v1.3.0

Different Battery Charge and Discharge Rates

It's now possible to define a different charge and discharge rate in the epl.Battery asset.

The example below defines a maximum charge and discharge rate of 2.0:

epl.Battery(power_mw=2.0)

The example below defines a maximum charge rate of 2.0 with a maximum discharge rate of 1.0:

epl.Battery(power_mw=2.0, discharge_power_mw=1.0)

Complex Objective Function Terms

A complex custom objective term allows you to construct an objective function with a complex set of costs and revenues.

For example, we can define an objective function that includes a cost for the maximum import above a threshold of 40:

{
    "function": "max_many_variables",
    "variables": {
        "asset_type": "site",
        "variable": "import_power_mwh",
    },
    "constant": 40,
    "coefficient": 200,
    "M": max(electric_load_mwh) * 10
}

See Complex Objective Function Terms in the documentation for more examples.

Custom Accounts

To accommodate complex custom objective functions, we have added the ability to include these custom costs and revenues as a custom account:

import energypylinear as epl

chp_size = 50
electric_efficiency = 0.5
electric_load_mwh = 0
electricity_prices = np.array([-1000, -750, -250, -100, 0, 10, 100, 1000])
export_charge = -500
export_threshold_mwh = 5
gas_prices = 20

assets = [
    epl.CHP(
        electric_efficiency_pct=electric_efficiency,
        electric_power_max_mw=chp_size,
    )
]
site = epl.Site(
    assets=assets,
    gas_prices=20,
    electricity_prices=np.array([-1000, -750, -250, -100, 0, 10, 100, 1000]),
    electric_load_mwh=electric_load_mwh,
)

terms: list[dict] = [
    {
        "asset_type": "site",
        "variable": "export_power_mwh",
        "interval_data": "electricity_prices",
        "coefficient": -1,
    },
    {
        "asset_type": "*",
        "variable": "gas_consumption_mwh",
        "interval_data": "gas_prices",
    },
    {
        "type": "complex",
        "function": "min_two_variables",
        "a": {
            "asset_type": "site",
            "variable": "export_power_mwh",
        },
        "b": 5.0,
        "coefficient": export_charge,
        "M": (
            electric_load_mwh
            + assets[0].cfg.electric_power_max_mw
            + export_threshold_mwh
        )
        * 1,
    },
]

simulation = site.optimize(
    verbose=4,
    objective={"terms": terms},
)

accounts = epl.get_accounts(simulation.results, custom_terms=terms[-1:])
print(accouts.custom)

<Account profit=15000.00 emissions=0.0000>

Optimization Status

The objective function value has been added to the epl.optimizer.OptimizationStatus object:

import energypylinear as epl

site = epl.Site(
    assets=[epl.Battery()],
    electricity_prices=np.array([-1000, -750, -250, -100, 0, 10, 100, 1000]),
)
simulation = site.optimize(verbose=4, objective="price")
print(simulation.status)

OptimizationStatus(status='Optimal', feasible=True, objective=-5811.11111)