EnergySystem

EnergySystem is the main entry point for setting up and running an optimization. You give it a portfolio of assets, scenario data, and time configuration -- then call .optimize().

Basic usage

from datetime import timedelta

from odys.energy_system import EnergySystem
from odys.energy_system_models.assets.generator import PowerGenerator
from odys.energy_system_models.assets.load import Load
from odys.energy_system_models.assets.portfolio import AssetPortfolio
from odys.energy_system_models.scenarios import Scenario

generator = PowerGenerator(name="gen", nominal_power=100.0, variable_cost=50.0)
load = Load(name="demand")

portfolio = AssetPortfolio()
portfolio.add_asset(generator)
portfolio.add_asset(load)

energy_system = EnergySystem(
    portfolio=portfolio,
    scenarios=Scenario(
        load_profiles={"demand": [60, 90, 40, 70]},
    ),
    timestep=timedelta(hours=1),
    number_of_steps=4,
    power_unit="MW",
)

result = energy_system.optimize()

Constructor parameters

Parameter	Type	Required	Default	Description
portfolio	AssetPortfolio	Yes	-	The collection of energy assets
scenarios	Scenario or list[StochasticScenario]	Yes	-	Scenario data (load profiles, prices, capacity profiles)
timestep	timedelta	Yes	-	Duration of each time period
number_of_steps	int	Yes	-	How many timesteps to optimize over
power_unit	str	Yes	-	Unit for power values: "W", "kW", or "MW"
markets	EnergyMarket or list[EnergyMarket] or None	No	None	Energy markets for buying/selling

Scenarios

For a single deterministic scenario, use Scenario:

from odys.energy_system_models.scenarios import Scenario

scenario = Scenario(
    load_profiles={"demand": [60, 90, 40, 70]},
    available_capacity_profiles={"gen": [100, 80, 100, 100]},
)

For stochastic optimization with multiple scenarios, pass a list of StochasticScenario:

from odys.energy_system_models.scenarios import StochasticScenario

scenarios = [
    StochasticScenario(
        name="low_wind",
        probability=0.3,
        load_profiles={"demand": [80, 90, 70, 60]},
        available_capacity_profiles={"wind": [30, 20, 40, 25]},
    ),
    StochasticScenario(
        name="high_wind",
        probability=0.7,
        load_profiles={"demand": [80, 90, 70, 60]},
        available_capacity_profiles={"wind": [120, 140, 100, 130]},
    ),
]

See Stochastic Optimization for more details.

Adding markets

To include energy markets, pass them via the markets parameter:

from odys.energy_system_models.markets import EnergyMarket

energy_system = EnergySystem(
    portfolio=portfolio,
    markets=(
        EnergyMarket(name="sdac", max_trading_volume_per_step=150),
        EnergyMarket(name="sidc", max_trading_volume_per_step=100),
    ),
    scenarios=scenario,
    timestep=timedelta(hours=1),
    number_of_steps=4,
    power_unit="MW",
)

Market prices are specified in the scenario, not on the market object. See Market for details.

Running the optimization

Call .optimize() to build and solve the mathematical model:

result = energy_system.optimize()

This returns an OptimizationResults object. See Optimization for how to read and interpret the results.

What happens under the hood

When you call .optimize(), odys:

1. Validates the full system configuration (asset names match scenario profiles, probabilities sum to 1, etc.)
2. Builds a mixed-integer linear program (MILP) using linopy
3. Solves it with the HiGHS solver
4. Wraps the solution in an OptimizationResults object

You don't need to interact with any of these internals -- but if you're curious, the API Reference has the full details.