pypsa-eur/scripts/prepare_sector_network.py

# coding: utf-8

import pypsa
import re
import os
import pytz

import pandas as pd
import numpy as np
import xarray as xr

from itertools import product
from scipy.stats import beta
from vresutils.costdata import annuity

from build_energy_totals import build_eea_co2, build_eurostat_co2, build_co2_totals
from helper import override_component_attrs

import logging
logger = logging.getLogger(__name__)

from types import SimpleNamespace
spatial = SimpleNamespace()


def define_spatial(nodes):
    """
    Namespace for spatial

    Parameters
    ----------
    nodes : list-like
    """

    global spatial
    global options

    spatial.nodes = nodes

    # biomass

    spatial.biomass = SimpleNamespace()

    if options["biomass_transport"]:
        spatial.biomass.nodes = nodes + " solid biomass"
        spatial.biomass.locations = nodes
        spatial.biomass.industry = nodes + " solid biomass for industry"
        spatial.biomass.industry_cc = nodes + " solid biomass for industry CC"
    else:
        spatial.biomass.nodes = ["EU solid biomass"]
        spatial.biomass.locations = ["EU"]
        spatial.biomass.industry = ["solid biomass for industry"]
        spatial.biomass.industry_cc = ["solid biomass for industry CC"]

    spatial.biomass.df = pd.DataFrame(vars(spatial.biomass), index=nodes)

    # co2

    spatial.co2 = SimpleNamespace()

    if options["co2_network"]:
        spatial.co2.nodes = nodes + " co2 stored"
        spatial.co2.locations = nodes
        spatial.co2.vents = nodes + " co2 vent"
    else:
        spatial.co2.nodes = ["co2 stored"]
        spatial.co2.locations = ["EU"]
        spatial.co2.vents = ["co2 vent"]

    spatial.co2.df = pd.DataFrame(vars(spatial.co2), index=nodes)


def emission_sectors_from_opts(opts):

    sectors = ["electricity"]
    if "T" in opts:
        sectors += [
            "rail non-elec",
            "road non-elec"
        ]
    if "H" in opts:
        sectors += [
            "residential non-elec",
            "services non-elec"
        ]
    if "I" in opts:
        sectors += [
            "industrial non-elec",
            "industrial processes",
            "domestic aviation",
            "international aviation",
            "domestic navigation",
            "international navigation"
        ]

    return sectors


def get(item, investment_year=None):
    """Check whether item depends on investment year"""
    if isinstance(item, dict):
        return item[investment_year]
    else:
        return item


def create_network_topology(n, prefix, connector=" -> "):
    """
    Create a network topology like the power transmission network.

    Parameters
    ----------
    n : pypsa.Network
    prefix : str
    connector : str

    Returns
    -------
    pd.DataFrame with columns bus0, bus1 and length
    """

    ln_attrs = ["bus0", "bus1", "length"]
    lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]

    candidates = pd.concat([
        n.lines[ln_attrs],
        n.links.loc[n.links.carrier == "DC", lk_attrs]
    ]).fillna(0)

    positive_order = candidates.bus0 < candidates.bus1
    candidates_p = candidates[positive_order]
    swap_buses = {"bus0": "bus1", "bus1": "bus0"}
    candidates_n = candidates[~positive_order].rename(columns=swap_buses)
    candidates = pd.concat([candidates_p, candidates_n])

    topo = candidates.groupby(["bus0", "bus1"], as_index=False).mean()
    topo.index = topo.apply(lambda c: prefix + c.bus0 + connector + c.bus1, axis=1)
    return topo


def co2_emissions_year(countries, opts, year):
    """
    Calculate CO2 emissions in one specific year (e.g. 1990 or 2018).
    """

    eea_co2 = build_eea_co2(year)

    # TODO: read Eurostat data from year > 2014
    # this only affects the estimation of CO2 emissions for BA, RS, AL, ME, MK
    if year > 2014:
        eurostat_co2 = build_eurostat_co2(year=2014)
    else:
        eurostat_co2 = build_eurostat_co2(year)

    co2_totals = build_co2_totals(eea_co2, eurostat_co2)

    sectors = emission_sectors_from_opts(opts)

    co2_emissions = co2_totals.loc[countries, sectors].sum().sum()

    # convert MtCO2 to GtCO2
    co2_emissions *= 0.001

    return co2_emissions


# TODO: move to own rule with sector-opts wildcard?
def build_carbon_budget(o, fn):
    """
    Distribute carbon budget following beta or exponential transition path.
    """
    # opts?

    if "be" in o:
        #beta decay
        carbon_budget = float(o[o.find("cb")+2:o.find("be")])
        be = float(o[o.find("be")+2:])
    if "ex" in o:
        #exponential decay
        carbon_budget = float(o[o.find("cb")+2:o.find("ex")])
        r = float(o[o.find("ex")+2:])

    countries = n.buses.country.dropna().unique()

    e_1990 = co2_emissions_year(countries, opts, year=1990)

    #emissions at the beginning of the path (last year available 2018)
    e_0 = co2_emissions_year(countries, opts, year=2018)

    planning_horizons = snakemake.config['scenario']['planning_horizons']
    t_0 = planning_horizons[0]

    if "be" in o:

        # final year in the path
        t_f = t_0 + (2 * carbon_budget / e_0).round(0)

        def beta_decay(t):
            cdf_term = (t - t_0) / (t_f - t_0)
            return (e_0 / e_1990) * (1 - beta.cdf(cdf_term, be, be))

        #emissions (relative to 1990)
        co2_cap = pd.Series({t: beta_decay(t) for t in planning_horizons}, name=o)

    if "ex" in o:

        T = carbon_budget / e_0
        m = (1 + np.sqrt(1 + r * T)) / T

        def exponential_decay(t):
            return (e_0 / e_1990) * (1 + (m + r) * (t - t_0)) * np.exp(-m * (t - t_0))

        co2_cap = pd.Series({t: exponential_decay(t) for t in planning_horizons}, name=o)

    # TODO log in Snakefile
    if not os.path.exists(fn):
        os.makedirs(fn)
    co2_cap.to_csv(fn, float_format='%.3f')


def add_lifetime_wind_solar(n, costs):
    """Add lifetime for solar and wind generators."""
    for carrier in ['solar', 'onwind', 'offwind']:
        gen_i = n.generators.index.str.contains(carrier)
        n.generators.loc[gen_i, "lifetime"] = costs.at[carrier, 'lifetime']


def create_network_topology(n, prefix, connector=" -> ", bidirectional=True):
    """
    Create a network topology like the power transmission network.

    Parameters
    ----------
    n : pypsa.Network
    prefix : str
    connector : str
    bidirectional : bool, default True
        True: one link for each connection
        False: one link for each connection and direction (back and forth)

    Returns
    -------
    pd.DataFrame with columns bus0, bus1 and length
    """

    ln_attrs = ["bus0", "bus1", "length"]
    lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]

    candidates = pd.concat([
        n.lines[ln_attrs],
        n.links.loc[n.links.carrier == "DC", lk_attrs]
    ]).fillna(0)

    positive_order = candidates.bus0 < candidates.bus1
    candidates_p = candidates[positive_order]
    swap_buses = {"bus0": "bus1", "bus1": "bus0"}
    candidates_n = candidates[~positive_order].rename(columns=swap_buses)
    candidates = pd.concat([candidates_p, candidates_n])

    def make_index(c):
        return prefix + c.bus0 + connector + c.bus1

    topo = candidates.groupby(["bus0", "bus1"], as_index=False).mean()
    topo.index = topo.apply(make_index, axis=1)

    if not bidirectional:
        topo_reverse = topo.copy()
        topo_reverse.rename(columns=swap_buses, inplace=True)
        topo_reverse.index = topo_reverse.apply(make_index, axis=1)
        topo = topo.append(topo_reverse)

    return topo


# TODO merge issue with PyPSA-Eur
def update_wind_solar_costs(n, costs):
    """
    Update costs for wind and solar generators added with pypsa-eur to those
    cost in the planning year
    """

    #NB: solar costs are also manipulated for rooftop
    #when distribution grid is inserted
    n.generators.loc[n.generators.carrier=='solar', 'capital_cost'] = costs.at['solar-utility', 'fixed']

    n.generators.loc[n.generators.carrier=='onwind', 'capital_cost'] = costs.at['onwind', 'fixed']

    #for offshore wind, need to calculated connection costs

    #assign clustered bus
    #map initial network -> simplified network
    busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0).squeeze()
    busmap_s.index = busmap_s.index.astype(str)
    busmap_s = busmap_s.astype(str)
    #map simplified network -> clustered network
    busmap = pd.read_csv(snakemake.input.busmap, index_col=0).squeeze()
    busmap.index = busmap.index.astype(str)
    busmap = busmap.astype(str)
    #map initial network -> clustered network
    clustermaps = busmap_s.map(busmap)

    #code adapted from pypsa-eur/scripts/add_electricity.py
    for connection in ['dc', 'ac']:
        tech = "offwind-" + connection
        profile = snakemake.input['profile_offwind_' + connection]
        with xr.open_dataset(profile) as ds:
            underwater_fraction = ds['underwater_fraction'].to_pandas()
            connection_cost = (snakemake.config['costs']['lines']['length_factor'] *
                               ds['average_distance'].to_pandas() *
                               (underwater_fraction *
                                costs.at[tech + '-connection-submarine', 'fixed'] +
                                (1. - underwater_fraction) *
                                costs.at[tech + '-connection-underground', 'fixed']))

            #convert to aggregated clusters with weighting
            weight = ds['weight'].to_pandas()

            #e.g. clusters == 37m means that VRE generators are left
            #at clustering of simplified network, but that they are
            #connected to 37-node network
            if snakemake.wildcards.clusters[-1:] == "m":
                genmap = busmap_s
            else:
                genmap = clustermaps

            connection_cost = (connection_cost*weight).groupby(genmap).sum()/weight.groupby(genmap).sum()

            capital_cost = (costs.at['offwind', 'fixed'] +
                            costs.at[tech + '-station', 'fixed'] +
                            connection_cost)

            logger.info("Added connection cost of {:0.0f}-{:0.0f} Eur/MW/a to {}"
                        .format(connection_cost[0].min(), connection_cost[0].max(), tech))

            n.generators.loc[n.generators.carrier==tech, 'capital_cost'] = capital_cost.rename(index=lambda node: node + ' ' + tech)


def add_carrier_buses(n, carriers):
    """
    Add buses to connect e.g. coal, nuclear and oil plants
    """
    if isinstance(carriers, str):
        carriers = [carriers]

    for carrier in carriers:

        n.add("Carrier", carrier)

        n.add("Bus",
            "EU " + carrier,
            location="EU",
            carrier=carrier
        )

        #capital cost could be corrected to e.g. 0.2 EUR/kWh * annuity and O&M
        n.add("Store",
            "EU " + carrier + " Store",
            bus="EU " + carrier,
            e_nom_extendable=True,
            e_cyclic=True,
            carrier=carrier,
        )

        n.add("Generator",
            "EU " + carrier,
            bus="EU " + carrier,
            p_nom_extendable=True,
            carrier=carrier,
            marginal_cost=costs.at[carrier, 'fuel']
        )


# TODO: PyPSA-Eur merge issue
def remove_elec_base_techs(n):
    """remove conventional generators (e.g. OCGT) and storage units (e.g. batteries and H2)
    from base electricity-only network, since they're added here differently using links
    """

    for c in n.iterate_components(snakemake.config["pypsa_eur"]):
        to_keep = snakemake.config["pypsa_eur"][c.name]
        to_remove = pd.Index(c.df.carrier.unique()).symmetric_difference(to_keep)
        print("Removing", c.list_name, "with carrier", to_remove)
        names = c.df.index[c.df.carrier.isin(to_remove)]
        print(names)
        n.mremove(c.name, names)
        n.carriers.drop(to_remove, inplace=True, errors="ignore")


# TODO: PyPSA-Eur merge issue
def remove_non_electric_buses(n):
    """
    remove buses from pypsa-eur with carriers which are not AC buses
    """
    print("drop buses from PyPSA-Eur with carrier: ", n.buses[~n.buses.carrier.isin(["AC", "DC"])].carrier.unique())
    n.buses = n.buses[n.buses.carrier.isin(["AC", "DC"])]


def patch_electricity_network(n):
    remove_elec_base_techs(n)
    remove_non_electric_buses(n)
    update_wind_solar_costs(n, costs)
    n.loads["carrier"] = "electricity"
    n.buses["location"] = n.buses.index
    # remove trailing white space of load index until new PyPSA version after v0.18.
    n.loads.rename(lambda x: x.strip(), inplace=True)
    n.loads_t.p_set.rename(lambda x: x.strip(), axis=1, inplace=True)


def add_co2_tracking(n, options):

    # minus sign because opposite to how fossil fuels used:
    # CH4 burning puts CH4 down, atmosphere up
    n.add("Carrier", "co2",
          co2_emissions=-1.)

    # this tracks CO2 in the atmosphere
    n.add("Bus",
        "co2 atmosphere",
        location="EU",
        carrier="co2"
    )

    # can also be negative
    n.add("Store",
        "co2 atmosphere",
        e_nom_extendable=True,
        e_min_pu=-1,
        carrier="co2",
        bus="co2 atmosphere"
    )

    # this tracks CO2 stored, e.g. underground
    n.madd("Bus",
        spatial.co2.nodes,
        location=spatial.co2.locations,
        carrier="co2 stored"
    )

    n.madd("Store",
        spatial.co2.nodes,
        e_nom_extendable=True,
        e_nom_max=np.inf,
        capital_cost=options['co2_sequestration_cost'],
        carrier="co2 stored",
        bus=spatial.co2.nodes
    )

    if options['co2_vent']:

        n.madd("Link",
            spatial.co2.vents,
            bus0=spatial.co2.nodes,
            bus1="co2 atmosphere",
            carrier="co2 vent",
            efficiency=1.,
            p_nom_extendable=True
        )


def add_co2_network(n, costs):

    logger.info("Adding CO2 network.")
    co2_links = create_network_topology(n, "CO2 pipeline ")

    cost_onshore = (1 - co2_links.underwater_fraction) * costs.at['CO2 pipeline', 'fixed'] * co2_links.length
    cost_submarine = co2_links.underwater_fraction * costs.at['CO2 submarine pipeline', 'fixed'] * co2_links.length
    capital_cost = cost_onshore + cost_submarine

    n.madd("Link",
        co2_links.index,
        bus0=co2_links.bus0.values + " co2 stored",
        bus1=co2_links.bus1.values + " co2 stored",
        p_min_pu=-1,
        p_nom_extendable=True,
        length=co2_links.length.values,
        capital_cost=capital_cost.values,
        carrier="CO2 pipeline",
        lifetime=costs.at['CO2 pipeline', 'lifetime']
    )


def add_dac(n, costs):

    heat_carriers = ["urban central heat"]
    heat_buses = n.buses.index[n.buses.carrier.isin(heat_carriers)]
    locations = n.buses.location[heat_buses]

    efficiency2 = -(costs.at['direct air capture', 'electricity-input'] + costs.at['direct air capture', 'compression-electricity-input'])
    efficiency3 = -(costs.at['direct air capture', 'heat-input'] - costs.at['direct air capture', 'compression-heat-output'])

    n.madd("Link",
        locations,
        suffix=" DAC",
        bus0="co2 atmosphere",
        bus1=spatial.co2.df.loc[locations, "nodes"].values,
        bus2=locations.values,
        bus3=heat_buses,
        carrier="DAC",
        capital_cost=costs.at['direct air capture', 'fixed'],
        efficiency=1.,
        efficiency2=efficiency2,
        efficiency3=efficiency3,
        p_nom_extendable=True,
        lifetime=costs.at['direct air capture', 'lifetime']
    )


def add_co2limit(n, Nyears=1., limit=0.):

    print("Adding CO2 budget limit as per unit of 1990 levels of", limit)

    countries = n.buses.country.dropna().unique()

    sectors = emission_sectors_from_opts(opts)

    # convert Mt to tCO2
    co2_totals = 1e6 * pd.read_csv(snakemake.input.co2_totals_name, index_col=0)

    co2_limit = co2_totals.loc[countries, sectors].sum().sum()

    co2_limit *= limit * Nyears

    n.add("GlobalConstraint",
        "CO2Limit",
        carrier_attribute="co2_emissions",
        sense="<=",
        constant=co2_limit
    )

# TODO PyPSA-Eur merge issue
def average_every_nhours(n, offset):
    logger.info(f'Resampling the network to {offset}')
    m = n.copy(with_time=False)

    # TODO is this still needed?
    #fix copying of network attributes
    #copied from pypsa/io.py, should be in pypsa/components.py#Network.copy()
    allowed_types = (float, int, bool, str) + tuple(np.typeDict.values())
    attrs = dict((attr, getattr(n, attr))
                 for attr in dir(n)
                 if (not attr.startswith("__") and
                     isinstance(getattr(n,attr), allowed_types)))
    for k,v in attrs.items():
        setattr(m,k,v)

    snapshot_weightings = n.snapshot_weightings.resample(offset).sum()
    m.set_snapshots(snapshot_weightings.index)
    m.snapshot_weightings = snapshot_weightings

    for c in n.iterate_components():
        pnl = getattr(m, c.list_name+"_t")
        for k, df in c.pnl.items():
            if not df.empty:
                if c.list_name == "stores" and k == "e_max_pu":
                    pnl[k] = df.resample(offset).min()
                elif c.list_name == "stores" and k == "e_min_pu":
                    pnl[k] = df.resample(offset).max()
                else:
                    pnl[k] = df.resample(offset).mean()

    return m


def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
    """
    Give a 24*7 long list of weekly hourly profiles, generate this for each
    country for the period dt_index, taking account of time zones and summer time.
    """

    weekly_profile = pd.Series(weekly_profile, range(24*7))

    week_df = pd.DataFrame(index=dt_index, columns=nodes)

    for node in nodes:
        timezone = pytz.timezone(pytz.country_timezones[node[:2]][0])
        tz_dt_index = dt_index.tz_convert(timezone)
        week_df[node] = [24 * dt.weekday() + dt.hour for dt in tz_dt_index]
        week_df[node] = week_df[node].map(weekly_profile)

    week_df = week_df.tz_localize(localize)

    return week_df


def cycling_shift(df, steps=1):
    """Cyclic shift on index of pd.Series|pd.DataFrame by number of steps"""
    df = df.copy()
    new_index = np.roll(df.index, steps)
    df.values[:] = df.reindex(index=new_index).values
    return df


def transport_degree_factor(
    temperature,
    deadband_lower=15,
    deadband_upper=20,
    lower_degree_factor=0.5,
    upper_degree_factor=1.6):
    """
    Work out how much energy demand in vehicles increases due to heating and cooling.
    There is a deadband where there is no increase.
    Degree factors are % increase in demand compared to no heating/cooling fuel consumption.
    Returns per unit increase in demand for each place and time
    """

    dd = temperature.copy()

    dd[(temperature > deadband_lower) & (temperature < deadband_upper)] = 0.

    dT_lower = deadband_lower - temperature[temperature < deadband_lower]
    dd[temperature < deadband_lower] = lower_degree_factor / 100 * dT_lower

    dT_upper = temperature[temperature > deadband_upper] - deadband_upper
    dd[temperature > deadband_upper] = upper_degree_factor / 100 * dT_upper

    return dd


# TODO separate sectors and move into own rules
def prepare_data(n):


    ##############
    #Heating
    ##############


    ashp_cop = xr.open_dataarray(snakemake.input.cop_air_total).to_pandas().reindex(index=n.snapshots)
    gshp_cop = xr.open_dataarray(snakemake.input.cop_soil_total).to_pandas().reindex(index=n.snapshots)

    solar_thermal = xr.open_dataarray(snakemake.input.solar_thermal_total).to_pandas().reindex(index=n.snapshots)
    # 1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
    solar_thermal = options['solar_cf_correction'] * solar_thermal / 1e3

    energy_totals = pd.read_csv(snakemake.input.energy_totals_name, index_col=0)

    nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.)
    nodal_energy_totals.index = pop_layout.index
    # district heat share not weighted by population
    district_heat_share = round(nodal_energy_totals["district heat share"],
                                ndigits=2)
    nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction, axis=0)

    # copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
    daily_space_heat_demand = xr.open_dataarray(snakemake.input.heat_demand_total).to_pandas().reindex(index=n.snapshots, method="ffill")

    intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)

    sectors = ["residential", "services"]
    uses = ["water", "space"]

    heat_demand = {}
    electric_heat_supply = {}
    for sector, use in product(sectors, uses):
        weekday = list(intraday_profiles[f"{sector} {use} weekday"])
        weekend = list(intraday_profiles[f"{sector} {use} weekend"])
        weekly_profile = weekday * 5 + weekend * 2
        intraday_year_profile = generate_periodic_profiles(
            daily_space_heat_demand.index.tz_localize("UTC"),
            nodes=daily_space_heat_demand.columns,
            weekly_profile=weekly_profile
        )

        if use == "space":
            heat_demand_shape = daily_space_heat_demand * intraday_year_profile
        else:
            heat_demand_shape = intraday_year_profile

        heat_demand[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(nodal_energy_totals[f"total {sector} {use}"]) * 1e6
        electric_heat_supply[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(nodal_energy_totals[f"electricity {sector} {use}"]) * 1e6

    heat_demand = pd.concat(heat_demand, axis=1)
    electric_heat_supply = pd.concat(electric_heat_supply, axis=1)

    # subtract from electricity load since heat demand already in heat_demand
    electric_nodes = n.loads.index[n.loads.carrier == "electricity"]
    n.loads_t.p_set[electric_nodes] = n.loads_t.p_set[electric_nodes] - electric_heat_supply.groupby(level=1, axis=1).sum()[electric_nodes]

    ##############
    #Transport
    ##############

    ## Get overall demand curve for all vehicles

    traffic = pd.read_csv(snakemake.input.traffic_data_KFZ, skiprows=2, usecols=["count"], squeeze=True)

    #Generate profiles
    transport_shape = generate_periodic_profiles(
        dt_index=n.snapshots.tz_localize("UTC"),
        nodes=pop_layout.index,
        weekly_profile=traffic.values
    )
    transport_shape = transport_shape / transport_shape.sum()

    transport_data = pd.read_csv(snakemake.input.transport_name, index_col=0)

    nodal_transport_data = transport_data.loc[pop_layout.ct].fillna(0.)
    nodal_transport_data.index = pop_layout.index
    nodal_transport_data["number cars"] = pop_layout["fraction"] * nodal_transport_data["number cars"]
    nodal_transport_data.loc[nodal_transport_data["average fuel efficiency"] == 0., "average fuel efficiency"] = transport_data["average fuel efficiency"].mean()


    # electric motors are more efficient, so alter transport demand

    plug_to_wheels_eta = options.get("bev_plug_to_wheel_efficiency", 0.2)
    battery_to_wheels_eta = plug_to_wheels_eta * options.get("bev_charge_efficiency", 0.9)

    efficiency_gain = nodal_transport_data["average fuel efficiency"] / battery_to_wheels_eta

    #get heating demand for correction to demand time series
    temperature = xr.open_dataarray(snakemake.input.temp_air_total).to_pandas()

    # correction factors for vehicle heating
    dd_ICE = transport_degree_factor(
        temperature,
        options['transport_heating_deadband_lower'],
        options['transport_heating_deadband_upper'],
        options['ICE_lower_degree_factor'],
        options['ICE_upper_degree_factor']
    )

    dd_EV = transport_degree_factor(
        temperature,
        options['transport_heating_deadband_lower'],
        options['transport_heating_deadband_upper'],
        options['EV_lower_degree_factor'],
        options['EV_upper_degree_factor']
    )

    # divide out the heating/cooling demand from ICE totals
    # and multiply back in the heating/cooling demand for EVs
    ice_correction = (transport_shape * (1 + dd_ICE)).sum() / transport_shape.sum()

    energy_totals_transport = nodal_energy_totals["total road"] + nodal_energy_totals["total rail"] - nodal_energy_totals["electricity rail"]

    transport = (transport_shape.multiply(energy_totals_transport) * 1e6 * Nyears).divide(efficiency_gain * ice_correction).multiply(1 + dd_EV)

    ## derive plugged-in availability for PKW's (cars)

    traffic = pd.read_csv(snakemake.input.traffic_data_Pkw, skiprows=2, usecols=["count"], squeeze=True)

    avail_max = options.get("bev_avail_max", 0.95)
    avail_mean = options.get("bev_avail_mean", 0.8)

    avail = avail_max - (avail_max - avail_mean) * (traffic - traffic.min()) / (traffic.mean() - traffic.min())

    avail_profile = generate_periodic_profiles(
        dt_index=n.snapshots.tz_localize("UTC"),
        nodes=pop_layout.index,
        weekly_profile=avail.values
    )

    dsm_week = np.zeros((24*7,))

    dsm_week[(np.arange(0,7,1) * 24 + options['bev_dsm_restriction_time'])] = options['bev_dsm_restriction_value']

    dsm_profile = generate_periodic_profiles(
        dt_index=n.snapshots.tz_localize("UTC"),
        nodes=pop_layout.index,
        weekly_profile=dsm_week
    )


    return nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share


# TODO checkout PyPSA-Eur script
def prepare_costs(cost_file, USD_to_EUR, discount_rate, Nyears, lifetime):

    #set all asset costs and other parameters
    costs = pd.read_csv(cost_file, index_col=[0,1]).sort_index()

    #correct units to MW and EUR
    costs.loc[costs.unit.str.contains("/kW"), "value"] *= 1e3
    costs.loc[costs.unit.str.contains("USD"), "value"] *= USD_to_EUR

    #min_count=1 is important to generate NaNs which are then filled by fillna
    costs = costs.loc[:, "value"].unstack(level=1).groupby("technology").sum(min_count=1)
    costs = costs.fillna({"CO2 intensity" : 0,
                          "FOM" : 0,
                          "VOM" : 0,
                          "discount rate" : discount_rate,
                          "efficiency" : 1,
                          "fuel" : 0,
                          "investment" : 0,
                          "lifetime" : lifetime
    })

    annuity_factor = lambda v: annuity(v["lifetime"], v["discount rate"]) + v["FOM"] / 100
    costs["fixed"] = [annuity_factor(v) * v["investment"] * Nyears for i, v in costs.iterrows()]

    return costs


def add_generation(n, costs):

    print("adding electricity generation")

    nodes = pop_layout.index

    fallback = {"OCGT": "gas"}
    conventionals = options.get("conventional_generation", fallback)

    add_carrier_buses(n, np.unique(list(conventionals.values())))

    for generator, carrier in conventionals.items():

        n.madd("Link",
            nodes + " " + generator,
            bus0="EU " + carrier,
            bus1=nodes,
            bus2="co2 atmosphere",
            marginal_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'VOM'], #NB: VOM is per MWel
            capital_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'fixed'], #NB: fixed cost is per MWel
            p_nom_extendable=True,
            carrier=generator,
            efficiency=costs.at[generator, 'efficiency'],
            efficiency2=costs.at[carrier, 'CO2 intensity'],
            lifetime=costs.at[generator, 'lifetime']
        )


def add_wave(n, wave_cost_factor):

    # TODO: handle in Snakefile
    wave_fn = "data/WindWaveWEC_GLTB.xlsx"

    #in kW
    capacity = pd.Series({"Attenuator": 750,
                          "F2HB": 1000,
                          "MultiPA": 600})

    #in EUR/MW
    annuity_factor = annuity(25,0.07) + 0.03
    costs = 1e6 * wave_cost_factor * annuity_factor * pd.Series({"Attenuator": 2.5,
                                                                 "F2HB": 2,
                                                                 "MultiPA": 1.5})

    sheets = pd.read_excel(wave_fn, sheet_name=["FirthForth", "Hebrides"],
                           usecols=["Attenuator", "F2HB", "MultiPA"],
                           index_col=0, skiprows=[0], parse_dates=True)

    wave = pd.concat([sheets[l].divide(capacity, axis=1) for l in locations],
                     keys=locations,
                     axis=1)

    for wave_type in costs.index:
        n.add("Generator",
            "Hebrides " + wave_type,
            bus="GB4 0", # TODO this location is hardcoded
            p_nom_extendable=True,
            carrier="wave",
            capital_cost=costs[wave_type],
            p_max_pu=wave["Hebrides", wave_type]
        )


def insert_electricity_distribution_grid(n, costs):
    # TODO pop_layout?
    # TODO options?

    print("Inserting electricity distribution grid with investment cost factor of",
          options['electricity_distribution_grid_cost_factor'])

    nodes = pop_layout.index

    cost_factor = options['electricity_distribution_grid_cost_factor']

    n.madd("Bus",
        nodes + " low voltage",
        location=nodes,
        carrier="low voltage"
    )

    n.madd("Link",
        nodes + " electricity distribution grid",
        bus0=nodes,
        bus1=nodes + " low voltage",
        p_nom_extendable=True,
        p_min_pu=-1,
        carrier="electricity distribution grid",
        efficiency=1,
        lifetime=costs.at['electricity distribution grid', 'lifetime'],
        capital_cost=costs.at['electricity distribution grid', 'fixed'] * cost_factor
    )

    # this catches regular electricity load and "industry electricity"
    loads = n.loads.index[n.loads.carrier.str.contains("electricity")]
    n.loads.loc[loads, "bus"] += " low voltage"

    bevs = n.links.index[n.links.carrier == "BEV charger"]
    n.links.loc[bevs, "bus0"] += " low voltage"

    v2gs = n.links.index[n.links.carrier == "V2G"]
    n.links.loc[v2gs, "bus1"] += " low voltage"

    hps = n.links.index[n.links.carrier.str.contains("heat pump")]
    n.links.loc[hps, "bus0"] += " low voltage"

    rh = n.links.index[n.links.carrier.str.contains("resistive heater")]
    n.links.loc[rh, "bus0"] += " low voltage"

    mchp = n.links.index[n.links.carrier.str.contains("micro gas")]
    n.links.loc[mchp, "bus1"] += " low voltage"

    # set existing solar to cost of utility cost rather the 50-50 rooftop-utility
    solar = n.generators.index[n.generators.carrier == "solar"]
    n.generators.loc[solar, "capital_cost"] = costs.at['solar-utility', 'fixed']
    if snakemake.wildcards.clusters[-1:] == "m":
        simplified_pop_layout = pd.read_csv(snakemake.input.simplified_pop_layout, index_col=0)
        pop_solar = simplified_pop_layout.total.rename(index = lambda x: x + " solar")
    else:
        pop_solar = pop_layout.total.rename(index = lambda x: x + " solar")

    # add max solar rooftop potential assuming 0.1 kW/m2 and 10 m2/person,
    # i.e. 1 kW/person (population data is in thousands of people) so we get MW
    potential = 0.1 * 10 * pop_solar

    n.madd("Generator",
        solar,
        suffix=" rooftop",
        bus=n.generators.loc[solar, "bus"] + " low voltage",
        carrier="solar rooftop",
        p_nom_extendable=True,
        p_nom_max=potential,
        marginal_cost=n.generators.loc[solar, 'marginal_cost'],
        capital_cost=costs.at['solar-rooftop', 'fixed'],
        efficiency=n.generators.loc[solar, 'efficiency'],
        p_max_pu=n.generators_t.p_max_pu[solar],
        lifetime=costs.at['solar-rooftop', 'lifetime']
    )

    n.add("Carrier", "home battery")

    n.madd("Bus",
        nodes + " home battery",
        location=nodes,
        carrier="home battery"
    )

    n.madd("Store",
        nodes + " home battery",
        bus=nodes + " home battery",
        e_cyclic=True,
        e_nom_extendable=True,
        carrier="home battery",
        capital_cost=costs.at['home battery storage', 'fixed'],
        lifetime=costs.at['battery storage', 'lifetime']
    )

    n.madd("Link",
        nodes + " home battery charger",
        bus0=nodes + " low voltage",
        bus1=nodes + " home battery",
        carrier="home battery charger",
        efficiency=costs.at['battery inverter', 'efficiency']**0.5,
        capital_cost=costs.at['home battery inverter', 'fixed'],
        p_nom_extendable=True,
        lifetime=costs.at['battery inverter', 'lifetime']
    )

    n.madd("Link",
        nodes + " home battery discharger",
        bus0=nodes + " home battery",
        bus1=nodes + " low voltage",
        carrier="home battery discharger",
        efficiency=costs.at['battery inverter', 'efficiency']**0.5,
        marginal_cost=options['marginal_cost_storage'],
        p_nom_extendable=True,
        lifetime=costs.at['battery inverter', 'lifetime']
    )


def insert_gas_distribution_costs(n, costs):
    # TODO options?

    f_costs = options['gas_distribution_grid_cost_factor']

    print("Inserting gas distribution grid with investment cost factor of", f_costs)

    capital_cost = costs.loc['electricity distribution grid']["fixed"] * f_costs

    # gas boilers
    gas_b = n.links.index[n.links.carrier.str.contains("gas boiler") &
                          (~n.links.carrier.str.contains("urban central"))]
    n.links.loc[gas_b, "capital_cost"] += capital_cost

    # micro CHPs
    mchp = n.links.index[n.links.carrier.str.contains("micro gas")]
    n.links.loc[mchp,  "capital_cost"] += capital_cost


def add_electricity_grid_connection(n, costs):

    carriers = ["onwind", "solar"]

    gens = n.generators.index[n.generators.carrier.isin(carriers)]

    n.generators.loc[gens, "capital_cost"] += costs.at['electricity grid connection', 'fixed']


def add_storage(n, costs):
    # TODO pop_layout
    # TODO options?

    print("adding electricity storage")

    nodes = pop_layout.index

    n.add("Carrier", "H2")

    n.madd("Bus",
        nodes + " H2",
        location=nodes,
        carrier="H2"
    )

    n.madd("Link",
        nodes + " H2 Electrolysis",
        bus1=nodes + " H2",
        bus0=nodes,
        p_nom_extendable=True,
        carrier="H2 Electrolysis",
        efficiency=costs.at["electrolysis", "efficiency"],
        capital_cost=costs.at["electrolysis", "fixed"],
        lifetime=costs.at['electrolysis', 'lifetime']
    )

    n.madd("Link",
        nodes + " H2 Fuel Cell",
        bus0=nodes + " H2",
        bus1=nodes,
        p_nom_extendable=True,
        carrier ="H2 Fuel Cell",
        efficiency=costs.at["fuel cell", "efficiency"],
        capital_cost=costs.at["fuel cell", "fixed"] * costs.at["fuel cell", "efficiency"], #NB: fixed cost is per MWel
        lifetime=costs.at['fuel cell', 'lifetime']
    )

    cavern_nodes = pd.DataFrame()
    if options['hydrogen_underground_storage']:
         h2_salt_cavern_potential = pd.read_csv(snakemake.input.h2_cavern, index_col=0, squeeze=True)
         h2_cavern_ct = h2_salt_cavern_potential[~h2_salt_cavern_potential.isna()]
         cavern_nodes = pop_layout[pop_layout.ct.isin(h2_cavern_ct.index)]

         h2_capital_cost = costs.at["hydrogen storage underground", "fixed"]

         # assumptions: weight storage potential in a country by population
         # TODO: fix with real geographic potentials
         # convert TWh to MWh with 1e6
         h2_pot = h2_cavern_ct.loc[cavern_nodes.ct]
         h2_pot.index = cavern_nodes.index
         h2_pot = h2_pot * cavern_nodes.fraction * 1e6

         n.madd("Store",
            cavern_nodes.index + " H2 Store",
            bus=cavern_nodes.index + " H2",
            e_nom_extendable=True,
            e_nom_max=h2_pot.values,
            e_cyclic=True,
            carrier="H2 Store",
            capital_cost=h2_capital_cost
        )

    # hydrogen stored overground (where not already underground)
    h2_capital_cost = costs.at["hydrogen storage tank incl. compressor", "fixed"]
    nodes_overground = cavern_nodes.index.symmetric_difference(nodes)

    n.madd("Store",
        nodes_overground + " H2 Store",
        bus=nodes_overground + " H2",
        e_nom_extendable=True,
        e_cyclic=True,
        carrier="H2 Store",
        capital_cost=h2_capital_cost
    )

    attrs = ["bus0", "bus1", "length"]
    h2_links = pd.DataFrame(columns=attrs)

    candidates = pd.concat({"lines": n.lines[attrs],
                            "links": n.links.loc[n.links.carrier == "DC", attrs]})

    for candidate in candidates.index:
        buses = [candidates.at[candidate, "bus0"], candidates.at[candidate, "bus1"]]
        buses.sort()
        name = f"H2 pipeline {buses[0]} -> {buses[1]}"
        if name not in h2_links.index:
            h2_links.at[name, "bus0"] = buses[0]
            h2_links.at[name, "bus1"] = buses[1]
            h2_links.at[name, "length"] = candidates.at[candidate, "length"]

    # TODO Add efficiency losses
    n.madd("Link",
        h2_links.index,
        bus0=h2_links.bus0.values + " H2",
        bus1=h2_links.bus1.values + " H2",
        p_min_pu=-1,
        p_nom_extendable=True,
        length=h2_links.length.values,
        capital_cost=costs.at['H2 (g) pipeline', 'fixed'] * h2_links.length.values,
        carrier="H2 pipeline",
        lifetime=costs.at['H2 (g) pipeline', 'lifetime']
    )

    n.add("Carrier", "battery")

    n.madd("Bus",
        nodes + " battery",
        location=nodes,
        carrier="battery"
    )

    n.madd("Store",
        nodes + " battery",
        bus=nodes + " battery",
        e_cyclic=True,
        e_nom_extendable=True,
        carrier="battery",
        capital_cost=costs.at['battery storage', 'fixed'],
        lifetime=costs.at['battery storage', 'lifetime']
    )

    n.madd("Link",
        nodes + " battery charger",
        bus0=nodes,
        bus1=nodes + " battery",
        carrier="battery charger",
        efficiency=costs.at['battery inverter', 'efficiency']**0.5,
        capital_cost=costs.at['battery inverter', 'fixed'],
        p_nom_extendable=True,
        lifetime=costs.at['battery inverter', 'lifetime']
    )

    n.madd("Link",
        nodes + " battery discharger",
        bus0=nodes + " battery",
        bus1=nodes,
        carrier="battery discharger",
        efficiency=costs.at['battery inverter', 'efficiency']**0.5,
        marginal_cost=options['marginal_cost_storage'],
        p_nom_extendable=True,
        lifetime=costs.at['battery inverter', 'lifetime']
    )

    if options['methanation']:

        n.madd("Link",
            spatial.nodes,
            suffix=" Sabatier",
            bus0=nodes + " H2",
            bus1="EU gas",
            bus2=spatial.co2.nodes,
            p_nom_extendable=True,
            carrier="Sabatier",
            efficiency=costs.at["methanation", "efficiency"],
            efficiency2=-costs.at["methanation", "efficiency"] * costs.at['gas', 'CO2 intensity'],
            capital_cost=costs.at["methanation", "fixed"] * costs.at["methanation", "efficiency"],  # costs given per kW_gas
            lifetime=costs.at['methanation', 'lifetime']
        )

    if options['helmeth']:

        n.madd("Link",
            spatial.nodes,
            suffix=" helmeth",
            bus0=nodes,
            bus1="EU gas",
            bus2=spatial.co2.nodes,
            carrier="helmeth",
            p_nom_extendable=True,
            efficiency=costs.at["helmeth", "efficiency"],
            efficiency2=-costs.at["helmeth", "efficiency"] * costs.at['gas', 'CO2 intensity'],
            capital_cost=costs.at["helmeth", "fixed"],
            lifetime=costs.at['helmeth', 'lifetime']
        )


    if options['SMR']:

        n.madd("Link",
            spatial.nodes,
            suffix=" SMR CC",
            bus0="EU gas",
            bus1=nodes + " H2",
            bus2="co2 atmosphere",
            bus3=spatial.co2.nodes,
            p_nom_extendable=True,
            carrier="SMR CC",
            efficiency=costs.at["SMR CC", "efficiency"],
            efficiency2=costs.at['gas', 'CO2 intensity'] * (1 - options["cc_fraction"]),
            efficiency3=costs.at['gas', 'CO2 intensity'] * options["cc_fraction"],
            capital_cost=costs.at["SMR CC", "fixed"],
            lifetime=costs.at['SMR CC', 'lifetime']
        )

        n.madd("Link",
            nodes + " SMR",
            bus0="EU gas",
            bus1=nodes + " H2",
            bus2="co2 atmosphere",
            p_nom_extendable=True,
            carrier="SMR",
            efficiency=costs.at["SMR", "efficiency"],
            efficiency2=costs.at['gas', 'CO2 intensity'],
            capital_cost=costs.at["SMR", "fixed"],
            lifetime=costs.at['SMR', 'lifetime']
        )


def add_land_transport(n, costs):
    # TODO options?

    print("adding land transport")

    fuel_cell_share = get(options["land_transport_fuel_cell_share"], investment_year)
    electric_share = get(options["land_transport_electric_share"], investment_year)
    ice_share = 1 - fuel_cell_share - electric_share

    print("FCEV share", fuel_cell_share)
    print("EV share", electric_share)
    print("ICEV share", ice_share)

    assert ice_share >= 0, "Error, more FCEV and EV share than 1."

    nodes = pop_layout.index

    if electric_share > 0:

        n.add("Carrier", "Li ion")

        n.madd("Bus",
            nodes,
            location=nodes,
            suffix=" EV battery",
            carrier="Li ion"
        )

        p_set = electric_share * (transport[nodes] + cycling_shift(transport[nodes], 1) + cycling_shift(transport[nodes], 2)) / 3

        n.madd("Load",
            nodes,
            suffix=" land transport EV",
            bus=nodes + " EV battery",
            carrier="land transport EV",
            p_set=p_set
        )


        p_nom = nodal_transport_data["number cars"] * options.get("bev_charge_rate", 0.011) * electric_share

        n.madd("Link",
            nodes,
            suffix= " BEV charger",
            bus0=nodes,
            bus1=nodes + " EV battery",
            p_nom=p_nom,
            carrier="BEV charger",
            p_max_pu=avail_profile[nodes],
            efficiency=options.get("bev_charge_efficiency", 0.9),
            #These were set non-zero to find LU infeasibility when availability = 0.25
            #p_nom_extendable=True,
            #p_nom_min=p_nom,
            #capital_cost=1e6,  #i.e. so high it only gets built where necessary
        )

    if electric_share > 0 and options["v2g"]:

        n.madd("Link",
            nodes,
            suffix=" V2G",
            bus1=nodes,
            bus0=nodes + " EV battery",
            p_nom=p_nom,
            carrier="V2G",
            p_max_pu=avail_profile[nodes],
            efficiency=options.get("bev_charge_efficiency", 0.9),
        )

    if electric_share > 0 and options["bev_dsm"]:

        e_nom = nodal_transport_data["number cars"] * options.get("bev_energy", 0.05) * options["bev_availability"] * electric_share

        n.madd("Store",
            nodes,
            suffix=" battery storage",
            bus=nodes + " EV battery",
            carrier="battery storage",
            e_cyclic=True,
            e_nom=e_nom,
            e_max_pu=1,
            e_min_pu=dsm_profile[nodes]
        )

    if fuel_cell_share > 0:

        n.madd("Load",
            nodes,
            suffix=" land transport fuel cell",
            bus=nodes + " H2",
            carrier="land transport fuel cell",
            p_set=fuel_cell_share / options['transport_fuel_cell_efficiency'] * transport[nodes]
        )

    if ice_share > 0:

        if "EU oil" not in n.buses.index:
            n.add("Bus",
                "EU oil",
                location="EU",
                carrier="oil"
            )

        ice_efficiency = options['transport_internal_combustion_efficiency']

        n.madd("Load",
            nodes,
            suffix=" land transport oil",
            bus="EU oil",
            carrier="land transport oil",
            p_set=ice_share / ice_efficiency * transport[nodes]
        )

        co2 = ice_share / ice_efficiency * transport[nodes].sum().sum() / 8760 * costs.at["oil", 'CO2 intensity']

        n.madd("Load",
            ["land transport oil emissions"],
            bus="co2 atmosphere",
            carrier="land transport oil emissions",
            p_set=-co2
        )


def add_heat(n, costs):
    # TODO options?
    # TODO pop_layout?

    print("adding heat")

    sectors = ["residential", "services"]


    nodes, dist_fraction, urban_fraction = create_nodes_for_heat_sector()

    #NB: must add costs of central heating afterwards (EUR 400 / kWpeak, 50a, 1% FOM from Fraunhofer ISE)

    # exogenously reduce space heat demand
    if options["reduce_space_heat_exogenously"]:
        dE = get(options["reduce_space_heat_exogenously_factor"], investment_year)
        print(f"assumed space heat reduction of {dE*100} %")
        for sector in sectors:
            heat_demand[sector + " space"] = (1 - dE) * heat_demand[sector + " space"]

    heat_systems = [
        "residential rural",
        "services rural",
        "residential urban decentral",
        "services urban decentral",
        "urban central"
    ]

    for name in heat_systems:

        name_type = "central" if name == "urban central" else "decentral"

        n.add("Carrier", name + " heat")

        n.madd("Bus",
            nodes[name] + f" {name} heat",
            location=nodes[name],
            carrier=name + " heat"
        )

        ## Add heat load

        for sector in sectors:
            # heat demand weighting
            if "rural" in name:
                factor = 1 - urban_fraction[nodes[name]]
            elif "urban central" in name:
                factor = dist_fraction[nodes[name]]
            elif "urban decentral" in name:
                factor = urban_fraction[nodes[name]] - \
                    dist_fraction[nodes[name]]
            else:
                factor = None

            if sector in name:
                heat_load = heat_demand[[sector + " water",sector + " space"]].groupby(level=1,axis=1).sum()[nodes[name]].multiply(factor)

        if name == "urban central":
            heat_load = heat_demand.groupby(level=1,axis=1).sum()[nodes[name]].multiply(factor * (1 + options['district_heating']['district_heating_loss']))

        n.madd("Load",
            nodes[name],
            suffix=f" {name} heat",
            bus=nodes[name] + f" {name} heat",
            carrier=name + " heat",
            p_set=heat_load
        )

        ## Add heat pumps

        heat_pump_type = "air" if "urban" in name else "ground"

        costs_name = f"{name_type} {heat_pump_type}-sourced heat pump"
        cop = {"air" : ashp_cop, "ground" : gshp_cop}
        efficiency = cop[heat_pump_type][nodes[name]] if options["time_dep_hp_cop"] else costs.at[costs_name, 'efficiency']

        n.madd("Link",
            nodes[name],
            suffix=f" {name} {heat_pump_type} heat pump",
            bus0=nodes[name],
            bus1=nodes[name] + f" {name} heat",
            carrier=f"{name} {heat_pump_type} heat pump",
            efficiency=efficiency,
            capital_cost=costs.at[costs_name, 'efficiency'] * costs.at[costs_name, 'fixed'],
            p_nom_extendable=True,
            lifetime=costs.at[costs_name, 'lifetime']
        )

        if options["tes"]:

            n.add("Carrier", name + " water tanks")

            n.madd("Bus",
                nodes[name] + f" {name} water tanks",
                location=nodes[name],
                carrier=name + " water tanks"
            )

            n.madd("Link",
                nodes[name] + f" {name} water tanks charger",
                bus0=nodes[name] + f" {name} heat",
                bus1=nodes[name] + f" {name} water tanks",
                efficiency=costs.at['water tank charger', 'efficiency'],
                carrier=name + " water tanks charger",
                p_nom_extendable=True
            )

            n.madd("Link",
                nodes[name] + f" {name} water tanks discharger",
                bus0=nodes[name] + f" {name} water tanks",
                bus1=nodes[name] + f" {name} heat",
                carrier=name + " water tanks discharger",
                efficiency=costs.at['water tank discharger', 'efficiency'],
                p_nom_extendable=True
            )


            if isinstance(options["tes_tau"], dict):
                tes_time_constant_days = options["tes_tau"][name_type]
            else:
                logger.warning("Deprecated: a future version will require you to specify 'tes_tau' ",
                               "for 'decentral' and 'central' separately.")
                tes_time_constant_days = options["tes_tau"] if name_type == "decentral" else 180.

            # conversion from EUR/m^3 to EUR/MWh for 40 K diff and 1.17 kWh/m^3/K
            capital_cost = costs.at[name_type + ' water tank storage', 'fixed'] / 0.00117 / 40

            n.madd("Store",
                nodes[name] + f" {name} water tanks",
                bus=nodes[name] + f" {name} water tanks",
                e_cyclic=True,
                e_nom_extendable=True,
                carrier=name + " water tanks",
                standing_loss=1 - np.exp(- 1 / 24 / tes_time_constant_days),
                capital_cost=capital_cost,
                lifetime=costs.at[name_type + ' water tank storage', 'lifetime']
            )

        if options["boilers"]:

            key = f"{name_type} resistive heater"

            n.madd("Link",
                nodes[name] + f" {name} resistive heater",
                bus0=nodes[name],
                bus1=nodes[name] + f" {name} heat",
                carrier=name + " resistive heater",
                efficiency=costs.at[key, 'efficiency'],
                capital_cost=costs.at[key, 'efficiency'] * costs.at[key, 'fixed'],
                p_nom_extendable=True,
                lifetime=costs.at[key, 'lifetime']
            )

            key = f"{name_type} gas boiler"

            n.madd("Link",
                nodes[name] + f" {name} gas boiler",
                p_nom_extendable=True,
                bus0="EU gas",
                bus1=nodes[name] + f" {name} heat",
                bus2="co2 atmosphere",
                carrier=name + " gas boiler",
                efficiency=costs.at[key, 'efficiency'],
                efficiency2=costs.at['gas', 'CO2 intensity'],
                capital_cost=costs.at[key, 'efficiency'] * costs.at[key, 'fixed'],
                lifetime=costs.at[key, 'lifetime']
            )

        if options["solar_thermal"]:

            n.add("Carrier", name + " solar thermal")

            n.madd("Generator",
                nodes[name],
                suffix=f" {name} solar thermal collector",
                bus=nodes[name] + f" {name} heat",
                carrier=name + " solar thermal",
                p_nom_extendable=True,
                capital_cost=costs.at[name_type + ' solar thermal', 'fixed'],
                p_max_pu=solar_thermal[nodes[name]],
                lifetime=costs.at[name_type + ' solar thermal', 'lifetime']
            )

        if options["chp"] and name == "urban central":

            # add gas CHP; biomass CHP is added in biomass section
            n.madd("Link",
                nodes[name] + " urban central gas CHP",
                bus0="EU gas",
                bus1=nodes[name],
                bus2=nodes[name] + " urban central heat",
                bus3="co2 atmosphere",
                carrier="urban central gas CHP",
                p_nom_extendable=True,
                capital_cost=costs.at['central gas CHP', 'fixed'] * costs.at['central gas CHP', 'efficiency'],
                marginal_cost=costs.at['central gas CHP', 'VOM'],
                efficiency=costs.at['central gas CHP', 'efficiency'],
                efficiency2=costs.at['central gas CHP', 'efficiency'] / costs.at['central gas CHP', 'c_b'],
                efficiency3=costs.at['gas', 'CO2 intensity'],
                lifetime=costs.at['central gas CHP', 'lifetime']
            )

            n.madd("Link",
                nodes[name] + " urban central gas CHP CC",
                bus0="EU gas",
                bus1=nodes[name],
                bus2=nodes[name] + " urban central heat",
                bus3="co2 atmosphere",
                bus4=spatial.co2.df.loc[nodes[name], "nodes"].values,
                carrier="urban central gas CHP CC",
                p_nom_extendable=True,
                capital_cost=costs.at['central gas CHP', 'fixed']*costs.at['central gas CHP', 'efficiency'] + costs.at['biomass CHP capture', 'fixed']*costs.at['gas', 'CO2 intensity'],
                marginal_cost=costs.at['central gas CHP', 'VOM'],
                efficiency=costs.at['central gas CHP', 'efficiency'] - costs.at['gas', 'CO2 intensity'] * (costs.at['biomass CHP capture', 'electricity-input'] + costs.at['biomass CHP capture', 'compression-electricity-input']),
                efficiency2=costs.at['central gas CHP', 'efficiency'] / costs.at['central gas CHP', 'c_b'] + costs.at['gas', 'CO2 intensity'] * (costs.at['biomass CHP capture', 'heat-output'] + costs.at['biomass CHP capture', 'compression-heat-output'] - costs.at['biomass CHP capture', 'heat-input']),
                efficiency3=costs.at['gas', 'CO2 intensity'] * (1-costs.at['biomass CHP capture', 'capture_rate']),
                efficiency4=costs.at['gas', 'CO2 intensity'] * costs.at['biomass CHP capture', 'capture_rate'],
                lifetime=costs.at['central gas CHP', 'lifetime']
            )

        if options["chp"] and options["micro_chp"] and name != "urban central":

            n.madd("Link",
                nodes[name] + f" {name} micro gas CHP",
                p_nom_extendable=True,
                bus0="EU gas",
                bus1=nodes[name],
                bus2=nodes[name] + f" {name} heat",
                bus3="co2 atmosphere",
                carrier=name + " micro gas CHP",
                efficiency=costs.at['micro CHP', 'efficiency'],
                efficiency2=costs.at['micro CHP', 'efficiency-heat'],
                efficiency3=costs.at['gas', 'CO2 intensity'],
                capital_cost=costs.at['micro CHP', 'fixed'],
                lifetime=costs.at['micro CHP', 'lifetime']
            )


    if options['retrofitting']['retro_endogen']:

        print("adding retrofitting endogenously")

        # resample heat demand temporal 'heat_demand_r' depending on in config
        # specified temporal resolution, to not overestimate retrofitting
        hours = list(filter(re.compile(r'^\d+h$', re.IGNORECASE).search, opts))
        if len(hours)==0:
            hours = [n.snapshots[1] - n.snapshots[0]]
        heat_demand_r =  heat_demand.resample(hours[0]).mean()

        # retrofitting data 'retro_data' with 'costs' [EUR/m^2] and heat
        # demand 'dE' [per unit of original heat demand] for each country and
        # different retrofitting strengths [additional insulation thickness in m]
        retro_data = pd.read_csv(snakemake.input.retro_cost_energy,
                                 index_col=[0, 1], skipinitialspace=True,
                                 header=[0, 1])
        # heated floor area [10^6 * m^2] per country
        floor_area = pd.read_csv(snakemake.input.floor_area, index_col=[0, 1])

        n.add("Carrier", "retrofitting")

        # share of space heat demand 'w_space' of total heat demand
        w_space = {}
        for sector in sectors:
            w_space[sector] = heat_demand_r[sector + " space"] / \
                (heat_demand_r[sector + " space"] + heat_demand_r[sector + " water"])
        w_space["tot"] = ((heat_demand_r["services space"] +
                           heat_demand_r["residential space"]) /
                           heat_demand_r.groupby(level=[1], axis=1).sum())


        for name in n.loads[n.loads.carrier.isin([x + " heat" for x in heat_systems])].index:

            node = n.buses.loc[name, "location"]
            ct = pop_layout.loc[node, "ct"]

            # weighting 'f' depending on the size of the population at the node
            f = urban_fraction[node] if "urban" in name else (1-urban_fraction[node])
            if f == 0:
                continue
            # get sector name ("residential"/"services"/or both "tot" for urban central)
            sec = [x if x in name else "tot" for x in sectors][0]

            # get floor aread at node and region (urban/rural) in m^2
            floor_area_node = ((pop_layout.loc[node].fraction
                                  * floor_area.loc[ct, "value"] * 10**6).loc[sec] * f)
            # total heat demand at node [MWh]
            demand = (n.loads_t.p_set[name].resample(hours[0])
                      .mean())

            # space heat demand at node [MWh]
            space_heat_demand = demand * w_space[sec][node]
            # normed time profile of space heat demand 'space_pu' (values between 0-1),
            # p_max_pu/p_min_pu of retrofitting generators
            space_pu = (space_heat_demand / space_heat_demand.max()).to_frame(name=node)

            # minimum heat demand 'dE' after retrofitting in units of original heat demand (values between 0-1)
            dE = retro_data.loc[(ct, sec), ("dE")]
            # get addtional energy savings 'dE_diff' between the different retrofitting strengths/generators at one node
            dE_diff = abs(dE.diff()).fillna(1-dE.iloc[0])
            # convert costs Euro/m^2 -> Euro/MWh
            capital_cost =  retro_data.loc[(ct, sec), ("cost")] * floor_area_node / \
                            ((1 - dE) * space_heat_demand.max())
            # number of possible retrofitting measures 'strengths' (set in list at config.yaml 'l_strength')
            # given in additional insulation thickness [m]
            # for each measure, a retrofitting generator is added at the node
            strengths = retro_data.columns.levels[1]

            # check that ambitious retrofitting has higher costs per MWh than moderate retrofitting
            if (capital_cost.diff() < 0).sum():
                print(f"Warning: costs are not linear for {ct} {sec}")
                s = capital_cost[(capital_cost.diff() < 0)].index
                strengths = strengths.drop(s)

            # reindex normed time profile of space heat demand back to hourly resolution
            space_pu = space_pu.reindex(index=heat_demand.index).fillna(method="ffill")

            # add for each retrofitting strength a generator with heat generation profile following the profile of the heat demand
            for strength in strengths:
                n.madd('Generator',
                    [node],
                    suffix=' retrofitting ' + strength + " " + name[6::],
                    bus=name,
                    carrier="retrofitting",
                    p_nom_extendable=True,
                    p_nom_max=dE_diff[strength] * space_heat_demand.max(), # maximum energy savings for this renovation strength
                    p_max_pu=space_pu,
                    p_min_pu=space_pu,
                    country=ct,
                    capital_cost=capital_cost[strength] * options['retrofitting']['cost_factor']
                )


def create_nodes_for_heat_sector():
    # TODO pop_layout

    # rural are areas with low heating density and individual heating
    # urban are areas with high heating density
    # urban can be split into district heating (central) and individual heating (decentral)

    ct_urban = pop_layout.urban.groupby(pop_layout["ct"]).sum()
    # distribution of urban population within a country
    pop_layout["urban_ct_fraction"] = pop_layout["urban"] / \
            pop_layout["ct"].map(ct_urban.get)

    sectors = ["residential", "services"]

    nodes = {}
    urban_fraction = pop_layout["urban"] / \
                     (pop_layout[["urban", "rural"]].sum(axis=1))

    for sector in sectors:
        nodes[sector + " rural"] = pop_layout.index
        nodes[sector + " urban decentral"] = pop_layout.index

    # maximum potential of urban demand covered by district heating
    central_fraction = options['district_heating']["potential"]
    # district heating share at each node
    dist_fraction_node = district_heat_share * pop_layout["urban_ct_fraction"] / pop_layout["fraction"]
    nodes["urban central"] = dist_fraction_node.index
    # if district heating share larger than urban fraction -> set urban
    # fraction to district heating share
    urban_fraction = pd.concat([urban_fraction, dist_fraction_node],
                               axis=1).max(axis=1)
    # difference of max potential and today's share of district heating
    diff = (urban_fraction * central_fraction) - dist_fraction_node
    progress = get(options["district_heating"]["potential"], investment_year)
    dist_fraction_node += diff * progress
    print("************************************")
    print(
        "the current DH share compared to the maximum possible is increased \
           \n by a progress factor of ",
        progress,
        "resulting DH share: ",
        dist_fraction_node)
    print("**********************************")

    return nodes, dist_fraction_node, urban_fraction


def add_biomass(n, costs):

    print("adding biomass")

    # biomass distributed at country level - i.e. transport within country allowed
    countries = n.buses.country.dropna().unique()

    biomass_potentials = pd.read_csv(snakemake.input.biomass_potentials, index_col=0)

    if options["biomass_transport"]:
        # potential per node distributed within country by population
        biomass_potentials_spatial = (biomass_potentials.loc[pop_layout.ct]
                            .set_index(pop_layout.index)
                            .mul(pop_layout.fraction, axis="index")
                            .rename(index=lambda x: x + " solid biomass"))
    else:
        biomass_potentials_spatial = biomass_potentials.sum()

    n.add("Carrier", "biogas")
    n.add("Carrier", "solid biomass")

    n.add("Bus",
        "EU biogas",
        location="EU",
        carrier="biogas"
    )

    n.madd("Bus",
        spatial.biomass.nodes,
        location=spatial.biomass.locations,
        carrier="solid biomass"
    )

    n.add("Store",
        "EU biogas",
        bus="EU biogas",
        carrier="biogas",
        e_nom=biomass_potentials.loc[countries, "biogas"].sum(),
        marginal_cost=costs.at['biogas', 'fuel'],
        e_initial=biomass_potentials.loc[countries, "biogas"].sum()
    )

    n.madd("Store",
        spatial.biomass.nodes,
        bus=spatial.biomass.nodes,
        carrier="solid biomass",
        e_nom=biomass_potentials_spatial["solid biomass"],
        marginal_cost=costs.at['solid biomass', 'fuel'],
        e_initial=biomass_potentials_spatial["solid biomass"]
    )

    n.add("Link",
        "biogas to gas",
        bus0="EU biogas",
        bus1="EU gas",
        bus2="co2 atmosphere",
        carrier="biogas to gas",
        capital_cost=costs.loc["biogas upgrading", "fixed"],
        marginal_cost=costs.loc["biogas upgrading", "VOM"],
        efficiency2=-costs.at['gas', 'CO2 intensity'],
        p_nom_extendable=True
    )

    if options["biomass_transport"]:

        transport_costs = pd.read_csv(
            snakemake.input.biomass_transport_costs,
            index_col=0,
            squeeze=True
        )

        # add biomass transport
        biomass_transport = create_network_topology(n, "biomass transport ", bidirectional=False)

        # costs
        bus0_costs = biomass_transport.bus0.apply(lambda x: transport_costs[x[:2]])
        bus1_costs = biomass_transport.bus1.apply(lambda x: transport_costs[x[:2]])
        biomass_transport["costs"] = pd.concat([bus0_costs, bus1_costs], axis=1).mean(axis=1)

        n.madd("Link",
            biomass_transport.index,
            bus0=biomass_transport.bus0 + " solid biomass",
            bus1=biomass_transport.bus1 + " solid biomass",
            p_nom_extendable=True,
            length=biomass_transport.length.values,
            marginal_cost=biomass_transport.costs * biomass_transport.length.values,
            capital_cost=1,
            carrier="solid biomass transport"
        )

    #AC buses with district heating
    urban_central = n.buses.index[n.buses.carrier == "urban central heat"]
    if not urban_central.empty and options["chp"]:
        urban_central = urban_central.str[:-len(" urban central heat")]

        key = 'central solid biomass CHP'

        n.madd("Link",
            urban_central + " urban central solid biomass CHP",
            bus0=spatial.biomass.df.loc[urban_central, "nodes"].values,
            bus1=urban_central,
            bus2=urban_central + " urban central heat",
            carrier="urban central solid biomass CHP",
            p_nom_extendable=True,
            capital_cost=costs.at[key, 'fixed'] * costs.at[key, 'efficiency'],
            marginal_cost=costs.at[key, 'VOM'],
            efficiency=costs.at[key, 'efficiency'],
            efficiency2=costs.at[key, 'efficiency-heat'],
            lifetime=costs.at[key, 'lifetime']
        )

        n.madd("Link",
            urban_central + " urban central solid biomass CHP CC",
            bus0=spatial.biomass.df.loc[urban_central, "nodes"].values,
            bus1=urban_central,
            bus2=urban_central + " urban central heat",
            bus3="co2 atmosphere",
            bus4=spatial.co2.df.loc[urban_central, "nodes"].values,
            carrier="urban central solid biomass CHP CC",
            p_nom_extendable=True,
            capital_cost=costs.at[key, 'fixed'] * costs.at[key, 'efficiency'] + costs.at['biomass CHP capture', 'fixed'] * costs.at['solid biomass', 'CO2 intensity'],
            marginal_cost=costs.at[key, 'VOM'],
            efficiency=costs.at[key, 'efficiency'] - costs.at['solid biomass', 'CO2 intensity'] * (costs.at['biomass CHP capture', 'electricity-input'] + costs.at['biomass CHP capture', 'compression-electricity-input']),
            efficiency2=costs.at[key, 'efficiency-heat'] + costs.at['solid biomass', 'CO2 intensity'] * (costs.at['biomass CHP capture', 'heat-output'] + costs.at['biomass CHP capture', 'compression-heat-output'] - costs.at['biomass CHP capture', 'heat-input']),
            efficiency3=-costs.at['solid biomass', 'CO2 intensity'] * costs.at['biomass CHP capture', 'capture_rate'],
            efficiency4=costs.at['solid biomass', 'CO2 intensity'] * costs.at['biomass CHP capture', 'capture_rate'],
            lifetime=costs.at[key, 'lifetime']
        )


def add_industry(n, costs):

    print("adding industrial demand")

    nodes = pop_layout.index

    # 1e6 to convert TWh to MWh
    industrial_demand = pd.read_csv(snakemake.input.industrial_demand, index_col=0) * 1e6

    n.madd("Bus",
        spatial.biomass.industry,
        location=spatial.biomass.locations,
        carrier="solid biomass for industry"
    )

    if options["biomass_transport"]:
        p_set = industrial_demand.loc[spatial.biomass.locations, "solid biomass"].rename(index=lambda x: x + " solid biomass for industry") / 8760
    else:
        p_set = industrial_demand["solid biomass"].sum() / 8760

    n.madd("Load",
        spatial.biomass.industry,
        bus=spatial.biomass.industry,
        carrier="solid biomass for industry",
        p_set=p_set
    )

    n.madd("Link",
        spatial.biomass.industry,
        bus0=spatial.biomass.nodes,
        bus1=spatial.biomass.industry,
        carrier="solid biomass for industry",
        p_nom_extendable=True,
        efficiency=1.
    )

    n.madd("Link",
        spatial.biomass.industry_cc,
        bus0=spatial.biomass.nodes,
        bus1=spatial.biomass.industry,
        bus2="co2 atmosphere",
        bus3=spatial.co2.nodes,
        carrier="solid biomass for industry CC",
        p_nom_extendable=True,
        capital_cost=costs.at["cement capture", "fixed"] * costs.at['solid biomass', 'CO2 intensity'],
        efficiency=0.9, # TODO: make config option
        efficiency2=-costs.at['solid biomass', 'CO2 intensity'] * costs.at["cement capture", "capture_rate"],
        efficiency3=costs.at['solid biomass', 'CO2 intensity'] * costs.at["cement capture", "capture_rate"],
        lifetime=costs.at['cement capture', 'lifetime']
    )

    n.add("Bus",
        "gas for industry",
        location="EU",
        carrier="gas for industry")

    n.add("Load",
        "gas for industry",
        bus="gas for industry",
        carrier="gas for industry",
        p_set=industrial_demand.loc[nodes, "methane"].sum() / 8760
    )

    n.add("Link",
        "gas for industry",
        bus0="EU gas",
        bus1="gas for industry",
        bus2="co2 atmosphere",
        carrier="gas for industry",
        p_nom_extendable=True,
        efficiency=1.,
        efficiency2=costs.at['gas', 'CO2 intensity']
    )

    n.madd("Link",
        spatial.co2.locations,
        suffix=" gas for industry CC",
        bus0="EU gas",
        bus1="gas for industry",
        bus2="co2 atmosphere",
        bus3=spatial.co2.nodes,
        carrier="gas for industry CC",
        p_nom_extendable=True,
        capital_cost=costs.at["cement capture", "fixed"] * costs.at['gas', 'CO2 intensity'],
        efficiency=0.9,
        efficiency2=costs.at['gas', 'CO2 intensity'] * (1 - costs.at["cement capture", "capture_rate"]),
        efficiency3=costs.at['gas', 'CO2 intensity'] * costs.at["cement capture", "capture_rate"],
        lifetime=costs.at['cement capture', 'lifetime']
    )


    n.madd("Load",
        nodes,
        suffix=" H2 for industry",
        bus=nodes + " H2",
        carrier="H2 for industry",
        p_set=industrial_demand.loc[nodes, "hydrogen"] / 8760
    )

    if options["shipping_hydrogen_liquefaction"]:

        n.madd("Bus",
            nodes,
            suffix=" H2 liquid",
            carrier="H2 liquid",
            location=nodes
        )

        n.madd("Link",
            nodes + " H2 liquefaction",
            bus0=nodes + " H2",
            bus1=nodes + " H2 liquid",
            carrier="H2 liquefaction",
            efficiency=costs.at["H2 liquefaction", 'efficiency'],
            capital_cost=costs.at["H2 liquefaction", 'fixed'],
            p_nom_extendable=True,
            lifetime=costs.at['H2 liquefaction', 'lifetime']
        )

        shipping_bus = nodes + " H2 liquid"
    else:
        shipping_bus = nodes + " H2"

    all_navigation = ["total international navigation", "total domestic navigation"]
    efficiency = options['shipping_average_efficiency'] / costs.at["fuel cell", "efficiency"]
    shipping_hydrogen_share = get(options['shipping_hydrogen_share'], investment_year)
    p_set = shipping_hydrogen_share * nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 * efficiency / 8760

    n.madd("Load",
        nodes,
        suffix=" H2 for shipping",
        bus=shipping_bus,
        carrier="H2 for shipping",
        p_set=p_set
    )

    if shipping_hydrogen_share < 1:

        shipping_oil_share = 1 - shipping_hydrogen_share

        p_set = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 / 8760.

        n.madd("Load",
            nodes,
            suffix=" shipping oil",
            bus="EU oil",
            carrier="shipping oil",
            p_set=p_set
        )

        co2 = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum().sum() * 1e6 / 8760 * costs.at["oil", "CO2 intensity"]

        n.add("Load",
            "shipping oil emissions",
            bus="co2 atmosphere",
            carrier="shipping oil emissions",
            p_set=-co2
        )

    if "EU oil" not in n.buses.index:

        n.add("Bus",
            "EU oil",
            location="EU",
            carrier="oil"
        )

    if "EU oil Store" not in n.stores.index:

        #could correct to e.g. 0.001 EUR/kWh * annuity and O&M
        n.add("Store",
            "EU oil Store",
            bus="EU oil",
            e_nom_extendable=True,
            e_cyclic=True,
            carrier="oil",
        )

    if "EU oil" not in n.generators.index:

        n.add("Generator",
            "EU oil",
            bus="EU oil",
            p_nom_extendable=True,
            carrier="oil",
            marginal_cost=costs.at["oil", 'fuel']
        )

    if options["oil_boilers"]:

        nodes_heat = create_nodes_for_heat_sector()[0]

        for name in ["residential rural", "services rural", "residential urban decentral", "services urban decentral"]:

            n.madd("Link",
                nodes_heat[name] + f" {name} oil boiler",
                p_nom_extendable=True,
                bus0="EU oil",
                bus1=nodes_heat[name] + f" {name}  heat",
                bus2="co2 atmosphere",
                carrier=f"{name} oil boiler",
                efficiency=costs.at['decentral oil boiler', 'efficiency'],
                efficiency2=costs.at['oil', 'CO2 intensity'],
                capital_cost=costs.at['decentral oil boiler', 'efficiency'] * costs.at['decentral oil boiler', 'fixed'],
                lifetime=costs.at['decentral oil boiler', 'lifetime']
            )

    n.madd("Link",
        nodes + " Fischer-Tropsch",
        bus0=nodes + " H2",
        bus1="EU oil",
        bus2=spatial.co2.nodes,
        carrier="Fischer-Tropsch",
        efficiency=costs.at["Fischer-Tropsch", 'efficiency'],
        capital_cost=costs.at["Fischer-Tropsch", 'fixed'],
        efficiency2=-costs.at["oil", 'CO2 intensity'] * costs.at["Fischer-Tropsch", 'efficiency'],
        p_nom_extendable=True,
        lifetime=costs.at['Fischer-Tropsch', 'lifetime']
    )

    n.add("Load",
        "naphtha for industry",
        bus="EU oil",
        carrier="naphtha for industry",
        p_set=industrial_demand.loc[nodes, "naphtha"].sum() / 8760
    )

    all_aviation = ["total international aviation", "total domestic aviation"]
    p_set = nodal_energy_totals.loc[nodes, all_aviation].sum(axis=1).sum() * 1e6 / 8760

    n.add("Load",
        "kerosene for aviation",
        bus="EU oil",
        carrier="kerosene for aviation",
        p_set=p_set
    )

    #NB: CO2 gets released again to atmosphere when plastics decay or kerosene is burned
    #except for the process emissions when naphtha is used for petrochemicals, which can be captured with other industry process emissions
    #tco2 per hour
    co2_release = ["naphtha for industry", "kerosene for aviation"]
    co2 = n.loads.loc[co2_release, "p_set"].sum() * costs.at["oil", 'CO2 intensity'] - industrial_demand.loc[nodes, "process emission from feedstock"].sum() / 8760

    n.add("Load",
        "oil emissions",
        bus="co2 atmosphere",
        carrier="oil emissions",
        p_set=-co2
    )

    # TODO simplify bus expression
    n.madd("Load",
        nodes,
        suffix=" low-temperature heat for industry",
        bus=[node + " urban central heat" if node + " urban central heat" in n.buses.index else node + " services urban decentral heat" for node in nodes],
        carrier="low-temperature heat for industry",
        p_set=industrial_demand.loc[nodes, "low-temperature heat"] / 8760
    )

    # remove today's industrial electricity demand by scaling down total electricity demand
    for ct in n.buses.country.dropna().unique():
        # TODO map onto n.bus.country
        loads_i = n.loads.index[(n.loads.index.str[:2] == ct) & (n.loads.carrier == "electricity")]
        if n.loads_t.p_set[loads_i].empty: continue
        factor = 1 - industrial_demand.loc[loads_i, "current electricity"].sum() / n.loads_t.p_set[loads_i].sum().sum()
        n.loads_t.p_set[loads_i] *= factor

    n.madd("Load",
        nodes,
        suffix=" industry electricity",
        bus=nodes,
        carrier="industry electricity",
        p_set=industrial_demand.loc[nodes, "electricity"] / 8760
    )

    n.add("Bus",
        "process emissions",
        location="EU",
        carrier="process emissions"
    )

    # this should be process emissions fossil+feedstock
    # then need load on atmosphere for feedstock emissions that are currently going to atmosphere via Link Fischer-Tropsch demand
    n.add("Load",
        "process emissions",
        bus="process emissions",
        carrier="process emissions",
        p_set=-industrial_demand.loc[nodes,["process emission", "process emission from feedstock"]].sum(axis=1).sum() / 8760
    )

    n.add("Link",
        "process emissions",
        bus0="process emissions",
        bus1="co2 atmosphere",
        carrier="process emissions",
        p_nom_extendable=True,
        efficiency=1.
    )

    #assume enough local waste heat for CC
    n.madd("Link",
        spatial.co2.locations,
        suffix=" process emissions CC",
        bus0="process emissions",
        bus1="co2 atmosphere",
        bus2=spatial.co2.nodes,
        carrier="process emissions CC",
        p_nom_extendable=True,
        capital_cost=costs.at["cement capture", "fixed"],
        efficiency=1 - costs.at["cement capture", "capture_rate"],
        efficiency2=costs.at["cement capture", "capture_rate"],
        lifetime=costs.at['cement capture', 'lifetime']
    )


def add_waste_heat(n):
    # TODO options?

    print("adding possibility to use industrial waste heat in district heating")

    #AC buses with district heating
    urban_central = n.buses.index[n.buses.carrier == "urban central heat"]
    if not urban_central.empty:
        urban_central = urban_central.str[:-len(" urban central heat")]

        # TODO what is the 0.95 and should it be a config option?
        if options['use_fischer_tropsch_waste_heat']:
            n.links.loc[urban_central + " Fischer-Tropsch", "bus3"] = urban_central + " urban central heat"
            n.links.loc[urban_central + " Fischer-Tropsch", "efficiency3"] = 0.95 - n.links.loc[urban_central + " Fischer-Tropsch", "efficiency"]

        if options['use_fuel_cell_waste_heat']:
            n.links.loc[urban_central + " H2 Fuel Cell", "bus2"] = urban_central + " urban central heat"
            n.links.loc[urban_central + " H2 Fuel Cell", "efficiency2"] = 0.95 - n.links.loc[urban_central + " H2 Fuel Cell", "efficiency"]


def decentral(n):
    """Removes the electricity transmission system."""
    n.lines.drop(n.lines.index, inplace=True)
    n.links.drop(n.links.index[n.links.carrier.isin(["DC", "B2B"])], inplace=True)


def remove_h2_network(n):

    n.links.drop(n.links.index[n.links.carrier == "H2 pipeline"], inplace=True)

    if "EU H2 Store" in n.stores.index:
        n.stores.drop("EU H2 Store", inplace=True)


def maybe_adjust_costs_and_potentials(n, opts):

    for o in opts:
        if "+" not in o: continue
        oo = o.split("+")
        carrier_list = np.hstack((n.generators.carrier.unique(), n.links.carrier.unique(),
                                n.stores.carrier.unique(), n.storage_units.carrier.unique()))
        suptechs = map(lambda c: c.split("-", 2)[0], carrier_list)
        if oo[0].startswith(tuple(suptechs)):
            carrier = oo[0]
            attr_lookup = {"p": "p_nom_max", "e": "e_nom_max", "c": "capital_cost"}
            attr = attr_lookup[oo[1][0]]
            factor = float(oo[1][1:])
            #beware if factor is 0 and p_nom_max is np.inf, 0*np.inf is nan
            if carrier == "AC":  # lines do not have carrier
                n.lines[attr] *= factor
            else:
                if attr == 'p_nom_max':
                    comps = {"Generator", "Link", "StorageUnit"}
                elif attr == 'e_nom_max':
                    comps = {"Store"}
                else:
                    comps = {"Generator", "Link", "StorageUnit", "Store"}
                for c in n.iterate_components(comps):
                    if carrier=='solar':
                        sel = c.df.carrier.str.contains(carrier) & ~c.df.carrier.str.contains("solar rooftop")
                    else:
                        sel = c.df.carrier.str.contains(carrier)
                    c.df.loc[sel,attr] *= factor
            print("changing", attr , "for", carrier, "by factor", factor)


# TODO this should rather be a config no wildcard
def limit_individual_line_extension(n, maxext):
    print(f"limiting new HVAC and HVDC extensions to {maxext} MW")
    n.lines['s_nom_max'] = n.lines['s_nom'] + maxext
    hvdc = n.links.index[n.links.carrier == 'DC']
    n.links.loc[hvdc, 'p_nom_max'] = n.links.loc[hvdc, 'p_nom'] + maxext

#%%
if __name__ == "__main__":
    if 'snakemake' not in globals():
        from helper import mock_snakemake
        snakemake = mock_snakemake(
            'prepare_sector_network',
            simpl='',
            opts="",
            clusters="37",
            lv=1.0,
            sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
            planning_horizons="2030",
        )

    logging.basicConfig(level=snakemake.config['logging_level'])

    options = snakemake.config["sector"]

    opts = snakemake.wildcards.sector_opts.split('-')

    investment_year = int(snakemake.wildcards.planning_horizons[-4:])

    overrides = override_component_attrs(snakemake.input.overrides)
    n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)

    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
    Nyears = n.snapshot_weightings.sum() / 8760

    costs = prepare_costs(snakemake.input.costs,
                          snakemake.config['costs']['USD2013_to_EUR2013'],
                          snakemake.config['costs']['discountrate'],
                          Nyears,
                          snakemake.config['costs']['lifetime'])

    patch_electricity_network(n)

    define_spatial(pop_layout.index)

    if snakemake.config["foresight"] == 'myopic':

        add_lifetime_wind_solar(n, costs)

        conventional = snakemake.config['existing_capacities']['conventional_carriers']
        add_carrier_buses(n, conventional)

    add_co2_tracking(n, options)

    add_generation(n, costs)

    add_storage(n, costs)

    # TODO merge with opts cost adjustment below
    for o in opts:
        if o[:4] == "wave":
            wave_cost_factor = float(o[4:].replace("p", ".").replace("m", "-"))
            print("Including wave generators with cost factor of", wave_cost_factor)
            add_wave(n, wave_cost_factor)
        if o[:4] == "dist":
            options['electricity_distribution_grid'] = True
            options['electricity_distribution_grid_cost_factor'] = float(o[4:].replace("p", ".").replace("m", "-"))
        if o == "biomasstransport":
            options["biomass_transport"] = True

    nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share = prepare_data(n)

    if "nodistrict" in opts:
        options["district_heating"]["progress"] = 0.0

    if "T" in opts:
        add_land_transport(n, costs)

    if "H" in opts:
        add_heat(n, costs)

    if "B" in opts:
        add_biomass(n, costs)

    if "I" in opts:
        add_industry(n, costs)

    if "I" in opts and "H" in opts:
        add_waste_heat(n)

    if options['dac']:
        add_dac(n, costs)

    if "decentral" in opts:
        decentral(n)

    if "noH2network" in opts:
        remove_h2_network(n)

    if options["co2_network"]:
        add_co2_network(n, costs)

    for o in opts:
        m = re.match(r'^\d+h$', o, re.IGNORECASE)
        if m is not None:
            n = average_every_nhours(n, m.group(0))
            break

    limit_type = "config"
    limit = get(snakemake.config["co2_budget"], investment_year)
    for o in opts:
        if not "cb" in o: continue
        limit_type = "carbon budget"
        fn = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/carbon_budget_distribution.csv'
        if not os.path.exists(fn):
            build_carbon_budget(o, fn)
        co2_cap = pd.read_csv(fn, index_col=0, squeeze=True)
        limit = co2_cap[investment_year]
        break
    for o in opts:
        if not "Co2L" in o: continue
        limit_type = "wildcard"
        limit = o[o.find("Co2L")+4:]
        limit = float(limit.replace("p", ".").replace("m", "-"))
        break
    print("add CO2 limit from", limit_type)
    add_co2limit(n, Nyears, limit)

    for o in opts:
        if not o[:10] == 'linemaxext': continue
        maxext = float(o[10:]) * 1e3
        limit_individual_line_extension(n, maxext)
        break

    if options['electricity_distribution_grid']:
        insert_electricity_distribution_grid(n, costs)

    maybe_adjust_costs_and_potentials(n, opts)

    if options['gas_distribution_grid']:
        insert_gas_distribution_costs(n, costs)

    if options['electricity_grid_connection']:
        add_electricity_grid_connection(n, costs)

    n.export_to_netcdf(snakemake.output[0])