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pypsa-eur/scripts/add_electricity.py
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# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
Adds electrical generators and existing hydro storage units to a base network.
Relevant Settings
-----------------
.. code:: yaml
costs:
year:
version:
dicountrate:
emission_prices:
electricity:
max_hours:
marginal_cost:
capital_cost:
conventional_carriers:
co2limit:
extendable_carriers:
estimate_renewable_capacities:
load:
scaling_factor:
renewable:
hydro:
carriers:
hydro_max_hours:
hydro_capital_cost:
lines:
length_factor:
.. seealso::
Documentation of the configuration file ``config/config.yaml`` at :ref:`costs_cf`,
:ref:`electricity_cf`, :ref:`load_cf`, :ref:`renewable_cf`, :ref:`lines_cf`
Inputs
------
- ``resources/costs.csv``: The database of cost assumptions for all included technologies for specific years from various sources; e.g. discount rate, lifetime, investment (CAPEX), fixed operation and maintenance (FOM), variable operation and maintenance (VOM), fuel costs, efficiency, carbon-dioxide intensity.
- ``data/bundle/hydro_capacities.csv``: Hydropower plant store/discharge power capacities, energy storage capacity, and average hourly inflow by country.
.. image:: img/hydrocapacities.png
:scale: 34 %
- ``data/geth2015_hydro_capacities.csv``: alternative to capacities above; not currently used!
- ``resources/load.csv`` Hourly per-country load profiles.
- ``resources/regions_onshore.geojson``: confer :ref:`busregions`
- ``resources/nuts3_shapes.geojson``: confer :ref:`shapes`
- ``resources/powerplants.csv``: confer :ref:`powerplants`
- ``resources/profile_{}.nc``: all technologies in ``config["renewables"].keys()``, confer :ref:`renewableprofiles`.
- ``networks/base.nc``: confer :ref:`base`
Outputs
-------
- ``networks/elec.nc``:
.. image:: img/elec.png
:scale: 33 %
Description
-----------
The rule :mod:`add_electricity` ties all the different data inputs from the preceding rules together into a detailed PyPSA network that is stored in ``networks/elec.nc``. It includes:
- today's transmission topology and transfer capacities (optionally including lines which are under construction according to the config settings ``lines: under_construction`` and ``links: under_construction``),
- today's thermal and hydro power generation capacities (for the technologies listed in the config setting ``electricity: conventional_carriers``), and
- today's load time-series (upsampled in a top-down approach according to population and gross domestic product)
It further adds extendable ``generators`` with **zero** capacity for
- photovoltaic, onshore and AC- as well as DC-connected offshore wind installations with today's locational, hourly wind and solar capacity factors (but **no** current capacities),
- additional open- and combined-cycle gas turbines (if ``OCGT`` and/or ``CCGT`` is listed in the config setting ``electricity: extendable_carriers``)
"""
import logging
import geopandas as gpd
import numpy as np
import pandas as pd
import powerplantmatching as pm
import pypsa
import xarray as xr
from _helpers import configure_logging, update_p_nom_max
from powerplantmatching.export import map_country_bus
from vresutils import transfer as vtransfer
idx = pd.IndexSlice
logger = logging.getLogger(__name__)
def normed(s):
return s / s.sum()
def calculate_annuity(n, r):
"""
Calculate the annuity factor for an asset with lifetime n years and.
discount rate of r, e.g. annuity(20, 0.05) * 20 = 1.6
"""
if isinstance(r, pd.Series):
return pd.Series(1 / n, index=r.index).where(
r == 0, r / (1.0 - 1.0 / (1.0 + r) ** n)
)
elif r > 0:
return r / (1.0 - 1.0 / (1.0 + r) ** n)
else:
return 1 / n
def add_missing_carriers_with_nice_names(n, config):
components = [n.buses, n.generators, n.lines, n.links, n.storage_units, n.stores]
for c in components:
missing_carrier = np.setdiff1d(c.carrier.unique(), n.carriers.index)
if len(missing_carrier):
n.madd("Carrier", missing_carrier)
carrier_i = n.carriers.index
nice_names = (
pd.Series(config["plotting"]["nice_names"])
.reindex(carrier_i)
.fillna(carrier_i.to_series().str.title())
)
n.carriers["nice_name"] = nice_names
colors = pd.Series(config["plotting"]["tech_colors"]).reindex(carrier_i)
if colors.isna().any():
missing_i = list(colors.index[colors.isna()])
logger.warning(f"tech_colors for carriers {missing_i} not defined in config.")
n.carriers["color"] = colors
def _add_missing_carriers_from_costs(n, costs, carriers):
missing_carriers = pd.Index(carriers).difference(n.carriers.index)
if missing_carriers.empty:
return
emissions_cols = (
costs.columns.to_series().loc[lambda s: s.str.endswith("_emissions")].values
)
suptechs = missing_carriers.str.split("-").str[0]
emissions = costs.loc[suptechs, emissions_cols].fillna(0.0)
emissions.index = missing_carriers
n.import_components_from_dataframe(emissions, "Carrier")
def load_costs(tech_costs, config, elec_config, Nyears=1.0):
# set all asset costs and other parameters
costs = pd.read_csv(tech_costs, index_col=[0, 1]).sort_index()
# correct units to MW
costs.loc[costs.unit.str.contains("/kW"), "value"] *= 1e3
costs.unit = costs.unit.str.replace("/kW", "/MW")
fill_values = config["fill_values"]
costs = costs.value.unstack().fillna(fill_values)
costs["capital_cost"] = (
(
calculate_annuity(costs["lifetime"], costs["discount rate"])
+ costs["FOM"] / 100.0
)
* costs["investment"]
* Nyears
)
costs.at["OCGT", "fuel"] = costs.at["gas", "fuel"]
costs.at["CCGT", "fuel"] = costs.at["gas", "fuel"]
costs["marginal_cost"] = costs["VOM"] + costs["fuel"] / costs["efficiency"]
costs = costs.rename(columns={"CO2 intensity": "co2_emissions"})
costs.at["OCGT", "co2_emissions"] = costs.at["gas", "co2_emissions"]
costs.at["CCGT", "co2_emissions"] = costs.at["gas", "co2_emissions"]
costs.at["solar", "capital_cost"] = (
config["rooftop_share"] * costs.at["solar-rooftop", "capital_cost"]
+ (1 - config["rooftop_share"]) * costs.at["solar-utility", "capital_cost"]
)
def costs_for_storage(store, link1, link2=None, max_hours=1.0):
capital_cost = link1["capital_cost"] + max_hours * store["capital_cost"]
if link2 is not None:
capital_cost += link2["capital_cost"]
return pd.Series(
dict(capital_cost=capital_cost, marginal_cost=0.0, co2_emissions=0.0)
)
max_hours = elec_config["max_hours"]
costs.loc["battery"] = costs_for_storage(
costs.loc["battery storage"],
costs.loc["battery inverter"],
max_hours=max_hours["battery"],
)
costs.loc["H2"] = costs_for_storage(
costs.loc["hydrogen storage underground"],
costs.loc["fuel cell"],
costs.loc["electrolysis"],
max_hours=max_hours["H2"],
)
for attr in ("marginal_cost", "capital_cost"):
overwrites = config.get(attr)
if overwrites is not None:
overwrites = pd.Series(overwrites)
costs.loc[overwrites.index, attr] = overwrites
return costs
def load_powerplants(ppl_fn):
carrier_dict = {
"ocgt": "OCGT",
"ccgt": "CCGT",
"bioenergy": "biomass",
"ccgt, thermal": "CCGT",
"hard coal": "coal",
}
return (
pd.read_csv(ppl_fn, index_col=0, dtype={"bus": "str"})
.powerplant.to_pypsa_names()
.rename(columns=str.lower)
.replace({"carrier": carrier_dict})
)
def attach_load(n, regions, load, nuts3_shapes, countries, scaling=1.0):
substation_lv_i = n.buses.index[n.buses["substation_lv"]]
regions = gpd.read_file(regions).set_index("name").reindex(substation_lv_i)
opsd_load = pd.read_csv(load, index_col=0, parse_dates=True).filter(items=countries)
logger.info(f"Load data scaled with scalling factor {scaling}.")
opsd_load *= scaling
nuts3 = gpd.read_file(nuts3_shapes).set_index("index")
def upsample(cntry, group):
l = opsd_load[cntry]
if len(group) == 1:
return pd.DataFrame({group.index[0]: l})
else:
nuts3_cntry = nuts3.loc[nuts3.country == cntry]
transfer = vtransfer.Shapes2Shapes(
group, nuts3_cntry.geometry, normed=False
).T.tocsr()
gdp_n = pd.Series(
transfer.dot(nuts3_cntry["gdp"].fillna(1.0).values), index=group.index
)
pop_n = pd.Series(
transfer.dot(nuts3_cntry["pop"].fillna(1.0).values), index=group.index
)
# relative factors 0.6 and 0.4 have been determined from a linear
# regression on the country to continent load data
factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
return pd.DataFrame(
factors.values * l.values[:, np.newaxis],
index=l.index,
columns=factors.index,
)
load = pd.concat(
[
upsample(cntry, group)
for cntry, group in regions.geometry.groupby(regions.country)
],
axis=1,
)
n.madd("Load", substation_lv_i, bus=substation_lv_i, p_set=load)
def update_transmission_costs(n, costs, length_factor=1.0):
# TODO: line length factor of lines is applied to lines and links.
# Separate the function to distinguish.
n.lines["capital_cost"] = (
n.lines["length"] * length_factor * costs.at["HVAC overhead", "capital_cost"]
)
if n.links.empty:
return
dc_b = n.links.carrier == "DC"
# If there are no dc links, then the 'underwater_fraction' column
# may be missing. Therefore we have to return here.
if n.links.loc[dc_b].empty:
return
costs = (
n.links.loc[dc_b, "length"]
* length_factor
* (
(1.0 - n.links.loc[dc_b, "underwater_fraction"])
* costs.at["HVDC overhead", "capital_cost"]
+ n.links.loc[dc_b, "underwater_fraction"]
* costs.at["HVDC submarine", "capital_cost"]
)
+ costs.at["HVDC inverter pair", "capital_cost"]
)
n.links.loc[dc_b, "capital_cost"] = costs
def attach_wind_and_solar(
n, costs, input_profiles, technologies, extendable_carriers, line_length_factor=1
):
# TODO: rename tech -> carrier, technologies -> carriers
_add_missing_carriers_from_costs(n, costs, technologies)
for tech in technologies:
if tech == "hydro":
continue
with xr.open_dataset(getattr(input_profiles, "profile_" + tech)) as ds:
if ds.indexes["bus"].empty:
continue
suptech = tech.split("-", 2)[0]
if suptech == "offwind":
underwater_fraction = ds["underwater_fraction"].to_pandas()
connection_cost = (
line_length_factor
* ds["average_distance"].to_pandas()
* (
underwater_fraction
* costs.at[tech + "-connection-submarine", "capital_cost"]
+ (1.0 - underwater_fraction)
* costs.at[tech + "-connection-underground", "capital_cost"]
)
)
capital_cost = (
costs.at["offwind", "capital_cost"]
+ costs.at[tech + "-station", "capital_cost"]
+ connection_cost
)
logger.info(
"Added connection cost of {:0.0f}-{:0.0f} Eur/MW/a to {}".format(
connection_cost.min(), connection_cost.max(), tech
)
)
else:
capital_cost = costs.at[tech, "capital_cost"]
n.madd(
"Generator",
ds.indexes["bus"],
" " + tech,
bus=ds.indexes["bus"],
carrier=tech,
p_nom_extendable=tech in extendable_carriers["Generator"],
p_nom_max=ds["p_nom_max"].to_pandas(),
weight=ds["weight"].to_pandas(),
marginal_cost=costs.at[suptech, "marginal_cost"],
capital_cost=capital_cost,
efficiency=costs.at[suptech, "efficiency"],
p_max_pu=ds["profile"].transpose("time", "bus").to_pandas(),
)
def attach_conventional_generators(
n,
costs,
ppl,
conventional_carriers,
extendable_carriers,
conventional_config,
conventional_inputs,
):
carriers = set(conventional_carriers) | set(extendable_carriers["Generator"])
_add_missing_carriers_from_costs(n, costs, carriers)
ppl = (
ppl.query("carrier in @carriers")
.join(costs, on="carrier", rsuffix="_r")
.rename(index=lambda s: "C" + str(s))
)
ppl["efficiency"] = ppl.efficiency.fillna(ppl.efficiency_r)
ppl["marginal_cost"] = (
ppl.carrier.map(costs.VOM) + ppl.carrier.map(costs.fuel) / ppl.efficiency
)
logger.info(
"Adding {} generators with capacities [GW] \n{}".format(
len(ppl), ppl.groupby("carrier").p_nom.sum().div(1e3).round(2)
)
)
n.madd(
"Generator",
ppl.index,
carrier=ppl.carrier,
bus=ppl.bus,
p_nom_min=ppl.p_nom.where(ppl.carrier.isin(conventional_carriers), 0),
p_nom=ppl.p_nom.where(ppl.carrier.isin(conventional_carriers), 0),
p_nom_extendable=ppl.carrier.isin(extendable_carriers["Generator"]),
efficiency=ppl.efficiency,
marginal_cost=ppl.marginal_cost,
capital_cost=ppl.capital_cost,
build_year=ppl.datein.fillna(0).astype(int),
lifetime=(ppl.dateout - ppl.datein).fillna(np.inf),
)
for carrier in conventional_config:
# Generators with technology affected
idx = n.generators.query("carrier == @carrier").index
for attr in list(set(conventional_config[carrier]) & set(n.generators)):
values = conventional_config[carrier][attr]
if f"conventional_{carrier}_{attr}" in conventional_inputs:
# Values affecting generators of technology k country-specific
# First map generator buses to countries; then map countries to p_max_pu
values = pd.read_csv(values, index_col=0).iloc[:, 0]
bus_values = n.buses.country.map(values)
n.generators[attr].update(
n.generators.loc[idx].bus.map(bus_values).dropna()
)
else:
# Single value affecting all generators of technology k indiscriminantely of country
n.generators.loc[idx, attr] = values
def attach_hydro(n, costs, ppl, profile_hydro, hydro_capacities, carriers, **config):
_add_missing_carriers_from_costs(n, costs, carriers)
ppl = (
ppl.query('carrier == "hydro"')
.reset_index(drop=True)
.rename(index=lambda s: str(s) + " hydro")
)
ror = ppl.query('technology == "Run-Of-River"')
phs = ppl.query('technology == "Pumped Storage"')
hydro = ppl.query('technology == "Reservoir"')
country = ppl["bus"].map(n.buses.country).rename("country")
inflow_idx = ror.index.union(hydro.index)
if not inflow_idx.empty:
dist_key = ppl.loc[inflow_idx, "p_nom"].groupby(country).transform(normed)
with xr.open_dataarray(profile_hydro) as inflow:
inflow_countries = pd.Index(country[inflow_idx])
missing_c = inflow_countries.unique().difference(
inflow.indexes["countries"]
)
assert missing_c.empty, (
f"'{profile_hydro}' is missing "
f"inflow time-series for at least one country: {', '.join(missing_c)}"
)
inflow_t = (
inflow.sel(countries=inflow_countries)
.rename({"countries": "name"})
.assign_coords(name=inflow_idx)
.transpose("time", "name")
.to_pandas()
.multiply(dist_key, axis=1)
)
if "ror" in carriers and not ror.empty:
n.madd(
"Generator",
ror.index,
carrier="ror",
bus=ror["bus"],
p_nom=ror["p_nom"],
efficiency=costs.at["ror", "efficiency"],
capital_cost=costs.at["ror", "capital_cost"],
weight=ror["p_nom"],
p_max_pu=(
inflow_t[ror.index]
.divide(ror["p_nom"], axis=1)
.where(lambda df: df <= 1.0, other=1.0)
),
)
if "PHS" in carriers and not phs.empty:
# fill missing max hours to config value and
# assume no natural inflow due to lack of data
max_hours = config.get("PHS_max_hours", 6)
phs = phs.replace({"max_hours": {0: max_hours}})
n.madd(
"StorageUnit",
phs.index,
carrier="PHS",
bus=phs["bus"],
p_nom=phs["p_nom"],
capital_cost=costs.at["PHS", "capital_cost"],
max_hours=phs["max_hours"],
efficiency_store=np.sqrt(costs.at["PHS", "efficiency"]),
efficiency_dispatch=np.sqrt(costs.at["PHS", "efficiency"]),
cyclic_state_of_charge=True,
)
if "hydro" in carriers and not hydro.empty:
hydro_max_hours = config.get("hydro_max_hours")
assert hydro_max_hours is not None, "No path for hydro capacities given."
hydro_stats = pd.read_csv(
hydro_capacities, comment="#", na_values="-", index_col=0
)
e_target = hydro_stats["E_store[TWh]"].clip(lower=0.2) * 1e6
e_installed = hydro.eval("p_nom * max_hours").groupby(hydro.country).sum()
e_missing = e_target - e_installed
missing_mh_i = hydro.query("max_hours.isnull()").index
if hydro_max_hours == "energy_capacity_totals_by_country":
# watch out some p_nom values like IE's are totally underrepresented
max_hours_country = (
e_missing / hydro.loc[missing_mh_i].groupby("country").p_nom.sum()
)
elif hydro_max_hours == "estimate_by_large_installations":
max_hours_country = (
hydro_stats["E_store[TWh]"] * 1e3 / hydro_stats["p_nom_discharge[GW]"]
)
max_hours_country.clip(0, inplace=True)
missing_countries = pd.Index(hydro["country"].unique()).difference(
max_hours_country.dropna().index
)
if not missing_countries.empty:
logger.warning(
"Assuming max_hours=6 for hydro reservoirs in the countries: {}".format(
", ".join(missing_countries)
)
)
hydro_max_hours = hydro.max_hours.where(
hydro.max_hours > 0, hydro.country.map(max_hours_country)
).fillna(6)
n.madd(
"StorageUnit",
hydro.index,
carrier="hydro",
bus=hydro["bus"],
p_nom=hydro["p_nom"],
max_hours=hydro_max_hours,
capital_cost=costs.at["hydro", "capital_cost"],
marginal_cost=costs.at["hydro", "marginal_cost"],
p_max_pu=1.0, # dispatch
p_min_pu=0.0, # store
efficiency_dispatch=costs.at["hydro", "efficiency"],
efficiency_store=0.0,
cyclic_state_of_charge=True,
inflow=inflow_t.loc[:, hydro.index],
)
def attach_extendable_generators(n, costs, ppl, carriers):
logger.warning(
"The function `attach_extendable_generators` is deprecated in v0.5.0."
)
_add_missing_carriers_from_costs(n, costs, carriers)
for tech in carriers:
if tech.startswith("OCGT"):
ocgt = (
ppl.query("carrier in ['OCGT', 'CCGT']")
.groupby("bus", as_index=False)
.first()
)
n.madd(
"Generator",
ocgt.index,
suffix=" OCGT",
bus=ocgt["bus"],
carrier=tech,
p_nom_extendable=True,
p_nom=0.0,
capital_cost=costs.at["OCGT", "capital_cost"],
marginal_cost=costs.at["OCGT", "marginal_cost"],
efficiency=costs.at["OCGT", "efficiency"],
)
elif tech.startswith("CCGT"):
ccgt = (
ppl.query("carrier in ['OCGT', 'CCGT']")
.groupby("bus", as_index=False)
.first()
)
n.madd(
"Generator",
ccgt.index,
suffix=" CCGT",
bus=ccgt["bus"],
carrier=tech,
p_nom_extendable=True,
p_nom=0.0,
capital_cost=costs.at["CCGT", "capital_cost"],
marginal_cost=costs.at["CCGT", "marginal_cost"],
efficiency=costs.at["CCGT", "efficiency"],
)
elif tech.startswith("nuclear"):
nuclear = (
ppl.query("carrier == 'nuclear'").groupby("bus", as_index=False).first()
)
n.madd(
"Generator",
nuclear.index,
suffix=" nuclear",
bus=nuclear["bus"],
carrier=tech,
p_nom_extendable=True,
p_nom=0.0,
capital_cost=costs.at["nuclear", "capital_cost"],
marginal_cost=costs.at["nuclear", "marginal_cost"],
efficiency=costs.at["nuclear", "efficiency"],
)
else:
raise NotImplementedError(
"Adding extendable generators for carrier "
"'{tech}' is not implemented, yet. "
"Only OCGT, CCGT and nuclear are allowed at the moment."
)
def attach_OPSD_renewables(n, tech_map):
tech_string = ", ".join(sum(tech_map.values(), []))
logger.info(f"Using OPSD renewable capacities for carriers {tech_string}.")
df = pm.data.OPSD_VRE().powerplant.convert_country_to_alpha2()
technology_b = ~df.Technology.isin(["Onshore", "Offshore"])
df["Fueltype"] = df.Fueltype.where(technology_b, df.Technology).replace(
{"Solar": "PV"}
)
df = df.query("Fueltype in @tech_map").powerplant.convert_country_to_alpha2()
for fueltype, carriers in tech_map.items():
gens = n.generators[lambda df: df.carrier.isin(carriers)]
buses = n.buses.loc[gens.bus.unique()]
gens_per_bus = gens.groupby("bus").p_nom.count()
caps = map_country_bus(df.query("Fueltype == @fueltype"), buses)
caps = caps.groupby(["bus"]).Capacity.sum()
caps = caps / gens_per_bus.reindex(caps.index, fill_value=1)
n.generators.p_nom.update(gens.bus.map(caps).dropna())
n.generators.p_nom_min.update(gens.bus.map(caps).dropna())
def estimate_renewable_capacities(n, config):
year = config["electricity"]["estimate_renewable_capacities"]["year"]
tech_map = config["electricity"]["estimate_renewable_capacities"][
"technology_mapping"
]
countries = config["countries"]
expansion_limit = config["electricity"]["estimate_renewable_capacities"][
"expansion_limit"
]
if not len(countries) or not len(tech_map):
return
capacities = pm.data.IRENASTAT().powerplant.convert_country_to_alpha2()
capacities = capacities.query(
"Year == @year and Technology in @tech_map and Country in @countries"
)
capacities = capacities.groupby(["Technology", "Country"]).Capacity.sum()
logger.info(
f"Heuristics applied to distribute renewable capacities [GW]: "
f"\n{capacities.groupby('Technology').sum().div(1e3).round(2)}"
)
for ppm_technology, techs in tech_map.items():
tech_i = n.generators.query("carrier in @techs").index
stats = capacities.loc[ppm_technology].reindex(countries, fill_value=0.0)
country = n.generators.bus[tech_i].map(n.buses.country)
existent = n.generators.p_nom[tech_i].groupby(country).sum()
missing = stats - existent
dist = n.generators_t.p_max_pu.mean() * n.generators.p_nom_max
n.generators.loc[tech_i, "p_nom"] += (
dist[tech_i]
.groupby(country)
.transform(lambda s: normed(s) * missing[s.name])
.where(lambda s: s > 0.1, 0.0) # only capacities above 100kW
)
n.generators.loc[tech_i, "p_nom_min"] = n.generators.loc[tech_i, "p_nom"]
if expansion_limit:
assert np.isscalar(expansion_limit)
logger.info(
f"Reducing capacity expansion limit to {expansion_limit*100:.2f}% of installed capacity."
)
n.generators.loc[tech_i, "p_nom_max"] = (
expansion_limit * n.generators.loc[tech_i, "p_nom_min"]
)
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("add_electricity")
configure_logging(snakemake)
n = pypsa.Network(snakemake.input.base_network)
Nyears = n.snapshot_weightings.objective.sum() / 8760.0
costs = load_costs(
snakemake.input.tech_costs,
snakemake.config["costs"],
snakemake.config["electricity"],
Nyears,
)
ppl = load_powerplants(snakemake.input.powerplants)
if "renewable_carriers" in snakemake.config["electricity"]:
renewable_carriers = set(snakemake.config["electricity"]["renewable_carriers"])
else:
logger.warning(
"Missing key `renewable_carriers` under config entry `electricity`. "
"In future versions, this will raise an error. "
"Falling back to carriers listed under `renewable`."
)
renewable_carriers = snakemake.config["renewable"]
extendable_carriers = snakemake.config["electricity"]["extendable_carriers"]
if not (set(renewable_carriers) & set(extendable_carriers["Generator"])):
logger.warning(
"No renewables found in config entry `extendable_carriers`. "
"In future versions, these have to be explicitly listed. "
"Falling back to all renewables."
)
conventional_carriers = snakemake.config["electricity"]["conventional_carriers"]
attach_load(
n,
snakemake.input.regions,
snakemake.input.load,
snakemake.input.nuts3_shapes,
snakemake.config["countries"],
snakemake.config["load"]["scaling_factor"],
)
update_transmission_costs(n, costs, snakemake.config["lines"]["length_factor"])
conventional_inputs = {
k: v for k, v in snakemake.input.items() if k.startswith("conventional_")
}
attach_conventional_generators(
n,
costs,
ppl,
conventional_carriers,
extendable_carriers,
snakemake.config.get("conventional", {}),
conventional_inputs,
)
attach_wind_and_solar(
n,
costs,
snakemake.input,
renewable_carriers,
extendable_carriers,
snakemake.config["lines"]["length_factor"],
)
if "hydro" in renewable_carriers:
conf = snakemake.config["renewable"]["hydro"]
attach_hydro(
n,
costs,
ppl,
snakemake.input.profile_hydro,
snakemake.input.hydro_capacities,
conf.pop("carriers", []),
**conf,
)
if "estimate_renewable_capacities" not in snakemake.config["electricity"]:
logger.warning(
"Missing key `estimate_renewable_capacities` under config entry `electricity`. "
"In future versions, this will raise an error. "
"Falling back to whether ``estimate_renewable_capacities_from_capacity_stats`` is in the config."
)
if (
"estimate_renewable_capacities_from_capacity_stats"
in snakemake.config["electricity"]
):
estimate_renewable_caps = {
"enable": True,
**snakemake.config["electricity"][
"estimate_renewable_capacities_from_capacity_stats"
],
}
else:
estimate_renewable_caps = {"enable": False}
else:
estimate_renewable_caps = snakemake.config["electricity"][
"estimate_renewable_capacities"
]
if "enable" not in estimate_renewable_caps:
logger.warning(
"Missing key `enable` under config entry `estimate_renewable_capacities`. "
"In future versions, this will raise an error. Falling back to False."
)
estimate_renewable_caps = {"enable": False}
if "from_opsd" not in estimate_renewable_caps:
logger.warning(
"Missing key `from_opsd` under config entry `estimate_renewable_capacities`. "
"In future versions, this will raise an error. "
"Falling back to whether `renewable_capacities_from_opsd` is non-empty."
)
from_opsd = bool(
snakemake.config["electricity"].get("renewable_capacities_from_opsd", False)
)
estimate_renewable_caps["from_opsd"] = from_opsd
if estimate_renewable_caps["enable"]:
if estimate_renewable_caps["from_opsd"]:
tech_map = snakemake.config["electricity"]["estimate_renewable_capacities"][
"technology_mapping"
]
attach_OPSD_renewables(n, tech_map)
estimate_renewable_capacities(n, snakemake.config)
update_p_nom_max(n)
add_missing_carriers_with_nice_names(n, snakemake.config)
n.meta = snakemake.config
n.export_to_netcdf(snakemake.output[0])
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