Solve network

Functions to add constraints and prepare the network for the solver. Associated with the solve_networks rule in the Snakefile.

add_battery_constraints(n)

Add constraint ensuring that charger = discharger, i.e. 1 * charger_size - efficiency * discharger_size = 0

Source code in workflow/scripts/solve_network.py

def add_battery_constraints(n: pypsa.Network):
    """
    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")

add_chp_constraints(n)

Add constraints to couple the heat and electricity output of CHP plants. Simplified treatment of extraction plant with Cb/Cv coeffs as per DKEA catalogue. Ignore the minimum electric power for heat output (aggregate over all units).

Parameters:

Name	Type	Description	Default
n	Network	the pypsa network object to which's model the constraints are added	required

Source code in workflow/scripts/solve_network.py

def add_chp_constraints(n: pypsa.Network):
    """Add constraints to couple the heat and electricity output of CHP plants.
      Simplified treatment of extraction plant with Cb/Cv coeffs as per DKEA catalogue.
      Ignore the minimum electric power for heat output (aggregate over all units).


    Args:
        n (pypsa.Network): the pypsa network object to which's model the constraints are added
    """
    electric = n.links.index.str.contains("CHP") & n.links.index.str.contains("generator")
    heat = n.links.index.str.contains("CHP") & n.links.index.str.contains("boiler")

    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.heat_to_power * 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")

    # max heat to power ratio (simplified extraction plant w/o min heat output in extraction mode)
    # heat can drop to zero to represent condensation mode
    if not n.links[electric].index.empty:
        lhs = p.loc[:, heat] * n.links.efficiency[heat] - p.loc[:, electric] * n.links.efficiency[
            electric
        ] * n.links.heat_to_power[electric].fillna(1)
        n.model.add_constraints(lhs <= 0, name="chplink-backpressure")

add_chp_constraints_new_attempt(n)

Add constraints to couple the heat and electricity output of CHP plants. Simplified treatment of extraction plant with max heat to power ratio. Ignore the minimum electric power for heat output (aggregate over all units).

Parameters:

Name	Type	Description	Default
n	Network	the pypsa network object to which's model the constraints are added	required

Source code in workflow/scripts/solve_network.py

def add_chp_constraints_new_attempt(n: pypsa.Network):
    """Add constraints to couple the heat and electricity output of CHP plants.
      Simplified treatment of extraction plant with max heat to power ratio.
      Ignore the minimum electric power for heat output (aggregate over all units).


    Args:
        n (pypsa.Network): the pypsa network object to which's model the constraints are added
    """

    elec = n.links.query("index.str.contains('chp', case=False) & index.str.contains('generator')")
    heat = n.links.query("index.str.contains('chp', case=False) & index.str.contains('boiler')")

    if not elec.shape == heat.shape:
        raise ValueError(
            "Incorrect definition of CHP units, check link names."
            " Each 'CHP generator' needs an associated 'CHP boiler'."
        )

    # elec = n.links.index.str.contains("CHP") & n.links.index.str.contains("generator")
    # heat = n.links.index.str.contains("CHP") & n.links.index.str.contains("boiler")

    elec_ext = sorted(elec.query("p_nom_extendable").index)
    heat_ext = sorted(heat.query("p_nom_extendable").index)

    elec_fix = elec.query("~p_nom_extendable").index
    heat_fix = heat.query("~p_nom_extendable").index

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

    # maximum capacity of heat set by ratio between heat and electricity
    if not elec.query("p_nom_extendable").empty:
        # maximising heat power under constraints that Pel/eta_el + Pth/eta_th <= p_nom_link
        # & noting that pypsa link powers are defined by input (fuel)
        # yields: p_nom_heat = p_nom_el * htpr / (htpr+eta_h/eta_el)
        htpr = n.links.p_nom_ratio[elec_ext].values
        alpha = n.links.efficiency[heat_ext].values / n.links.efficiency[elec_ext].values
        lhs = p_nom.loc[elec_ext] * htpr / (htpr + alpha) - p_nom.loc[heat_ext]
        n.model.add_constraints(lhs == 0, name="chplink-fix_max_heat")

        # top_iso_fuel_line ext (is it still relevant?)
        rename = {"Link-ext": "Link"}
        lhs = p.loc[:, elec_ext] + p.loc[:, heat_ext] - p_nom.rename(rename).loc[elec_ext]
        n.model.add_constraints(lhs <= 0, name="chplink-top_iso_fuel_line_ext")

    # max heat to power ratio
    grouper_heat = pd.concat([heat.location, heat.build_year], axis=1)
    grouper_elec = pd.concat([elec.location, elec.build_year], axis=1)
    p_heat = n.model["Link-p"].loc[:, heat.index]
    p_elec = n.model["Link-p"].loc[:, elec.index]
    lhs = (p_heat * heat.efficiency).groupby(grouper_heat).sum()
    rhs = (p_elec * elec.efficiency * elec.p_nom_ratio.fillna(1)).groupby(grouper_elec).sum()
    n.model.add_constraints(lhs <= rhs, name="chplink-max_heat_to_power")

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

add_co2_constraints_prices(network, co2_control)

Add co2 constraints or prices

Parameters:

Name	Type	Description	Default
network	Network	the network to which prices or constraints are to be added	required
co2_control	dict	the config	required

Raises:

Type	Description
ValueError	unrecognised co2 control option

Source code in workflow/scripts/solve_network.py

def add_co2_constraints_prices(network: pypsa.Network, co2_control: dict):
    """Add co2 constraints or prices

    Args:
        network (pypsa.Network): the network to which prices or constraints are to be added
        co2_control (dict): the config

    Raises:
        ValueError: unrecognised co2 control option
    """

    if co2_control["control"] is None:
        pass
    elif co2_control["control"] == "price":
        logger.info("Adding CO2 price to marginal costs of generators and storage units")
        add_emission_prices(network, emission_prices={"co2": co2_control["co2_pr_or_limit"]})

    elif co2_control["control"].startswith("budget"):
        co2_limit = co2_control["co2_pr_or_limit"]
        logger.info("Adding CO2 constraint based on scenario {co2_limit}")
        network.add(
            "GlobalConstraint",
            "co2_limit",
            type="primary_energy",
            carrier_attribute="co2_emissions",
            sense="<=",
            constant=co2_limit,
        )
    else:
        logger.error(f"Unhandled CO2 control config {co2_control} due to unknown control.")
        raise ValueError(f"Unhandled CO2 control config {co2_control} due to unknown control")

add_emission_prices(n, emission_prices={'co2': 0.0}, exclude_co2=False)

From pypsa-eur: add GHG price to marginal costs of generators and storage units

Parameters:

Name	Type	Description	Default
n	Network	the pypsa network	required
emission_prices	dict	emission prices per GHG. Defaults to {"co2": 0.0}.	{'co2': 0.0}
exclude_co2	bool	do not charge for CO2 emissions. Defaults to False.	False

Source code in workflow/scripts/solve_network.py

def add_emission_prices(n: pypsa.Network, emission_prices={"co2": 0.0}, exclude_co2=False):
    """From pypsa-eur: add GHG price to marginal costs of generators and storage units

    Args:
        n (pypsa.Network): the pypsa network
        emission_prices (dict, optional): emission prices per GHG. Defaults to {"co2": 0.0}.
        exclude_co2 (bool, optional): do not charge for CO2 emissions. Defaults to False.
    """
    if exclude_co2:
        emission_prices.pop("co2")
    em_price = (
        pd.Series(emission_prices).rename(lambda x: x + "_emissions")
        * n.carriers.filter(like="_emissions")
    ).sum(axis=1)

    n.meta.update({"emission_prices": emission_prices})

    gen_em_price = n.generators.carrier.map(em_price) / n.generators.efficiency

    n.generators["marginal_cost"] += gen_em_price
    n.generators_t["marginal_cost"] += gen_em_price[n.generators_t["marginal_cost"].columns]
    # storage units su
    su_em_price = n.storage_units.carrier.map(em_price) / n.storage_units.efficiency_dispatch
    n.storage_units["marginal_cost"] += su_em_price

    logger.info("Added emission prices to marginal costs of generators and storage units")
    logger.info(f"\tEmission prices: {emission_prices}")

add_land_use_constraint(n, planning_horizons)

Add land use constraints for renewable energy potential. This ensures that the brownfield + greenfield vre installations for each generator tech do not exceed the technical potential.

Parameters:

Name	Type	Description	Default
n	Network	the network object to add the constraints to	required
planning_horizons	str | int	the planning horizon year as string	required

Source code in workflow/scripts/solve_network.py

def add_land_use_constraint(n: pypsa.Network, planning_horizons: str | int) -> None:
    """
    Add land use constraints for renewable energy potential. This ensures that the brownfield +
     greenfield vre installations for each generator tech do not exceed the technical potential.

    Args:
        n (pypsa.Network): the network object to add the constraints to
        planning_horizons (str | int): the planning horizon year as string
    """
    # warning: this will miss existing offwind which is not classed AC-DC and has carrier 'offwind'

    for carrier in [
        "solar",
        "solar thermal",
        "onwind",
        "offwind",
        "offwind-ac",
        "offwind-dc",
        "offwind-float",
    ]:
        ext_i = (n.generators.carrier == carrier) & ~n.generators.p_nom_extendable
        grouper = n.generators.loc[ext_i].index.str.replace(f" {carrier}.*$", "", regex=True)
        existing = n.generators.loc[ext_i, "p_nom"].groupby(grouper).sum()
        existing.index += f" {carrier}"
        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"] = n.generators["p_nom_max"].clip(lower=0)

add_operational_reserve_margin(n, config)

Build operational reserve margin constraints based on the formulation given in https://genxproject.github.io/GenX.jl/stable/Model_Reference/core/#GenX.operational_reserves_core!-Tuple{JuMP.Model,%20Dict,%20Dict}

The constraint is network wide and not at each node!

Parameters:

Name	Type	Description	Default
n	Network	the network object to optimize	required
config	dict	the configuration dictionary	required

Example

config.yaml requires to specify operational_reserve: operational_reserve: 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

Source code in workflow/scripts/solve_network.py

def add_operational_reserve_margin(n: pypsa.network, config):
    """
    Build operational reserve margin constraints based on the formulation given in
    https://genxproject.github.io/GenX.jl/stable/Model_Reference/core/#GenX.operational_reserves_core!-Tuple{JuMP.Model,%20Dict,%20Dict}

    The constraint is network wide and not at each node!

    Args:
        n (pypsa.Network): the network object to optimize
        config (dict): the configuration dictionary

    Example:
        config.yaml requires to specify operational_reserve:
        operational_reserve:
            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["operational_reserve"]
    VRE_TECHS = config["Techs"].get("non_dispatchable", ["onwind", "offwind", "solar"])
    EPSILON_LOAD, EPSILON_VRES = reserve_config["epsilon_load"], reserve_config["epsilon_vres"]
    CONTINGENCY = float(reserve_config["contingency"])

    # AC producers
    ac_mask = n.generators.bus.map(n.buses.carrier) == "AC"
    ac_buses = n.buses.query("carrier =='AC'").index
    attached_carriers = filter_carriers(n, "AC")
    # conceivably a link could have a negative efficiency and flow towards bus0 - don't consider
    prod_links = n.links.query("carrier in @attached_carriers & not bus0 in @ac_buses")
    transport_links = prod_links.bus0.map(n.buses.carrier) == prod_links.bus1.map(n.buses.carrier)
    prod_links = prod_links.loc[transport_links == False]
    prod_gen = n.generators.loc[ac_mask]
    producers_all = prod_links.index.append(prod_gen.index)
    producers_all.name = "Producers-p"

    # RSERVES
    n.model.add_variables(0, np.inf, coords=[n.snapshots, prod_gen.index], name="Generator-r")
    n.model.add_variables(0, np.inf, coords=[n.snapshots, prod_links.index], name="Link-r")

    # Define Reserve and weigh VRES by their mean availability ("capacity credit")
    vres_gen = prod_gen.query("carrier in @VRE_TECHS")
    non_vre = prod_gen.index.difference(vres_gen.index)
    # full capacity credit for non-VRE producers (questionable, maybe should be weighted by availability)
    summed_reserve = (n.model["Link-r"] * prod_links.efficiency).sum("Link") + n.model[
        "Generator-r"
    ].loc[:, non_vre].sum("Generator")

    # VRE capacity credit & margin reqs
    ext_idx = vres_gen.query("p_nom_extendable").index
    avail = n.generators_t.p_max_pu.loc[:, vres_gen.index]
    vres_idx = avail.columns

    if not vres_gen.empty:
        # Reserve score based on actual avail (perfect foresight) not mean/expected avail
        vre_reserve_score = (n.model["Generator-r"].loc[:, vres_gen.index] * avail).sum("Generator")
        summed_reserve += vre_reserve_score
    if not ext_idx.empty and not vres_idx.empty:
        # reqs from brownfield VRE generators. epsilon is the margin for VRES forecast error
        avail_factor = n.generators_t.p_max_pu[ext_idx]
        p_nom_vres = n.model["Generator-p_nom"].loc[ext_idx].rename({"Generator-ext": "Generator"})
        vre_req_ext = (p_nom_vres * (EPSILON_VRES * xr.DataArray(avail_factor))).sum("Generator")
    else:
        vre_req_ext = 0

    if not vres_idx.empty:
        # reqs extendable VRE generators
        avail_factor = n.generators_t.p_max_pu[vres_idx.difference(ext_idx)]
        renewable_capacity = n.generators.p_nom[vres_idx.difference(ext_idx)]
        vre_req_fix = (avail_factor * renewable_capacity).sum(axis=1)
    else:
        vre_req_fix = 0

    lhs = summed_reserve - vre_req_ext
    # Right-hand-side
    demand = get_as_dense(n, "Load", "p_set").sum(axis=1)
    rhs = EPSILON_LOAD * demand + EPSILON_VRES * vre_req_fix + CONTINGENCY

    n.model.add_constraints(lhs >= rhs, name="Reserve-margin")

    # Need additional constraints (reserve + dispatch <= p_nom): gen_r + gen_p <= gen_p_nom (capacity)
    to_constrain = {"Link": prod_links, "Generator": prod_gen}
    for component, producer in to_constrain.items():
        logger.info(f"adding secondary reserve constraint for {component}s")

        fix_i = producer.query("p_nom_extendable==False").index
        ext_i = producer.query("p_nom_extendable==True").index

        dispatch = n.model[f"{component}-p"].loc[:, producer.index]
        reserve = n.model[f"{component}-r"].loc[:, ext_i.union(fix_i)]

        capacity_variable = n.model[f"{component}-p_nom"].loc[ext_i]
        capacity_variable = capacity_variable.rename({f"{component}-ext": f"{component}"})
        capacity_fixed = getattr(n, component.lower() + "s").p_nom[fix_i]

        p_max_pu = get_as_dense(n, f"{component}", "p_max_pu")

        lhs = dispatch + reserve
        # MAY have to check what happens in case pmaxpu is not defined for all items
        rhs = capacity_variable * p_max_pu[ext_i] + (p_max_pu[fix_i] * capacity_fixed)
        n.model.add_constraints(
            lhs - rhs.loc[lhs.indexes] <= 0, name=f"{component}-p-reserve-upper"
        )

add_remind_paid_off_constraints(n)

Paid-off components can be placed wherever PyPSA wants but have a total limit.

Add constraints to ensure that the paid off capacity from REMIND is not exceeded across the network & that it does not exceed the technical potential.

Parameters:

Name	Type	Description	Default
n	Network	the network object to which's model the constraints are added	required

Source code in workflow/scripts/solve_network.py

def add_remind_paid_off_constraints(n: pypsa.Network) -> None:
    """
    Paid-off components can be placed wherever PyPSA wants but have a total limit.

    Add constraints to ensure that the paid off capacity from REMIND is not
    exceeded across the network & that it does not exceed the technical potential.

    Args:
        n (pypsa.Network): the network object to which's model the constraints are added
    """

    if not n.config["run"].get("is_remind_coupled", False):
        logger.info("Skipping paid off constraints as REMIND is not coupled")
        return

    # In coupled-mode components (Generators, Links,..) have limits p/e_nom_rcl & a tech_group
    # These columns are added by `add_existing_baseyear.add_paid_off_capacity`.
    # p/e_nom_rcl is the availale paid-off capacity per tech group and is nan for non paid-off (usual) generators.
    # rcl is a legacy name from Aodenweller
    for component in ["Generator", "Link", "Store"]:
        prefix = "e" if component == "Store" else "p"
        paid_off_col = f"{prefix}_nom_max_rcl"

        paid_off = getattr(n, component.lower() + "s").copy()
        # if there are no paid_off components
        if paid_off_col not in paid_off.columns:
            continue
        else:
            paid_off.dropna(subset=[paid_off_col], inplace=True)

        paid_off_totals = paid_off.set_index("tech_group")[paid_off_col].drop_duplicates()

        # LHS: p_nom per technology grp < totals
        groupers = [paid_off["tech_group"]]
        grouper_lhs = xr.DataArray(pd.MultiIndex.from_arrays(groupers), dims=[f"{component}-ext"])
        p_nom_groups = (
            n.model[f"{component}-{prefix}_nom"].loc[paid_off.index].groupby(grouper_lhs).sum()
        )

        # get indices to sort RHS. the grouper is multi-indexed (legacy from PyPSA-Eur)
        idx = p_nom_groups.indexes["group"]
        idx = [x[0] for x in idx]

        # Add constraint
        if not p_nom_groups.empty:
            n.model.add_constraints(
                p_nom_groups <= paid_off_totals[idx].values,
                name=f"paidoff_cap_totals_{component.lower()}",
            )

    # === ensure normal e/p_nom_max is respected for (paid_off + normal) components
    # e.g. if PV has 100MW tech potential at nodeA, paid_off+normal p_nom_opt <100MW
    for component in ["Generator", "Link", "Store"]:
        paidoff_comp = getattr(n, component.lower() + "s").copy()

        prefix = "e" if component == "Store" else "p"
        paid_off_col = f"{prefix}_nom_max_rcl"
        # if there are no paid_off components
        if paid_off_col not in paidoff_comp.columns:
            continue
        else:
            paidoff_comp.dropna(subset=[paid_off_col], inplace=True)

        # techs that only exist as paid-off don't have usual counterparts
        remind_only = n.config["existing_capacities"].get("remind_only_tech_groups", [])  # no qa: F
        paidoff_comp = paidoff_comp.query("tech_group not in @remind_only")

        if paidoff_comp.empty:
            continue

        # find equivalent usual (not paid-off) components
        usual_comps_idx = paidoff_comp.index.str.replace("_paid_off", "")
        usual_comps = getattr(n, component.lower() + "s").loc[usual_comps_idx].copy()
        usual_comps = usual_comps[~usual_comps.p_nom_max.isin([np.inf, np.nan])]

        paid_off_wlimits = paidoff_comp.loc[usual_comps.index + "_paid_off"]
        to_constrain = pd.concat([usual_comps, paid_off_wlimits], axis=0)
        if to_constrain.empty:
            continue
        to_constrain.rename_axis(index=f"{component}-ext", inplace=True)
        # otherwise n.model query will fail. This is needed in case freeze_compoents was used
        # it is fine so long as p_nom is zero for the frozen components
        to_constrain = to_constrain.query("p_nom_extendable==True")
        to_constrain["grouper"] = to_constrain.index.str.replace("_paid_off", "")

        grouper = xr.DataArray(to_constrain.grouper, dims=[f"{component}-ext"])

        lhs = n.model[f"{component}-{prefix}_nom"].loc[to_constrain.index].groupby(grouper).sum()
        # RHS
        idx = lhs.indexes["grouper"]

        if not lhs.empty:
            n.model.add_constraints(
                lhs <= usual_comps.loc[idx].p_nom_max.values,
                name=f"constrain_paidoff&usual_{component}_potential",
            )

add_transmission_constraints(n)

Add constraint ensuring that transmission lines p_nom are the same for both directions, i.e. p_nom positive = p_nom negative

Parameters:

Name	Type	Description	Default
n	Network	the network object to optimize	required

Source code in workflow/scripts/solve_network.py

def add_transmission_constraints(n: pypsa.Network):
    """
    Add constraint ensuring that transmission lines p_nom are the same for both directions, i.e.
    p_nom positive = p_nom negative

    Args:
        n (pypsa.Network): the network object to optimize
    """

    if not n.links.p_nom_extendable.any():
        return

    positive_bool = n.links.index.str.contains("positive")
    negative_bool = n.links.index.str.contains("reversed")

    positive_ext = n.links[positive_bool].query("p_nom_extendable").index
    negative_ext = n.links[negative_bool].query("p_nom_extendable").index

    lhs = n.model["Link-p_nom"].loc[positive_ext]
    rhs = n.model["Link-p_nom"].loc[negative_ext]

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

add_water_tank_charger_constraints(n, config)

Add constraint ensuring that centra water tank charger = discharger & limit p_nom/e_nom ratio, i.e. Args: n (pypsa.Network): the network object to optimize config (dict): the snakemake configuration dictionary

Source code in workflow/scripts/solve_network.py

def add_water_tank_charger_constraints(n: pypsa.Network, config: dict):
    """
    Add constraint ensuring that centra water tank charger = discharger & limit p_nom/e_nom ratio, i.e.
    Args:
        n (pypsa.Network): the network object to optimize
        config (dict): the snakemake configuration dictionary
    """

    discharger_bool = n.links.index.str.contains(
        "water tanks discharger & not index.str.contains('decentral')"
    )
    charger_bool = n.links.index.str.contains(
        "water tanks charger & not index.str.contains('decentral')"
    )

    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-water-tank-charger_ratio")

    # limit the p_nom/e_nom ratio
    central_tanks = n.stores.query("carrier == 'water tanks' & not index.str.contains('decentral')")
    central_dischargers_ext = n.links.query(
        "carrier == 'water tanks' and index.str.contains('discharger') and not index.str.contains('decentral')"
    ).query("p_nom_extendable")

    grouper_s = central_tanks.location.rename_axis("Store-ext")
    grouper_l = central_dischargers_ext.rename_axis("Link-ext").location
    p_nom_over_e_nom = config["water_tanks"].get("p_nom_over_e_nom", 0.2)
    lhs = (
        n.model["Store-e_nom"].loc[central_tanks.index].groupby(grouper_s).sum() * p_nom_over_e_nom
        - n.model["Link-p_nom"].loc[central_dischargers_ext.index].groupby(grouper_l).sum()
    )
    n.model.add_constraints(lhs >= 0, name="Central_Water_Tank_p_nom_over_e_nom")

    # limit the p_nom/e_nom ratio to 1 for decentral tanks
    decentral_tanks = n.stores.query("carrier == 'water tanks' & index.str.contains('decentral')")
    decentral_dischargers_ext = n.links.query(
        "carrier == 'water tanks' and index.str.contains('discharger') and index.str.contains('decentral')"
    ).query("p_nom_extendable")
    decentral_chargers_ext = n.links.query(
        "carrier == 'water tanks' and not index.str.contains('discharger') and index.str.contains('decentral')"
    ).query("p_nom_extendable")

    grouper_s = decentral_tanks.location.rename_axis("Store-ext")
    grouper_l = decentral_dischargers_ext.rename_axis("Link-ext").location
    lhs = (
        n.model["Store-e_nom"].loc[decentral_tanks.index].groupby(grouper_s).sum()
        - n.model["Link-p_nom"].loc[decentral_dischargers_ext.index].groupby(grouper_l).sum()
    )

    n.model.add_constraints(lhs >= 0, name="Decentral_Water_Tank_p_nom_over_e_nom")
    lhs = (
        n.model["Store-e_nom"].loc[decentral_tanks.index].groupby(grouper_s).sum()
        - n.model["Link-p_nom"].loc[decentral_dischargers_ext.index].groupby(grouper_l).sum()
    )
    grouper_l = decentral_chargers_ext.rename_axis("Link-ext").location
    lhs = (
        n.model["Store-e_nom"].loc[decentral_tanks.index].groupby(grouper_s).sum()
        - n.model["Link-p_nom"].loc[decentral_chargers_ext.index].groupby(grouper_l).sum()
    )
    n.model.add_constraints(lhs >= 0, name="Decentral_Water_Tank_p_nom_over_e_nom_charger")

extra_functionality(n, _)

Add supplementary constraints to the network model. pypsa.linopf.network_lopf. 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.

Parameters:

Name	Type	Description	Default
n	Network	the network object to optimize	required
_		dummy for compatibility with pypsa solve	required

Source code in workflow/scripts/solve_network.py

def extra_functionality(n: pypsa.Network, _) -> None:
    """
    Add supplementary constraints to the network model. ``pypsa.linopf.network_lopf``.
    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.

    Args:
        n (pypsa.Network): the network object to optimize
        _: dummy for compatibility with pypsa solve
    """
    config = n.config
    add_battery_constraints(n)
    add_transmission_constraints(n)
    if config["heat_coupling"]:
        add_water_tank_charger_constraints(n, config)
        add_chp_constraints(n)
        # add_chp_constraints_new_attempt(n)
    if config["run"].get("is_remind_coupled", False):
        logger.info("Adding remind paid off constraints")
        add_remind_paid_off_constraints(n)

    reserve = config.get("operational_reserve", {})
    if reserve.get("activate", False):
        logger.info("Adding operational reserve margin constraints")
        add_operational_reserve_margin(n, config)

    logger.info("Added extra functionality to the network model")

freeze_components(n, config, exclude=['H2 turbine'])

Set p_nom_extendable=False for the components in the network. Applies to vre_techs and conventional technologies not in the exclude list.

Parameters:

Name	Type	Description	Default
n	Network	the network object	required
config	dict	the configuration dictionary	required
exclude	list	list of technologies to exclude from freezing. Defaults to ["OCGT"]	['H2 turbine']

Source code in workflow/scripts/solve_network.py

def freeze_components(n: pypsa.Network, config: dict, exclude: list = ["H2 turbine"]):
    """Set p_nom_extendable=False for the components in the network.
    Applies to vre_techs and conventional technologies not in the exclude list.

    Args:
        n (pypsa.Network): the network object
        config (dict): the configuration dictionary
        exclude (list, optional): list of technologies to exclude from freezing.
            Defaults to ["OCGT"]
    """

    # Freeze VRE and conventional techs
    freeze = config["Techs"]["vre_techs"] + config["Techs"]["conv_techs"]
    freeze = [f for f in freeze if f not in exclude]
    if "coal boiler" in freeze:
        freeze += ["coal boiler central", "coal boiler decentral"]
    if "gas boiler" in freeze:
        freeze += ["gas boiler central", "gas boiler decentral"]

    # very ugly -> how to make more robust?
    to_fix = {
        "OCGT": "gas OCGT",
        "CCGT": "gas CCGT",
        "CCGT-CCS": "gas ccs",
        "coal power plant": "coal",
        "coal-CCS": "coal ccs",
    }
    freeze += [to_fix[k] for k in to_fix if k in freeze]

    for comp in ["generators", "links"]:
        query = "carrier in @freeze & p_nom_extendable == True"
        components = getattr(n, comp)
        # p_nom_max_rcl.isna(): exclude paid_off as needed
        if "p_nom_max_rcl" in components.columns:
            query += " & p_nom_max_rcl.isna()"
        mask = components.query(query).index
        components.loc[mask, "p_nom_extendable"] = False

prepare_network(n, solve_opts, config, plan_year, co2_pathway)

Prepare the network for the solver,

Parameters:

Name	Type	Description	Default
n	Network	the network object to optimize	required
solve_opts	dict	solving options	required
config	dict	the snakemake configuration dictionary	required
plan_year	int	planning horizon year for which network is solved	required
co2_pathway	str	the CO2 pathway name to use	required

Returns:

Type	Description
Network	pypsa.Network: network object with additional constraints

Source code in workflow/scripts/solve_network.py

def prepare_network(
    n: pypsa.Network, solve_opts: dict, config: dict, plan_year: int, co2_pathway: str
) -> pypsa.Network:
    """Prepare the network for the solver,

    Args:
        n (pypsa.Network): the network object to optimize
        solve_opts (dict): solving options
        config (dict): the snakemake configuration dictionary
        plan_year (int): planning horizon year for which network is solved
        co2_pathway (str): the CO2 pathway name to use

    Returns:
        pypsa.Network: network object with additional constraints
    """

    co2_opts = ConfigManager(config).fetch_co2_restriction(co2_pathway, int(plan_year))
    add_co2_constraints_prices(n, co2_opts)

    if "clip_p_max_pu" in solve_opts:
        for df in (n.generators_t.p_max_pu, n.storage_units_t.inflow):
            df.where(df > solve_opts["clip_p_max_pu"], other=0.0, inplace=True)

    # TODO duplicated with freeze components
    if solve_opts.get("load_shedding"):
        n.add("Carrier", "Load Shedding")
        buses_i = n.buses.query("carrier == 'AC'").index
        n.add(
            "Generator",
            buses_i,
            " load",
            bus=buses_i,
            carrier="Load Shedding",
            marginal_cost=solve_opts.get("voll", 1e5),  # EUR/MWh
            # intersect between macroeconomic and surveybased
            # willingness to pay
            # http://journal.frontiersin.org/article/10.3389/fenrg.2015.00055/full
            p_nom=1e6,  # MW
        )

    if solve_opts.get("noisy_costs"):
        for t in n.iterate_components(n.one_port_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 config["run"].get("is_remind_coupled", False) & (
        config["existing_capacities"].get("freeze_new", False)
    ):
        freeze_components(
            n,
            config,
            exclude=config["existing_capacities"].get("never_freeze", []),
        )

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

    if config["existing_capacities"].get("add", False):
        add_land_use_constraint(n, plan_year)

    return n

set_transmission_limit(n, kind, factor, n_years=1)

Set global transimission limit constraints - adapted from pypsa-eur

Parameters:

Name	Type	Description	Default
n	Network	the network object	required
kind	str	the kind of limit to set, either 'c' for cost or 'v' for volume or l for length	required
factor	float or str	the factor to apply to the base year quantity, per year	required
n_years	int	the number of years to consider for the limit. Defaults to 1.	1

Source code in workflow/scripts/solve_network.py

def set_transmission_limit(n: pypsa.Network, kind: str, factor: float, n_years=1):
    """
    Set global transimission limit constraints - adapted from pypsa-eur

    Args:
        n (pypsa.Network): the network object
        kind (str): the kind of limit to set, either 'c' for cost or 'v' for volume or l for length
        factor (float or str): the factor to apply to the base year quantity, per year
        n_years (int, optional): the number of years to consider for the limit. Defaults to 1.
    """
    logger.info(
        f"Adding global transmission limit for {kind} with factor {factor}/year & {n_years} years"
    )
    links_dc = n.links.query("carrier in ['AC','DC']").index
    # links_dc_rev = n.links.query("carrier in ['AC','DC'] & Link.str.contains('reverse')").index

    _lines_s_nom = (
        np.sqrt(3)
        * n.lines.type.map(n.line_types.i_nom)
        * n.lines.num_parallel
        * n.lines.bus0.map(n.buses.v_nom)
    )
    lines_s_nom = n.lines.s_nom.where(n.lines.type == "", _lines_s_nom)

    col = "capital_cost" if kind == "c" else "length"
    ref = lines_s_nom @ n.lines[col] + n.links.loc[links_dc, "p_nom"] @ n.links.loc[links_dc, col]

    if factor == "opt" or float(factor) ** n_years > 1.0:
        n.lines["s_nom_min"] = lines_s_nom
        n.lines["s_nom_extendable"] = True

        n.links.loc[links_dc, "p_nom_extendable"] = True

    elif float(factor) ** n_years == 1.0:
        # if factor is 1.0, then we do not need to extend
        n.lines["s_nom_min"] = lines_s_nom
        n.lines["s_nom_extendable"] = False
        n.links.loc[links_dc, "p_nom_extendable"] = False

        # factor = 1 + 1e-7  # to avoid numerical issues with the constraints

    elif float(factor) ** n_years < 1.0:
        n.lines["s_nom_min"] = 0
        n.links.loc[links_dc, "p_nom_min"] = 0
        # n.links.loc[links_dc_rev, "p_nom_min"] = 0

    if factor != "opt":
        con_type = "expansion_cost" if kind == "c" else "volume_expansion"
        rhs = float(factor) ** n_years * ref
        logger.info(
            f"Adding global transmission limit for {kind} to {float(factor)**n_years} current value"
        )
        n.add(
            "GlobalConstraint",
            f"l{kind}_limit",
            type=f"transmission_{con_type}_limit",
            sense="<=",
            constant=rhs,
            carrier_attribute="AC, DC",
        )

solve_network(n, config, solving, opts='', **kwargs)

Perform the optimisation Args: n (pypsa.Network): the pypsa network object config (dict): the configuration dictionary solving (dict): the solving configuration dictionary opts (str): optional wildcards such as ll (not used in pypsa-china)

Returns:

Type	Description
Network	pypsa.Network: the optimized network

Source code in workflow/scripts/solve_network.py

def solve_network(
    n: pypsa.Network, config: dict, solving: dict, opts: str = "", **kwargs
) -> pypsa.Network:
    """Perform the optimisation
    Args:
        n (pypsa.Network): the pypsa network object
        config (dict): the configuration dictionary
        solving (dict): the solving configuration dictionary
        opts (str): optional wildcards such as ll (not used in pypsa-china)

    Returns:
        pypsa.Network: the optimized network
    """
    set_of_options = solving["solver"]["options"]
    solver_options = solving["solver_options"][set_of_options] if set_of_options else {}
    solver_name = solving["solver"]["name"]
    cf_solving = 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)
    transmission_losses = cf_solving.get("transmission_losses", 0)

    # 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,
            transmission_losses=transmission_losses,
            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,
            transmission_losses=transmission_losses,
            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