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pre-commit-ci[bot] 2023-04-06 13:57:26 +00:00
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7 changed files with 188 additions and 173 deletions
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2
rules/collect.smk
View File

@ -65,6 +65,7 @@ rule solve_sector_networks:
**config["scenario"] **config["scenario"]
), ),
rule solve_sector_networks_perfect: rule solve_sector_networks_perfect:
input: input:
expand( expand(
@ -73,6 +74,7 @@ rule solve_sector_networks_perfect:
**config["scenario"] **config["scenario"]
), ),
rule plot_networks: rule plot_networks:
input: input:
expand( expand(

5
rules/solve_perfect.smk
View File

@ -172,10 +172,7 @@ rule make_summary_perfect:
log: log:
LOGS + "make_summary_perfect.log", LOGS + "make_summary_perfect.log",
benchmark: benchmark:
( (BENCHMARKS + "make_summary_perfect")
BENCHMARKS
+ "make_summary_perfect"
)
conda: conda:
"../envs/environment.yaml" "../envs/environment.yaml"
script: script:

114
scripts/make_summary_perfect.py
View File

@ -4,39 +4,33 @@
# SPDX-License-Identifier: MIT # SPDX-License-Identifier: MIT
""" """
Create summary CSV files for all scenario runs with perfect foresight Create summary CSV files for all scenario runs with perfect foresight including
including costs, capacities, capacity factors, curtailment, energy balances, costs, capacities, capacity factors, curtailment, energy balances, prices and
prices and other metrics. other metrics.
""" """
from six import iteritems
import pandas as pd
import numpy as np import numpy as np
import pandas as pd
import pypsa import pypsa
from pypsa.descriptors import (
nominal_attrs,
get_active_assets,
)
from _helpers import override_component_attrs from _helpers import override_component_attrs
from make_summary import (
assign_carriers,
assign_locations,
calculate_cfs,
calculate_nodal_cfs,
calculate_nodal_costs,
)
from prepare_sector_network import prepare_costs from prepare_sector_network import prepare_costs
from pypsa.descriptors import get_active_assets, nominal_attrs
from make_summary import (assign_carriers, assign_locations, from six import iteritems
calculate_cfs, calculate_nodal_cfs,
calculate_nodal_costs)
idx = pd.IndexSlice idx = pd.IndexSlice
opt_name = {"Store": "e", "Line": "s", "Transformer": "s"} opt_name = {"Store": "e", "Line": "s", "Transformer": "s"}
def calculate_costs(n, label, costs):
def calculate_costs(n, label, costs):
investments = n.investment_periods investments = n.investment_periods
cols = pd.MultiIndex.from_product( cols = pd.MultiIndex.from_product(
[ [
@ -65,9 +59,7 @@ def calculate_costs(n, label, costs):
n.investment_period_weightings["objective"] n.investment_period_weightings["objective"]
/ n.investment_period_weightings["years"] / n.investment_period_weightings["years"]
) )
capital_costs_grouped = ( capital_costs_grouped = capital_costs.groupby(c.df.carrier).sum().mul(discount)
capital_costs.groupby(c.df.carrier).sum().mul(discount)
)
capital_costs_grouped = pd.concat([capital_costs_grouped], keys=["capital"]) capital_costs_grouped = pd.concat([capital_costs_grouped], keys=["capital"])
capital_costs_grouped = pd.concat([capital_costs_grouped], keys=[c.list_name]) capital_costs_grouped = pd.concat([capital_costs_grouped], keys=[c.list_name])
@ -176,7 +168,6 @@ def calculate_nodal_capacities(n, label, nodal_capacities):
def calculate_capacities(n, label, capacities): def calculate_capacities(n, label, capacities):
investments = n.investment_periods investments = n.investment_periods
cols = pd.MultiIndex.from_product( cols = pd.MultiIndex.from_product(
[ [
@ -202,9 +193,7 @@ def calculate_capacities(n, label, capacities):
caps = c.df[opt_name.get(c.name, "p") + "_nom_opt"] caps = c.df[opt_name.get(c.name, "p") + "_nom_opt"]
caps = active.mul(caps, axis=0) caps = active.mul(caps, axis=0)
capacities_grouped = ( capacities_grouped = (
caps.groupby(c.df.carrier) caps.groupby(c.df.carrier).sum().drop("load", errors="ignore")
.sum()
.drop("load", errors="ignore")
) )
capacities_grouped = pd.concat([capacities_grouped], keys=[c.list_name]) capacities_grouped = pd.concat([capacities_grouped], keys=[c.list_name])
@ -218,7 +207,6 @@ def calculate_capacities(n, label, capacities):
def calculate_curtailment(n, label, curtailment): def calculate_curtailment(n, label, curtailment):
avail = ( avail = (
n.generators_t.p_max_pu.multiply(n.generators.p_nom_opt) n.generators_t.p_max_pu.multiply(n.generators.p_nom_opt)
.sum() .sum()
@ -233,9 +221,7 @@ def calculate_curtailment(n, label, curtailment):
def calculate_energy(n, label, energy): def calculate_energy(n, label, energy):
for c in n.iterate_components(n.one_port_components | n.branch_components): for c in n.iterate_components(n.one_port_components | n.branch_components):
if c.name in n.one_port_components: if c.name in n.one_port_components:
c_energies = ( c_energies = (
c.pnl.p.multiply(n.snapshot_weightings, axis=0) c.pnl.p.multiply(n.snapshot_weightings, axis=0)
@ -265,7 +251,10 @@ def calculate_energy(n, label, energy):
def calculate_supply(n, label, supply): def calculate_supply(n, label, supply):
"""calculate the max dispatch of each component at the buses aggregated by carrier""" """
Calculate the max dispatch of each component at the buses aggregated by
carrier.
"""
bus_carriers = n.buses.carrier.unique() bus_carriers = n.buses.carrier.unique()
@ -274,7 +263,6 @@ def calculate_supply(n, label, supply):
bus_map.at[""] = False bus_map.at[""] = False
for c in n.iterate_components(n.one_port_components): for c in n.iterate_components(n.one_port_components):
items = c.df.index[c.df.bus.map(bus_map).fillna(False)] items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
if len(items) == 0: if len(items) == 0:
@ -294,9 +282,7 @@ def calculate_supply(n, label, supply):
supply.loc[s.index, label] = s supply.loc[s.index, label] = s
for c in n.iterate_components(n.branch_components): for c in n.iterate_components(n.branch_components):
for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]: for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
items = c.df.index[c.df["bus" + end].map(bus_map).fillna(False)] items = c.df.index[c.df["bus" + end].map(bus_map).fillna(False)]
if len(items) == 0: if len(items) == 0:
@ -317,7 +303,10 @@ def calculate_supply(n, label, supply):
def calculate_supply_energy(n, label, supply_energy): def calculate_supply_energy(n, label, supply_energy):
"""calculate the total energy supply/consuption of each component at the buses aggregated by carrier""" """
Calculate the total energy supply/consuption of each component at the buses
aggregated by carrier.
"""
investments = n.investment_periods investments = n.investment_periods
cols = pd.MultiIndex.from_product( cols = pd.MultiIndex.from_product(
@ -338,7 +327,6 @@ def calculate_supply_energy(n, label, supply_energy):
bus_map.at[""] = False bus_map.at[""] = False
for c in n.iterate_components(n.one_port_components): for c in n.iterate_components(n.one_port_components):
items = c.df.index[c.df.bus.map(bus_map).fillna(False)] items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
if len(items) == 0: if len(items) == 0:
@ -351,7 +339,9 @@ def calculate_supply_energy(n, label, supply_energy):
if i in ["oil", "co2", "H2"]: if i in ["oil", "co2", "H2"]:
if c.name == "Load": if c.name == "Load":
c.df.loc[items, "carrier"] = [load.split("-202")[0] for load in items] c.df.loc[items, "carrier"] = [
load.split("-202")[0] for load in items
]
if i == "oil" and c.name == "Generator": if i == "oil" and c.name == "Generator":
c.df.loc[items, "carrier"] = "imported oil" c.df.loc[items, "carrier"] = "imported oil"
s = ( s = (
@ -373,9 +363,7 @@ def calculate_supply_energy(n, label, supply_energy):
supply_energy.loc[s.index, label] = s.values supply_energy.loc[s.index, label] = s.values
for c in n.iterate_components(n.branch_components): for c in n.iterate_components(n.branch_components):
for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]: for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
items = c.df.index[c.df["bus" + str(end)].map(bus_map).fillna(False)] items = c.df.index[c.df["bus" + str(end)].map(bus_map).fillna(False)]
if len(items) == 0: if len(items) == 0:
@ -405,7 +393,6 @@ def calculate_supply_energy(n, label, supply_energy):
def calculate_metrics(n, label, metrics): def calculate_metrics(n, label, metrics):
metrics = metrics.reindex( metrics = metrics.reindex(
pd.Index( pd.Index(
[ [
@ -438,15 +425,10 @@ def calculate_metrics(n, label, metrics):
def calculate_prices(n, label, prices): def calculate_prices(n, label, prices):
prices = prices.reindex(prices.index.union(n.buses.carrier.unique())) prices = prices.reindex(prices.index.union(n.buses.carrier.unique()))
# WARNING: this is time-averaged, see weighted_prices for load-weighted average # WARNING: this is time-averaged, see weighted_prices for load-weighted average
prices[label] = ( prices[label] = n.buses_t.marginal_price.mean().groupby(n.buses.carrier).mean()
n.buses_t.marginal_price.mean()
.groupby(n.buses.carrier)
.mean()
)
return prices return prices
@ -484,7 +466,6 @@ def calculate_weighted_prices(n, label, weighted_prices):
} }
for carrier in link_loads: for carrier in link_loads:
if carrier == "electricity": if carrier == "electricity":
suffix = "" suffix = ""
elif carrier[:5] == "space": elif carrier[:5] == "space":
@ -503,7 +484,6 @@ def calculate_weighted_prices(n, label, weighted_prices):
load = n.loads_t.p_set.reindex(buses, axis=1) load = n.loads_t.p_set.reindex(buses, axis=1)
for tech in link_loads[carrier]: for tech in link_loads[carrier]:
names = n.links.index[n.links.index.to_series().str[-len(tech) :] == tech] names = n.links.index[n.links.index.to_series().str[-len(tech) :] == tech]
if names.empty: if names.empty:
@ -585,7 +565,6 @@ def calculate_market_values(n, label, market_values):
def calculate_price_statistics(n, label, price_statistics): def calculate_price_statistics(n, label, price_statistics):
price_statistics = price_statistics.reindex( price_statistics = price_statistics.reindex(
price_statistics.index.union( price_statistics.index.union(
pd.Index(["zero_hours", "mean", "standard_deviation"]) pd.Index(["zero_hours", "mean", "standard_deviation"])
@ -604,9 +583,7 @@ def calculate_price_statistics(n, label, price_statistics):
df.shape[0] * df.shape[1] df.shape[0] * df.shape[1]
) )
price_statistics.at["mean", label] = ( price_statistics.at["mean", label] = n.buses_t.marginal_price[buses].mean().mean()
n.buses_t.marginal_price[buses].mean().mean()
)
price_statistics.at["standard_deviation", label] = ( price_statistics.at["standard_deviation", label] = (
n.buses_t.marginal_price[buses].droplevel(0).unstack().std() n.buses_t.marginal_price[buses].droplevel(0).unstack().std()
@ -616,7 +593,6 @@ def calculate_price_statistics(n, label, price_statistics):
def calculate_co2_emissions(n, label, df): def calculate_co2_emissions(n, label, df):
carattr = "co2_emissions" carattr = "co2_emissions"
emissions = n.carriers.query(f"{carattr} != 0")[carattr] emissions = n.carriers.query(f"{carattr} != 0")[carattr]
@ -642,11 +618,7 @@ def calculate_co2_emissions(n, label, df):
emitted = n.generators_t.p[gens.index].mul(em_pu) emitted = n.generators_t.p[gens.index].mul(em_pu)
emitted_grouped = ( emitted_grouped = (
emitted.groupby(level=0) emitted.groupby(level=0).sum().groupby(n.generators.carrier, axis=1).sum().T
.sum()
.groupby(n.generators.carrier, axis=1)
.sum()
.T
) )
df = df.reindex(emitted_grouped.index.union(df.index)) df = df.reindex(emitted_grouped.index.union(df.index))
@ -681,7 +653,6 @@ outputs = [
def make_summaries(networks_dict): def make_summaries(networks_dict):
columns = pd.MultiIndex.from_tuples( columns = pd.MultiIndex.from_tuples(
networks_dict.keys(), names=["cluster", "lv", "opt"] networks_dict.keys(), names=["cluster", "lv", "opt"]
) )
@ -694,9 +665,7 @@ def make_summaries(networks_dict):
for label, filename in iteritems(networks_dict): for label, filename in iteritems(networks_dict):
print(label, filename) print(label, filename)
try: try:
n = pypsa.Network( n = pypsa.Network(filename, override_component_attrs=overrides)
filename, override_component_attrs=overrides
)
except OSError: except OSError:
print(label, " not solved yet.") print(label, " not solved yet.")
continue continue
@ -715,7 +684,6 @@ def make_summaries(networks_dict):
def to_csv(df): def to_csv(df):
for key in df: for key in df:
df[key] = df[key].apply(lambda x: pd.to_numeric(x)) df[key] = df[key].apply(lambda x: pd.to_numeric(x))
df[key].to_csv(snakemake.output[key]) df[key].to_csv(snakemake.output[key])
@ -726,22 +694,22 @@ if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing # Detect running outside of snakemake and mock snakemake for testing
if "snakemake" not in globals(): if "snakemake" not in globals():
from _helpers import mock_snakemake from _helpers import mock_snakemake
snakemake = mock_snakemake('make_summary_perfect')
snakemake = mock_snakemake("make_summary_perfect")
networks_dict = { networks_dict = {
(clusters, lv, opts+sector_opts) : (clusters, lv, opts + sector_opts): "results/"
"results/" + snakemake.config['run']["name"] + f'postnetworks/elec_s{simpl}_{clusters}_l{lv}_{opts}_{sector_opts}_brownfield_all_years.nc' \ + snakemake.config["run"]["name"]
for simpl in snakemake.config['scenario']['simpl'] \ + f"postnetworks/elec_s{simpl}_{clusters}_l{lv}_{opts}_{sector_opts}_brownfield_all_years.nc"
for clusters in snakemake.config['scenario']['clusters'] \ for simpl in snakemake.config["scenario"]["simpl"]
for opts in snakemake.config['scenario']['opts'] \ for clusters in snakemake.config["scenario"]["clusters"]
for sector_opts in snakemake.config['scenario']['sector_opts'] \ for opts in snakemake.config["scenario"]["opts"]
for lv in snakemake.config['scenario']['ll'] \ for sector_opts in snakemake.config["scenario"]["sector_opts"]
for lv in snakemake.config["scenario"]["ll"]
} }
print(networks_dict) print(networks_dict)
nyears = 1 nyears = 1
costs_db = prepare_costs( costs_db = prepare_costs(
snakemake.input.costs, snakemake.input.costs,

21
scripts/plot_summary.py
View File

@ -44,7 +44,7 @@ def rename_techs(label):
"land transport fuel cell", "land transport fuel cell",
"land transport oil", "land transport oil",
"H2 for industry", "H2 for industry",
"shipping oil" "shipping oil",
] ]
rename_if_contains_dict = { rename_if_contains_dict = {
@ -277,7 +277,6 @@ def plot_balances():
i for i in balances_df.index.levels[0] if i not in co2_carriers i for i in balances_df.index.levels[0] if i not in co2_carriers
] ]
for k, v in balances.items(): for k, v in balances.items():
df = balances_df.loc[v] df = balances_df.loc[v]
df = df.groupby(df.index.get_level_values(2)).sum() df = df.groupby(df.index.get_level_values(2)).sum()
@ -462,18 +461,21 @@ def plot_carbon_budget_distribution(input_eurostat, input_eea):
plt.rcParams["xtick.labelsize"] = 20 plt.rcParams["xtick.labelsize"] = 20
plt.rcParams["ytick.labelsize"] = 20 plt.rcParams["ytick.labelsize"] = 20
path_cb = "results/" + snakemake.params.RDIR + "csvs/" path_cb = "results/" + snakemake.params.RDIR + "csvs/"
countries = snakemake.config["countries"] countries = snakemake.config["countries"]
emissions_scope = snakemake.config["energy"]["emissions"] emissions_scope = snakemake.config["energy"]["emissions"]
# this only affects the estimation of CO2 emissions for BA, RS, AL, ME, MK # this only affects the estimation of CO2 emissions for BA, RS, AL, ME, MK
report_year = snakemake.config["energy"]["eurostat_report_year"] report_year = snakemake.config["energy"]["eurostat_report_year"]
e_1990 = co2_emissions_year( e_1990 = co2_emissions_year(
countries, input_eurostat, input_eea, opts, emissions_scope, countries,
report_year, year=1990 input_eurostat,
input_eea,
opts,
emissions_scope,
report_year,
year=1990,
) )
CO2_CAP = pd.read_csv(path_cb + "carbon_budget_distribution.csv", index_col=0) CO2_CAP = pd.read_csv(path_cb + "carbon_budget_distribution.csv", index_col=0)
plt.figure(figsize=(10, 7)) plt.figure(figsize=(10, 7))
@ -483,7 +485,6 @@ def plot_carbon_budget_distribution(input_eurostat, input_eea):
ax1.set_ylim([0, 5]) ax1.set_ylim([0, 5])
ax1.set_xlim([1990, snakemake.config["scenario"]["planning_horizons"][-1] + 1]) ax1.set_xlim([1990, snakemake.config["scenario"]["planning_horizons"][-1] + 1])
ax1.plot(e_1990 * CO2_CAP[o], linewidth=3, color="dodgerblue", label=None) ax1.plot(e_1990 * CO2_CAP[o], linewidth=3, color="dodgerblue", label=None)
emissions = historical_emissions(countries) emissions = historical_emissions(countries)
@ -560,6 +561,7 @@ def plot_carbon_budget_distribution(input_eurostat, input_eea):
path_cb_plot = "results/" + snakemake.params.RDIR + "/graphs/" path_cb_plot = "results/" + snakemake.params.RDIR + "/graphs/"
plt.savefig(path_cb_plot + "carbon_budget_plot.pdf", dpi=300) plt.savefig(path_cb_plot + "carbon_budget_plot.pdf", dpi=300)
# %% # %%
if __name__ == "__main__": if __name__ == "__main__":
if "snakemake" not in globals(): if "snakemake" not in globals():
@ -581,5 +583,6 @@ if __name__ == "__main__":
opts = sector_opts.split("-") opts = sector_opts.split("-")
for o in opts: for o in opts:
if "cb" in o: if "cb" in o:
plot_carbon_budget_distribution(snakemake.input.eurostat, plot_carbon_budget_distribution(
snakemake.input.co2) snakemake.input.eurostat, snakemake.input.co2
)

113
scripts/prepare_perfect_foresight.py
View File

@ -7,20 +7,24 @@
Concats pypsa networks of single investment periods to one network. Concats pypsa networks of single investment periods to one network.
""" """
import pypsa
import pandas as pd
from _helpers import override_component_attrs, update_config_with_sector_opts
from pypsa.io import import_components_from_dataframe
from add_existing_baseyear import add_build_year_to_new_assets
from six import iterkeys
from pypsa.descriptors import expand_series
import re
import logging import logging
import re
import pandas as pd
import pypsa
from _helpers import override_component_attrs, update_config_with_sector_opts
from add_existing_baseyear import add_build_year_to_new_assets
from pypsa.descriptors import expand_series
from pypsa.io import import_components_from_dataframe
from six import iterkeys
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
# helper functions --------------------------------------------------- # helper functions ---------------------------------------------------
def get_missing(df, n, c): def get_missing(df, n, c):
"""Get in network n missing assets of df for component c. """
Get in network n missing assets of df for component c.
Input: Input:
df: pandas DataFrame, static values of pypsa components df: pandas DataFrame, static values of pypsa components
@ -35,15 +39,18 @@ def get_missing(df, n, c):
def get_social_discount(t, r=0.01): def get_social_discount(t, r=0.01):
"""Calculate for a given time t the social discount.""" """
Calculate for a given time t the social discount.
"""
return 1 / (1 + r) ** t return 1 / (1 + r) ** t
def get_investment_weighting(time_weighting, r=0.01): def get_investment_weighting(time_weighting, r=0.01):
"""Define cost weighting. """
Define cost weighting.
Returns cost weightings depending on the the time_weighting (pd.Series) Returns cost weightings depending on the the time_weighting
and the social discountrate r (pd.Series) and the social discountrate r
""" """
end = time_weighting.cumsum() end = time_weighting.cumsum()
start = time_weighting.cumsum().shift().fillna(0) start = time_weighting.cumsum().shift().fillna(0)
@ -52,6 +59,7 @@ def get_investment_weighting(time_weighting, r=0.01):
axis=1, axis=1,
) )
def add_year_to_constraints(n, baseyear): def add_year_to_constraints(n, baseyear):
""" """
Parameters Parameters
@ -62,28 +70,27 @@ def add_year_to_constraints(n, baseyear):
""" """
for c in n.iterate_components(["GlobalConstraint"]): for c in n.iterate_components(["GlobalConstraint"]):
c.df["investment_period"] = baseyear c.df["investment_period"] = baseyear
c.df.rename(index=lambda x: x + "-" + str(baseyear), inplace=True) c.df.rename(index=lambda x: x + "-" + str(baseyear), inplace=True)
# -------------------------------------------------------------------- # --------------------------------------------------------------------
def concat_networks(years): def concat_networks(years):
"""Concat given pypsa networks and adds build_year. """
Concat given pypsa networks and adds build_year.
Return: Return:
n : pypsa.Network for the whole planning horizon n : pypsa.Network for the whole planning horizon
""" """
# input paths of sector coupling networks # input paths of sector coupling networks
network_paths = [snakemake.input.brownfield_network] + [ network_paths = [snakemake.input.brownfield_network] + [
snakemake.input[f"network_{year}"] for year in years[1:]] snakemake.input[f"network_{year}"] for year in years[1:]
]
# final concatenated network # final concatenated network
overrides = override_component_attrs(snakemake.input.overrides) overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(override_component_attrs=overrides) n = pypsa.Network(override_component_attrs=overrides)
# iterate over single year networks and concat to perfect foresight network # iterate over single year networks and concat to perfect foresight network
for i, network_path in enumerate(network_paths): for i, network_path in enumerate(network_paths):
year = years[i] year = years[i]
@ -102,7 +109,6 @@ def concat_networks(years):
for component in network.iterate_components( for component in network.iterate_components(
["Generator", "Link", "Store", "Load", "Line", "StorageUnit"] ["Generator", "Link", "Store", "Load", "Line", "StorageUnit"]
): ):
df_year = component.df.copy() df_year = component.df.copy()
missing = get_missing(df_year, n, component.list_name) missing = get_missing(df_year, n, component.list_name)
@ -117,14 +123,14 @@ def concat_networks(years):
pnl = getattr(n, component.list_name + "_t") pnl = getattr(n, component.list_name + "_t")
for k in iterkeys(component.pnl): for k in iterkeys(component.pnl):
pnl_year = component.pnl[k].copy().reindex(snapshots, level=1) pnl_year = component.pnl[k].copy().reindex(snapshots, level=1)
if pnl_year.empty and ~(component.name=="Load" and k=="p_set"): continue if pnl_year.empty and ~(component.name == "Load" and k == "p_set"):
continue
if component.name == "Load": if component.name == "Load":
static_load = network.loads.loc[network.loads.p_set != 0] static_load = network.loads.loc[network.loads.p_set != 0]
static_load_t = expand_series( static_load_t = expand_series(static_load.p_set, network_sns).T
static_load.p_set, network_sns pnl_year = pd.concat(
).T [pnl_year.reindex(network_sns), static_load_t], axis=1
pnl_year = pd.concat([pnl_year.reindex(network_sns), )
static_load_t], axis=1)
columns = (pnl[k].columns.union(pnl_year.columns)).unique() columns = (pnl[k].columns.union(pnl_year.columns)).unique()
pnl[k] = pnl[k].reindex(columns=columns) pnl[k] = pnl[k].reindex(columns=columns)
pnl[k].loc[pnl_year.index, pnl_year.columns] = pnl_year pnl[k].loc[pnl_year.index, pnl_year.columns] = pnl_year
@ -134,7 +140,6 @@ def concat_networks(years):
cols = pnl_year.columns.difference(pnl[k].columns) cols = pnl_year.columns.difference(pnl[k].columns)
pnl[k] = pd.concat([pnl[k], pnl_year[cols]], axis=1) pnl[k] = pd.concat([pnl[k], pnl_year[cols]], axis=1)
n.snapshot_weightings.loc[year, :] = network.snapshot_weightings.values n.snapshot_weightings.loc[year, :] = network.snapshot_weightings.values
# (3) global constraints # (3) global constraints
@ -148,8 +153,9 @@ def concat_networks(years):
time_w = n.investment_periods.to_series().diff().shift(-1).fillna(method="ffill") time_w = n.investment_periods.to_series().diff().shift(-1).fillna(method="ffill")
n.investment_period_weightings["years"] = time_w n.investment_period_weightings["years"] = time_w
# set objective weightings # set objective weightings
objective_w = get_investment_weighting(n.investment_period_weightings["years"], objective_w = get_investment_weighting(
social_discountrate) n.investment_period_weightings["years"], social_discountrate
)
n.investment_period_weightings["objective"] = objective_w n.investment_period_weightings["objective"] = objective_w
# all former static loads are now time-dependent -> set static = 0 # all former static loads are now time-dependent -> set static = 0
n.loads["p_set"] = 0 n.loads["p_set"] = 0
@ -157,9 +163,12 @@ def concat_networks(years):
return n return n
def adjust_stores(n): def adjust_stores(n):
"""Make sure that stores still behave cyclic over one year and not whole """
modelling horizon.""" Make sure that stores still behave cyclic over one year and not whole
modelling horizon.
"""
# cylclic constraint # cylclic constraint
cyclic_i = n.stores[n.stores.e_cyclic].index cyclic_i = n.stores[n.stores.e_cyclic].index
n.stores.loc[cyclic_i, "e_cyclic_per_period"] = True n.stores.loc[cyclic_i, "e_cyclic_per_period"] = True
@ -177,9 +186,12 @@ def adjust_stores(n):
return n return n
def set_phase_out(n, carrier, ct, phase_out_year): def set_phase_out(n, carrier, ct, phase_out_year):
"""Set planned phase outs for given carrier,country (ct) and planned year """
of phase out (phase_out_year).""" Set planned phase outs for given carrier,country (ct) and planned year of
phase out (phase_out_year).
"""
df = n.links[(n.links.carrier.isin(carrier)) & (n.links.bus1.str[:2] == ct)] df = n.links[(n.links.carrier.isin(carrier)) & (n.links.bus1.str[:2] == ct)]
# assets which are going to be phased out before end of their lifetime # assets which are going to be phased out before end of their lifetime
assets_i = df[df[["build_year", "lifetime"]].sum(axis=1) > phase_out_year].index assets_i = df[df[["build_year", "lifetime"]].sum(axis=1) > phase_out_year].index
@ -187,16 +199,19 @@ def set_phase_out(n, carrier, ct, phase_out_year):
# adjust lifetime # adjust lifetime
n.links.loc[assets_i, "lifetime"] = (phase_out_year - build_year).astype(float) n.links.loc[assets_i, "lifetime"] = (phase_out_year - build_year).astype(float)
def set_all_phase_outs(n): def set_all_phase_outs(n):
# TODO move this to a csv or to the config # TODO move this to a csv or to the config
planned= [(["nuclear"], "DE", 2022), planned = [
(["nuclear"], "DE", 2022),
(["nuclear"], "BE", 2025), (["nuclear"], "BE", 2025),
(["nuclear"], "ES", 2027), (["nuclear"], "ES", 2027),
(["coal", "lignite"], "DE", 2038), (["coal", "lignite"], "DE", 2038),
(["coal", "lignite"], "ES", 2027), (["coal", "lignite"], "ES", 2027),
(["coal", "lignite"], "FR", 2022), (["coal", "lignite"], "FR", 2022),
(["coal", "lignite"], "GB", 2024), (["coal", "lignite"], "GB", 2024),
(["coal", "lignite"], "IT", 2025)] (["coal", "lignite"], "IT", 2025),
]
for carrier, ct, phase_out_year in planned: for carrier, ct, phase_out_year in planned:
set_phase_out(n, carrier, ct, phase_out_year) set_phase_out(n, carrier, ct, phase_out_year)
# remove assets which are already phased out # remove assets which are already phased out
@ -205,7 +220,9 @@ def set_all_phase_outs(n):
def set_carbon_constraints(n, opts): def set_carbon_constraints(n, opts):
"""Add global constraints for carbon emissions.""" """
Add global constraints for carbon emissions.
"""
budget = None budget = None
for o in opts: for o in opts:
# other budgets # other budgets
@ -243,7 +260,9 @@ def set_carbon_constraints(n, opts):
first_year = n.snapshots.levels[0][0] first_year = n.snapshots.levels[0][0]
time_weightings = n.investment_period_weightings.loc[first_year, "years"] time_weightings = n.investment_period_weightings.loc[first_year, "years"]
co2min = emissions_2019 - ((first_year - 2019) * annual_reduction) co2min = emissions_2019 - ((first_year - 2019) * annual_reduction)
logger.info("add minimum emissions for {} of {} t CO2/a".format(first_year, co2min)) logger.info(
"add minimum emissions for {} of {} t CO2/a".format(first_year, co2min)
)
n.add( n.add(
"GlobalConstraint", "GlobalConstraint",
"Co2min", "Co2min",
@ -255,30 +274,36 @@ def set_carbon_constraints(n, opts):
) )
return n return n
# %% # %%
if __name__ == "__main__": if __name__ == "__main__":
if 'snakemake' not in globals(): if "snakemake" not in globals():
from _helpers import mock_snakemake from _helpers import mock_snakemake
snakemake = mock_snakemake( snakemake = mock_snakemake(
'prepare_perfect_foresight', "prepare_perfect_foresight",
simpl='', simpl="",
opts="", opts="",
clusters="37", clusters="37",
ll=1.0, ll=1.0,
sector_opts='cb40ex0-2190H-T-H-B-solar+p3-dist1', sector_opts="cb40ex0-2190H-T-H-B-solar+p3-dist1",
) )
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts) update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
# parameters ----------------------------------------------------------- # parameters -----------------------------------------------------------
years = snakemake.config["scenario"]["planning_horizons"] years = snakemake.config["scenario"]["planning_horizons"]
opts = snakemake.wildcards.sector_opts.split('-') opts = snakemake.wildcards.sector_opts.split("-")
social_discountrate = snakemake.config["costs"]["social_discountrate"] social_discountrate = snakemake.config["costs"]["social_discountrate"]
for o in opts: for o in opts:
if "sdr" in o: if "sdr" in o:
social_discountrate = float(o.replace("sdr", "")) / 100 social_discountrate = float(o.replace("sdr", "")) / 100
logger.info("Concat networks of investment period {} with social discount rate of {}%" logger.info(
.format(years, social_discountrate*100)) "Concat networks of investment period {} with social discount rate of {}%".format(
years, social_discountrate * 100
)
)
# concat prenetworks of planning horizon to single network ------------ # concat prenetworks of planning horizon to single network ------------
n = concat_networks(years) n = concat_networks(years)
@ -289,7 +314,7 @@ if __name__ == "__main__":
n = adjust_stores(n) n = adjust_stores(n)
# set carbon constraints # set carbon constraints
opts = snakemake.wildcards.sector_opts.split('-') opts = snakemake.wildcards.sector_opts.split("-")
n = set_carbon_constraints(n, opts) n = set_carbon_constraints(n, opts)
# export network # export network

31
scripts/prepare_sector_network.py
View File

@ -246,12 +246,24 @@ def build_carbon_budget(o, input_eurostat, input_eea, fn, emissions_scope, repor
countries = snakemake.config["countries"] countries = snakemake.config["countries"]
e_1990 = co2_emissions_year( e_1990 = co2_emissions_year(
countries, input_eurostat, input_eea, opts, emissions_scope, report_year, year=1990 countries,
input_eurostat,
input_eea,
opts,
emissions_scope,
report_year,
year=1990,
) )
# emissions at the beginning of the path (last year available 2018) # emissions at the beginning of the path (last year available 2018)
e_0 = co2_emissions_year( e_0 = co2_emissions_year(
countries, input_eurostat, input_eea, opts, emissions_scope, report_year, year=2018 countries,
input_eurostat,
input_eea,
opts,
emissions_scope,
report_year,
year=2018,
) )
planning_horizons = snakemake.config["scenario"]["planning_horizons"] planning_horizons = snakemake.config["scenario"]["planning_horizons"]
@ -1130,7 +1142,9 @@ def add_storage_and_grids(n, costs):
e_cyclic=True, e_cyclic=True,
carrier="H2 Store", carrier="H2 Store",
capital_cost=h2_capital_cost, capital_cost=h2_capital_cost,
lifetime=costs.at["hydrogen storage tank type 1 including compressor", "lifetime"], lifetime=costs.at[
"hydrogen storage tank type 1 including compressor", "lifetime"
],
) )
if options["gas_network"] or options["H2_retrofit"]: if options["gas_network"] or options["H2_retrofit"]:
@ -3287,7 +3301,7 @@ if __name__ == "__main__":
spatial = define_spatial(pop_layout.index, options) spatial = define_spatial(pop_layout.index, options)
if snakemake.config["foresight"] in ['myopic', 'perfect']: if snakemake.config["foresight"] in ["myopic", "perfect"]:
add_lifetime_wind_solar(n, costs) add_lifetime_wind_solar(n, costs)
conventional = snakemake.config["existing_capacities"]["conventional_carriers"] conventional = snakemake.config["existing_capacities"]["conventional_carriers"]
@ -3369,7 +3383,12 @@ if __name__ == "__main__":
emissions_scope = snakemake.config["energy"]["emissions"] emissions_scope = snakemake.config["energy"]["emissions"]
report_year = snakemake.config["energy"]["eurostat_report_year"] report_year = snakemake.config["energy"]["eurostat_report_year"]
build_carbon_budget( build_carbon_budget(
o, snakemake.input.eurostat, snakemake.input.co2, fn, emissions_scope, report_year o,
snakemake.input.eurostat,
snakemake.input.co2,
fn,
emissions_scope,
report_year,
) )
co2_cap = pd.read_csv(fn, index_col=0).squeeze() co2_cap = pd.read_csv(fn, index_col=0).squeeze()
limit = co2_cap.loc[investment_year] limit = co2_cap.loc[investment_year]
@ -3402,7 +3421,7 @@ if __name__ == "__main__":
if options["electricity_grid_connection"]: if options["electricity_grid_connection"]:
add_electricity_grid_connection(n, costs) add_electricity_grid_connection(n, costs)
first_year_multi = (snakemake.config["foresight"] in ['myopic', 'perfect']) and ( first_year_multi = (snakemake.config["foresight"] in ["myopic", "perfect"]) and (
snakemake.config["scenario"]["planning_horizons"][0] == investment_year snakemake.config["scenario"]["planning_horizons"][0] == investment_year
) )

1
scripts/solve_network.py
View File

@ -629,6 +629,7 @@ def solve_network(n, config, opts="", **kwargs):
return n return n
# %% # %%
if __name__ == "__main__": if __name__ == "__main__":
if "snakemake" not in globals(): if "snakemake" not in globals():