Optimize for a Forecast

energypylinear has the ability to optimize for both actuals & forecasts.

An asset (or site) can be used to model the variance between optimizing for actual & forecast prices.

Setup Battery Asset

import energypylinear as epl

#  interval data
electricity_prices = [100, 50, 200, -100, 0, 200, 100, -100]
forecasts = [-100, 0, 200, 100, -100, 100, 50, 200]

#  battery asset
asset = epl.battery.Battery(power_mw=2, capacity_mwh=4, efficiency=0.9)

Optimize with Perfect Foresight

#  optimize with perfect foresight
actual = asset.optimize(electricity_prices=electricity_prices, verbose=False)

# create accounts for the two scenarios 
perfect_foresight = epl.get_accounts(actual.interval_data, actual.simulation, verbose=False)
print(f"{perfect_foresight=}")

perfect_foresight=<Accounts profit=1057.78 emissions=0.0822>

Optimize to a Forecast

#  optimize to the forecast
forecast = asset.optimize(electricity_prices=forecasts, verbose=False)

# in the forecast we use the actual interval_data, not the forecast interval_data
forecast_account = epl.get_accounts(actual.interval_data, forecast.simulation, verbose=False)
print(f"{forecast_account=}")

forecast_account=<Accounts profit=-140.00 emissions=0.0800>

Calculate Variance Between Accounts

variance = perfect_foresight - forecast_account
print(f"{variance=}")

variance=<Account profit=1197.78 emissions=0.0022>

See examples/forecast-accuracy.py for a complete example.