Stochastic Scenarios

This example shows how to optimize across multiple scenarios with different wind outputs, using generators and a battery.

Source: examples/example2.py

What it demonstrates

• Defining multiple StochasticScenario instances with probabilities
• Varying available_capacity_profiles across scenarios (different wind conditions)
• How the optimizer balances dispatch decisions across uncertain futures

The setup

Two generators:

• gen1: A conventional generator (100 MW, 200 $/MWh)
• wind_farm: A wind generator (150 MW, 100 $/MWh) with variable output

Plus a battery (200 MW, 100 MWh) and a fixed load.

We define two equally likely scenarios:

• default: Moderate wind availability
• high_wind: More wind available in the first few timesteps

Code

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, LoadType
from odys.energy_system_models.assets.portfolio import AssetPortfolio
from odys.energy_system_models.assets.storage import Battery
from odys.energy_system_models.scenarios import StochasticScenario

generator_1 = PowerGenerator(
    name="gen1",
    nominal_power=100.0,
    variable_cost=200.0,
    min_up_time=1,
    ramp_down=100,
)
generator_2 = PowerGenerator(
    name="wind_farm",
    nominal_power=150.0,
    variable_cost=100.0,
)
battery_1 = Battery(
    name="battery1",
    max_power=200.0,
    capacity=100,
    efficiency_charging=0.9,
    efficiency_discharging=0.8,
    soc_start=1.0,
    soc_end=0.5,
)
load = Load(name="load", type=LoadType.Fixed)

portfolio = AssetPortfolio()
portfolio.add_asset(generator_1)
portfolio.add_asset(generator_2)
portfolio.add_asset(battery_1)
portfolio.add_asset(load)

scenarios = [
    StochasticScenario(
        name="default",
        probability=0.5,
        available_capacity_profiles={
            "gen1": [100, 100, 100, 50, 50, 50, 50],
            "wind_farm": [100, 100, 100, 50, 50, 50, 50],
        },
        load_profiles={
            "load": [180, 180, 150, 50, 80, 90, 100],
        },
    ),
    StochasticScenario(
        name="high_wind",
        probability=0.5,
        available_capacity_profiles={
            "gen1": [100, 100, 100, 50, 50, 50, 50],
            "wind_farm": [150, 150, 100, 50, 50, 50, 50],
        },
        load_profiles={
            "load": [180, 180, 150, 50, 80, 90, 100],
        },
    ),
]

energy_system = EnergySystem(
    portfolio=portfolio,
    timestep=timedelta(minutes=30),
    number_of_steps=7,
    scenarios=scenarios,
    power_unit="MW",
)

result = energy_system.optimize()

Reading the results

print(result.generators.power)      # dispatch per scenario
print(result.batteries.net_power)    # battery behavior per scenario
print(result.to_dataframe)           # everything combined

Since we have two scenarios, the results include a scenario dimension. You can compare how the optimizer dispatches differently under each wind condition.

What to look for

• The wind_farm is cheaper, so the optimizer uses it first. In the high_wind scenario, it can produce more.
• gen1 is expensive (200 $/MWh) and only runs when the wind farm and battery can't cover the load.
• The battery shifts energy across timesteps to minimize total cost, considering both scenarios.