pypsa-eur/scripts/add_electricity.py

# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later

# coding: utf-8
"""
Adds electrical generators and existing hydro storage units to a base network.

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

.. code:: yaml

    costs:
        year:
        USD2013_to_EUR2013:
        dicountrate:
        emission_prices:

    electricity:
        max_hours:
        marginal_cost:
        capital_cost:
        conventional_carriers:
        co2limit:
        extendable_carriers:
            Generator:
        estimate_renewable_capacities_from_capacity_stats:

    load:
        scaling_factor:

    renewable: (keys)
        hydro:
            carriers:
            hydro_max_hours:
            hydro_capital_cost:

    lines:
        length_factor:

.. seealso::
    Documentation of the configuration file ``config.yaml`` at :ref:`costs_cf`,
    :ref:`electricity_cf`, :ref:`load_cf`, :ref:`renewable_cf`, :ref:`lines_cf`

Inputs
------

- ``data/costs.csv``: The database of cost assumptions for all included technologies for specific years from various sources; e.g. discount rate, lifetime, investment (CAPEX), fixed operation and maintenance (FOM), variable operation and maintenance (VOM), fuel costs, efficiency, carbon-dioxide intensity.
- ``data/bundle/hydro_capacities.csv``: Hydropower plant store/discharge power capacities, energy storage capacity, and average hourly inflow by country.

    .. image:: ../img/hydrocapacities.png
        :scale: 34 %

- ``data/geth2015_hydro_capacities.csv``: alternative to capacities above; NOT CURRENTLY USED!


- ``resources/opsd_load.csv`` Hourly per-country load profiles.
- ``resources/regions_onshore.geojson``: confer :ref:`busregions`
- ``resources/nuts3_shapes.geojson``: confer :ref:`shapes`
- ``resources/powerplants.csv``: confer :ref:`powerplants`
- ``resources/profile_{}.nc``: all technologies in ``config["renewables"].keys()``, confer :ref:`renewableprofiles`.
- ``networks/base.nc``: confer :ref:`base`

Outputs
-------

- ``networks/elec.nc``:

    .. image:: ../img/elec.png
            :scale: 33 %

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

The rule :mod:`add_electricity` ties all the different data inputs from the preceding rules together into a detailed PyPSA network that is stored in ``networks/elec.nc``. It includes:

- today's transmission topology and transfer capacities (optionally including lines which are under construction according to the config settings ``lines: under_construction`` and ``links: under_construction``),
- today's thermal and hydro power generation capacities (for the technologies listed in the config setting ``electricity: conventional_carriers``), and
- today's load time-series (upsampled in a top-down approach according to population and gross domestic product)

It further adds extendable ``generators`` with **zero** capacity for

- photovoltaic, onshore and AC- as well as DC-connected offshore wind installations with today's locational, hourly wind and solar capacity factors (but **no** current capacities),
- additional open- and combined-cycle gas turbines (if ``OCGT`` and/or ``CCGT`` is listed in the config setting ``electricity: extendable_carriers``)
"""

import logging
from _helpers import configure_logging

import pypsa
import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import powerplantmatching as ppm

from vresutils.costdata import annuity
from vresutils.load import timeseries_opsd
from vresutils import transfer as vtransfer

idx = pd.IndexSlice

logger = logging.getLogger(__name__)


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


def _add_missing_carriers_from_costs(n, costs, carriers):
    missing_carriers = pd.Index(carriers).difference(n.carriers.index)
    if missing_carriers.empty: return

    emissions_cols = costs.columns.to_series()\
                           .loc[lambda s: s.str.endswith('_emissions')].values
    suptechs = missing_carriers.str.split('-').str[0]
    emissions = costs.loc[suptechs, emissions_cols].fillna(0.)
    emissions.index = missing_carriers
    n.import_components_from_dataframe(emissions, 'Carrier')


def load_costs(Nyears=1., tech_costs=None, config=None, elec_config=None):
    if tech_costs is None:
        tech_costs = snakemake.input.tech_costs

    if config is None:
        config = snakemake.config['costs']

    # set all asset costs and other parameters
    costs = pd.read_csv(tech_costs, index_col=list(range(3))).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"] *= config['USD2013_to_EUR2013']

    costs = (costs.loc[idx[:,config['year'],:], "value"]
             .unstack(level=2).groupby("technology").sum(min_count=1))

    costs = costs.fillna({"CO2 intensity" : 0,
                          "FOM" : 0,
                          "VOM" : 0,
                          "discount rate" : config['discountrate'],
                          "efficiency" : 1,
                          "fuel" : 0,
                          "investment" : 0,
                          "lifetime" : 25})

    costs["capital_cost"] = ((annuity(costs["lifetime"], costs["discount rate"]) +
                             costs["FOM"]/100.) *
                             costs["investment"] * Nyears)

    costs.at['OCGT', 'fuel'] = costs.at['gas', 'fuel']
    costs.at['CCGT', 'fuel'] = costs.at['gas', 'fuel']

    costs['marginal_cost'] = costs['VOM'] + costs['fuel'] / costs['efficiency']

    costs = costs.rename(columns={"CO2 intensity": "co2_emissions"})

    costs.at['OCGT', 'co2_emissions'] = costs.at['gas', 'co2_emissions']
    costs.at['CCGT', 'co2_emissions'] = costs.at['gas', 'co2_emissions']

    costs.at['solar', 'capital_cost'] = 0.5*(costs.at['solar-rooftop', 'capital_cost'] +
                                             costs.at['solar-utility', 'capital_cost'])

    def costs_for_storage(store, link1, link2=None, max_hours=1.):
        capital_cost = link1['capital_cost'] + max_hours * store['capital_cost']
        if link2 is not None:
            capital_cost += link2['capital_cost']
        return pd.Series(dict(capital_cost=capital_cost,
                              marginal_cost=0.,
                              co2_emissions=0.))

    if elec_config is None:
        elec_config = snakemake.config['electricity']
    max_hours = elec_config['max_hours']
    costs.loc["battery"] = \
        costs_for_storage(costs.loc["battery storage"], costs.loc["battery inverter"],
                          max_hours=max_hours['battery'])
    costs.loc["H2"] = \
        costs_for_storage(costs.loc["hydrogen storage"], costs.loc["fuel cell"],
                          costs.loc["electrolysis"], max_hours=max_hours['H2'])

    for attr in ('marginal_cost', 'capital_cost'):
        overwrites = config.get(attr)
        if overwrites is not None:
            overwrites = pd.Series(overwrites)
            costs.loc[overwrites.index, attr] = overwrites

    return costs


def load_powerplants(ppl_fn=None):
    if ppl_fn is None:
        ppl_fn = snakemake.input.powerplants
    carrier_dict = {'ocgt': 'OCGT', 'ccgt': 'CCGT', 'bioenergy': 'biomass',
                    'ccgt, thermal': 'CCGT', 'hard coal': 'coal'}
    return (pd.read_csv(ppl_fn, index_col=0, dtype={'bus': 'str'})
            .powerplant.to_pypsa_names()
            .rename(columns=str.lower).drop(columns=['efficiency'])
            .replace({'carrier': carrier_dict}))


def attach_load(n):
    substation_lv_i = n.buses.index[n.buses['substation_lv']]
    regions = (gpd.read_file(snakemake.input.regions).set_index('name')
               .reindex(substation_lv_i))
    opsd_load = (pd.read_csv(snakemake.input.load, index_col=0, parse_dates=True)
                .filter(items=snakemake.config['countries']))

    scaling = snakemake.config.get('load', {}).get('scaling_factor', 1.0)
    logger.info(f"Load data scaled with scalling factor {scaling}.")
    opsd_load *= scaling

    nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index('index')

    def upsample(cntry, group):
        l = opsd_load[cntry]
        if len(group) == 1:
            return pd.DataFrame({group.index[0]: l})
        else:
            nuts3_cntry = nuts3.loc[nuts3.country == cntry]
            transfer = vtransfer.Shapes2Shapes(group, nuts3_cntry.geometry,
                                               normed=False).T.tocsr()
            gdp_n = pd.Series(transfer.dot(nuts3_cntry['gdp'].fillna(1.).values),
                              index=group.index)
            pop_n = pd.Series(transfer.dot(nuts3_cntry['pop'].fillna(1.).values),
                              index=group.index)

            # relative factors 0.6 and 0.4 have been determined from a linear
            # regression on the country to continent load data
            # (refer to vresutils.load._upsampling_weights)
            factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
            return pd.DataFrame(factors.values * l.values[:,np.newaxis],
                                index=l.index, columns=factors.index)

    load = pd.concat([upsample(cntry, group) for cntry, group
                      in regions.geometry.groupby(regions.country)], axis=1)

    n.madd("Load", substation_lv_i, bus=substation_lv_i, p_set=load)


def update_transmission_costs(n, costs, length_factor=1.0, simple_hvdc_costs=False):
    n.lines['capital_cost'] = (n.lines['length'] * length_factor *
                               costs.at['HVAC overhead', 'capital_cost'])

    if n.links.empty: return

    dc_b = n.links.carrier == 'DC'
    if simple_hvdc_costs:
        costs = (n.links.loc[dc_b, 'length'] * length_factor *
                 costs.at['HVDC overhead', 'capital_cost'])
    else:
        costs = (n.links.loc[dc_b, 'length'] * length_factor *
                ((1. - n.links.loc[dc_b, 'underwater_fraction']) *
                costs.at['HVDC overhead', 'capital_cost'] +
                n.links.loc[dc_b, 'underwater_fraction'] *
                costs.at['HVDC submarine', 'capital_cost']) +
                costs.at['HVDC inverter pair', 'capital_cost'])
    n.links.loc[dc_b, 'capital_cost'] = costs


def attach_wind_and_solar(n, costs):
    for tech in snakemake.config['renewable']:
        if tech == 'hydro': continue

        n.add("Carrier", name=tech)
        with xr.open_dataset(getattr(snakemake.input, 'profile_' + tech)) as ds:
            if ds.indexes['bus'].empty: continue

            suptech = tech.split('-', 2)[0]
            if suptech == 'offwind':
                underwater_fraction = ds['underwater_fraction'].to_pandas()
                connection_cost = (snakemake.config['lines']['length_factor'] *
                                   ds['average_distance'].to_pandas() *
                                   (underwater_fraction *
                                    costs.at[tech + '-connection-submarine', 'capital_cost'] +
                                    (1. - underwater_fraction) *
                                    costs.at[tech + '-connection-underground', 'capital_cost']))
                capital_cost = (costs.at['offwind', 'capital_cost'] +
                                costs.at[tech + '-station', 'capital_cost'] +
                                connection_cost)
                logger.info("Added connection cost of {:0.0f}-{:0.0f} Eur/MW/a to {}"
                            .format(connection_cost.min(), connection_cost.max(), tech))
            else:
                capital_cost = costs.at[tech, 'capital_cost']

            n.madd("Generator", ds.indexes['bus'], ' ' + tech,
                   bus=ds.indexes['bus'],
                   carrier=tech,
                   p_nom_extendable=True,
                   p_nom_max=ds['p_nom_max'].to_pandas(),
                   weight=ds['weight'].to_pandas(),
                   marginal_cost=costs.at[suptech, 'marginal_cost'],
                   capital_cost=capital_cost,
                   efficiency=costs.at[suptech, 'efficiency'],
                   p_max_pu=ds['profile'].transpose('time', 'bus').to_pandas())


def attach_conventional_generators(n, costs, ppl):
    carriers = snakemake.config['electricity']['conventional_carriers']

    _add_missing_carriers_from_costs(n, costs, carriers)

    ppl = (ppl.query('carrier in @carriers').join(costs, on='carrier')
           .rename(index=lambda s: 'C' + str(s)))

    logger.info('Adding {} generators with capacities\n{}'
                .format(len(ppl), ppl.groupby('carrier').p_nom.sum()))

    n.madd("Generator", ppl.index,
           carrier=ppl.carrier,
           bus=ppl.bus,
           p_nom=ppl.p_nom,
           efficiency=ppl.efficiency,
           marginal_cost=ppl.marginal_cost,
           capital_cost=0)

    logger.warning(f'Capital costs for conventional generators put to 0 EUR/MW.')


def attach_hydro(n, costs, ppl):
    if 'hydro' not in snakemake.config['renewable']: return
    c = snakemake.config['renewable']['hydro']
    carriers = c.get('carriers', ['ror', 'PHS', 'hydro'])

    _add_missing_carriers_from_costs(n, costs, carriers)

    ppl = ppl.query('carrier == "hydro"').reset_index(drop=True)\
             .rename(index=lambda s: str(s) + ' hydro')
    ror = ppl.query('technology == "Run-Of-River"')
    phs = ppl.query('technology == "Pumped Storage"')
    hydro = ppl.query('technology == "Reservoir"')

    country = ppl['bus'].map(n.buses.country).rename("country")

    inflow_idx = ror.index | hydro.index
    if not inflow_idx.empty:
        dist_key = ppl.loc[inflow_idx, 'p_nom'].groupby(country).transform(normed)

        with xr.open_dataarray(snakemake.input.profile_hydro) as inflow:
            inflow_countries = pd.Index(country[inflow_idx])
            missing_c = (inflow_countries.unique()
                         .difference(inflow.indexes['countries']))
            assert missing_c.empty, (f"'{snakemake.input.profile_hydro}' is missing "
                f"inflow time-series for at least one country: {', '.join(missing_c)}")

            inflow_t = (inflow.sel(countries=inflow_countries)
                        .rename({'countries': 'name'})
                        .assign_coords(name=inflow_idx)
                        .transpose('time', 'name')
                        .to_pandas()
                        .multiply(dist_key, axis=1))

    if 'ror' in carriers and not ror.empty:
        n.madd("Generator", ror.index,
               carrier='ror',
               bus=ror['bus'],
               p_nom=ror['p_nom'],
               efficiency=costs.at['ror', 'efficiency'],
               capital_cost=costs.at['ror', 'capital_cost'],
               weight=ror['p_nom'],
               p_max_pu=(inflow_t[ror.index]
                         .divide(ror['p_nom'], axis=1)
                         .where(lambda df: df<=1., other=1.)))

    if 'PHS' in carriers and not phs.empty:
        # fill missing max hours to config value and
        # assume no natural inflow due to lack of data
        phs = phs.replace({'max_hours': {0: c['PHS_max_hours']}})
        n.madd('StorageUnit', phs.index,
               carrier='PHS',
               bus=phs['bus'],
               p_nom=phs['p_nom'],
               capital_cost=costs.at['PHS', 'capital_cost'],
               max_hours=phs['max_hours'],
               efficiency_store=np.sqrt(costs.at['PHS','efficiency']),
               efficiency_dispatch=np.sqrt(costs.at['PHS','efficiency']),
               cyclic_state_of_charge=True)

    if 'hydro' in carriers and not hydro.empty:
        hydro_max_hours = c.get('hydro_max_hours')
        hydro_stats = pd.read_csv(snakemake.input.hydro_capacities,
                                   comment="#", na_values='-', index_col=0)
        e_target = hydro_stats["E_store[TWh]"].clip(lower=0.2) * 1e6
        e_installed = hydro.eval('p_nom * max_hours').groupby(hydro.country).sum()
        e_missing = e_target - e_installed
        missing_mh_i = hydro.query('max_hours == 0').index

        if hydro_max_hours == 'energy_capacity_totals_by_country':
            # watch out some p_nom values like IE's are totally underrepresented
            max_hours_country = e_missing / \
                                hydro.loc[missing_mh_i].groupby('country').p_nom.sum()

        elif hydro_max_hours == 'estimate_by_large_installations':
            max_hours_country = hydro_stats['E_store[TWh]'] * 1e3 / \
                                hydro_stats['p_nom_discharge[GW]']

        missing_countries = (pd.Index(hydro['country'].unique())
                             .difference(max_hours_country.dropna().index))
        if not missing_countries.empty:
            logger.warning("Assuming max_hours=6 for hydro reservoirs in the countries: {}"
                           .format(", ".join(missing_countries)))
        hydro_max_hours = hydro.max_hours.where(hydro.max_hours > 0,
                                hydro.country.map(max_hours_country)).fillna(6)

        n.madd('StorageUnit', hydro.index, carrier='hydro',
               bus=hydro['bus'],
               p_nom=hydro['p_nom'],
               max_hours=hydro_max_hours,
               capital_cost=(costs.at['hydro', 'capital_cost']
                             if c.get('hydro_capital_cost') else 0.),
               marginal_cost=costs.at['hydro', 'marginal_cost'],
               p_max_pu=1.,  # dispatch
               p_min_pu=0.,  # store
               efficiency_dispatch=costs.at['hydro', 'efficiency'],
               efficiency_store=0.,
               cyclic_state_of_charge=True,
               inflow=inflow_t.loc[:, hydro.index])


def attach_extendable_generators(n, costs, ppl):
    elec_opts = snakemake.config['electricity']
    carriers = pd.Index(elec_opts['extendable_carriers']['Generator'])

    _add_missing_carriers_from_costs(n, costs, carriers)

    for tech in carriers:
        if tech.startswith('OCGT'):
            ocgt = ppl.query("carrier in ['OCGT', 'CCGT']").groupby('bus', as_index=False).first()
            n.madd('Generator', ocgt.index,
                   suffix=' OCGT',
                   bus=ocgt['bus'],
                   carrier=tech,
                   p_nom_extendable=True,
                   p_nom=0.,
                   capital_cost=costs.at['OCGT', 'capital_cost'],
                   marginal_cost=costs.at['OCGT', 'marginal_cost'],
                   efficiency=costs.at['OCGT', 'efficiency'])

        elif tech.startswith('CCGT'):
            ccgt = ppl.query("carrier in ['OCGT', 'CCGT']").groupby('bus', as_index=False).first()
            n.madd('Generator', ccgt.index,
                   suffix=' CCGT',
                   bus=ccgt['bus'],
                   carrier=tech,
                   p_nom_extendable=True,
                   p_nom=0.,
                   capital_cost=costs.at['CCGT', 'capital_cost'],
                   marginal_cost=costs.at['CCGT', 'marginal_cost'],
                   efficiency=costs.at['CCGT', 'efficiency'])

        elif tech.startswith('nuclear'):
            nuclear = ppl.query("carrier == 'nuclear'").groupby('bus', as_index=False).first()
            n.madd('Generator', nuclear.index,
                suffix=' nuclear',
                bus=nuclear['bus'],
                carrier=tech,
                p_nom_extendable=True,
                p_nom=0.,
                capital_cost=costs.at['nuclear', 'capital_cost'],
                marginal_cost=costs.at['nuclear', 'marginal_cost'],
                efficiency=costs.at['nuclear', 'efficiency'])

        else:
            raise NotImplementedError(f"Adding extendable generators for carrier "
                                      "'{tech}' is not implemented, yet. "
                                      "Only OCGT, CCGT and nuclear are allowed at the moment.")


def estimate_renewable_capacities(n, tech_map=None):
    if tech_map is None:
        tech_map = (snakemake.config['electricity']
                    .get('estimate_renewable_capacities_from_capacity_stats', {}))

    if len(tech_map) == 0: return

    capacities = (ppm.data.Capacity_stats().powerplant.convert_country_to_alpha2()
                  [lambda df: df.Energy_Source_Level_2]
                  .set_index(['Fueltype', 'Country']).sort_index())

    countries = n.buses.country.unique()

    for ppm_fueltype, techs in tech_map.items():
        tech_capacities = capacities.loc[ppm_fueltype, 'Capacity']\
                                    .reindex(countries, fill_value=0.)
        tech_i = n.generators.query('carrier in @techs').index
        n.generators.loc[tech_i, 'p_nom'] = (
            (n.generators_t.p_max_pu[tech_i].mean() *
             n.generators.loc[tech_i, 'p_nom_max']) # maximal yearly generation
             .groupby(n.generators.bus.map(n.buses.country))
             .transform(lambda s: normed(s) * tech_capacities.at[s.name])
             .where(lambda s: s>0.1, 0.))  # only capacities above 100kW


def add_nice_carrier_names(n, config=None):
    if config is None: config = snakemake.config
    carrier_i = n.carriers.index
    nice_names = (pd.Series(config['plotting']['nice_names'])
                  .reindex(carrier_i).fillna(carrier_i.to_series().str.title()))
    n.carriers['nice_name'] = nice_names
    colors = pd.Series(config['plotting']['tech_colors']).reindex(carrier_i)
    if colors.isna().any():
        missing_i = list(colors.index[colors.isna()])
        logger.warning(f'tech_colors for carriers {missing_i} not defined '
                       'in config.')
    n.carriers['color'] = colors


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

    n = pypsa.Network(snakemake.input.base_network)
    Nyears = n.snapshot_weightings.sum() / 8760.

    costs = load_costs(Nyears)
    ppl = load_powerplants()

    attach_load(n)

    update_transmission_costs(n, costs)

    attach_conventional_generators(n, costs, ppl)
    attach_wind_and_solar(n, costs)
    attach_hydro(n, costs, ppl)
    attach_extendable_generators(n, costs, ppl)

    estimate_renewable_capacities(n)
    add_nice_carrier_names(n)

    n.export_to_netcdf(snakemake.output[0])