replace the word "config" to "params" in functions
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virio-andreyana
GitHub
parent
8208ac033b
commit
8af1fe5649
@ -277,6 +277,7 @@ rule add_electricity:
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countries=config["countries"],
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renewable=config["renewable"],
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electricity=config["electricity"],
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conventional=config.get("conventional", {})
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costs=config["costs"],
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input:
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**{
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@ -135,7 +135,7 @@ def _add_missing_carriers_from_costs(n, costs, carriers):
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n.import_components_from_dataframe(emissions, "Carrier")
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def load_costs(tech_costs, config, elec_config, Nyears=1.0):
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def load_costs(tech_costs, params, elec_params, Nyears=1.0):
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# set all asset costs and other parameters
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costs = pd.read_csv(tech_costs, index_col=[0, 1]).sort_index()
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@ -143,7 +143,7 @@ def load_costs(tech_costs, config, elec_config, Nyears=1.0):
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costs.loc[costs.unit.str.contains("/kW"), "value"] *= 1e3
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costs.unit = costs.unit.str.replace("/kW", "/MW")
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fill_values = config["fill_values"]
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fill_values = params["fill_values"]
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costs = costs.value.unstack().fillna(fill_values)
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costs["capital_cost"] = (
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@ -166,8 +166,8 @@ def load_costs(tech_costs, config, elec_config, Nyears=1.0):
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costs.at["CCGT", "co2_emissions"] = costs.at["gas", "co2_emissions"]
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costs.at["solar", "capital_cost"] = (
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config["rooftop_share"] * costs.at["solar-rooftop", "capital_cost"]
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+ (1 - config["rooftop_share"]) * costs.at["solar-utility", "capital_cost"]
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params["rooftop_share"] * costs.at["solar-rooftop", "capital_cost"]
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+ (1 - params["rooftop_share"]) * costs.at["solar-utility", "capital_cost"]
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)
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def costs_for_storage(store, link1, link2=None, max_hours=1.0):
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@ -178,7 +178,7 @@ def load_costs(tech_costs, config, elec_config, Nyears=1.0):
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dict(capital_cost=capital_cost, marginal_cost=0.0, co2_emissions=0.0)
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)
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max_hours = elec_config["max_hours"]
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max_hours = elec_params["max_hours"]
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costs.loc["battery"] = costs_for_storage(
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costs.loc["battery storage"],
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costs.loc["battery inverter"],
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@ -192,7 +192,7 @@ def load_costs(tech_costs, config, elec_config, Nyears=1.0):
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)
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for attr in ("marginal_cost", "capital_cost"):
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overwrites = config.get(attr)
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overwrites = params.get(attr)
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if overwrites is not None:
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overwrites = pd.Series(overwrites)
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costs.loc[overwrites.index, attr] = overwrites
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@ -356,7 +356,7 @@ def attach_conventional_generators(
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ppl,
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conventional_carriers,
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extendable_carriers,
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conventional_config,
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conventional_params,
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conventional_inputs,
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):
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carriers = set(conventional_carriers) | set(extendable_carriers["Generator"])
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@ -393,12 +393,12 @@ def attach_conventional_generators(
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lifetime=(ppl.dateout - ppl.datein).fillna(np.inf),
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)
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for carrier in conventional_config:
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for carrier in conventional_params:
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# Generators with technology affected
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idx = n.generators.query("carrier == @carrier").index
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for attr in list(set(conventional_config[carrier]) & set(n.generators)):
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values = conventional_config[carrier][attr]
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for attr in list(set(conventional_params[carrier]) & set(n.generators)):
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values = conventional_params[carrier][attr]
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if f"conventional_{carrier}_{attr}" in conventional_inputs:
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# Values affecting generators of technology k country-specific
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@ -413,7 +413,7 @@ def attach_conventional_generators(
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n.generators.loc[idx, attr] = values
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def attach_hydro(n, costs, ppl, profile_hydro, hydro_capacities, carriers, **config):
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def attach_hydro(n, costs, ppl, profile_hydro, hydro_capacities, carriers, **params):
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_add_missing_carriers_from_costs(n, costs, carriers)
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ppl = (
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@ -468,9 +468,9 @@ def attach_hydro(n, costs, ppl, profile_hydro, hydro_capacities, carriers, **con
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)
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if "PHS" in carriers and not phs.empty:
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# fill missing max hours to config value and
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# fill missing max hours to params value and
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# assume no natural inflow due to lack of data
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max_hours = config.get("PHS_max_hours", 6)
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max_hours = params.get("PHS_max_hours", 6)
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phs = phs.replace({"max_hours": {0: max_hours}})
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n.madd(
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"StorageUnit",
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@ -486,7 +486,7 @@ def attach_hydro(n, costs, ppl, profile_hydro, hydro_capacities, carriers, **con
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)
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if "hydro" in carriers and not hydro.empty:
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hydro_max_hours = config.get("hydro_max_hours")
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hydro_max_hours = params.get("hydro_max_hours")
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assert hydro_max_hours is not None, "No path for hydro capacities given."
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@ -636,13 +636,12 @@ def attach_OPSD_renewables(n, tech_map):
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n.generators.p_nom_min.update(gens.bus.map(caps).dropna())
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def estimate_renewable_capacities(n, config):
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year = config["electricity"]["estimate_renewable_capacities"]["year"]
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tech_map = config["electricity"]["estimate_renewable_capacities"][
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def estimate_renewable_capacities(n, electricity_params, countries):
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year = electricity_params["estimate_renewable_capacities"]["year"]
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tech_map = electricity_params["estimate_renewable_capacities"][
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"technology_mapping"
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]
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countries = config["countries"]
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expansion_limit = config["electricity"]["estimate_renewable_capacities"][
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expansion_limit = electricity_params["estimate_renewable_capacities"][
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"expansion_limit"
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]
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@ -759,7 +758,7 @@ if __name__ == "__main__":
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ppl,
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conventional_carriers,
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extendable_carriers,
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snakemake.config.get("conventional", {}),
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snakemake.params.get("conventional", {}),
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conventional_inputs,
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)
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@ -773,15 +772,15 @@ if __name__ == "__main__":
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)
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if "hydro" in renewable_carriers:
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conf = snakemake.params["renewable"]["hydro"]
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para = snakemake.params["renewable"]["hydro"]
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attach_hydro(
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n,
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costs,
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ppl,
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snakemake.input.profile_hydro,
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snakemake.input.hydro_capacities,
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conf.pop("carriers", []),
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**conf,
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para.pop("carriers", []),
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**para,
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)
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if "estimate_renewable_capacities" not in snakemake.params["electricity"]:
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@ -829,7 +828,7 @@ if __name__ == "__main__":
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"technology_mapping"
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]
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attach_OPSD_renewables(n, tech_map)
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estimate_renewable_capacities(n, snakemake.config)
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estimate_renewable_capacities(n, snakemake.params["electricity"],snakemake.params["countries"])
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update_p_nom_max(n)
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@ -210,9 +210,9 @@ if __name__ == "__main__":
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snakemake = mock_snakemake("build_biomass_potentials", simpl="", clusters="5")
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config = snakemake.params["biomass"]
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year = config["year"]
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scenario = config["scenario"]
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params = snakemake.params["biomass"]
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year = params["year"]
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scenario = params["scenario"]
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enspreso = enspreso_biomass_potentials(year, scenario)
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@ -228,7 +228,7 @@ if __name__ == "__main__":
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df.to_csv(snakemake.output.biomass_potentials_all)
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grouper = {v: k for k, vv in config["classes"].items() for v in vv}
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grouper = {v: k for k, vv in params["classes"].items() for v in vv}
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df = df.groupby(grouper, axis=1).sum()
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df *= 1e6 # TWh/a to MWh/a
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@ -737,7 +737,7 @@ if __name__ == "__main__":
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logging.basicConfig(level=snakemake.config["logging"]["level"])
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config = snakemake.params["energy"]
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params = snakemake.params["energy"]
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nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index("index")
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population = nuts3["pop"].groupby(nuts3.country).sum()
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@ -745,7 +745,7 @@ if __name__ == "__main__":
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countries = snakemake.params["countries"]
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idees_countries = pd.Index(countries).intersection(eu28)
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data_year = config["energy_totals_year"]
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data_year = params["energy_totals_year"]
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report_year = snakemake.params["energy"]["eurostat_report_year"]
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input_eurostat = snakemake.input.eurostat
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eurostat = build_eurostat(input_eurostat, countries, report_year, data_year)
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@ -755,7 +755,7 @@ if __name__ == "__main__":
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energy = build_energy_totals(countries, eurostat, swiss, idees)
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energy.to_csv(snakemake.output.energy_name)
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base_year_emissions = config["base_emissions_year"]
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base_year_emissions = params["base_emissions_year"]
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emissions_scope = snakemake.params["energy"]["emissions"]
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eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope)
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eurostat_co2 = build_eurostat_co2(
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@ -130,7 +130,7 @@ if __name__ == "__main__":
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snakemake = mock_snakemake("build_hydro_profile")
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configure_logging(snakemake)
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config_hydro = snakemake.params["renewable"]["hydro"]
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params_hydro = snakemake.params["renewable"]["hydro"]
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cutout = atlite.Cutout(snakemake.input.cutout)
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countries = snakemake.params["countries"]
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@ -151,7 +151,7 @@ if __name__ == "__main__":
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normalize_using_yearly=eia_stats,
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)
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if "clip_min_inflow" in config_hydro:
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inflow = inflow.where(inflow > config_hydro["clip_min_inflow"], 0)
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if "clip_min_inflow" in params_hydro:
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inflow = inflow.where(inflow > params_hydro["clip_min_inflow"], 0)
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inflow.to_netcdf(snakemake.output[0])
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@ -101,8 +101,8 @@ def add_ammonia_energy_demand(demand):
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def get_ammonia_by_fuel(x):
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fuels = {
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"gas": config["MWh_CH4_per_tNH3_SMR"],
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"electricity": config["MWh_elec_per_tNH3_SMR"],
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"gas": params["MWh_CH4_per_tNH3_SMR"],
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"electricity": params["MWh_elec_per_tNH3_SMR"],
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}
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return pd.Series({k: x * v for k, v in fuels.items()})
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@ -112,7 +112,7 @@ def add_ammonia_energy_demand(demand):
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index=demand.index, fill_value=0.0
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)
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ammonia = pd.DataFrame({"ammonia": ammonia * config["MWh_NH3_per_tNH3"]}).T
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ammonia = pd.DataFrame({"ammonia": ammonia * params["MWh_NH3_per_tNH3"]}).T
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demand["Ammonia"] = ammonia.unstack().reindex(index=demand.index, fill_value=0.0)
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@ -178,8 +178,8 @@ if __name__ == "__main__":
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snakemake = mock_snakemake("build_industrial_energy_demand_per_country_today")
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config = snakemake.params["industry"]
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year = config.get("reference_year", 2015)
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params = snakemake.params["industry"]
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year = params.get("reference_year", 2015)
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countries = pd.Index(snakemake.params["countries"])
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demand = industrial_energy_demand(countries.intersection(eu28), year)
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@ -264,9 +264,9 @@ def separate_basic_chemicals(demand, year):
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# assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals
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distribution_key = demand["Basic chemicals"] / demand["Basic chemicals"].sum()
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demand["HVC"] = config["HVC_production_today"] * 1e3 * distribution_key
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demand["Chlorine"] = config["chlorine_production_today"] * 1e3 * distribution_key
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demand["Methanol"] = config["methanol_production_today"] * 1e3 * distribution_key
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demand["HVC"] = params["HVC_production_today"] * 1e3 * distribution_key
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demand["Chlorine"] = params["chlorine_production_today"] * 1e3 * distribution_key
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demand["Methanol"] = params["methanol_production_today"] * 1e3 * distribution_key
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demand.drop(columns=["Basic chemicals"], inplace=True)
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@ -283,7 +283,7 @@ if __name__ == "__main__":
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year = snakemake.params["industry"]["reference_year"]
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config = snakemake.params["industry"]
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params = snakemake.params["industry"]
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jrc_dir = snakemake.input.jrc
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eurostat_dir = snakemake.input.eurostat
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@ -15,7 +15,7 @@ if __name__ == "__main__":
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snakemake = mock_snakemake("build_industrial_production_per_country_tomorrow")
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config = snakemake.params["industry"]
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params = snakemake.params["industry"]
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investment_year = int(snakemake.wildcards.planning_horizons)
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@ -25,8 +25,8 @@ if __name__ == "__main__":
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keys = ["Integrated steelworks", "Electric arc"]
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total_steel = production[keys].sum(axis=1)
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st_primary_fraction = get(config["St_primary_fraction"], investment_year)
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dri_fraction = get(config["DRI_fraction"], investment_year)
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st_primary_fraction = get(params["St_primary_fraction"], investment_year)
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dri_fraction = get(params["DRI_fraction"], investment_year)
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int_steel = production["Integrated steelworks"].sum()
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fraction_persistent_primary = st_primary_fraction * total_steel.sum() / int_steel
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@ -51,7 +51,7 @@ if __name__ == "__main__":
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key_pri = "Aluminium - primary production"
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key_sec = "Aluminium - secondary production"
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al_primary_fraction = get(config["Al_primary_fraction"], investment_year)
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al_primary_fraction = get(params["Al_primary_fraction"], investment_year)
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fraction_persistent_primary = (
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al_primary_fraction * total_aluminium.sum() / production[key_pri].sum()
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)
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@ -60,15 +60,15 @@ if __name__ == "__main__":
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production[key_sec] = total_aluminium - production[key_pri]
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production["HVC (mechanical recycling)"] = (
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get(config["HVC_mechanical_recycling_fraction"], investment_year)
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get(params["HVC_mechanical_recycling_fraction"], investment_year)
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* production["HVC"]
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)
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production["HVC (chemical recycling)"] = (
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get(config["HVC_chemical_recycling_fraction"], investment_year)
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get(params["HVC_chemical_recycling_fraction"], investment_year)
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* production["HVC"]
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)
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production["HVC"] *= get(config["HVC_primary_fraction"], investment_year)
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production["HVC"] *= get(params["HVC_primary_fraction"], investment_year)
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fn = snakemake.output.industrial_production_per_country_tomorrow
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production.to_csv(fn, float_format="%.2f")
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@ -185,10 +185,10 @@ def iron_and_steel():
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df[sector] = df["Electric arc"]
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# add H2 consumption for DRI at 1.7 MWh H2 /ton steel
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df.at["hydrogen", sector] = config["H2_DRI"]
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df.at["hydrogen", sector] = params["H2_DRI"]
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# add electricity consumption in DRI shaft (0.322 MWh/tSl)
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df.at["elec", sector] += config["elec_DRI"]
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df.at["elec", sector] += params["elec_DRI"]
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## Integrated steelworks
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# could be used in combination with CCS)
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@ -383,19 +383,19 @@ def chemicals_industry():
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assert s_emi.index[0] == sector
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# convert from MtHVC/a to ktHVC/a
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s_out = config["HVC_production_today"] * 1e3
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s_out = params["HVC_production_today"] * 1e3
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# tCO2/t material
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df.loc["process emission", sector] += (
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s_emi["Process emissions"]
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- config["petrochemical_process_emissions"] * 1e3
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- config["NH3_process_emissions"] * 1e3
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- params["petrochemical_process_emissions"] * 1e3
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- params["NH3_process_emissions"] * 1e3
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) / s_out
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# emissions originating from feedstock, could be non-fossil origin
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# tCO2/t material
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df.loc["process emission from feedstock", sector] += (
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config["petrochemical_process_emissions"] * 1e3
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params["petrochemical_process_emissions"] * 1e3
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) / s_out
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# convert from ktoe/a to GWh/a
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@ -405,18 +405,18 @@ def chemicals_industry():
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# subtract ammonia energy demand (in ktNH3/a)
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ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
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ammonia_total = ammonia.loc[ammonia.index.intersection(eu28), str(year)].sum()
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df.loc["methane", sector] -= ammonia_total * config["MWh_CH4_per_tNH3_SMR"]
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df.loc["elec", sector] -= ammonia_total * config["MWh_elec_per_tNH3_SMR"]
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df.loc["methane", sector] -= ammonia_total * params["MWh_CH4_per_tNH3_SMR"]
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df.loc["elec", sector] -= ammonia_total * params["MWh_elec_per_tNH3_SMR"]
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# subtract chlorine demand
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chlorine_total = config["chlorine_production_today"]
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df.loc["hydrogen", sector] -= chlorine_total * config["MWh_H2_per_tCl"]
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df.loc["elec", sector] -= chlorine_total * config["MWh_elec_per_tCl"]
|
||||
chlorine_total = params["chlorine_production_today"]
|
||||
df.loc["hydrogen", sector] -= chlorine_total * params["MWh_H2_per_tCl"]
|
||||
df.loc["elec", sector] -= chlorine_total * params["MWh_elec_per_tCl"]
|
||||
|
||||
# subtract methanol demand
|
||||
methanol_total = config["methanol_production_today"]
|
||||
df.loc["methane", sector] -= methanol_total * config["MWh_CH4_per_tMeOH"]
|
||||
df.loc["elec", sector] -= methanol_total * config["MWh_elec_per_tMeOH"]
|
||||
methanol_total = params["methanol_production_today"]
|
||||
df.loc["methane", sector] -= methanol_total * params["MWh_CH4_per_tMeOH"]
|
||||
df.loc["elec", sector] -= methanol_total * params["MWh_elec_per_tMeOH"]
|
||||
|
||||
# MWh/t material
|
||||
df.loc[sources, sector] = df.loc[sources, sector] / s_out
|
||||
@ -427,37 +427,37 @@ def chemicals_industry():
|
||||
|
||||
sector = "HVC (mechanical recycling)"
|
||||
df[sector] = 0.0
|
||||
df.loc["elec", sector] = config["MWh_elec_per_tHVC_mechanical_recycling"]
|
||||
df.loc["elec", sector] = params["MWh_elec_per_tHVC_mechanical_recycling"]
|
||||
|
||||
# HVC chemical recycling
|
||||
|
||||
sector = "HVC (chemical recycling)"
|
||||
df[sector] = 0.0
|
||||
df.loc["elec", sector] = config["MWh_elec_per_tHVC_chemical_recycling"]
|
||||
df.loc["elec", sector] = params["MWh_elec_per_tHVC_chemical_recycling"]
|
||||
|
||||
# Ammonia
|
||||
|
||||
sector = "Ammonia"
|
||||
df[sector] = 0.0
|
||||
if snakemake.params["sector"].get("ammonia", False):
|
||||
df.loc["ammonia", sector] = config["MWh_NH3_per_tNH3"]
|
||||
df.loc["ammonia", sector] = params["MWh_NH3_per_tNH3"]
|
||||
else:
|
||||
df.loc["hydrogen", sector] = config["MWh_H2_per_tNH3_electrolysis"]
|
||||
df.loc["elec", sector] = config["MWh_elec_per_tNH3_electrolysis"]
|
||||
df.loc["hydrogen", sector] = params["MWh_H2_per_tNH3_electrolysis"]
|
||||
df.loc["elec", sector] = params["MWh_elec_per_tNH3_electrolysis"]
|
||||
|
||||
# Chlorine
|
||||
|
||||
sector = "Chlorine"
|
||||
df[sector] = 0.0
|
||||
df.loc["hydrogen", sector] = config["MWh_H2_per_tCl"]
|
||||
df.loc["elec", sector] = config["MWh_elec_per_tCl"]
|
||||
df.loc["hydrogen", sector] = params["MWh_H2_per_tCl"]
|
||||
df.loc["elec", sector] = params["MWh_elec_per_tCl"]
|
||||
|
||||
# Methanol
|
||||
|
||||
sector = "Methanol"
|
||||
df[sector] = 0.0
|
||||
df.loc["methane", sector] = config["MWh_CH4_per_tMeOH"]
|
||||
df.loc["elec", sector] = config["MWh_elec_per_tMeOH"]
|
||||
df.loc["methane", sector] = params["MWh_CH4_per_tMeOH"]
|
||||
df.loc["elec", sector] = params["MWh_elec_per_tMeOH"]
|
||||
|
||||
# Other chemicals
|
||||
|
||||
@ -1465,10 +1465,10 @@ if __name__ == "__main__":
|
||||
|
||||
snakemake = mock_snakemake("build_industry_sector_ratios")
|
||||
|
||||
# TODO make config option
|
||||
# TODO make params option
|
||||
year = 2015
|
||||
|
||||
config = snakemake.params["industry"]
|
||||
params = snakemake.params["industry"]
|
||||
|
||||
df = pd.concat(
|
||||
[
|
||||
|
||||
@ -64,7 +64,7 @@ Inputs
|
||||
- ``resources/offshore_shapes.geojson``: confer :ref:`shapes`
|
||||
- ``resources/regions_onshore.geojson``: (if not offshore wind), confer :ref:`busregions`
|
||||
- ``resources/regions_offshore.geojson``: (if offshore wind), :ref:`busregions`
|
||||
- ``"cutouts/" + config["renewable"][{technology}]['cutout']``: :ref:`cutout`
|
||||
- ``"cutouts/" + params["renewable"][{technology}]['cutout']``: :ref:`cutout`
|
||||
- ``networks/base.nc``: :ref:`base`
|
||||
|
||||
Outputs
|
||||
@ -204,14 +204,14 @@ if __name__ == "__main__":
|
||||
|
||||
nprocesses = int(snakemake.threads)
|
||||
noprogress = snakemake.config["run"].get("disable_progressbar", True)
|
||||
config = snakemake.params["renewable"][snakemake.wildcards.technology]
|
||||
resource = config["resource"] # pv panel config / wind turbine config
|
||||
correction_factor = config.get("correction_factor", 1.0)
|
||||
capacity_per_sqkm = config["capacity_per_sqkm"]
|
||||
p_nom_max_meth = config.get("potential", "conservative")
|
||||
params = snakemake.params["renewable"][snakemake.wildcards.technology]
|
||||
resource = params["resource"] # pv panel params / wind turbine params
|
||||
correction_factor = params.get("correction_factor", 1.0)
|
||||
capacity_per_sqkm = params["capacity_per_sqkm"]
|
||||
p_nom_max_meth = params.get("potential", "conservative")
|
||||
|
||||
if isinstance(config.get("corine", {}), list):
|
||||
config["corine"] = {"grid_codes": config["corine"]}
|
||||
if isinstance(params.get("corine", {}), list):
|
||||
params["corine"] = {"grid_codes": params["corine"]}
|
||||
|
||||
if correction_factor != 1.0:
|
||||
logger.info(f"correction_factor is set as {correction_factor}")
|
||||
@ -229,13 +229,13 @@ if __name__ == "__main__":
|
||||
regions = regions.set_index("name").rename_axis("bus")
|
||||
buses = regions.index
|
||||
|
||||
res = config.get("excluder_resolution", 100)
|
||||
res = params.get("excluder_resolution", 100)
|
||||
excluder = atlite.ExclusionContainer(crs=3035, res=res)
|
||||
|
||||
if config["natura"]:
|
||||
if params["natura"]:
|
||||
excluder.add_raster(snakemake.input.natura, nodata=0, allow_no_overlap=True)
|
||||
|
||||
corine = config.get("corine", {})
|
||||
corine = params.get("corine", {})
|
||||
if "grid_codes" in corine:
|
||||
codes = corine["grid_codes"]
|
||||
excluder.add_raster(snakemake.input.corine, codes=codes, invert=True, crs=3035)
|
||||
@ -246,28 +246,28 @@ if __name__ == "__main__":
|
||||
snakemake.input.corine, codes=codes, buffer=buffer, crs=3035
|
||||
)
|
||||
|
||||
if "ship_threshold" in config:
|
||||
if "ship_threshold" in params:
|
||||
shipping_threshold = (
|
||||
config["ship_threshold"] * 8760 * 6
|
||||
params["ship_threshold"] * 8760 * 6
|
||||
) # approximation because 6 years of data which is hourly collected
|
||||
func = functools.partial(np.less, shipping_threshold)
|
||||
excluder.add_raster(
|
||||
snakemake.input.ship_density, codes=func, crs=4326, allow_no_overlap=True
|
||||
)
|
||||
|
||||
if config.get("max_depth"):
|
||||
if params.get("max_depth"):
|
||||
# lambda not supported for atlite + multiprocessing
|
||||
# use named function np.greater with partially frozen argument instead
|
||||
# and exclude areas where: -max_depth > grid cell depth
|
||||
func = functools.partial(np.greater, -config["max_depth"])
|
||||
func = functools.partial(np.greater, -params["max_depth"])
|
||||
excluder.add_raster(snakemake.input.gebco, codes=func, crs=4326, nodata=-1000)
|
||||
|
||||
if "min_shore_distance" in config:
|
||||
buffer = config["min_shore_distance"]
|
||||
if "min_shore_distance" in params:
|
||||
buffer = params["min_shore_distance"]
|
||||
excluder.add_geometry(snakemake.input.country_shapes, buffer=buffer)
|
||||
|
||||
if "max_shore_distance" in config:
|
||||
buffer = config["max_shore_distance"]
|
||||
if "max_shore_distance" in params:
|
||||
buffer = params["max_shore_distance"]
|
||||
excluder.add_geometry(
|
||||
snakemake.input.country_shapes, buffer=buffer, invert=True
|
||||
)
|
||||
@ -309,7 +309,7 @@ if __name__ == "__main__":
|
||||
p_nom_max = capacities / max_cap_factor
|
||||
else:
|
||||
raise AssertionError(
|
||||
'Config key `potential` should be one of "simple" '
|
||||
'params key `potential` should be one of "simple" '
|
||||
f'(default) or "conservative", not "{p_nom_max_meth}"'
|
||||
)
|
||||
|
||||
@ -358,13 +358,13 @@ if __name__ == "__main__":
|
||||
# select only buses with some capacity and minimal capacity factor
|
||||
ds = ds.sel(
|
||||
bus=(
|
||||
(ds["profile"].mean("time") > config.get("min_p_max_pu", 0.0))
|
||||
& (ds["p_nom_max"] > config.get("min_p_nom_max", 0.0))
|
||||
(ds["profile"].mean("time") > params.get("min_p_max_pu", 0.0))
|
||||
& (ds["p_nom_max"] > params.get("min_p_nom_max", 0.0))
|
||||
)
|
||||
)
|
||||
|
||||
if "clip_p_max_pu" in config:
|
||||
min_p_max_pu = config["clip_p_max_pu"]
|
||||
if "clip_p_max_pu" in params:
|
||||
min_p_max_pu = params["clip_p_max_pu"]
|
||||
ds["profile"] = ds["profile"].where(ds["profile"] >= min_p_max_pu, 0)
|
||||
|
||||
ds.to_netcdf(snakemake.output.profile)
|
||||
|
||||
@ -727,7 +727,7 @@ def cycling_shift(df, steps=1):
|
||||
return df
|
||||
|
||||
|
||||
def prepare_costs(cost_file, config, nyears):
|
||||
def prepare_costs(cost_file, params, nyears):
|
||||
# set all asset costs and other parameters
|
||||
costs = pd.read_csv(cost_file, index_col=[0, 1]).sort_index()
|
||||
|
||||
@ -739,7 +739,7 @@ def prepare_costs(cost_file, config, nyears):
|
||||
costs.loc[:, "value"].unstack(level=1).groupby("technology").sum(min_count=1)
|
||||
)
|
||||
|
||||
costs = costs.fillna(config["fill_values"])
|
||||
costs = costs.fillna(params["fill_values"])
|
||||
|
||||
def annuity_factor(v):
|
||||
return annuity(v["lifetime"], v["discount rate"]) + v["FOM"] / 100
|
||||
|
||||
Loading…
Reference in New Issue
Block a user