Optimize for Carbon

energypylinear has the ability to optimize for both price and carbon as optimization objectives.

This ability comes from two things - an objective function, which can be either for price or carbon, along with accounting of both price and carbon emissions.

We can dispatch a battery to minimize carbon emissions by passing in objective='carbon':

Setup Battery Asset

import energypylinear as epl

#  interval data
electricity_prices = [100, 50, 200, -100, 0, 200, 100, -100]
electricity_carbon_intensities = [0.1, 0.2, 0.1, 0.15, 0.01, 0.7, 0.5, 0.01]

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

Optimize for Carbon

#  optimize for carbon
carbon = asset.optimize(
    electricity_prices=electricity_prices,
    electricity_carbon_intensities=electricity_carbon_intensities,
    objective='carbon',
    verbose=False
)

carbon_account = epl.get_accounts(
    carbon.interval_data,
    carbon.simulation,
    verbose=False
)
print(f"{carbon_account=}")

carbon_account=<Accounts profit=134.44 emissions=-2.2733>

Optimize for Money

We can compare these results above with a simulation that optimizes for price, using a energypylinear.accounting.Account to compare both simulations.

Our optimization for price has a high negative cost.

The optimization for carbon has lower emissions, but at a higher cost:

#  optimize for money
price = asset.optimize(
    electricity_prices=electricity_prices,
    verbose=False
)

#  get an account representing the difference between the two
price_account = epl.get_accounts(
    price.interval_data,
    price.simulation,
    verbose=False
)
print(f"{price_account=}")

price_account=<Accounts profit=1057.78 emissions=0.0822>

Calculate Variance Between Accounts

variance = price_account - carbon_account
print(f"{variance=}")
print(f"{-variance.cost / variance.emissions:.2f} $/tC")

variance=<Account profit=923.33 emissions=2.3556>
391.98 $/tC