577 lines
23 KiB
Python
577 lines
23 KiB
Python
# coding: utf-8
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import logging
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logger = logging.getLogger(__name__)
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import pandas as pd
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idx = pd.IndexSlice
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import numpy as np
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import xarray as xr
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import pypsa
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import yaml
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from prepare_sector_network import prepare_costs, define_spatial, cluster_heat_buses
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from helper import override_component_attrs, update_config_with_sector_opts
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from types import SimpleNamespace
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spatial = SimpleNamespace()
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def add_build_year_to_new_assets(n, baseyear):
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"""
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Parameters
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----------
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n : pypsa.Network
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baseyear : int
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year in which optimized assets are built
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"""
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# Give assets with lifetimes and no build year the build year baseyear
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for c in n.iterate_components(["Link", "Generator", "Store"]):
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assets = c.df.index[(c.df.lifetime!=np.inf) & (c.df.build_year==0)]
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c.df.loc[assets, "build_year"] = baseyear
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# add -baseyear to name
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rename = pd.Series(c.df.index, c.df.index)
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rename[assets] += "-" + str(baseyear)
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c.df.rename(index=rename, inplace=True)
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# rename time-dependent
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selection = (
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n.component_attrs[c.name].type.str.contains("series")
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& n.component_attrs[c.name].status.str.contains("Input")
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)
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for attr in n.component_attrs[c.name].index[selection]:
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c.pnl[attr].rename(columns=rename, inplace=True)
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def add_existing_renewables(df_agg):
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"""
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Append existing renewables to the df_agg pd.DataFrame
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with the conventional power plants.
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"""
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cc = pd.read_csv(snakemake.input.country_codes, index_col=0)
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carriers = {
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"solar": "solar",
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"onwind": "onwind",
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"offwind": "offwind-ac"
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}
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for tech in ['solar', 'onwind', 'offwind']:
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carrier = carriers[tech]
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df = pd.read_csv(snakemake.input[f"existing_{tech}"], index_col=0).fillna(0.)
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df.columns = df.columns.astype(int)
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rename_countries = {
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'Czechia': 'Czech Republic',
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'UK': 'United Kingdom',
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'Bosnia Herzg': 'Bosnia Herzegovina',
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'North Macedonia': 'Macedonia'
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}
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df.rename(index=rename_countries, inplace=True)
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df.rename(index=cc["2 letter code (ISO-3166-2)"], inplace=True)
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# calculate yearly differences
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df.insert(loc=0, value=.0, column='1999')
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df = df.diff(axis=1).drop('1999', axis=1).clip(lower=0)
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# distribute capacities among nodes according to capacity factor
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# weighting with nodal_fraction
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elec_buses = n.buses.index[n.buses.carrier == "AC"].union(n.buses.index[n.buses.carrier == "DC"])
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nodal_fraction = pd.Series(0., elec_buses)
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for country in n.buses.loc[elec_buses, "country"].unique():
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gens = n.generators.index[(n.generators.index.str[:2] == country) & (n.generators.carrier == carrier)]
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cfs = n.generators_t.p_max_pu[gens].mean()
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cfs_key = cfs / cfs.sum()
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nodal_fraction.loc[n.generators.loc[gens, "bus"]] = cfs_key.values
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nodal_df = df.loc[n.buses.loc[elec_buses, "country"]]
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nodal_df.index = elec_buses
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nodal_df = nodal_df.multiply(nodal_fraction, axis=0)
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for year in nodal_df.columns:
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for node in nodal_df.index:
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name = f"{node}-{tech}-{year}"
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capacity = nodal_df.loc[node, year]
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if capacity > 0.:
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df_agg.at[name, "Fueltype"] = tech
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df_agg.at[name, "Capacity"] = capacity
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df_agg.at[name, "DateIn"] = year
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df_agg.at[name, "cluster_bus"] = node
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def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, baseyear):
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"""
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Parameters
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----------
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n : pypsa.Network
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grouping_years :
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intervals to group existing capacities
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costs :
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to read lifetime to estimate YearDecomissioning
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baseyear : int
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"""
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print("adding power capacities installed before baseyear from powerplants.csv")
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df_agg = pd.read_csv(snakemake.input.powerplants, index_col=0)
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rename_fuel = {
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'Hard Coal': 'coal',
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'Lignite': 'lignite',
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'Nuclear': 'nuclear',
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'Oil': 'oil',
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'OCGT': 'OCGT',
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'CCGT': 'CCGT',
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'Natural Gas': 'gas',
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'Bioenergy': 'urban central solid biomass CHP',
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}
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fueltype_to_drop = [
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'Hydro',
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'Wind',
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'Solar',
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'Geothermal',
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'Waste',
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'Other',
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'CCGT, Thermal'
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]
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technology_to_drop = [
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'Pv',
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'Storage Technologies'
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]
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# drop unused fueltyps and technologies
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df_agg.drop(df_agg.index[df_agg.Fueltype.isin(fueltype_to_drop)], inplace=True)
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df_agg.drop(df_agg.index[df_agg.Technology.isin(technology_to_drop)], inplace=True)
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df_agg.Fueltype = df_agg.Fueltype.map(rename_fuel)
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# Intermediate fix for DateIn & DateOut
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# Fill missing DateIn
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biomass_i = df_agg.loc[df_agg.Fueltype=='urban central solid biomass CHP'].index
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mean = df_agg.loc[biomass_i, 'DateIn'].mean()
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df_agg.loc[biomass_i, 'DateIn'] = df_agg.loc[biomass_i, 'DateIn'].fillna(int(mean))
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# Fill missing DateOut
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dateout = df_agg.loc[biomass_i, 'DateIn'] + snakemake.config['costs']['lifetime']
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df_agg.loc[biomass_i, 'DateOut'] = df_agg.loc[biomass_i, 'DateOut'].fillna(dateout)
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# drop assets which are already phased out / decommissioned
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phased_out = df_agg[df_agg["DateOut"]<baseyear].index
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df_agg.drop(phased_out, inplace=True)
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# calculate remaining lifetime before phase-out (+1 because assuming
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# phase out date at the end of the year)
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df_agg["lifetime"] = df_agg.DateOut - df_agg.DateIn + 1
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# assign clustered bus
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busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0).squeeze()
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busmap = pd.read_csv(snakemake.input.busmap, index_col=0).squeeze()
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inv_busmap = {}
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for k, v in busmap.items():
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inv_busmap[v] = inv_busmap.get(v, []) + [k]
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clustermaps = busmap_s.map(busmap)
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clustermaps.index = clustermaps.index.astype(int)
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df_agg["cluster_bus"] = df_agg.bus.map(clustermaps)
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# include renewables in df_agg
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add_existing_renewables(df_agg)
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df_agg["grouping_year"] = np.take(
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grouping_years,
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np.digitize(df_agg.DateIn, grouping_years, right=True)
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)
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df = df_agg.pivot_table(
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index=["grouping_year", 'Fueltype'],
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columns='cluster_bus',
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values='Capacity',
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aggfunc='sum'
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)
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lifetime = df_agg.pivot_table(
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index=["grouping_year", 'Fueltype'],
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columns='cluster_bus',
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values='lifetime',
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aggfunc='mean' # currently taken mean for clustering lifetimes
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)
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carrier = {
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"OCGT": "gas",
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"CCGT": "gas",
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"coal": "coal",
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"oil": "oil",
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"lignite": "lignite",
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"nuclear": "uranium",
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'urban central solid biomass CHP': "biomass",
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}
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for grouping_year, generator in df.index:
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# capacity is the capacity in MW at each node for this
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capacity = df.loc[grouping_year, generator]
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capacity = capacity[~capacity.isna()]
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capacity = capacity[capacity > snakemake.config['existing_capacities']['threshold_capacity']]
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suffix = '-ac' if generator == 'offwind' else ''
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name_suffix = f' {generator}{suffix}-{grouping_year}'
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asset_i = capacity.index + name_suffix
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if generator in ['solar', 'onwind', 'offwind']:
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# to consider electricity grid connection costs or a split between
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# solar utility and rooftop as well, rather take cost assumptions
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# from existing network than from the cost database
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capital_cost = n.generators.loc[n.generators.carrier==generator+suffix, "capital_cost"].mean()
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marginal_cost = n.generators.loc[n.generators.carrier==generator+suffix, "marginal_cost"].mean()
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# check if assets are already in network (e.g. for 2020)
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already_build = n.generators.index.intersection(asset_i)
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new_build = asset_i.difference(n.generators.index)
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# this is for the year 2020
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if not already_build.empty:
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n.generators.loc[already_build, "p_nom_min"] = capacity.loc[already_build.str.replace(name_suffix, "")].values
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new_capacity = capacity.loc[new_build.str.replace(name_suffix, "")]
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if 'm' in snakemake.wildcards.clusters:
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for ind in new_capacity.index:
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# existing capacities are split evenly among regions in every country
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inv_ind = [i for i in inv_busmap[ind]]
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# for offshore the splitting only includes coastal regions
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inv_ind = [i for i in inv_ind if (i + name_suffix) in n.generators.index]
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p_max_pu = n.generators_t.p_max_pu[[i + name_suffix for i in inv_ind]]
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p_max_pu.columns=[i + name_suffix for i in inv_ind ]
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n.madd("Generator",
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[i + name_suffix for i in inv_ind],
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bus=ind,
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carrier=generator,
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p_nom=new_capacity[ind] / len(inv_ind), # split among regions in a country
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marginal_cost=marginal_cost,
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capital_cost=capital_cost,
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efficiency=costs.at[generator, 'efficiency'],
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p_max_pu=p_max_pu,
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build_year=grouping_year,
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lifetime=costs.at[generator,'lifetime']
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)
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else:
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p_max_pu = n.generators_t.p_max_pu[capacity.index + f' {generator}{suffix}-{baseyear}']
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if not new_build.empty:
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n.madd("Generator",
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new_capacity.index,
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suffix=' ' + name_suffix,
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bus=new_capacity.index,
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carrier=generator,
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p_nom=new_capacity,
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marginal_cost=marginal_cost,
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capital_cost=capital_cost,
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efficiency=costs.at[generator, 'efficiency'],
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p_max_pu=p_max_pu.rename(columns=n.generators.bus),
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build_year=grouping_year,
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lifetime=costs.at[generator, 'lifetime']
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)
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else:
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bus0 = vars(spatial)[carrier[generator]].nodes
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if "EU" not in vars(spatial)[carrier[generator]].locations:
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bus0 = bus0.intersection(capacity.index + " gas")
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already_build = n.links.index.intersection(asset_i)
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new_build = asset_i.difference(n.links.index)
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lifetime_assets = lifetime.loc[grouping_year,generator].dropna()
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# this is for the year 2020
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if not already_build.empty:
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n.links.loc[already_build, "p_nom_min"] = capacity.loc[already_build.str.replace(name_suffix, "")].values
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if not new_build.empty:
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new_capacity = capacity.loc[new_build.str.replace(name_suffix, "")]
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if generator!="urban central solid biomass CHP":
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n.madd("Link",
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new_capacity.index,
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suffix= name_suffix,
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bus0=bus0,
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bus1=new_capacity.index,
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bus2="co2 atmosphere",
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carrier=generator,
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marginal_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'VOM'], #NB: VOM is per MWel
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capital_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'fixed'], #NB: fixed cost is per MWel
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p_nom=new_capacity / costs.at[generator, 'efficiency'],
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efficiency=costs.at[generator, 'efficiency'],
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efficiency2=costs.at[carrier[generator], 'CO2 intensity'],
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build_year=grouping_year,
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lifetime=lifetime_assets.loc[new_capacity.index],
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)
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else:
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key = 'central solid biomass CHP'
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n.madd("Link",
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new_capacity.index,
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suffix= name_suffix,
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bus0=spatial.biomass.df.loc[new_capacity.index]["nodes"].values,
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bus1=new_capacity.index,
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bus2=new_capacity.index + " urban central heat",
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carrier=generator,
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p_nom=new_capacity / costs.at[key, 'efficiency'],
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capital_cost=costs.at[key, 'fixed'] * costs.at[key, 'efficiency'],
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marginal_cost=costs.at[key, 'VOM'],
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efficiency=costs.at[key, 'efficiency'],
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build_year=grouping_year,
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efficiency2=costs.at[key, 'efficiency-heat'],
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lifetime=lifetime_assets.loc[new_capacity.index]
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)
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# check if existing capacities are larger than technical potential
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existing_large = n.generators[n.generators["p_nom_min"] > n.generators["p_nom_max"]].index
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if len(existing_large):
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logger.warning(f"Existing capacities larger than technical potential for {existing_large},\
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adjust technical potential to existing capacities")
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n.generators.loc[existing_large, "p_nom_max"] = n.generators.loc[existing_large, "p_nom_min"]
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def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime):
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"""
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Parameters
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----------
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n : pypsa.Network
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baseyear : last year covered in the existing capacities database
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grouping_years : intervals to group existing capacities
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linear decommissioning of heating capacities from 2020 to 2045 is
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currently assumed heating capacities split between residential and
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services proportional to heating load in both 50% capacities
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in rural busess 50% in urban buses
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"""
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print("adding heating capacities installed before baseyear")
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# Add existing heating capacities, data comes from the study
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# "Mapping and analyses of the current and future (2020 - 2030)
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# heating/cooling fuel deployment (fossil/renewables) "
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# https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
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# file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
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# TODO start from original file
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# retrieve existing heating capacities
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techs = [
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'gas boiler',
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'oil boiler',
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'resistive heater',
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'air heat pump',
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'ground heat pump'
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]
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df = pd.read_csv(snakemake.input.existing_heating, index_col=0, header=0)
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# data for Albania, Montenegro and Macedonia not included in database
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df.loc['Albania'] = np.nan
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df.loc['Montenegro'] = np.nan
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df.loc['Macedonia'] = np.nan
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df.fillna(0., inplace=True)
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# convert GW to MW
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df *= 1e3
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cc = pd.read_csv(snakemake.input.country_codes, index_col=0)
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df.rename(index=cc["2 letter code (ISO-3166-2)"], inplace=True)
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# coal and oil boilers are assimilated to oil boilers
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df['oil boiler'] = df['oil boiler'] + df['coal boiler']
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df.drop(['coal boiler'], axis=1, inplace=True)
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# distribute technologies to nodes by population
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
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nodal_df = df.loc[pop_layout.ct]
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nodal_df.index = pop_layout.index
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nodal_df = nodal_df.multiply(pop_layout.fraction, axis=0)
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# split existing capacities between residential and services
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# proportional to energy demand
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ratio_residential=pd.Series([(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] /
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(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] +
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n.loads_t.p_set.sum()['{} services rural heat'.format(node)] ))
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for node in nodal_df.index], index=nodal_df.index)
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for tech in techs:
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nodal_df['residential ' + tech] = nodal_df[tech] * ratio_residential
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nodal_df['services ' + tech] = nodal_df[tech] * (1 - ratio_residential)
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names = [
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"residential rural",
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"services rural",
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"residential urban decentral",
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"services urban decentral",
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"urban central"
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]
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nodes = {}
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p_nom = {}
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for name in names:
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name_type = "central" if name == "urban central" else "decentral"
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nodes[name] = pd.Index([n.buses.at[index, "location"] for index in n.buses.index[n.buses.index.str.contains(name) & n.buses.index.str.contains('heat')]])
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heat_pump_type = "air" if "urban" in name else "ground"
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heat_type= "residential" if "residential" in name else "services"
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if name == "urban central":
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p_nom[name] = nodal_df['air heat pump'][nodes[name]]
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else:
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p_nom[name] = nodal_df[f'{heat_type} {heat_pump_type} heat pump'][nodes[name]]
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# Add heat pumps
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costs_name = f"decentral {heat_pump_type}-sourced heat pump"
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cop = {"air": ashp_cop, "ground": gshp_cop}
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if time_dep_hp_cop:
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efficiency = cop[heat_pump_type][nodes[name]]
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else:
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efficiency = costs.at[costs_name, 'efficiency']
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for i, grouping_year in enumerate(grouping_years):
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if int(grouping_year) + default_lifetime <= int(baseyear):
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continue
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# installation is assumed to be linear for the past 25 years (default lifetime)
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ratio = (int(grouping_year) - int(grouping_years[i-1])) / default_lifetime
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n.madd("Link",
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nodes[name],
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suffix=f" {name} {heat_pump_type} heat pump-{grouping_year}",
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bus0=nodes[name],
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bus1=nodes[name] + " " + name + " heat",
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carrier=f"{name} {heat_pump_type} heat pump",
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efficiency=efficiency,
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capital_cost=costs.at[costs_name, 'efficiency'] * costs.at[costs_name, 'fixed'],
|
|
p_nom=p_nom[name] * ratio / costs.at[costs_name, 'efficiency'],
|
|
build_year=int(grouping_year),
|
|
lifetime=costs.at[costs_name, 'lifetime']
|
|
)
|
|
|
|
# add resistive heater, gas boilers and oil boilers
|
|
# (50% capacities to rural buses, 50% to urban buses)
|
|
n.madd("Link",
|
|
nodes[name],
|
|
suffix=f" {name} resistive heater-{grouping_year}",
|
|
bus0=nodes[name],
|
|
bus1=nodes[name] + " " + name + " heat",
|
|
carrier=name + " resistive heater",
|
|
efficiency=costs.at[name_type + ' resistive heater', 'efficiency'],
|
|
capital_cost=costs.at[name_type + ' resistive heater', 'efficiency'] * costs.at[name_type + ' resistive heater', 'fixed'],
|
|
p_nom=0.5 * nodal_df[f'{heat_type} resistive heater'][nodes[name]] * ratio / costs.at[name_type + ' resistive heater', 'efficiency'],
|
|
build_year=int(grouping_year),
|
|
lifetime=costs.at[costs_name, 'lifetime']
|
|
)
|
|
|
|
|
|
n.madd("Link",
|
|
nodes[name],
|
|
suffix= f" {name} gas boiler-{grouping_year}",
|
|
bus0=spatial.gas.nodes,
|
|
bus1=nodes[name] + " " + name + " heat",
|
|
bus2="co2 atmosphere",
|
|
carrier=name + " gas boiler",
|
|
efficiency=costs.at[name_type + ' gas boiler', 'efficiency'],
|
|
efficiency2=costs.at['gas', 'CO2 intensity'],
|
|
capital_cost=costs.at[name_type + ' gas boiler', 'efficiency'] * costs.at[name_type + ' gas boiler', 'fixed'],
|
|
p_nom=0.5*nodal_df[f'{heat_type} gas boiler'][nodes[name]] * ratio / costs.at[name_type + ' gas boiler', 'efficiency'],
|
|
build_year=int(grouping_year),
|
|
lifetime=costs.at[name_type + ' gas boiler', 'lifetime']
|
|
)
|
|
|
|
n.madd("Link",
|
|
nodes[name],
|
|
suffix=f" {name} oil boiler-{grouping_year}",
|
|
bus0=spatial.oil.nodes,
|
|
bus1=nodes[name] + " " + name + " heat",
|
|
bus2="co2 atmosphere",
|
|
carrier=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'],
|
|
p_nom=0.5 * nodal_df[f'{heat_type} oil boiler'][nodes[name]] * ratio / costs.at['decentral oil boiler', 'efficiency'],
|
|
build_year=int(grouping_year),
|
|
lifetime=costs.at[name_type + ' gas boiler', 'lifetime']
|
|
)
|
|
|
|
# delete links with p_nom=nan corresponding to extra nodes in country
|
|
n.mremove("Link", [index for index in n.links.index.to_list() if str(grouping_year) in index and np.isnan(n.links.p_nom[index])])
|
|
|
|
# delete links with capacities below threshold
|
|
threshold = snakemake.config['existing_capacities']['threshold_capacity']
|
|
n.mremove("Link", [index for index in n.links.index.to_list() if str(grouping_year) in index and n.links.p_nom[index] < threshold])
|
|
|
|
#%%
|
|
if __name__ == "__main__":
|
|
if 'snakemake' not in globals():
|
|
from helper import mock_snakemake
|
|
snakemake = mock_snakemake(
|
|
'add_existing_baseyear',
|
|
simpl='',
|
|
clusters="45",
|
|
lv=1.0,
|
|
opts='',
|
|
sector_opts='8760H-T-H-B-I-A-solar+p3-dist1',
|
|
planning_horizons=2020,
|
|
)
|
|
|
|
logging.basicConfig(level=snakemake.config['logging_level'])
|
|
|
|
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
|
|
|
|
options = snakemake.config["sector"]
|
|
opts = snakemake.wildcards.sector_opts.split('-')
|
|
|
|
baseyear = snakemake.config['scenario']["planning_horizons"][0]
|
|
|
|
overrides = override_component_attrs(snakemake.input.overrides)
|
|
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
|
|
# define spatial resolution of carriers
|
|
spatial = define_spatial(n.buses[n.buses.carrier=="AC"].index, options)
|
|
add_build_year_to_new_assets(n, baseyear)
|
|
|
|
Nyears = n.snapshot_weightings.generators.sum() / 8760.
|
|
costs = prepare_costs(
|
|
snakemake.input.costs,
|
|
snakemake.config['costs']['USD2013_to_EUR2013'],
|
|
snakemake.config['costs']['discountrate'],
|
|
Nyears,
|
|
snakemake.config['costs']['lifetime']
|
|
)
|
|
|
|
grouping_years_power = snakemake.config['existing_capacities']['grouping_years_power']
|
|
grouping_years_heat = snakemake.config['existing_capacities']['grouping_years_heat']
|
|
add_power_capacities_installed_before_baseyear(n, grouping_years_power, costs, baseyear)
|
|
|
|
if "H" in opts:
|
|
time_dep_hp_cop = options["time_dep_hp_cop"]
|
|
ashp_cop = xr.open_dataarray(snakemake.input.cop_air_total).to_pandas().reindex(index=n.snapshots)
|
|
gshp_cop = xr.open_dataarray(snakemake.input.cop_soil_total).to_pandas().reindex(index=n.snapshots)
|
|
default_lifetime = snakemake.config['costs']['lifetime']
|
|
add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years_heat,
|
|
ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime)
|
|
|
|
if options.get("cluster_heat_buses", False):
|
|
cluster_heat_buses(n)
|
|
|
|
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
|
|
|
|
n.export_to_netcdf(snakemake.output[0])
|