Source code for dispaset.preprocessing.data_check

"""
This files gathers different functions used in the DispaSET to check the input
data

__author__ = 'Sylvain Quoilin (sylvain.quoilin@ec.europa.eu)'
"""

import os
import sys
import numpy as np
import pandas as pd
import logging
from pandas.api.types import is_numeric_dtype

from ..common import commons  # Load fuel types, technologies, timestep, etc


[docs]def isVRE(tech): ''' Function that returns true the technology is a variable renewable energy technology ''' return tech in ['HROR','PHOT','WTON','WTOF']
[docs]def isStorage(tech): ''' Function that returns true the technology is a storage technology ''' return tech in ['HDAM','HPHS','CAES','BATS','BEVS','THMS','P2GS']
[docs]def check_AvailabilityFactors(plants, AF): ''' Function that checks the validity of the provided availability factors and warns if a default value of 100% is used. ''' RES = ['WTON', 'WTOF', 'PHOT', 'HROR'] for i,v in plants.iterrows(): u = v['Unit'] t = v['Technology'] if t in RES and u not in AF: logging.error('Unit ' + str(u) + ' (technology ' + t + ') does not appear in the availbilityFactors table. Please provide') raise ValueError('Please provide RES AF timeseries for '+str(u)) if u in AF: if pd.isna(AF[u]).any(): Nna = pd.isna(AF[u]).count() logging.warning('The Availability factor of unit {} for technology {} contains {} empty values.'.format(str(u),t,Nna)) df_af = AF[u].dropna() if (df_af == 1).all(axis=None): logging.debug('The availability factor of unit ' + str(u) + ' + for technology ' + t + ' is always 100%!') if ((df_af < 0) | (df_af > 1)).any(axis=None): Nup = df_af[df_af>1].count() Ndo = df_af[df_af<0].count() logging.error('The Availability factor of unit {} for technology {} should be between 0 and 1. There are {} values above 1.0 and {} below 0.0'.format(str(u),t,Nup,Ndo)) else: logging.error('Unit ' + str(u) + ' (technology ' + t + ') does not appear in the availbilityFactors table. Its values will be set to 100%!')
[docs]def check_FlexibleDemand(flex): ''' Function that checks the validity of the provided flexibility demand time series ''' if (flex.dropna().values < 0).any(): logging.error('Some flexibility demand values are negative. They must be comprised between 0 and 1') sys.exit(1) if (flex.dropna().values > 1).any(): logging.error('Some flexibility demand values are more than 1. They must be comprised between 0 and 1') sys.exit(1)
[docs]def check_clustering(plants,plants_merged): ''' Function that checks that the installed capacities are still equal after the clustering process :param plants: Non-clustered list of units :param plants_merged: clustered list of units ''' # First, list all pairs of technology - fuel techs = pd.DataFrame( [[plants.Technology[idx],plants.Fuel[idx]] for idx in plants.index] ) techs.drop_duplicates(inplace=True) for i in techs.index: tech = (techs.loc[i,0],techs.loc[i,1]) units_old = plants[(plants.Technology == tech[0]) & (plants.Fuel == tech[1])] units_new = plants_merged[(plants_merged.Technology == tech[0]) & (plants_merged.Fuel == tech[1])] P_old = (units_old.PowerCapacity * units_old.Nunits).sum() P_new = (units_new.PowerCapacity * units_new.Nunits).sum() if np.abs(P_old - P_new)/(P_old + 0.0001) > 0.01: logging.error('The installed capacity for technology "' + tech[0] + '" and fuel "' + tech[1] + '" is not equal between the original units table (P = ' + str(P_old) + ') and the clustered table (P = ' + str(P_new) + ')') sys.exit(1) # Check the overall installed storage capacity: List_tech_storage = ['HDAM', 'HPHS', 'BATS', 'BEVS', 'CAES', 'THMS'] isstorage = pd.Series(index=plants.index,dtype='bool') for u in isstorage.index: isstorage[u] = plants.Technology[u] in List_tech_storage isstorage_merged = pd.Series(index=plants_merged.index,dtype='bool') for u in isstorage_merged.index: isstorage_merged[u] = plants_merged.Technology[u] in List_tech_storage TotalStorage = (plants.STOCapacity[isstorage]*plants.Nunits[isstorage]).sum() TotalStorage_merged = (plants_merged.STOCapacity[isstorage_merged]*plants_merged.Nunits[isstorage_merged]).sum() if np.abs(TotalStorage - TotalStorage_merged)/(TotalStorage + 0.0001) > 0.01: logging.error('The total installed storage capacity is not equal between the original units table (' + str(TotalStorage) + ') and the clustered table (' + str(TotalStorage_merged) + ')') #sys.exit(1) return True
[docs]def check_MinMaxFlows(df_min,df_max): ''' Function that checks that there is no incompatibility between the minimum and maximum flows ''' if (df_min > df_max).any(): pos = np.where(df_min > df_max) logging.critical('ERROR: At least one minimum flow is higher than the maximum flow, for example in line number ' + str(pos[0][0]) + ' and time step ' + str(pos[1][0])) sys.exit(1) if (df_max < 0).any(): pos = np.where(df_max < 0) logging.critical('ERROR: At least one maximum flow is negative, for example in line number ' + str(pos[0][0]) + ' and time step ' + str(pos[1][0])) sys.exit(1) return True
[docs]def check_sto(config, plants,raw_data=True): """ Function that checks the storage plant characteristics """ if raw_data: keys = ['STOCapacity','STOSelfDischarge','STOMaxChargingPower','STOChargingEfficiency'] NonNaNKeys = ['STOCapacity'] else: keys = ['StorageCapacity','StorageSelfDischarge','StorageChargingCapacity','StorageChargingEfficiency'] NonNaNKeys = ['StorageCapacity'] if 'StorageInitial' in plants: logging.warning('The "StorageInitial" column is present in the power plant table, although it is deprecated (it should now be defined in the ReservoirLevel data table). It will not be considered.') for key in keys: if key not in plants: logging.critical('The power plants data does not contain the field "' + key + '", which is mandatory for storage units') sys.exit(1) for key in NonNaNKeys: for u in plants.index: if 'Unit' in plants: unitname = plants.loc[u,'Unit'] else: unitname = str(u) if isinstance(plants.loc[u, key], str): logging.critical('A non numeric value was detected in the power plants inputs for parameter "' + key + '"') sys.exit(1) if np.isnan(plants.loc[u, key]): logging.critical('The power plants data is missing for unit ' + unitname + ' and parameter "' + key + '"') sys.exit(1) return True
[docs]def check_p2h(config, plants): """ Function that checks the p2h unit characteristics """ keys = ['COP'] NonNaNKeys = ['COP'] StrKeys = [] if len(plants)==0: # If there are no P2HT units, exit the check return True for key in keys: if key not in plants: logging.critical('The power plants data does not contain the field "' + key + '", which is mandatory for P2HT units') sys.exit(1) for key in NonNaNKeys: for u in plants.index: if 'Unit' in plants: unitname = plants.loc[u,'Unit'] else: unitname = str(u) if type(plants.loc[u, key]) == str: logging.critical('A non numeric value was detected in the power plants inputs for parameter "' + key + '"') sys.exit(1) if np.isnan(plants.loc[u, key]): logging.critical('The power plants data is missing for unit number ' + unitname + ' and parameter "' + key + '"') sys.exit(1) for key in StrKeys: for u in plants.index: if 'Unit' in plants: unitname = plants.loc[u,'Unit'] else: unitname = str(u) if not isinstance(plants.loc[u, key], str): logging.critical( 'A numeric value was detected in the power plants inputs for parameter "' + key + '". This column should contain strings only.') sys.exit(1) elif plants.loc[u, key] == '': logging.critical('An empty value was detected in the power plants inputs for unit "' + unitname + '" and parameter "' + key + '"') sys.exit(1) # Check the COP values: for u in plants.index: if plants.loc[u,'COP'] < 0 or plants.loc[u,'COP'] > 20: logging.critical('The COP value of p2h units must be comprised between 0 and 20. The provided value for unit ' + u + ' is "' + str(plants.loc[u,'COP'] + '"')) sys.exit(1) return True
[docs]def check_chp(config, plants): """ Function that checks the CHP plant characteristics """ keys = ['CHPType','CHPPowerToHeat','CHPPowerLossFactor'] NonNaNKeys = ['CHPPowerToHeat','CHPPowerLossFactor'] StrKeys = ['CHPType'] for key in keys: if key not in plants: logging.critical('The power plants data does not contain the field "' + key + '", which is mandatory for CHP units') sys.exit(1) for key in NonNaNKeys: for u in plants.index: if 'Unit' in plants: unitname = plants.loc[u,'Unit'] else: unitname = str(u) if type(plants.loc[u, key]) == str: logging.critical('A non numeric value was detected in the power plants inputs for parameter "' + key + '"') sys.exit(1) if np.isnan(plants.loc[u, key]): logging.critical('The power plants data is missing for unit number ' + unitname + ' and parameter "' + key + '"') sys.exit(1) for key in StrKeys: for u in plants.index: if 'Unit' in plants: unitname = plants.loc[u,'Unit'] else: unitname = str(u) if not isinstance(plants.loc[u, key], str): logging.critical( 'A numeric value was detected in the power plants inputs for parameter "' + key + '". This column should contain strings only.') sys.exit(1) elif plants.loc[u, key] == '': logging.critical('An empty value was detected in the power plants inputs for unit "' + unitname + '" and parameter "' + key + '"') sys.exit(1) # Check the efficiency values: for u in plants.index: if 'Unit' in plants: unitname = plants.loc[u,'Unit'] else: unitname = str(u) plant_PowerCapacity = plants.loc[u,'PowerCapacity'] plant_MaxHeat = plants.loc[u, 'CHPMaxHeat'] plant_powertoheat = plants.loc[u,'CHPPowerToHeat'] plant_powerlossfactor = plants.loc[u,'CHPPowerLossFactor'] if plants.loc[u,'CHPType'].lower() not in ['extraction','back-pressure', 'p2h']: logging.critical('The value of CHPType should be "extraction", "back-pressure" or "p2h". The type of unit ' + u + ' is "' + str(plants.loc[u,'CHPType'] + '"')) sys.exit(1) if 0 > plant_powertoheat > 10: logging.critical('The value of CHPPowerToHeat should be higher or equal to zero and lower than 10. Unit ' + u + ' has a value of ' + str(plant_powertoheat)) sys.exit(1) if 0 > plant_powerlossfactor > 1 and plants.loc[u,'CHPType'].lower() != 'p2h': logging.critical('The value of CHPPowerLossFactor should be higher or equal to zero and lower than 1. Unit ' + u + ' has a value of ' + str(plant_powerlossfactor)) sys.exit(1) if plants.loc[u,'CHPType'].lower() == 'back-pressure' and plant_powerlossfactor != 0: logging.critical('The value of CHPPowerLossFactor must be zero if the CHP types is "back-pressure". Unit ' + u + ' has a value of ' + str(plant_powerlossfactor)) sys.exit(1) if plants.loc[u, 'CHPType'].lower() == 'extraction': intersection_MaxHeat = plant_PowerCapacity / plant_powertoheat if not pd.isnull(plant_MaxHeat): if intersection_MaxHeat < plant_MaxHeat: logging.warning('Given Maximum heat CHPMaxHeat ({}) is higher than the intersection point of the two other constraints ({}) ' '(power loss factor and backpressure line) therefore it will not be ignored'.format(plant_MaxHeat, intersection_MaxHeat) ) plant_MaxHeat = intersection_MaxHeat else: plant_MaxHeat = intersection_MaxHeat # Calculating the nominal total efficiency at the highest point: if plants.loc[u,'CHPType'].lower() != 'p2h': Fuel = (plant_PowerCapacity + plant_powerlossfactor * plant_MaxHeat)/plants.loc[u,'Efficiency'] # F = (P + C_v * Q)/eta_condensation TotalEfficiency = (plant_PowerCapacity + plant_MaxHeat) / Fuel # eta_tot = (P + Q) / F logging.debug('Highest overall efficiency of CHP plant {} is {:.2f}'.format(u,TotalEfficiency)) if TotalEfficiency < 0 or TotalEfficiency > 1.14: logging.critical('The calculated value of the total CHP efficiency for unit ' + unitname + ' is ' + str(TotalEfficiency) + ', which is unrealistic!') sys.exit(1) if TotalEfficiency > 0.95: logging.warning('The calculated value of the total CHP efficiency for unit ' + unitname + ' is ' + str(TotalEfficiency) + ', which is very high!') # Check the optional MaxHeatCapacity parameter. While it adds another realistic boundary it is not a required parameter for the definition of the CHP's operational envelope.: if 'CHPMaxHeat' in plants: for u in plants.index: plant_MaxHeat = plants.loc[u, 'CHPMaxHeat'] if plant_MaxHeat <=0: logging.warning('CHPMaxHeat for plant {} is {} which shuts down any heat production.'.format(u, plant_MaxHeat)) # Check the optional heat storage values: if 'STOCapacity' in plants: for u in plants.index: Qdot = plants.loc[u,'PowerCapacity']/plants.loc[u,'CHPPowerToHeat'] if plants.loc[u,'STOCapacity'] < Qdot * 0.5 : logging.warning('Unit ' + unitname + ': The value of the thermal storage capacity (' + str(plants.loc[u,'STOCapacity']) + 'MWh) seems very low compared to its thermal power (' + str(Qdot) + 'MW).') elif plants.loc[u,'STOCapacity'] > Qdot * 24: logging.warning('Unit ' + unitname + ': The value of the thermal storage capacity (' + str(plants.loc[u,'STOCapacity']) + 'MWh) seems very high compared to its thermal power (' + str(Qdot) + 'MW).') if 'STOSelfDischarge' in plants: for u in plants.index: if plants.loc[u,'STOSelfDischarge'] < 0 : logging.error('Unit ' + unitname + ': The value of the thermal storage self-discharge (' + str(plants.loc[u,'STOSelfDischarge']*100) + '%/day) cannot be negative') sys.exit(1) elif plants.loc[u,'STOSelfDischarge'] > 1: logging.warning('Unit ' + unitname + ': The value of the thermal storage self-discharge (' + str(plants.loc[u,'STOSelfDischarge']*100) + '%/day) seems very high') elif plants.loc[u,'STOSelfDischarge'] > 24: logging.error('Unit ' + unitname + ': The value of the thermal storage self-discharge (' + str(plants.loc[u,'STOSelfDischarge']*100) + '%/day) is too high') sys.exit(1) return True
[docs]def check_units(config, plants): """ Function that checks the power plant characteristics """ keys = ['Unit', 'Fuel', 'Zone', 'Technology', 'PowerCapacity', 'PartLoadMin', 'RampUpRate', 'RampDownRate', 'StartUpTime', 'MinUpTime', 'MinDownTime', 'NoLoadCost', 'StartUpCost', 'Efficiency', 'CO2Intensity'] NonNaNKeys = ['PowerCapacity', 'PartLoadMin', 'RampUpRate', 'RampDownRate', 'Efficiency', 'RampingCost', 'CO2Intensity'] StrKeys = ['Unit', 'Zone', 'Fuel', 'Technology'] # Special treatment for the Optional key Nunits: if 'Nunits' in plants: keys.append('Nunits') NonNaNKeys.append('Nunits') if any([not float(x).is_integer() for x in plants['Nunits']]): logging.error('Some values are not integers in the "Nunits" column of the plant database') sys.exit(1) else: logging.info('The columns "Nunits" is not present in the power plant database. A value of one will be assumed by default') for key in keys: if key not in plants: logging.critical('The power plants data does not contain the field "' + key + '", which is mandatory') sys.exit(1) for key in NonNaNKeys: for u in plants.index: if type(plants.loc[u, key]) == str: logging.critical('A non numeric value was detected in the power plants inputs for parameter "' + key + '"') sys.exit(1) if np.isnan(plants.loc[u, key]): logging.critical('The power plants data is missing for unit number ' + str(u) + ' and parameter "' + key + '"') sys.exit(1) for key in StrKeys: for u in plants.index: if not type(plants.loc[u, key]) == str: logging.critical( 'A numeric value was detected in the power plants inputs for parameter "' + key + '". This column should contain strings only.') sys.exit(1) elif plants.loc[u, key] == '': logging.critical('An empty value was detected in the power plants inputs for unit "' + str( u) + '" and parameter "' + key + '"') sys.exit(1) lower = {'PowerCapacity': 0, 'PartLoadMin': 0, 'StartUpTime': 0, 'MinUpTime': 0, 'MinDownTime': 0, 'NoLoadCost': 0, 'StartUpCost': 0} lower_hard = {'RampUpRate': 0, 'RampDownRate': 0, 'Efficiency': 0} higher = {'PartLoadMin': 1, 'Efficiency': 1} higher_time = {'MinUpTime': 0, 'MinDownTime': 0} # 'StartUpTime':0, # Special treatment for the Optional key Nunits: if 'Nunits' in plants: lower_hard['Nunits'] = 0 if len(plants['Unit'].unique()) != len(plants['Unit']): duplicates = plants['Unit'][plants['Unit'].duplicated()].tolist() logging.error('The names of the power plants are not unique. The following names are duplicates: ' + str(duplicates) + '. "' + str(duplicates[0] + '" appears for example in the following zones: ' + str(plants.Zone[plants['Unit']==duplicates[0]].tolist()))) sys.exit(1) for key in lower: if any(plants[key] < lower[key]): plantlist = plants[plants[key] < lower[key]] plantlist = plantlist['Unit'].tolist() logging.critical( 'The value of ' + key + ' should be higher or equal to zero. A negative value has been found for units ' + str( plantlist)) sys.exit(1) for key in lower_hard: if any(plants[key] <= lower_hard[key]): plantlist = plants[plants[key] <= lower_hard[key]] plantlist = plantlist['Unit'].tolist() logging.critical( 'The value of ' + key + ' should be strictly higher than zero. A null or negative value has been found for units ' + str( plantlist)) sys.exit(1) for key in higher: if any(plants[key] > higher[key]): plantlist = plants[plants[key] > higher[key]] plantlist = plantlist['Unit'].tolist() logging.critical( 'The value of ' + key + ' should be lower or equal to one. A higher value has been found for units ' + str( plantlist)) sys.exit(1) for key in higher_time: if any(plants[key] >= config['HorizonLength'] * 24): plantlist = plants[plants[key] >= config['HorizonLength'] * 24] plantlist = plantlist['Unit'].tolist() logging.critical('The value of ' + key + ' should be lower than the horizon length (' + str( config['HorizonLength'] * 24) + ' hours). A higher value has been found for units ' + str(plantlist)) sys.exit(1) # Checking che compatibility between the selected simulation time and the power plant constraints: if config['SimulationType'] in ('LP','LP clustered') : for key in ['NoLoadCost', 'PartLoadMin', 'MinEfficiency', 'StartUpTime']: if (plants[key] > 0).any(): logging.error('Non-null value(s) have been found for key ' + key + ' in the power plant list. \ This cannot be modelled with the ' + config['SimulationType'] + ' formulation and \ will therefore not be considered.') return True
[docs]def check_heat_demand(plants,data): ''' Function that checks the validity of the heat demand profiles :param plants: List of CHP plants ''' plants.index = plants['Unit'] plants_heating = plants[[str(plants['CHPType'][u]).lower() in commons['types_CHP'] or plants.loc[u,'Technology']=='P2HT' for u in plants.index]] plants_chp = plants[[str(plants['CHPType'][u]).lower() in commons['types_CHP'] for u in plants.index]] for u in data: if u in plants_heating.index: if 'Nunits' in plants_heating: Nunits = plants.loc[u,'Nunits'] else: Nunits = 1 if (data[u] == 0).all(): logging.critical('Heat demand data for CHP unit "' + u + '" is either no found or always equal to zero') else: logging.warning('The heat demand profile with header "' + str(u) + '" does not correspond to any CHP plant. It will be ignored.') if u in plants_chp.index: plant_CHP_type = plants.loc[u,'CHPType'].lower() if pd.isnull(plants.loc[u, 'CHPMaxHeat']): plant_Qmax = +np.inf else: plant_Qmax = plants.loc[u,'CHPMaxHeat'] if plant_CHP_type == 'extraction': Qmin = 0 Qmax = min(plants.loc[u, 'PowerCapacity'] / plants.loc[u, 'CHPPowerToHeat'], plant_Qmax) * Nunits elif plant_CHP_type == 'back-pressure': Qmin = plants.loc[u,'PowerCapacity'] * plants.loc[u,'PartLoadMin'] /plants.loc[u,'CHPPowerToHeat'] Qmax = min(plants.loc[u, 'PowerCapacity'] / plants.loc[u, 'CHPPowerToHeat'], plant_Qmax) * Nunits elif plant_CHP_type == 'p2h': Qmin = 0 Qmax = plant_Qmax * Nunits else: logging.error('The CHP type for unit ' + u + ' is not valid.') if np.isnan(Qmax) and plant_CHP_type!='p2h': logging.error('CHPPowerToHeat is not defined for unit ' + str(u) + ' appearing in the heat demand profiles') sys.exit(1) elif data[u].max() > Qmax: logging.warning('The maximum thermal demand for unit ' + str(u) + ' (' + str(data[u].max()) + ') is higher than its thermal capacity (' + str(Qmax) + '). Slack heat will be used to cover that.') if data[u].min() < Qmin: logging.warning('The minimum thermal demand for unit ' + str(u) + ' (' + str(data[u].min()) + ') is lower than its minimum thermal generation (' + str(Qmin) + ' MWth)') # check that a heating demand has been provided for all heating technologies for u in plants_heating.index: if u not in data: logging.critical('No heat demand data was found for unit ' + u) sys.exit(1) return True
[docs]def check_reserves(Reserve2D,Reserve2U,Load): ''' Function that checks the validity of the reserve requirement time series ''' for z in Load.columns: if z in Reserve2U: if (Reserve2U[z] < 0).any(): logging.critical('The reserve 2U table contains negative values for zone ' + z) sys.exit(1) if (Load[z] - Reserve2U[z] < 0).any(): logging.critical('The reserve 2U table contains negative values higher than demand for zone ' + z) sys.exit(1) else: logging.warning('No 2U reserve requirement data has been found for zone ' + z + '. Using the standard formula') if z in Reserve2D: if (Reserve2D[z] < 0).any(): logging.critical('The reserve 2D table contains negative values for zone ' + z) sys.exit(1) if (Load[z] - Reserve2D[z] < 0).any(): logging.critical('The reserve 2D table contains negative values higher than demand for zone ' + z) sys.exit(1) else: logging.warning('No 2D reserve requirement data has been found for zone ' + z + '. Using the standard formula')
[docs]def check_temperatures(plants,Temperatures): ''' Function that checks the presence and validity of the temperatures profiles for units with temperature-dependent characteristics :param plants: List of all units ''' plants.index = plants['Unit'] if 'Tnominal' in plants and is_numeric_dtype(plants['Tnominal']): plants_T = plants[plants.Tnominal > 0] if 'coef_COP_a' not in plants or 'coef_COP_b' not in plants: logging.critical('Columns coef_COP_a and coef_COP_b must be defined in the units table') sys.exit(1) else: plants_T= pd.DataFrame(columns=plants.columns) for u in plants_T.index: if plants_T.loc[u,'Zone'] not in Temperatures: logging.critical('No temperature data has been found for zone ' + plants_T.loc[u,'Zone'] +", although it is required for temperature-dependent unit " + u) sys.exit(1) return True
[docs]def check_df(df, StartDate=None, StopDate=None, name=''): """ Function that check the time series provided as inputs """ if isinstance(df.index, pd.DatetimeIndex): if not StartDate in df.index: logging.warning('The start date ' + str(StartDate) + ' is not in the index of the provided dataframe') if not StopDate in df.index: logging.warning('The stop date ' + str(StopDate) + ' is not in the index of the provided dataframe') if any(np.isnan(df)): for key in df: missing = np.sum(np.isnan(df[key])) # pos = np.where(np.isnan(df.sum(axis=1))) # idx_pos = [df.index[i] for i in pos] if missing > 1: logging.warning('There are ' + str(missing) + ' missing entries in the column ' + key + ' of the dataframe ' + name) if not df.columns.is_unique: logging.error('The column headers of table "' + name + '" are not unique!. The following headers are duplicated: ' + str(df.columns.get_duplicates())) sys.exit(1) return True
[docs]def check_simulation_environment(SimulationPath, store_type='pickle', firstline=7): """ Function to test the validity of disapset inputs :param SimulationPath: Path to the simulation folder :param store_type: choose between: "list", "excel", "pickle" :param firstline: Number of the first line in the data (only if type=='excel') """ import cPickle # minimum list of variable required for dispaSET: list_sets = [ 'h', 'd', 'mk', 'n', 'c', 'p', 'l', 'f', 's', 't', 'tr', 'u'] list_param = [ 'AvailabilityFactor', 'CostFixed', 'CostShutDown', 'Curtailment', 'Demand', 'Efficiency', 'Fuel', 'CostVariable', 'FuelPrice', 'Markup', 'CostStartUp', 'EmissionMaximum', 'EmissionRate', 'FlowMaximum', 'FlowMinimum', 'LineNode', 'Location', 'LoadShedding', 'OutageFactor', 'PermitPrice', 'PriceTransmission', 'PowerCapacity', 'PartLoadMin', 'RampUpMaximum', 'RampDownMaximum', 'RampStartUpMaximum', 'RampShutDownMaximum', 'Reserve', 'StorageDischargeEfficiency', 'StorageCapacity', 'StorageInflow', 'StorageOutflow', 'StorageInitial', 'StorageMinimum', 'StorageChargingEfficiency', 'StorageChargingCapacity', 'Technology', 'TimeDownMinimum', 'TimeUpMinimum', 'TimeDownInitial', 'TimeUpInitial', 'PowerInitial'] if store_type == 'list': if isinstance(SimulationPath, list): # The list of sets and parameters has been passed directly to the function, checking that all are present: SimulationPath_vars = [SimulationPath[i]['name'] for i in range(len(SimulationPath))] for var in list_sets + list_param: if var not in SimulationPath_vars: logging.critical('The variable "' + var + '" has not been found in the list of input variables') sys.exit(1) else: logging.critical('The argument must a list. Please correct or change the "type" argument') sys.exit(1) elif store_type == 'pickle': if os.path.exists(SimulationPath): if os.path.isfile(os.path.join(SimulationPath, 'Inputs.p')): vars = cPickle.load(open(os.path.join(SimulationPath, 'Inputs.p'), 'rb')) arg_vars = [vars[i]['name'] for i in range(len(vars))] for var in list_sets + list_param: if var not in arg_vars: logging.critical('Found Pickle file but does not contain valid DispaSET input (' + var + ' missing)') sys.exit(1) else: logging.critical('Could not find the Inputs.p file in the specified directory') sys.exit(1) else: logging.critical('The function argument is not a valid directory') sys.exit(1) elif store_type == 'excel': if os.path.exists(SimulationPath): if not os.path.isfile(os.path.join(SimulationPath, 'InputDispa-SET - Sets.xlsx')): logging.critical("Could not find the file 'InputDispa-SET - Sets.xlsx'") sys.exit(1) for var in list_param: if os.path.isfile(os.path.join(SimulationPath, 'InputDispa-SET - ' + var + '.xlsx')): a = 1 else: logging.critical("Could not find the file 'InputDispa-SET - " + var + ".xlsx'") sys.exit(1) else: logging.critical('The function argument is not a valid directory') sys.exit(1) else: logging.critical('The "type" parameter must be one of the following : "list", "excel", "pickle"') sys.exit(1)