pypsa.Network.pf

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pypsa.Network.pf#

Network.pf(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:

  • snapshots (list-like|single snapshot) – 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) – 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 False 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:

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)

Return type:

dict

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