# -*- coding: utf-8 -*- # SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors # # SPDX-License-Identifier: MIT """ Adds all sector-coupling components to the network, including demand and supply technologies for the buildings, transport and industry sectors. """ import logging import os import re from itertools import product import networkx as nx import numpy as np import pandas as pd import pypsa import xarray as xr from _helpers import generate_periodic_profiles, update_config_with_sector_opts from add_electricity import calculate_annuity, sanitize_carriers from build_energy_totals import build_co2_totals, build_eea_co2, build_eurostat_co2 from networkx.algorithms import complement from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation from pypsa.geo import haversine_pts from pypsa.io import import_components_from_dataframe from scipy.stats import beta logger = logging.getLogger(__name__) from types import SimpleNamespace spatial = SimpleNamespace() from packaging.version import Version, parse pd_version = parse(pd.__version__) agg_group_kwargs = dict(numeric_only=False) if pd_version >= Version("1.3") else {} def define_spatial(nodes, options): """ Namespace for spatial. Parameters ---------- nodes : list-like """ global spatial spatial.nodes = nodes # biomass spatial.biomass = SimpleNamespace() if options.get("biomass_spatial", options["biomass_transport"]): spatial.biomass.nodes = nodes + " solid biomass" spatial.biomass.locations = nodes spatial.biomass.industry = nodes + " solid biomass for industry" spatial.biomass.industry_cc = nodes + " solid biomass for industry CC" else: spatial.biomass.nodes = ["EU solid biomass"] spatial.biomass.locations = ["EU"] spatial.biomass.industry = ["solid biomass for industry"] spatial.biomass.industry_cc = ["solid biomass for industry CC"] spatial.biomass.df = pd.DataFrame(vars(spatial.biomass), index=nodes) # co2 spatial.co2 = SimpleNamespace() if options["co2_spatial"]: spatial.co2.nodes = nodes + " co2 stored" spatial.co2.locations = nodes spatial.co2.vents = nodes + " co2 vent" spatial.co2.process_emissions = nodes + " process emissions" else: spatial.co2.nodes = ["co2 stored"] spatial.co2.locations = ["EU"] spatial.co2.vents = ["co2 vent"] spatial.co2.process_emissions = ["process emissions"] spatial.co2.df = pd.DataFrame(vars(spatial.co2), index=nodes) # gas spatial.gas = SimpleNamespace() if options["gas_network"]: spatial.gas.nodes = nodes + " gas" spatial.gas.locations = nodes spatial.gas.biogas = nodes + " biogas" spatial.gas.industry = nodes + " gas for industry" spatial.gas.industry_cc = nodes + " gas for industry CC" spatial.gas.biogas_to_gas = nodes + " biogas to gas" else: spatial.gas.nodes = ["EU gas"] spatial.gas.locations = ["EU"] spatial.gas.biogas = ["EU biogas"] spatial.gas.industry = ["gas for industry"] spatial.gas.biogas_to_gas = ["EU biogas to gas"] if options.get("co2_spatial", options["co2network"]): spatial.gas.industry_cc = nodes + " gas for industry CC" else: spatial.gas.industry_cc = ["gas for industry CC"] spatial.gas.df = pd.DataFrame(vars(spatial.gas), index=nodes) # ammonia if options.get("ammonia"): spatial.ammonia = SimpleNamespace() if options.get("ammonia") == "regional": spatial.ammonia.nodes = nodes + " NH3" spatial.ammonia.locations = nodes else: spatial.ammonia.nodes = ["EU NH3"] spatial.ammonia.locations = ["EU"] spatial.ammonia.df = pd.DataFrame(vars(spatial.ammonia), index=nodes) # hydrogen spatial.h2 = SimpleNamespace() spatial.h2.nodes = nodes + " H2" spatial.h2.locations = nodes # methanol spatial.methanol = SimpleNamespace() spatial.methanol.nodes = ["EU methanol"] spatial.methanol.locations = ["EU"] # oil spatial.oil = SimpleNamespace() spatial.oil.nodes = ["EU oil"] spatial.oil.locations = ["EU"] # uranium spatial.uranium = SimpleNamespace() spatial.uranium.nodes = ["EU uranium"] spatial.uranium.locations = ["EU"] # coal spatial.coal = SimpleNamespace() spatial.coal.nodes = ["EU coal"] spatial.coal.locations = ["EU"] # lignite spatial.lignite = SimpleNamespace() spatial.lignite.nodes = ["EU lignite"] spatial.lignite.locations = ["EU"] return spatial from types import SimpleNamespace spatial = SimpleNamespace() def emission_sectors_from_opts(opts): sectors = ["electricity"] if "T" in opts: sectors += ["rail non-elec", "road non-elec"] if "H" in opts: sectors += ["residential non-elec", "services non-elec"] if "I" in opts: sectors += [ "industrial non-elec", "industrial processes", "domestic aviation", "international aviation", "domestic navigation", "international navigation", ] if "A" in opts: sectors += ["agriculture"] return sectors def get(item, investment_year=None): """ Check whether item depends on investment year. """ if isinstance(item, dict): return item[investment_year] else: return item def co2_emissions_year( countries, input_eurostat, opts, emissions_scope, report_year, input_co2, year ): """ Calculate CO2 emissions in one specific year (e.g. 1990 or 2018). """ eea_co2 = build_eea_co2(input_co2, year, emissions_scope) # TODO: read Eurostat data from year > 2014 # this only affects the estimation of CO2 emissions for BA, RS, AL, ME, MK if year > 2014: eurostat_co2 = build_eurostat_co2( input_eurostat, countries, report_year, year=2014 ) else: eurostat_co2 = build_eurostat_co2(input_eurostat, countries, report_year, year) co2_totals = build_co2_totals(countries, eea_co2, eurostat_co2) sectors = emission_sectors_from_opts(opts) co2_emissions = co2_totals.loc[countries, sectors].sum().sum() # convert MtCO2 to GtCO2 co2_emissions *= 0.001 return co2_emissions # TODO: move to own rule with sector-opts wildcard? def build_carbon_budget(o, input_eurostat, fn, emissions_scope, report_year): """ Distribute carbon budget following beta or exponential transition path. """ # opts? if "be" in o: # beta decay carbon_budget = float(o[o.find("cb") + 2 : o.find("be")]) be = float(o[o.find("be") + 2 :]) if "ex" in o: # exponential decay carbon_budget = float(o[o.find("cb") + 2 : o.find("ex")]) r = float(o[o.find("ex") + 2 :]) countries = snakemake.params.countries e_1990 = co2_emissions_year( countries, input_eurostat, opts, emissions_scope, report_year, input_co2, year=1990, ) # emissions at the beginning of the path (last year available 2018) e_0 = co2_emissions_year( countries, input_eurostat, opts, emissions_scope, report_year, input_co2, year=2018, ) planning_horizons = snakemake.params.planning_horizons t_0 = planning_horizons[0] if "be" in o: # final year in the path t_f = t_0 + (2 * carbon_budget / e_0).round(0) def beta_decay(t): cdf_term = (t - t_0) / (t_f - t_0) return (e_0 / e_1990) * (1 - beta.cdf(cdf_term, be, be)) # emissions (relative to 1990) co2_cap = pd.Series({t: beta_decay(t) for t in planning_horizons}, name=o) if "ex" in o: T = carbon_budget / e_0 m = (1 + np.sqrt(1 + r * T)) / T def exponential_decay(t): return (e_0 / e_1990) * (1 + (m + r) * (t - t_0)) * np.exp(-m * (t - t_0)) co2_cap = pd.Series( {t: exponential_decay(t) for t in planning_horizons}, name=o ) # TODO log in Snakefile csvs_folder = fn.rsplit("/", 1)[0] if not os.path.exists(csvs_folder): os.makedirs(csvs_folder) co2_cap.to_csv(fn, float_format="%.3f") def add_lifetime_wind_solar(n, costs): """ Add lifetime for solar and wind generators. """ for carrier in ["solar", "onwind", "offwind"]: gen_i = n.generators.index.str.contains(carrier) n.generators.loc[gen_i, "lifetime"] = costs.at[carrier, "lifetime"] def haversine(p): coord0 = n.buses.loc[p.bus0, ["x", "y"]].values coord1 = n.buses.loc[p.bus1, ["x", "y"]].values return 1.5 * haversine_pts(coord0, coord1) def create_network_topology( n, prefix, carriers=["DC"], connector=" -> ", bidirectional=True ): """ Create a network topology from transmission lines and link carrier selection. Parameters ---------- n : pypsa.Network prefix : str carriers : list-like connector : str bidirectional : bool, default True True: one link for each connection False: one link for each connection and direction (back and forth) Returns ------- pd.DataFrame with columns bus0, bus1, length, underwater_fraction """ ln_attrs = ["bus0", "bus1", "length"] lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"] lk_attrs = n.links.columns.intersection(lk_attrs) candidates = pd.concat( [n.lines[ln_attrs], n.links.loc[n.links.carrier.isin(carriers), lk_attrs]] ).fillna(0) # base network topology purely on location not carrier candidates["bus0"] = candidates.bus0.map(n.buses.location) candidates["bus1"] = candidates.bus1.map(n.buses.location) positive_order = candidates.bus0 < candidates.bus1 candidates_p = candidates[positive_order] swap_buses = {"bus0": "bus1", "bus1": "bus0"} candidates_n = candidates[~positive_order].rename(columns=swap_buses) candidates = pd.concat([candidates_p, candidates_n]) def make_index(c): return prefix + c.bus0 + connector + c.bus1 topo = candidates.groupby(["bus0", "bus1"], as_index=False).mean() topo.index = topo.apply(make_index, axis=1) if not bidirectional: topo_reverse = topo.copy() topo_reverse.rename(columns=swap_buses, inplace=True) topo_reverse.index = topo_reverse.apply(make_index, axis=1) topo = pd.concat([topo, topo_reverse]) return topo # TODO merge issue with PyPSA-Eur def update_wind_solar_costs(n, costs): """ Update costs for wind and solar generators added with pypsa-eur to those cost in the planning year. """ # NB: solar costs are also manipulated for rooftop # when distribution grid is inserted n.generators.loc[n.generators.carrier == "solar", "capital_cost"] = costs.at[ "solar-utility", "fixed" ] n.generators.loc[n.generators.carrier == "onwind", "capital_cost"] = costs.at[ "onwind", "fixed" ] # for offshore wind, need to calculated connection costs # assign clustered bus # map initial network -> simplified network busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0).squeeze() busmap_s.index = busmap_s.index.astype(str) busmap_s = busmap_s.astype(str) # map simplified network -> clustered network busmap = pd.read_csv(snakemake.input.busmap, index_col=0).squeeze() busmap.index = busmap.index.astype(str) busmap = busmap.astype(str) # map initial network -> clustered network clustermaps = busmap_s.map(busmap) # code adapted from pypsa-eur/scripts/add_electricity.py for connection in ["dc", "ac"]: tech = "offwind-" + connection profile = snakemake.input["profile_offwind_" + connection] with xr.open_dataset(profile) as ds: underwater_fraction = ds["underwater_fraction"].to_pandas() connection_cost = ( snakemake.params.length_factor * ds["average_distance"].to_pandas() * ( underwater_fraction * costs.at[tech + "-connection-submarine", "fixed"] + (1.0 - underwater_fraction) * costs.at[tech + "-connection-underground", "fixed"] ) ) # convert to aggregated clusters with weighting weight = ds["weight"].to_pandas() # e.g. clusters == 37m means that VRE generators are left # at clustering of simplified network, but that they are # connected to 37-node network if snakemake.wildcards.clusters[-1:] == "m": genmap = busmap_s else: genmap = clustermaps connection_cost = (connection_cost * weight).groupby( genmap ).sum() / weight.groupby(genmap).sum() capital_cost = ( costs.at["offwind", "fixed"] + costs.at[tech + "-station", "fixed"] + connection_cost ) logger.info( "Added connection cost of {:0.0f}-{:0.0f} Eur/MW/a to {}".format( connection_cost[0].min(), connection_cost[0].max(), tech ) ) n.generators.loc[ n.generators.carrier == tech, "capital_cost" ] = capital_cost.rename(index=lambda node: node + " " + tech) def add_carrier_buses(n, carrier, nodes=None): """ Add buses to connect e.g. coal, nuclear and oil plants. """ if nodes is None: nodes = vars(spatial)[carrier].nodes location = vars(spatial)[carrier].locations # skip if carrier already exists if carrier in n.carriers.index: return if not isinstance(nodes, pd.Index): nodes = pd.Index(nodes) n.add("Carrier", carrier) unit = "MWh_LHV" if carrier == "gas" else "MWh_th" n.madd("Bus", nodes, location=location, carrier=carrier, unit=unit) # capital cost could be corrected to e.g. 0.2 EUR/kWh * annuity and O&M n.madd( "Store", nodes + " Store", bus=nodes, e_nom_extendable=True, e_cyclic=True, carrier=carrier, capital_cost=0.2 * costs.at[carrier, "discount rate"], # preliminary value to avoid zeros ) n.madd( "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. """ to_drop = list(n.buses.query("carrier not in ['AC', 'DC']").carrier.unique()) if to_drop: 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, 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", ) # this tracks CO2 stored, e.g. underground n.madd( "Bus", spatial.co2.nodes, location=spatial.co2.locations, carrier="co2 stored", unit="t_co2", ) 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 stored") else: e_nom_max = np.inf n.madd( "Store", spatial.co2.nodes, e_nom_extendable=True, e_nom_max=e_nom_max, capital_cost=options["co2_sequestration_cost"], carrier="co2 stored", bus=spatial.co2.nodes, ) n.add("Carrier", "co2 stored") 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 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="<=", 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 / ( cf_industry["MWh_elec_per_tNH3_electrolysis"] / cf_industry["MWh_NH3_per_tNH3"] ), # output: MW_NH3 per MW_elec efficiency2=-cf_industry["MWh_H2_per_tNH3_electrolysis"] / cf_industry["MWh_elec_per_tNH3_electrolysis"], # input: MW_H2 per MW_elec capital_cost=costs.at["Haber-Bosch", "fixed"], 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 add_wave(n, wave_cost_factor): # TODO: handle in Snakefile wave_fn = "data/WindWaveWEC_GLTB.xlsx" # in kW capacity = pd.Series({"Attenuator": 750, "F2HB": 1000, "MultiPA": 600}) # in EUR/MW annuity_factor = calculate_annuity(25, 0.07) + 0.03 costs = ( 1e6 * wave_cost_factor * annuity_factor * pd.Series({"Attenuator": 2.5, "F2HB": 2, "MultiPA": 1.5}) ) sheets = pd.read_excel( wave_fn, sheet_name=["FirthForth", "Hebrides"], usecols=["Attenuator", "F2HB", "MultiPA"], index_col=0, skiprows=[0], parse_dates=True, ) wave = pd.concat( [sheets[l].divide(capacity, axis=1) for l in locations], keys=locations, axis=1 ) for wave_type in costs.index: n.add( "Generator", "Hebrides " + wave_type, bus="GB4 0", # TODO this location is hardcoded p_nom_extendable=True, carrier="wave", capital_cost=costs[wave_type], p_max_pu=wave["Hebrides", wave_type], ) def insert_electricity_distribution_grid(n, costs): # TODO pop_layout? # TODO options? 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.loc["electricity distribution grid"]["fixed"] * f_costs # gas boilers gas_b = n.links.index[ n.links.carrier.str.contains("gas boiler") & (~n.links.carrier.str.contains("urban central")) ] n.links.loc[gas_b, "capital_cost"] += capital_cost # micro CHPs mchp = n.links.index[n.links.carrier.str.contains("micro gas")] n.links.loc[mchp, "capital_cost"] += capital_cost def add_electricity_grid_connection(n, costs): carriers = ["onwind", "solar"] gens = n.generators.index[n.generators.carrier.isin(carriers)] n.generators.loc[gens, "capital_cost"] += costs.at[ "electricity grid connection", "fixed" ] def add_storage_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 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 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) p_nom = gas_input_nodes.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 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) augmentation = list( k_edge_augmentation(G, k_edge, avail=complement_edges.values) ) if augmentation: 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"] ) # 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, 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", efficiency=costs.at["methanation", "efficiency"], efficiency2=-costs.at["methanation", "efficiency"] * costs.at["gas", "CO2 intensity"], capital_cost=costs.at["methanation", "fixed"] * costs.at["methanation", "efficiency"], # costs given per kW_gas lifetime=costs.at["methanation", "lifetime"], ) if options["helmeth"]: n.madd( "Link", spatial.nodes, suffix=" helmeth", bus0=nodes, bus1=spatial.gas.nodes, bus2=spatial.co2.nodes, carrier="helmeth", p_nom_extendable=True, efficiency=costs.at["helmeth", "efficiency"], efficiency2=-costs.at["helmeth", "efficiency"] * costs.at["gas", "CO2 intensity"], capital_cost=costs.at["helmeth", "fixed"], lifetime=costs.at["helmeth", "lifetime"], ) if options.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"]: 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"], ) 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") nhours = n.snapshot_weightings.generators.sum() 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, location=nodes, suffix=" EV battery", 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: if "oil" not in n.buses.carrier.unique(): n.madd( "Bus", spatial.oil.nodes, location=spatial.oil.locations, carrier="oil", unit="MWh_LHV", ) ice_efficiency = options["transport_internal_combustion_efficiency"] n.madd( "Load", nodes, suffix=" land transport oil", bus=spatial.oil.nodes, carrier="land transport oil", p_set=ice_share / ice_efficiency * transport[nodes], ) co2 = ( ice_share / ice_efficiency * transport[nodes].sum().sum() / nhours * costs.at["oil", "CO2 intensity"] ) n.add( "Load", "land transport oil emissions", bus="co2 atmosphere", carrier="land transport oil emissions", p_set=-co2, ) def build_heat_demand(n): # copy forward the daily average heat demand into each hour, so it can be multiplied by the intraday profile daily_space_heat_demand = ( xr.open_dataarray(snakemake.input.heat_demand_total) .to_pandas() .reindex(index=n.snapshots, method="ffill") ) intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0) sectors = ["residential", "services"] uses = ["water", "space"] heat_demand = {} electric_heat_supply = {} for sector, use in product(sectors, uses): weekday = list(intraday_profiles[f"{sector} {use} weekday"]) weekend = list(intraday_profiles[f"{sector} {use} weekend"]) weekly_profile = weekday * 5 + weekend * 2 intraday_year_profile = generate_periodic_profiles( daily_space_heat_demand.index.tz_localize("UTC"), nodes=daily_space_heat_demand.columns, weekly_profile=weekly_profile, ) if use == "space": heat_demand_shape = daily_space_heat_demand * intraday_year_profile else: heat_demand_shape = intraday_year_profile heat_demand[f"{sector} {use}"] = ( heat_demand_shape / heat_demand_shape.sum() ).multiply(pop_weighted_energy_totals[f"total {sector} {use}"]) * 1e6 electric_heat_supply[f"{sector} {use}"] = ( heat_demand_shape / heat_demand_shape.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.groupby(level=1, axis=1).sum()[electric_nodes] ) return heat_demand def add_heat(n, costs): logger.info("Add heat sector") sectors = ["residential", "services"] heat_demand = build_heat_demand(n) nodes, dist_fraction, urban_fraction = create_nodes_for_heat_sector() # NB: must add costs of central heating afterwards (EUR 400 / kWpeak, 50a, 1% FOM from Fraunhofer ISE) # exogenously reduce space heat demand if options["reduce_space_heat_exogenously"]: dE = get(options["reduce_space_heat_exogenously_factor"], investment_year) 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 for name in heat_systems: name_type = "central" if name == "urban central" else "decentral" n.add("Carrier", name + " heat") n.madd( "Bus", nodes[name] + f" {name} heat", location=nodes[name], carrier=name + " heat", unit="MWh_th", ) ## Add heat load for sector in sectors: # heat demand weighting if "rural" in name: factor = 1 - urban_fraction[nodes[name]] elif "urban central" in name: factor = dist_fraction[nodes[name]] elif "urban decentral" in name: factor = urban_fraction[nodes[name]] - dist_fraction[nodes[name]] else: raise NotImplementedError( f" {name} not in " f"heat systems: {heat_systems}" ) if sector in name: heat_load = ( heat_demand[[sector + " water", sector + " space"]] .groupby(level=1, axis=1) .sum()[nodes[name]] .multiply(factor) ) if name == "urban central": heat_load = ( heat_demand.groupby(level=1, axis=1) .sum()[nodes[name]] .multiply( factor * (1 + options["district_heating"]["district_heating_loss"]) ) ) n.madd( "Load", nodes[name], suffix=f" {name} heat", bus=nodes[name] + f" {name} heat", carrier=name + " heat", p_set=heat_load, ) ## Add heat pumps heat_pump_type = "air" if "urban" in name else "ground" costs_name = f"{name_type} {heat_pump_type}-sourced heat pump" efficiency = ( cop[heat_pump_type][nodes[name]] if options["time_dep_hp_cop"] else costs.at[costs_name, "efficiency"] ) n.madd( "Link", nodes[name], suffix=f" {name} {heat_pump_type} heat pump", bus0=nodes[name], bus1=nodes[name] + f" {name} heat", carrier=f"{name} {heat_pump_type} heat pump", efficiency=efficiency, capital_cost=costs.at[costs_name, "efficiency"] * costs.at[costs_name, "fixed"], p_nom_extendable=True, lifetime=costs.at[costs_name, "lifetime"], ) if options["tes"]: n.add("Carrier", name + " water tanks") n.madd( "Bus", nodes[name] + f" {name} water tanks", location=nodes[name], carrier=name + " water tanks", unit="MWh_th", ) n.madd( "Link", nodes[name] + f" {name} water tanks charger", bus0=nodes[name] + f" {name} heat", bus1=nodes[name] + f" {name} water tanks", efficiency=costs.at["water tank charger", "efficiency"], carrier=name + " water tanks charger", p_nom_extendable=True, ) n.madd( "Link", nodes[name] + f" {name} water tanks discharger", bus0=nodes[name] + f" {name} water tanks", bus1=nodes[name] + f" {name} heat", carrier=name + " water tanks discharger", efficiency=costs.at["water tank discharger", "efficiency"], p_nom_extendable=True, ) if isinstance(options["tes_tau"], dict): tes_time_constant_days = options["tes_tau"][name_type] else: logger.warning( "Deprecated: a future version will require you to specify 'tes_tau' ", "for 'decentral' and 'central' separately.", ) tes_time_constant_days = ( options["tes_tau"] if name_type == "decentral" else 180.0 ) n.madd( "Store", nodes[name] + f" {name} water tanks", bus=nodes[name] + f" {name} water tanks", e_cyclic=True, e_nom_extendable=True, carrier=name + " water tanks", standing_loss=1 - np.exp(-1 / 24 / tes_time_constant_days), capital_cost=costs.at[name_type + " water tank storage", "fixed"], lifetime=costs.at[name_type + " water tank storage", "lifetime"], ) if options["boilers"]: key = f"{name_type} resistive heater" n.madd( "Link", nodes[name] + f" {name} resistive heater", bus0=nodes[name], bus1=nodes[name] + f" {name} heat", carrier=name + " resistive heater", efficiency=costs.at[key, "efficiency"], capital_cost=costs.at[key, "efficiency"] * costs.at[key, "fixed"], p_nom_extendable=True, lifetime=costs.at[key, "lifetime"], ) key = f"{name_type} gas boiler" n.madd( "Link", nodes[name] + f" {name} gas boiler", p_nom_extendable=True, bus0=spatial.gas.df.loc[nodes[name], "nodes"].values, bus1=nodes[name] + f" {name} heat", bus2="co2 atmosphere", carrier=name + " gas boiler", efficiency=costs.at[key, "efficiency"], efficiency2=costs.at["gas", "CO2 intensity"], capital_cost=costs.at[key, "efficiency"] * costs.at[key, "fixed"], lifetime=costs.at[key, "lifetime"], ) if options["solar_thermal"]: n.add("Carrier", name + " solar thermal") n.madd( "Generator", nodes[name], suffix=f" {name} solar thermal collector", bus=nodes[name] + f" {name} heat", carrier=name + " solar thermal", p_nom_extendable=True, capital_cost=costs.at[name_type + " solar thermal", "fixed"], p_max_pu=solar_thermal[nodes[name]], lifetime=costs.at[name_type + " solar thermal", "lifetime"], ) if options["chp"] and name == "urban central": # add gas CHP; biomass CHP is added in biomass section n.madd( "Link", nodes[name] + " urban central gas CHP", bus0=spatial.gas.df.loc[nodes[name], "nodes"].values, bus1=nodes[name], bus2=nodes[name] + " urban central heat", bus3="co2 atmosphere", carrier="urban central gas CHP", p_nom_extendable=True, capital_cost=costs.at["central gas CHP", "fixed"] * costs.at["central gas CHP", "efficiency"], marginal_cost=costs.at["central gas CHP", "VOM"], efficiency=costs.at["central gas CHP", "efficiency"], efficiency2=costs.at["central gas CHP", "efficiency"] / costs.at["central gas CHP", "c_b"], efficiency3=costs.at["gas", "CO2 intensity"], lifetime=costs.at["central gas CHP", "lifetime"], ) n.madd( "Link", nodes[name] + " urban central gas CHP CC", bus0=spatial.gas.df.loc[nodes[name], "nodes"].values, bus1=nodes[name], bus2=nodes[name] + " urban central heat", bus3="co2 atmosphere", bus4=spatial.co2.df.loc[nodes[name], "nodes"].values, carrier="urban central gas CHP CC", p_nom_extendable=True, capital_cost=costs.at["central gas CHP", "fixed"] * costs.at["central gas CHP", "efficiency"] + costs.at["biomass CHP capture", "fixed"] * costs.at["gas", "CO2 intensity"], marginal_cost=costs.at["central gas CHP", "VOM"], efficiency=costs.at["central gas CHP", "efficiency"] - costs.at["gas", "CO2 intensity"] * ( costs.at["biomass CHP capture", "electricity-input"] + costs.at["biomass CHP capture", "compression-electricity-input"] ), efficiency2=costs.at["central gas CHP", "efficiency"] / costs.at["central gas CHP", "c_b"] + costs.at["gas", "CO2 intensity"] * ( costs.at["biomass CHP capture", "heat-output"] + costs.at["biomass CHP capture", "compression-heat-output"] - costs.at["biomass CHP capture", "heat-input"] ), efficiency3=costs.at["gas", "CO2 intensity"] * (1 - costs.at["biomass CHP capture", "capture_rate"]), efficiency4=costs.at["gas", "CO2 intensity"] * costs.at["biomass CHP capture", "capture_rate"], lifetime=costs.at["central gas CHP", "lifetime"], ) if options["chp"] and options["micro_chp"] and name != "urban central": n.madd( "Link", nodes[name] + f" {name} micro gas CHP", p_nom_extendable=True, bus0=spatial.gas.df.loc[nodes[name], "nodes"].values, bus1=nodes[name], bus2=nodes[name] + f" {name} heat", bus3="co2 atmosphere", carrier=name + " micro gas CHP", efficiency=costs.at["micro CHP", "efficiency"], efficiency2=costs.at["micro CHP", "efficiency-heat"], efficiency3=costs.at["gas", "CO2 intensity"], capital_cost=costs.at["micro CHP", "fixed"], lifetime=costs.at["micro CHP", "lifetime"], ) if options["retrofitting"]["retro_endogen"]: 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_energy, index_col=[0, 1], skipinitialspace=True, header=[0, 1], ) # heated floor area [10^6 * m^2] per country floor_area = pd.read_csv(snakemake.input.floor_area, index_col=[0, 1]) n.add("Carrier", "retrofitting") # share of space heat demand 'w_space' of total heat demand w_space = {} for sector in sectors: w_space[sector] = heat_demand_r[sector + " space"] / ( heat_demand_r[sector + " space"] + heat_demand_r[sector + " water"] ) w_space["tot"] = ( heat_demand_r["services space"] + heat_demand_r["residential space"] ) / heat_demand_r.groupby(level=[1], axis=1).sum() for name in n.loads[ n.loads.carrier.isin([x + " heat" for x in heat_systems]) ].index: node = n.buses.loc[name, "location"] ct = pop_layout.loc[node, "ct"] # weighting 'f' depending on the size of the population at the node f = urban_fraction[node] if "urban" in name else (1 - urban_fraction[node]) if f == 0: continue # get sector name ("residential"/"services"/or both "tot" for urban central) sec = [x if x in name else "tot" for x in sectors][0] # get floor aread at node and region (urban/rural) in m^2 floor_area_node = ( pop_layout.loc[node].fraction * floor_area.loc[ct, "value"] * 10**6 ).loc[sec] * f # total heat demand at node [MWh] demand = n.loads_t.p_set[name].resample(hours[0]).mean() # space heat demand at node [MWh] space_heat_demand = demand * w_space[sec][node] # normed time profile of space heat demand 'space_pu' (values between 0-1), # p_max_pu/p_min_pu of retrofitting generators space_pu = (space_heat_demand / space_heat_demand.max()).to_frame(name=node) # minimum heat demand 'dE' after retrofitting in units of original heat demand (values between 0-1) dE = retro_data.loc[(ct, sec), ("dE")] # get 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()) ) # 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).fillna(method="ffill") # add for each retrofitting strength a generator with heat generation profile following the profile of the heat demand for strength in strengths: n.madd( "Generator", [node], suffix=" retrofitting " + strength + " " + name[6::], bus=name, carrier="retrofitting", p_nom_extendable=True, p_nom_max=dE_diff[strength] * space_heat_demand.max(), # maximum energy savings for this renovation strength p_max_pu=space_pu, p_min_pu=space_pu, country=ct, capital_cost=capital_cost[strength] * options["retrofitting"]["cost_factor"], ) def create_nodes_for_heat_sector(): # TODO pop_layout # rural are areas with low heating density and individual heating # urban are areas with high heating density # urban can be split into district heating (central) and individual heating (decentral) ct_urban = pop_layout.urban.groupby(pop_layout.ct).sum() # distribution of urban population within a country pop_layout["urban_ct_fraction"] = pop_layout.urban / pop_layout.ct.map(ct_urban.get) sectors = ["residential", "services"] nodes = {} urban_fraction = pop_layout.urban / pop_layout[["rural", "urban"]].sum(axis=1) for sector in sectors: nodes[sector + " rural"] = pop_layout.index nodes[sector + " urban decentral"] = pop_layout.index district_heat_share = pop_weighted_energy_totals["district heat share"] # maximum potential of urban demand covered by district heating central_fraction = options["district_heating"]["potential"] # district heating share at each node dist_fraction_node = ( district_heat_share * pop_layout["urban_ct_fraction"] / pop_layout["fraction"] ) nodes["urban central"] = dist_fraction_node.index # if district heating share larger than urban fraction -> set urban # fraction to district heating share urban_fraction = pd.concat([urban_fraction, dist_fraction_node], axis=1).max(axis=1) # difference of max potential and today's share of district heating diff = (urban_fraction * central_fraction) - dist_fraction_node progress = get(options["district_heating"]["progress"], investment_year) dist_fraction_node += diff * progress logger.info( f"Increase district heating share by a progress factor of {progress:.2%} " f"resulting in new average share of {dist_fraction_node.mean():.2%}" ) return nodes, dist_fraction_node, urban_fraction 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.loc["biogas upgrading", "fixed"], marginal_cost=costs.loc["biogas upgrading", "VOM"], efficiency2=-costs.at["gas", "CO2 intensity"], p_nom_extendable=True, ) if options["biomass_transport"]: # 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, ) # 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_heat = create_nodes_for_heat_sector()[0] for name in [ "residential rural", "services rural", "residential urban decentral", "services urban decentral", ]: n.madd( "Link", nodes_heat[name] + f" {name} biomass boiler", p_nom_extendable=True, bus0=spatial.biomass.df.loc[nodes_heat[name], "nodes"].values, bus1=nodes_heat[name] + 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"], 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.loc["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.loc["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.loc["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.loc["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, ) n.madd( "Link", spatial.biomass.industry_cc, bus0=spatial.biomass.nodes, bus1=spatial.biomass.industry, bus2="co2 atmosphere", bus3=spatial.co2.nodes, carrier="solid biomass for industry CC", p_nom_extendable=True, capital_cost=costs.at["cement capture", "fixed"] * costs.at["solid biomass", "CO2 intensity"], efficiency=0.9, # TODO: make config option efficiency2=-costs.at["solid biomass", "CO2 intensity"] * costs.at["cement capture", "capture_rate"], efficiency3=costs.at["solid biomass", "CO2 intensity"] * costs.at["cement capture", "capture_rate"], lifetime=costs.at["cement capture", "lifetime"], ) n.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 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.sum() * efficiency n.madd( "Load", spatial.methanol.nodes, suffix=" shipping methanol", bus=spatial.methanol.nodes, carrier="shipping methanol", p_set=p_set_methanol, ) # 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 co2 = p_set_methanol / options["MWh_MeOH_per_tCO2"] n.add( "Load", "shipping methanol emissions", bus="co2 atmosphere", carrier="shipping methanol emissions", p_set=-co2, ) if shipping_oil_share: p_set_oil = shipping_oil_share * p_set.sum() n.madd( "Load", spatial.oil.nodes, suffix=" shipping oil", bus=spatial.oil.nodes, carrier="shipping oil", p_set=p_set_oil, ) co2 = p_set_oil * costs.at["oil", "CO2 intensity"] n.add( "Load", "shipping oil emissions", bus="co2 atmosphere", carrier="shipping oil emissions", p_set=-co2, ) 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", [oil_bus + " Store" for oil_bus in spatial.oil.nodes], 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 options["oil_boilers"]: nodes_heat = create_nodes_for_heat_sector()[0] for name in [ "residential rural", "services rural", "residential urban decentral", "services urban decentral", ]: n.madd( "Link", nodes_heat[name] + f" {name} oil boiler", p_nom_extendable=True, bus0=spatial.oil.nodes, bus1=nodes_heat[name] + f" {name} heat", bus2="co2 atmosphere", carrier=f"{name} oil boiler", efficiency=costs.at["decentral oil boiler", "efficiency"], efficiency2=costs.at["oil", "CO2 intensity"], capital_cost=costs.at["decentral oil boiler", "efficiency"] * costs.at["decentral oil boiler", "fixed"], lifetime=costs.at["decentral oil boiler", "lifetime"], ) n.madd( "Link", nodes + " Fischer-Tropsch", bus0=nodes + " H2", bus1=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 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"], ) demand_factor = options.get("HVC_demand_factor", 1) p_set = demand_factor * industrial_demand.loc[nodes, "naphtha"].sum() / nhours if demand_factor != 1: logger.warning(f"Changing HVC demand by {demand_factor*100-100:+.2f}%.") n.madd( "Load", ["naphtha for industry"], bus=spatial.oil.nodes, carrier="naphtha for industry", p_set=p_set, ) demand_factor = options.get("aviation_demand_factor", 1) all_aviation = ["total international aviation", "total domestic aviation"] p_set = ( demand_factor * pop_weighted_energy_totals.loc[nodes, all_aviation].sum(axis=1).sum() * 1e6 / nhours ) if demand_factor != 1: logger.warning(f"Changing aviation demand by {demand_factor*100-100:+.2f}%.") n.madd( "Load", ["kerosene for aviation"], bus=spatial.oil.nodes, carrier="kerosene for aviation", p_set=p_set, ) # NB: CO2 gets released again to atmosphere when plastics decay or kerosene is burned # except for the process emissions when naphtha is used for petrochemicals, which can be captured with other industry process emissions # tco2 per hour co2_release = ["naphtha for industry", "kerosene for aviation"] co2 = ( n.loads.loc[co2_release, "p_set"].sum() * costs.at["oil", "CO2 intensity"] - industrial_demand.loc[nodes, "process emission from feedstock"].sum() / nhours ) n.add( "Load", "oil emissions", bus="co2 atmosphere", carrier="oil emissions", p_set=-co2, ) # TODO simplify bus expression n.madd( "Load", nodes, suffix=" low-temperature heat for industry", bus=[ node + " urban central heat" if node + " urban central heat" in n.buses.index else node + " services urban decentral heat" for node in nodes ], carrier="low-temperature heat for industry", p_set=industrial_demand.loc[nodes, "low-temperature heat"] / 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", ) sel = ["process emission", "process emission from feedstock"] if options["co2_spatial"] or options["co2network"]: p_set = ( -industrial_demand.loc[nodes, sel] .sum(axis=1) .rename(index=lambda x: x + " process emissions") / nhours ) else: p_set = -industrial_demand.loc[nodes, sel].sum(axis=1).sum() / nhours # this should be process emissions fossil+feedstock # then need load on atmosphere for feedstock emissions that are currently going to atmosphere via Link Fischer-Tropsch demand n.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, ) def add_waste_heat(n): # TODO options? logger.info("Add possibility to use industrial waste heat in district heating") # AC buses with district heating urban_central = n.buses.index[n.buses.carrier == "urban central heat"] if not urban_central.empty: urban_central = urban_central.str[: -len(" urban central heat")] # TODO what is the 0.95 and should it be a config option? if options["use_fischer_tropsch_waste_heat"]: n.links.loc[urban_central + " Fischer-Tropsch", "bus3"] = ( urban_central + " urban central heat" ) n.links.loc[urban_central + " Fischer-Tropsch", "efficiency3"] = ( 0.95 - n.links.loc[urban_central + " Fischer-Tropsch", "efficiency"] ) # TODO integrate usable waste heat efficiency into technology-data from DEA if options.get("use_electrolysis_waste_heat", False): 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"]: 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" ] 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 * 1e6 / nhours, ) if oil_share > 0: n.madd( "Load", ["agriculture machinery oil"], bus=spatial.oil.nodes, carrier="agriculture machinery oil", p_set=oil_share * machinery_nodal_energy.sum() * 1e6 / nhours, ) co2 = ( oil_share * machinery_nodal_energy.sum() * 1e6 / nhours * costs.at["oil", "CO2 intensity"] ) n.add( "Load", "agriculture machinery oil emissions", bus="co2 atmosphere", carrier="agriculture machinery oil emissions", p_set=-co2, ) 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: if "+" not in o: continue oo = o.split("+") carrier_list = np.hstack( ( n.generators.carrier.unique(), n.links.carrier.unique(), n.stores.carrier.unique(), n.storage_units.carrier.unique(), ) ) suptechs = map(lambda c: c.split("-", 2)[0], carrier_list) if oo[0].startswith(tuple(suptechs)): carrier = oo[0] attr_lookup = {"p": "p_nom_max", "e": "e_nom_max", "c": "capital_cost"} attr = attr_lookup[oo[1][0]] factor = float(oo[1][1:]) # beware if factor is 0 and p_nom_max is np.inf, 0*np.inf is nan if carrier == "AC": # lines do not have carrier n.lines[attr] *= factor else: if attr == "p_nom_max": comps = {"Generator", "Link", "StorageUnit"} elif attr == "e_nom_max": comps = {"Store"} else: comps = {"Generator", "Link", "StorageUnit", "Store"} for c in n.iterate_components(comps): if carrier == "solar": sel = c.df.carrier.str.contains( carrier ) & ~c.df.carrier.str.contains("solar rooftop") else: sel = c.df.carrier.str.contains(carrier) c.df.loc[sel, attr] *= factor 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": "sum", "s_nom": "sum", "v_nom": "max", "v_mag_pu_max": "min", "v_mag_pu_min": "max", "p_nom_max": "sum", "s_nom_max": "sum", "p_nom_min": "sum", "s_nom_min": "sum", "v_ang_min": "max", "v_ang_max": "min", "terrain_factor": "mean", "num_parallel": "sum", "p_set": "sum", "e_initial": "sum", "e_nom": "sum", "e_nom_max": "sum", "e_nom_min": "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(): pnl[k].rename( columns=lambda x: x.replace("residential ", "").replace( "services ", "" ), inplace=True, ) pnl[k] = pnl[k].groupby(level=0, axis=1).agg(agg[k], **agg_group_kwargs) # 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: 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" ) 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 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="5", ll="v1.5", 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 == "myopic": add_lifetime_wind_solar(n, costs) conventional = snakemake.params.conventional_carriers for carrier in conventional: add_carrier_buses(n, carrier) add_co2_tracking(n, 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] == "wave": wave_cost_factor = float(o[4:].replace("p", ".").replace("m", "-")) logger.info( f"Including wave generators with cost factor of {wave_cost_factor}" ) add_wave(n, wave_cost_factor) if o[:4] == "dist": options["electricity_distribution_grid"] = True options["electricity_distribution_grid_cost_factor"] = float( o[4:].replace("p", ".").replace("m", "-") ) if o == "biomasstransport": options["biomass_transport"] = True if "nodistrict" in opts: options["district_heating"]["progress"] = 0.0 if "T" in opts: add_land_transport(n, costs) if "H" in opts: add_heat(n, costs) if "B" in opts: add_biomass(n, costs) if options["ammonia"]: add_ammonia(n, costs) if "I" in opts: add_industry(n, costs) if "I" in opts and "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 ) 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) first_year_myopic = (snakemake.params.foresight == "myopic") 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])