NetworkPowerFlowMixin

Mixin class for network power flow methods.

Class inherits to pypsa.Network. All attributes and methods can be used within any Network instance.

Methods:

• calculate_dependent_values –

  Calculate per unit impedances and append voltages to lines and shunt impedances.

• lpf –

  Linear power flow for generic network.

• lpf_contingency –

  Compute linear power flow for a selection of branch outages.

• pf –

  Full non-linear power flow for generic network.

pypsa.Network.calculate_dependent_values

calculate_dependent_values() -> None

Calculate per unit impedances and append voltages to lines and shunt impedances.

pypsa.Network.lpf

lpf(
    n: Network,
    snapshots: Sequence | None = None,
    skip_pre: bool = False,
) -> None

Linear power flow for generic network.

Parameters:

• n (Network) –

  The network to run the power flow on.

• snapshots (list-like|single snapshot, default: None ) –

  A subset or an elements of n.snapshots on which to run the power flow, defaults to n.snapshots

• skip_pre (bool, default: False ) –

  Skip the preliminary steps of computing topology, calculating dependent values and finding bus controls.

pypsa.Network.lpf_contingency

lpf_contingency(
    snapshots: Sequence
    | str
    | int
    | Timestamp
    | None = None,
    branch_outages: Sequence | None = None,
) -> DataFrame

Compute linear power flow for a selection of branch outages.

Parameters:

• snapshots (list-like|single snapshot, default: None ) –

  A subset or an elements of n.snapshots on which to run the power flow, defaults to n.snapshots NB: currently this only works for a single snapshot

• branch_outages (list - like, default: None ) –

  A list of passive branches which are to be tested for outages. If None, it's take as all n.passive_branches_i()

Returns:

• p0 ( DataFrame ) –

  num_passive_branch x num_branch_outages DataFrame of new power flows

pypsa.Network.pf

pf(
    n: Network,
    snapshots: Sequence | None = None,
    skip_pre: bool = False,
    x_tol: float = 1e-06,
    use_seed: bool = False,
    distribute_slack: bool = False,
    slack_weights: str = "p_set",
) -> Dict

Full non-linear power flow for generic network.

Parameters:

• n (Network) –

  The network to run the power flow on.

• snapshots (list-like|single snapshot, default: None ) –

  A subset or an elements of n.snapshots on which to run the power flow, defaults to n.snapshots

• skip_pre (bool, default: False ) –

  Skip the preliminary steps of computing topology, calculating dependent values and finding bus controls.

• x_tol (float, default: 1e-06 ) –

  Tolerance for Newton-Raphson power flow.

• use_seed (bool, default: False ) –

  Use a seed for the initial guess for the Newton-Raphson algorithm.

• distribute_slack (bool, default: False ) –

  If True, distribute the slack power across generators proportional to generator dispatch by default or according to the distribution scheme provided in slack_weights. If alse only the slack generator takes up the slack.

• slack_weights (dict | str, default: 'p_set' ) –

  Distribution scheme describing how to determine the fraction of the total slack power (of each sub network individually) a bus of the sub-network takes up. Default is to distribute proportional to generator dispatch ('p_set'). Another option is to distribute proportional to (optimised) nominal capacity ('p_nom' or 'p_nom_opt'). Custom weights can be specified via a dictionary that has a key for each sub-network index (n.sub_networks.index) and a pandas.Series/dict with buses or generators of the corresponding sub-network as index/keys. When specifying custom weights with buses as index/keys the slack power of a bus is distributed among its generators in proportion to their nominal capacity (p_nom) if given, otherwise evenly.

Returns:

• dict –

  Dictionary with keys 'n_iter', 'converged', 'error' and dataframe values indicating number of iterations, convergence status, and iteration error for each snapshot (rows) and sub_network (columns)