Create new OperationalLife.py file

[Kiel-SFU]

Currently, ResidualCapacity.py generates ResidualCapacity.csv and OperationalLife.csv. This is because variables created when making OperationalLife.csv are needed to make ResidualCapacity.csv. We could have ResidualCapacity.py rely upon a new OperationalLife.py file that does its work separately.

For example, the data from the variable opLife are used in both the Operational Life and Residual Capacity parts of ResidualCapacity.py.

Canada-U.S.-ElecTrade/scripts/ResidualCapacity.py

Lines 39 to 44 in 3873057

	#Create a dictionary from the tech, year values for use with residual capacity
	techs = dfOperationalLife['TECHNOLOGY'].tolist()
	opLifeYears = dfOperationalLife['YEARS'].tolist()
	opLife = {}
	for i in range(len(techs)):
	opLife[techs[i]] = opLifeYears[i]

[trevorb1]

Good idea! We will also need to make sure that ResidualCapacity.py is dependent on OperationalLife.py in the snakefile

[trevorb1]

For reference on what we should break into the new operational_life.py file...

Canada-U.S.-ElecTrade/scripts/ResidualCapacity.py

Lines 31 to 83 in c8af7e6

	def getCanOperationalLife():
	# PURPOSE: Creates opertionalLife file from USA data
	# INPUT: N/A
	# OUTPUT: opLifeData = Canadian Operational Life Data
	# opLife = Dictionary from the tech, year values for use with residual capacity
	# techs = List of technologies pertaining to Operational Life
	# techListTrade = Trade names without duplicates
	# dfTrade = Raw Trade datafile
	continent = functions.getFromYaml('continent')
	canSubregions = functions.getFromYaml('regions_dict')['CAN'] # Canadian subregions
	#read in raw operational life values
	dfOperationalLife = pd.read_csv('../dataSources/OperationalLifeTechnology.csv')
	#remove P2G and FCL columns
	rowsToDrop = []
	for i in range(len(dfOperationalLife)):
	tech = dfOperationalLife['TECHNOLOGY'].iloc[i]
	if (tech == 'P2G') or (tech == 'FCL'):
	rowsToDrop.append(i)
	dfOperationalLife = dfOperationalLife.drop(rowsToDrop)
	dfOperationalLife.reset_index()
	#Create a dictionary from the tech, year values for use with residual capacity
	techs = dfOperationalLife['TECHNOLOGY'].tolist()
	opLifeYears = dfOperationalLife['YEARS'].tolist()
	opLife = {}
	for i in range(len(techs)):
	opLife[techs[i]] = opLifeYears[i]
	#List to hold output operational life values
	#columns = region, technology, value
	opLifeData = []
	#save operational life to list
	for subregion in canSubregions:
	for tech, value in opLife.items():
	techName = 'PWR' + tech + 'CAN' + subregion + '01'
	opLifeData.append([continent,techName,value])
	# get trade tech names and save operational life values
	dfTrade = pd.read_csv('../dataSources/Trade.csv')
	# get list of all the trade technologies
	techListTrade = dfTrade['TECHNOLOGY'].tolist()
	techListTrade = list(set(techListTrade)) #remove duplicates
	# hardcode in operational life of 100 years
	for tech in techListTrade:
	opLifeData.append([continent,tech,100])
	return opLifeData, opLife, techs, techListTrade, dfTrade