Optimization Model

The typical optimisation of an energy system in solph is the dispatch optimisation (Dispatch vs. Investment Optimization), which means that the use of the sources is optimised to satisfy the demand at least costs. The capacities of component must be fixed, i.e. the attribute nominal_capacity of a flow assigned to the component should be used

>>> from oemof import solph
>>> my_energysystem = solph.EnergySystem(
...     timeindex=solph.create_time_index(2025, number=24)
... )

>>> bus_electricity = solph.Bus("electricity")
>>> bus_gas = solph.Bus("gas")
>>> gas_grid = solph.components.Source(
...     label="gas grid",
...     outputs={
...         bus_gas: solph.Flow(
...             nominal_capacity=1000,
...             variable_costs=50
...         )
...     },
... )

>>> electricity_grid = solph.components.Sink(
...     label="electricity grid",
...     inputs={
...         bus_electricity: solph.Flow(
...             fix=100,
...             nominal_capacity=1
...         )
...     },
... )

Variable costs can be defined for each components. For example, the cost for gas could be defined in the gas source The operating costs of the gas power plant could be defined in gas power plant converter.

>>> power_plant = solph.components.Converter(
...     label="gas_power_plant",
...     inputs={
...         bus_gas: solph.Flow()
...     },
...     outputs={
...         bus_electricity: solph.Flow()
...     },
...     conversion_factors={bus_electricity: 0.58},
... )

>>> my_energysystem.add(
...    bus_electricity, bus_gas, gas_grid, electricity_grid, power_plant
... )

Costs do not have to be monetary costs but could be emissions or other variable units.

Furthermore, it is possible to optimise the capacity of different components using the investment mode (see Investment optimization).

Note

Since v0.5.1, there also is the possibility to have multi-period (i.e. dynamic) investments over longer-time horizon which is in experimental state (see Multi-period optimization (experimental)).

# set up a simple least cost optimisation
>>> om = solph.Model(my_energysystem)

# solve the energy model using the CBC solver
>>> result = om.solve(solver='cbc')

If you want to analyse the lp-file to see all equations and bounds you can write it into a file. In that case, it is recommended to reduce the number of timesteps to 3, to increase the readability of the file.

Solver

For a list on possible solver please have a look at SOLVER_SECTION_LINK