# -*- coding: utf-8 -*- # SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors # # SPDX-License-Identifier: MIT """ Prepares brownfield data from previous planning horizon. """ import logging import numpy as np import pandas as pd import pypsa from _helpers import update_config_with_sector_opts from add_existing_baseyear import add_build_year_to_new_assets logger = logging.getLogger(__name__) idx = pd.IndexSlice def add_brownfield(n, n_p, year): logger.info(f"Preparing brownfield for the year {year}") # electric transmission grid set optimised capacities of previous as minimum n.lines.s_nom_min = n_p.lines.s_nom_opt dc_i = n.links[n.links.carrier == "DC"].index n.links.loc[dc_i, "p_nom_min"] = n_p.links.loc[dc_i, "p_nom_opt"] for c in n_p.iterate_components(["Link", "Generator", "Store"]): attr = "e" if c.name == "Store" else "p" # first, remove generators, links and stores that track # CO2 or global EU values since these are already in n n_p.mremove(c.name, c.df.index[c.df.lifetime == np.inf]) # remove assets whose build_year + lifetime < year n_p.mremove(c.name, c.df.index[c.df.build_year + c.df.lifetime < year]) # remove assets if their optimized nominal capacity is lower than a threshold # since CHP heat Link is proportional to CHP electric Link, make sure threshold is compatible chp_heat = c.df.index[ (c.df[f"{attr}_nom_extendable"] & c.df.index.str.contains("urban central")) & c.df.index.str.contains("CHP") & c.df.index.str.contains("heat") ] threshold = snakemake.params.threshold_capacity if not chp_heat.empty: threshold_chp_heat = ( threshold * c.df.efficiency[chp_heat.str.replace("heat", "electric")].values * c.df.p_nom_ratio[chp_heat.str.replace("heat", "electric")].values / c.df.efficiency[chp_heat].values ) n_p.mremove( c.name, chp_heat[c.df.loc[chp_heat, f"{attr}_nom_opt"] < threshold_chp_heat], ) n_p.mremove( c.name, c.df.index[ (c.df[f"{attr}_nom_extendable"] & ~c.df.index.isin(chp_heat)) & (c.df[f"{attr}_nom_opt"] < threshold) ], ) # copy over assets but fix their capacity c.df[f"{attr}_nom"] = c.df[f"{attr}_nom_opt"] c.df[f"{attr}_nom_extendable"] = False n.import_components_from_dataframe(c.df, c.name) # copy time-dependent selection = n.component_attrs[c.name].type.str.contains( "series" ) & n.component_attrs[c.name].status.str.contains("Input") for tattr in n.component_attrs[c.name].index[selection]: n.import_series_from_dataframe(c.pnl[tattr], c.name, tattr) # deal with gas network pipe_carrier = ["gas pipeline"] if snakemake.params.H2_retrofit: # drop capacities of previous year to avoid duplicating to_drop = n.links.carrier.isin(pipe_carrier) & (n.links.build_year != year) n.mremove("Link", n.links.loc[to_drop].index) # subtract the already retrofitted from today's gas grid capacity h2_retrofitted_fixed_i = n.links[ (n.links.carrier == "H2 pipeline retrofitted") & (n.links.build_year != year) ].index gas_pipes_i = n.links[n.links.carrier.isin(pipe_carrier)].index CH4_per_H2 = 1 / snakemake.params.H2_retrofit_capacity_per_CH4 fr = "H2 pipeline retrofitted" to = "gas pipeline" # today's pipe capacity pipe_capacity = n.links.loc[gas_pipes_i, "p_nom"] # already retrofitted capacity from gas -> H2 already_retrofitted = ( n.links.loc[h2_retrofitted_fixed_i, "p_nom"] .rename(lambda x: x.split("-2")[0].replace(fr, to)) .groupby(level=0) .sum() ) remaining_capacity = ( pipe_capacity - CH4_per_H2 * already_retrofitted.reindex(index=pipe_capacity.index).fillna(0) ) n.links.loc[gas_pipes_i, "p_nom"] = remaining_capacity else: new_pipes = n.links.carrier.isin(pipe_carrier) & ( n.links.build_year == year ) n.links.loc[new_pipes, "p_nom"] = 0.0 n.links.loc[new_pipes, "p_nom_min"] = 0.0 def disable_grid_expansion_if_LV_limit_hit(n): if "lv_limit" not in n.global_constraints.index: return total_expansion = ( n.lines.eval("s_nom_min * length").sum() + n.links.query("carrier == 'DC'").eval("p_nom_min * length").sum() ).sum() lv_limit = n.global_constraints.at["lv_limit", "constant"] # allow small numerical differences if lv_limit - total_expansion < 1: logger.info("LV is already reached, disabling expansion and LV limit") extendable_acs = n.lines.query("s_nom_extendable").index n.lines.loc[extendable_acs, "s_nom_extendable"] = False n.lines.loc[extendable_acs, "s_nom"] = n.lines.loc[extendable_acs, "s_nom_min"] extendable_dcs = n.links.query("carrier == 'DC' and p_nom_extendable").index n.links.loc[extendable_dcs, "p_nom_extendable"] = False n.links.loc[extendable_dcs, "p_nom"] = n.links.loc[extendable_dcs, "p_nom_min"] n.global_constraints.drop("lv_limit", inplace=True) if __name__ == "__main__": if "snakemake" not in globals(): from _helpers import mock_snakemake snakemake = mock_snakemake( "add_brownfield", simpl="", clusters="37", opts="", ll="v1.0", sector_opts="168H-T-H-B-I-dist1", planning_horizons=2030, ) logging.basicConfig(level=snakemake.config["logging"]["level"]) update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts) logger.info(f"Preparing brownfield from the file {snakemake.input.network_p}") year = int(snakemake.wildcards.planning_horizons) n = pypsa.Network(snakemake.input.network) add_build_year_to_new_assets(n, year) n_p = pypsa.Network(snakemake.input.network_p) add_brownfield(n, n_p, year) disable_grid_expansion_if_LV_limit_hit(n) n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards))) n.export_to_netcdf(snakemake.output[0])