pypsa.SubNetwork.pf#

SubNetwork.pf(snapshots=None, skip_pre=False, x_tol=1e-06, use_seed=False, distribute_slack=False, slack_weights='p_set')#

Non-linear power flow for connected sub-network.

Parameters:

snapshots (list-like|single snapshot) – A subset or an elements of network.snapshots on which to run the power flow, defaults to network.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 (pandas.Series|str, default 'p_set') – Distribution scheme describing how to determine the fraction of the total slack power a bus of the subnetwork 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 provided via a pandas.Series/dict that has the buses or the generators of the subnetwork as index/keys. When using 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:

pypsa.SubNetwork.pf