fluxopt¶

Energy system optimization with linopy — detailed dispatch, scaled to multi-period planning.

Composable elements

Build models from Flow, Bus, Converter, Storage, and Effect — clear separation of physics, costs, and topology.
xarray-native

Time series, parameters, and results as xr.Dataset — vectorized constraints via linopy.
Sizing & status

Capacity optimization and on/off behavior as first-class concerns, not bolt-ons.
HiGHS out of the box

Open-source MIP solver bundled. Swap in Gurobi, CPLEX, or any linopy-supported backend.
Math, documented

Every constraint has a formulation page with notation, derivation, and the line of code that emits it.
Companion ecosystem

Lean core, optional companions for plotting, YAML loading, and (planned) interactive marimo apps.

# A gas boiler covers a heat demand, minimizing fuel cost
from datetime import datetime
from fluxopt import Carrier, Converter, Effect, Flow, Port, optimize

result = optimize(
    timesteps=[datetime(2024, 1, 1, h) for h in range(4)],
    carriers=[Carrier('gas'), Carrier('heat')],
    effects=[Effect('cost')],
    ports=[
        Port('grid', imports=[
            Flow('gas', size=500, effects_per_flow_hour={'cost': 0.04})
        ]),
        Port('demand', exports=[
            Flow('heat', size=100, fixed_relative_profile=[0.4, 0.7, 0.5, 0.6])
        ])
    ],
    converters=[
        Converter.boiler(
            'boiler',
            thermal_efficiency=0.9,
            fuel_flow=Flow('gas', size=300),
            thermal_flow=Flow('heat', size=200)
        )
    ],
    objective_effects='cost',
)

print(f"Total cost: {result.objective:.2f}")
print(result.flow_rates)

Where to next¶

Learn by example

Seven executable notebooks from quickstart through investment and piecewise conversion.

Notebooks
Math reference

Notation, objective, and per-element formulations.

Math
API reference

Auto-generated from source — every public class, every parameter.

API