Store

Use the Store class to represent an energy store with unlimited power capacity and given energy capacity (e_nom), in order to optimize its active power over time (StoreOptimizationDynamicResults.p).

Use the ExtendableStore class to represent a hypothetical store with an energy capacity that is flexible, in order to also optimize the energy capacity (ExtendableStoreOptimizationStaticResults.e_nom_opt).

API Reference

Store and ExtendableStore both inherit from the BaseStore parent class.

`BaseStore`

Bases: ENomExtendableComponent[T]

Stores provide fundamental inter-temporal storage functionality not limited in charging or discharging power.

PyPSA user guide for this component.

`bus: str = Field(min_length=1)`

Name of bus to which store is attached.

`carrier: str | None = Field(default=None, min_length=1)`

Carrier of the store.

`e_nom_extendable: bool`

Switch to allow capacity e_nom to be extended in optimisation.

`e_min_pu: float | Series[T] = Field(default=0.0, ge=0.0, le=1.0)`

Minimal value of e relative to e_nom for the optimisation.

`e_max_pu: float | Series[T] = Field(default=1.0, ge=0.0, le=1.0)`

Maximal value of e relative to e_nom for the optimisation.

`e_initial: float = Field(default=0.0, ge=0.0)`

Energy before the snapshots in the optimisation.

`e_initial_per_period: bool = False`

Switch: if True, then at the beginning of each investment period e is set to e_initial.

`e_cyclic: bool = False`

Switch: if True, then e_initial is ignored and the initial energy is set to the final energy for the group of snapshots in the optimisation.

`e_cyclic_per_period: bool = False`

Switch: if True, then the cyclic constraints are applied to each investment period separately.

`p_set: float | Series[T] | None = Field(default=None, ge=0.0)`

Active power set point (for power flow only).

`q_set: float | Series[T] = Field(default=0.0, ge=0.0)`

Reactive power set point (for power flow only).

`e_set: float | Series[T] | None = Field(default=None, ge=0.0)`

Fixed energy filling level set point (for optimisation only).

`sign: SignType = +1`

Sign denoting orientation of the energy variable (e).

`marginal_cost: float | Series[T] = Field(default=0.0, ge=0.0)`

Marginal cost applied to both charging and discharging of 1 MWh.

`marginal_cost_quadratic: float | Series[T] = Field(default=0.0, ge=0.0)`

Quadratic marginal cost of applied to charging and discharging of 1 MWh.

`marginal_cost_storage: float | Series[T] = Field(default=0.0, ge=0.0)`

Marginal cost of energy storage of 1 MWh for one hour.

`standing_loss: float | Series[T] = Field(default=0.0, ge=0.0)`

Losses per hour to energy level.

`active: bool = True`

Whether to consider the component in optimisation or not.

`build_year: int | None = None`

Build year.

`lifetime: float | None = Field(default=None, gt=0.0)`

Lifetime.

`Store`

Bases: BaseStore[T]

`e_nom: float = Field(default=0.0, ge=0.0)`

Nominal energy capacity (i.e. limit on e).

`ExtendableStore`

Bases: BaseStore[T]

`e_nom_mod: float = Field(default=0.0, ge=0.0)`

Nominal energy capacity of the store module. Introduces integer variables if set.

`e_nom_min: float = Field(default=0.0, ge=0.0)`

Minimum value of e_nom_opt.

`e_nom_max: float | None = Field(default=None, gt=0.0)`

Maximum value of e_nom_opt.

`e_nom_set: float | None = Field(default=None, ge=0.0)`

Set value of e_nom_opt.

`capital_cost: float = Field(default=0.0, ge=0.0)`

Fixed period costs of extending e_nom by 1 MWh, including periodized investment costs and periodic fixed O&M costs (e.g. annuitized investment costs).

`StoreBaseDynamicResults`

Bases: BaseDynamicResults

`p: pandas.DataFrame`

Power at bus (positive if net supply).

`StoreOptimizationDynamicResults`

Bases: StoreBaseDynamicResults

`e: pandas.DataFrame`

Energy level of store as calculated by the optimisation.

`mu_upper: pandas.DataFrame`

Shadow price of upper e_nom limit.

`mu_lower: pandas.DataFrame`

Shadow price of lower e_nom limit.

`mu_energy_balance: pandas.DataFrame`

Shadow price of storage consistency equations (i.e. water values).

`StorePfDynamicResults`

Bases: StoreBaseDynamicResults

`StoreNonlinearPfDynamicResults`

Bases: StorePfDynamicResults

`q: pandas.DataFrame`

Reactive power (positive if net supply).