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07224751e5
pypsa-eur/scripts/add_electricity.py
Fabian Hofmann 07224751e5
Merge branch 'master' into validation
2023-06-29 08:10:52 +02:00

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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
from itertools import product
import geopandas as gpd
import numpy as np
import pandas as pd
import powerplantmatching as pm
import pypsa
import scipy.sparse as sparse
import xarray as xr
from _helpers import configure_logging, update_p_nom_max
from powerplantmatching.export import map_country_bus
from shapely.prepared import prep
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_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, max_hours, 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)
)
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 shapes_to_shapes(orig, dest):
"""
Adopted from vresutils.transfer.Shapes2Shapes()
"""
orig_prepped = list(map(prep, orig))
transfer = sparse.lil_matrix((len(dest), len(orig)), dtype=float)
for i, j in product(range(len(dest)), range(len(orig))):
if orig_prepped[j].intersects(dest[i]):
area = orig[j].intersection(dest[i]).area
transfer[i, j] = area / dest[i].area
return transfer
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 = shapes_to_shapes(group, nuts3_cntry.geometry).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,
fuel_price,
ppl,
conventional_carriers,
extendable_carriers,
conventional_params,
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)
fuel_price = (fuel_price.assign(OCGT=fuel_price['gas'],
CCGT=fuel_price['gas'])
.drop("gas", axis=1))
fuel_price = fuel_price.reindex(ppl.carrier, axis=1)
fuel_price.fillna(costs.fuel, inplace=True)
fuel_price.columns = ppl.index
marginal_cost = (
(fuel_price.div(ppl.efficiency)).add(ppl.carrier.map(costs.VOM))
)
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=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_params:
# Generators with technology affected
idx = n.generators.query("carrier == @carrier").index
for attr in list(set(conventional_params[carrier]) & set(n.generators)):
values = conventional_params[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(
snakemake.input[f"conventional_{carrier}_{attr}"], 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, **params):
_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 params value and
# assume no natural inflow due to lack of data
max_hours = params.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 = params.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, year, tech_map, expansion_limit, countries):
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"]
)
def add_nice_carrier_names(n, config):
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
#%%
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("add_electricity")
configure_logging(snakemake)
params = snakemake.params
n = pypsa.Network(snakemake.input.base_network)
Nyears = n.snapshot_weightings.objective.sum() / 8760.0
costs = load_costs(
snakemake.input.tech_costs,
params.costs,
params.electricity["max_hours"],
Nyears,
)
ppl = load_powerplants(snakemake.input.powerplants)
attach_load(
n,
snakemake.input.regions,
snakemake.input.load,
snakemake.input.nuts3_shapes,
params.countries,
params.scaling_factor,
)
update_transmission_costs(n, costs, params.length_factor)
renewable_carriers = set(params.electricity["renewable_carriers"])
extendable_carriers = params.electricity["extendable_carriers"]
conventional_carriers = params.electricity["conventional_carriers"]
conventional_inputs = {
k: v for k, v in snakemake.input.items() if k.startswith("conventional_")
}
m_fuel_price = pd.read_csv(snakemake.input.fuel_price,
index_col=[0], header=[0])
m_fuel_price.index = pd.date_range(start='2019-01-01', end='2019-12-01',
freq='MS')
fuel_price = m_fuel_price.reindex(n.snapshots).fillna(method="ffill")
attach_conventional_generators(
n,
costs,
fuel_price,
ppl,
conventional_carriers,
extendable_carriers,
params.conventional,
conventional_inputs,
)
attach_wind_and_solar(
n,
costs,
snakemake.input,
renewable_carriers,
extendable_carriers,
params.length_factor,
)
if "hydro" in renewable_carriers:
para = params.renewable["hydro"]
attach_hydro(
n,
costs,
ppl,
snakemake.input.profile_hydro,
snakemake.input.hydro_capacities,
para.pop("carriers", []),
**para,
)
estimate_renewable_caps = params.electricity["estimate_renewable_capacities"]
if estimate_renewable_caps["enable"]:
tech_map = estimate_renewable_caps["technology_mapping"]
expansion_limit = estimate_renewable_caps["expansion_limit"]
year = estimate_renewable_caps["year"]
if estimate_renewable_caps["from_opsd"]:
attach_OPSD_renewables(n, tech_map)
estimate_renewable_capacities(
n, year, tech_map, expansion_limit, params.countries
)
update_p_nom_max(n)
add_nice_carrier_names(n, snakemake.config)
n.meta = snakemake.config
n.export_to_netcdf(snakemake.output[0])
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