StochasticScenario

A StochasticScenario extends Scenario with a name and a probability, letting you model multiple possible futures. The optimizer finds a dispatch plan that performs well across all scenarios, weighted by how likely each one is.

Basic usage

from odys.energy_system_models.scenarios import StochasticScenario

low_wind = StochasticScenario(
    name="low_wind",
    probability=0.3,
    available_capacity_profiles={
        "wind_farm": [30, 20, 40, 25],
    },
    load_profiles={"demand": [100, 120, 80, 90]},
)

high_wind = StochasticScenario(
    name="high_wind",
    probability=0.7,
    available_capacity_profiles={
        "wind_farm": [120, 140, 100, 130],
    },
    load_profiles={"demand": [100, 120, 80, 90]},
)

Fields

StochasticScenario inherits all fields from Scenario and adds two:

Field	Type	Required	Default	Description
name	str	Yes	-	Unique identifier for the scenario
probability	float	Yes	-	Probability (0-1) of this scenario occurring
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

Validation rules

Odys enforces two rules when you pass a list of stochastic scenarios to the EnergySystem:

1. Probabilities must sum to 1.0 -- if they don't, you'll get a ValueError
2. Names must be unique -- duplicated names also raise a ValueError

# This will fail: probabilities sum to 0.8
scenarios = [
    StochasticScenario(name="a", probability=0.5, load_profiles={"demand": [100]}),
    StochasticScenario(name="b", probability=0.3, load_profiles={"demand": [100]}),
]

# This will fail: duplicate names
scenarios = [
    StochasticScenario(name="scenario", probability=0.5, load_profiles={"demand": [100]}),
    StochasticScenario(name="scenario", probability=0.5, load_profiles={"demand": [100]}),
]

Using stochastic scenarios

Pass a list of scenarios to the EnergySystem instead of a single Scenario:

from datetime import timedelta

from odys.energy_system import EnergySystem

energy_system = EnergySystem(
    portfolio=portfolio,
    scenarios=[low_wind, high_wind],
    timestep=timedelta(hours=1),
    number_of_steps=4,
    power_unit="MW",
)

result = energy_system.optimize()

Everything else works the same -- the optimizer just considers multiple futures instead of one.

What can vary across scenarios

Each scenario can have different values for any combination of:

• available_capacity_profiles -- model different wind/solar outputs
• load_profiles -- model demand uncertainty
• market_prices -- model price volatility

This means you can capture several types of uncertainty in a single optimization run.

Scenario vs StochasticScenario

	Scenario	StochasticScenario
Number	Exactly one	Two or more in a list
Probability	Implicit 1.0	Explicit, must sum to 1.0
Name	Not needed	Required, must be unique
Use case	Deterministic dispatch	Decisions under uncertainty

Next steps

For a deeper dive into stochastic optimization concepts -- stage-fixed decisions, results with multiple scenarios, and how the optimizer weighs probabilities -- see Stochastic Optimization.