92d00a0c83
This allows them to cover heat demand peaks e.g. 10% higher than those in the data. The disadvantage of manipulating the costs is that the capacity is then not quite right. This way at least the costs are right. Doing it properly would require introducing artificial peaks, but this creates new problems (e.g. what is going on with wind/solar/other demand).
617 lines
23 KiB
Python
617 lines
23 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Adds existing power and heat generation capacities for initial planning
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horizon.
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"""
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import logging
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from types import SimpleNamespace
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import country_converter as coco
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import numpy as np
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import pandas as pd
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import pypsa
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import xarray as xr
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from _helpers import update_config_with_sector_opts
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from add_electricity import sanitize_carriers
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from prepare_sector_network import cluster_heat_buses, define_spatial, prepare_costs
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logger = logging.getLogger(__name__)
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cc = coco.CountryConverter()
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idx = pd.IndexSlice
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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] += f"-{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 = n.component_attrs[c.name].type.str.contains(
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"series"
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) & n.component_attrs[c.name].status.str.contains("Input")
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for attr in n.component_attrs[c.name].index[selection]:
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c.pnl[attr] = c.pnl[attr].rename(columns=rename)
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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 with the conventional
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power plants.
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"""
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carriers = {"solar": "solar", "onwind": "onwind", "offwind": "offwind-ac"}
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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(0.0)
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df.columns = df.columns.astype(int)
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df.index = cc.convert(df.index, to="iso2")
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# calculate yearly differences
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df.insert(loc=0, value=0.0, 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(
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n.buses.index[n.buses.carrier == "DC"]
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)
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nodal_fraction = pd.Series(0.0, elec_buses)
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for country in n.buses.loc[elec_buses, "country"].unique():
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gens = n.generators.index[
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(n.generators.index.str[:2] == country)
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& (n.generators.carrier == carrier)
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]
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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.groupby(
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n.generators.loc[gens, "bus"]
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).sum()
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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 > 0.0:
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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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logger.debug(
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f"Adding power capacities installed before {baseyear} from powerplants.csv"
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)
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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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"Bioenergy": "urban central solid biomass CHP",
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}
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# Replace Fueltype "Natural Gas" with the respective technology (OCGT or CCGT)
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df_agg.loc[df_agg["Fueltype"] == "Natural Gas", "Fueltype"] = df_agg.loc[
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df_agg["Fueltype"] == "Natural Gas", "Technology"
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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 = ["Pv", "Storage Technologies"]
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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 = (
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df_agg.loc[biomass_i, "DateIn"]
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+ snakemake.params.costs["fill_values"]["lifetime"]
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)
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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, 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[
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capacity > snakemake.params.existing_capacities["threshold_capacity"]
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]
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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[
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n.generators.carrier == generator + suffix, "capital_cost"
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].mean()
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marginal_cost = n.generators.loc[
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n.generators.carrier == generator + suffix, "marginal_cost"
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].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[
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already_build.str.replace(name_suffix, "")
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].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 = list(inv_busmap[ind])
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# for offshore the splitting only includes coastal regions
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inv_ind = [
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i for i in inv_ind if (i + name_suffix) in n.generators.index
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]
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p_max_pu = n.generators_t.p_max_pu[
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[i + name_suffix for i in inv_ind]
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]
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p_max_pu.columns = [i + name_suffix for i in inv_ind]
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n.madd(
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"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]
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/ 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[
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capacity.index + f" {generator}{suffix}-{baseyear}"
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]
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if not new_build.empty:
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n.madd(
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"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 + " " + carrier[generator])
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# check for missing bus
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missing_bus = pd.Index(bus0).difference(n.buses.index)
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if not missing_bus.empty:
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logger.info(f"add buses {bus0}")
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n.madd(
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"Bus",
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bus0,
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carrier=generator,
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location=vars(spatial)[carrier[generator]].locations,
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unit="MWh_el",
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)
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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[
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already_build.str.replace(name_suffix, "")
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].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(
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"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"]
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* costs.at[generator, "VOM"], # NB: VOM is per MWel
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capital_cost=costs.at[generator, "efficiency"]
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* 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(
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"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"]
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* 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[
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n.generators["p_nom_min"] > n.generators["p_nom_max"]
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].index
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if len(existing_large):
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logger.warning(
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f"Existing capacities larger than technical potential for {existing_large},\
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adjust technical potential to existing capacities"
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)
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n.generators.loc[existing_large, "p_nom_max"] = n.generators.loc[
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existing_large, "p_nom_min"
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]
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def add_heating_capacities_installed_before_baseyear(
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n,
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baseyear,
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grouping_years,
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ashp_cop,
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gshp_cop,
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time_dep_hp_cop,
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costs,
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default_lifetime,
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):
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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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logger.debug(f"Adding heating capacities installed before {baseyear}")
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existing_heating = pd.read_csv(
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snakemake.input.existing_heating_distribution, header=[0, 1], index_col=0
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)
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techs = existing_heating.columns.get_level_values(1).unique()
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for name in existing_heating.columns.get_level_values(0).unique():
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name_type = "central" if name == "urban central" else "decentral"
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nodes = pd.Index(n.buses.location[n.buses.index.str.contains(f"{name} heat")])
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heat_pump_type = "air" if "urban" in name else "ground"
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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]
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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 default_lifetime years
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ratio = (int(grouping_year) - int(grouping_years[i - 1])) / default_lifetime
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n.madd(
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"Link",
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nodes,
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suffix=f" {name} {heat_pump_type} heat pump-{grouping_year}",
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bus0=nodes,
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bus1=nodes + " " + 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"]
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* costs.at[costs_name, "fixed"],
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p_nom=existing_heating.loc[nodes, (name, f"{heat_pump_type} heat pump")]
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* ratio
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/ costs.at[costs_name, "efficiency"],
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build_year=int(grouping_year),
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lifetime=costs.at[costs_name, "lifetime"],
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)
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# add resistive heater, gas boilers and oil boilers
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n.madd(
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"Link",
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nodes,
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suffix=f" {name} resistive heater-{grouping_year}",
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bus0=nodes,
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bus1=nodes + " " + name + " heat",
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carrier=name + " resistive heater",
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efficiency=costs.at[f"{name_type} resistive heater", "efficiency"],
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capital_cost=(
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costs.at[f"{name_type} resistive heater", "efficiency"]
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* costs.at[f"{name_type} resistive heater", "fixed"]
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),
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p_nom=(
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existing_heating.loc[nodes, (name, "resistive heater")]
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* ratio
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/ costs.at[f"{name_type} resistive heater", "efficiency"]
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),
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build_year=int(grouping_year),
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lifetime=costs.at[f"{name_type} resistive heater", "lifetime"],
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)
|
|
|
|
n.madd(
|
|
"Link",
|
|
nodes,
|
|
suffix=f" {name} gas boiler-{grouping_year}",
|
|
bus0="EU gas" if "EU gas" in spatial.gas.nodes else nodes + " gas",
|
|
bus1=nodes + " " + name + " heat",
|
|
bus2="co2 atmosphere",
|
|
carrier=name + " gas boiler",
|
|
efficiency=costs.at[f"{name_type} gas boiler", "efficiency"],
|
|
efficiency2=costs.at["gas", "CO2 intensity"],
|
|
capital_cost=(
|
|
costs.at[f"{name_type} gas boiler", "efficiency"]
|
|
* costs.at[f"{name_type} gas boiler", "fixed"]
|
|
),
|
|
p_nom=(
|
|
existing_heating.loc[nodes, (name, "gas boiler")]
|
|
* ratio
|
|
/ costs.at[f"{name_type} gas boiler", "efficiency"]
|
|
),
|
|
build_year=int(grouping_year),
|
|
lifetime=costs.at[f"{name_type} gas boiler", "lifetime"],
|
|
)
|
|
|
|
n.madd(
|
|
"Link",
|
|
nodes,
|
|
suffix=f" {name} oil boiler-{grouping_year}",
|
|
bus0=spatial.oil.nodes,
|
|
bus1=nodes + " " + 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=(
|
|
existing_heating.loc[nodes, (name, "oil boiler")]
|
|
* ratio
|
|
/ costs.at["decentral oil boiler", "efficiency"]
|
|
),
|
|
build_year=int(grouping_year),
|
|
lifetime=costs.at[f"{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.params.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 _helpers import mock_snakemake
|
|
|
|
snakemake = mock_snakemake(
|
|
"add_existing_baseyear",
|
|
# configfiles="config/test/config.myopic.yaml",
|
|
simpl="",
|
|
clusters="37",
|
|
ll="v1.0",
|
|
opts="",
|
|
sector_opts="1p7-4380H-T-H-B-I-A-dist1",
|
|
planning_horizons=2020,
|
|
)
|
|
|
|
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("-")
|
|
|
|
baseyear = snakemake.params.baseyear
|
|
|
|
n = pypsa.Network(snakemake.input.network)
|
|
|
|
# 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() / 8760.0
|
|
costs = prepare_costs(
|
|
snakemake.input.costs,
|
|
snakemake.params.costs,
|
|
Nyears,
|
|
)
|
|
|
|
grouping_years_power = snakemake.params.existing_capacities["grouping_years_power"]
|
|
grouping_years_heat = snakemake.params.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.params.existing_capacities[
|
|
"default_heating_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)))
|
|
|
|
sanitize_carriers(n, snakemake.config)
|
|
|
|
n.export_to_netcdf(snakemake.output[0])
|