Scenario

A Scenario defines the operating conditions for your energy system -- load demand, generator availability, and market prices over time. It's the bridge between your asset definitions and the actual timeseries data the optimizer works with.

Basic usage

from odys.energy_system_models.scenarios import Scenario

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

This tells the optimizer: "here's what demand looks like over four timesteps." The key "demand" must match the name of a Load in your portfolio.

Fields

Field	Type	Required	Default	Description
load_profiles	dict[str, list[float]]	No	None	Load values per timestep, keyed by load name
available_capacity_profiles	dict[str, list[float]]	No	None	Max available capacity per timestep, keyed by generator name
market_prices	dict[str, list[float]]	No	None	Market prices per timestep, keyed by market name

All fields are optional -- you only include what you need. If a field is omitted, the optimizer won't apply that constraint (e.g., generators without an available_capacity_profiles entry can produce up to their nominal_power).

Load profiles

Specify how much power each load demands at each timestep:

scenario = Scenario(
    load_profiles={
        "factory": [100, 120, 80, 90],
        "office": [20, 25, 15, 20],
    },
)

The keys must match the name of a Load in your portfolio.

Available capacity profiles

Cap the output of specific generators over time. This is how you model variable renewable generation like wind or solar:

scenario = Scenario(
    available_capacity_profiles={
        "wind_farm": [80, 60, 90, 70],
        "solar": [0, 50, 80, 30],
    },
    load_profiles={"demand": [100, 120, 80, 90]},
)

The keys must match the name of a PowerGenerator in your portfolio. At each timestep, the generator can't produce more than the value specified here (or its nominal_power, whichever is lower).

Market prices

Provide price timeseries for each Market:

scenario = Scenario(
    market_prices={
        "day_ahead": [50, 55, 45, 60],
    },
    load_profiles={"demand": [100, 120, 80, 90]},
)

The keys must match the name of an EnergyMarket passed to the EnergySystem.

Putting it all together

A scenario with all three fields might look like:

from odys.energy_system_models.scenarios import Scenario

scenario = Scenario(
    load_profiles={
        "demand": [100, 120, 80, 90],
    },
    available_capacity_profiles={
        "wind_farm": [80, 60, 90, 70],
    },
    market_prices={
        "day_ahead": [50, 55, 45, 60],
    },
)

Then pass it to the EnergySystem:

energy_system = EnergySystem(
    portfolio=portfolio,
    scenarios=scenario,
    timestep=timedelta(hours=1),
    number_of_steps=4,
    power_unit="MW",
)

When you need multiple scenarios

A single Scenario represents one deterministic future. If you want to optimize under uncertainty -- accounting for multiple possible outcomes -- use StochasticScenario instead.