1a477d6b32
Now the script build_district_heat_share.py does what the old function create_nodes_for_heating() in prepare_sector_networks.py did. There is no need to build nodes lists for each heating sector, since all nodes have district heating now.
3749 lines
124 KiB
Python
Executable File
3749 lines
124 KiB
Python
Executable File
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Adds all sector-coupling components to the network, including demand and supply
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technologies for the buildings, transport and industry sectors.
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"""
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import logging
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import os
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import re
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from itertools import product
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from types import SimpleNamespace
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import networkx as nx
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import numpy as np
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import pandas as pd
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import pypsa
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import xarray as xr
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from _helpers import update_config_with_sector_opts
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from add_electricity import calculate_annuity, sanitize_carriers
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from build_energy_totals import build_co2_totals, build_eea_co2, build_eurostat_co2
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from networkx.algorithms import complement
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from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation
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from packaging.version import Version, parse
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from pypsa.geo import haversine_pts
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from pypsa.io import import_components_from_dataframe
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from scipy.stats import beta
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spatial = SimpleNamespace()
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logger = logging.getLogger(__name__)
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pd_version = parse(pd.__version__)
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agg_group_kwargs = dict(numeric_only=False) if pd_version >= Version("1.3") else {}
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def define_spatial(nodes, options):
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"""
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Namespace for spatial.
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Parameters
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----------
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nodes : list-like
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"""
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global spatial
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spatial.nodes = nodes
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# biomass
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spatial.biomass = SimpleNamespace()
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if options.get("biomass_spatial", options["biomass_transport"]):
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spatial.biomass.nodes = nodes + " solid biomass"
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spatial.biomass.locations = nodes
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spatial.biomass.industry = nodes + " solid biomass for industry"
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spatial.biomass.industry_cc = nodes + " solid biomass for industry CC"
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else:
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spatial.biomass.nodes = ["EU solid biomass"]
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spatial.biomass.locations = ["EU"]
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spatial.biomass.industry = ["solid biomass for industry"]
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spatial.biomass.industry_cc = ["solid biomass for industry CC"]
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spatial.biomass.df = pd.DataFrame(vars(spatial.biomass), index=nodes)
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# co2
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spatial.co2 = SimpleNamespace()
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if options["co2_spatial"]:
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spatial.co2.nodes = nodes + " co2 stored"
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spatial.co2.locations = nodes
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spatial.co2.vents = nodes + " co2 vent"
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spatial.co2.process_emissions = nodes + " process emissions"
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else:
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spatial.co2.nodes = ["co2 stored"]
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spatial.co2.locations = ["EU"]
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spatial.co2.vents = ["co2 vent"]
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spatial.co2.process_emissions = ["process emissions"]
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spatial.co2.df = pd.DataFrame(vars(spatial.co2), index=nodes)
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# gas
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spatial.gas = SimpleNamespace()
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if options["gas_network"]:
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spatial.gas.nodes = nodes + " gas"
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spatial.gas.locations = nodes
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spatial.gas.biogas = nodes + " biogas"
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spatial.gas.industry = nodes + " gas for industry"
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spatial.gas.industry_cc = nodes + " gas for industry CC"
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spatial.gas.biogas_to_gas = nodes + " biogas to gas"
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spatial.gas.biogas_to_gas_cc = nodes + "biogas to gas CC"
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else:
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spatial.gas.nodes = ["EU gas"]
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spatial.gas.locations = ["EU"]
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spatial.gas.biogas = ["EU biogas"]
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spatial.gas.industry = ["gas for industry"]
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spatial.gas.biogas_to_gas = ["EU biogas to gas"]
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if options.get("biomass_spatial", options["biomass_transport"]):
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spatial.gas.biogas_to_gas_cc = nodes + " biogas to gas CC"
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else:
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spatial.gas.biogas_to_gas_cc = ["EU biogas to gas CC"]
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if options.get("co2_spatial", options["co2network"]):
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spatial.gas.industry_cc = nodes + " gas for industry CC"
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else:
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spatial.gas.industry_cc = ["gas for industry CC"]
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spatial.gas.df = pd.DataFrame(vars(spatial.gas), index=nodes)
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# ammonia
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if options.get("ammonia"):
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spatial.ammonia = SimpleNamespace()
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if options.get("ammonia") == "regional":
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spatial.ammonia.nodes = nodes + " NH3"
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spatial.ammonia.locations = nodes
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else:
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spatial.ammonia.nodes = ["EU NH3"]
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spatial.ammonia.locations = ["EU"]
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spatial.ammonia.df = pd.DataFrame(vars(spatial.ammonia), index=nodes)
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# hydrogen
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spatial.h2 = SimpleNamespace()
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spatial.h2.nodes = nodes + " H2"
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spatial.h2.locations = nodes
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# methanol
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# beware: unlike other carriers, uses locations rather than locations+carriername
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# this allows to avoid separation between nodes and locations
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spatial.methanol = SimpleNamespace()
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spatial.methanol.nodes = ["EU methanol"]
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spatial.methanol.locations = ["EU"]
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if options["regional_methanol_demand"]:
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spatial.methanol.demand_locations = nodes
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spatial.methanol.shipping = nodes + " shipping methanol"
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else:
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spatial.methanol.demand_locations = ["EU"]
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spatial.methanol.shipping = ["EU shipping methanol"]
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# oil
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spatial.oil = SimpleNamespace()
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spatial.oil.nodes = ["EU oil"]
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spatial.oil.locations = ["EU"]
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if options["regional_oil_demand"]:
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spatial.oil.demand_locations = nodes
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spatial.oil.naphtha = nodes + " naphtha for industry"
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spatial.oil.kerosene = nodes + " kerosene for aviation"
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spatial.oil.shipping = nodes + " shipping oil"
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spatial.oil.agriculture_machinery = nodes + " agriculture machinery oil"
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spatial.oil.land_transport = nodes + " land transport oil"
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else:
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spatial.oil.demand_locations = ["EU"]
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spatial.oil.naphtha = ["EU naphtha for industry"]
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spatial.oil.kerosene = ["EU kerosene for aviation"]
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spatial.oil.shipping = ["EU shipping oil"]
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spatial.oil.agriculture_machinery = ["EU agriculture machinery oil"]
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spatial.oil.land_transport = ["EU land transport oil"]
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# uranium
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spatial.uranium = SimpleNamespace()
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spatial.uranium.nodes = ["EU uranium"]
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spatial.uranium.locations = ["EU"]
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# coal
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spatial.coal = SimpleNamespace()
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spatial.coal.nodes = ["EU coal"]
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spatial.coal.locations = ["EU"]
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# lignite
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spatial.lignite = SimpleNamespace()
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spatial.lignite.nodes = ["EU lignite"]
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spatial.lignite.locations = ["EU"]
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return spatial
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spatial = SimpleNamespace()
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def emission_sectors_from_opts(opts):
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sectors = ["electricity"]
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if "T" in opts:
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sectors += ["rail non-elec", "road non-elec"]
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if "H" in opts:
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sectors += ["residential non-elec", "services non-elec"]
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if "I" in opts:
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sectors += [
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"industrial non-elec",
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"industrial processes",
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"domestic aviation",
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"international aviation",
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"domestic navigation",
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"international navigation",
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]
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if "A" in opts:
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sectors += ["agriculture"]
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return sectors
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def get(item, investment_year=None):
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"""
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Check whether item depends on investment year.
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"""
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return item[investment_year] if isinstance(item, dict) else item
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def co2_emissions_year(
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countries, input_eurostat, opts, emissions_scope, report_year, input_co2, year
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):
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"""
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Calculate CO2 emissions in one specific year (e.g. 1990 or 2018).
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"""
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eea_co2 = build_eea_co2(input_co2, year, emissions_scope)
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# TODO: read Eurostat data from year > 2014
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# this only affects the estimation of CO2 emissions for BA, RS, AL, ME, MK
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if year > 2014:
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eurostat_co2 = build_eurostat_co2(
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input_eurostat, countries, report_year, year=2014
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)
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else:
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eurostat_co2 = build_eurostat_co2(input_eurostat, countries, report_year, year)
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co2_totals = build_co2_totals(countries, eea_co2, eurostat_co2)
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sectors = emission_sectors_from_opts(opts)
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co2_emissions = co2_totals.loc[countries, sectors].sum().sum()
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# convert MtCO2 to GtCO2
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co2_emissions *= 0.001
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return co2_emissions
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# TODO: move to own rule with sector-opts wildcard?
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def build_carbon_budget(o, input_eurostat, fn, emissions_scope, report_year):
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"""
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Distribute carbon budget following beta or exponential transition path.
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"""
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# opts?
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if "be" in o:
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# beta decay
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carbon_budget = float(o[o.find("cb") + 2 : o.find("be")])
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be = float(o[o.find("be") + 2 :])
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if "ex" in o:
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# exponential decay
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carbon_budget = float(o[o.find("cb") + 2 : o.find("ex")])
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r = float(o[o.find("ex") + 2 :])
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countries = snakemake.params.countries
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e_1990 = co2_emissions_year(
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countries,
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input_eurostat,
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opts,
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emissions_scope,
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report_year,
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input_co2,
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year=1990,
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)
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# emissions at the beginning of the path (last year available 2018)
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e_0 = co2_emissions_year(
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countries,
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input_eurostat,
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opts,
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emissions_scope,
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report_year,
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input_co2,
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year=2018,
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)
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planning_horizons = snakemake.params.planning_horizons
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if not isinstance(planning_horizons, list):
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planning_horizons = [planning_horizons]
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t_0 = planning_horizons[0]
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if "be" in o:
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# final year in the path
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t_f = t_0 + (2 * carbon_budget / e_0).round(0)
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def beta_decay(t):
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cdf_term = (t - t_0) / (t_f - t_0)
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return (e_0 / e_1990) * (1 - beta.cdf(cdf_term, be, be))
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# emissions (relative to 1990)
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co2_cap = pd.Series({t: beta_decay(t) for t in planning_horizons}, name=o)
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if "ex" in o:
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T = carbon_budget / e_0
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m = (1 + np.sqrt(1 + r * T)) / T
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def exponential_decay(t):
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return (e_0 / e_1990) * (1 + (m + r) * (t - t_0)) * np.exp(-m * (t - t_0))
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co2_cap = pd.Series(
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{t: exponential_decay(t) for t in planning_horizons}, name=o
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)
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# TODO log in Snakefile
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csvs_folder = fn.rsplit("/", 1)[0]
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if not os.path.exists(csvs_folder):
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os.makedirs(csvs_folder)
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co2_cap.to_csv(fn, float_format="%.3f")
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def add_lifetime_wind_solar(n, costs):
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"""
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Add lifetime for solar and wind generators.
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"""
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for carrier in ["solar", "onwind", "offwind"]:
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gen_i = n.generators.index.str.contains(carrier)
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n.generators.loc[gen_i, "lifetime"] = costs.at[carrier, "lifetime"]
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def haversine(p):
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coord0 = n.buses.loc[p.bus0, ["x", "y"]].values
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coord1 = n.buses.loc[p.bus1, ["x", "y"]].values
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return 1.5 * haversine_pts(coord0, coord1)
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def create_network_topology(
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n, prefix, carriers=["DC"], connector=" -> ", bidirectional=True
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):
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"""
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Create a network topology from transmission lines and link carrier
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selection.
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Parameters
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----------
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n : pypsa.Network
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prefix : str
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carriers : list-like
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connector : str
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bidirectional : bool, default True
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True: one link for each connection
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False: one link for each connection and direction (back and forth)
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Returns
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-------
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pd.DataFrame with columns bus0, bus1, length, underwater_fraction
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"""
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ln_attrs = ["bus0", "bus1", "length"]
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lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]
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lk_attrs = n.links.columns.intersection(lk_attrs)
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candidates = pd.concat(
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[n.lines[ln_attrs], n.links.loc[n.links.carrier.isin(carriers), lk_attrs]]
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).fillna(0)
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# base network topology purely on location not carrier
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candidates["bus0"] = candidates.bus0.map(n.buses.location)
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candidates["bus1"] = candidates.bus1.map(n.buses.location)
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positive_order = candidates.bus0 < candidates.bus1
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candidates_p = candidates[positive_order]
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swap_buses = {"bus0": "bus1", "bus1": "bus0"}
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candidates_n = candidates[~positive_order].rename(columns=swap_buses)
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candidates = pd.concat([candidates_p, candidates_n])
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def make_index(c):
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return prefix + c.bus0 + connector + c.bus1
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topo = candidates.groupby(["bus0", "bus1"], as_index=False).mean()
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topo.index = topo.apply(make_index, axis=1)
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if not bidirectional:
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topo_reverse = topo.copy()
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topo_reverse.rename(columns=swap_buses, inplace=True)
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topo_reverse.index = topo_reverse.apply(make_index, axis=1)
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topo = pd.concat([topo, topo_reverse])
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return topo
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# TODO merge issue with PyPSA-Eur
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def update_wind_solar_costs(n, costs):
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"""
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Update costs for wind and solar generators added with pypsa-eur to those
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cost in the planning year.
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"""
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# NB: solar costs are also manipulated for rooftop
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# when distribution grid is inserted
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n.generators.loc[n.generators.carrier == "solar", "capital_cost"] = costs.at[
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"solar-utility", "fixed"
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]
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n.generators.loc[n.generators.carrier == "onwind", "capital_cost"] = costs.at[
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"onwind", "fixed"
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]
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# for offshore wind, need to calculated connection costs
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# assign clustered bus
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# map initial network -> simplified network
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busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0).squeeze()
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busmap_s.index = busmap_s.index.astype(str)
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busmap_s = busmap_s.astype(str)
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# map simplified network -> clustered network
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busmap = pd.read_csv(snakemake.input.busmap, index_col=0).squeeze()
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busmap.index = busmap.index.astype(str)
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busmap = busmap.astype(str)
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# map initial network -> clustered network
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clustermaps = busmap_s.map(busmap)
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# code adapted from pypsa-eur/scripts/add_electricity.py
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for connection in ["dc", "ac"]:
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tech = "offwind-" + connection
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profile = snakemake.input["profile_offwind_" + connection]
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with xr.open_dataset(profile) as ds:
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underwater_fraction = ds["underwater_fraction"].to_pandas()
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connection_cost = (
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snakemake.params.length_factor
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* ds["average_distance"].to_pandas()
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* (
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underwater_fraction
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* costs.at[tech + "-connection-submarine", "fixed"]
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+ (1.0 - underwater_fraction)
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* costs.at[tech + "-connection-underground", "fixed"]
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)
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)
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# convert to aggregated clusters with weighting
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weight = ds["weight"].to_pandas()
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# e.g. clusters == 37m means that VRE generators are left
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# at clustering of simplified network, but that they are
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# connected to 37-node network
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genmap = (
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busmap_s if snakemake.wildcards.clusters[-1:] == "m" else clustermaps
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)
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connection_cost = (connection_cost * weight).groupby(
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genmap
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).sum() / weight.groupby(genmap).sum()
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capital_cost = (
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costs.at["offwind", "fixed"]
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+ costs.at[tech + "-station", "fixed"]
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+ connection_cost
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)
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logger.info(
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"Added connection cost of {:0.0f}-{:0.0f} Eur/MW/a to {}".format(
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connection_cost.min(), connection_cost.max(), tech
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)
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)
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n.generators.loc[
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n.generators.carrier == tech, "capital_cost"
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] = capital_cost.rename(index=lambda node: node + " " + tech)
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def add_carrier_buses(n, carrier, nodes=None):
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"""
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Add buses to connect e.g. coal, nuclear and oil plants.
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"""
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if nodes is None:
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nodes = vars(spatial)[carrier].nodes
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location = vars(spatial)[carrier].locations
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# skip if carrier already exists
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if carrier in n.carriers.index:
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return
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if not isinstance(nodes, pd.Index):
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nodes = pd.Index(nodes)
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n.add("Carrier", carrier)
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unit = "MWh_LHV" if carrier == "gas" else "MWh_th"
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# preliminary value for non-gas carriers to avoid zeros
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capital_cost = costs.at["gas storage", "fixed"] if carrier == "gas" else 0.02
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n.madd("Bus", nodes, location=location, carrier=carrier, unit=unit)
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n.madd(
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"Store",
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nodes + " Store",
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bus=nodes,
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e_nom_extendable=True,
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e_cyclic=True,
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carrier=carrier,
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capital_cost=capital_cost,
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)
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n.madd(
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"Generator",
|
|
nodes,
|
|
bus=nodes,
|
|
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.params.pypsa_eur):
|
|
to_keep = snakemake.params.pypsa_eur[c.name]
|
|
to_remove = pd.Index(c.df.carrier.unique()).symmetric_difference(to_keep)
|
|
if to_remove.empty:
|
|
continue
|
|
logger.info(f"Removing {c.list_name} with carrier {list(to_remove)}")
|
|
names = c.df.index[c.df.carrier.isin(to_remove)]
|
|
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.
|
|
"""
|
|
if to_drop := list(n.buses.query("carrier not in ['AC', 'DC']").carrier.unique()):
|
|
logger.info(f"Drop buses from PyPSA-Eur with carrier: {to_drop}")
|
|
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
|
|
n.buses["unit"] = "MWh_el"
|
|
# 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, costs, options):
|
|
# minus sign because opposite to how fossil fuels used:
|
|
# CH4 burning puts CH4 down, atmosphere up
|
|
n.add("Carrier", "co2", co2_emissions=-1.0)
|
|
|
|
# this tracks CO2 in the atmosphere
|
|
n.add("Bus", "co2 atmosphere", location="EU", carrier="co2", unit="t_co2")
|
|
|
|
# can also be negative
|
|
n.add(
|
|
"Store",
|
|
"co2 atmosphere",
|
|
e_nom_extendable=True,
|
|
e_min_pu=-1,
|
|
carrier="co2",
|
|
bus="co2 atmosphere",
|
|
)
|
|
|
|
# add CO2 tanks
|
|
n.madd(
|
|
"Bus",
|
|
spatial.co2.nodes,
|
|
location=spatial.co2.locations,
|
|
carrier="co2 stored",
|
|
unit="t_co2",
|
|
)
|
|
|
|
n.madd(
|
|
"Store",
|
|
spatial.co2.nodes,
|
|
e_nom_extendable=True,
|
|
capital_cost=costs.at["CO2 storage tank", "fixed"],
|
|
carrier="co2 stored",
|
|
e_cyclic=True,
|
|
bus=spatial.co2.nodes,
|
|
)
|
|
n.add("Carrier", "co2 stored")
|
|
|
|
# this tracks CO2 sequestered, e.g. underground
|
|
sequestration_buses = pd.Index(spatial.co2.nodes).str.replace(
|
|
" stored", " sequestered"
|
|
)
|
|
n.madd(
|
|
"Bus",
|
|
sequestration_buses,
|
|
location=spatial.co2.locations,
|
|
carrier="co2 sequestered",
|
|
unit="t_co2",
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
sequestration_buses,
|
|
bus0=spatial.co2.nodes,
|
|
bus1=sequestration_buses,
|
|
carrier="co2 sequestered",
|
|
efficiency=1.0,
|
|
p_nom_extendable=True,
|
|
)
|
|
|
|
if options["regional_co2_sequestration_potential"]["enable"]:
|
|
upper_limit = (
|
|
options["regional_co2_sequestration_potential"]["max_size"] * 1e3
|
|
) # Mt
|
|
annualiser = options["regional_co2_sequestration_potential"]["years_of_storage"]
|
|
e_nom_max = pd.read_csv(
|
|
snakemake.input.sequestration_potential, index_col=0
|
|
).squeeze()
|
|
e_nom_max = (
|
|
e_nom_max.reindex(spatial.co2.locations)
|
|
.fillna(0.0)
|
|
.clip(upper=upper_limit)
|
|
.mul(1e6)
|
|
/ annualiser
|
|
) # t
|
|
e_nom_max = e_nom_max.rename(index=lambda x: x + " co2 sequestered")
|
|
else:
|
|
e_nom_max = np.inf
|
|
|
|
n.madd(
|
|
"Store",
|
|
sequestration_buses,
|
|
e_nom_extendable=True,
|
|
e_nom_max=e_nom_max,
|
|
capital_cost=options["co2_sequestration_cost"],
|
|
bus=sequestration_buses,
|
|
lifetime=options["co2_sequestration_lifetime"],
|
|
carrier="co2 sequestered",
|
|
)
|
|
|
|
n.add("Carrier", "co2 sequestered")
|
|
|
|
if options["co2_vent"]:
|
|
n.madd(
|
|
"Link",
|
|
spatial.co2.vents,
|
|
bus0=spatial.co2.nodes,
|
|
bus1="co2 atmosphere",
|
|
carrier="co2 vent",
|
|
efficiency=1.0,
|
|
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
|
|
cost_factor = snakemake.config["sector"]["co2_network_cost_factor"]
|
|
capital_cost *= cost_factor
|
|
|
|
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_allam(n, costs):
|
|
logger.info("Adding Allam cycle gas power plants.")
|
|
|
|
nodes = pop_layout.index
|
|
|
|
n.madd(
|
|
"Link",
|
|
nodes,
|
|
suffix=" allam",
|
|
bus0=spatial.gas.df.loc[nodes, "nodes"].values,
|
|
bus1=nodes,
|
|
bus2=spatial.co2.df.loc[nodes, "nodes"].values,
|
|
carrier="allam",
|
|
p_nom_extendable=True,
|
|
# TODO: add costs to technology-data
|
|
capital_cost=0.6 * 1.5e6 * 0.1, # efficiency * EUR/MW * annuity
|
|
marginal_cost=2,
|
|
efficiency=0.6,
|
|
efficiency2=costs.at["gas", "CO2 intensity"],
|
|
lifetime=30.0,
|
|
)
|
|
|
|
|
|
def add_dac(n, costs):
|
|
heat_carriers = ["urban central heat", "services urban decentral 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",
|
|
heat_buses.str.replace(" heat", " 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=1.0,
|
|
efficiency2=efficiency2,
|
|
efficiency3=efficiency3,
|
|
p_nom_extendable=True,
|
|
lifetime=costs.at["direct air capture", "lifetime"],
|
|
)
|
|
|
|
|
|
def add_co2limit(n, nyears=1.0, limit=0.0):
|
|
logger.info(f"Adding CO2 budget limit as per unit of 1990 levels of {limit}")
|
|
|
|
countries = snakemake.params.countries
|
|
|
|
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="<=",
|
|
type="co2_atmosphere",
|
|
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)
|
|
|
|
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 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 prepare_costs(cost_file, params, nyears):
|
|
# 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
|
|
|
|
# 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(params["fill_values"])
|
|
|
|
def annuity_factor(v):
|
|
return calculate_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):
|
|
logger.info("Adding electricity generation")
|
|
|
|
nodes = pop_layout.index
|
|
|
|
fallback = {"OCGT": "gas"}
|
|
conventionals = options.get("conventional_generation", fallback)
|
|
|
|
for generator, carrier in conventionals.items():
|
|
carrier_nodes = vars(spatial)[carrier].nodes
|
|
|
|
add_carrier_buses(n, carrier, carrier_nodes)
|
|
|
|
n.madd(
|
|
"Link",
|
|
nodes + " " + generator,
|
|
bus0=carrier_nodes,
|
|
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_ammonia(n, costs):
|
|
logger.info("Adding ammonia carrier with synthesis, cracking and storage")
|
|
|
|
nodes = pop_layout.index
|
|
|
|
cf_industry = snakemake.params.industry
|
|
|
|
n.add("Carrier", "NH3")
|
|
|
|
n.madd(
|
|
"Bus", spatial.ammonia.nodes, location=spatial.ammonia.locations, carrier="NH3"
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
nodes,
|
|
suffix=" Haber-Bosch",
|
|
bus0=nodes,
|
|
bus1=spatial.ammonia.nodes,
|
|
bus2=nodes + " H2",
|
|
p_nom_extendable=True,
|
|
carrier="Haber-Bosch",
|
|
efficiency=1 / costs.at["Haber-Bosch", "electricity-input"],
|
|
efficiency2=-costs.at["Haber-Bosch", "hydrogen-input"]
|
|
/ costs.at["Haber-Bosch", "electricity-input"],
|
|
capital_cost=costs.at["Haber-Bosch", "fixed"]
|
|
/ costs.at["Haber-Bosch", "electricity-input"],
|
|
marginal_cost=costs.at["Haber-Bosch", "VOM"]
|
|
/ costs.at["Haber-Bosch", "electricity-input"],
|
|
lifetime=costs.at["Haber-Bosch", "lifetime"],
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
nodes,
|
|
suffix=" ammonia cracker",
|
|
bus0=spatial.ammonia.nodes,
|
|
bus1=nodes + " H2",
|
|
p_nom_extendable=True,
|
|
carrier="ammonia cracker",
|
|
efficiency=1 / cf_industry["MWh_NH3_per_MWh_H2_cracker"],
|
|
capital_cost=costs.at["Ammonia cracker", "fixed"]
|
|
/ cf_industry["MWh_NH3_per_MWh_H2_cracker"], # given per MW_H2
|
|
lifetime=costs.at["Ammonia cracker", "lifetime"],
|
|
)
|
|
|
|
# Ammonia Storage
|
|
n.madd(
|
|
"Store",
|
|
spatial.ammonia.nodes,
|
|
suffix=" ammonia store",
|
|
bus=spatial.ammonia.nodes,
|
|
e_nom_extendable=True,
|
|
e_cyclic=True,
|
|
carrier="ammonia store",
|
|
capital_cost=costs.at["NH3 (l) storage tank incl. liquefaction", "fixed"],
|
|
lifetime=costs.at["NH3 (l) storage tank incl. liquefaction", "lifetime"],
|
|
)
|
|
|
|
|
|
def insert_electricity_distribution_grid(n, costs):
|
|
# TODO pop_layout?
|
|
# TODO options?
|
|
|
|
cost_factor = options["electricity_distribution_grid_cost_factor"]
|
|
|
|
logger.info(
|
|
f"Inserting electricity distribution grid with investment cost factor of {cost_factor:.2f}"
|
|
)
|
|
|
|
nodes = pop_layout.index
|
|
|
|
n.madd(
|
|
"Bus",
|
|
nodes + " low voltage",
|
|
location=nodes,
|
|
carrier="low voltage",
|
|
unit="MWh_el",
|
|
)
|
|
|
|
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" and
|
|
# "agriculture machinery electric" and "agriculture electricity"
|
|
loads = n.loads.index[n.loads.carrier.str.contains("electric")]
|
|
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",
|
|
unit="MWh_el",
|
|
)
|
|
|
|
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"]
|
|
|
|
logger.info(
|
|
f"Inserting gas distribution grid with investment cost factor of {f_costs}"
|
|
)
|
|
|
|
capital_cost = costs.at["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_and_grids(n, costs):
|
|
logger.info("Add hydrogen storage")
|
|
|
|
nodes = pop_layout.index
|
|
|
|
n.add("Carrier", "H2")
|
|
|
|
n.madd("Bus", nodes + " H2", location=nodes, carrier="H2", unit="MWh_LHV")
|
|
|
|
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"],
|
|
)
|
|
|
|
if options["hydrogen_fuel_cell"]:
|
|
logger.info("Adding hydrogen fuel cell for re-electrification.")
|
|
|
|
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"],
|
|
)
|
|
|
|
if options["hydrogen_turbine"]:
|
|
logger.info(
|
|
"Adding hydrogen turbine for re-electrification. Assuming OCGT technology costs."
|
|
)
|
|
# TODO: perhaps replace with hydrogen-specific technology assumptions.
|
|
|
|
n.madd(
|
|
"Link",
|
|
nodes + " H2 turbine",
|
|
bus0=nodes + " H2",
|
|
bus1=nodes,
|
|
p_nom_extendable=True,
|
|
carrier="H2 turbine",
|
|
efficiency=costs.at["OCGT", "efficiency"],
|
|
capital_cost=costs.at["OCGT", "fixed"]
|
|
* costs.at["OCGT", "efficiency"], # NB: fixed cost is per MWel
|
|
marginal_cost=costs.at["OCGT", "VOM"],
|
|
lifetime=costs.at["OCGT", "lifetime"],
|
|
)
|
|
|
|
cavern_types = snakemake.params.sector["hydrogen_underground_storage_locations"]
|
|
h2_caverns = pd.read_csv(snakemake.input.h2_cavern, index_col=0)
|
|
|
|
if (
|
|
not h2_caverns.empty
|
|
and options["hydrogen_underground_storage"]
|
|
and set(cavern_types).intersection(h2_caverns.columns)
|
|
):
|
|
h2_caverns = h2_caverns[cavern_types].sum(axis=1)
|
|
|
|
# only use sites with at least 2 TWh potential
|
|
h2_caverns = h2_caverns[h2_caverns > 2]
|
|
|
|
# convert TWh to MWh
|
|
h2_caverns = h2_caverns * 1e6
|
|
|
|
# clip at 1000 TWh for one location
|
|
h2_caverns.clip(upper=1e9, inplace=True)
|
|
|
|
logger.info("Add hydrogen underground storage")
|
|
|
|
h2_capital_cost = costs.at["hydrogen storage underground", "fixed"]
|
|
|
|
n.madd(
|
|
"Store",
|
|
h2_caverns.index + " H2 Store",
|
|
bus=h2_caverns.index + " H2",
|
|
e_nom_extendable=True,
|
|
e_nom_max=h2_caverns.values,
|
|
e_cyclic=True,
|
|
carrier="H2 Store",
|
|
capital_cost=h2_capital_cost,
|
|
lifetime=costs.at["hydrogen storage underground", "lifetime"],
|
|
)
|
|
|
|
# hydrogen stored overground (where not already underground)
|
|
h2_capital_cost = costs.at[
|
|
"hydrogen storage tank type 1 including compressor", "fixed"
|
|
]
|
|
nodes_overground = h2_caverns.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,
|
|
)
|
|
|
|
if options["gas_network"] or options["H2_retrofit"]:
|
|
fn = snakemake.input.clustered_gas_network
|
|
gas_pipes = pd.read_csv(fn, index_col=0)
|
|
|
|
if options["gas_network"]:
|
|
logger.info(
|
|
"Add natural gas infrastructure, incl. LNG terminals, production, storage and entry-points."
|
|
)
|
|
|
|
if options["H2_retrofit"]:
|
|
gas_pipes["p_nom_max"] = gas_pipes.p_nom
|
|
gas_pipes["p_nom_min"] = 0.0
|
|
# 0.1 EUR/MWkm/a to prefer decommissioning to address degeneracy
|
|
gas_pipes["capital_cost"] = 0.1 * gas_pipes.length
|
|
else:
|
|
gas_pipes["p_nom_max"] = np.inf
|
|
gas_pipes["p_nom_min"] = gas_pipes.p_nom
|
|
gas_pipes["capital_cost"] = (
|
|
gas_pipes.length * costs.at["CH4 (g) pipeline", "fixed"]
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
gas_pipes.index,
|
|
bus0=gas_pipes.bus0 + " gas",
|
|
bus1=gas_pipes.bus1 + " gas",
|
|
p_min_pu=gas_pipes.p_min_pu,
|
|
p_nom=gas_pipes.p_nom,
|
|
p_nom_extendable=True,
|
|
p_nom_max=gas_pipes.p_nom_max,
|
|
p_nom_min=gas_pipes.p_nom_min,
|
|
length=gas_pipes.length,
|
|
capital_cost=gas_pipes.capital_cost,
|
|
tags=gas_pipes.name,
|
|
carrier="gas pipeline",
|
|
lifetime=costs.at["CH4 (g) pipeline", "lifetime"],
|
|
)
|
|
|
|
# remove fossil generators where there is neither
|
|
# production, LNG terminal, nor entry-point beyond system scope
|
|
|
|
fn = snakemake.input.gas_input_nodes_simplified
|
|
gas_input_nodes = pd.read_csv(fn, index_col=0)
|
|
|
|
unique = gas_input_nodes.index.unique()
|
|
gas_i = n.generators.carrier == "gas"
|
|
internal_i = ~n.generators.bus.map(n.buses.location).isin(unique)
|
|
|
|
remove_i = n.generators[gas_i & internal_i].index
|
|
n.generators.drop(remove_i, inplace=True)
|
|
|
|
input_types = ["lng", "pipeline", "production"]
|
|
p_nom = gas_input_nodes[input_types].sum(axis=1).rename(lambda x: x + " gas")
|
|
n.generators.loc[gas_i, "p_nom_extendable"] = False
|
|
n.generators.loc[gas_i, "p_nom"] = p_nom
|
|
|
|
# add existing gas storage capacity
|
|
gas_i = n.stores.carrier == "gas"
|
|
e_nom = (
|
|
gas_input_nodes["storage"]
|
|
.rename(lambda x: x + " gas Store")
|
|
.reindex(n.stores.index)
|
|
.fillna(0.0)
|
|
* 1e3
|
|
) # MWh_LHV
|
|
e_nom.clip(
|
|
upper=e_nom.quantile(0.98), inplace=True
|
|
) # limit extremely large storage
|
|
n.stores.loc[gas_i, "e_nom_min"] = e_nom
|
|
|
|
# add candidates for new gas pipelines to achieve full connectivity
|
|
|
|
G = nx.Graph()
|
|
|
|
gas_buses = n.buses.loc[n.buses.carrier == "gas", "location"]
|
|
G.add_nodes_from(np.unique(gas_buses.values))
|
|
|
|
sel = gas_pipes.p_nom > 1500
|
|
attrs = ["bus0", "bus1", "length"]
|
|
G.add_weighted_edges_from(gas_pipes.loc[sel, attrs].values)
|
|
|
|
# find all complement edges
|
|
complement_edges = pd.DataFrame(complement(G).edges, columns=["bus0", "bus1"])
|
|
complement_edges["length"] = complement_edges.apply(haversine, axis=1)
|
|
|
|
# apply k_edge_augmentation weighted by length of complement edges
|
|
k_edge = options.get("gas_network_connectivity_upgrade", 3)
|
|
if augmentation := list(
|
|
k_edge_augmentation(G, k_edge, avail=complement_edges.values)
|
|
):
|
|
new_gas_pipes = pd.DataFrame(augmentation, columns=["bus0", "bus1"])
|
|
new_gas_pipes["length"] = new_gas_pipes.apply(haversine, axis=1)
|
|
|
|
new_gas_pipes.index = new_gas_pipes.apply(
|
|
lambda x: f"gas pipeline new {x.bus0} <-> {x.bus1}", axis=1
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
new_gas_pipes.index,
|
|
bus0=new_gas_pipes.bus0 + " gas",
|
|
bus1=new_gas_pipes.bus1 + " gas",
|
|
p_min_pu=-1, # new gas pipes are bidirectional
|
|
p_nom_extendable=True,
|
|
length=new_gas_pipes.length,
|
|
capital_cost=new_gas_pipes.length
|
|
* costs.at["CH4 (g) pipeline", "fixed"],
|
|
carrier="gas pipeline new",
|
|
lifetime=costs.at["CH4 (g) pipeline", "lifetime"],
|
|
)
|
|
|
|
if options["H2_retrofit"]:
|
|
logger.info("Add retrofitting options of existing CH4 pipes to H2 pipes.")
|
|
|
|
fr = "gas pipeline"
|
|
to = "H2 pipeline retrofitted"
|
|
h2_pipes = gas_pipes.rename(index=lambda x: x.replace(fr, to))
|
|
|
|
n.madd(
|
|
"Link",
|
|
h2_pipes.index,
|
|
bus0=h2_pipes.bus0 + " H2",
|
|
bus1=h2_pipes.bus1 + " H2",
|
|
p_min_pu=-1.0, # allow that all H2 retrofit pipelines can be used in both directions
|
|
p_nom_max=h2_pipes.p_nom * options["H2_retrofit_capacity_per_CH4"],
|
|
p_nom_extendable=True,
|
|
length=h2_pipes.length,
|
|
capital_cost=costs.at["H2 (g) pipeline repurposed", "fixed"]
|
|
* h2_pipes.length,
|
|
tags=h2_pipes.name,
|
|
carrier="H2 pipeline retrofitted",
|
|
lifetime=costs.at["H2 (g) pipeline repurposed", "lifetime"],
|
|
)
|
|
|
|
if options.get("H2_network", True):
|
|
logger.info("Add options for new hydrogen pipelines.")
|
|
|
|
h2_pipes = create_network_topology(
|
|
n, "H2 pipeline ", carriers=["DC", "gas pipeline"]
|
|
)
|
|
|
|
h2_pipes["p_nom"] = 0.0
|
|
|
|
if "custom_h2_pipelines" in snakemake.input:
|
|
fn = snakemake.input.custom_h2_pipelines
|
|
custom_pipes = pd.read_csv(fn, index_col=0)
|
|
|
|
h2_pipes = pd.concat([h2_pipes, custom_pipes])
|
|
|
|
# drop duplicates according to buses (order can be different) and keep pipe with highest p_nom
|
|
h2_pipes["buses_sorted"] = h2_pipes[["bus0", "bus1"]].apply(sorted, axis=1)
|
|
h2_pipes = (
|
|
h2_pipes.sort_values("p_nom")
|
|
.drop_duplicates(subset=["buses_sorted"], keep="last")
|
|
.drop(columns="buses_sorted")
|
|
)
|
|
|
|
# TODO Add efficiency losses
|
|
n.madd(
|
|
"Link",
|
|
h2_pipes.index,
|
|
bus0=h2_pipes.bus0.values + " H2",
|
|
bus1=h2_pipes.bus1.values + " H2",
|
|
p_min_pu=-1,
|
|
p_nom_extendable=True,
|
|
p_nom_min=h2_pipes.p_nom.values,
|
|
length=h2_pipes.length.values,
|
|
capital_cost=costs.at["H2 (g) pipeline", "fixed"] * h2_pipes.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", unit="MWh_el")
|
|
|
|
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=spatial.gas.nodes,
|
|
bus2=spatial.co2.nodes,
|
|
p_nom_extendable=True,
|
|
carrier="Sabatier",
|
|
p_min_pu=options.get("min_part_load_methanation", 0),
|
|
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.get("coal_cc"):
|
|
n.madd(
|
|
"Link",
|
|
spatial.nodes,
|
|
suffix=" coal CC",
|
|
bus0=spatial.coal.nodes,
|
|
bus1=spatial.nodes,
|
|
bus2="co2 atmosphere",
|
|
bus3=spatial.co2.nodes,
|
|
marginal_cost=costs.at["coal", "efficiency"]
|
|
* costs.at["coal", "VOM"], # NB: VOM is per MWel
|
|
capital_cost=costs.at["coal", "efficiency"] * costs.at["coal", "fixed"]
|
|
+ costs.at["biomass CHP capture", "fixed"]
|
|
* costs.at["coal", "CO2 intensity"], # NB: fixed cost is per MWel
|
|
p_nom_extendable=True,
|
|
carrier="coal",
|
|
efficiency=costs.at["coal", "efficiency"],
|
|
efficiency2=costs.at["coal", "CO2 intensity"]
|
|
* (1 - costs.at["biomass CHP capture", "capture_rate"]),
|
|
efficiency3=costs.at["coal", "CO2 intensity"]
|
|
* costs.at["biomass CHP capture", "capture_rate"],
|
|
lifetime=costs.at["coal", "lifetime"],
|
|
)
|
|
|
|
if options["SMR_cc"]:
|
|
n.madd(
|
|
"Link",
|
|
spatial.nodes,
|
|
suffix=" SMR CC",
|
|
bus0=spatial.gas.nodes,
|
|
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"],
|
|
)
|
|
|
|
if options["SMR"]:
|
|
n.madd(
|
|
"Link",
|
|
nodes + " SMR",
|
|
bus0=spatial.gas.nodes,
|
|
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?
|
|
|
|
logger.info("Add land transport")
|
|
|
|
transport = pd.read_csv(
|
|
snakemake.input.transport_demand, index_col=0, parse_dates=True
|
|
)
|
|
number_cars = pd.read_csv(snakemake.input.transport_data, index_col=0)[
|
|
"number cars"
|
|
]
|
|
avail_profile = pd.read_csv(
|
|
snakemake.input.avail_profile, index_col=0, parse_dates=True
|
|
)
|
|
dsm_profile = pd.read_csv(
|
|
snakemake.input.dsm_profile, index_col=0, parse_dates=True
|
|
)
|
|
|
|
fuel_cell_share = get(options["land_transport_fuel_cell_share"], investment_year)
|
|
electric_share = get(options["land_transport_electric_share"], investment_year)
|
|
ice_share = get(options["land_transport_ice_share"], investment_year)
|
|
|
|
total_share = fuel_cell_share + electric_share + ice_share
|
|
if total_share != 1:
|
|
logger.warning(
|
|
f"Total land transport shares sum up to {total_share:.2%}, corresponding to increased or decreased demand assumptions."
|
|
)
|
|
|
|
logger.info(f"FCEV share: {fuel_cell_share*100}%")
|
|
logger.info(f"EV share: {electric_share*100}%")
|
|
logger.info(f"ICEV share: {ice_share*100}%")
|
|
|
|
nodes = pop_layout.index
|
|
|
|
if electric_share > 0:
|
|
n.add("Carrier", "Li ion")
|
|
|
|
n.madd(
|
|
"Bus",
|
|
nodes,
|
|
suffix=" EV battery",
|
|
location=nodes,
|
|
carrier="Li ion",
|
|
unit="MWh_el",
|
|
)
|
|
|
|
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 = 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 = (
|
|
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:
|
|
add_carrier_buses(n, "oil")
|
|
|
|
ice_efficiency = options["transport_internal_combustion_efficiency"]
|
|
|
|
p_set_land_transport_oil = (
|
|
ice_share
|
|
/ ice_efficiency
|
|
* transport[nodes].rename(columns=lambda x: x + " land transport oil")
|
|
)
|
|
|
|
if not options["regional_oil_demand"]:
|
|
p_set_land_transport_oil = p_set_land_transport_oil.sum(axis=1).to_frame(
|
|
name="EU land transport oil"
|
|
)
|
|
|
|
n.madd(
|
|
"Bus",
|
|
spatial.oil.land_transport,
|
|
location=spatial.oil.demand_locations,
|
|
carrier="land transport oil",
|
|
unit="land transport",
|
|
)
|
|
|
|
n.madd(
|
|
"Load",
|
|
spatial.oil.land_transport,
|
|
bus=spatial.oil.land_transport,
|
|
carrier="land transport oil",
|
|
p_set=p_set_land_transport_oil,
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
spatial.oil.land_transport,
|
|
bus0=spatial.oil.nodes,
|
|
bus1=spatial.oil.land_transport,
|
|
bus2="co2 atmosphere",
|
|
carrier="land transport oil",
|
|
efficiency2=costs.at["oil", "CO2 intensity"],
|
|
p_nom_extendable=True,
|
|
)
|
|
|
|
|
|
def build_heat_demand(n):
|
|
|
|
heat_demand_shape = xr.open_dataset(snakemake.input.hourly_heat_demand_total).to_dataframe().unstack(level=1)
|
|
|
|
sectors = ["residential", "services"]
|
|
uses = ["water", "space"]
|
|
|
|
heat_demand = {}
|
|
electric_heat_supply = {}
|
|
for sector, use in product(sectors, uses):
|
|
|
|
name = f"{sector} {use}"
|
|
|
|
heat_demand[name] = (
|
|
heat_demand_shape[name] / heat_demand_shape[name].sum()
|
|
).multiply(pop_weighted_energy_totals[f"total {sector} {use}"]) * 1e6
|
|
electric_heat_supply[f"{sector} {use}"] = (
|
|
heat_demand_shape[name] / heat_demand_shape[name].sum()
|
|
).multiply(pop_weighted_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.T.groupby(level=1).sum().T[electric_nodes]
|
|
)
|
|
|
|
return heat_demand
|
|
|
|
|
|
def add_heat(n, costs):
|
|
logger.info("Add heat sector")
|
|
|
|
sectors = ["residential", "services"]
|
|
|
|
heat_demand = build_heat_demand(n)
|
|
|
|
district_heat_info = pd.read_csv(snakemake.input.district_heat_share,
|
|
index_col=0)
|
|
dist_fraction = district_heat_info["district fraction of node"]
|
|
urban_fraction = district_heat_info["urban fraction"]
|
|
|
|
# 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)
|
|
logger.info(f"Assumed space heat reduction of {dE:.2%}")
|
|
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",
|
|
]
|
|
|
|
cop = {
|
|
"air": xr.open_dataarray(snakemake.input.cop_air_total)
|
|
.to_pandas()
|
|
.reindex(index=n.snapshots),
|
|
"ground": xr.open_dataarray(snakemake.input.cop_soil_total)
|
|
.to_pandas()
|
|
.reindex(index=n.snapshots),
|
|
}
|
|
|
|
if options["solar_thermal"]:
|
|
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
|
|
|
|
nodes = pop_layout.index
|
|
|
|
for name in heat_systems:
|
|
name_type = "central" if name == "urban central" else "decentral"
|
|
|
|
n.add("Carrier", name + " heat")
|
|
|
|
n.madd(
|
|
"Bus",
|
|
nodes + f" {name} heat",
|
|
location=nodes,
|
|
carrier=name + " heat",
|
|
unit="MWh_th",
|
|
)
|
|
|
|
if name == "urban central" and options.get("central_heat_vent"):
|
|
n.madd(
|
|
"Generator",
|
|
nodes + f" {name} heat vent",
|
|
bus=nodes + f" {name} heat",
|
|
location=nodes,
|
|
carrier=name + " heat vent",
|
|
p_nom_extendable=True,
|
|
p_max_pu=0,
|
|
p_min_pu=-1,
|
|
unit="MWh_th",
|
|
)
|
|
|
|
## Add heat load
|
|
|
|
for sector in sectors:
|
|
# heat demand weighting
|
|
if "rural" in name:
|
|
factor = 1 - urban_fraction[nodes]
|
|
elif "urban central" in name:
|
|
factor = dist_fraction[nodes]
|
|
elif "urban decentral" in name:
|
|
factor = urban_fraction[nodes] - dist_fraction[nodes]
|
|
else:
|
|
raise NotImplementedError(
|
|
f" {name} not in " f"heat systems: {heat_systems}"
|
|
)
|
|
|
|
if sector in name:
|
|
heat_load = (
|
|
heat_demand[[sector + " water", sector + " space"]]
|
|
.T.groupby(level=1)
|
|
.sum()
|
|
.T[nodes]
|
|
.multiply(factor)
|
|
)
|
|
|
|
if name == "urban central":
|
|
heat_load = (
|
|
heat_demand.T.groupby(level=1)
|
|
.sum()
|
|
.T[nodes]
|
|
.multiply(
|
|
factor * (1 + options["district_heating"]["district_heating_loss"])
|
|
)
|
|
)
|
|
|
|
n.madd(
|
|
"Load",
|
|
nodes,
|
|
suffix=f" {name} heat",
|
|
bus=nodes + 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"
|
|
efficiency = (
|
|
cop[heat_pump_type][nodes]
|
|
if options["time_dep_hp_cop"]
|
|
else costs.at[costs_name, "efficiency"]
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
nodes,
|
|
suffix=f" {name} {heat_pump_type} heat pump",
|
|
bus0=nodes,
|
|
bus1=nodes + 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 + f" {name} water tanks",
|
|
location=nodes,
|
|
carrier=name + " water tanks",
|
|
unit="MWh_th",
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
nodes + f" {name} water tanks charger",
|
|
bus0=nodes + f" {name} heat",
|
|
bus1=nodes + f" {name} water tanks",
|
|
efficiency=costs.at["water tank charger", "efficiency"],
|
|
carrier=name + " water tanks charger",
|
|
p_nom_extendable=True,
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
nodes + f" {name} water tanks discharger",
|
|
bus0=nodes + f" {name} water tanks",
|
|
bus1=nodes + 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 180.0
|
|
)
|
|
|
|
n.madd(
|
|
"Store",
|
|
nodes + f" {name} water tanks",
|
|
bus=nodes + 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=costs.at[name_type + " water tank storage", "fixed"],
|
|
lifetime=costs.at[name_type + " water tank storage", "lifetime"],
|
|
)
|
|
|
|
if options["resistive_heaters"]:
|
|
key = f"{name_type} resistive heater"
|
|
|
|
n.madd(
|
|
"Link",
|
|
nodes + f" {name} resistive heater",
|
|
bus0=nodes,
|
|
bus1=nodes + 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"],
|
|
)
|
|
|
|
if options["boilers"]:
|
|
key = f"{name_type} gas boiler"
|
|
|
|
n.madd(
|
|
"Link",
|
|
nodes + f" {name} gas boiler",
|
|
p_nom_extendable=True,
|
|
bus0=spatial.gas.df.loc[nodes, "nodes"].values,
|
|
bus1=nodes + 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,
|
|
suffix=f" {name} solar thermal collector",
|
|
bus=nodes + 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],
|
|
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 + " urban central gas CHP",
|
|
bus0=spatial.gas.df.loc[nodes, "nodes"].values,
|
|
bus1=nodes,
|
|
bus2=nodes + " 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 + " urban central gas CHP CC",
|
|
bus0=spatial.gas.df.loc[nodes, "nodes"].values,
|
|
bus1=nodes,
|
|
bus2=nodes + " urban central heat",
|
|
bus3="co2 atmosphere",
|
|
bus4=spatial.co2.df.loc[nodes, "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 + f" {name} micro gas CHP",
|
|
p_nom_extendable=True,
|
|
bus0=spatial.gas.df.loc[nodes, "nodes"].values,
|
|
bus1=nodes,
|
|
bus2=nodes + 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"]:
|
|
logger.info("Add 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,
|
|
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.T.groupby(level=[1]).sum().T
|
|
|
|
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
|
|
if "urban central" in name:
|
|
f = dist_fraction[node]
|
|
elif "urban decentral" in name:
|
|
f = urban_fraction[node] - dist_fraction[node]
|
|
else:
|
|
f = 1 - urban_fraction[node]
|
|
if f == 0:
|
|
continue
|
|
# get sector name ("residential"/"services"/or both "tot" for urban central)
|
|
if "urban central" in name:
|
|
sec = "tot"
|
|
if "residential" in name:
|
|
sec = "residential"
|
|
if "services" in name:
|
|
sec = "services"
|
|
|
|
# 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)
|
|
.fillna(0)
|
|
)
|
|
|
|
# minimum heat demand 'dE' after retrofitting in units of original heat demand (values between 0-1)
|
|
dE = retro_data.loc[(ct, sec), ("dE")]
|
|
# get additional 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())
|
|
)
|
|
if space_heat_demand.max() == 0:
|
|
capital_cost = capital_cost.apply(lambda b: 0 if b == np.inf else b)
|
|
|
|
# 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():
|
|
logger.warning(f"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).ffill()
|
|
|
|
# add for each retrofitting strength a generator with heat generation profile following the profile of the heat demand
|
|
for strength in strengths:
|
|
node_name = " ".join(name.split(" ")[2::])
|
|
n.madd(
|
|
"Generator",
|
|
[node],
|
|
suffix=" retrofitting " + strength + " " + node_name,
|
|
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 add_biomass(n, costs):
|
|
logger.info("Add biomass")
|
|
|
|
biomass_potentials = pd.read_csv(snakemake.input.biomass_potentials, index_col=0)
|
|
|
|
# need to aggregate potentials if gas not nodally resolved
|
|
if options["gas_network"]:
|
|
biogas_potentials_spatial = biomass_potentials["biogas"].rename(
|
|
index=lambda x: x + " biogas"
|
|
)
|
|
else:
|
|
biogas_potentials_spatial = biomass_potentials["biogas"].sum()
|
|
|
|
if options.get("biomass_spatial", options["biomass_transport"]):
|
|
solid_biomass_potentials_spatial = biomass_potentials["solid biomass"].rename(
|
|
index=lambda x: x + " solid biomass"
|
|
)
|
|
else:
|
|
solid_biomass_potentials_spatial = biomass_potentials["solid biomass"].sum()
|
|
|
|
n.add("Carrier", "biogas")
|
|
n.add("Carrier", "solid biomass")
|
|
|
|
n.madd(
|
|
"Bus",
|
|
spatial.gas.biogas,
|
|
location=spatial.gas.locations,
|
|
carrier="biogas",
|
|
unit="MWh_LHV",
|
|
)
|
|
|
|
n.madd(
|
|
"Bus",
|
|
spatial.biomass.nodes,
|
|
location=spatial.biomass.locations,
|
|
carrier="solid biomass",
|
|
unit="MWh_LHV",
|
|
)
|
|
|
|
n.madd(
|
|
"Store",
|
|
spatial.gas.biogas,
|
|
bus=spatial.gas.biogas,
|
|
carrier="biogas",
|
|
e_nom=biogas_potentials_spatial,
|
|
marginal_cost=costs.at["biogas", "fuel"],
|
|
e_initial=biogas_potentials_spatial,
|
|
)
|
|
|
|
n.madd(
|
|
"Store",
|
|
spatial.biomass.nodes,
|
|
bus=spatial.biomass.nodes,
|
|
carrier="solid biomass",
|
|
e_nom=solid_biomass_potentials_spatial,
|
|
marginal_cost=costs.at["solid biomass", "fuel"],
|
|
e_initial=solid_biomass_potentials_spatial,
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
spatial.gas.biogas_to_gas,
|
|
bus0=spatial.gas.biogas,
|
|
bus1=spatial.gas.nodes,
|
|
bus2="co2 atmosphere",
|
|
carrier="biogas to gas",
|
|
capital_cost=costs.at["biogas", "fixed"]
|
|
+ costs.at["biogas upgrading", "fixed"],
|
|
marginal_cost=costs.at["biogas upgrading", "VOM"],
|
|
efficiency=costs.at["biogas", "efficiency"],
|
|
efficiency2=-costs.at["gas", "CO2 intensity"],
|
|
p_nom_extendable=True,
|
|
)
|
|
|
|
if options.get("biogas_upgrading_cc"):
|
|
# Assuming for costs that the CO2 from upgrading is pure, such as in amine scrubbing. I.e., with and without CC is
|
|
# equivalent. Adding biomass CHP capture because biogas is often small-scale and decentral so further
|
|
# from e.g. CO2 grid or buyers. This is a proxy for the added cost for e.g. a raw biogas pipeline to a central upgrading facility
|
|
n.madd(
|
|
"Link",
|
|
spatial.gas.biogas_to_gas_cc,
|
|
bus0=spatial.gas.biogas,
|
|
bus1=spatial.gas.nodes,
|
|
bus2=spatial.co2.nodes,
|
|
bus3="co2 atmosphere",
|
|
carrier="biogas to gas CC",
|
|
capital_cost=costs.at["biogas CC", "fixed"]
|
|
+ costs.at["biogas upgrading", "fixed"]
|
|
+ costs.at["biomass CHP capture", "fixed"]
|
|
* costs.at["biogas CC", "CO2 stored"],
|
|
marginal_cost=costs.at["biogas CC", "VOM"]
|
|
+ costs.at["biogas upgrading", "VOM"],
|
|
efficiency=costs.at["biogas CC", "efficiency"],
|
|
efficiency2=costs.at["biogas CC", "CO2 stored"]
|
|
* costs.at["biogas CC", "capture rate"],
|
|
efficiency3=-costs.at["gas", "CO2 intensity"]
|
|
- costs.at["biogas CC", "CO2 stored"]
|
|
* costs.at["biogas CC", "capture rate"],
|
|
p_nom_extendable=True,
|
|
)
|
|
|
|
if options["biomass_transport"]:
|
|
# add biomass transport
|
|
transport_costs = pd.read_csv(
|
|
snakemake.input.biomass_transport_costs, index_col=0
|
|
)
|
|
transport_costs = transport_costs.squeeze()
|
|
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=False,
|
|
p_nom=5e4,
|
|
length=biomass_transport.length.values,
|
|
marginal_cost=biomass_transport.costs * biomass_transport.length.values,
|
|
carrier="solid biomass transport",
|
|
)
|
|
|
|
elif options["biomass_spatial"]:
|
|
# add artificial biomass generators at nodes which include transport costs
|
|
transport_costs = pd.read_csv(
|
|
snakemake.input.biomass_transport_costs, index_col=0
|
|
)
|
|
transport_costs = transport_costs.squeeze()
|
|
bus_transport_costs = spatial.biomass.nodes.to_series().apply(
|
|
lambda x: transport_costs[x[:2]]
|
|
)
|
|
average_distance = 200 # km #TODO: validate this assumption
|
|
|
|
n.madd(
|
|
"Generator",
|
|
spatial.biomass.nodes,
|
|
bus=spatial.biomass.nodes,
|
|
carrier="solid biomass",
|
|
p_nom=10000,
|
|
marginal_cost=costs.at["solid biomass", "fuel"]
|
|
+ bus_transport_costs * average_distance,
|
|
)
|
|
n.add(
|
|
"GlobalConstraint",
|
|
"biomass limit",
|
|
carrier_attribute="solid biomass",
|
|
sense="<=",
|
|
constant=biomass_potentials["solid biomass"].sum(),
|
|
type="operational_limit",
|
|
)
|
|
|
|
# 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"],
|
|
)
|
|
|
|
if options["biomass_boiler"]:
|
|
# TODO: Add surcharge for pellets
|
|
nodes = pop_layout.index
|
|
for name in [
|
|
"residential rural",
|
|
"services rural",
|
|
"residential urban decentral",
|
|
"services urban decentral",
|
|
]:
|
|
n.madd(
|
|
"Link",
|
|
nodes + f" {name} biomass boiler",
|
|
p_nom_extendable=True,
|
|
bus0=spatial.biomass.df.loc[nodes, "nodes"].values,
|
|
bus1=nodes + f" {name} heat",
|
|
carrier=name + " biomass boiler",
|
|
efficiency=costs.at["biomass boiler", "efficiency"],
|
|
capital_cost=costs.at["biomass boiler", "efficiency"]
|
|
* costs.at["biomass boiler", "fixed"],
|
|
marginal_cost=costs.at["biomass boiler", "pelletizing cost"],
|
|
lifetime=costs.at["biomass boiler", "lifetime"],
|
|
)
|
|
|
|
# Solid biomass to liquid fuel
|
|
if options["biomass_to_liquid"]:
|
|
n.madd(
|
|
"Link",
|
|
spatial.biomass.nodes,
|
|
suffix=" biomass to liquid",
|
|
bus0=spatial.biomass.nodes,
|
|
bus1=spatial.oil.nodes,
|
|
bus2="co2 atmosphere",
|
|
carrier="biomass to liquid",
|
|
lifetime=costs.at["BtL", "lifetime"],
|
|
efficiency=costs.at["BtL", "efficiency"],
|
|
efficiency2=-costs.at["solid biomass", "CO2 intensity"]
|
|
+ costs.at["BtL", "CO2 stored"],
|
|
p_nom_extendable=True,
|
|
capital_cost=costs.at["BtL", "fixed"],
|
|
marginal_cost=costs.at["BtL", "efficiency"] * costs.at["BtL", "VOM"],
|
|
)
|
|
|
|
# TODO: Update with energy penalty
|
|
n.madd(
|
|
"Link",
|
|
spatial.biomass.nodes,
|
|
suffix=" biomass to liquid CC",
|
|
bus0=spatial.biomass.nodes,
|
|
bus1=spatial.oil.nodes,
|
|
bus2="co2 atmosphere",
|
|
bus3=spatial.co2.nodes,
|
|
carrier="biomass to liquid",
|
|
lifetime=costs.at["BtL", "lifetime"],
|
|
efficiency=costs.at["BtL", "efficiency"],
|
|
efficiency2=-costs.at["solid biomass", "CO2 intensity"]
|
|
+ costs.at["BtL", "CO2 stored"] * (1 - costs.at["BtL", "capture rate"]),
|
|
efficiency3=costs.at["BtL", "CO2 stored"] * costs.at["BtL", "capture rate"],
|
|
p_nom_extendable=True,
|
|
capital_cost=costs.at["BtL", "fixed"]
|
|
+ costs.at["biomass CHP capture", "fixed"] * costs.at["BtL", "CO2 stored"],
|
|
marginal_cost=costs.at["BtL", "efficiency"] * costs.at["BtL", "VOM"],
|
|
)
|
|
|
|
# BioSNG from solid biomass
|
|
if options["biosng"]:
|
|
n.madd(
|
|
"Link",
|
|
spatial.biomass.nodes,
|
|
suffix=" solid biomass to gas",
|
|
bus0=spatial.biomass.nodes,
|
|
bus1=spatial.gas.nodes,
|
|
bus3="co2 atmosphere",
|
|
carrier="BioSNG",
|
|
lifetime=costs.at["BioSNG", "lifetime"],
|
|
efficiency=costs.at["BioSNG", "efficiency"],
|
|
efficiency3=-costs.at["solid biomass", "CO2 intensity"]
|
|
+ costs.at["BioSNG", "CO2 stored"],
|
|
p_nom_extendable=True,
|
|
capital_cost=costs.at["BioSNG", "fixed"],
|
|
marginal_cost=costs.at["BioSNG", "efficiency"] * costs.at["BioSNG", "VOM"],
|
|
)
|
|
|
|
# TODO: Update with energy penalty for CC
|
|
n.madd(
|
|
"Link",
|
|
spatial.biomass.nodes,
|
|
suffix=" solid biomass to gas CC",
|
|
bus0=spatial.biomass.nodes,
|
|
bus1=spatial.gas.nodes,
|
|
bus2=spatial.co2.nodes,
|
|
bus3="co2 atmosphere",
|
|
carrier="BioSNG",
|
|
lifetime=costs.at["BioSNG", "lifetime"],
|
|
efficiency=costs.at["BioSNG", "efficiency"],
|
|
efficiency2=costs.at["BioSNG", "CO2 stored"]
|
|
* costs.at["BioSNG", "capture rate"],
|
|
efficiency3=-costs.at["solid biomass", "CO2 intensity"]
|
|
+ costs.at["BioSNG", "CO2 stored"]
|
|
* (1 - costs.at["BioSNG", "capture rate"]),
|
|
p_nom_extendable=True,
|
|
capital_cost=costs.at["BioSNG", "fixed"]
|
|
+ costs.at["biomass CHP capture", "fixed"]
|
|
* costs.at["BioSNG", "CO2 stored"],
|
|
marginal_cost=costs.at["BioSNG", "efficiency"] * costs.at["BioSNG", "VOM"],
|
|
)
|
|
|
|
|
|
def add_industry(n, costs):
|
|
logger.info("Add industrial demand")
|
|
|
|
nodes = pop_layout.index
|
|
nhours = n.snapshot_weightings.generators.sum()
|
|
nyears = nhours / 8760
|
|
|
|
# 1e6 to convert TWh to MWh
|
|
industrial_demand = (
|
|
pd.read_csv(snakemake.input.industrial_demand, index_col=0) * 1e6
|
|
) * nyears
|
|
|
|
n.madd(
|
|
"Bus",
|
|
spatial.biomass.industry,
|
|
location=spatial.biomass.locations,
|
|
carrier="solid biomass for industry",
|
|
unit="MWh_LHV",
|
|
)
|
|
|
|
if options.get("biomass_spatial", options["biomass_transport"]):
|
|
p_set = (
|
|
industrial_demand.loc[spatial.biomass.locations, "solid biomass"].rename(
|
|
index=lambda x: x + " solid biomass for industry"
|
|
)
|
|
/ nhours
|
|
)
|
|
else:
|
|
p_set = industrial_demand["solid biomass"].sum() / nhours
|
|
|
|
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=1.0,
|
|
)
|
|
|
|
if len(spatial.biomass.industry_cc) <= 1 and len(spatial.co2.nodes) > 1:
|
|
link_names = nodes + " " + spatial.biomass.industry_cc
|
|
else:
|
|
link_names = spatial.biomass.industry_cc
|
|
|
|
n.madd(
|
|
"Link",
|
|
link_names,
|
|
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.madd(
|
|
"Bus",
|
|
spatial.gas.industry,
|
|
location=spatial.gas.locations,
|
|
carrier="gas for industry",
|
|
unit="MWh_LHV",
|
|
)
|
|
|
|
gas_demand = industrial_demand.loc[nodes, "methane"] / nhours
|
|
|
|
if options["gas_network"]:
|
|
spatial_gas_demand = gas_demand.rename(index=lambda x: x + " gas for industry")
|
|
else:
|
|
spatial_gas_demand = gas_demand.sum()
|
|
|
|
n.madd(
|
|
"Load",
|
|
spatial.gas.industry,
|
|
bus=spatial.gas.industry,
|
|
carrier="gas for industry",
|
|
p_set=spatial_gas_demand,
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
spatial.gas.industry,
|
|
bus0=spatial.gas.nodes,
|
|
bus1=spatial.gas.industry,
|
|
bus2="co2 atmosphere",
|
|
carrier="gas for industry",
|
|
p_nom_extendable=True,
|
|
efficiency=1.0,
|
|
efficiency2=costs.at["gas", "CO2 intensity"],
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
spatial.gas.industry_cc,
|
|
bus0=spatial.gas.nodes,
|
|
bus1=spatial.gas.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"] / nhours,
|
|
)
|
|
|
|
shipping_hydrogen_share = get(options["shipping_hydrogen_share"], investment_year)
|
|
shipping_methanol_share = get(options["shipping_methanol_share"], investment_year)
|
|
shipping_oil_share = get(options["shipping_oil_share"], investment_year)
|
|
|
|
total_share = shipping_hydrogen_share + shipping_methanol_share + shipping_oil_share
|
|
if total_share != 1:
|
|
logger.warning(
|
|
f"Total shipping shares sum up to {total_share:.2%}, corresponding to increased or decreased demand assumptions."
|
|
)
|
|
|
|
domestic_navigation = pop_weighted_energy_totals.loc[
|
|
nodes, "total domestic navigation"
|
|
].squeeze()
|
|
international_navigation = (
|
|
pd.read_csv(snakemake.input.shipping_demand, index_col=0).squeeze() * nyears
|
|
)
|
|
all_navigation = domestic_navigation + international_navigation
|
|
p_set = all_navigation * 1e6 / nhours
|
|
|
|
if shipping_hydrogen_share:
|
|
oil_efficiency = options.get(
|
|
"shipping_oil_efficiency", options.get("shipping_average_efficiency", 0.4)
|
|
)
|
|
efficiency = oil_efficiency / costs.at["fuel cell", "efficiency"]
|
|
shipping_hydrogen_share = get(
|
|
options["shipping_hydrogen_share"], investment_year
|
|
)
|
|
|
|
if options["shipping_hydrogen_liquefaction"]:
|
|
n.madd(
|
|
"Bus",
|
|
nodes,
|
|
suffix=" H2 liquid",
|
|
carrier="H2 liquid",
|
|
location=nodes,
|
|
unit="MWh_LHV",
|
|
)
|
|
|
|
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"
|
|
|
|
efficiency = (
|
|
options["shipping_oil_efficiency"] / costs.at["fuel cell", "efficiency"]
|
|
)
|
|
p_set_hydrogen = shipping_hydrogen_share * p_set * efficiency
|
|
|
|
n.madd(
|
|
"Load",
|
|
nodes,
|
|
suffix=" H2 for shipping",
|
|
bus=shipping_bus,
|
|
carrier="H2 for shipping",
|
|
p_set=p_set_hydrogen,
|
|
)
|
|
|
|
if shipping_methanol_share:
|
|
n.madd(
|
|
"Bus",
|
|
spatial.methanol.nodes,
|
|
carrier="methanol",
|
|
location=spatial.methanol.locations,
|
|
unit="MWh_LHV",
|
|
)
|
|
|
|
n.madd(
|
|
"Store",
|
|
spatial.methanol.nodes,
|
|
suffix=" Store",
|
|
bus=spatial.methanol.nodes,
|
|
e_nom_extendable=True,
|
|
e_cyclic=True,
|
|
carrier="methanol",
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
spatial.h2.locations + " methanolisation",
|
|
bus0=spatial.h2.nodes,
|
|
bus1=spatial.methanol.nodes,
|
|
bus2=nodes,
|
|
bus3=spatial.co2.nodes,
|
|
carrier="methanolisation",
|
|
p_nom_extendable=True,
|
|
p_min_pu=options.get("min_part_load_methanolisation", 0),
|
|
capital_cost=costs.at["methanolisation", "fixed"]
|
|
* options["MWh_MeOH_per_MWh_H2"], # EUR/MW_H2/a
|
|
marginal_cost=options["MWh_MeOH_per_MWh_H2"]
|
|
* costs.at["methanolisation", "VOM"],
|
|
lifetime=costs.at["methanolisation", "lifetime"],
|
|
efficiency=options["MWh_MeOH_per_MWh_H2"],
|
|
efficiency2=-options["MWh_MeOH_per_MWh_H2"] / options["MWh_MeOH_per_MWh_e"],
|
|
efficiency3=-options["MWh_MeOH_per_MWh_H2"] / options["MWh_MeOH_per_tCO2"],
|
|
)
|
|
|
|
efficiency = (
|
|
options["shipping_oil_efficiency"] / options["shipping_methanol_efficiency"]
|
|
)
|
|
|
|
p_set_methanol = (
|
|
shipping_methanol_share
|
|
* p_set.rename(lambda x: x + " shipping methanol")
|
|
* efficiency
|
|
)
|
|
|
|
if not options["regional_methanol_demand"]:
|
|
p_set_methanol = p_set_methanol.sum()
|
|
|
|
n.madd(
|
|
"Bus",
|
|
spatial.methanol.shipping,
|
|
location=spatial.methanol.demand_locations,
|
|
carrier="shipping methanol",
|
|
unit="MWh_LHV",
|
|
)
|
|
|
|
n.madd(
|
|
"Load",
|
|
spatial.methanol.shipping,
|
|
bus=spatial.methanol.shipping,
|
|
carrier="shipping methanol",
|
|
p_set=p_set_methanol,
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
spatial.methanol.shipping,
|
|
bus0=spatial.methanol.nodes,
|
|
bus1=spatial.methanol.shipping,
|
|
bus2="co2 atmosphere",
|
|
carrier="shipping methanol",
|
|
p_nom_extendable=True,
|
|
efficiency2=1
|
|
/ options[
|
|
"MWh_MeOH_per_tCO2"
|
|
], # CO2 intensity methanol based on stoichiometric calculation with 22.7 GJ/t methanol (32 g/mol), CO2 (44 g/mol), 277.78 MWh/TJ = 0.218 t/MWh
|
|
)
|
|
|
|
if "oil" not in n.buses.carrier.unique():
|
|
n.madd(
|
|
"Bus",
|
|
spatial.oil.nodes,
|
|
location=spatial.oil.locations,
|
|
carrier="oil",
|
|
unit="MWh_LHV",
|
|
)
|
|
|
|
if "oil" not in n.stores.carrier.unique():
|
|
# could correct to e.g. 0.001 EUR/kWh * annuity and O&M
|
|
n.madd(
|
|
"Store",
|
|
spatial.oil.nodes,
|
|
suffix=" Store",
|
|
bus=spatial.oil.nodes,
|
|
e_nom_extendable=True,
|
|
e_cyclic=True,
|
|
carrier="oil",
|
|
)
|
|
|
|
if "oil" not in n.generators.carrier.unique():
|
|
n.madd(
|
|
"Generator",
|
|
spatial.oil.nodes,
|
|
bus=spatial.oil.nodes,
|
|
p_nom_extendable=True,
|
|
carrier="oil",
|
|
marginal_cost=costs.at["oil", "fuel"],
|
|
)
|
|
|
|
if shipping_oil_share:
|
|
p_set_oil = shipping_oil_share * p_set.rename(lambda x: x + " shipping oil")
|
|
|
|
if not options["regional_oil_demand"]:
|
|
p_set_oil = p_set_oil.sum()
|
|
|
|
n.madd(
|
|
"Bus",
|
|
spatial.oil.shipping,
|
|
location=spatial.oil.demand_locations,
|
|
carrier="shipping oil",
|
|
unit="MWh_LHV",
|
|
)
|
|
|
|
n.madd(
|
|
"Load",
|
|
spatial.oil.shipping,
|
|
bus=spatial.oil.shipping,
|
|
carrier="shipping oil",
|
|
p_set=p_set_oil,
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
spatial.oil.shipping,
|
|
bus0=spatial.oil.nodes,
|
|
bus1=spatial.oil.shipping,
|
|
bus2="co2 atmosphere",
|
|
carrier="shipping oil",
|
|
p_nom_extendable=True,
|
|
efficiency2=costs.at["oil", "CO2 intensity"],
|
|
)
|
|
|
|
if options["oil_boilers"]:
|
|
nodes = pop_layout.index
|
|
|
|
for name in [
|
|
"residential rural",
|
|
"services rural",
|
|
"residential urban decentral",
|
|
"services urban decentral",
|
|
]:
|
|
n.madd(
|
|
"Link",
|
|
nodes + f" {name} oil boiler",
|
|
p_nom_extendable=True,
|
|
bus0=spatial.oil.nodes,
|
|
bus1=nodes + 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=spatial.oil.nodes,
|
|
bus2=spatial.co2.nodes,
|
|
carrier="Fischer-Tropsch",
|
|
efficiency=costs.at["Fischer-Tropsch", "efficiency"],
|
|
capital_cost=costs.at["Fischer-Tropsch", "fixed"]
|
|
* costs.at["Fischer-Tropsch", "efficiency"], # EUR/MW_H2/a
|
|
marginal_cost=costs.at["Fischer-Tropsch", "efficiency"]
|
|
* costs.at["Fischer-Tropsch", "VOM"],
|
|
efficiency2=-costs.at["oil", "CO2 intensity"]
|
|
* costs.at["Fischer-Tropsch", "efficiency"],
|
|
p_nom_extendable=True,
|
|
p_min_pu=options.get("min_part_load_fischer_tropsch", 0),
|
|
lifetime=costs.at["Fischer-Tropsch", "lifetime"],
|
|
)
|
|
|
|
# naphtha
|
|
demand_factor = options.get("HVC_demand_factor", 1)
|
|
if demand_factor != 1:
|
|
logger.warning(f"Changing HVC demand by {demand_factor*100-100:+.2f}%.")
|
|
|
|
p_set_plastics = (
|
|
demand_factor
|
|
* industrial_demand.loc[nodes, "naphtha"].rename(
|
|
lambda x: x + " naphtha for industry"
|
|
)
|
|
/ nhours
|
|
)
|
|
|
|
if not options["regional_oil_demand"]:
|
|
p_set_plastics = p_set_plastics.sum()
|
|
|
|
n.madd(
|
|
"Bus",
|
|
spatial.oil.naphtha,
|
|
location=spatial.oil.demand_locations,
|
|
carrier="naphtha for industry",
|
|
unit="MWh_LHV",
|
|
)
|
|
|
|
n.madd(
|
|
"Load",
|
|
spatial.oil.naphtha,
|
|
bus=spatial.oil.naphtha,
|
|
carrier="naphtha for industry",
|
|
p_set=p_set_plastics,
|
|
)
|
|
|
|
# some CO2 from naphtha are process emissions from steam cracker
|
|
# rest of CO2 released to atmosphere either in waste-to-energy or decay
|
|
process_co2_per_naphtha = (
|
|
industrial_demand.loc[nodes, "process emission from feedstock"].sum()
|
|
/ industrial_demand.loc[nodes, "naphtha"].sum()
|
|
)
|
|
emitted_co2_per_naphtha = costs.at["oil", "CO2 intensity"] - process_co2_per_naphtha
|
|
|
|
n.madd(
|
|
"Link",
|
|
spatial.oil.naphtha,
|
|
bus0=spatial.oil.nodes,
|
|
bus1=spatial.oil.naphtha,
|
|
bus2="co2 atmosphere",
|
|
bus3=spatial.co2.process_emissions,
|
|
carrier="naphtha for industry",
|
|
p_nom_extendable=True,
|
|
efficiency2=emitted_co2_per_naphtha,
|
|
efficiency3=process_co2_per_naphtha,
|
|
)
|
|
|
|
# aviation
|
|
demand_factor = options.get("aviation_demand_factor", 1)
|
|
if demand_factor != 1:
|
|
logger.warning(f"Changing aviation demand by {demand_factor*100-100:+.2f}%.")
|
|
|
|
all_aviation = ["total international aviation", "total domestic aviation"]
|
|
|
|
p_set = (
|
|
demand_factor
|
|
* pop_weighted_energy_totals.loc[nodes, all_aviation].sum(axis=1)
|
|
* 1e6
|
|
/ nhours
|
|
).rename(lambda x: x + " kerosene for aviation")
|
|
|
|
if not options["regional_oil_demand"]:
|
|
p_set = p_set.sum()
|
|
|
|
n.madd(
|
|
"Bus",
|
|
spatial.oil.kerosene,
|
|
location=spatial.oil.demand_locations,
|
|
carrier="kerosene for aviation",
|
|
unit="MWh_LHV",
|
|
)
|
|
|
|
n.madd(
|
|
"Load",
|
|
spatial.oil.kerosene,
|
|
bus=spatial.oil.kerosene,
|
|
carrier="kerosene for aviation",
|
|
p_set=p_set,
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
spatial.oil.kerosene,
|
|
bus0=spatial.oil.nodes,
|
|
bus1=spatial.oil.kerosene,
|
|
bus2="co2 atmosphere",
|
|
carrier="kerosene for aviation",
|
|
p_nom_extendable=True,
|
|
efficiency2=costs.at["oil", "CO2 intensity"],
|
|
)
|
|
|
|
# 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"] / nhours,
|
|
)
|
|
|
|
# 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"] / nhours,
|
|
)
|
|
|
|
n.madd(
|
|
"Bus",
|
|
spatial.co2.process_emissions,
|
|
location=spatial.co2.locations,
|
|
carrier="process emissions",
|
|
unit="t_co2",
|
|
)
|
|
|
|
if options["co2_spatial"] or options["co2network"]:
|
|
p_set = (
|
|
-industrial_demand.loc[nodes, "process emission"].rename(
|
|
index=lambda x: x + " process emissions"
|
|
)
|
|
/ nhours
|
|
)
|
|
else:
|
|
p_set = -industrial_demand.loc[nodes, "process emission"].sum() / nhours
|
|
|
|
n.madd(
|
|
"Load",
|
|
spatial.co2.process_emissions,
|
|
bus=spatial.co2.process_emissions,
|
|
carrier="process emissions",
|
|
p_set=p_set,
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
spatial.co2.process_emissions,
|
|
bus0=spatial.co2.process_emissions,
|
|
bus1="co2 atmosphere",
|
|
carrier="process emissions",
|
|
p_nom_extendable=True,
|
|
efficiency=1.0,
|
|
)
|
|
|
|
# assume enough local waste heat for CC
|
|
n.madd(
|
|
"Link",
|
|
spatial.co2.locations,
|
|
suffix=" process emissions CC",
|
|
bus0=spatial.co2.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"],
|
|
)
|
|
|
|
if options.get("ammonia"):
|
|
if options["ammonia"] == "regional":
|
|
p_set = (
|
|
industrial_demand.loc[spatial.ammonia.locations, "ammonia"].rename(
|
|
index=lambda x: x + " NH3"
|
|
)
|
|
/ nhours
|
|
)
|
|
else:
|
|
p_set = industrial_demand["ammonia"].sum() / nhours
|
|
|
|
n.madd(
|
|
"Load",
|
|
spatial.ammonia.nodes,
|
|
bus=spatial.ammonia.nodes,
|
|
carrier="NH3",
|
|
p_set=p_set,
|
|
)
|
|
|
|
primary_steel = get(
|
|
snakemake.config["industry"]["St_primary_fraction"], investment_year
|
|
)
|
|
dri_steel = get(snakemake.config["industry"]["DRI_fraction"], investment_year)
|
|
bof_steel = primary_steel - dri_steel
|
|
|
|
if bof_steel > 0:
|
|
add_carrier_buses(n, "coal")
|
|
|
|
mwh_coal_per_mwh_coke = 1.366 # from eurostat energy balance
|
|
p_set = (
|
|
industrial_demand["coal"].sum()
|
|
+ mwh_coal_per_mwh_coke * industrial_demand["coke"].sum()
|
|
) / nhours
|
|
|
|
n.madd(
|
|
"Load",
|
|
spatial.coal.nodes,
|
|
suffix=" for industry",
|
|
bus=spatial.coal.nodes,
|
|
carrier="coal for industry",
|
|
p_set=p_set,
|
|
)
|
|
|
|
|
|
def add_waste_heat(n):
|
|
# TODO options?
|
|
|
|
logger.info("Add possibility to use industrial waste heat in district heating")
|
|
cf_industry = snakemake.params.industry
|
|
|
|
# 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")]
|
|
|
|
link_carriers = n.links.carrier.unique()
|
|
|
|
# TODO what is the 0.95 and should it be a config option?
|
|
if (
|
|
options["use_fischer_tropsch_waste_heat"]
|
|
and "Fischer-Tropsch" in link_carriers
|
|
):
|
|
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_methanation_waste_heat"] and "Sabatier" in link_carriers:
|
|
n.links.loc[urban_central + " Sabatier", "bus3"] = (
|
|
urban_central + " urban central heat"
|
|
)
|
|
n.links.loc[urban_central + " Sabatier", "efficiency3"] = (
|
|
0.95 - n.links.loc[urban_central + " Sabatier", "efficiency"]
|
|
)
|
|
|
|
# DEA quotes 15% of total input (11% of which are high-value heat)
|
|
if options["use_haber_bosch_waste_heat"] and "Haber-Bosch" in link_carriers:
|
|
n.links.loc[urban_central + " Haber-Bosch", "bus3"] = (
|
|
urban_central + " urban central heat"
|
|
)
|
|
total_energy_input = (
|
|
cf_industry["MWh_H2_per_tNH3_electrolysis"]
|
|
+ cf_industry["MWh_elec_per_tNH3_electrolysis"]
|
|
) / cf_industry["MWh_NH3_per_tNH3"]
|
|
electricity_input = (
|
|
cf_industry["MWh_elec_per_tNH3_electrolysis"]
|
|
/ cf_industry["MWh_NH3_per_tNH3"]
|
|
)
|
|
n.links.loc[urban_central + " Haber-Bosch", "efficiency3"] = (
|
|
0.15 * total_energy_input / electricity_input
|
|
)
|
|
|
|
if (
|
|
options["use_methanolisation_waste_heat"]
|
|
and "methanolisation" in link_carriers
|
|
):
|
|
n.links.loc[urban_central + " methanolisation", "bus4"] = (
|
|
urban_central + " urban central heat"
|
|
)
|
|
n.links.loc[urban_central + " methanolisation", "efficiency4"] = (
|
|
costs.at["methanolisation", "heat-output"]
|
|
/ costs.at["methanolisation", "hydrogen-input"]
|
|
)
|
|
|
|
# TODO integrate usable waste heat efficiency into technology-data from DEA
|
|
if (
|
|
options.get("use_electrolysis_waste_heat", False)
|
|
and "H2 Electrolysis" in link_carriers
|
|
):
|
|
n.links.loc[urban_central + " H2 Electrolysis", "bus2"] = (
|
|
urban_central + " urban central heat"
|
|
)
|
|
n.links.loc[urban_central + " H2 Electrolysis", "efficiency2"] = (
|
|
0.84 - n.links.loc[urban_central + " H2 Electrolysis", "efficiency"]
|
|
)
|
|
|
|
if options["use_fuel_cell_waste_heat"] and "H2 Fuel Cell" in link_carriers:
|
|
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 add_agriculture(n, costs):
|
|
logger.info("Add agriculture, forestry and fishing sector.")
|
|
|
|
nodes = pop_layout.index
|
|
nhours = n.snapshot_weightings.generators.sum()
|
|
|
|
# electricity
|
|
|
|
n.madd(
|
|
"Load",
|
|
nodes,
|
|
suffix=" agriculture electricity",
|
|
bus=nodes,
|
|
carrier="agriculture electricity",
|
|
p_set=pop_weighted_energy_totals.loc[nodes, "total agriculture electricity"]
|
|
* 1e6
|
|
/ nhours,
|
|
)
|
|
|
|
# heat
|
|
|
|
n.madd(
|
|
"Load",
|
|
nodes,
|
|
suffix=" agriculture heat",
|
|
bus=nodes + " services rural heat",
|
|
carrier="agriculture heat",
|
|
p_set=pop_weighted_energy_totals.loc[nodes, "total agriculture heat"]
|
|
* 1e6
|
|
/ nhours,
|
|
)
|
|
|
|
# machinery
|
|
|
|
electric_share = get(
|
|
options["agriculture_machinery_electric_share"], investment_year
|
|
)
|
|
oil_share = get(options["agriculture_machinery_oil_share"], investment_year)
|
|
|
|
total_share = electric_share + oil_share
|
|
if total_share != 1:
|
|
logger.warning(
|
|
f"Total agriculture machinery shares sum up to {total_share:.2%}, corresponding to increased or decreased demand assumptions."
|
|
)
|
|
|
|
machinery_nodal_energy = (
|
|
pop_weighted_energy_totals.loc[nodes, "total agriculture machinery"] * 1e6
|
|
)
|
|
|
|
if electric_share > 0:
|
|
efficiency_gain = (
|
|
options["agriculture_machinery_fuel_efficiency"]
|
|
/ options["agriculture_machinery_electric_efficiency"]
|
|
)
|
|
|
|
n.madd(
|
|
"Load",
|
|
nodes,
|
|
suffix=" agriculture machinery electric",
|
|
bus=nodes,
|
|
carrier="agriculture machinery electric",
|
|
p_set=electric_share / efficiency_gain * machinery_nodal_energy / nhours,
|
|
)
|
|
|
|
if oil_share > 0:
|
|
p_set = (
|
|
oil_share
|
|
* machinery_nodal_energy.rename(lambda x: x + " agriculture machinery oil")
|
|
/ nhours
|
|
)
|
|
|
|
if not options["regional_oil_demand"]:
|
|
p_set = p_set.sum()
|
|
|
|
n.madd(
|
|
"Bus",
|
|
spatial.oil.agriculture_machinery,
|
|
location=spatial.oil.demand_locations,
|
|
carrier="agriculture machinery oil",
|
|
unit="MWh_LHV",
|
|
)
|
|
|
|
n.madd(
|
|
"Load",
|
|
spatial.oil.agriculture_machinery,
|
|
bus=spatial.oil.agriculture_machinery,
|
|
carrier="agriculture machinery oil",
|
|
p_set=p_set,
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
spatial.oil.agriculture_machinery,
|
|
bus0=spatial.oil.nodes,
|
|
bus1=spatial.oil.agriculture_machinery,
|
|
bus2="co2 atmosphere",
|
|
carrier="agriculture machinery oil",
|
|
p_nom_extendable=True,
|
|
efficiency2=costs.at["oil", "CO2 intensity"],
|
|
)
|
|
|
|
|
|
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.str.contains("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:
|
|
flags = ["+e", "+p", "+m", "+c"]
|
|
if all(flag not in o for flag in flags):
|
|
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",
|
|
"m": "marginal_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
|
|
logger.info(f"changing {attr} for {carrier} by factor {factor}")
|
|
|
|
|
|
def limit_individual_line_extension(n, maxext):
|
|
logger.info(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
|
|
|
|
|
|
aggregate_dict = {
|
|
"p_nom": pd.Series.sum,
|
|
"s_nom": pd.Series.sum,
|
|
"v_nom": "max",
|
|
"v_mag_pu_max": "min",
|
|
"v_mag_pu_min": "max",
|
|
"p_nom_max": pd.Series.sum,
|
|
"s_nom_max": pd.Series.sum,
|
|
"p_nom_min": pd.Series.sum,
|
|
"s_nom_min": pd.Series.sum,
|
|
"v_ang_min": "max",
|
|
"v_ang_max": "min",
|
|
"terrain_factor": "mean",
|
|
"num_parallel": "sum",
|
|
"p_set": "sum",
|
|
"e_initial": "sum",
|
|
"e_nom": pd.Series.sum,
|
|
"e_nom_max": pd.Series.sum,
|
|
"e_nom_min": pd.Series.sum,
|
|
"state_of_charge_initial": "sum",
|
|
"state_of_charge_set": "sum",
|
|
"inflow": "sum",
|
|
"p_max_pu": "first",
|
|
"x": "mean",
|
|
"y": "mean",
|
|
}
|
|
|
|
|
|
def cluster_heat_buses(n):
|
|
"""
|
|
Cluster residential and service heat buses to one representative bus.
|
|
|
|
This can be done to save memory and speed up optimisation
|
|
"""
|
|
|
|
def define_clustering(attributes, aggregate_dict):
|
|
"""Define how attributes should be clustered.
|
|
Input:
|
|
attributes : pd.Index()
|
|
aggregate_dict: dictionary (key: name of attribute, value
|
|
clustering method)
|
|
|
|
Returns:
|
|
agg : clustering dictionary
|
|
"""
|
|
keys = attributes.intersection(aggregate_dict.keys())
|
|
agg = dict(
|
|
zip(
|
|
attributes.difference(keys),
|
|
["first"] * len(df.columns.difference(keys)),
|
|
)
|
|
)
|
|
for key in keys:
|
|
agg[key] = aggregate_dict[key]
|
|
return agg
|
|
|
|
logger.info("Cluster residential and service heat buses.")
|
|
components = ["Bus", "Carrier", "Generator", "Link", "Load", "Store"]
|
|
|
|
for c in n.iterate_components(components):
|
|
df = c.df
|
|
cols = df.columns[df.columns.str.contains("bus") | (df.columns == "carrier")]
|
|
|
|
# rename columns and index
|
|
df[cols] = df[cols].apply(
|
|
lambda x: x.str.replace("residential ", "").str.replace("services ", ""),
|
|
axis=1,
|
|
)
|
|
df = df.rename(
|
|
index=lambda x: x.replace("residential ", "").replace("services ", "")
|
|
)
|
|
|
|
# cluster heat nodes
|
|
# static dataframe
|
|
agg = define_clustering(df.columns, aggregate_dict)
|
|
df = df.groupby(level=0).agg(agg, **agg_group_kwargs)
|
|
# time-varying data
|
|
pnl = c.pnl
|
|
agg = define_clustering(pd.Index(pnl.keys()), aggregate_dict)
|
|
for k in pnl.keys():
|
|
|
|
def renamer(s):
|
|
return s.replace("residential ", "").replace("services ", "")
|
|
|
|
pnl[k] = pnl[k].T.groupby(renamer).agg(agg[k], **agg_group_kwargs).T
|
|
|
|
# remove unclustered assets of service/residential
|
|
to_drop = c.df.index.difference(df.index)
|
|
n.mremove(c.name, to_drop)
|
|
# add clustered assets
|
|
to_add = df.index.difference(c.df.index)
|
|
import_components_from_dataframe(n, df.loc[to_add], c.name)
|
|
|
|
|
|
def apply_time_segmentation(
|
|
n, segments, solver_name="cbc", overwrite_time_dependent=True
|
|
):
|
|
"""
|
|
Aggregating time series to segments with different lengths.
|
|
|
|
Input:
|
|
n: pypsa Network
|
|
segments: (int) number of segments in which the typical period should be
|
|
subdivided
|
|
solver_name: (str) name of solver
|
|
overwrite_time_dependent: (bool) overwrite time dependent data of pypsa network
|
|
with typical time series created by tsam
|
|
"""
|
|
try:
|
|
import tsam.timeseriesaggregation as tsam
|
|
except ImportError:
|
|
raise ModuleNotFoundError(
|
|
"Optional dependency 'tsam' not found." "Install via 'pip install tsam'"
|
|
)
|
|
|
|
# get all time-dependent data
|
|
columns = pd.MultiIndex.from_tuples([], names=["component", "key", "asset"])
|
|
raw = pd.DataFrame(index=n.snapshots, columns=columns)
|
|
for c in n.iterate_components():
|
|
for attr, pnl in c.pnl.items():
|
|
# exclude e_min_pu which is used for SOC of EVs in the morning
|
|
if not pnl.empty and attr != "e_min_pu":
|
|
df = pnl.copy()
|
|
df.columns = pd.MultiIndex.from_product([[c.name], [attr], df.columns])
|
|
raw = pd.concat([raw, df], axis=1)
|
|
|
|
# normalise all time-dependent data
|
|
annual_max = raw.max().replace(0, 1)
|
|
raw = raw.div(annual_max, level=0)
|
|
|
|
# get representative segments
|
|
agg = tsam.TimeSeriesAggregation(
|
|
raw,
|
|
hoursPerPeriod=len(raw),
|
|
noTypicalPeriods=1,
|
|
noSegments=int(segments),
|
|
segmentation=True,
|
|
solver=solver_name,
|
|
)
|
|
segmented = agg.createTypicalPeriods()
|
|
|
|
weightings = segmented.index.get_level_values("Segment Duration")
|
|
offsets = np.insert(np.cumsum(weightings[:-1]), 0, 0)
|
|
timesteps = [raw.index[0] + pd.Timedelta(f"{offset}h") for offset in offsets]
|
|
snapshots = pd.DatetimeIndex(timesteps)
|
|
sn_weightings = pd.Series(
|
|
weightings, index=snapshots, name="weightings", dtype="float64"
|
|
)
|
|
logger.info(f"Distribution of snapshot durations:\n{weightings.value_counts()}")
|
|
|
|
n.set_snapshots(sn_weightings.index)
|
|
n.snapshot_weightings = n.snapshot_weightings.mul(sn_weightings, axis=0)
|
|
|
|
# overwrite time-dependent data with timeseries created by tsam
|
|
if overwrite_time_dependent:
|
|
values_t = segmented.mul(annual_max).set_index(snapshots)
|
|
for component, key in values_t.columns.droplevel(2).unique():
|
|
n.pnl(component)[key] = values_t[component, key]
|
|
|
|
return n
|
|
|
|
|
|
def set_temporal_aggregation(n, opts, solver_name):
|
|
"""
|
|
Aggregate network temporally.
|
|
"""
|
|
for o in opts:
|
|
# temporal averaging
|
|
m = re.match(r"^\d+h$", o, re.IGNORECASE)
|
|
if m is not None:
|
|
n = average_every_nhours(n, m.group(0))
|
|
break
|
|
# representative snapshots
|
|
m = re.match(r"(^\d+)sn$", o, re.IGNORECASE)
|
|
if m is not None:
|
|
sn = int(m[1])
|
|
logger.info(f"Use every {sn} snapshot as representative")
|
|
n.set_snapshots(n.snapshots[::sn])
|
|
n.snapshot_weightings *= sn
|
|
break
|
|
# segments with package tsam
|
|
m = re.match(r"^(\d+)seg$", o, re.IGNORECASE)
|
|
if m is not None:
|
|
segments = int(m[1])
|
|
logger.info(f"Use temporal segmentation with {segments} segments")
|
|
n = apply_time_segmentation(n, segments, solver_name=solver_name)
|
|
break
|
|
return n
|
|
|
|
|
|
def lossy_bidirectional_links(n, carrier, efficiencies={}):
|
|
"Split bidirectional links into two unidirectional links to include transmission losses."
|
|
|
|
carrier_i = n.links.query("carrier == @carrier").index
|
|
|
|
if (
|
|
not any((v != 1.0) or (v >= 0) for v in efficiencies.values())
|
|
or carrier_i.empty
|
|
):
|
|
return
|
|
|
|
efficiency_static = efficiencies.get("efficiency_static", 1)
|
|
efficiency_per_1000km = efficiencies.get("efficiency_per_1000km", 1)
|
|
compression_per_1000km = efficiencies.get("compression_per_1000km", 0)
|
|
|
|
logger.info(
|
|
f"Specified losses for {carrier} transmission "
|
|
f"(static: {efficiency_static}, per 1000km: {efficiency_per_1000km}, compression per 1000km: {compression_per_1000km}). "
|
|
"Splitting bidirectional links."
|
|
)
|
|
|
|
n.links.loc[carrier_i, "p_min_pu"] = 0
|
|
n.links.loc[
|
|
carrier_i, "efficiency"
|
|
] = efficiency_static * efficiency_per_1000km ** (
|
|
n.links.loc[carrier_i, "length"] / 1e3
|
|
)
|
|
rev_links = (
|
|
n.links.loc[carrier_i].copy().rename({"bus0": "bus1", "bus1": "bus0"}, axis=1)
|
|
)
|
|
rev_links["length_original"] = rev_links["length"]
|
|
rev_links["capital_cost"] = 0
|
|
rev_links["length"] = 0
|
|
rev_links["reversed"] = True
|
|
rev_links.index = rev_links.index.map(lambda x: x + "-reversed")
|
|
|
|
n.links = pd.concat([n.links, rev_links], sort=False)
|
|
n.links["reversed"] = n.links["reversed"].fillna(False)
|
|
n.links["length_original"] = n.links["length_original"].fillna(n.links.length)
|
|
|
|
# do compression losses after concatenation to take electricity consumption at bus0 in either direction
|
|
carrier_i = n.links.query("carrier == @carrier").index
|
|
if compression_per_1000km > 0:
|
|
n.links.loc[carrier_i, "bus2"] = n.links.loc[carrier_i, "bus0"].map(
|
|
n.buses.location
|
|
) # electricity
|
|
n.links.loc[carrier_i, "efficiency2"] = (
|
|
-compression_per_1000km * n.links.loc[carrier_i, "length_original"] / 1e3
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if "snakemake" not in globals():
|
|
from _helpers import mock_snakemake
|
|
|
|
snakemake = mock_snakemake(
|
|
"prepare_sector_network",
|
|
configfiles="test/config.overnight.yaml",
|
|
simpl="",
|
|
opts="",
|
|
clusters="37",
|
|
ll="v1.0",
|
|
sector_opts="CO2L0-24H-T-H-B-I-A-solar+p3-dist1",
|
|
planning_horizons="2030",
|
|
)
|
|
|
|
logging.basicConfig(level=snakemake.config["logging"]["level"])
|
|
|
|
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
|
|
|
|
options = snakemake.params.sector
|
|
|
|
opts = snakemake.wildcards.sector_opts.split("-")
|
|
|
|
investment_year = int(snakemake.wildcards.planning_horizons[-4:])
|
|
|
|
n = pypsa.Network(snakemake.input.network)
|
|
|
|
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
|
|
nhours = n.snapshot_weightings.generators.sum()
|
|
nyears = nhours / 8760
|
|
|
|
costs = prepare_costs(
|
|
snakemake.input.costs,
|
|
snakemake.params.costs,
|
|
nyears,
|
|
)
|
|
|
|
pop_weighted_energy_totals = (
|
|
pd.read_csv(snakemake.input.pop_weighted_energy_totals, index_col=0) * nyears
|
|
)
|
|
|
|
patch_electricity_network(n)
|
|
|
|
spatial = define_spatial(pop_layout.index, options)
|
|
|
|
if snakemake.params.foresight in ["myopic", "perfect"]:
|
|
add_lifetime_wind_solar(n, costs)
|
|
|
|
conventional = snakemake.params.conventional_carriers
|
|
for carrier in conventional:
|
|
add_carrier_buses(n, carrier)
|
|
|
|
add_co2_tracking(n, costs, options)
|
|
|
|
add_generation(n, costs)
|
|
|
|
add_storage_and_grids(n, costs)
|
|
|
|
# TODO merge with opts cost adjustment below
|
|
for o in opts:
|
|
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
|
|
|
|
if "nodistrict" in opts:
|
|
options["district_heating"]["progress"] = 0.0
|
|
|
|
if "nowasteheat" in opts:
|
|
logger.info("Disabling waste heat.")
|
|
options["use_fischer_tropsch_waste_heat"] = False
|
|
options["use_methanolisation_waste_heat"] = False
|
|
options["use_haber_bosch_waste_heat"] = False
|
|
options["use_methanation_waste_heat"] = False
|
|
options["use_fuel_cell_waste_heat"] = False
|
|
options["use_electrolysis_waste_heat"] = False
|
|
|
|
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 options["ammonia"]:
|
|
add_ammonia(n, costs)
|
|
|
|
if "I" in opts:
|
|
add_industry(n, costs)
|
|
|
|
if "H" in opts:
|
|
add_waste_heat(n)
|
|
|
|
if "A" in opts: # requires H and I
|
|
add_agriculture(n, costs)
|
|
|
|
if options["dac"]:
|
|
add_dac(n, costs)
|
|
|
|
if "decentral" in opts:
|
|
decentral(n)
|
|
|
|
if "noH2network" in opts:
|
|
remove_h2_network(n)
|
|
|
|
if options["co2network"]:
|
|
add_co2_network(n, costs)
|
|
|
|
if options["allam_cycle"]:
|
|
add_allam(n, costs)
|
|
|
|
solver_name = snakemake.config["solving"]["solver"]["name"]
|
|
n = set_temporal_aggregation(n, opts, solver_name)
|
|
|
|
limit_type = "config"
|
|
limit = get(snakemake.params.co2_budget, investment_year)
|
|
for o in opts:
|
|
if "cb" not in o:
|
|
continue
|
|
limit_type = "carbon budget"
|
|
fn = "results/" + snakemake.params.RDIR + "/csvs/carbon_budget_distribution.csv"
|
|
if not os.path.exists(fn):
|
|
emissions_scope = snakemake.params.emissions_scope
|
|
report_year = snakemake.params.eurostat_report_year
|
|
input_co2 = snakemake.input.co2
|
|
build_carbon_budget(
|
|
o,
|
|
snakemake.input.eurostat,
|
|
fn,
|
|
emissions_scope,
|
|
report_year,
|
|
input_co2,
|
|
)
|
|
co2_cap = pd.read_csv(fn, index_col=0).squeeze()
|
|
limit = co2_cap.loc[investment_year]
|
|
break
|
|
for o in opts:
|
|
if "Co2L" not in o:
|
|
continue
|
|
limit_type = "wildcard"
|
|
limit = o[o.find("Co2L") + 4 :]
|
|
limit = float(limit.replace("p", ".").replace("m", "-"))
|
|
break
|
|
logger.info(f"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)
|
|
|
|
for k, v in options["transmission_efficiency"].items():
|
|
lossy_bidirectional_links(n, k, v)
|
|
|
|
# Workaround: Remove lines with conflicting (and unrealistic) properties
|
|
# cf. https://github.com/PyPSA/pypsa-eur/issues/444
|
|
if snakemake.config["solving"]["options"]["transmission_losses"]:
|
|
idx = n.lines.query("num_parallel == 0").index
|
|
logger.info(
|
|
f"Removing {len(idx)} line(s) with properties conflicting with transmission losses functionality."
|
|
)
|
|
n.mremove("Line", idx)
|
|
|
|
first_year_myopic = (snakemake.params.foresight in ["myopic", "perfect"]) and (
|
|
snakemake.params.planning_horizons[0] == investment_year
|
|
)
|
|
|
|
if options.get("cluster_heat_buses", False) and not first_year_myopic:
|
|
cluster_heat_buses(n)
|
|
|
|
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
|
|
|
|
sanitize_carriers(n, snakemake.config)
|
|
|
|
n.export_to_netcdf(snakemake.output[0])
|