PyPSA/test/test_pf_distributed_slack.py at master · bipulroy8/PyPSA · GitHub

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# -*- coding: utf-8 -*-

from numpy.testing import assert_array_almost_equal as equal


def normed(s):
    return s / s.sum()


def test_pf_distributed_slack(scipy_network):
    network = scipy_network
    network.set_snapshots(network.snapshots[:2])

    # There are some infeasibilities without line extensions
    network.lines.s_max_pu = 0.7
    network.lines.loc[["316", "527", "602"], "s_nom"] = 1715
    network.storage_units.state_of_charge_initial = 0.0

    network.lopf(network.snapshots, solver_name="glpk", formulation="kirchhoff")

    # For the PF, set the P to the optimised P
    network.generators_t.p_set = network.generators_t.p
    network.storage_units_t.p_set = network.storage_units_t.p

    # set all buses to PV, since we don't know what Q set points are
    network.generators.control = "PV"

    # Need some PQ buses so that Jacobian doesn't break
    f = network.generators[network.generators.bus == "492"]
    network.generators.loc[f.index, "control"] = "PQ"

    # by dispatch
    network.pf(distribute_slack=True, slack_weights="p_set")

    equal(
        network.generators_t.p_set.apply(normed, axis=1),
        (network.generators_t.p - network.generators_t.p_set).apply(normed, axis=1),
    )

    # by capacity
    network.pf(distribute_slack=True, slack_weights="p_nom")

    slack_shares_by_capacity = (
        network.generators_t.p - network.generators_t.p_set
    ).apply(normed, axis=1)

    for index, row in slack_shares_by_capacity.iterrows():
        equal(network.generators.p_nom.pipe(normed).fillna(0), row)

    # by custom weights (mirror 'capacity' via custom slack weights by bus)
    custom_weights = {}
    for sub_network in network.sub_networks.obj:
        buses_o = sub_network.buses_o
        custom_weights[sub_network.name] = (
            sub_network.generators()
            .groupby("bus")
            .sum()
            .p_nom.reindex(buses_o)
            .pipe(normed)
            .fillna(0)
        )

    network.pf(distribute_slack=True, slack_weights=custom_weights)

    equal(
        slack_shares_by_capacity,
        (network.generators_t.p - network.generators_t.p_set).apply(normed, axis=1),
    )

    # by custom weights (mirror 'capacity' via custom slack weights by generators)
    custom_weights = {}
    for sub_network in network.sub_networks.obj:
        custom_weights[
            sub_network.name
        ] = sub_network.generators().p_nom  # weights do not sum up to 1

    network.pf(distribute_slack=True, slack_weights=custom_weights)

    equal(
        slack_shares_by_capacity,
        (network.generators_t.p - network.generators_t.p_set).apply(normed, axis=1),
    )