RTS-GMLC to openTEPES format

RTS_Data/FormattedData/openTEPES/Create_openTEPES_InputData/SourceDataToDictionaries.py

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+# Libraries
+import time
+import pandas as pd
+
+
+def GettingDataTo_oTDict(_path_data, _path_file, CaseName):
+    print('Transforming data to get the oT_Dict files ****')
+
+    StartTime = time.time()
+
+    # reading data from the folder SourceData
+    df_bus    = pd.read_csv(_path_data+'/SourceData/bus.csv'   )
+    df_branch = pd.read_csv(_path_data+'/SourceData/branch.csv')
+    df_gen    = pd.read_csv(_path_data+'/SourceData/gen.csv'   )
+
+    # reading data from the folder timeseries_data_file
+    df_TS_CSP = pd.read_csv(_path_data + '/timeseries_data_files/CSP/DAY_AHEAD_Natural_Inflow.csv')
+
+
+    # Extracting regions
+    pRegions = pd.Series(['Region_1'])
+    pRegions.to_frame(name='Region').to_csv(_path_file+'/RTS-GMLC/oT_Dict_Region_'+CaseName+'.csv', sep=',', index=False)
+
+    # Extracting areas
+    IdxAreas  = df_bus['Area'].unique()
+    pAreas     = pd.Series(['Area_'+str(int(i)) for i in IdxAreas])
+    pAreas.to_frame(name='Area').to_csv(_path_file+'/RTS-GMLC/oT_Dict_Area_'+CaseName+'.csv', sep=',', index=False)
+
+    # Extracting zones
+    IdxZones  = df_bus['Zone'].unique()
+    pZones     = pd.Series(['Zone_'+str(int(i)) for i in IdxZones])
+    pZones.to_frame(name='Zone').to_csv(_path_file+'/RTS-GMLC/oT_Dict_Zone_'+CaseName+'.csv', sep=',', index=False)
+
+    # Extracting nodes
+    IdxNodes  = df_bus['Bus ID'].unique()
+    pNodes     = pd.Series(['Node_'+str(int(i)) for i in IdxNodes])
+    pNodes.to_frame(name='Node').to_csv(_path_file+'/RTS-GMLC/oT_Dict_Node_'+CaseName+'.csv', sep=',', index=False)
+
+    # From Node to Zone
+    Nodes = ['Node_' + str(int(df_bus['Bus ID'][i])) for i in df_bus.index]
+    Zones = ['Zone_' + str(int(df_bus['Zone'  ][i])) for i in df_bus.index]
+    pNodeToZone = pd.DataFrame({'Node': Nodes, 'Zone': Zones})
+    pNodeToZone.to_csv(_path_file+'/RTS-GMLC/oT_Dict_NodeToZone_'+CaseName+'.csv', sep=',', index=False)
+
+    # From Zone to Area
+    Areas = ['Area_' + str(int(df_bus['Area'][i])) for i in df_bus.index]
+    pZoneToArea = pd.DataFrame({'Zone': Zones, 'Area': Areas}).set_index(['Zone', 'Area'])
+    pZoneToArea = pZoneToArea[~pZoneToArea.index.duplicated(keep='first')].reset_index()
+    pZoneToArea.to_csv(_path_file+'/RTS-GMLC/oT_Dict_ZoneToArea_'+CaseName+'.csv', sep=',', index=False)
+
+    # From Area to Region
+    pAreaToRegion = pAreas.to_frame('Area')
+    pAreaToRegion['Region'] = 'Region_1'
+    pAreaToRegion.to_csv(_path_file + '/RTS-GMLC/oT_Dict_AreaToRegion_' + CaseName + '.csv', sep=',', index=False)
+
+    # Defining circuits
+    pCircuit = pd.Series(['eac1', 'eac2', 'eac3', 'eac4'])
+    pCircuit.to_frame(name='Circuit').to_csv(_path_file + '/RTS-GMLC/oT_Dict_Circuit_' + CaseName + '.csv', sep=',', index=False)
+
+    # Determining generators
+    IdxGenerator = df_gen['GEN UID'].unique()
+    pGenerator   = pd.Series([i for i in IdxGenerator])
+    pGenerator.to_frame(name='Generator').to_csv(_path_file+'/RTS-GMLC/oT_Dict_Generation_'+CaseName+'.csv', sep=',', index=False)
+
+    # Defining line types
+    pLineType = pd.Series(['AC', 'DC'])
+    pLineType.to_frame(name='LineType').to_csv(_path_file + '/RTS-GMLC/oT_Dict_Line_' + CaseName + '.csv', sep=',', index=False)
+
+    # Defining load levels
+    df_TS_CSP['Month' ] = df_TS_CSP.Month.map("{:02}".format)
+    df_TS_CSP['Day'   ] = df_TS_CSP.Day.map("{:02}".format)
+    df_TS_CSP['Period'] = df_TS_CSP.Period.map("{:02}".format)
+    LoadLevels   = [str(df_TS_CSP['Month'][i])+str(df_TS_CSP['Day'][i])+str(df_TS_CSP['Period'][i]) for i in df_TS_CSP.index]
+    pLoadLevels  = pd.DataFrame({'LoadLevel': LoadLevels})
+    pLoadLevels.to_csv(_path_file + '/RTS-GMLC/oT_Dict_LoadLevel_' + CaseName + '.csv', sep=',', index=False)
+
+    # Defining the Period
+    IdxPeriod    = df_TS_CSP['Year'].unique()
+    pPeriod      = pd.Series([i for i in IdxPeriod])
+    pPeriod.to_frame(name='Period').to_csv(_path_file+'/RTS-GMLC/oT_Dict_Period_'+CaseName+'.csv', sep=',', index=False)
+
+    # Defining the Scenario
+    pScenarios   = pd.Series(['sc01'])
+    pScenarios.to_frame(name='Scenario').to_csv(_path_file+'/RTS-GMLC/oT_Dict_Scenario_'+CaseName+'.csv', sep=',', index=False)
+
+    # Defining the Stage
+    pStage       = pd.Series(['st0'])
+    pStage.to_frame(name='Stage').to_csv(_path_file+'/RTS-GMLC/oT_Dict_Stage_'+CaseName+'.csv', sep=',', index=False)
+
+    # Defining the storage type
+    pStorageType = pd.Series(['Daily', 'Weekly'])
+    pStorageType.to_frame(name='StorageType').to_csv(_path_file+'/RTS-GMLC/oT_Dict_Storage_'+CaseName+'.csv', sep=',', index=False)
+
+    # Defining the generation technologies
+    IdxTechno    = df_gen['Fuel'].unique()
+    pTechnology  = pd.Series([i for i in IdxTechno])
+    pTechnology.to_frame(name='Technology').to_csv(_path_file+'/RTS-GMLC/oT_Dict_Technology_'+CaseName+'.csv', sep=',', index=False)
+
+    oT_Dict_Time    = time.time() - StartTime
+    print('oT_Dict files generation               ... ', round(oT_Dict_Time), 's')

RTS_Data/FormattedData/openTEPES/Create_openTEPES_InputData/__init__.py

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+"""
+Open Generation, Storage, and Transmission Operation and Expansion Planning Model with RES and ESS (openTEPES)
+
+
+    Args:
+        case:   Name of the folder where the CSV files of the case are found
+        dir:    Main path where the case folder can be found
+        solver: Name of the solver
+
+    Returns:
+        Output results in CSV files that are found in the case folder.
+
+    # Examples:
+    #     >>> import openTEPES as oT
+    #     >>> oT.routine("9n", "C:\\Users\\UserName\\Documents\\GitHub\\openTEPES", "glpk")
+"""
+# __version__ = "4.8.0"
+
+from .SourceDataToDictionaries import GettingDataTo_oTDict
+from .SourceDataToData         import GettingDataTo_oTData

RTS_Data/FormattedData/openTEPES/Create_openTEPES_RTS-GMLC.py

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+# Libraries
+import os
+import pandas as pd
+
+import Create_openTEPES_InputData as ID
+
+CaseName = 'RTS-GMLC'
+
+# Setting up the path
+_path_data = os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..', '..'))
+_path_file = os.path.dirname(__file__)
+
+print('*** Creating the case RTS-GLC in openTEPES format ****')
+
+ID.GettingDataTo_oTDict(_path_data, _path_file, CaseName)
+
+ID.GettingDataTo_oTData(_path_data, _path_file, CaseName)
+
+print('*** End                                           ****')

RTS_Data/FormattedData/openTEPES/README.md

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+**The workflow to create the input data for openTEPES based on the RTS-GMLC is as follows:**
+
+1. Run the python script Create_openTEPES_RTS-GMLC.py in the openTEPES directory. This file will import data from the SourceData folder and then write files out into the subfolder *RTS-GMLC*, which are used in the next step. 
+2. The files (CSV files) in the subfolder *RTS-GMLC* are in the openTEPES format which are classified between dictionaries and data. Then, the openTEPES should be installed by:
+```
+pip install openTEPES
+```
+3. Execute the openTEPES following the instruction (those written in its repository). Give the path of the subfolder *openTEPES* and the CaseName (RTS-GMLC).
+4. The solution will be on the subfolder *RTS-GMLC* in CSV and HTML files.