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NetworkPowerFlowMixin 


              flowchart TD
              pypsa.Network[NetworkPowerFlowMixin]
              pypsa.network.abstract._NetworkABC[_NetworkABC]

                              pypsa.network.abstract._NetworkABC --> pypsa.Network
                


              click pypsa.Network href "" "pypsa.Network"
              click pypsa.network.abstract._NetworkABC href "" "pypsa.network.abstract._NetworkABC"

Mixin class for network power flow methods.

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

Subclassed by:

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.

Referenced by:

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

Referenced by:

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)

Referenced by: