357 lines
12 KiB
Python
357 lines
12 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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Concats pypsa networks of single investment periods to one network.
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"""
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import logging
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import re
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import pandas as pd
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import pypsa
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from _helpers import update_config_with_sector_opts
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from add_existing_baseyear import add_build_year_to_new_assets
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from pypsa.descriptors import expand_series
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from pypsa.io import import_components_from_dataframe
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from six import iterkeys
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logger = logging.getLogger(__name__)
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# helper functions ---------------------------------------------------
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def get_missing(df, n, c):
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"""
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Get in network n missing assets of df for component c.
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Input:
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df: pandas DataFrame, static values of pypsa components
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n : pypsa Network to which new assets should be added
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c : string, pypsa component.list_name (e.g. "generators")
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Return:
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pd.DataFrame with static values of missing assets
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"""
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df_final = getattr(n, c)
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missing_i = df.index.difference(df_final.index)
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return df.loc[missing_i]
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def get_social_discount(t, r=0.01):
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"""
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Calculate for a given time t the social discount.
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"""
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return 1 / (1 + r) ** t
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def get_investment_weighting(time_weighting, r=0.01):
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"""
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Define cost weighting.
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Returns cost weightings depending on the the time_weighting
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(pd.Series) and the social discountrate r
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"""
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end = time_weighting.cumsum()
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start = time_weighting.cumsum().shift().fillna(0)
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return pd.concat([start, end], axis=1).apply(
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lambda x: sum([get_social_discount(t, r) for t in range(int(x[0]), int(x[1]))]),
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axis=1,
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)
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def add_year_to_constraints(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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for c in n.iterate_components(["GlobalConstraint"]):
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c.df["investment_period"] = baseyear
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c.df.rename(index=lambda x: x + "-" + str(baseyear), inplace=True)
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# --------------------------------------------------------------------
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def concat_networks(years):
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"""
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Concat given pypsa networks and adds build_year.
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Return:
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n : pypsa.Network for the whole planning horizon
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"""
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# input paths of sector coupling networks
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network_paths = [snakemake.input.brownfield_network] + [
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snakemake.input[f"network_{year}"] for year in years[1:]
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]
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# final concatenated network
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n = pypsa.Network()
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# iterate over single year networks and concat to perfect foresight network
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for i, network_path in enumerate(network_paths):
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year = years[i]
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network = pypsa.Network(network_path)
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network.lines["carrier"] = "AC"
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add_build_year_to_new_assets(network, year)
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# static ----------------------------------
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# (1) add buses and carriers
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for component in network.iterate_components(["Bus", "Carrier"]):
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df_year = component.df
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# get missing assets
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missing = get_missing(df_year, n, component.list_name)
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import_components_from_dataframe(n, missing, component.name)
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# (2) add generators, links, stores and loads
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for component in network.iterate_components(
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["Generator", "Link", "Store", "Load", "Line", "StorageUnit"]
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):
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df_year = component.df.copy()
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missing = get_missing(df_year, n, component.list_name)
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import_components_from_dataframe(n, missing, component.name)
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# time variant --------------------------------------------------
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network_sns = pd.MultiIndex.from_product([[year], network.snapshots])
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snapshots = n.snapshots.drop("now", errors="ignore").union(network_sns)
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n.set_snapshots(snapshots)
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for component in network.iterate_components():
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pnl = getattr(n, component.list_name + "_t")
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for k in iterkeys(component.pnl):
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pnl_year = component.pnl[k].copy().reindex(snapshots, level=1)
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if pnl_year.empty and ~(component.name == "Load" and k == "p_set"):
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continue
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if component.name == "Load":
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static_load = network.loads.loc[network.loads.p_set != 0]
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static_load_t = expand_series(static_load.p_set, network_sns).T
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pnl_year = pd.concat(
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[pnl_year.reindex(network_sns), static_load_t], axis=1
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)
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columns = (pnl[k].columns.union(pnl_year.columns)).unique()
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pnl[k] = pnl[k].reindex(columns=columns)
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pnl[k].loc[pnl_year.index, pnl_year.columns] = pnl_year
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else:
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# this is to avoid adding multiple times assets with infinit lifetime as ror
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cols = pnl_year.columns.difference(pnl[k].columns)
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pnl[k] = pd.concat([pnl[k], pnl_year[cols]], axis=1)
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n.snapshot_weightings.loc[year, :] = network.snapshot_weightings.values
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# (3) global constraints
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for component in network.iterate_components(["GlobalConstraint"]):
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add_year_to_constraints(network, year)
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import_components_from_dataframe(n, component.df, component.name)
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# set investment periods
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n.investment_periods = n.snapshots.levels[0]
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# weighting of the investment period -> assuming last period same weighting as the period before
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time_w = n.investment_periods.to_series().diff().shift(-1).fillna(method="ffill")
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n.investment_period_weightings["years"] = time_w
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# set objective weightings
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objective_w = get_investment_weighting(
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n.investment_period_weightings["years"], social_discountrate
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)
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n.investment_period_weightings["objective"] = objective_w
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# all former static loads are now time-dependent -> set static = 0
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n.loads["p_set"] = 0
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n.loads_t.p_set.fillna(0, inplace=True)
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return n
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def adjust_stores(n):
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"""
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Make sure that stores still behave cyclic over one year and not whole
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modelling horizon.
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"""
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# cylclic constraint
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cyclic_i = n.stores[n.stores.e_cyclic].index
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n.stores.loc[cyclic_i, "e_cyclic_per_period"] = True
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n.stores.loc[cyclic_i, "e_cyclic"] = False
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# non cyclic store assumptions
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non_cyclic_store = ["co2", "co2 stored", "solid biomass", "biogas", "Li ion"]
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co2_i = n.stores[n.stores.carrier.isin(non_cyclic_store)].index
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n.stores.loc[co2_i, "e_cyclic_per_period"] = False
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n.stores.loc[co2_i, "e_cyclic"] = False
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# e_initial at beginning of each investment period
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e_initial_store = ["solid biomass", "biogas"]
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co2_i = n.stores[n.stores.carrier.isin(e_initial_store)].index
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n.stores.loc[co2_i, "e_initial_per_period"] = True
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# n.stores.loc[co2_i, "e_initial"] *= 10
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# n.stores.loc[co2_i, "e_nom"] *= 10
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e_initial_store = ["co2 stored"]
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co2_i = n.stores[n.stores.carrier.isin(e_initial_store)].index
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n.stores.loc[co2_i, "e_initial_per_period"] = True
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return n
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def set_phase_out(n, carrier, ct, phase_out_year):
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"""
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Set planned phase outs for given carrier,country (ct) and planned year of
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phase out (phase_out_year).
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"""
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df = n.links[(n.links.carrier.isin(carrier)) & (n.links.bus1.str[:2] == ct)]
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# assets which are going to be phased out before end of their lifetime
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assets_i = df[df[["build_year", "lifetime"]].sum(axis=1) > phase_out_year].index
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build_year = n.links.loc[assets_i, "build_year"]
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# adjust lifetime
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n.links.loc[assets_i, "lifetime"] = (phase_out_year - build_year).astype(float)
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def set_all_phase_outs(n):
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# TODO move this to a csv or to the config
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planned = [
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(["nuclear"], "DE", 2022),
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(["nuclear"], "BE", 2025),
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(["nuclear"], "ES", 2027),
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(["coal", "lignite"], "DE", 2038),
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(["coal", "lignite"], "ES", 2027),
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(["coal", "lignite"], "FR", 2022),
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(["coal", "lignite"], "GB", 2024),
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(["coal", "lignite"], "IT", 2025),
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]
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for carrier, ct, phase_out_year in planned:
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set_phase_out(n, carrier, ct, phase_out_year)
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# remove assets which are already phased out
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remove_i = n.links[n.links[["build_year", "lifetime"]].sum(axis=1) < years[0]].index
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n.mremove("Link", remove_i)
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def set_carbon_constraints(n, opts):
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"""
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Add global constraints for carbon emissions.
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"""
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budget = None
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for o in opts:
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# other budgets
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m = re.match(r"^\d+p\d$", o, re.IGNORECASE)
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if m is not None:
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budget = snakemake.config["co2_budget"][m.group(0)] * 1e9
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if budget != None:
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logger.info("add carbon budget of {}".format(budget))
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n.add(
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"GlobalConstraint",
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"Budget",
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type="Co2Budget",
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carrier_attribute="co2_emissions",
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sense="<=",
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constant=budget,
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investment_period=n.investment_periods[-1]
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)
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# drop other CO2 limits
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drop_i = n.global_constraints[n.global_constraints.type=="co2_limit"].index
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n.mremove("GlobalConstraint", drop_i)
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n.add(
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"GlobalConstraint",
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"carbon_neutral",
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type="co2_limit",
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carrier_attribute="co2_emissions",
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sense="<=",
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constant=0,
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investment_period=n.investment_periods[-1]
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)
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# set minimum CO2 emission constraint to avoid too fast reduction
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if "co2min" in opts:
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emissions_1990 = 4.53693
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emissions_2019 = 3.344096
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target_2030 = 0.45 * emissions_1990
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annual_reduction = (emissions_2019 - target_2030) / 11
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first_year = n.snapshots.levels[0][0]
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time_weightings = n.investment_period_weightings.loc[first_year, "years"]
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co2min = emissions_2019 - ((first_year - 2019) * annual_reduction)
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logger.info(
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"add minimum emissions for {} of {} t CO2/a".format(first_year, co2min)
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)
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n.add(
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"GlobalConstraint",
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f"Co2Min-{first_year}",
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type="Co2min",
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carrier_attribute="co2_emissions",
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sense=">=",
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investment_period=first_year,
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constant=co2min * 1e9 * time_weightings,
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)
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return n
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def adjust_lvlimit(n):
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c = "GlobalConstraint"
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cols = ['carrier_attribute', 'sense', "constant", "type"]
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glc_type = "transmission_volume_expansion_limit"
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if (n.df(c)[n.df(c).type==glc_type][cols].nunique()==1).all():
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glc = n.df(c)[n.df(c).type==glc_type][cols].iloc[[0]]
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glc.index = pd.Index(["lv_limit"])
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remove_i = n.df(c)[n.df(c).type==glc_type].index
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n.mremove(c, remove_i)
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import_components_from_dataframe(n, glc, c)
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return n
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def adjust_CO2_glc(n):
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c = "GlobalConstraint"
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glc_name = "CO2Limit"
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glc_type = "primary_energy"
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mask = (n.df(c).index.str.contains(glc_name)) & (n.df(c).type==glc_type)
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n.df(c).loc[mask, "type"] = "co2_limit"
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return n
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# %%
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from _helpers import mock_snakemake
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snakemake = mock_snakemake(
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"prepare_perfect_foresight",
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simpl="",
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opts="",
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clusters="37",
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ll="v1.0",
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sector_opts="2p0-4380H-T-H-B-I-A-solar+p3-dist1",
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)
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update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
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# parameters -----------------------------------------------------------
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years = snakemake.config["scenario"]["planning_horizons"]
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opts = snakemake.wildcards.sector_opts.split("-")
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social_discountrate = snakemake.config["costs"]["social_discountrate"]
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for o in opts:
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if "sdr" in o:
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social_discountrate = float(o.replace("sdr", "")) / 100
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logger.info(
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"Concat networks of investment period {} with social discount rate of {}%".format(
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years, social_discountrate * 100
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)
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)
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# concat prenetworks of planning horizon to single network ------------
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n = concat_networks(years)
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# adjust global constraints lv limit if the same for all years
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n = adjust_lvlimit(n)
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# adjust global constraints CO2 limit
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n = adjust_CO2_glc(n)
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# set phase outs
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set_all_phase_outs(n)
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# adjust stores to multi period investment
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n = adjust_stores(n)
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# set carbon constraints
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opts = snakemake.wildcards.sector_opts.split("-")
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n = set_carbon_constraints(n, opts)
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# export network
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n.export_to_netcdf(snakemake.output[0])
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