def __init__(self, value): ): deepgetattr(A, attr) + +@pytest.mark.unit +def test_generate_daily_data_logger_messages(excel_data): + raw_data = excel_data['BaseCaseTax'] + with pytest.raises( + ValueError, + match=(f"tried to generate daily data, but horizon length of {9000} exceeds raw_data length of {len(raw_data)}"), + ): + m = PriceTakerModel(horizon_length=9000) + daily_data = m.generate_daily_data(raw_data, day_list=list(range(365))) + + def dfc_design(m, params, capacity_range=(650, 900)): _dfc_capacity = params["dfc_capacity"] _ng_flow = params["ng_flow"] From 1fc5f53b4597c7c9b6484b88a935fedd9b50b7a9 Mon Sep 17 00:00:00 2001 From: Daniel Laky Date: Thu, 22 Feb 2024 11:03:22 -0500 Subject: [PATCH 055/126] Updated data processing workflow Removed reconfigure data as it was poor coding practice (i.e., all functionality was done by generate_daily_data). Updated all tests to accommodate. Updated all functions in price_taker_model to accommodate. Also added warning messages for when sheet and column are not defined along with test for these warning messages. --- .../multiperiod/price_taker_model.py | 59 +++++------------- .../multiperiod/tests/FLECCS_no_sheet.xlsx | Bin 0 -> 477567 bytes .../multiperiod/tests/test_price_taker.py | 43 +++++++++---- 3 files changed, 47 insertions(+), 55 deletions(-) create mode 100644 idaes/apps/grid_integration/multiperiod/tests/FLECCS_no_sheet.xlsx diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 984b3eed69..5d5f0872ac 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -76,18 +76,18 @@ def horizon_length(self, value): raise ValueError(f"horizon_length must be an integer, but {value} is not an integer") self._horizon_length = value - def generate_daily_data(self,raw_data,day_list): + def generate_daily_data(self, raw_data): """ Function used to generate the daily data in a usable format from the raw data provided. Args: - raw_data: LMP data in raw format - day_list: List of days to be used from the raw data + raw_data: LMP data as a column of values Returns: - daily_data: Correctly formatted LMP data for later use + daily_data: Correctly formatted daily LMP data for later use """ + day_list = list(range(1, (len(raw_data) // self._horizon_length) + 1)) daily_data = pd.DataFrame(columns=day_list) @@ -107,34 +107,6 @@ def generate_daily_data(self,raw_data,day_list): return daily_data - def reconfigure_raw_data(self, raw_data): - """ - Reconfigures the raw price series data into a usable form - - Args: - raw_data: imported price series data - - Returns: - daily_data: reconfigured price series data - scenarios: ??? Add description ??? - """ - - # Get column headings - column_head = raw_data.columns.tolist() - # Remove the date/time column - scenarios = column_head[2:] - - # Creating an empty dataframe to store daily data for clustering - day_list = list(range(1, (len(raw_data) // self._horizon_length) + 1)) - - # Generate daily data - for i in scenarios[0:1]: - daily_data = self.generate_daily_data( - raw_data=raw_data[i], day_list=day_list - ) - - return daily_data, scenarios - def get_optimal_n_clusters( self, daily_data, @@ -237,7 +209,7 @@ def cluster_lmp_data(self, raw_data, n_clusters): raise ValueError(f"n_clusters must be > 0, but {n_clusters} is provided.") # reconfiguring raw data - daily_data, scenarios = self.reconfigure_raw_data(raw_data) + daily_data = self.generate_daily_data(raw_data) # KMeans clustering with the optimal number of clusters kmeans = KMeans(n_clusters=n_clusters).fit(daily_data.transpose()) @@ -306,10 +278,21 @@ def append_lmp_data( raise ValueError(f"The file path {file_path} does not exist. Please check your file path.") if '.xls' in path_to_file[-5:]: + if sheet is None: + _logger.warning( + f"Excel file was provided but no sheet was specified. Using the first sheet of the excel file." + ) + sheet = 0 full_data = pd.read_excel( path_to_file, sheet_name=[sheet])[sheet] elif '.csv' in path_to_file[-5:]: full_data = pd.read_csv(path_to_file, ) + if column_name is None: + _logger.warning( + f"Data was provided but no column name was provided. Using the first column of the data." + ) + column_name = full_data.columns[0] + if horizon_length is not None: self.horizon_length = horizon_length @@ -372,7 +355,7 @@ def append_lmp_data( self.LMP = {} self.WEIGHTS = {} - price_data = full_data + price_data = full_data[column_name] lmp_data, weights = self.cluster_lmp_data( price_data, n_clusters ) @@ -432,14 +415,6 @@ def build_multiperiod_model(self, **kwargs): blk.LMP = self.LMP[p] - - - #TODO: (1) Need to determine whether the minimum opterating power can be a parameter - # or whether it largly depends on the capacity of the plant. - # (2) Need to determine if the start up and shutdown limits can be modeled as bulk amounts vs - # a percentage of capacity - # (3) How to model/code the cosntraints in without knowing what variable - # these constraints will be applied to. def add_ramping_constraints( self, op_blk, diff --git a/idaes/apps/grid_integration/multiperiod/tests/FLECCS_no_sheet.xlsx 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zeh0Yz2fz;1^WAD{CJMvH{(w+@OJ(?Ydv-!;7^bEcK}C$-}~Ti{_%aRKmDrT wvD}6Jj`hpSx{vkeA`B{NX{UKtIhbiFy0LsA^Z~y=R literal 0 HcmV?d00001 diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index 40636059de..694a02c54f 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -46,7 +46,7 @@ def test_daily_data_size(excel_data): m = PriceTakerModel() # Generate price data for each hour of every day in the data - daily_data, scenarios = m.reconfigure_raw_data(excel_data) + daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) # Check that there is a row for each horizon length in a representative day assert len(daily_data) == m.horizon_length @@ -59,7 +59,7 @@ def test_determine_optimal_num_clusters(excel_data): # Older versions get n_clusters = 10, Newer versions n_clusters = 11 m = PriceTakerModel() - daily_data, scenarios = m.reconfigure_raw_data(excel_data) + daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data) assert n_clusters == 10 @@ -69,7 +69,7 @@ def test_determine_optimal_num_clusters(excel_data): def test_elbow_plot(excel_data): m = PriceTakerModel() - daily_data, scenarios = m.reconfigure_raw_data(excel_data) + daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) m.get_optimal_n_clusters(daily_data, plot=True) assert plt.gcf() is not None @@ -79,9 +79,9 @@ def test_elbow_plot(excel_data): def test_cluster_lmp_data(excel_data): m = PriceTakerModel() - daily_data, scenarios = m.reconfigure_raw_data(excel_data) + daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data) - lmp_data, weights = m.cluster_lmp_data(excel_data, n_clusters) + lmp_data, weights = m.cluster_lmp_data(excel_data['BaseCaseTax'], n_clusters=n_clusters) sum_of_weights = 0 for i in range(1, n_clusters + 1): @@ -96,7 +96,7 @@ def test_init_logger_messages(excel_data, caplog): with caplog.at_level(idaeslog.WARNING): m = PriceTakerModel() - daily_data, scenarios = m.reconfigure_raw_data(excel_data) + daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) m.get_optimal_n_clusters(daily_data) assert f"kmax was not set - using a default value of 30." in caplog.text @@ -174,7 +174,7 @@ def test_optimal_clusters_logger_messages(excel_data): ): m = PriceTakerModel() - daily_data, scenarios = m.reconfigure_raw_data(excel_data) + daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin[1], kmax=kmax[2]) with pytest.raises( @@ -183,7 +183,7 @@ def test_optimal_clusters_logger_messages(excel_data): ): m = PriceTakerModel() - daily_data, scenarios = m.reconfigure_raw_data(excel_data) + daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin[2], kmax=kmax[1]) with pytest.raises( @@ -192,7 +192,7 @@ def test_optimal_clusters_logger_messages(excel_data): ): m = PriceTakerModel() - daily_data, scenarios = m.reconfigure_raw_data(excel_data) + daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin[0], kmax=kmax[2]) @@ -202,7 +202,7 @@ def test_optimal_clusters_logger_messages(excel_data): ): m = PriceTakerModel() - daily_data, scenarios = m.reconfigure_raw_data(excel_data) + daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin[2], kmax=kmax[0]) m.cluster_lmp_data(excel_data, n_clusters) @@ -212,7 +212,7 @@ def test_optimal_clusters_logger_messages(excel_data): ): m = PriceTakerModel() - daily_data, scenarios = m.reconfigure_raw_data(excel_data) + daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin[2], kmax=kmax[2]) # TODO: The below test is not working because our data doesn't ever seem to arrive at an n_clusters close to kmax @@ -291,7 +291,7 @@ def test_ramping_constraint_logger_messages(excel_data): @pytest.mark.unit -def test_append_lmp_data_logger_messages(excel_data): +def test_append_lmp_data_logger_messages(excel_data, caplog): file_path = "FLECCS.xlsx" n_clusters = [-5, 1.7, 10] with pytest.raises( @@ -316,6 +316,23 @@ def test_append_lmp_data_logger_messages(excel_data): m = PriceTakerModel() m.append_lmp_data(file_path, sheet='2035 - NREL', column_name='MiNg_$100_CAISO', n_clusters=n_clusters[2], horizon_length=24,) + file_path = "FLECCS_no_sheet.xlsx" + caplog.clear() + with caplog.at_level(idaeslog.WARNING): + m = PriceTakerModel() + + m.append_lmp_data(file_path, column_name=1, n_clusters=n_clusters[2], horizon_length=24,) + + assert f"Excel file was provided but no sheet was specified. Using the first sheet of the excel file." in caplog.text + + caplog.clear() + with caplog.at_level(idaeslog.WARNING): + m = PriceTakerModel() + + m.append_lmp_data(file_path, sheet=0, n_clusters=n_clusters[2], horizon_length=24,) + + assert f"Data was provided but no column name was provided. Using the first column of the data." in caplog.text + @pytest.mark.unit def test_cluster_lmp_data_logger_messages(excel_data): n_clusters = [-5, 1.7, 10] @@ -357,7 +374,7 @@ def test_generate_daily_data_logger_messages(excel_data): match=(f"tried to generate daily data, but horizon length of {9000} exceeds raw_data length of {len(raw_data)}"), ): m = PriceTakerModel(horizon_length=9000) - daily_data = m.generate_daily_data(raw_data, day_list=list(range(365))) + daily_data = m.generate_daily_data(raw_data) def dfc_design(m, params, capacity_range=(650, 900)): From e991392828582f29a44630983213a7017c469a27 Mon Sep 17 00:00:00 2001 From: Daniel Laky Date: Thu, 22 Feb 2024 11:17:28 -0500 Subject: [PATCH 056/126] Small update to append_lmp_data Updated lmp_data to allow for non-integer columns to be used. Ultimately, the user should be responsible for column naming and correlating LMP data. Code is now more general. --- .../multiperiod/price_taker_model.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 5d5f0872ac..54c5f7bec8 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -305,7 +305,7 @@ def append_lmp_data( # editing the data if isinstance(column_name, list) and n_clusters is not None: # Multiple years and representative days - self.set_years = [int(y) for y in column_name] + self.set_years = [y for y in column_name] self.set_days = RangeSet(1,n_clusters) self._n_time_points = self.horizon_length if self.horizon_length is not None else 24 self.set_time = RangeSet(self._n_time_points) @@ -313,12 +313,11 @@ def append_lmp_data( self.LMP = {} self.WEIGHTS = {} - for year in column_name: - price_data = full_data[year] + for y in column_name: + price_data = full_data[y] lmp_data, weights = self.cluster_lmp_data( price_data, n_clusters ) - y = int(year) for d in self.set_days: for t in self.set_time: @@ -329,16 +328,15 @@ def append_lmp_data( elif isinstance(column_name, list): # Multiple years, use fullyear price signal for each year - self.set_years = [int(y) for y in column_name] + self.set_years = [y for y in column_name] self.set_days = None self._n_time_points = len(full_data) self.set_time = RangeSet(self._n_time_points) self.LMP = {} - for year in column_name: - price_data = full_data[year] - y = int(year) + for y in column_name: + price_data = full_data[y] for t in self.set_time: self.LMP[t, y] = price_data[t - 1] From f2725cc89805999b2792af92f7464a512020308f Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Tue, 27 Feb 2024 09:23:57 -0500 Subject: [PATCH 057/126] Updated get_optimal_n_clusters Found a case where the warning message should play (and added comments that a few test could be deleted as functions work properly but output may depend on external packages of kneed and scikit-learn). Also fixed up get_optimal_n_clusters and cluster_lmp_data as there were some unnecessary loops, lines of code, and if statements to test. --- .../multiperiod/price_taker_model.py | 34 +++++-------- .../multiperiod/tests/test_price_taker.py | 51 +++++++++++++------ 2 files changed, 49 insertions(+), 36 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index 54c5f7bec8..cdffbee149 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -82,7 +82,7 @@ def generate_daily_data(self, raw_data): from the raw data provided. Args: - raw_data: LMP data as a column of values + raw_data: Columnar data for a given LMP signal Returns: daily_data: Correctly formatted daily LMP data for later use @@ -119,7 +119,7 @@ def get_optimal_n_clusters( given price signal. Args: - daily_data: reconfigured price series data + daily_data: LMP signal grouped by days (output of generate_daily_data function) kmin: minimum number of clusters kmax: maximum number of clusters plot: flag to determine if an elbow plot should be displayed @@ -146,13 +146,12 @@ def get_optimal_n_clusters( if kmin >= kmax: raise ValueError(f"kmin must be less than kmax, but {kmin} >= {kmax}") - k_values = range(kmin, kmax) + k_values = range(kmin, kmax + 1) inertia_values = [] np.random.seed(self._seed) # Compute the inertia (SSE) for k clusters - for k in k_values: kmeans = KMeans(n_clusters=k).fit(daily_data.transpose()) inertia_values.append(kmeans.inertia_) @@ -163,9 +162,12 @@ def get_optimal_n_clusters( ) n_clusters = elbow_point.knee + if n_clusters is None: + raise ValueError(f"Could not find elbow point for given kmin, kmax. Consider increasing the range of kmin, kmax.") + print(f"Optimal # of clusters is: {n_clusters}") - if n_clusters + 2 >= kmax: + if int(n_clusters) + 2 >= kmax: _logger.warning( f"Optimal number of clusters is close to kmax: {kmax}. Consider increasing kmax." ) @@ -189,7 +191,7 @@ def cluster_lmp_data(self, raw_data, n_clusters): techniques for clustering. Args: - raw_data: imported price series data + raw_data: Columnar data for a given LMP signal n_clusters: number of clusters desired for the data (representative days) Returns: @@ -217,26 +219,16 @@ def cluster_lmp_data(self, raw_data, n_clusters): labels = kmeans.labels_ # Set any centroid values that are < 1e-4 to 0 to avoid noise - for d in range(n_clusters): - for t in range(24): - if centroids[d][t] < 1e-4: - centroids[d][t] = 0 - - n_clusters_list = range(0, n_clusters) - weights_counter = np.zeros(n_clusters) + centroids = centroids * (centroids >= 1e-4) # Compute weight for each cluster by counting its occurrences in the dataset - for j in range(0, len(labels)): - for k in n_clusters_list: - if labels[j] == k: - weights_counter[k] += 1 + unique_labels, weights_counter = np.unique(labels, return_counts=True) # Create dicts for lmp data and the weight of each cluster rep_days_data = {} weights_data = {} - - rep_days_data = pd.DataFrame(centroids.transpose(), columns = range(1,n_clusters+1)) + rep_days_data = pd.DataFrame(centroids.transpose(), columns=range(1,n_clusters+1)) lmp_data = rep_days_data.to_dict() weights_data = pd.DataFrame(weights_counter) weights_data.index = np.arange(1, len(weights_data) + 1) @@ -499,8 +491,8 @@ def add_ramping_constraints( blk = deepgetattr(self.mp_model.period[t],op_blk) blk._add_capacity_aux_vars() - act_startup_rate= {t: deepgetattr(self.mp_model.period[t], op_blk + "." + capacity_var + "_startup") for t in self.mp_model.period} - act_shutdown_rate= {t: deepgetattr(self.mp_model.period[t], op_blk + "." + capacity_var + "_shutdown") for t in self.mp_model.period} + act_startup_rate = {t: deepgetattr(self.mp_model.period[t], op_blk + "." + capacity_var + "_startup") for t in self.mp_model.period} + act_shutdown_rate = {t: deepgetattr(self.mp_model.period[t], op_blk + "." + capacity_var + "_shutdown") for t in self.mp_model.period} act_op_mode_rate = {t: deepgetattr(self.mp_model.period[t], op_blk + "." + capacity_var + "_op_mode") for t in self.mp_model.period} elif linearization == False: var_capacity = deepgetattr(self, design_blk + "." + capacity_var) diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index 694a02c54f..23a9849320 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -54,15 +54,18 @@ def test_daily_data_size(excel_data): @pytest.mark.unit def test_determine_optimal_num_clusters(excel_data): - # Test fails because the optimal number of clusters is different - # based on which version of scikit-learn and kneed you have. + # Added a range for optimal cluster values based on how the + # plot appears visually. Test can be removed in the future if + # failure occurs. This may depend on scikit-learn and kneed and + # the interaction thereof. + # Older versions get n_clusters = 10, Newer versions n_clusters = 11 m = PriceTakerModel() daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data) - assert n_clusters == 10 + assert 9 <= n_clusters <= 15 @pytest.mark.unit @@ -77,6 +80,8 @@ def test_elbow_plot(excel_data): @pytest.mark.unit def test_cluster_lmp_data(excel_data): + # This function gets within both if statement expression in the + # cluster_lmp_data function currently. m = PriceTakerModel() daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) @@ -165,7 +170,7 @@ def test_min_up_down_time_logger_messages(excel_data): @pytest.mark.unit -def test_optimal_clusters_logger_messages(excel_data): +def test_optimal_clusters_logger_messages(excel_data, caplog): kmin = [-5, 10.2, 9] kmax = [-5, 10.2, 8] with pytest.raises( @@ -215,17 +220,33 @@ def test_optimal_clusters_logger_messages(excel_data): daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin[2], kmax=kmax[2]) - # TODO: The below test is not working because our data doesn't ever seem to arrive at an n_clusters close to kmax - # caplog.clear() - # with caplog.at_level(idaeslog.WARNING): - # m = PriceTakerModel() - # kmin = 1 - # kmax = 7 - # - # daily_data, scenarios = m.reconfigure_raw_data(excel_data) - # m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) - # - # assert f"Optimal number of clusters is close to kmax: {kmax}. Consider increasing kmax." in caplog.text + with pytest.raises( + ValueError, + match=(f"Could not find elbow point for given kmin, kmax. Consider increasing the range of kmin, kmax."), + ): + m = PriceTakerModel() + + kmin = 9 + kmax = 10 + + daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) + n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) + + # Ideally the following test will work, however this will depend on the version of scikit-learn and + # kneed. It is possible that if these packages change, the test will fail. In that case, the test + # could be removed as the function works properly, but would not be covered. + caplog.clear() + with caplog.at_level(idaeslog.WARNING): + m = PriceTakerModel() + kmin = 9 + kmax = 14 + + daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) + m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) + + assert f"Optimal number of clusters is close to kmax: {kmax}. Consider increasing kmax." in caplog.text + + @pytest.mark.unit From 522023b3bb5151faba00923101b9b97f209e2603 Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Tue, 27 Feb 2024 13:17:53 -0500 Subject: [PATCH 058/126] Updated cash flow construction Updated the ability to make hourly cashflows. Now the user can provide all cashflows associated with all operation block models and the total cost and total revenue for each time point is generated to be used in the build cashflow function for the objective function. Still need to add tests for each of the logger messages (but the function appears to be working correctly). --- .../multiperiod/price_taker_model.py | 130 +++++++++++------- .../multiperiod/tests/test_price_taker.py | 15 +- 2 files changed, 94 insertions(+), 51 deletions(-) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index cdffbee149..c41cc487f0 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -428,8 +428,7 @@ def add_ramping_constraints( op_blk: String of the name of the operation model block, ex: ("fs.op_name") design_blk: String of the name of the design model block, ex: ("m.design_name") capacity_var: String of the name of the entity on the model the ramping constraints - will be applied to, ex: ("total_power") OR integer/float value of the - capacity value + will be applied to, ex: ("total_power") ramping_var: String of the name of the variable that the ramping constraints will be applied to constraint_type: String to choose between linear and nonlinear constraints. Valid @@ -474,16 +473,11 @@ def add_ramping_constraints( var_ramping = {t: deepgetattr(self.mp_model.period[t], op_blk + "." + ramping_var) for t in self.mp_model.period} if constraint_type == "linear": - if isinstance(capacity_var,str): - var_capacity = deepgetattr(self, design_blk + "." + capacity_var) - act_startup_rate = {t: var_capacity * start_up[t] for t in self.mp_model.period} - act_shutdown_rate = {t: var_capacity * shut_down[t] for t in self.mp_model.period} - act_op_mode_rate = {t: var_capacity * op_mode[t] for t in self.mp_model.period} + var_capacity = deepgetattr(self, design_blk + "." + capacity_var) + act_startup_rate = {t: var_capacity * start_up[t] for t in self.mp_model.period} + act_shutdown_rate = {t: var_capacity * shut_down[t] for t in self.mp_model.period} + act_op_mode_rate = {t: var_capacity * op_mode[t] for t in self.mp_model.period} - if isinstance(capacity_var, (int,float)): - act_startup_rate = {t: capacity_var * start_up[t] for t in self.mp_model.period} - act_shutdown_rate = {t: capacity_var * shut_down[t] for t in self.mp_model.period} - act_op_mode_rate = {t: capacity_var * op_mode[t] for t in self.mp_model.period} elif constraint_type == "nonlinear": if linearization == True: if hasattr(self, "capacity_startup") == False: @@ -652,49 +646,69 @@ def build_hourly_cashflows(self, revenue_streams=['elec_revenue',], costs=None): """ period = self.mp_model.period + count_op_blks = 0 for p in period: - for cost_name in costs: - setattr(self.mp_model.period[p], cost_name, 0) - - for revenue_name in revenue_streams: - setattr(self.mp_model.period[p], revenue_name, 0) + # Set net profit contribution expressions to 0 + total_cost_expr = 0 + total_revenue_expr = 0 + if costs is None: + _logger.warning( + f"No costs were provided while building the hourly cashflow. Costs will be set to 0." + ) + costs = [] - non_fuel_vom = 0 - fuel_cost = 0 - elec_revenue = 0 - carbon_price = 0 - - # ToDo: Add multiple revenue streams to the model (full user control) - # ToDo: Make costs a list as well (full user control) - # ToDo: Add check for whether or not OperationModelData blocks were found - # (Should throw warning if none were found.) + if revenue_streams is None: + _logger.warning( + f"No revenues were provided while building the hourly cashflow. Revenues will be set to 0." + ) + revenue_streams = [] for blk in period[p].component_data_objects(Block): if isinstance(blk, OperationModelData): - non_fuel_vom += blk.non_fuel_vom - fuel_cost += blk.fuel_cost - elec_revenue += blk.elec_revenue - carbon_price += blk.carbon_price - - period[p].non_fuel_vom = Expression(expr=non_fuel_vom) - period[p].fuel_cost = Expression(expr=fuel_cost) - period[p].elec_revenue = Expression(expr=elec_revenue) - period[p].carbon_price = Expression(expr=carbon_price) - - period[p].net_cash_inflow = Expression( - expr=period[p].elec_revenue - - period[p].non_fuel_vom - - period[p].fuel_cost - - period[p].carbon_price - ) + count_op_blks += 1 + + # Add costs for each block. If more than one block, may have + # costs that exist on one block and not on another. (i.e., coproduction) + for ind, cost in enumerate(costs): + curr_cost = 0 + try: + curr_cost += deepgetattr(blk, costs[ind]) + except: + pass + total_cost_expr += curr_cost + + # Add revenue streams for each block. If more than one block, may have + # revenues that exist on one block and not on another. (i.e., coproduction) + for ind, rev in enumerate(revenue_streams): + curr_rev = 0 + try: + curr_rev += deepgetattr(blk, revenue_streams[ind]) + except: + pass + total_revenue_expr += curr_rev + + # Set total cost expression + self.mp_model.period[p].total_cost = Expression(expr=total_cost_expr) + + # Set total revenue expression + self.mp_model.period[p].total_revenue = Expression(expr=total_revenue_expr) + + # Individual cost expression can be found on the operation model itself. No need to add these here. + + period[p].net_cash_inflow = Expression(expr=period[p].total_revenue - period[p].total_cost) + + if count_op_blks < 1: + _logger.warning( + f"build_hourly_cashflows was called but no operation blocks were found so hourly cashflow of the model was set to 0. If you have hourly costs, please manually assign them." + ) def build_cashflows( self, lifetime=30, discount_rate=0.08, corp_tax=0.2, - other_costs=0, - other_revenue=0, + other_costs=None, + other_revenue=None, objective="NPV", ): """ @@ -707,9 +721,12 @@ def build_cashflows( Must be between 0 and 1. corp_tax: Fractional value of corporate tax used in NPV calculations. ??? What are the restrictions on tax ??? - other_costs: Additional costs that are not captured - other_revenue: - objective: + other_costs: List of costs not captured by capex and fom that are + defined on the model. + other_revenue: List of revenues not captured by capex and fom that are + defined on the price take model. + objective: String to choose which objective form is used in the model. + Options: ["NPV", "Annualized NPV", "Net Profit"] Returns: @@ -720,8 +737,11 @@ def build_cashflows( # ToDo: Add check for whether or not DesignModelData blocks were found # (Should throw warning if none were found.) + count_des_blks = 0 for blk in self.component_data_objects(Block): if isinstance(blk, DesignModelData): + count_des_blks += 1 + capex_expr += blk.capex fom_expr += blk.fom @@ -740,7 +760,7 @@ def build_cashflows( self.net_cash_inflow_calculation = Constraint( expr=self.NET_CASH_INFLOW == sum(self.mp_model.period[p].net_cash_inflow for p in self.mp_model.period) # added period block name to the net_cash_inflow callout - ) # added period block name to mp_model, to kthe len, and made a range list to loop over + ) # added period block name to mp_model, to the len, and made a range list to loop over self.CORP_TAX = Var(within=NonNegativeReals, doc="Corporate tax") self.corp_tax_calculation = Constraint( @@ -771,11 +791,27 @@ def build_cashflows( expr=self.NET_PROFIT - (1 / constant_cf_factor) * self.CAPEX, ) + obj_set = False + if objective == "NPV": self.obj = Objective(expr=self.NPV, sense=maximize) + obj_set = True elif objective == "Annualized NPV": self.obj = Objective(expr=self.Annualized_NPV, sense=maximize) + obj_set = True elif objective == "Net Profit": self.obj = Objective(expr=self.NET_PROFIT, sense=maximize) + obj_set = True + + if not obj_set: + _logger.warning( + f"build_cashflows was called, but the objective type provided, {objective}, is invalid. The objective has been set to 0. Please manually add your cost objective if you require one." + ) + self.obj = Objective(expr=0, sense=maximize) + + if count_des_blks < 1: + _logger.warning( + f"build_cashflows was called, but no design blocks were found so capex and FOM are 0. Please manually add your cost objective if you require one." + ) diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py index 23a9849320..135fead4a4 100644 --- a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -170,7 +170,7 @@ def test_min_up_down_time_logger_messages(excel_data): @pytest.mark.unit -def test_optimal_clusters_logger_messages(excel_data, caplog): +def test_optimal_clusters_logger_messages(excel_data): kmin = [-5, 10.2, 9] kmax = [-5, 10.2, 8] with pytest.raises( @@ -232,9 +232,12 @@ def test_optimal_clusters_logger_messages(excel_data, caplog): daily_data = m.generate_daily_data(excel_data['BaseCaseTax']) n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) + +@pytest.mark.unit +def test_optimal_clusters_close_to_kmax(excel_data, caplog): # Ideally the following test will work, however this will depend on the version of scikit-learn and # kneed. It is possible that if these packages change, the test will fail. In that case, the test - # could be removed as the function works properly, but would not be covered. + # could be removed as the function works properly, but some lines of code will not be covered. caplog.clear() with caplog.at_level(idaeslog.WARNING): m = PriceTakerModel() @@ -245,8 +248,6 @@ def test_optimal_clusters_logger_messages(excel_data, caplog): m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) assert f"Optimal number of clusters is close to kmax: {kmax}. Consider increasing kmax." in caplog.text - - @pytest.mark.unit @@ -436,3 +437,9 @@ def dfc_design(m, params, capacity_range=(650, 900)): expr=_fom_factor * m.capex, doc="Fixed O&M cost [in 1000$/year]", ) + +# Test cases to be done (4 total): +# (LMP1, RR1, MUD1, OBJ1) +# (LMP2, RR2, MUD2, OBJ2) +# (LMP3, RR3, MUD2, OBJ3) +# (LMP4, MUD1, RR1, OBJ1) \ No newline at end of file From 22dbe128986117b65dbe831b734f0dd8b0d9cda1 Mon Sep 17 00:00:00 2001 From: Daniel Laky <29078718+dlakes94@users.noreply.github.com> Date: Tue, 27 Feb 2024 16:31:23 -0500 Subject: [PATCH 059/126] Added tests for cashflows Added tests for some of the logger warning messages from the build_hourly_cashflows and build_cashflows functions. Through these tests, most of the building code has also been tested. Remaining are two cases that will cover the rest of the build cases, options for the operational block, and the nonlinear case studies. Also, I am considering adding a function to add the capacity limitation (both linear and nonlinear w/ and w/o linearization). --- .../design_and_operation_models.py | 63 --- .../multiperiod/price_taker_model.py | 22 +- .../multiperiod/tests/FLECCS_shortened.xlsx | Bin 0 -> 1190504 bytes .../multiperiod/tests/test_price_taker.py | 449 ++++++++++++++++-- 4 files changed, 425 insertions(+), 109 deletions(-) create mode 100644 idaes/apps/grid_integration/multiperiod/tests/FLECCS_shortened.xlsx diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py index 363ab5d3ee..933f2851fc 100644 --- a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -52,52 +52,13 @@ class DesignModelData(SkeletonUnitModelData): doc="Dictionary containing arguments needed for model_func", ), ) - CONFIG.declare( - "declare_cost_expr", - ConfigValue( - default=True, - domain=In([True, False]), - doc="Should capex and fom attributes be defined?", - ), - ) - - CONFIG.declare( - "declare_build_vars", - ConfigValue( - default=True, - domain=In([True, False]), - doc="Should build_unit be declared/defined?", - ), - ) # noinspection PyAttributeOutsideInit def build(self): super().build() - - if self.config.declare_build_vars: - self.build_unit = Var( - within=Binary, - doc="Binary var: 1 if we choose to build the unit, 0 otherwise", - ) - # Build the model self.config.model_func(self, **self.config.model_args) - cost_expr_list = { - "capex": "CAPEX of the unit/process", - "fom": "Annual Fixed O&M cost for the unit/process", - } - - if self.config.declare_cost_expr: - for ce in cost_expr_list.keys(): - if not hasattr(self, ce): - # TODO: Use IDAES warning - print( - f"declare_cost_expr is set to True, but {ce} is not defined in {self.config.model_func.__name__}" - ) - print(f"setting {ce} to zero in {self.name}") - setattr(self, ce, Expression(expr=0, doc=cost_expr_list[ce])) - @declare_process_block_class("OperationModel") class OperationModelData(SkeletonUnitModelData): @@ -127,14 +88,6 @@ class OperationModelData(SkeletonUnitModelData): doc="Dictionary containing arguments needed for model_func", ), ) - CONFIG.declare( - "declare_cost_expr", - ConfigValue( - default=True, - domain=In([True, False]), - doc="Should capex and fom attributes be defined?", - ), - ) CONFIG.declare( "declare_op_vars", ConfigValue( @@ -190,22 +143,6 @@ def build(self): self.config.model_func(self, **self.config.model_args) - cost_expr_list = { - "non_fuel_vom": "Variable O&M cost excluding fuel/power", - "fuel_cost": "Cost of fuel/power required for the unit/process", - "elec_revenue": "Electricity revenue generated by the unit/process", - "carbon_price": "Carbon price associated with CO2 emissions", - } - - if self.config.declare_cost_expr: - for ce in cost_expr_list.keys(): - if not hasattr(self, ce): - # TODO: Use IDAES warning - print( - f"declare_cost_expr is set to True, but {ce} is not defined in {self.config.model_func.__name__}" - ) - print(f"setting {ce} to zero in {self.name}") - setattr(self, ce, Expression(expr=0, doc=cost_expr_list[ce])) if isinstance(self.config.capacity_var, str): self._add_capacity_aux_vars() diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py index c41cc487f0..3322476b10 100644 --- a/idaes/apps/grid_integration/multiperiod/price_taker_model.py +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -399,7 +399,7 @@ def build_multiperiod_model(self, **kwargs): if not LMP_exists: raise ValueError(f"OperationModelData has been defined to automatically " + f"populate LMP data. However, m.LMP does not exist. " + - f"Please run append_lmp_data() first or set the " + + f"Please run the append_lmp_data function first or set the " + f"append_lmp_data attribute to False when configuring " + f"your OperationModelData object.") blk.LMP = self.LMP[p] @@ -616,7 +616,7 @@ def minimum_down_time_con(b, t): 1 - op_mode[self.mp_model.set_period.at(t)]) - def build_hourly_cashflows(self, revenue_streams=['elec_revenue',], costs=None): + def build_hourly_cashflows(self, revenue_streams=None, costs=None): """ Adds an expression for the net cash inflow for each operational block. This is the new cash inflow for each time period of the @@ -707,8 +707,8 @@ def build_cashflows( lifetime=30, discount_rate=0.08, corp_tax=0.2, - other_costs=None, - other_revenue=None, + other_costs=0, + other_revenue=0, objective="NPV", ): """ @@ -721,10 +721,8 @@ def build_cashflows( Must be between 0 and 1. corp_tax: Fractional value of corporate tax used in NPV calculations. ??? What are the restrictions on tax ??? - other_costs: List of costs not captured by capex and fom that are - defined on the model. - other_revenue: List of revenues not captured by capex and fom that are - defined on the price take model. + other_costs: Pyomo expression for other costs + other_revenue: Pyomo expression for other sources of revenue objective: String to choose which objective form is used in the model. Options: ["NPV", "Annualized NPV", "Net Profit"] @@ -735,8 +733,6 @@ def build_cashflows( capex_expr = 0 fom_expr = 0 - # ToDo: Add check for whether or not DesignModelData blocks were found - # (Should throw warning if none were found.) count_des_blks = 0 for blk in self.component_data_objects(Block): if isinstance(blk, DesignModelData): @@ -759,9 +755,9 @@ def build_cashflows( self.NET_CASH_INFLOW = Var(doc="Net cash inflow") self.net_cash_inflow_calculation = Constraint( expr=self.NET_CASH_INFLOW - == sum(self.mp_model.period[p].net_cash_inflow for p in self.mp_model.period) # added period block name to the net_cash_inflow callout - ) # added period block name to mp_model, to the len, and made a range list to loop over - + == sum(self.mp_model.period[p].net_cash_inflow for p in self.mp_model.period) + ) + self.CORP_TAX = Var(within=NonNegativeReals, doc="Corporate tax") self.corp_tax_calculation = Constraint( expr=self.CORP_TAX diff --git 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