def test_between_two_values():
"""Check that the average price value is between the local value and the international value"""
fuel_1 = Fuel_Price(local_prices, price_gas, price_coal, local_production,
baseline)
assert pd.concat([fuel_1.international_prices["Gas"], local_prices["Gas"]], axis=1)\
.min(axis=1).all() <= fuel_1.average_price["Gas"].all()
assert fuel_1.average_price["Gas"].all() <= pd.concat(
[fuel_1.international_prices["Gas"], local_prices["Gas"]],
axis=1).max(axis=1).all()
def carbon_captured(self, headers):
"""Dataframe comparing the carbon captured in the two runs, for key years."""
cc_bau = self.BAU.carbon_captured()
cc_alt = self.ALT.carbon_captured()
cc_diff = cc_alt - cc_bau
table = pd.concat([cc_bau, cc_alt, cc_diff], axis=1)
table.columns = headers
return table
def carbon_intensity(self, headers):
"""Dataframe comparing the CO2 intensity of electricity in the two runs, for key years."""
ci_bau = self.BAU.carbon_intensity()
ci_alt = self.ALT.carbon_intensity()
ci_diff = ci_alt - ci_bau
table = pd.concat([ci_bau, ci_alt, ci_diff], axis=1)
table.columns = headers
return table
def emission_sum(self, headers):
"""Dataframe comparing total intertemporal CO2 emissions of the two runs, by technology."""
es_BAU = self.BAU.emission_sum()
es_ALT = self.ALT.emission_sum()
es_diff = es_ALT - es_BAU
d = pd.concat([es_BAU, es_ALT, es_diff], axis=1)
d.columns = headers
return d
def total(self, headers):
"""Dataframe comparing the key results of the two runs."""
units = self.BAU.total().iloc[:, 1]
total_BAU = self.BAU.total().iloc[:, 0] # Only the values
total_ALT = self.ALT.total().iloc[:, 0]
total_diff = total_ALT - total_BAU
d = pd.concat([total_BAU, total_ALT, total_diff, units], axis=1)
d.columns = headers + ['Units']
return d
def __init__(self, plan, parameter):
self.plan = plan
self.parameter = parameter
def pv(variable):
return present_value(
variable, parameter.discount_rate).sum() # Sum on technologies
self.total_production = pv(plan.production[sources])
self.investment = (plan.additions * MW * parameter.construction_cost *
USD / kW / MUSD)
self.total_investment = pv(self.investment)
self.salvage_value = pd.concat([
residual_value(plan.additions, parameter.plant_accounting_life,
fuel) for fuel in sources
],
axis=1)
self.total_salvage_value = pv(self.salvage_value)
self.fixed_OM_cost = (plan.capacities * MW *
parameter.fixed_operating_cost * USD / kW / MUSD)
self.total_fixed_OM_cost = pv(self.fixed_OM_cost)
self.variable_OM_cost = (plan.production * GWh *
parameter.variable_operating_cost * USD /
MWh / MUSD)
self.total_variable_OM_cost = pv(self.variable_OM_cost)
self.heat_used = (plan.production * GWh * parameter.heat_rate * Btu /
kWh / TBtu)
self.fuel_cost = (self.heat_used * TBtu * parameter.heat_price * USD /
MBtu / MUSD)
self.total_fuel_cost = pv(self.fuel_cost)
self.total_cost = (self.total_investment - self.total_salvage_value +
self.total_fixed_OM_cost +
self.total_variable_OM_cost + self.total_fuel_cost)
self.lcoe = self.total_cost / self.total_production
self.emissions = (plan.production * GWh * parameter.emission_factor *
g / kWh / kt)
self.emissions["Total"] = self.emissions.sum(axis=1)
self.total_emissions = self.emissions["Total"].sum() * kt / Gt
self.capture = (plan.production * GWh * parameter.capture_factor * g /
kWh / kt)
self.capture["Total"] = self.capture.sum(axis=1) * kt / Mt
self.total_capture = self.capture["Total"].sum() * Mt / Gt
self.external_cost = (self.emissions["Total"] * kt *
parameter.carbon_price * USD / t / MUSD)
self.total_external_cost = pv(self.external_cost)
self.signature = '#' + plan.digest + "-" + parameter.digest
def test_between_two_values():
"""Check that the averaged price is between the local value and the international value."""
fuel_1 = price_fuel(local_prices, price_gas, price_coal, local_production,
baseline)
averaged = fuel_1.average_price["Gas"].all()
prices = pd.concat([fuel_1.import_prices["Gas"], local_prices["Gas"]],
axis=1)
lower = prices.min(axis=1).all()
upper = prices.max(axis=1).all()
assert lower <= averaged <= upper
additions = additions.groupby(["year", "fuel"]).capacity_MW.sum()
additions = additions.unstack()
additions.drop("ND*", axis=1, inplace=True)
# 2016 - 2030 capacity additions for the four renewable technologies
additions["Solar"] = fill_in(capacities_PDP7A.Solar)
additions["Wind"] = fill_in(capacities_PDP7A.Wind)
additions["Biomass"] = fill_in(capacities_PDP7A.Biomass)
additions["SmallHydro"] = fill_in(capacities_PDP7A.SmallHydro)
additions["Import"] = fill_in(capacities_PDP7A.Import)
# 1974 - 2015 capacity additions and cleanup
additions = pd.concat([capacity_past, additions])
additions = additions[fuels + ["PumpedStorage", "Import"]].fillna(0)
# 2031 - 2050 scenario definition
increment = {
"Coal": 0,
"Gas": 750,
"Oil": 20,
"BigHydro": 0,
"SmallHydro": 50,
"Biomass": 50,
"Wind": 900,
"Solar": 1000,
"PumpedStorage": 50,
def __init__(self, plan, parameter):
self.plan = plan
self.parameter = parameter
def pv(variable):
"""Present value of a variable summed across technologies."""
return present_value(variable, parameter.DISCOUNT_RATE).sum()
self.total_production = pv(plan.production[SOURCES])
self.investment = (plan.additions * MW
* parameter.construction_cost * USD / kW
/ MUSD)
self.total_investment = pv(self.investment)
self.salvage_value = pd.concat([residual_value(plan.additions,
parameter.plant_accounting_life,
fuel)
for fuel in SOURCES],
axis=1)
self.total_salvage_value = pv(self.salvage_value)
self.fixed_om_cost = (plan.capacities * MW *
parameter.fixed_operating_cost * USD / kW
/ MUSD)
self.total_fixed_om_cost = pv(self.fixed_om_cost)
self.variable_OM_cost = (plan.production * GWh
* parameter.variable_operating_cost * USD / MWh
/ MUSD)
self.total_variable_OM_cost = pv(self.variable_OM_cost)
self.heat_used = (plan.production * GWh
* parameter.heat_rate * Btu / kWh
/ TBtu)
self.fuel_cost = (self.heat_used * TBtu
* parameter.heat_price * USD / MBtu
/ MUSD)
self.total_fuel_cost = pv(self.fuel_cost)
self.total_cost = (self.total_investment - self.total_salvage_value
+ self.total_fixed_om_cost + self.total_variable_OM_cost
+ self.total_fuel_cost)
self.lcoe = self.total_cost / self.total_production
self.emissions = (plan.production * GWh
* parameter.EMISSION_FACTOR * g / kWh
/ kt)
self.emissions["Total"] = self.emissions.sum(axis=1)
self.total_emissions = self.emissions["Total"].sum() * kt / Gt
self.capture = (plan.production * GWh
* parameter.capture_factor * g / kWh
/ kt)
self.capture["Total"] = self.capture.sum(axis=1) * kt / Mt
self.total_capture = self.capture["Total"].sum() * Mt / Gt
self.external_cost = (self.emissions["Total"] * kt
* parameter.CARBON_PRICE * USD / t
/ MUSD)
self.total_external_cost = pv(self.external_cost)
self.signature = '#' + plan.digest + "-" + parameter.digest
additions = additions.groupby(["year", "fuel"]).capacity_MW.sum()
additions = additions.unstack()
additions.drop("ND*", axis=1, inplace=True)
# 2016 - 2030 capacity additions for the four renewable technologies
additions["Solar"] = fill_in(capacities_PDP7A.Solar)
additions["Wind"] = fill_in(capacities_PDP7A.Wind)
additions["Biomass"] = fill_in(capacities_PDP7A.Biomass)
additions["SmallHydro"] = fill_in(capacities_PDP7A.SmallHydro)
additions["Import"] = fill_in(capacities_PDP7A.Import)
# 1974 - 2015 capacity additions and cleanup
additions = pd.concat([CAPACITY_PAST, additions])
# FIXME: PLANT_TYPE + ["PumpedStorage", "Import"] is technologies
additions = additions[PLANT_TYPE + ["PumpedStorage", "Import"]].fillna(0)
# 2031 - 2050 scenario definition
increment = {
"Coal": 0,
"Gas": 750,
"Oil": 20,
"BigHydro": 0,
"SmallHydro": 50,
"Biomass": 50,
"Wind": 900,
"Solar": 1000,