pypsa-eur/scripts/solve_network.py

# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT

"""
Solves optimal operation and capacity for a network with the option to
iteratively optimize while updating line reactances.

This script is used for optimizing the electrical network as well as the
sector coupled network.

Relevant Settings
-----------------

.. code:: yaml

    solving:
        options:
            formulation:
            clip_p_max_pu:
            load_shedding:
            noisy_costs:
            nhours:
            min_iterations:
            max_iterations:
            skip_iterations:
            track_iterations:
        solver:
            name:
            options:

.. seealso::
    Documentation of the configuration file ``config.yaml`` at
    :ref:`electricity_cf`, :ref:`solving_cf`, :ref:`plotting_cf`

Inputs
------

- ``networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: confer :ref:`prepare`

Outputs
-------

- ``results/networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: Solved PyPSA network including optimisation results

    .. image:: img/results.png
        :scale: 40 %

Description
-----------

Total annual system costs are minimised with PyPSA. The full formulation of the
linear optimal power flow (plus investment planning
is provided in the
`documentation of PyPSA <https://pypsa.readthedocs.io/en/latest/optimal_power_flow.html#linear-optimal-power-flow>`_.

The optimization is based on the ``pyomo=False`` setting in the :func:`network.lopf` and  :func:`pypsa.linopf.ilopf` function.
Additionally, some extra constraints specified in :mod:`prepare_network` are added.

Solving the network in multiple iterations is motivated through the dependence of transmission line capacities and impedances.
As lines are expanded their electrical parameters change, which renders the optimisation bilinear even if the power flow
equations are linearized.
To retain the computational advantage of continuous linear programming, a sequential linear programming technique
is used, where in between iterations the line impedances are updated.
Details (and errors made through this heuristic) are discussed in the paper

- Fabian Neumann and Tom Brown. `Heuristics for Transmission Expansion Planning in Low-Carbon Energy System Models <https://arxiv.org/abs/1907.10548>`_), *16th International Conference on the European Energy Market*, 2019. `arXiv:1907.10548 <https://arxiv.org/abs/1907.10548>`_.

.. warning::

    Capital costs of existing network components are not included in the objective function,
    since for the optimisation problem they are just a constant term (no influence on optimal result).

    Therefore, these capital costs are not included in ``network.objective``!
    If you want to calculate the full total annual system costs add these to the objective value.

.. tip::

    The rule :mod:`solve_all_networks` runs
    for all ``scenario`` s in the configuration file
    the rule :mod:`solve_network`.
"""
import logging
import re

import numpy as np
import pandas as pd
import pypsa
import xarray as xr
from _helpers import (
    configure_logging,
    override_component_attrs,
    update_config_with_sector_opts,
)
from vresutils.benchmark import memory_logger

logger = logging.getLogger(__name__)
pypsa.pf.logger.setLevel(logging.WARNING)


def add_land_use_constraint(n, config):
    if "m" in snakemake.wildcards.clusters:
        _add_land_use_constraint_m(n, config)
    else:
        _add_land_use_constraint(n, config)


def _add_land_use_constraint(n, config):
    # warning: this will miss existing offwind which is not classed AC-DC and has carrier 'offwind'

    for carrier in ["solar", "onwind", "offwind-ac", "offwind-dc"]:
        ext_i = (n.generators.carrier == carrier) & ~n.generators.p_nom_extendable
        existing = (
            n.generators.loc[ext_i, "p_nom"]
            .groupby(n.generators.bus.map(n.buses.location))
            .sum()
        )
        existing.index += " " + carrier + "-" + snakemake.wildcards.planning_horizons
        n.generators.loc[existing.index, "p_nom_max"] -= existing

    # check if existing capacities are larger than technical potential
    existing_large = n.generators[
        n.generators["p_nom_min"] > n.generators["p_nom_max"]
    ].index
    if len(existing_large):
        logger.warning(
            f"Existing capacities larger than technical potential for {existing_large},\
                       adjust technical potential to existing capacities"
        )
        n.generators.loc[existing_large, "p_nom_max"] = n.generators.loc[
            existing_large, "p_nom_min"
        ]

    n.generators.p_nom_max.clip(lower=0, inplace=True)


def _add_land_use_constraint_m(n, config):
    # if generators clustering is lower than network clustering, land_use accounting is at generators clusters

    planning_horizons = config["scenario"]["planning_horizons"]
    grouping_years = config["existing_capacities"]["grouping_years"]
    current_horizon = snakemake.wildcards.planning_horizons

    for carrier in ["solar", "onwind", "offwind-ac", "offwind-dc"]:
        existing = n.generators.loc[n.generators.carrier == carrier, "p_nom"]
        ind = list(
            set(
                [
                    i.split(sep=" ")[0] + " " + i.split(sep=" ")[1]
                    for i in existing.index
                ]
            )
        )

        previous_years = [
            str(y)
            for y in planning_horizons + grouping_years
            if y < int(snakemake.wildcards.planning_horizons)
        ]

        for p_year in previous_years:
            ind2 = [
                i for i in ind if i + " " + carrier + "-" + p_year in existing.index
            ]
            sel_current = [i + " " + carrier + "-" + current_horizon for i in ind2]
            sel_p_year = [i + " " + carrier + "-" + p_year for i in ind2]
            n.generators.loc[sel_current, "p_nom_max"] -= existing.loc[
                sel_p_year
            ].rename(lambda x: x[:-4] + current_horizon)

    n.generators.p_nom_max.clip(lower=0, inplace=True)


def add_co2_sequestration_limit(n, limit=200):
    """
    Add a global constraint on the amount of Mt CO2 that can be sequestered.
    """
    n.carriers.loc["co2 stored", "co2_absorptions"] = -1
    n.carriers.co2_absorptions = n.carriers.co2_absorptions.fillna(0)

    limit = limit * 1e6
    for o in opts:
        if "seq" not in o:
            continue
        limit = float(o[o.find("seq") + 3 :]) * 1e6
        break

    n.add(
        "GlobalConstraint",
        "co2_sequestration_limit",
        sense="<=",
        constant=limit,
        type="primary_energy",
        carrier_attribute="co2_absorptions",
    )


def prepare_network(n, solve_opts=None, config=None):
    if "clip_p_max_pu" in solve_opts:
        for df in (
            n.generators_t.p_max_pu,
            n.generators_t.p_min_pu,  # TODO: check if this can be removed
            n.storage_units_t.inflow,
        ):
            df.where(df > solve_opts["clip_p_max_pu"], other=0.0, inplace=True)

    if solve_opts.get("load_shedding"):
        # intersect between macroeconomic and surveybased willingness to pay
        # http://journal.frontiersin.org/article/10.3389/fenrg.2015.00055/full
        # TODO: retrieve color and nice name from config
        n.add("Carrier", "load", color="#dd2e23", nice_name="Load shedding")
        buses_i = n.buses.query("carrier == 'AC'").index
        if not np.isscalar(load_shedding):
            # TODO: do not scale via sign attribute (use Eur/MWh instead of Eur/kWh)
            load_shedding = 1e2  # Eur/kWh

        n.madd(
            "Generator",
            buses_i,
            " load",
            bus=n.buses.index,
            carrier="load",
            sign=1e-3,  # Adjust sign to measure p and p_nom in kW instead of MW
            marginal_cost=load_shedding,  # Eur/kWh
            p_nom=1e9,  # kW
        )

    if solve_opts.get("noisy_costs"):
        for t in n.iterate_components():
            # if 'capital_cost' in t.df:
            #    t.df['capital_cost'] += 1e1 + 2.*(np.random.random(len(t.df)) - 0.5)
            if "marginal_cost" in t.df:
                t.df["marginal_cost"] += 1e-2 + 2e-3 * (
                    np.random.random(len(t.df)) - 0.5
                )

        for t in n.iterate_components(["Line", "Link"]):
            t.df["capital_cost"] += (
                1e-1 + 2e-2 * (np.random.random(len(t.df)) - 0.5)
            ) * t.df["length"]

    if solve_opts.get("nhours"):
        nhours = solve_opts["nhours"]
        n.set_snapshots(n.snapshots[:nhours])
        n.snapshot_weightings[:] = 8760.0 / nhours

    if config["foresight"] == "myopic":
        add_land_use_constraint(n, config)

    if n.stores.carrier.eq("co2 stored").any():
        limit = config["sector"].get("co2_sequestration_potential", 200)
        add_co2_sequestration_limit(n, limit=limit)

    return n


def add_CCL_constraints(n, config):
    """
    Add CCL (country & carrier limit) constraint to the network.

    Add minimum and maximum levels of generator nominal capacity per carrier
    for individual countries. Opts and path for agg_p_nom_minmax.csv must be defined
    in config.yaml. Default file is available at data/agg_p_nom_minmax.csv.

    Parameters
    ----------
    n : pypsa.Network
    config : dict

    Example
    -------
    scenario:
        opts: [Co2L-CCL-24H]
    electricity:
        agg_p_nom_limits: data/agg_p_nom_minmax.csv
    """
    agg_p_nom_minmax = pd.read_csv(
        config["electricity"]["agg_p_nom_limits"], index_col=[0, 1]
    )
    logger.info("Adding generation capacity constraints per carrier and country")
    p_nom = n.model["Generator-p_nom"]

    gens = n.generators.query("p_nom_extendable").rename_axis(index="Generator-ext")
    grouper = [gens.bus.map(n.buses.country), gens.carrier]
    grouper = xr.DataArray(pd.MultiIndex.from_arrays(grouper), dims=["Generator-ext"])
    lhs = p_nom.groupby(grouper).sum().rename(bus="country")

    minimum = xr.DataArray(agg_p_nom_minmax["min"].dropna()).rename(dim_0="group")
    index = minimum.indexes["group"].intersection(lhs.indexes["group"])
    if not index.empty:
        n.model.add_constraints(
            lhs.sel(group=index) >= minimum.loc[index], name="agg_p_nom_min"
        )

    maximum = xr.DataArray(agg_p_nom_minmax["max"].dropna()).rename(dim_0="group")
    index = maximum.indexes["group"].intersection(lhs.indexes["group"])
    if not index.empty:
        n.model.add_constraints(
            lhs.sel(group=index) <= maximum.loc[index], name="agg_p_nom_max"
        )


def add_EQ_constraints(n, o, scaling=1e-1):
    """
    Add equity constraints to the network.

    Currently this is only implemented for the electricity sector only.

    Opts must be specified in the config.yaml.

    Parameters
    ----------
    n : pypsa.Network
    o : str

    Example
    -------
    scenario:
        opts: [Co2L-EQ0.7-24H]

    Require each country or node to on average produce a minimal share
    of its total electricity consumption itself. Example: EQ0.7c demands each country
    to produce on average at least 70% of its consumption; EQ0.7 demands
    each node to produce on average at least 70% of its consumption.
    """
    # TODO: Generalize to cover myopic and other sectors?
    float_regex = "[0-9]*\.?[0-9]+"
    level = float(re.findall(float_regex, o)[0])
    if o[-1] == "c":
        ggrouper = n.generators.bus.map(n.buses.country).to_xarray()
        lgrouper = n.loads.bus.map(n.buses.country).to_xarray()
        sgrouper = n.storage_units.bus.map(n.buses.country).to_xarray()
    else:
        ggrouper = n.generators.bus.to_xarray()
        lgrouper = n.loads.bus.to_xarray()
        sgrouper = n.storage_units.bus.to_xarray()
    load = (
        n.snapshot_weightings.generators
        @ n.loads_t.p_set.groupby(lgrouper, axis=1).sum()
    )
    inflow = (
        n.snapshot_weightings.stores
        @ n.storage_units_t.inflow.groupby(sgrouper, axis=1).sum()
    )
    inflow = inflow.reindex(load.index).fillna(0.0)
    rhs = scaling * (level * load - inflow)
    p = n.model["Generator-p"]
    lhs_gen = (
        (p * (n.snapshot_weightings.generators * scaling))
        .groupby(ggrouper)
        .sum()
        .sum("snapshot")
    )
    # TODO: double check that this is really needed, why do have to subtract the spillage
    if not n.storage_units_t.inflow.empty:
        spillage = n.model["StorageUnit-spill"]
        lhs_spill = (
            (spillage * (-n.snapshot_weightings.stores * scaling))
            .groupby(sgrouper)
            .sum()
            .sum("snapshot")
        )
        lhs = lhs_gen + lhs_spill
    else:
        lhs = lhs_gen
    n.model.add_constraints(lhs >= rhs, name="equity_min")


def add_BAU_constraints(n, config):
    """
    Add a per-carrier minimal overall capacity.

    BAU_mincapacities and opts must be adjusted in the config.yaml.

    Parameters
    ----------
    n : pypsa.Network
    config : dict

    Example
    -------
    scenario:
        opts: [Co2L-BAU-24H]
    electricity:
        BAU_mincapacities:
            solar: 0
            onwind: 0
            OCGT: 100000
            offwind-ac: 0
            offwind-dc: 0
    Which sets minimum expansion across all nodes e.g. in Europe to 100GW.
    OCGT bus 1 + OCGT bus 2 + ... > 100000
    """
    mincaps = pd.Series(config["electricity"]["BAU_mincapacities"])
    p_nom = n.model["Generator-p_nom"]
    ext_i = n.generators.query("p_nom_extendable")
    ext_carrier_i = xr.DataArray(ext_i.carrier.rename_axis("Generator-ext"))
    lhs = p_nom.groupby(ext_carrier_i).sum()
    index = mincaps.index.intersection(lhs.indexes["carrier"])
    rhs = mincaps[index].rename_axis("carrier")
    n.model.add_constraints(lhs >= rhs, name="bau_mincaps")


# TODO: think about removing or make per country
def add_SAFE_constraints(n, config):
    """
    Add a capacity reserve margin of a certain fraction above the peak demand.
    Renewable generators and storage do not contribute. Ignores network.

    Parameters
    ----------
        n : pypsa.Network
        config : dict

    Example
    -------
    config.yaml requires to specify opts:

    scenario:
        opts: [Co2L-SAFE-24H]
    electricity:
        SAFE_reservemargin: 0.1
    Which sets a reserve margin of 10% above the peak demand.
    """
    peakdemand = n.loads_t.p_set.sum(axis=1).max()
    margin = 1.0 + config["electricity"]["SAFE_reservemargin"]
    reserve_margin = peakdemand * margin
    # TODO: do not take this from the plotting config!
    conv_techs = config["plotting"]["conv_techs"]
    ext_gens_i = n.generators.query("carrier in @conv_techs & p_nom_extendable").index
    p_nom = n.model["Generator-p_nom"].loc[ext_gens_i]
    lhs = p_nom.sum()
    exist_conv_caps = n.generators.query(
        "~p_nom_extendable & carrier in @conv_techs"
    ).p_nom.sum()
    rhs = reserve_margin - exist_conv_caps
    n.model.add_constraints(lhs >= rhs, name="safe_mintotalcap")


def add_operational_reserve_margin(n, sns, config):
    """
    Build reserve margin constraints based on the formulation given in
    https://genxproject.github.io/GenX/dev/core/#Reserves.

    Parameters
    ----------
        n : pypsa.Network
        sns: pd.DatetimeIndex
        config : dict

    Example:
    --------
    config.yaml requires to specify operational_reserve:
    operational_reserve: # like https://genxproject.github.io/GenX/dev/core/#Reserves
        activate: true
        epsilon_load: 0.02 # percentage of load at each snapshot
        epsilon_vres: 0.02 # percentage of VRES at each snapshot
        contingency: 400000 # MW
    """
    reserve_config = config["electricity"]["operational_reserve"]
    EPSILON_LOAD = reserve_config["epsilon_load"]
    EPSILON_VRES = reserve_config["epsilon_vres"]
    CONTINGENCY = reserve_config["contingency"]

    # Reserve Variables
    n.model.add_variables(
        0, np.inf, coords=[sns, n.generators.index], name="Generator-r"
    )
    reserve = n.model["Generator-r"]
    lhs = reserve.sum("Generator")

    # Share of extendable renewable capacities
    ext_i = n.generators.query("p_nom_extendable").index
    vres_i = n.generators_t.p_max_pu.columns
    if not ext_i.empty and not vres_i.empty:
        capacity_factor = n.generators_t.p_max_pu[vres_i.intersection(ext_i)]
        p_nom_vres = (
            n.model["Generator-p_nom"]
            .loc[vres_i.intersection(ext_i)]
            .rename({"Generator-ext": "Generator"})
        )
        lhs = lhs + (p_nom_vres * (-EPSILON_VRES * capacity_factor)).sum()

    # Total demand per t
    demand = n.loads_t.p_set.sum(axis=1)

    # VRES potential of non extendable generators
    capacity_factor = n.generators_t.p_max_pu[vres_i.difference(ext_i)]
    renewable_capacity = n.generators.p_nom[vres_i.difference(ext_i)]
    potential = (capacity_factor * renewable_capacity).sum(axis=1)

    # Right-hand-side
    rhs = EPSILON_LOAD * demand + EPSILON_VRES * potential + CONTINGENCY

    n.model.add_constraints(lhs >= rhs, name="reserve_margin")

    reserve = n.model["Generator-r"]

    lhs = n.model.constraints["Generator-fix-p-upper"].lhs
    lhs = lhs + reserve.loc[:, lhs.coords["Generator-fix"]].drop("Generator")
    rhs = n.model.constraints["Generator-fix-p-upper"].rhs
    n.model.add_constraints(lhs <= rhs, name="Generator-fix-p-upper-reserve")

    lhs = n.model.constraints["Generator-ext-p-upper"].lhs
    lhs = lhs + reserve.loc[:, lhs.coords["Generator-ext"]].drop("Generator")
    rhs = n.model.constraints["Generator-ext-p-upper"].rhs
    n.model.add_constraints(lhs >= rhs, name="Generator-ext-p-upper-reserve")


def add_battery_constraints(n):
    """
    Add constraint ensuring that charger = discharger, i.e.
    1 * charger_size - efficiency * discharger_size = 0
    """
    if not n.links.p_nom_extendable.any():
        return

    discharger_bool = n.links.index.str.contains("battery discharger")
    charger_bool = n.links.index.str.contains("battery charger")

    dischargers_ext = n.links[discharger_bool].query("p_nom_extendable").index
    chargers_ext = n.links[charger_bool].query("p_nom_extendable").index

    eff = n.links.efficiency[dischargers_ext].values
    lhs = (
        n.model["Link-p_nom"].loc[chargers_ext]
        - n.model["Link-p_nom"].loc[dischargers_ext] * eff
    )

    n.model.add_constraints(lhs == 0, name="Link-charger_ratio")


def add_chp_constraints(n):
    electric = (
        n.links.index.str.contains("urban central")
        & n.links.index.str.contains("CHP")
        & n.links.index.str.contains("electric")
    )
    heat = (
        n.links.index.str.contains("urban central")
        & n.links.index.str.contains("CHP")
        & n.links.index.str.contains("heat")
    )

    electric_ext = n.links[electric].query("p_nom_extendable").index
    heat_ext = n.links[heat].query("p_nom_extendable").index

    electric_fix = n.links[electric].query("~p_nom_extendable").index
    heat_fix = n.links[heat].query("~p_nom_extendable").index

    p = n.model["Link-p"]  # dimension: [time, link]

    # output ratio between heat and electricity and top_iso_fuel_line for extendable
    if not electric_ext.empty:
        p_nom = n.model["Link-p_nom"]

        lhs = (
            p_nom.loc[electric_ext]
            * (n.links.p_nom_ratio * n.links.efficiency)[electric_ext].values
            - p_nom.loc[heat_ext] * n.links.efficiency[heat_ext].values
        )
        n.model.add_constraints(lhs == 0, name="chplink-fix_p_nom_ratio")

        rename = {"Link-ext": "Link"}
        lhs = (
            p.loc[:, electric_ext]
            + p.loc[:, heat_ext]
            - p_nom.rename(rename).loc[electric_ext]
        )
        n.model.add_constraints(lhs <= 0, name="chplink-top_iso_fuel_line_ext")

    # top_iso_fuel_line for fixed
    if not electric_fix.empty:
        lhs = p.loc[:, electric_fix] + p.loc[:, heat_fix]
        rhs = n.links.p_nom[electric_fix]
        n.model.add_constraints(lhs <= rhs, name="chplink-top_iso_fuel_line_fix")

    # back-pressure
    if not electric.empty:
        lhs = (
            p.loc[:, heat] * (n.links.efficiency[heat] * n.links.c_b[electric].values)
            - p.loc[:, electric] * n.links.efficiency[electric]
        )
        n.model.add_constraints(lhs <= rhs, name="chplink-backpressure")


def add_pipe_retrofit_constraint(n):
    """
    Add constraint for retrofitting existing CH4 pipelines to H2 pipelines.
    """
    gas_pipes_i = n.links.query("carrier == 'gas pipeline' and p_nom_extendable").index
    h2_retrofitted_i = n.links.query(
        "carrier == 'H2 pipeline retrofitted' and p_nom_extendable"
    ).index

    if h2_retrofitted_i.empty or gas_pipes_i.empty:
        return

    p_nom = n.model["Link-p_nom"]

    CH4_per_H2 = 1 / n.config["sector"]["H2_retrofit_capacity_per_CH4"]
    lhs = p_nom.loc[gas_pipes_i] + CH4_per_H2 * p_nom.loc[h2_retrofitted_i]
    rhs = n.links.p_nom[gas_pipes_i].rename_axis("Link-ext")

    n.model.add_constraints(lhs == rhs, name="Link-pipe_retrofit")


def extra_functionality(n, snapshots):
    """
    Collects supplementary constraints which will be passed to
    ``pypsa.optimization.optimize``.

    If you want to enforce additional custom constraints, this is a good
    location to add them. The arguments ``opts`` and
    ``snakemake.config`` are expected to be attached to the network.
    """
    opts = n.opts
    config = n.config
    if "BAU" in opts and n.generators.p_nom_extendable.any():
        add_BAU_constraints(n, config)
    if "SAFE" in opts and n.generators.p_nom_extendable.any():
        add_SAFE_constraints(n, config)
    if "CCL" in opts and n.generators.p_nom_extendable.any():
        add_CCL_constraints(n, config)
    reserve = config["electricity"].get("operational_reserve", {})
    if reserve.get("activate"):
        add_operational_reserve_margin(n, snapshots, config)
    for o in opts:
        if "EQ" in o:
            add_EQ_constraints(n, o)
    add_battery_constraints(n)
    add_pipe_retrofit_constraint(n)


def solve_network(n, config, opts="", **kwargs):
    set_of_options = config["solving"]["solver"]["options"]
    solver_options = (
        config["solving"]["solver_options"][set_of_options] if set_of_options else {}
    )
    solver_name = config["solving"]["solver"]["name"]
    cf_solving = config["solving"]["options"]
    track_iterations = cf_solving.get("track_iterations", False)
    min_iterations = cf_solving.get("min_iterations", 4)
    max_iterations = cf_solving.get("max_iterations", 6)

    # add to network for extra_functionality
    n.config = config
    n.opts = opts

    skip_iterations = cf_solving.get("skip_iterations", False)
    if not n.lines.s_nom_extendable.any():
        skip_iterations = True
        logger.info("No expandable lines found. Skipping iterative solving.")

    if skip_iterations:
        status, condition = n.optimize(
            solver_name=solver_name,
            extra_functionality=extra_functionality,
            **solver_options,
            **kwargs,
        )
    else:
        status, condition = n.optimize.optimize_transmission_expansion_iteratively(
            solver_name=solver_name,
            track_iterations=track_iterations,
            min_iterations=min_iterations,
            max_iterations=max_iterations,
            extra_functionality=extra_functionality,
            **solver_options,
            **kwargs,
        )

    if status != "ok":
        logger.warning(
            f"Solving status '{status}' with termination condition '{condition}'"
        )
    if "infeasible" in condition:
        raise RuntimeError("Solving status 'infeasible'")

    return n


if __name__ == "__main__":
    if "snakemake" not in globals():
        from _helpers import mock_snakemake

        snakemake = mock_snakemake(
            "solve_sector_network",
            configfiles="test/config.overnight.yaml",
            simpl="",
            opts="",
            clusters="5",
            ll="v1.5",
            sector_opts="CO2L0-24H-T-H-B-I-A-solar+p3-dist1",
            planning_horizons="2030",
        )
    configure_logging(snakemake)
    update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)

    opts = (snakemake.wildcards.opts + "-" + snakemake.wildcards.sector_opts).split("-")
    opts = [o for o in opts if o != ""]
    solve_opts = snakemake.config["solving"]["options"]

    np.random.seed(solve_opts.get("seed", 123))

    fn = getattr(snakemake.log, "memory", None)
    with memory_logger(filename=fn, interval=30.0) as mem:
        if "overrides" in snakemake.input.keys():
            overrides = override_component_attrs(snakemake.input.overrides)
            n = pypsa.Network(
                snakemake.input.network, override_component_attrs=overrides
            )
        else:
            n = pypsa.Network(snakemake.input.network)

        n = prepare_network(n, solve_opts, config=snakemake.config)

        n = solve_network(
            n, config=snakemake.config, opts=opts, log_fn=snakemake.log.solver
        )

        n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
        n.export_to_netcdf(snakemake.output[0])

    logger.info("Maximum memory usage: {}".format(mem.mem_usage))