Odys

Optimize energy portfolios under uncertainty.

Documentation

Examples

Odys is a modern Python framework for optimizing multi-asset energy systems.

The key features are:

• Optimization under uncertainty: Stochastic optimization is the default, not an afterthought. Multi-asset, multi-market from day one with built-in CVaR risk management.
• Multi-solver support: Swap between HiGHS (default), Gurobi, CPLEX, or SCIP with a single configuration change.
• Simple API: Define your assets, describe the uncertainty through scenarios, and call .optimize(). No boilerplate, no configuration files.
• Transparent math: Every constraint and objective term is documented with equations. You know exactly what the solver sees.

Installation

pip install odys

Requires Python 3.11+. For commercial solvers (Gurobi, CPLEX, SCIP), see the solver docs.

Quick example

Define your assets, describe the scenario, and call .optimize():

from datetime import timedelta

from odys import AssetPortfolio, EnergySystem, FixedLoad, Generator, Scenario

generator = Generator(name="gen", nominal_power=100.0, variable_cost=50.0)
load = FixedLoad(name="demand")

portfolio = AssetPortfolio([generator, load])

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

result = energy_system.optimize()
print(result.generators.power)

time  generator
0     gen          60.0
1     gen          90.0
2     gen          40.0
3     gen          70.0
Name: generator_power, dtype: float64

See the documentation for the full workflow, or check the examples for complete worked scenarios.

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

MIT