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pre-commit-ci[bot] 2023-08-30 10:05:16 +00:00
parent 9ec7442c69
commit bdaa646ed6
10 changed files with 287 additions and 239 deletions
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8
config/config.perfect.yaml
View File

@ -87,10 +87,10 @@ co2_budget:
2050: 0.000
# update of IPCC 6th AR compared to the 1.5SR. (discussed here: https://twitter.com/JoeriRogelj/status/1424743828339167233)
1p5 : 34.2 # 25.7 # Budget in Gt CO2 for 1.5 for Europe, global 420 Gt, assuming per capita share
1p6 : 43.259666 # 35 # Budget in Gt CO2 for 1.6 for Europe, global 580 Gt
1p7 : 51.4 # 45 # Budget in Gt CO2 for 1.7 for Europe, global 800 Gt
2p0 : 69.778 # 73.9 # Budget in Gt CO2 for 2 for Europe, global 1170 Gt
1p5: 34.2 # 25.7 # Budget in Gt CO2 for 1.5 for Europe, global 420 Gt, assuming per capita share
1p6: 43.259666 # 35 # Budget in Gt CO2 for 1.6 for Europe, global 580 Gt
1p7: 51.4 # 45 # Budget in Gt CO2 for 1.7 for Europe, global 800 Gt
2p0: 69.778 # 73.9 # Budget in Gt CO2 for 2 for Europe, global 1170 Gt
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#electricity
electricity:

8
rules/postprocess.smk
View File

@ -7,7 +7,9 @@ localrules:
copy_config,
copy_conda_env,
if config["foresight"] != "perfect":
rule plot_network:
params:
foresight=config["foresight"],
@ -34,7 +36,9 @@ if config["foresight"] != "perfect":
script:
"../scripts/plot_network.py"
if config["foresight"] == "perfect":
rule plot_network:
params:
foresight=config["foresight"],
@ -55,15 +59,13 @@ if config["foresight"] == "perfect":
mem_mb=10000,
benchmark:
BENCHMARKS
+ "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years_benchmark",
+"postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years_benchmark"
conda:
"../envs/environment.yaml"
script:
"../scripts/plot_network.py"
rule copy_config:
params:
RDIR=RDIR,

3
rules/solve_perfect.smk
View File

@ -190,7 +190,4 @@ rule make_summary_perfect:
"../scripts/make_summary_perfect.py"
ruleorder: add_existing_baseyear > add_brownfield

7
scripts/add_existing_baseyear.py
View File

@ -309,11 +309,12 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
missing_bus = pd.Index(bus0).difference(n.buses.index)
if not missing_bus.empty:
logger.info(f"add buses {bus0}")
n.madd("Bus",
n.madd(
"Bus",
bus0,
carrier=generator,
location=vars(spatial)[carrier[generator]].locations,
unit="MWh_el"
unit="MWh_el",
)
already_build = n.links.index.intersection(asset_i)
@ -617,7 +618,7 @@ def add_heating_capacities_installed_before_baseyear(
)
# drop assets which are at the end of their lifetime
links_i = n.links[(n.links.build_year+n.links.lifetime<=baseyear)].index
links_i = n.links[(n.links.build_year + n.links.lifetime <= baseyear)].index
n.mremove("Link", links_i)

10
scripts/build_industrial_distribution_key.py
View File

@ -99,12 +99,15 @@ def prepare_hotmaps_database(regions):
# the .sjoin can lead to duplicates if a geom is in two regions
if gdf.index.duplicated().any():
import pycountry
# get all duplicated entries
duplicated_i = gdf.index[gdf.index.duplicated()]
# convert from raw data country name to iso-2-code
s = df.loc[duplicated_i, "Country"].apply(lambda x: pycountry.countries.lookup(x).alpha_2)
s = df.loc[duplicated_i, "Country"].apply(
lambda x: pycountry.countries.lookup(x).alpha_2
)
# Get a boolean mask where gdf's country column matches s's values for the same index
mask = gdf['country'] == gdf.index.map(s)
mask = gdf["country"] == gdf.index.map(s)
# Filter gdf using the mask
gdf_filtered = gdf[mask]
# concat not duplicated and filtered gdf
@ -160,7 +163,8 @@ def build_nodal_distribution_key(hotmaps, regions, countries):
return keys
#%%
# %%
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake

24
scripts/make_summary_perfect.py
View File

@ -235,20 +235,28 @@ def calculate_energy(n, label, energy):
if c.name in n.one_port_components:
c_energies = (
c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
.groupby(level=0).sum()
.groupby(level=0)
.sum()
.multiply(c.df.sign)
.groupby(c.df.carrier, axis=1)
.sum()
)
else:
c_energies = pd.DataFrame(0.0, columns=c.df.carrier.unique(), index=n.investment_periods)
c_energies = pd.DataFrame(
0.0, columns=c.df.carrier.unique(), index=n.investment_periods
)
for port in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
totals = c.pnl["p" + port].multiply(n.snapshot_weightings.generators, axis=0).groupby(level=0).sum()
totals = (
c.pnl["p" + port]
.multiply(n.snapshot_weightings.generators, axis=0)
.groupby(level=0)
.sum()
)
# remove values where bus is missing (bug in nomopyomo)
no_bus = c.df.index[c.df["bus" + port] == ""]
totals[no_bus] = float(n.component_attrs[c.name].loc[
"p" + port, "default"
])
totals[no_bus] = float(
n.component_attrs[c.name].loc["p" + port, "default"]
)
c_energies -= totals.groupby(c.df.carrier, axis=1).sum()
c_energies = pd.concat([c_energies.T], keys=[c.list_name])
@ -703,10 +711,12 @@ if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("make_summary_perfect")
run = snakemake.config["run"]["name"]
if run!="": run += "/"
if run != "":
run += "/"
networks_dict = {
(clusters, lv, opts + sector_opts): "results/"

76
scripts/plot_network.py
View File

@ -913,9 +913,11 @@ def plot_series(network, carrier="AC", name="test"):
)
def plot_map_perfect(network, components=["Link", "Store", "StorageUnit", "Generator"],
bus_size_factor=1.7e10):
def plot_map_perfect(
network,
components=["Link", "Store", "StorageUnit", "Generator"],
bus_size_factor=1.7e10,
):
n = network.copy()
assign_location(n)
# Drop non-electric buses so they don't clutter the plot
@ -926,41 +928,48 @@ def plot_map_perfect(network, components=["Link", "Store", "StorageUnit", "Gener
costs = {}
for comp in components:
df_c = n.df(comp)
if df_c.empty: continue
if df_c.empty:
continue
df_c["nice_group"] = df_c.carrier.map(rename_techs_tyndp)
attr = "e_nom_opt" if comp == "Store" else "p_nom_opt"
active = pd.concat(
[
n.get_active_assets(comp, inv_p).rename(inv_p)
for inv_p in investments
],
[n.get_active_assets(comp, inv_p).rename(inv_p) for inv_p in investments],
axis=1,
).astype(int)
capital_cost = n.df(comp)[attr] * n.df(comp).capital_cost
capital_cost_t = ((active.mul(capital_cost, axis=0))
.groupby([n.df(comp).location,
n.df(comp).nice_group]).sum())
capital_cost_t = (
(active.mul(capital_cost, axis=0))
.groupby([n.df(comp).location, n.df(comp).nice_group])
.sum()
)
capital_cost_t.drop("load", level=1, inplace=True, errors="ignore")
costs[comp] = capital_cost_t
costs = pd.concat(costs).groupby(level=[1,2]).sum()
costs.drop(costs[costs.sum(axis=1)==0].index, inplace=True)
costs = pd.concat(costs).groupby(level=[1, 2]).sum()
costs.drop(costs[costs.sum(axis=1) == 0].index, inplace=True)
new_columns = (preferred_order.intersection(costs.index.levels[1])
.append(costs.index.levels[1].difference(preferred_order)))
new_columns = preferred_order.intersection(costs.index.levels[1]).append(
costs.index.levels[1].difference(preferred_order)
)
costs = costs.reindex(new_columns, level=1)
for item in new_columns:
if item not in snakemake.config['plotting']['tech_colors']:
print("Warning!",item,"not in config/plotting/tech_colors, assign random color")
snakemake.config['plotting']['tech_colors'] = "pink"
if item not in snakemake.config["plotting"]["tech_colors"]:
print(
"Warning!",
item,
"not in config/plotting/tech_colors, assign random color",
)
snakemake.config["plotting"]["tech_colors"] = "pink"
n.links.drop(n.links.index[(n.links.carrier != "DC") & (
n.links.carrier != "B2B")], inplace=True)
n.links.drop(
n.links.index[(n.links.carrier != "DC") & (n.links.carrier != "B2B")],
inplace=True,
)
# drop non-bus
to_drop = costs.index.levels[0].symmetric_difference(n.buses.index)
@ -972,7 +981,7 @@ def plot_map_perfect(network, components=["Link", "Store", "StorageUnit", "Gener
costs.index = pd.MultiIndex.from_tuples(costs.index.values)
# PDF has minimum width, so set these to zero
line_lower_threshold = 500.
line_lower_threshold = 500.0
line_upper_threshold = 1e4
linewidth_factor = 2e3
ac_color = "gray"
@ -981,29 +990,29 @@ def plot_map_perfect(network, components=["Link", "Store", "StorageUnit", "Gener
line_widths = n.lines.s_nom_opt
link_widths = n.links.p_nom_opt
linewidth_factor = 2e3
line_lower_threshold = 0.
line_lower_threshold = 0.0
title = "Today's transmission"
line_widths[line_widths < line_lower_threshold] = 0.
link_widths[link_widths < line_lower_threshold] = 0.
line_widths[line_widths < line_lower_threshold] = 0.0
link_widths[link_widths < line_lower_threshold] = 0.0
line_widths[line_widths > line_upper_threshold] = line_upper_threshold
link_widths[link_widths > line_upper_threshold] = line_upper_threshold
for year in costs.columns:
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
fig.set_size_inches(7, 6)
fig.suptitle(year)
n.plot(
bus_sizes=costs[year] / bus_size_factor,
bus_colors=snakemake.config['plotting']['tech_colors'],
bus_colors=snakemake.config["plotting"]["tech_colors"],
line_colors=ac_color,
link_colors=dc_color,
line_widths=line_widths / linewidth_factor,
link_widths=link_widths / linewidth_factor,
ax=ax, **map_opts
ax=ax,
**map_opts,
)
sizes = [20, 10, 5]
@ -1051,15 +1060,12 @@ def plot_map_perfect(network, components=["Link", "Store", "StorageUnit", "Gener
frameon=False,
)
fig.savefig(
snakemake.output[f"map_{year}"],
transparent=True,
bbox_inches="tight"
snakemake.output[f"map_{year}"], transparent=True, bbox_inches="tight"
)
#%%
# %%
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
@ -1085,9 +1091,11 @@ if __name__ == "__main__":
map_opts["boundaries"] = regions.total_bounds[[0, 2, 1, 3]] + [-1, 1, -1, 1]
if snakemake.params["foresight"] == "perfect":
plot_map_perfect(n,
plot_map_perfect(
n,
components=["Link", "Store", "StorageUnit", "Generator"],
bus_size_factor=2e10)
bus_size_factor=2e10,
)
else:
plot_map(
n,

36
scripts/plot_summary.py
View File

@ -276,8 +276,6 @@ def plot_balances():
i for i in balances_df.index.levels[0] if i not in co2_carriers
]
for k, v in balances.items():
df = balances_df.loc[v]
df = df.groupby(df.index.get_level_values(2)).sum()
@ -357,7 +355,6 @@ def plot_balances():
fig.savefig(snakemake.output.balances[:-10] + k + ".pdf", bbox_inches="tight")
def historical_emissions(countries):
"""
Read historical emissions to add them to the carbon budget plot.
@ -393,17 +390,18 @@ def historical_emissions(countries):
e["other LULUCF"] = "4.H - Other LULUCF"
pol = ["CO2"] # ["All greenhouse gases - (CO2 equivalent)"]
if "GB" in countries:
countries.remove("GB")
countries.append("UK")
year = df.index.levels[0][df.index.levels[0]>=1990]
year = df.index.levels[0][df.index.levels[0] >= 1990]
missing = pd.Index(countries).difference(df.index.levels[2])
if not missing.empty:
logger.warning(f"The following countries are missing and not considered when plotting historic CO2 emissions: {missing}")
logger.warning(
f"The following countries are missing and not considered when plotting historic CO2 emissions: {missing}"
)
countries = pd.Index(df.index.levels[2]).intersection(countries)
idx = pd.IndexSlice
@ -469,24 +467,26 @@ def plot_carbon_budget_distribution(input_eurostat):
# historic emissions
countries = snakemake.params.countries
e_1990 = co2_emissions_year(
countries, input_eurostat, opts, snakemake, year=1990
)
e_1990 = co2_emissions_year(countries, input_eurostat, opts, snakemake, year=1990)
emissions = historical_emissions(countries)
# add other years https://sdi.eea.europa.eu/data/0569441f-2853-4664-a7cd-db969ef54de0
emissions.loc[2019] = 2.971372
emissions.loc[2020] = 2.691958
emissions.loc[2021] = 2.869355
if snakemake.config["foresight"] == "myopic":
path_cb = "results/" + snakemake.params.RDIR + "/csvs/"
co2_cap = pd.read_csv(path_cb + "carbon_budget_distribution.csv", index_col=0)[["cb"]]
co2_cap = pd.read_csv(path_cb + "carbon_budget_distribution.csv", index_col=0)[
["cb"]
]
co2_cap *= e_1990
else:
supply_energy = pd.read_csv(snakemake.input.balances,
index_col=[0,1,2], header=[0,1,2, 3])
co2_cap = supply_energy.loc["co2"].droplevel(0).drop("co2").sum().unstack().T/1e9
supply_energy = pd.read_csv(
snakemake.input.balances, index_col=[0, 1, 2], header=[0, 1, 2, 3]
)
co2_cap = (
supply_energy.loc["co2"].droplevel(0).drop("co2").sum().unstack().T / 1e9
)
co2_cap.rename(index=lambda x: int(x), inplace=True)
plt.figure(figsize=(10, 7))
@ -550,7 +550,6 @@ def plot_carbon_budget_distribution(input_eurostat):
for col in co2_cap.columns:
ax1.plot(co2_cap[col], linewidth=3, label=col)
ax1.legend(
fancybox=True, fontsize=18, loc=(0.01, 0.01), facecolor="white", frameon=True
)
@ -559,7 +558,8 @@ def plot_carbon_budget_distribution(input_eurostat):
path = snakemake.output.balances.split("balances")[0] + "carbon_budget.pdf"
plt.savefig(path, bbox_inches="tight")
#%%
# %%
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
@ -578,5 +578,7 @@ if __name__ == "__main__":
for sector_opts in snakemake.params.sector_opts:
opts = sector_opts.split("-")
if any(["cb" in o for o in opts]) or (snakemake.config["foresight"]=="perfect"):
if any(["cb" in o for o in opts]) or (
snakemake.config["foresight"] == "perfect"
):
plot_carbon_budget_distribution(snakemake.input.eurostat)

70
scripts/prepare_perfect_foresight.py
View File

@ -8,14 +8,16 @@ Concats pypsa networks of single investment periods to one network.
import logging
import re
import pandas as pd
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
from pypsa.descriptors import expand_series
from pypsa.io import import_components_from_dataframe
from six import iterkeys
logger = logging.getLogger(__name__)
@ -38,8 +40,8 @@ def get_missing(df, n, c):
def get_social_discount(t, r=0.01):
"""
Calculate for a given time t and social discount rate r [per unit]
the social discount.
Calculate for a given time t and social discount rate r [per unit] the
social discount.
"""
return 1 / (1 + r) ** t
@ -77,13 +79,16 @@ def add_year_to_constraints(n, baseyear):
def hvdc_transport_model(n):
"""
Convert AC lines to DC links for multi-decade optimisation with
line expansion. Losses of DC links are assumed to be 3% per 1000km
Convert AC lines to DC links for multi-decade optimisation with line
expansion.
Losses of DC links are assumed to be 3% per 1000km
"""
logger.info("Convert AC lines to DC links to perform multi-decade optimisation.")
n.madd("Link",
n.madd(
"Link",
n.lines.index,
bus0=n.lines.bus0,
bus1=n.lines.bus1,
@ -95,7 +100,8 @@ def hvdc_transport_model(n):
marginal_cost=0,
carrier="DC",
length=n.lines.length,
capital_cost=n.lines.capital_cost)
capital_cost=n.lines.capital_cost,
)
# Remove AC lines
logger.info("Removing AC lines")
@ -103,8 +109,9 @@ def hvdc_transport_model(n):
n.mremove("Line", lines_rm)
# Set efficiency of all DC links to include losses depending on length
n.links.loc[n.links.carrier == 'DC', 'efficiency'] = 1 - 0.03 * n.links.loc[
n.links.carrier == 'DC', 'length'] / 1000
n.links.loc[n.links.carrier == "DC", "efficiency"] = (
1 - 0.03 * n.links.loc[n.links.carrier == "DC", "length"] / 1000
)
def adjust_electricity_grid(n, year, years):
@ -121,17 +128,16 @@ def adjust_electricity_grid(n, year, years):
investment periods
"""
n.lines["carrier"] = "AC"
links_i = n.links[n.links.carrier=="DC"].index
links_i = n.links[n.links.carrier == "DC"].index
if n.lines.s_nom_extendable.any() or n.links.loc[links_i, "p_nom_extendable"].any():
hvdc_transport_model(n)
links_i = n.links[n.links.carrier=="DC"].index
links_i = n.links[n.links.carrier == "DC"].index
n.links.loc[links_i, "lifetime"] = 100
if year!= years[0]:
if year != years[0]:
n.links.loc[links_i, "p_nom_min"] = 0
n.links.loc[links_i, "p_nom"] = 0
# --------------------------------------------------------------------
def concat_networks(years):
"""
@ -246,7 +252,6 @@ def adjust_stores(n):
co2_i = n.stores[n.stores.carrier.isin(e_initial_store)].index
n.stores.loc[co2_i, "e_initial_per_period"] = True
return n
@ -309,11 +314,11 @@ def set_carbon_constraints(n, opts):
carrier_attribute="co2_emissions",
sense="<=",
constant=budget,
investment_period=n.investment_periods[-1]
investment_period=n.investment_periods[-1],
)
# drop other CO2 limits
drop_i = n.global_constraints[n.global_constraints.type=="co2_limit"].index
drop_i = n.global_constraints[n.global_constraints.type == "co2_limit"].index
n.mremove("GlobalConstraint", drop_i)
n.add(
@ -323,10 +328,9 @@ def set_carbon_constraints(n, opts):
carrier_attribute="co2_emissions",
sense="<=",
constant=0,
investment_period=n.investment_periods[-1]
investment_period=n.investment_periods[-1],
)
# set minimum CO2 emission constraint to avoid too fast reduction
if "co2min" in opts:
emissions_1990 = 4.53693
@ -354,16 +358,16 @@ def set_carbon_constraints(n, opts):
def adjust_lvlimit(n):
"""
Convert global constraints for single investment period to one uniform
if all attributes stay the same.
Convert global constraints for single investment period to one uniform if
all attributes stay the same.
"""
c = "GlobalConstraint"
cols = ['carrier_attribute', 'sense', "constant", "type"]
cols = ["carrier_attribute", "sense", "constant", "type"]
glc_type = "transmission_volume_expansion_limit"
if (n.df(c)[n.df(c).type==glc_type][cols].nunique()==1).all():
glc = n.df(c)[n.df(c).type==glc_type][cols].iloc[[0]]
if (n.df(c)[n.df(c).type == glc_type][cols].nunique() == 1).all():
glc = n.df(c)[n.df(c).type == glc_type][cols].iloc[[0]]
glc.index = pd.Index(["lv_limit"])
remove_i = n.df(c)[n.df(c).type==glc_type].index
remove_i = n.df(c)[n.df(c).type == glc_type].index
n.mremove(c, remove_i)
import_components_from_dataframe(n, glc, c)
@ -374,7 +378,7 @@ def adjust_CO2_glc(n):
c = "GlobalConstraint"
glc_name = "CO2Limit"
glc_type = "primary_energy"
mask = (n.df(c).index.str.contains(glc_name)) & (n.df(c).type==glc_type)
mask = (n.df(c).index.str.contains(glc_name)) & (n.df(c).type == glc_type)
n.df(c).loc[mask, "type"] = "co2_limit"
return n
@ -382,8 +386,9 @@ def adjust_CO2_glc(n):
def add_H2_boilers(n):
"""
Gas boilers can be retrofitted to run with H2. Add H2 boilers for heating
for all existing gas boilers.
Gas boilers can be retrofitted to run with H2.
Add H2 boilers for heating for all existing gas boilers.
"""
c = "Link"
logger.info("Add H2 boilers.")
@ -394,8 +399,12 @@ def add_H2_boilers(n):
# adjust bus 0
df["bus0"] = df.bus1.map(n.buses.location) + " H2"
# rename carrier and index
df["carrier"] = df.carrier.apply(lambda x: x.replace("gas boiler", "retrofitted H2 boiler"))
df.rename(index = lambda x: x.replace("gas boiler", "retrofitted H2 boiler"), inplace=True)
df["carrier"] = df.carrier.apply(
lambda x: x.replace("gas boiler", "retrofitted H2 boiler")
)
df.rename(
index=lambda x: x.replace("gas boiler", "retrofitted H2 boiler"), inplace=True
)
# todo, costs for retrofitting
df["capital_costs"] = 100
# set existing capacity to zero
@ -470,6 +479,7 @@ def apply_time_segmentation_perfect(
return n
def set_temporal_aggregation_SEG(n, opts, solver_name):
"""
Aggregate network temporally with tsam.
@ -483,6 +493,8 @@ def set_temporal_aggregation_SEG(n, opts, solver_name):
n = apply_time_segmentation_perfect(n, segments, solver_name=solver_name)
break
return n
# %%
if __name__ == "__main__":
if "snakemake" not in globals():

82
scripts/solve_network.py
View File

@ -32,10 +32,9 @@ import re
import numpy as np
import pandas as pd
import pypsa
from pypsa.descriptors import get_activity_mask
import xarray as xr
from _helpers import configure_logging, update_config_with_sector_opts
from pypsa.descriptors import get_activity_mask
from vresutils.benchmark import memory_logger
logger = logging.getLogger(__name__)
@ -51,31 +50,37 @@ def add_land_use_constraint(n, planning_horizons, config):
def add_land_use_constraint_perfect(n):
"""Add global constraints for tech capacity limit.
"""
Add global constraints for tech capacity limit.
"""
logger.info("Add land-use constraint for perfect foresight")
def compress_series(s):
def compress_series(s):
def process_group(group):
if group.nunique() == 1:
return pd.Series(group.iloc[0], index=[None])
else:
return group
return s.groupby(level=[0,1]).apply(process_group)
return s.groupby(level=[0, 1]).apply(process_group)
def new_index_name(t):
# Convert all elements to string and filter out None values
parts = [str(x) for x in t if x is not None]
# Join with space, but use a dash for the last item if not None
return ' '.join(parts[:2]) + (f'-{parts[-1]}' if len(parts) > 2 else '')
return " ".join(parts[:2]) + (f"-{parts[-1]}" if len(parts) > 2 else "")
def check_p_min_p_max(p_nom_max):
p_nom_min = n.generators[ext_i].groupby(grouper).sum().p_nom_min
p_nom_min = p_nom_min.reindex(p_nom_max.index)
check = (p_nom_min.groupby(level=[0,1]).sum()>p_nom_max.groupby(level=[0,1]).min())
check = (
p_nom_min.groupby(level=[0, 1]).sum()
> p_nom_max.groupby(level=[0, 1]).min()
)
if check.sum():
logger.warning(f"summed p_min_pu values at node larger than technical potential {check[check].index}")
logger.warning(
f"summed p_min_pu values at node larger than technical potential {check[check].index}"
)
grouper = [n.generators.carrier, n.generators.bus, n.generators.build_year]
ext_i = n.generators.p_nom_extendable
@ -85,8 +90,7 @@ def add_land_use_constraint_perfect(n):
p_nom_max = p_nom_max[~p_nom_max.isin([np.inf, np.nan])]
# carrier
carriers = p_nom_max.index.get_level_values(0).unique()
gen_i = n.generators[(n.generators.carrier.isin(carriers)) &
(ext_i)].index
gen_i = n.generators[(n.generators.carrier.isin(carriers)) & (ext_i)].index
n.generators.loc[gen_i, "p_nom_min"] = 0
# check minimum capacities
check_p_min_p_max(p_nom_max)
@ -94,12 +98,14 @@ def add_land_use_constraint_perfect(n):
# p_nom_max = compress_series(p_nom_max)
# adjust name to fit syntax of nominal constraint per bus
df = p_nom_max.reset_index()
df["name"] = df.apply(lambda row: f"nom_max_{row['carrier']}"
+ (f"_{row['build_year']}" if row['build_year'] is not None else ""),
axis=1)
df["name"] = df.apply(
lambda row: f"nom_max_{row['carrier']}"
+ (f"_{row['build_year']}" if row["build_year"] is not None else ""),
axis=1,
)
for name in df.name.unique():
df_carrier = df[df.name==name]
df_carrier = df[df.name == name]
bus = df_carrier.bus
n.buses.loc[bus, name] = df_carrier.p_nom_max.values
@ -227,7 +233,7 @@ def add_carbon_constraint(n, snapshots):
last_i = last.set_index([last.index, last.timestep]).index
final_e = n.model["Store-e"].loc[last_i, stores.index]
time_i = pd.IndexSlice[time_valid, :]
lhs = final_e.loc[time_i,:] - final_e.shift(snapshot=1).loc[time_i,:]
lhs = final_e.loc[time_i, :] - final_e.shift(snapshot=1).loc[time_i, :]
n.model.add_constraints(lhs <= rhs, name=f"GlobalConstraint-{name}")
@ -254,13 +260,14 @@ def add_carbon_budget_constraint(n, snapshots):
last_i = last.set_index([last.index, last.timestep]).index
final_e = n.model["Store-e"].loc[last_i, stores.index]
time_i = pd.IndexSlice[time_valid, :]
lhs = final_e.loc[time_i,:] * weighting
lhs = final_e.loc[time_i, :] * weighting
n.model.add_constraints(lhs <= rhs, name=f"GlobalConstraint-{name}")
def add_max_growth(n, config):
"""Add maximum growth rates for different carriers
"""
Add maximum growth rates for different carriers.
"""
opts = config["sector"]["limit_max_growth"]
@ -268,19 +275,24 @@ def add_max_growth(n, config):
factor = n.investment_period_weightings.years.max() * opts["factor"]
for carrier in opts["max_growth"].keys():
max_per_period = opts["max_growth"][carrier] * factor
logger.info(f"set maximum growth rate per investment period of {carrier} to {max_per_period} GW.")
logger.info(
f"set maximum growth rate per investment period of {carrier} to {max_per_period} GW."
)
n.carriers.loc[carrier, "max_growth"] = max_per_period * 1e3
for carrier in opts["max_relative_growth"].keys():
max_r_per_period = opts["max_relative_growth"][carrier]
logger.info(f"set maximum relative growth per investment period of {carrier} to {max_r_per_period}.")
logger.info(
f"set maximum relative growth per investment period of {carrier} to {max_r_per_period}."
)
n.carriers.loc[carrier, "max_relative_growth"] = max_r_per_period
return n
def add_retrofit_gas_boiler_constraint(n, snapshots):
"""Allow retrofitting of existing gas boilers to H2 boilers.
"""
Allow retrofitting of existing gas boilers to H2 boilers.
"""
c = "Link"
logger.info("Add constraint for retrofitting gas boilers to H2 boilers.")
@ -290,23 +302,25 @@ def add_retrofit_gas_boiler_constraint(n, snapshots):
mask = n.links.carrier.str.contains("retrofitted H2 boiler")
h2_i = n.links[mask].index
n.links.loc[gas_i, "p_nom_extendable"] = True
p_nom = n.links.loc[gas_i, "p_nom"]
n.links.loc[gas_i, "p_nom"] = 0
# heat profile
cols = n.loads_t.p_set.columns[n.loads_t.p_set.columns.str.contains("heat")
cols = n.loads_t.p_set.columns[
n.loads_t.p_set.columns.str.contains("heat")
& ~n.loads_t.p_set.columns.str.contains("industry")
& ~n.loads_t.p_set.columns.str.contains("agriculture")]
profile = n.loads_t.p_set[cols].div(n.loads_t.p_set[cols].groupby(level=0).max(), level=0)
& ~n.loads_t.p_set.columns.str.contains("agriculture")
]
profile = n.loads_t.p_set[cols].div(
n.loads_t.p_set[cols].groupby(level=0).max(), level=0
)
# to deal if max value is zero
profile.fillna(0, inplace=True)
profile.rename(columns=n.loads.bus.to_dict(), inplace=True)
profile = profile.reindex(columns=n.links.loc[gas_i, "bus1"])
profile.columns = gas_i
rhs = profile.mul(p_nom)
dispatch = n.model["Link-p"]
@ -320,8 +334,6 @@ def add_retrofit_gas_boiler_constraint(n, snapshots):
n.model.add_constraints(lhs == rhs, name="gas_retrofit")
def prepare_network(
n,
solve_opts=None,
@ -756,7 +768,6 @@ def add_pipe_retrofit_constraint(n):
n.model.add_constraints(lhs == rhs, name="Link-pipe_retrofit")
def extra_functionality(n, snapshots):
"""
Collects supplementary constraints which will be passed to
@ -792,7 +803,9 @@ def solve_network(n, config, solving, opts="", **kwargs):
set_of_options = solving["solver"]["options"]
cf_solving = solving["options"]
kwargs["multi_investment_periods"] = True if config["foresight"] == "perfect" else False
kwargs["multi_investment_periods"] = (
True if config["foresight"] == "perfect" else False
)
kwargs["solver_options"] = (
solving["solver_options"][set_of_options] if set_of_options else {}
)
@ -838,7 +851,8 @@ def solve_network(n, config, solving, opts="", **kwargs):
return n
#%%
# %%
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
@ -878,8 +892,9 @@ if __name__ == "__main__":
co2_sequestration_potential=snakemake.params["co2_sequestration_potential"],
)
with memory_logger(filename=getattr(snakemake.log, 'memory', None), interval=30.) as mem:
with memory_logger(
filename=getattr(snakemake.log, "memory", None), interval=30.0
) as mem:
n = solve_network(
n,
config=snakemake.config,
@ -890,8 +905,5 @@ if __name__ == "__main__":
logger.info("Maximum memory usage: {}".format(mem.mem_usage))
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])