adjust summary functions
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lisazeyen
parent
f2ff8beae8
commit
5989c287c4
@ -1,3 +1,15 @@
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# -*- 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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Create summary CSV files for all scenario runs with perfect foresight
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including costs, capacities, capacity factors, curtailment, energy balances,
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prices and other metrics.
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"""
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from six import iteritems
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import pandas as pd
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@ -11,7 +23,7 @@ from pypsa.descriptors import (
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get_active_assets,
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)
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from helper import override_component_attrs
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from _helpers import override_component_attrs
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from prepare_sector_network import prepare_costs
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@ -50,8 +62,8 @@ def calculate_costs(n, label, costs):
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).astype(int)
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capital_costs = active.mul(capital_costs, axis=0)
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discount = (
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n.investment_period_weightings["objective_weightings"]
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/ n.investment_period_weightings["time_weightings"]
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n.investment_period_weightings["objective"]
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/ n.investment_period_weightings["years"]
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)
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capital_costs_grouped = (
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capital_costs.groupby(c.df.carrier).sum().mul(discount)
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@ -66,20 +78,20 @@ def calculate_costs(n, label, costs):
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if c.name == "Link":
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p = (
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c.pnl.p0.multiply(n.snapshot_weightings.generator_weightings, axis=0)
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c.pnl.p0.multiply(n.snapshot_weightings.generators, axis=0)
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.groupby(level=0)
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.sum()
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)
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elif c.name == "Line":
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continue
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elif c.name == "StorageUnit":
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p_all = c.pnl.p.multiply(n.snapshot_weightings.store_weightings, axis=0)
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p_all = c.pnl.p.multiply(n.snapshot_weightings.stores, axis=0)
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p_all[p_all < 0.0] = 0.0
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p = p_all.groupby(level=0).sum()
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else:
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p = (
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round(c.pnl.p, ndigits=2)
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.multiply(n.snapshot_weightings.generator_weightings, axis=0)
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.multiply(n.snapshot_weightings.generators, axis=0)
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.groupby(level=0)
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.sum()
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)
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@ -285,7 +297,7 @@ def calculate_supply(n, label, supply):
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for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
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items = c.df.index[c.df["bus" + end].map(bus_map, na_action=False)]
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items = c.df.index[c.df["bus" + end].map(bus_map).fillna(False)]
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if len(items) == 0:
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continue
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@ -333,9 +345,9 @@ def calculate_supply_energy(n, label, supply_energy):
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continue
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if c.name == "Generator":
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weightings = n.snapshot_weightings.generator_weightings
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weightings = n.snapshot_weightings.generators
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else:
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weightings = n.snapshot_weightings.store_weightings
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weightings = n.snapshot_weightings.stores
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if i in ["oil", "co2", "H2"]:
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if c.name=="Load":
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@ -364,7 +376,7 @@ def calculate_supply_energy(n, label, supply_energy):
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for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
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items = c.df.index[c.df["bus" + str(end)].map(bus_map, na_action=False)]
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items = c.df.index[c.df["bus" + str(end)].map(bus_map).fillna(False)]
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if len(items) == 0:
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continue
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@ -373,7 +385,7 @@ def calculate_supply_energy(n, label, supply_energy):
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(-1)
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* c.pnl["p" + end]
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.reindex(items, axis=1)
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.multiply(n.snapshot_weightings.objective_weightings, axis=0)
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.multiply(n.snapshot_weightings.objective, axis=0)
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.groupby(level=0)
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.sum()
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.groupby(c.df.loc[items, "carrier"], axis=1)
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@ -593,11 +605,11 @@ def calculate_price_statistics(n, label, price_statistics):
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)
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price_statistics.at["mean", label] = (
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n.buses_t.marginal_price[buses].unstack().mean()
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n.buses_t.marginal_price[buses].mean().mean()
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)
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price_statistics.at["standard_deviation", label] = (
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n.buses_t.marginal_price[buses].unstack().std()
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n.buses_t.marginal_price[buses].droplevel(0).unstack().std()
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)
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return price_statistics
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@ -612,7 +624,7 @@ def calculate_co2_emissions(n, label, df):
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return
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weightings = n.snapshot_weightings.mul(
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n.investment_period_weightings["time_weightings"]
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n.investment_period_weightings["years"]
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.reindex(n.snapshots)
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.fillna(method="bfill")
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.fillna(1.0),
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@ -624,7 +636,7 @@ def calculate_co2_emissions(n, label, df):
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if not gens.empty:
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em_pu = gens.carrier.map(emissions) / gens.efficiency
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em_pu = (
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weightings["generator_weightings"].to_frame("weightings")
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weightings["generators"].to_frame("weightings")
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@ em_pu.to_frame("weightings").T
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)
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emitted = n.generators_t.p[gens.index].mul(em_pu)
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@ -648,52 +660,6 @@ def calculate_co2_emissions(n, label, df):
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return df
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def calculate_cumulative_capacities(n, label, cum_cap):
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# TODO
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investments = n.investment_periods
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cols = pd.MultiIndex.from_product(
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[
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cum_cap.columns.levels[0],
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cum_cap.columns.levels[1],
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cum_cap.columns.levels[2],
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investments,
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],
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names=cum_cap.columns.names[:3] + ["year"],
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)
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cum_cap = cum_cap.reindex(cols, axis=1)
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learn_i = n.carriers[n.carriers.learning_rate != 0].index
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for c, attr in nominal_attrs.items():
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if "carrier" not in n.df(c) or n.df(c).empty:
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continue
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caps = (
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n.df(c)[n.df(c).carrier.isin(learn_i)]
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.groupby([n.df(c).carrier, n.df(c).build_year])[
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opt_name.get(c, "p") + "_nom_opt"
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]
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.sum()
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)
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if caps.empty:
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continue
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caps = round(
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caps.unstack().reindex(columns=investments).fillna(0).cumsum(axis=1)
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)
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cum_cap = cum_cap.reindex(caps.index.union(cum_cap.index))
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cum_cap.loc[caps.index, label] = caps.values
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return cum_cap
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outputs = [
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"nodal_costs",
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"nodal_capacities",
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@ -711,14 +677,13 @@ outputs = [
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"market_values",
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"metrics",
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"co2_emissions",
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"cumulative_capacities",
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]
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def make_summaries(networks_dict):
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columns = pd.MultiIndex.from_tuples(
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networks_dict.keys(), names=["cluster", "lv", "opt", "year"]
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networks_dict.keys(), names=["cluster", "lv", "opt"]
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)
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df = {}
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@ -760,30 +725,28 @@ def to_csv(df):
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if __name__ == "__main__":
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# Detect running outside of snakemake and mock snakemake for testing
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if "snakemake" not in globals():
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from helper import mock_snakemake
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from _helpers import mock_snakemake
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snakemake = mock_snakemake('make_summary_perfect')
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networks_dict = {
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(clusters, lv, opts+sector_opts) :
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snakemake.config['results_dir'] + snakemake.config['run'] + f'/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_brownfield_all_years.nc' \
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"results/" + snakemake.config['run']["name"] + f'/postnetworks/elec_s{simpl}_{clusters}_l{lv}_{opts}_{sector_opts}_brownfield_all_years.nc' \
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for simpl in snakemake.config['scenario']['simpl'] \
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for clusters in snakemake.config['scenario']['clusters'] \
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for opts in snakemake.config['scenario']['opts'] \
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for sector_opts in snakemake.config['scenario']['sector_opts'] \
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for lv in snakemake.config['scenario']['lv'] \
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for lv in snakemake.config['scenario']['ll'] \
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}
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print(networks_dict)
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Nyears = 1
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nyears = 1
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costs_db = prepare_costs(
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snakemake.input.costs,
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snakemake.config["costs"]["USD2013_to_EUR2013"],
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snakemake.config["costs"]["discountrate"],
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Nyears,
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snakemake.config["costs"]["lifetime"],
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snakemake.config["costs"],
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nyears,
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)
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df = make_summaries(networks_dict)
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