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# !/usr/bin/env python3
# -*- coding: utf-8 -*-
# Author: Dennis Dreier, Copyright 2019
# OSeMOSYS version: OSeMOSYS_2017_11_08
__doc__ = """
========================================================================================================================
OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling
========================================================================================================================
OSeMOSYS-PuLP
This is the educational version of OSeMOSYS-PuLP
========================================================================================================================
OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling
Please cite this software by using the following reference of the original scientific article:
Dennis Dreier, Mark Howells, OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling.
Energies 2019, 12, 1382, https://doi.org/10.3390/en12071382
Additional references to be cited for the OSeMOSYS modelling framework (see DOI links for complete references):
Howells et al. (2011), https://doi.org/10.1016/j.enpol.2011.06.033
Gardumi et al. (2018), https://doi.org/10.1016/j.esr.2018.03.005
Other sources:
OSeMOSYS GitHub: https://github.com/OSeMOSYS/
OSeMOSYS website: http://www.osemosys.org/
OpTIMUS community: http://www.optimus.community/
========================================================================================================================
"""
import os
import datetime as dt
import logging
import numpy as np
import pandas as pd
import pulp
logging.basicConfig(level=logging.DEBUG)
logging.info("{}\tScript started.".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
# ----------------------------------------------------------------------------------------------------------------------
# SETUP - DATA SOURCES and MONTE CARLO SIMULATION
# ----------------------------------------------------------------------------------------------------------------------
# Input data
inputFile = "UTOPIA_BASE.xlsx" # Update with actual filename
# Settings
inputDir = ".\Input_Data\\"
modelName = inputFile.split('.')[0]
sheetSets = "SETS"
sheetParams = "PARAMETERS"
sheetParamsDefault = "PARAMETERS_DEFAULT"
sheetMcs = "MCS"
sheetMcsNum = "MCS_num"
outputDir = ".\Output_Data\\"
outputFile = f"{modelName}_result.xlsx"
# ----------------------------------------------------------------------------------------------------------------------
# FUNCTIONS
# ----------------------------------------------------------------------------------------------------------------------
def newVar(name, lb, ub, cat, *indices):
"""
This function create a new variable having a lower bound (lb),
upper bound (ub), category (cat), using indices from SETS
"""
_name = name
for index in indices:
_name = "{}_{}".format(_name, index)
return pulp.LpVariable(_name, lowBound=lb, upBound=ub, cat=cat)
def loadData(filePath, sheetSets, sheetParams, sheetParamsDefault, sheetMcs, sheetMcsNum):
"""
This function loads all data from the input data set to dataframes.
"""
# Data: SETS
sets_df = pd.read_excel(io=filePath, sheet_name=sheetSets)
sets_df['REGION'] = sets_df['REGION'].astype(str)
sets_df['REGION2'] = sets_df['REGION2'].astype(str)
sets_df['DAYTYPE'] = sets_df['DAYTYPE'].astype(str)
sets_df['EMISSION'] = sets_df['EMISSION'].astype(str)
sets_df['FUEL'] = sets_df['FUEL'].astype(str)
sets_df['DAILYTIMEBRACKET'] = sets_df['DAILYTIMEBRACKET'].astype(str)
sets_df['SEASON'] = sets_df['SEASON'].astype(str)
sets_df['TIMESLICE'] = sets_df['TIMESLICE'].astype(str)
sets_df['MODE_OF_OPERATION'] = sets_df['MODE_OF_OPERATION'].astype(str)
sets_df['STORAGE'] = sets_df['STORAGE'].astype(str)
sets_df['TECHNOLOGY'] = sets_df['TECHNOLOGY'].astype(str)
sets_df['YEAR'] = sets_df['YEAR'].astype(str)
sets_df['FLEXIBLEDEMANDTYPE'] = sets_df['FLEXIBLEDEMANDTYPE'].astype(str)
# Data: PARAMETERS
p_df = pd.read_excel(io=filePath, sheet_name=sheetParams)
p_df = p_df.fillna(0)
p_df['PARAM'] = p_df['PARAM'].astype(str)
p_df['VALUE'] = p_df['VALUE'].apply(pd.to_numeric, downcast='signed')
p_df['REGION'] = p_df['REGION'].astype(str)
p_df['REGION2'] = p_df['REGION2'].astype(str)
p_df['DAYTYPE'] = p_df['DAYTYPE'].astype(int)
p_df['DAYTYPE'] = p_df['DAYTYPE'].astype(str)
p_df['EMISSION'] = p_df['EMISSION'].astype(str)
p_df['FUEL'] = p_df['FUEL'].astype(str)
p_df['DAILYTIMEBRACKET'] = p_df['DAILYTIMEBRACKET'].astype(int)
p_df['DAILYTIMEBRACKET'] = p_df['DAILYTIMEBRACKET'].astype(str)
p_df['SEASON'] = p_df['SEASON'].astype(int)
p_df['SEASON'] = p_df['SEASON'].astype(str)
p_df['TIMESLICE'] = p_df['TIMESLICE'].astype(str)
p_df['MODE_OF_OPERATION'] = p_df['MODE_OF_OPERATION'].astype(int)
p_df['MODE_OF_OPERATION'] = p_df['MODE_OF_OPERATION'].astype(str)
p_df['STORAGE'] = p_df['STORAGE'].astype(str)
p_df['TECHNOLOGY'] = p_df['TECHNOLOGY'].astype(str)
p_df['YEAR'] = p_df['YEAR'].astype(int)
p_df['YEAR'] = p_df['YEAR'].astype(str)
# Data: Parameters default values
p_default_df = pd.read_excel(io=filePath, sheet_name=sheetParamsDefault)
p_default_df = p_default_df.fillna(0)
p_default_df['PARAM'] = p_default_df['PARAM'].astype(str)
p_default_df['VALUE'] = p_default_df['VALUE'].apply(pd.to_numeric, downcast='signed')
# Data: Monte Carlo Simulation (MCS)
mcs_df = pd.read_excel(io=filePath, sheet_name=sheetMcs)
mcs_df = mcs_df.fillna(0)
mcs_df['DEFAULT_SETTING'] = mcs_df['DEFAULT_SETTING'].apply(pd.to_numeric, downcast='signed')
mcs_df['DEFAULT_SETTING'] = mcs_df['DEFAULT_SETTING'].astype(int)
mcs_df['REL_SD'] = mcs_df['REL_SD'].apply(pd.to_numeric, downcast='signed')
mcs_df['REL_MIN'] = mcs_df['REL_MIN'].apply(pd.to_numeric, downcast='signed')
mcs_df['REL_MAX'] = mcs_df['REL_MAX'].apply(pd.to_numeric, downcast='signed')
mcs_df['DISTRIBUTION'] = mcs_df['DISTRIBUTION'].astype(str)
mcs_df['ARRAY'] = [[float(i) for i in str(x).split(",")] for x in mcs_df['ARRAY']]
mcs_df['PARAM'] = mcs_df['PARAM'].astype(str)
mcs_df['REGION'] = mcs_df['REGION'].astype(str)
mcs_df['DAYTYPE'] = mcs_df['DAYTYPE'].astype(int)
mcs_df['DAYTYPE'] = mcs_df['DAYTYPE'].astype(str)
mcs_df['EMISSION'] = mcs_df['EMISSION'].astype(str)
mcs_df['FUEL'] = mcs_df['FUEL'].astype(str)
mcs_df['DAILYTIMEBRACKET'] = mcs_df['DAILYTIMEBRACKET'].astype(int)
mcs_df['DAILYTIMEBRACKET'] = mcs_df['DAILYTIMEBRACKET'].astype(str)
mcs_df['SEASON'] = mcs_df['SEASON'].astype(int)
mcs_df['SEASON'] = mcs_df['SEASON'].astype(str)
mcs_df['TIMESLICE'] = mcs_df['TIMESLICE'].astype(str)
mcs_df['MODE_OF_OPERATION'] = mcs_df['MODE_OF_OPERATION'].astype(int)
mcs_df['MODE_OF_OPERATION'] = mcs_df['MODE_OF_OPERATION'].astype(str)
mcs_df['STORAGE'] = mcs_df['STORAGE'].astype(str)
mcs_df['TECHNOLOGY'] = mcs_df['TECHNOLOGY'].astype(str)
mcs_df['YEAR'] = mcs_df['YEAR'].astype(int)
mcs_df['YEAR'] = mcs_df['YEAR'].astype(str)
# Number of MCS simulations
mcs_num_df = pd.read_excel(io=filePath, sheet_name=sheetMcsNum)
mcs_num = mcs_num_df.at[0, 'MCS_num']
return sets_df, p_df, p_default_df, mcs_df, mcs_num
def generateRandomData(reference, list):
"""
This function generates random data for the parameters included in the Monte Carlo Simulations.
reference (format: float): mean for normal distribution, mode for both triangular and uniform distributions
dist: type of distribution. Choose from: "normal", "triangular", "uniform" (format: string)
rel_sd: relative standard deviation from mean or mode. Unit: percent as decimals (format: float)
rel_min: relative minimum deviation from mean or mode. Unit: percent as decimals (format: float), must be a negative value
rel_max: relative maximum deviation from mean or mode. Unit: percent as decimals (format: float), must be a positive value
array: array with potential values. One value out of the array will be randomly chosen.
==================================================================================================================
Note: To use the reference value without any distribution, then write as input in the excel file in the tab "MCS":
Columns: PARAM: "parameter name", DEFAULT_SETTING: "1", DIST: "normal", REL_SD: "0".
This will make the code to choose the reference value as defined for the model without MCS.
"""
dist, rel_sd, rel_min, rel_max, array = list[0], list[1], list[2], list[3], list[4]
if dist == "normal":
value = np.random.normal(reference, rel_sd * reference, 1)[
0] # mean, standard deviation, generate 1 value at the time
elif dist == "triangular":
value = np.random.triangular((1 + rel_min) * reference, reference, (1 + rel_max) * reference, 1)[
0] # minimum value, mode, maximum value, generate 1 value at the time
elif dist == "uniform":
value = np.random.uniform((1 + rel_min) * reference, (1 + rel_max) * reference, 1)[
0] # minimum value, maximum value, generate 1 value at the time
elif dist == "choice":
if len(array) > 1:
value = np.random.choice(array)
else:
logging.error("ERROR: Review MCS_df array column. Expected length of array: larger than 1, but is: 0 or 1")
else:
logging.error("ERROR: Select an available distribution, review input data and/or add default input data for this parameter.")
return
# This if condition prevents input errors caused by negative values for the parameters
if value >= 0:
return value
else:
return 0
def saveResultsTemporary(dataframe, model_name, scenario):
# Activate variable names in "var_dict" to be included in the results,
# or comment out all redundant variables.
df = dataframe
var_dict = {
######## Demands #############
#"RateOfDemand": ["r", "l", "f", "y"],
"Demand": ["r", "l", "f", "y"],
######## Storage #############
#"RateOfStorageCharge": ["r", "s", "ls", "ld", "lh", "y"],
#"RateOfStorageDischarge": ["r", "s", "ls", "ld", "lh", "y"],
#"NetChargeWithinYear": ["r", "s", "ls", "ld", "lh", "y"],
#"NetChargeWithinDay": ["r", "s", "ls", "ld", "lh", "y"],
#"StorageLevelYearStart": ["r", "s", "y"],
#"StorageLevelYearFinish": ["r", "s", "y"],
#"StorageLevelSeasonStart": ["r", "s", "ls", "y"],
#"StorageLevelDayTypeStart": ["r", "s", "ls", "ld", "y"],
#"StorageLevelDayTypeFinish": ["r", "s", "ls", "ld", "y"],
#"StorageLowerLimit": ["r", "s", "y"],
#"StorageUpperLimit": ["r", "s", "y"],
#"AccumulatedNewStorageCapacity": ["r", "s", "y"],
#"NewStorageCapacity": ["r", "s", "y"],
#"CapitalInvestmentStorage": ["r", "s", "y"],
#"DiscountedCapitalInvestmentStorage": ["r", "s", "y"],
#"SalvageValueStorage": ["r", "s", "y"],
#"DiscountedSalvageValueStorage": ["r", "s", "y"],
#"TotalDiscountedStorageCost": ["r", "s", "y"],
######### Capacity Variables #############
#"NumberOfNewTechnologyUnits": ["r", "t", "y"],
"NewCapacity": ["r", "t", "y"],
#"AccumulatedNewCapacity": ["r", "t", "y"],
"TotalCapacityAnnual": ["r", "t", "y"],
######### Activity Variables #############
#"RateOfActivity": ["r", "l", "t", "m", "y"],
#"RateOfTotalActivity": ["r", "t", "l", "y"],
#"TotalTechnologyAnnualActivity": ["r", "t", "y"],
#"TotalAnnualTechnologyActivityByMode": ["r", "t", "m", "y"],
#"TotalTechnologyModelPeriodActivity": ["r", "t"],
#"RateOfProductionByTechnologyByMode": ["r", "l", "t", "m", "f", "y"],
#"RateOfProductionByTechnology": ["r", "l", "t", "f", "y"],
#"ProductionByTechnology": ["r", "l", "t", "f", "y"],
#"ProductionByTechnologyAnnual": ["r", "t", "f", "y"],
#"RateOfProduction": ["r", "l", "f", "y"],
#"Production": ["r", "l", "f", "y"],
#"RateOfUseByTechnologyByMode": ["r", "l", "t", "m", "f", "y"],
#"RateOfUseByTechnology": ["r", "l", "t", "f", "y"],
#"UseByTechnologyAnnual": ["r", "t", "f", "y"],
#"RateOfUse": ["r", "l", "f", "y"],
#"UseByTechnology": ["r", "l", "t", "f", "y"],
#"Use": ["r", "l", "f", "y"],
#"Trade": ["r", "rr", "l", "f", "y"],
#"TradeAnnual": ["r", "rr", "f", "y"],
#"ProductionAnnual": ["r", "f", "y"],
"UseAnnual": ["r", "f", "y"],
######### Costing Variables #############
"CapitalInvestment": ["r", "t", "y"],
#"DiscountedCapitalInvestment": ["r", "t", "y"],
#"SalvageValue": ["r", "t", "y"],
#"DiscountedSalvageValue": ["r", "t", "y"],
#"OperatingCost": ["r", "t", "y"],
#"DiscountedOperatingCost": ["r", "t", "y"],
#"AnnualVariableOperatingCost": ["r", "t", "y"],
#"AnnualFixedOperatingCost": ["r", "t", "y"],
#"TotalDiscountedCostByTechnology": ["r", "t", "y"],
#"TotalDiscountedCost": ["r", "y"],
#"ModelPeriodCostByRegion": ["r"],
######### Reserve Margin #############
#"TotalCapacityInReserveMargin": ["r", "y"],
#"DemandNeedingReserveMargin": ["r", "l", "y"],
######### RE Gen Target #############
#"TotalREProductionAnnual": ["r", "y"],
#"RETotalProductionOfTargetFuelAnnual": ["r", "y"],
######### Emissions #############
#"AnnualTechnologyEmissionByMode": ["r", "t", "e", "m", "y"],
#"AnnualTechnologyEmission": ["r", "t", "e", "y"],
#"AnnualTechnologyEmissionPenaltyByEmission": ["r", "t", "e", "y"],
#"AnnualTechnologyEmissionsPenalty": ["r", "t", "y"],
#"DiscountedTechnologyEmissionsPenalty": ["r", "t", "y"],
"AnnualEmissions": ["r", "e", "y"],
"ModelPeriodEmissions": ["r", "e"]
}
# Objective value ("cost")
temp_df = pd.DataFrame(columns=[
'SCENARIO',
'VAR_NAME',
'VAR_VALUE',
'REGION',
'REGION2',
'DAYTYPE',
'EMISSION',
'FUEL',
'DAILYTIMEBRACKET',
'SEASON',
'TIMESLICE',
'MODE_OF_OPERATION',
'STORAGE',
'TECHNOLOGY',
'YEAR',
'FLEXIBLEDEMANDTYPE'])
temp_df.at[0, 'SCENARIO'] = scenario
temp_df.at[0, 'VAR_NAME'] = "cost"
temp_df.at[0, 'VAR_VALUE'] = model_name.objective.value()
temp_df.at[0, 'REGION'] = " "
temp_df.at[0, 'REGION2'] = " "
temp_df.at[0, 'DAYTYPE'] = " "
temp_df.at[0, 'EMISSION'] = " "
temp_df.at[0, 'FUEL'] = " "
temp_df.at[0, 'DAILYTIMEBRACKET'] = " "
temp_df.at[0, 'SEASON'] = " "
temp_df.at[0, 'TIMESLICE'] = " "
temp_df.at[0, 'MODE_OF_OPERATION'] = " "
temp_df.at[0, 'STORAGE'] = " "
temp_df.at[0, 'TECHNOLOGY'] = " "
temp_df.at[0, 'YEAR'] = " "
temp_df.at[0, 'FLEXIBLEDEMANDTYPE'] = " "
df = pd.concat([df, temp_df])
# Variables values (only variables that are included in var_dict)
selected_variables = [variable for key in var_dict.keys() for variable in model_name.variables() if key == variable.name.split("_")[0]]
for var in selected_variables:
# Temporal dataframe in loop
temp_df = pd.DataFrame(columns=[
'SCENARIO',
'VAR_NAME',
'VAR_VALUE',
'REGION',
'REGION2',
'DAYTYPE',
'EMISSION',
'FUEL',
'DAILYTIMEBRACKET',
'SEASON',
'TIMESLICE',
'MODE_OF_OPERATION',
'STORAGE',
'TECHNOLOGY',
'YEAR',
'FLEXIBLEDEMANDTYPE'])
# Variable name
var_name = var.name.split("_")[0]
# Variable indices
var_concrete_indices_list = var.name.split("_")[1:]
# Variable abstract indices
var_abstract_indices_list = var_dict[var_name]
# Dictionary
abstract_dict = {key: "" for key in ["r", "rr", "ld", "e", "f", "lh", "ls", "l", "m", "s", "t", "y", "fdt"]} # default value: " "
concrete_dict = {key: value for key, value in zip(var_abstract_indices_list, var_concrete_indices_list)}
data_dict = {**abstract_dict, **concrete_dict} # Merge dictionaries
# Write data to temporary dataframe
temp_df.at[0, 'SCENARIO'] = scenario
temp_df.at[0, 'VAR_NAME'] = var.name.split("_")[0]
temp_df.at[0, 'VAR_VALUE'] = var.varValue
temp_df.at[0, 'REGION'] = data_dict["r"]
temp_df.at[0, 'REGION2'] = data_dict["rr"]
temp_df.at[0, 'DAYTYPE'] = data_dict["ld"]
temp_df.at[0, 'EMISSION'] = data_dict["e"]
temp_df.at[0, 'FUEL'] = data_dict["f"]
temp_df.at[0, 'DAILYTIMEBRACKET'] = data_dict["lh"]
temp_df.at[0, 'SEASON'] = data_dict["ls"]
temp_df.at[0, 'TIMESLICE'] = data_dict["l"]
temp_df.at[0, 'MODE_OF_OPERATION'] = data_dict["m"]
temp_df.at[0, 'STORAGE'] = data_dict["s"]
temp_df.at[0, 'TECHNOLOGY'] = data_dict["t"]
temp_df.at[0, 'YEAR'] = data_dict["y"]
temp_df.at[0, 'FLEXIBLEDEMANDTYPE'] = data_dict["fdt"]
df = pd.concat([df, temp_df])
return df
def saveResults(dataframe, fileDir, fileName):
"""
This function saves all results to an Excel file.
"""
_df = dataframe
# Shorten abstract variable names to keep Excel worksheet name limit of 31 characters
_df['VAR_NAME'].replace(
regex={'Total': 'Tot', 'Annual': 'Ann', 'Technology': 'Tech', 'Discounted': 'Disc', 'Production': 'Prod'},
inplace=True)
name_list = _df['VAR_NAME'].unique()
dataframe_list = [_df[_df['VAR_NAME'] == str(name)] for name in name_list]
if not os.path.exists(fileDir):
os.makedirs(fileDir)
writer = pd.ExcelWriter(os.path.join(fileDir, fileName))
for d, name in zip(dataframe_list, name_list):
d.to_excel(writer, sheet_name=name, index=False)
writer.save()
return
# ----------------------------------------------------------------------------------------------------------------------
# LOAD DATA
# ----------------------------------------------------------------------------------------------------------------------
inputPath = os.path.join(inputDir, inputFile)
sets_df, p_df, p_default_df, mcs_df, mcs_num = loadData(inputPath, sheetSets, sheetParams, sheetParamsDefault, sheetMcs, sheetMcsNum)
mcs_parameters = mcs_df['PARAM'].unique() # list of parameters to be included in monte carlo simulation
logging.info("{}\tData is loaded.".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
# ----------------------------------------------------------------------------------------------------------------------
# SETS
# ----------------------------------------------------------------------------------------------------------------------
REGION = [r for r in sets_df['REGION'] if r != 'nan']
REGION2 = [rr for rr in sets_df['REGION2'] if rr != 'nan']
DAYTYPE = [str(int(float(ld))) for ld in sets_df['DAYTYPE'] if ld != 'nan']
EMISSION = [e for e in sets_df['EMISSION'] if e != 'nan']
FUEL = [f for f in sets_df['FUEL'] if f != 'nan']
DAILYTIMEBRACKET = [str(int(float(lh))) for lh in sets_df['DAILYTIMEBRACKET'] if lh != 'nan']
SEASON = [str(int(float(ls))) for ls in sets_df['SEASON'] if ls != 'nan']
TIMESLICE = [l for l in sets_df['TIMESLICE'] if l != 'nan']
MODE_OF_OPERATION = [str(int(float(m))) for m in sets_df['MODE_OF_OPERATION'] if m != 'nan']
STORAGE = [s for s in sets_df['STORAGE'] if s != 'nan']
TECHNOLOGY = [t for t in sets_df['TECHNOLOGY'] if t != 'nan']
YEAR = [str(int(float(y))) for y in sets_df['YEAR'] if y != 'nan']
FLEXIBLEDEMANDTYPE = [fdt for fdt in sets_df['FLEXIBLEDEMANDTYPE'] if fdt != 'nan']
logging.info("{}\tSets are created.".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
# ----------------------------------------------------------------------------------------------------------------------
# PARAMETERS AND DATA
# ----------------------------------------------------------------------------------------------------------------------
######## Global #########
# YearSplit
YearSplit = p_df[p_df['PARAM'] == "YearSplit"][['TIMESLICE', 'YEAR', 'VALUE']].groupby('TIMESLICE')\
.apply(lambda df: df.set_index('YEAR')['VALUE'].to_dict()).to_dict()
# DiscountRate
DiscountRate_default_value = p_default_df[p_default_df['PARAM'] == "DiscountRate"].VALUE.iat[0]
DiscountRate_specified = tuple([(str(r)) for r in p_df[p_df['PARAM'] == "DiscountRate"].REGION])
DiscountRate = {str(r): p_df[(p_df['PARAM'] == "DiscountRate") & (p_df['REGION'] == r)].VALUE.iat[0]\
if (str(r)) in DiscountRate_specified else DiscountRate_default_value for r in REGION}
# DaySplit
DaySplit_default_value = p_default_df[p_default_df['PARAM'] == "DaySplit"].VALUE.iat[0]
DaySplit_specified = tuple([(str(lh), str(y)) for lh, y in zip(
p_df[p_df['PARAM'] == "DaySplit"].DAILYTIMEBRACKET, p_df[p_df['PARAM'] == "DaySplit"].YEAR)])
DaySplit = {str(lh): {str(y): p_df[(p_df['PARAM'] == "DaySplit") & (p_df['DAILYTIMEBRACKET'] == lh) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(lh), str(y)) in DaySplit_specified else DaySplit_default_value for y in YEAR} for lh in DAILYTIMEBRACKET}
# Conversionls
Conversionls_default_value = p_default_df[p_default_df['PARAM'] == "Conversionls"].VALUE.iat[0]
Conversionls_specified = tuple([(str(l), str(ls)) for l, ls in zip(p_df[p_df['PARAM'] == "Conversionls"].TIMESLICE, p_df[p_df['PARAM'] == "Conversionls"].SEASON)])
Conversionls = {str(l): {str(ls): p_df[(p_df['PARAM'] == "Conversionls") & (p_df['TIMESLICE'] == l) & (p_df['SEASON'] == ls)].VALUE.iat[0] if (str(l), str(ls)) in Conversionls_specified else Conversionls_default_value for ls in SEASON} for l in TIMESLICE}
# Conversionld
Conversionld_default_value = p_default_df[p_default_df['PARAM'] == "Conversionld"].VALUE.iat[0]
Conversionld_specified = tuple([(str(l), str(ld)) for l, ld in zip(p_df[p_df['PARAM'] == "Conversionld"].TIMESLICE, p_df[p_df['PARAM'] == "Conversionld"].DAYTYPE)])
Conversionld = {str(l): {str(ld): p_df[(p_df['PARAM'] == "Conversionld") & (p_df['TIMESLICE'] == l) & (p_df['DAYTYPE'] == ld)].VALUE.iat[0] if (str(l), str(ld)) in Conversionld_specified else Conversionld_default_value for ld in DAYTYPE} for l in TIMESLICE}
# Conversionlh
Conversionlh_default_value = p_default_df[p_default_df['PARAM'] == "Conversionlh"].VALUE.iat[0]
Conversionlh_specified = tuple([(str(l), str(lh)) for l, lh in zip(p_df[p_df['PARAM'] == "Conversionlh"].TIMESLICE, p_df[p_df['PARAM'] == "Conversionlh"].DAILYTIMEBRACKET)])
Conversionlh = {str(l): {str(lh): p_df[(p_df['PARAM'] == "Conversionlh") & (p_df['TIMESLICE'] == l) & (p_df['DAILYTIMEBRACKET'] == lh)].VALUE.iat[0] if (str(l), str(lh)) in Conversionlh_specified else Conversionlh_default_value for lh in DAILYTIMEBRACKET} for l in TIMESLICE}
# DaysInDayType
DaysInDayType_default_value = p_default_df[p_default_df['PARAM'] == "DaysInDayType"].VALUE.iat[0]
DaysInDayType_specified = tuple([(str(r),str(f),str(y)) for r, f, y in zip(p_df[p_df['PARAM'] == "DaysInDayType"].SEASON, p_df[p_df['PARAM'] == "DaysInDayType"].DAYTYPE, p_df[p_df['PARAM'] == "DaysInDayType"].YEAR)])
DaysInDayType = {str(ls): {str(ld): {str(y): p_df[(p_df['PARAM'] == "DaysInDayType") & (p_df['SEASON'] == ls) & (p_df['DAYTYPE'] == ld) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(ls),str(ld),str(y)) in DaysInDayType_specified else DaysInDayType_default_value for y in YEAR} for ld in DAYTYPE} for ls in SEASON}
# TradeRoute
TradeRoute_default_value = p_default_df[p_default_df['PARAM'] == "TradeRoute"].VALUE.iat[0]
TradeRoute_specified = tuple([(str(r),str(rr),str(f),str(y)) for r, rr, f, y in zip(p_df[p_df['PARAM'] == "TradeRoute"].REGION, p_df[p_df['PARAM'] == "TradeRoute"].REGION2, p_df[p_df['PARAM'] == "TradeRoute"].FUEL, p_df[p_df['PARAM'] == "TradeRoute"].YEAR)])
TradeRoute = {str(r): {str(rr): {str(f): {str(y): p_df[(p_df['PARAM'] == "TradeRoute") & (p_df['REGION'] == r) & (p_df['REGION2'] == rr) & (p_df['FUEL'] == f) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(rr),str(f),str(y)) in TradeRoute_specified else TradeRoute_default_value for y in YEAR} for f in FUEL} for rr in REGION2} for r in REGION}
# DepreciationMethod
DepreciationMethod_default_value = p_default_df[p_default_df['PARAM'] == "DepreciationMethod"].VALUE.iat[0]
DepreciationMethod_specified = tuple([(str(r)) for r in p_df[p_df['PARAM'] == "DepreciationMethod"].REGION])
DepreciationMethod = {str(r): p_df[(p_df['PARAM'] == "DepreciationMethod") & (p_df['REGION'] == r)].VALUE.iat[0] if (str(r)) in DepreciationMethod_specified else DepreciationMethod_default_value for r in REGION}
######## Demands #########
# SpecifiedAnnualDemand
SpecifiedAnnualDemand_default_value = p_default_df[p_default_df['PARAM'] == "SpecifiedAnnualDemand"].VALUE.iat[0]
SpecifiedAnnualDemand_specified = tuple([(str(r),str(f),str(y)) for r, f, y in zip(p_df[p_df['PARAM'] == "SpecifiedAnnualDemand"].REGION, p_df[p_df['PARAM'] == "SpecifiedAnnualDemand"].FUEL, p_df[p_df['PARAM'] == "SpecifiedAnnualDemand"].YEAR)])
SpecifiedAnnualDemand = {str(r): {str(f): {str(y): p_df[(p_df['PARAM'] == "SpecifiedAnnualDemand") & (p_df['REGION'] == r) & (p_df['FUEL'] == f) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(f),str(y)) in SpecifiedAnnualDemand_specified else SpecifiedAnnualDemand_default_value for y in YEAR} for f in FUEL} for r in REGION}
# SpecifiedDemandProfile
SpecifiedDemandProfile_default_value = p_default_df[p_default_df['PARAM'] == "SpecifiedDemandProfile"].VALUE.iat[0]
SpecifiedDemandProfile_specified = tuple([(str(r),str(f),str(l),str(y)) for r, f, l, y in zip(p_df[p_df['PARAM'] == "SpecifiedDemandProfile"].REGION, p_df[p_df['PARAM'] == "SpecifiedDemandProfile"].FUEL, p_df[p_df['PARAM'] == "SpecifiedDemandProfile"].TIMESLICE, p_df[p_df['PARAM'] == "SpecifiedDemandProfile"].YEAR)])
SpecifiedDemandProfile = {str(r): {str(f): {str(l): {str(y): p_df[(p_df['PARAM'] == "SpecifiedDemandProfile") & (p_df['REGION'] == r) & (p_df['FUEL'] == f) & (p_df['TIMESLICE'] == l) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(f),str(l),str(y)) in SpecifiedDemandProfile_specified else SpecifiedDemandProfile_default_value for y in YEAR} for l in TIMESLICE} for f in FUEL} for r in REGION}
# AccumulatedAnnualDemand
AccumulatedAnnualDemand_default_value = p_default_df[p_default_df['PARAM'] == "AccumulatedAnnualDemand"].VALUE.iat[0]
AccumulatedAnnualDemand_specified = tuple([(str(r),str(f),str(y)) for r, f, y in zip(p_df[p_df['PARAM'] == "AccumulatedAnnualDemand"].REGION, p_df[p_df['PARAM'] == "AccumulatedAnnualDemand"].FUEL, p_df[p_df['PARAM'] == "AccumulatedAnnualDemand"].YEAR)])
AccumulatedAnnualDemand = {str(r): {str(f): {str(y): p_df[(p_df['PARAM'] == "AccumulatedAnnualDemand") & (p_df['REGION'] == r) & (p_df['FUEL'] == f) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(f),str(y)) in AccumulatedAnnualDemand_specified else AccumulatedAnnualDemand_default_value for y in YEAR} for f in FUEL} for r in REGION}
######### Performance #########
# CapacityToActivityUnit
CapacityToActivityUnit_default_value = p_default_df[p_default_df['PARAM'] == "CapacityToActivityUnit"].VALUE.iat[0]
CapacityToActivityUnit_specified = tuple([(str(r), str(t)) for r, t in zip(p_df[p_df['PARAM'] == "CapacityToActivityUnit"].REGION, p_df[p_df['PARAM'] == "CapacityToActivityUnit"].TECHNOLOGY)])
CapacityToActivityUnit = {str(r): {str(t): p_df[(p_df['PARAM'] == "CapacityToActivityUnit") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t)].VALUE.iat[0] if (str(r), str(t)) in CapacityToActivityUnit_specified else CapacityToActivityUnit_default_value for t in TECHNOLOGY} for r in REGION}
# TechWithCapacityNeededToMeetPeakTS
TechWithCapacityNeededToMeetPeakTS_default_value = p_default_df[p_default_df['PARAM'] == "TechWithCapacityNeededToMeetPeakTS"].VALUE.iat[0]
TechWithCapacityNeededToMeetPeakTS_specified = tuple([(str(r), str(t)) for r, t in zip(p_df[p_df['PARAM'] == "TechWithCapacityNeededToMeetPeakTS"].REGION, p_df[p_df['PARAM'] == "TechWithCapacityNeededToMeetPeakTS"].TECHNOLOGY)])
TechWithCapacityNeededToMeetPeakTS = {str(r): {str(t): p_df[(p_df['PARAM'] == "TechWithCapacityNeededToMeetPeakTS") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t)].VALUE.iat[0] if (str(r), str(t)) in TechWithCapacityNeededToMeetPeakTS_specified else TechWithCapacityNeededToMeetPeakTS_default_value for t in TECHNOLOGY} for r in REGION}
# CapacityFactor
CapacityFactor_default_value = p_default_df[p_default_df['PARAM'] == "CapacityFactor"].VALUE.iat[0]
CapacityFactor_specified = tuple([(str(r),str(t),str(l),str(y)) for r, t, l, y in zip(p_df[p_df['PARAM'] == "CapacityFactor"].REGION, p_df[p_df['PARAM'] == "CapacityFactor"].TECHNOLOGY, p_df[p_df['PARAM'] == "CapacityFactor"].TIMESLICE, p_df[p_df['PARAM'] == "CapacityFactor"].YEAR)])
CapacityFactor = {str(r): {str(t): {str(l): {str(y): p_df[(p_df['PARAM'] == "CapacityFactor") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y) & (p_df['TIMESLICE'] == l)].VALUE.iat[0] if (str(r),str(t),str(l),str(y)) in CapacityFactor_specified else CapacityFactor_default_value for y in YEAR} for l in TIMESLICE} for t in TECHNOLOGY} for r in REGION}
# AvailabilityFactor
AvailabilityFactor_default_value = p_default_df[p_default_df['PARAM'] == "AvailabilityFactor"].VALUE.iat[0]
AvailabilityFactor_specified = tuple([(str(r),str(t),str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "AvailabilityFactor"].REGION, p_df[p_df['PARAM'] == "AvailabilityFactor"].TECHNOLOGY, p_df[p_df['PARAM'] == "AvailabilityFactor"].YEAR)])
AvailabilityFactor = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "AvailabilityFactor") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(y)) in AvailabilityFactor_specified else AvailabilityFactor_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# OperationalLife
OperationalLife_default_value = p_default_df[p_default_df['PARAM'] == "OperationalLife"].VALUE.iat[0]
OperationalLife_specified = tuple([(str(r), str(t)) for r, t in zip(p_df[p_df['PARAM'] == "OperationalLife"].REGION, p_df[p_df['PARAM'] == "OperationalLife"].TECHNOLOGY)])
OperationalLife = {str(r): {str(t): p_df[(p_df['PARAM'] == "OperationalLife") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t)].VALUE.iat[0] if (str(r), str(t)) in OperationalLife_specified else OperationalLife_default_value for t in TECHNOLOGY} for r in REGION}
# ResidualCapacity
ResidualCapacity_default_value = p_default_df[p_default_df['PARAM'] == "ResidualCapacity"].VALUE.iat[0]
ResidualCapacity_specified = tuple([(str(r),str(t),str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "ResidualCapacity"].REGION, p_df[p_df['PARAM'] == "ResidualCapacity"].TECHNOLOGY, p_df[p_df['PARAM'] == "ResidualCapacity"].YEAR)])
ResidualCapacity = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "ResidualCapacity") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(y)) in ResidualCapacity_specified else ResidualCapacity_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# InputActivityRatio
InputActivityRatio_default_value = p_default_df[p_default_df['PARAM'] == "InputActivityRatio"].VALUE.iat[0]
InputActivityRatio_specified = tuple([(str(r),str(t),str(f),str(m),str(y)) for r, t, f, m, y in zip(p_df[p_df['PARAM'] == "InputActivityRatio"].REGION, p_df[p_df['PARAM'] == "InputActivityRatio"].TECHNOLOGY, p_df[p_df['PARAM'] == "InputActivityRatio"].FUEL, p_df[p_df['PARAM'] == "InputActivityRatio"].MODE_OF_OPERATION, p_df[p_df['PARAM'] == "InputActivityRatio"].YEAR)])
InputActivityRatio = {str(r): {str(t): {str(f): {str(m): {str(y): p_df[(p_df['PARAM'] == "InputActivityRatio") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['FUEL'] == f) & (p_df['MODE_OF_OPERATION'] == m) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(f),str(m),str(y)) in InputActivityRatio_specified else InputActivityRatio_default_value for y in YEAR} for m in MODE_OF_OPERATION} for f in FUEL} for t in TECHNOLOGY} for r in REGION}
# OutputActivityRatio
OutputActivityRatio_default_value = p_default_df[p_default_df['PARAM'] == "OutputActivityRatio"].VALUE.iat[0]
OutputActivityRatio_specified = tuple([(str(r),str(t),str(f),str(m),str(y)) for r, t, f, m, y in zip(p_df[p_df['PARAM'] == "OutputActivityRatio"].REGION, p_df[p_df['PARAM'] == "OutputActivityRatio"].TECHNOLOGY, p_df[p_df['PARAM'] == "OutputActivityRatio"].FUEL, p_df[p_df['PARAM'] == "OutputActivityRatio"].MODE_OF_OPERATION, p_df[p_df['PARAM'] == "OutputActivityRatio"].YEAR)])
OutputActivityRatio = {str(r): {str(t): {str(f): {str(m): {str(y): p_df[(p_df['PARAM'] == "OutputActivityRatio") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['FUEL'] == f) & (p_df['MODE_OF_OPERATION'] == m) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(f),str(m),str(y)) in OutputActivityRatio_specified else OutputActivityRatio_default_value for y in YEAR} for m in MODE_OF_OPERATION} for f in FUEL} for t in TECHNOLOGY} for r in REGION}
######### Technology Costs #########
# CapitalCost
CapitalCost_default_value = p_default_df[p_default_df['PARAM'] == "CapitalCost"].VALUE.iat[0]
CapitalCost_specified = tuple([(str(r),str(t),str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "CapitalCost"].REGION, p_df[p_df['PARAM'] == "CapitalCost"].TECHNOLOGY, p_df[p_df['PARAM'] == "CapitalCost"].YEAR)])
CapitalCost = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "CapitalCost") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(y)) in CapitalCost_specified else CapitalCost_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# VariableCost
VariableCost_default_value = p_default_df[p_default_df['PARAM'] == "VariableCost"].VALUE.iat[0]
VariableCost_specified = tuple([(str(r),str(t),str(m),str(y)) for r, t, m, y in zip(p_df[p_df['PARAM'] == "VariableCost"].REGION, p_df[p_df['PARAM'] == "VariableCost"].TECHNOLOGY, p_df[p_df['PARAM'] == "VariableCost"].MODE_OF_OPERATION, p_df[p_df['PARAM'] == "VariableCost"].YEAR)])
VariableCost = {str(r): {str(t): {str(m): {str(y): p_df[(p_df['PARAM'] == "VariableCost") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['MODE_OF_OPERATION'] == m) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(m),str(y)) in VariableCost_specified else VariableCost_default_value for y in YEAR} for m in MODE_OF_OPERATION} for t in TECHNOLOGY} for r in REGION}
# FixedCost
FixedCost_default_value = p_default_df[p_default_df['PARAM'] == "FixedCost"].VALUE.iat[0]
FixedCost_specified = tuple([(str(r),str(t),str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "FixedCost"].REGION, p_df[p_df['PARAM'] == "FixedCost"].TECHNOLOGY, p_df[p_df['PARAM'] == "FixedCost"].YEAR)])
FixedCost = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "FixedCost") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(y)) in FixedCost_specified else FixedCost_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
######### Storage #########
# TechnologyToStorage
TechnologyToStorage_default_value = p_default_df[p_default_df['PARAM'] == "TechnologyToStorage"].VALUE.iat[0]
TechnologyToStorage_specified = tuple([(str(r),str(t),str(s),str(m)) for r, t, s, m in zip(p_df[p_df['PARAM'] == "TechnologyToStorage"].REGION, p_df[p_df['PARAM'] == "TechnologyToStorage"].TECHNOLOGY, p_df[p_df['PARAM'] == "TechnologyToStorage"].STORAGE, p_df[p_df['PARAM'] == "TechnologyToStorage"].MODE_OF_OPERATION)])
TechnologyToStorage = {str(r): {str(t): {str(s): {str(m): p_df[(p_df['PARAM'] == "TechnologyToStorage") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['STORAGE'] == s) & (p_df['MODE_OF_OPERATION'] == m)].VALUE.iat[0] if (str(r),str(t),str(s),str(m)) in TechnologyToStorage_specified else TechnologyToStorage_default_value for m in MODE_OF_OPERATION} for s in STORAGE} for t in TECHNOLOGY} for r in REGION}
# TechnologyFromStorage
TechnologyFromStorage_default_value = p_default_df[p_default_df['PARAM'] == "TechnologyFromStorage"].VALUE.iat[0]
TechnologyFromStorage_specified = tuple([(str(r),str(t),str(s),str(m)) for r, t, s, m in zip(p_df[p_df['PARAM'] == "TechnologyFromStorage"].REGION, p_df[p_df['PARAM'] == "TechnologyFromStorage"].TECHNOLOGY, p_df[p_df['PARAM'] == "TechnologyFromStorage"].STORAGE, p_df[p_df['PARAM'] == "TechnologyFromStorage"].MODE_OF_OPERATION)])
TechnologyFromStorage = {str(r): {str(t): {str(s): {str(m): p_df[(p_df['PARAM'] == "TechnologyFromStorage") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['STORAGE'] == s) & (p_df['MODE_OF_OPERATION'] == m)].VALUE.iat[0] if (str(r),str(t),str(s),str(m)) in TechnologyFromStorage_specified else TechnologyFromStorage_default_value for m in MODE_OF_OPERATION} for s in STORAGE} for t in TECHNOLOGY} for r in REGION}
# StorageLevelStart
StorageLevelStart_default_value = p_default_df[p_default_df['PARAM'] == "StorageLevelStart"].VALUE.iat[0]
StorageLevelStart_specified = tuple([(str(r), str(s)) for r, s in zip(p_df[p_df['PARAM'] == "StorageLevelStart"].REGION, p_df[p_df['PARAM'] == "StorageLevelStart"].STORAGE)])
StorageLevelStart = {str(r): {str(s): p_df[(p_df['PARAM'] == "StorageLevelStart") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s)].VALUE.iat[0] if (str(r), str(s)) in StorageLevelStart_specified else StorageLevelStart_default_value for s in STORAGE} for r in REGION}
# StorageMaxChargeRate
StorageMaxChargeRate_default_value = p_default_df[p_default_df['PARAM'] == "StorageMaxChargeRate"].VALUE.iat[0]
StorageMaxChargeRate_specified = tuple([(str(r), str(s)) for r, s in zip(p_df[p_df['PARAM'] == "StorageMaxChargeRate"].REGION, p_df[p_df['PARAM'] == "StorageMaxChargeRate"].STORAGE)])
StorageMaxChargeRate = {str(r): {str(s): p_df[(p_df['PARAM'] == "StorageMaxChargeRate") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s)].VALUE.iat[0] if (str(r), str(s)) in StorageMaxChargeRate_specified else StorageMaxChargeRate_default_value for s in STORAGE} for r in REGION}
# StorageMaxDischargeRate
StorageMaxDischargeRate_default_value = p_default_df[p_default_df['PARAM'] == "StorageMaxDischargeRate"].VALUE.iat[0]
StorageMaxDischargeRate_specified = tuple([(str(r), str(s)) for r, s in zip(p_df[p_df['PARAM'] == "StorageMaxDischargeRate"].REGION, p_df[p_df['PARAM'] == "StorageMaxDischargeRate"].STORAGE)])
StorageMaxDischargeRate = {str(r): {str(s): p_df[(p_df['PARAM'] == "StorageMaxDischargeRate") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s)].VALUE.iat[0] if (str(r), str(s)) in StorageMaxDischargeRate_specified else StorageMaxDischargeRate_default_value for s in STORAGE} for r in REGION}
# MinStorageCharge
MinStorageCharge_default_value = p_default_df[p_default_df['PARAM'] == "MinStorageCharge"].VALUE.iat[0]
MinStorageCharge_specified = tuple([(str(r), str(s), str(y)) for r, s, y in zip(p_df[p_df['PARAM'] == "MinStorageCharge"].REGION, p_df[p_df['PARAM'] == "MinStorageCharge"].STORAGE, p_df[p_df['PARAM'] == "MinStorageCharge"].YEAR)])
MinStorageCharge = {str(r): {str(s): {str(y): p_df[(p_df['PARAM'] == "MinStorageCharge") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(s), str(y)) in MinStorageCharge_specified else MinStorageCharge_default_value for y in YEAR} for s in STORAGE} for r in REGION}
# OperationalLifeStorage
OperationalLifeStorage_default_value = p_default_df[p_default_df['PARAM'] == "OperationalLifeStorage"].VALUE.iat[0]
OperationalLifeStorage_specified = tuple([(str(r), str(s)) for r, s in zip(p_df[p_df['PARAM'] == "OperationalLifeStorage"].REGION, p_df[p_df['PARAM'] == "OperationalLifeStorage"].STORAGE)])
OperationalLifeStorage = {str(r): {str(s): p_df[(p_df['PARAM'] == "OperationalLifeStorage") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s)].VALUE.iat[0] if (str(r), str(s)) in OperationalLifeStorage_specified else OperationalLifeStorage_default_value for s in STORAGE} for r in REGION}
# CapitalCostStorage
CapitalCostStorage_default_value = p_default_df[p_default_df['PARAM'] == "CapitalCostStorage"].VALUE.iat[0]
CapitalCostStorage_specified = tuple([(str(r), str(s), str(y)) for r, s, y in zip(p_df[p_df['PARAM'] == "CapitalCostStorage"].REGION, p_df[p_df['PARAM'] == "CapitalCostStorage"].STORAGE, p_df[p_df['PARAM'] == "CapitalCostStorage"].YEAR)])
CapitalCostStorage = {str(r): {str(s): {str(y): p_df[(p_df['PARAM'] == "CapitalCostStorage") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(s), str(y)) in CapitalCostStorage_specified else CapitalCostStorage_default_value for y in YEAR} for s in STORAGE} for r in REGION}
# ResidualStorageCapacity
ResidualStorageCapacity_default_value = p_default_df[p_default_df['PARAM'] == "ResidualStorageCapacity"].VALUE.iat[0]
ResidualStorageCapacity_specified = tuple([(str(r), str(s), str(y)) for r, s, y in zip(p_df[p_df['PARAM'] == "ResidualStorageCapacity"].REGION, p_df[p_df['PARAM'] == "ResidualStorageCapacity"].STORAGE, p_df[p_df['PARAM'] == "ResidualStorageCapacity"].YEAR)])
ResidualStorageCapacity = {str(r): {str(s): {str(y): p_df[(p_df['PARAM'] == "ResidualStorageCapacity") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(s), str(y)) in ResidualStorageCapacity_specified else ResidualStorageCapacity_default_value for y in YEAR} for s in STORAGE} for r in REGION}
######### Capacity Constraints #########
# CapacityOfOneTechnologyUnit
CapacityOfOneTechnologyUnit_default_value = p_default_df[p_default_df['PARAM'] == "CapacityOfOneTechnologyUnit"].VALUE.iat[0]
CapacityOfOneTechnologyUnit_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "CapacityOfOneTechnologyUnit"].REGION, p_df[p_df['PARAM'] == "CapacityOfOneTechnologyUnit"].TECHNOLOGY, p_df[p_df['PARAM'] == "CapacityOfOneTechnologyUnit"].YEAR)])
CapacityOfOneTechnologyUnit = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "CapacityOfOneTechnologyUnit") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in CapacityOfOneTechnologyUnit_specified else CapacityOfOneTechnologyUnit_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# TotalAnnualMaxCapacity
TotalAnnualMaxCapacity_default_value = p_default_df[p_default_df['PARAM'] == "TotalAnnualMaxCapacity"].VALUE.iat[0]
TotalAnnualMaxCapacity_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "TotalAnnualMaxCapacity"].REGION, p_df[p_df['PARAM'] == "TotalAnnualMaxCapacity"].TECHNOLOGY, p_df[p_df['PARAM'] == "TotalAnnualMaxCapacity"].YEAR)])
TotalAnnualMaxCapacity = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "TotalAnnualMaxCapacity") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in TotalAnnualMaxCapacity_specified else TotalAnnualMaxCapacity_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# TotalAnnualMinCapacity
TotalAnnualMinCapacity_default_value = p_default_df[p_default_df['PARAM'] == "TotalAnnualMinCapacity"].VALUE.iat[0]
TotalAnnualMinCapacity_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "TotalAnnualMinCapacity"].REGION, p_df[p_df['PARAM'] == "TotalAnnualMinCapacity"].TECHNOLOGY, p_df[p_df['PARAM'] == "TotalAnnualMinCapacity"].YEAR)])
TotalAnnualMinCapacity = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "TotalAnnualMinCapacity") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in TotalAnnualMinCapacity_specified else TotalAnnualMinCapacity_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
######### Investment Constraints #########
# TotalAnnualMaxCapacityInvestment
TotalAnnualMaxCapacityInvestment_default_value = p_default_df[p_default_df['PARAM'] == "TotalAnnualMaxCapacityInvestment"].VALUE.iat[0]
TotalAnnualMaxCapacityInvestment_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "TotalAnnualMaxCapacityInvestment"].REGION, p_df[p_df['PARAM'] == "TotalAnnualMaxCapacityInvestment"].TECHNOLOGY, p_df[p_df['PARAM'] == "TotalAnnualMaxCapacityInvestment"].YEAR)])
TotalAnnualMaxCapacityInvestment = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "TotalAnnualMaxCapacityInvestment") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in TotalAnnualMaxCapacityInvestment_specified else TotalAnnualMaxCapacityInvestment_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# TotalAnnualMinCapacityInvestment
TotalAnnualMinCapacityInvestment_default_value = p_default_df[p_default_df['PARAM'] == "TotalAnnualMinCapacityInvestment"].VALUE.iat[0]
TotalAnnualMinCapacityInvestment_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "TotalAnnualMinCapacityInvestment"].REGION, p_df[p_df['PARAM'] == "TotalAnnualMinCapacityInvestment"].TECHNOLOGY, p_df[p_df['PARAM'] == "TotalAnnualMinCapacityInvestment"].YEAR)])
TotalAnnualMinCapacityInvestment = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "TotalAnnualMinCapacityInvestment") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in TotalAnnualMinCapacityInvestment_specified else TotalAnnualMinCapacityInvestment_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
######### Activity Constraints #########
# TotalTechnologyAnnualActivityUpperLimit
TotalTechnologyAnnualActivityUpperLimit_default_value = p_default_df[p_default_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit"].VALUE.iat[0]
TotalTechnologyAnnualActivityUpperLimit_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit"].REGION, p_df[p_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit"].TECHNOLOGY, p_df[p_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit"].YEAR)])
TotalTechnologyAnnualActivityUpperLimit = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in TotalTechnologyAnnualActivityUpperLimit_specified else TotalTechnologyAnnualActivityUpperLimit_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# TotalTechnologyAnnualActivityLowerLimit
TotalTechnologyAnnualActivityLowerLimit_default_value = p_default_df[p_default_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit"].VALUE.iat[0]
TotalTechnologyAnnualActivityLowerLimit_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit"].REGION, p_df[p_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit"].TECHNOLOGY, p_df[p_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit"].YEAR)])
TotalTechnologyAnnualActivityLowerLimit = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in TotalTechnologyAnnualActivityLowerLimit_specified else TotalTechnologyAnnualActivityLowerLimit_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# TotalTechnologyModelPeriodActivityUpperLimit
TotalTechnologyModelPeriodActivityUpperLimit_default_value = p_default_df[p_default_df['PARAM'] == "TotalTechnologyModelPeriodActivityUpperLimit"].VALUE.iat[0]
TotalTechnologyModelPeriodActivityUpperLimit_specified = tuple([(str(r), str(t)) for r, t in zip(p_df[p_df['PARAM'] == "TotalTechnologyModelPeriodActivityUpperLimit"].REGION, p_df[p_df['PARAM'] == "TotalTechnologyModelPeriodActivityUpperLimit"].TECHNOLOGY)])
TotalTechnologyModelPeriodActivityUpperLimit = {str(r): {str(t): p_df[(p_df['PARAM'] == "TotalTechnologyModelPeriodActivityUpperLimit") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t)].VALUE.iat[0] if (str(r), str(t)) in TotalTechnologyModelPeriodActivityUpperLimit_specified else TotalTechnologyModelPeriodActivityUpperLimit_default_value for t in TECHNOLOGY} for r in REGION}
# TotalTechnologyModelPeriodActivityLowerLimit
TotalTechnologyModelPeriodActivityLowerLimit_default_value = p_default_df[p_default_df['PARAM'] == "TotalTechnologyModelPeriodActivityLowerLimit"].VALUE.iat[0]
TotalTechnologyModelPeriodActivityLowerLimit_specified = tuple([(str(r), str(t)) for r, t in zip(p_df[p_df['PARAM'] == "TotalTechnologyModelPeriodActivityLowerLimit"].REGION, p_df[p_df['PARAM'] == "TotalTechnologyModelPeriodActivityLowerLimit"].TECHNOLOGY)])
TotalTechnologyModelPeriodActivityLowerLimit = {str(r): {str(t): p_df[(p_df['PARAM'] == "TotalTechnologyModelPeriodActivityLowerLimit") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t)].VALUE.iat[0] if (str(r), str(t)) in TotalTechnologyModelPeriodActivityLowerLimit_specified else TotalTechnologyModelPeriodActivityLowerLimit_default_value for t in TECHNOLOGY} for r in REGION}
######### Reserve Margin #########
# ReserveMarginTagTechnology
ReserveMarginTagTechnology_default_value = p_default_df[p_default_df['PARAM'] == "ReserveMarginTagTechnology"].VALUE.iat[0]
ReserveMarginTagTechnology_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "ReserveMarginTagTechnology"].REGION, p_df[p_df['PARAM'] == "ReserveMarginTagTechnology"].TECHNOLOGY, p_df[p_df['PARAM'] == "ReserveMarginTagTechnology"].YEAR)])
ReserveMarginTagTechnology = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "ReserveMarginTagTechnology") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in ReserveMarginTagTechnology_specified else ReserveMarginTagTechnology_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# ReserveMarginTagFuel
ReserveMarginTagFuel_default_value = p_default_df[p_default_df['PARAM'] == "ReserveMarginTagFuel"].VALUE.iat[0]
ReserveMarginTagFuel_specified = tuple([(str(r), str(f), str(y)) for r, f, y in zip(p_df[p_df['PARAM'] == "ReserveMarginTagFuel"].REGION, p_df[p_df['PARAM'] == "ReserveMarginTagFuel"].FUEL, p_df[p_df['PARAM'] == "ReserveMarginTagFuel"].YEAR)])
ReserveMarginTagFuel = {str(r): {str(f): {str(y): p_df[(p_df['PARAM'] == "ReserveMarginTagFuel") & (p_df['REGION'] == r) & (p_df['FUEL'] == f) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(f), str(y)) in ReserveMarginTagFuel_specified else ReserveMarginTagFuel_default_value for y in YEAR} for f in FUEL} for r in REGION}
# ReserveMargin
ReserveMargin_default_value = p_default_df[p_default_df['PARAM'] == "ReserveMargin"].VALUE.iat[0]
ReserveMargin_specified = tuple([(str(r), str(y)) for r, y in zip(p_df[p_df['PARAM'] == "ReserveMargin"].REGION, p_df[p_df['PARAM'] == "ReserveMargin"].YEAR)])
ReserveMargin = {str(r): {str(y): p_df[(p_df['PARAM'] == "ReserveMargin") & (p_df['REGION'] == r) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(y)) in ReserveMargin_specified else ReserveMargin_default_value for y in YEAR} for r in REGION}
######### RE Generation Target #########
# RETagTechnology
RETagTechnology_default_value = p_default_df[p_default_df['PARAM'] == "RETagTechnology"].VALUE.iat[0]
RETagTechnology_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "RETagTechnology"].REGION, p_df[p_df['PARAM'] == "RETagTechnology"].TECHNOLOGY, p_df[p_df['PARAM'] == "RETagTechnology"].YEAR)])
RETagTechnology = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "RETagTechnology") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in RETagTechnology_specified else RETagTechnology_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# RETagFuel
RETagFuel_default_value = p_default_df[p_default_df['PARAM'] == "RETagFuel"].VALUE.iat[0]
RETagFuel_specified = tuple([(str(r), str(f), str(y)) for r, f, y in zip(p_df[p_df['PARAM'] == "RETagFuel"].REGION, p_df[p_df['PARAM'] == "RETagFuel"].FUEL, p_df[p_df['PARAM'] == "RETagFuel"].YEAR)])
RETagFuel = {str(r): {str(f): {str(y): p_df[(p_df['PARAM'] == "RETagFuel") & (p_df['REGION'] == r) & (p_df['FUEL'] == f) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(f), str(y)) in RETagFuel_specified else RETagFuel_default_value for y in YEAR} for f in FUEL} for r in REGION}
# REMinProductionTarget
REMinProductionTarget_default_value = p_default_df[p_default_df['PARAM'] == "REMinProductionTarget"].VALUE.iat[0]
REMinProductionTarget_specified = tuple([(str(r), str(y)) for r, y in zip(p_df[p_df['PARAM'] == "REMinProductionTarget"].REGION, p_df[p_df['PARAM'] == "REMinProductionTarget"].YEAR)])
REMinProductionTarget = {str(r): {str(y): p_df[(p_df['PARAM'] == "REMinProductionTarget") & (p_df['REGION'] == r) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(y)) in REMinProductionTarget_specified else REMinProductionTarget_default_value for y in YEAR} for r in REGION}
######### Emissions & Penalties #########
# EmissionActivityRatio
EmissionActivityRatio_default_value = p_default_df[p_default_df['PARAM'] == "EmissionActivityRatio"].VALUE.iat[0]
EmissionActivityRatio_specified = tuple([(str(r),str(t),str(e),str(m),str(y)) for r, t, e, m, y in zip(p_df[p_df['PARAM'] == "EmissionActivityRatio"].REGION, p_df[p_df['PARAM'] == "EmissionActivityRatio"].TECHNOLOGY, p_df[p_df['PARAM'] == "EmissionActivityRatio"].EMISSION, p_df[p_df['PARAM'] == "EmissionActivityRatio"].MODE_OF_OPERATION, p_df[p_df['PARAM'] == "EmissionActivityRatio"].YEAR)])
EmissionActivityRatio = {str(r): {str(t): {str(e): {str(m): {str(y): p_df[(p_df['PARAM'] == "EmissionActivityRatio") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['EMISSION'] == e) & (p_df['MODE_OF_OPERATION'] == m) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(e),str(m),str(y)) in EmissionActivityRatio_specified else EmissionActivityRatio_default_value for y in YEAR} for m in MODE_OF_OPERATION} for e in EMISSION} for t in TECHNOLOGY} for r in REGION}
# EmissionsPenalty
EmissionsPenalty_default_value = p_default_df[p_default_df['PARAM'] == "EmissionsPenalty"].VALUE.iat[0]
EmissionsPenalty_specified = tuple([(str(r), str(e), str(y)) for r, e, y in zip(p_df[p_df['PARAM'] == "EmissionsPenalty"].REGION, p_df[p_df['PARAM'] == "EmissionsPenalty"].EMISSION, p_df[p_df['PARAM'] == "EmissionsPenalty"].YEAR)])
EmissionsPenalty = {str(r): {str(e): {str(y): p_df[(p_df['PARAM'] == "EmissionsPenalty") & (p_df['REGION'] == r) & (p_df['EMISSION'] == e) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(e), str(y)) in EmissionsPenalty_specified else EmissionsPenalty_default_value for y in YEAR} for e in EMISSION} for r in REGION}
# AnnualExogenousEmission
AnnualExogenousEmission_default_value = p_default_df[p_default_df['PARAM'] == "AnnualExogenousEmission"].VALUE.iat[0]
AnnualExogenousEmission_specified = tuple([(str(r), str(e), str(y)) for r, e, y in zip(p_df[p_df['PARAM'] == "AnnualExogenousEmission"].REGION, p_df[p_df['PARAM'] == "AnnualExogenousEmission"].EMISSION, p_df[p_df['PARAM'] == "AnnualExogenousEmission"].YEAR)])
AnnualExogenousEmission = {str(r): {str(e): {str(y): p_df[(p_df['PARAM'] == "AnnualExogenousEmission") & (p_df['REGION'] == r) & (p_df['EMISSION'] == e) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(e), str(y)) in AnnualExogenousEmission_specified else AnnualExogenousEmission_default_value for y in YEAR} for e in EMISSION} for r in REGION}
# AnnualEmissionLimit
AnnualEmissionLimit_default_value = p_default_df[p_default_df['PARAM'] == "AnnualEmissionLimit"].VALUE.iat[0]
AnnualEmissionLimit_specified = tuple([(str(r), str(e), str(y)) for r, e, y in zip(p_df[p_df['PARAM'] == "AnnualEmissionLimit"].REGION, p_df[p_df['PARAM'] == "AnnualEmissionLimit"].EMISSION, p_df[p_df['PARAM'] == "AnnualEmissionLimit"].YEAR)])
AnnualEmissionLimit = {str(r): {str(e): {str(y): p_df[(p_df['PARAM'] == "AnnualEmissionLimit") & (p_df['REGION'] == r) & (p_df['EMISSION'] == e) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(e), str(y)) in AnnualEmissionLimit_specified else AnnualEmissionLimit_default_value for y in YEAR} for e in EMISSION} for r in REGION}
# ModelPeriodExogenousEmission
ModelPeriodExogenousEmission_default_value = p_default_df[p_default_df['PARAM'] == "ModelPeriodExogenousEmission"].VALUE.iat[0]
ModelPeriodExogenousEmission_specified = tuple([(str(r), str(e)) for r, e in zip(p_df[p_df['PARAM'] == "ModelPeriodExogenousEmission"].REGION, p_df[p_df['PARAM'] == "ModelPeriodExogenousEmission"].EMISSION)])
ModelPeriodExogenousEmission = {str(r): {str(e): p_df[(p_df['PARAM'] == "ModelPeriodExogenousEmission") & (p_df['REGION'] == r) & (p_df['EMISSION'] == e)].VALUE.iat[0] if (str(r), str(e)) in ModelPeriodExogenousEmission_specified else ModelPeriodExogenousEmission_default_value for e in EMISSION} for r in REGION}
# ModelPeriodEmissionLimit
ModelPeriodEmissionLimit_default_value = p_default_df[p_default_df['PARAM'] == "ModelPeriodEmissionLimit"].VALUE.iat[0]
ModelPeriodEmissionLimit_specified = tuple([(str(r), str(e)) for r, e in zip(p_df[p_df['PARAM'] == "ModelPeriodEmissionLimit"].REGION, p_df[p_df['PARAM'] == "ModelPeriodEmissionLimit"].EMISSION)])
ModelPeriodEmissionLimit = {str(r): {str(e): p_df[(p_df['PARAM'] == "ModelPeriodEmissionLimit") & (p_df['REGION'] == r) & (p_df['EMISSION'] == e)].VALUE.iat[0] if (str(r), str(e)) in ModelPeriodEmissionLimit_specified else ModelPeriodEmissionLimit_default_value for e in EMISSION} for r in REGION}
logging.info("{}\tParameters are created.".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
i = 0
while i <= mcs_num:
######### Simulation loops #########
logging.info("{}\tModel run #{}".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), i))
# ------------------------------------------------------------------------------------------------------------------
# MODEL INITIALIZATION
# ------------------------------------------------------------------------------------------------------------------
model = pulp.LpProblem(modelName, pulp.LpMinimize)
# ------------------------------------------------------------------------------------------------------------------
# MODEL VARIABLES
# ------------------------------------------------------------------------------------------------------------------
######## Demands #########
RateOfDemand = {str(r): {str(l): {str(f): {str(y): newVar("RateOfDemand", 0, None, 'Continuous', r, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for r in REGION}
Demand = {str(r): {str(l): {str(f): {str(y): newVar("Demand", 0, None, 'Continuous', r, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for r in REGION}
######## Storage #########
RateOfStorageCharge = {str(r): {str(s): {str(ls): {str(ld): {str(lh): {str(y): newVar("RateOfStorageCharge", 0, None, 'Continuous', r, s, ls, ld, lh, y) for y in YEAR} for lh in DAILYTIMEBRACKET} for ld in DAYTYPE} for ls in SEASON} for s in STORAGE} for r in REGION}
RateOfStorageDischarge = {str(r): {str(s): {str(ls): {str(ld): {str(lh): {str(y): newVar("RateOfStorageDischarge", None, None, 'Continuous', r, s, ls, ld, lh, y) for y in YEAR} for lh in DAILYTIMEBRACKET} for ld in DAYTYPE} for ls in SEASON} for s in STORAGE} for r in REGION}
NetChargeWithinYear = {str(r): {str(s): {str(ls): {str(ld): {str(lh): {str(y): newVar("NetChargeWithinYear", None, None, 'Continuous', r, s, ls, ld, lh, y) for y in YEAR} for lh in DAILYTIMEBRACKET} for ld in DAYTYPE} for ls in SEASON} for s in STORAGE} for r in REGION}
NetChargeWithinDay = {str(r): {str(s): {str(ls): {str(ld): {str(lh): {str(y): newVar("NetChargeWithinDay", None, None, 'Continuous', r, s, ls, ld, lh, y) for y in YEAR} for lh in DAILYTIMEBRACKET} for ld in DAYTYPE} for ls in SEASON} for s in STORAGE} for r in REGION}
StorageLevelYearStart = {str(r): {str(s): {str(y): newVar("StorageLevelYearStart", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
StorageLevelYearFinish = {str(r): {str(s): {str(y): newVar("StorageLevelYearFinish", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
StorageLevelSeasonStart = {str(r): {str(s): {str(ls): {str(y): newVar("StorageLevelSeasonStart", 0, None, 'Continuous', r, s, ls, y) for y in YEAR} for ls in SEASON} for s in STORAGE} for r in REGION}
StorageLevelDayTypeStart = {str(r): {str(s): {str(ls): {str(ld): {str(y): newVar("StorageLevelDayTypeStart", 0, None, 'Continuous', r, s, ls, ld, y) for y in YEAR} for ld in DAYTYPE} for ls in SEASON} for s in STORAGE} for r in REGION}
StorageLevelDayTypeFinish = {str(r): {str(s): {str(ls): {str(ld): {str(y): newVar("StorageLevelDayTypeFinish", 0, None, 'Continuous', r, s, ls, ld, y) for y in YEAR} for ld in DAYTYPE} for ls in SEASON} for s in STORAGE} for r in REGION}
StorageLowerLimit = {str(r): {str(s): {str(y): newVar("StorageLowerLimit", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
StorageUpperLimit = {str(r): {str(s): {str(y): newVar("StorageUpperLimit", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
AccumulatedNewStorageCapacity = {str(r): {str(s): {str(y): newVar("AccumulatedNewStorageCapacity", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
NewStorageCapacity = {str(r): {str(s): {str(y): newVar("NewStorageCapacity", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
CapitalInvestmentStorage = {str(r): {str(s): {str(y): newVar("CapitalInvestmentStorage", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
DiscountedCapitalInvestmentStorage = {str(r): {str(s): {str(y): newVar("DiscountedCapitalInvestmentStorage", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
SalvageValueStorage = {str(r): {str(s): {str(y): newVar("SalvageValueStorage", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
DiscountedSalvageValueStorage = {str(r): {str(s): {str(y): newVar("DiscountedSalvageValueStorage", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
TotalDiscountedStorageCost = {str(r): {str(s): {str(y): newVar("TotalDiscountedStorageCost", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
######### Capacity Variables #########
NumberOfNewTechnologyUnits = {str(r): {str(t): {str(y): newVar("NumberOfNewTechnologyUnits", 0, None, 'Integer', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
NewCapacity = {str(r): {str(t): {str(y): newVar("NewCapacity", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
AccumulatedNewCapacity = {str(r): {str(t): {str(y): newVar("AccumulatedNewCapacity", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
TotalCapacityAnnual = {str(r): {str(t): {str(y): newVar("TotalCapacityAnnual", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
######### Activity Variables #########
RateOfActivity = {str(r): {str(l): {str(t): {str(m): {str(y): newVar("RateOfActivity", 0, None, 'Continuous', r, l, t, m, y) for y in YEAR} for m in MODE_OF_OPERATION} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
RateOfTotalActivity = {str(r): {str(t): {str(l): {str(y): newVar("RateOfTotalActivity", 0, None, 'Continuous', r, t, l, y) for y in YEAR} for l in TIMESLICE} for t in TECHNOLOGY} for r in REGION}
TotalTechnologyAnnualActivity = {str(r): {str(t): {str(y): newVar("TotalTechnologyAnnualActivity", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
TotalAnnualTechnologyActivityByMode = {str(r): {str(t): {str(m): {str(y): newVar("TotalAnnualTechnologyActivityByMode", 0, None, 'Continuous', r, t, m, y) for y in YEAR} for m in MODE_OF_OPERATION} for t in TECHNOLOGY} for r in REGION}
TotalTechnologyModelPeriodActivity = {str(r): {str(t): newVar("TotalTechnologyModelPeriodActivity", None, None, 'Continuous', r, t) for t in TECHNOLOGY} for r in REGION}
RateOfProductionByTechnologyByMode = {str(r): {str(l): {str(t): {str(m): {str(f): {str(y): newVar("RateOfProductionByTechnologyByMode", 0, None, 'Continuous', r, l, t, m, f, y) for y in YEAR} for f in FUEL} for m in MODE_OF_OPERATION} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
RateOfProductionByTechnology = {str(r): {str(l): {str(t): {str(f): {str(y): newVar("RateOfProductionByTechnology", 0, None, 'Continuous', r, l, t, f, y) for y in YEAR} for f in FUEL} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
ProductionByTechnology = {str(r): {str(l): {str(t): {str(f): {str(y): newVar("ProductionByTechnology", 0, None, 'Continuous', r, l, t, f, y) for y in YEAR} for f in FUEL} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
ProductionByTechnologyAnnual = {str(r): {str(t): {str(f): {str(y): newVar("ProductionByTechnologyAnnual", 0, None, 'Continuous', r, t, f, y) for y in YEAR} for f in FUEL} for t in TECHNOLOGY} for r in REGION}
RateOfProduction = {str(r): {str(l): {str(f): {str(y): newVar("RateOfProduction", 0, None, 'Continuous', r, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for r in REGION}
Production = {str(r): {str(l): {str(f): {str(y): newVar("Production", 0, None, 'Continuous', r, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for r in REGION}
RateOfUseByTechnologyByMode = {str(r): {str(l): {str(t): {str(m): {str(f): {str(y): newVar("RateOfUseByTechnologyByMode", 0, None, 'Continuous', r, l, t, m, f, y) for y in YEAR} for f in FUEL} for m in MODE_OF_OPERATION} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
RateOfUseByTechnology = {str(r): {str(l): {str(t): {str(f): {str(y): newVar("RateOfUseByTechnology", 0, None, 'Continuous', r, l, t, f, y) for y in YEAR} for f in FUEL} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
UseByTechnologyAnnual = {str(r): {str(t): {str(f): {str(y): newVar("UseByTechnologyAnnual", 0, None, 'Continuous', r, t, f, y) for y in YEAR} for f in FUEL} for t in TECHNOLOGY} for r in REGION}
RateOfUse = {str(r): {str(l): {str(f): {str(y): newVar("RateOfUse", 0, None, 'Continuous', r, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for r in REGION}
UseByTechnology = {str(r): {str(l): {str(t): {str(f): {str(y): newVar("UseByTechnology", 0, None, 'Continuous', r, l, t, f, y) for y in YEAR} for f in FUEL} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
Use = {str(r): {str(l): {str(f): {str(y): newVar("Use", 0, None, 'Continuous', r, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for r in REGION}
Trade = {str(r): {str(rr): {str(l): {str(f): {str(y): newVar("Trade", None, None, 'Continuous', r, rr, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for rr in REGION2} for r in REGION}
TradeAnnual = {str(r): {str(rr): {str(f): {str(y): newVar("TradeAnnual", None, None, 'Continuous', r, rr, f, y) for y in YEAR} for f in FUEL} for rr in REGION2} for r in REGION}
ProductionAnnual = {str(r): {str(f): {str(y): newVar("ProductionAnnual", 0, None, 'Continuous', r, f, y) for y in YEAR} for f in FUEL} for r in REGION}
UseAnnual = {str(r): {str(f): {str(y): newVar("UseAnnual", 0, None, 'Continuous', r, f, y) for y in YEAR} for f in FUEL} for r in REGION}
######### Costing Variables #########
CapitalInvestment = {str(r): {str(t): {str(y): newVar("CapitalInvestment", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
DiscountedCapitalInvestment = {str(r): {str(t): {str(y): newVar("DiscountedCapitalInvestment", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
SalvageValue = {str(r): {str(t): {str(y): newVar("SalvageValue", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
DiscountedSalvageValue = {str(r): {str(t): {str(y): newVar("DiscountedSalvageValue", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
OperatingCost = {str(r): {str(t): {str(y): newVar("OperatingCost", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
DiscountedOperatingCost = {str(r): {str(t): {str(y): newVar("DiscountedOperatingCost", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
AnnualVariableOperatingCost = {str(r): {str(t): {str(y): newVar("AnnualVariableOperatingCost", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
AnnualFixedOperatingCost = {str(r): {str(t): {str(y): newVar("AnnualFixedOperatingCost", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
TotalDiscountedCostByTechnology = {str(r): {str(t): {str(y): newVar("TotalDiscountedCostByTechnology", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
TotalDiscountedCost = {str(r): {str(y): newVar("TotalDiscountedCost", 0, None, 'Continuous', r, y) for y in YEAR} for r in REGION}
ModelPeriodCostByRegion = {str(r): newVar("ModelPeriodCostByRegion", 0, None, 'Continuous', r) for r in REGION}
######### Reserve Margin #########
TotalCapacityInReserveMargin = {str(r): {str(y): newVar("TotalCapacityInReserveMargin", 0, None, 'Continuous', r, y) for y in YEAR} for r in REGION}
DemandNeedingReserveMargin = {str(r): {str(l): {str(y): newVar("DemandNeedingReserveMargin", 0, None, 'Continuous', r, l, y) for y in YEAR} for l in TIMESLICE} for r in REGION}
######### RE Gen Target #########
TotalREProductionAnnual = {str(r): {str(y): newVar("TotalREProductionAnnual", None, None, 'Continuous', r, y) for y in YEAR} for r in REGION}
RETotalProductionOfTargetFuelAnnual = {str(r): {str(y): newVar("RETotalProductionOfTargetFuelAnnual", None, None, 'Continuous', r, y) for y in YEAR} for r in REGION}
######### Emissions #########
AnnualTechnologyEmissionByMode = {str(r): {str(t): {str(e): {str(m): {str(y): newVar("AnnualTechnologyEmissionByMode", 0, None, 'Continuous', r, t, e, m, y) for y in YEAR} for m in MODE_OF_OPERATION} for e in EMISSION} for t in TECHNOLOGY} for r in REGION}
AnnualTechnologyEmission = {str(r): {str(t): {str(e): {str(y): newVar("AnnualTechnologyEmission", 0, None, 'Continuous', r, t, e, y) for y in YEAR} for e in EMISSION} for t in TECHNOLOGY} for r in REGION}
AnnualTechnologyEmissionPenaltyByEmission = {str(r): {str(t): {str(e): {str(y): newVar("AnnualTechnologyEmissionPenaltyByEmission", 0, None, 'Continuous', r, t, e, y) for y in YEAR} for e in EMISSION} for t in TECHNOLOGY} for r in REGION}
AnnualTechnologyEmissionsPenalty = {str(r): {str(t): {str(y): newVar("AnnualTechnologyEmissionsPenalty", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
DiscountedTechnologyEmissionsPenalty = {str(r): {str(t): {str(y): newVar("DiscountedTechnologyEmissionsPenalty", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
AnnualEmissions = {str(r): {str(e): {str(y): newVar("AnnualEmissions", 0, None, 'Continuous', r, e, y) for y in YEAR} for e in EMISSION} for r in REGION}
ModelPeriodEmissions = {str(r): {str(e): newVar("ModelPeriodEmissions", 0, None, 'Continuous', r, e) for e in EMISSION} for r in REGION}
logging.info("{}\tVariables are created".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
# ------------------------------------------------------------------------------------------------------------------
# OBJECTIVE FUNCTION
# ------------------------------------------------------------------------------------------------------------------
cost = pulp.LpVariable("cost", cat='Continuous')
model += cost, "Objective"
model += cost == pulp.lpSum([TotalDiscountedCost[r][y] for r in REGION for y in YEAR]), "Cost_function"
# ------------------------------------------------------------------------------------------------------------------
# CONSTRAINTS
# ------------------------------------------------------------------------------------------------------------------
for r in REGION:
for l in TIMESLICE:
for f in FUEL:
for y in YEAR:
# EQ_SpecifiedDemand
model += RateOfDemand[r][l][f][y] == SpecifiedAnnualDemand[r][f][y] * SpecifiedDemandProfile[r][f][l][y] / YearSplit[l][y], ""
######### Capacity Adequacy A #########
for r in REGION:
for t in TECHNOLOGY:
for y in YEAR:
# CAa1_TotalNewCapacity
model += AccumulatedNewCapacity[r][t][y] == pulp.lpSum([NewCapacity[r][t][yy] for yy in YEAR if (float(int(y) - int(yy)) < OperationalLife[r][t]) and (int(y) - int(yy) >= 0)]), ""
# CAa2_TotalAnnualCapacity
model += TotalCapacityAnnual[r][t][y] == AccumulatedNewCapacity[r][t][y] + ResidualCapacity[r][t][y], ""
for l in TIMESLICE:
# CAa3_TotalActivityOfEachTechnology
model += RateOfTotalActivity[r][t][l][y] == pulp.lpSum([RateOfActivity[r][l][t][m][y] for m in MODE_OF_OPERATION]), ""
# CAa4_Constraint_Capacity
model += RateOfTotalActivity[r][t][l][y] <= TotalCapacityAnnual[r][t][y] * CapacityFactor[r][t][l][y] * CapacityToActivityUnit[r][t], ""
if CapacityOfOneTechnologyUnit[r][t][y] != 0:
# CAa5_TotalNewCapacity
model += NewCapacity[r][t][y] == CapacityOfOneTechnologyUnit[r][t][y] * NumberOfNewTechnologyUnits[r][t][y], ""
######### Capacity Adequacy B #########
for r in REGION:
for t in TECHNOLOGY:
for y in YEAR:
# CAb1_PlannedMaintenance
model += pulp.lpSum([RateOfTotalActivity[r][t][l][y] * YearSplit[l][y] for l in TIMESLICE]) <= pulp.lpSum([TotalCapacityAnnual[r][t][y] * CapacityFactor[r][t][l][y] * YearSplit[l][y] for l in TIMESLICE]) * AvailabilityFactor[r][t][y] * CapacityToActivityUnit[r][t], ""
######### Energy Balance A #########
for r in REGION:
for l in TIMESLICE:
for f in FUEL:
for y in YEAR:
for t in TECHNOLOGY:
for m in MODE_OF_OPERATION:
# EBa1_RateOfFuelProduction1
if OutputActivityRatio[r][t][f][m][y] != 0:
model += RateOfProductionByTechnologyByMode[r][l][t][m][f][y] == RateOfActivity[r][l][t][m][y] * OutputActivityRatio[r][t][f][m][y], ""
else:
model += RateOfProductionByTechnologyByMode[r][l][t][m][f][y] == 0, ""
# EBa2_RateOfFuelProduction2
model += RateOfProductionByTechnology[r][l][t][f][y] == pulp.lpSum([RateOfProductionByTechnologyByMode[r][l][t][m][f][y] for m in MODE_OF_OPERATION if OutputActivityRatio[r][t][f][m][y] != 0]), ""
# EBa3_RateOfFuelProduction3
model += RateOfProduction[r][l][f][y] == pulp.lpSum([RateOfProductionByTechnology[r][l][t][f][y] for t in TECHNOLOGY]), ""
for t in TECHNOLOGY:
for m in MODE_OF_OPERATION:
# EBa4_RateOfFuelUse1
if InputActivityRatio[r][t][f][m][y] != 0:
model += RateOfUseByTechnologyByMode[r][l][t][m][f][y] == RateOfActivity[r][l][t][m][y] * InputActivityRatio[r][t][f][m][y], ""
# EBa5_RateOfFuelUse2
model += RateOfUseByTechnology[r][l][t][f][y] == pulp.lpSum([RateOfUseByTechnologyByMode[r][l][t][m][f][y] for m in MODE_OF_OPERATION if InputActivityRatio[r][t][f][m][y] != 0]), ""
# EBa6_RateOfFuelUse3
model += RateOfUse[r][l][f][y] == pulp.lpSum([RateOfUseByTechnology[r][l][t][f][y] for t in TECHNOLOGY]), ""
# EBa7_EnergyBalanceEachTS1
model += Production[r][l][f][y] == RateOfProduction[r][l][f][y] * YearSplit[l][y], ""
# EBa8_EnergyBalanceEachTS2
model += Use[r][l][f][y] == RateOfUse[r][l][f][y] * YearSplit[l][y], ""
# EBa9_EnergyBalanceEachTS3
model += Demand[r][l][f][y] == RateOfDemand[r][l][f][y] * YearSplit[l][y], ""
for rr in REGION2:
# EBa10_EnergyBalanceEachTS4
model += Trade[r][rr][l][f][y] == -Trade[rr][r][l][f][y], ""
# EBa11_EnergyBalanceEachTS5
model += Production[r][l][f][y] >= Demand[r][l][f][y] + Use[r][l][f][y] + pulp.lpSum([Trade[r][rr][l][f][y] * TradeRoute[r][rr][f][y] for rr in REGION2]), ""
######### Energy Balance B #########
for r in REGION:
for f in FUEL:
for y in YEAR:
# EBb1_EnergyBalanceEachYear1
model += ProductionAnnual[r][f][y] == pulp.lpSum([Production[r][l][f][y] for l in TIMESLICE]), ""
# EBb2_EnergyBalanceEachYear2
model += UseAnnual[r][f][y] == pulp.lpSum([Use[r][l][f][y] for l in TIMESLICE]), ""
for rr in REGION2:
# EBb3_EnergyBalanceEachYear3
model += TradeAnnual[r][rr][f][y] == pulp.lpSum([Trade[r][rr][l][f][y] for l in TIMESLICE]), ""
# EBb4_EnergyBalanceEachYear4
model += ProductionAnnual[r][f][y] >= UseAnnual[r][f][y] + pulp.lpSum([TradeAnnual[r][rr][f][y] * TradeRoute[r][rr][f][y] for rr in REGION2]) + AccumulatedAnnualDemand[r][f][y], ""
######### Accounting Technology Production/Use #########
for r in REGION:
for t in TECHNOLOGY:
for y in YEAR:
for l in TIMESLICE:
for f in FUEL:
# Acc1_FuelProductionByTechnology
model += ProductionByTechnology[r][l][t][f][y] == RateOfProductionByTechnology[r][l][t][f][y] * YearSplit[l][y], ""
# Acc2_FuelUseByTechnology
model += UseByTechnology[r][l][t][f][y] == RateOfUseByTechnology[r][l][t][f][y] * YearSplit[l][y], ""
for m in MODE_OF_OPERATION:
# Acc3_AverageAnnualRateOfActivity
model += TotalAnnualTechnologyActivityByMode[r][t][m][y] == pulp.lpSum([RateOfActivity[r][l][t][m][y] * YearSplit[l][y] for l in TIMESLICE]), ""
# Acc4_ModelPeriodCostByRegion
model += ModelPeriodCostByRegion[r] == pulp.lpSum([TotalDiscountedCost[r][y] for y in YEAR]), ""
######### Storage Equations #########
for r in REGION:
for s in STORAGE:
for y in YEAR:
for ls in SEASON:
for ld in DAYTYPE:
for lh in DAILYTIMEBRACKET:
# S1_RateOfStorageCharge
model += RateOfStorageCharge[r][s][ls][ld][lh][y] == pulp.lpSum([RateOfActivity[r][l][t][m][y] * TechnologyToStorage[r][t][s][m] * Conversionls[l][ls] * Conversionld[l][ld] * Conversionlh[l][lh] for t in TECHNOLOGY for m in MODE_OF_OPERATION for l in TIMESLICE if TechnologyToStorage[r][t][s][m] > 0]), ""
# S2_RateOfStorageDischarge
model += RateOfStorageDischarge[r][s][ls][ld][lh][y] == pulp.lpSum([RateOfActivity[r][l][t][m][y] * TechnologyFromStorage[r][t][s][m] * Conversionls[l][ls] * Conversionld[l][ld] * Conversionlh[l][lh] for t in TECHNOLOGY for m in MODE_OF_OPERATION for l in TIMESLICE if TechnologyFromStorage[r][t][s][m] > 0]), ""
# S3_NetChargeWithinYear
model += NetChargeWithinYear[r][s][ls][ld][lh][y] == pulp.lpSum([(RateOfStorageCharge[r][s][ls][ld][lh][y] - RateOfStorageDischarge[r][s][ls][ld][lh][y]) * YearSplit[l][y] * Conversionls[l][ls] * Conversionld[l][ld] * Conversionlh[l][lh] for l in TIMESLICE if (Conversionls[l][ls] > 0) and (Conversionld[l][ld] > 0) and (Conversionlh[l][lh] > 0)]), ""