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Fix empty array error

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In case there are no extendable solar generators in the network, the constraint is not implemented. Without this condition, an 'empty array' error will arise.
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Parisra 2024-04-29 15:58:42 +02:00 committed by GitHub
parent c66549c577
commit ea0e2f66ce
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1 changed files with 38 additions and 63 deletions
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101
scripts/solve_network.py
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@ -228,76 +228,51 @@ def add_solar_potential_constraints(n, config):
"solar-hsat": config["renewable"]["solar"]["capacity_per_sqkm"]
/ config["renewable"]["solar-hsat"]["capacity_per_sqkm"],
}
gen_index = n.generators[n.generators.p_nom_extendable].index
filters = [
("solar", True),
("thermal", False),
("rooftop", False),
] ## filter all utility solar generation except solar thermal
solar = reduce(
lambda gen_index, f: gen_index[gen_index.str.contains(f[0]) == f[1]],
filters,
gen_index,
)
solar_today = n.generators[
(n.generators.carrier == "solar") & (n.generators.p_nom_extendable)
].index
solar_hsat = n.generators[(n.generators.carrier == "solar-hsat")].index
land_use = pd.DataFrame(1, index=solar, columns=["land_use_factor"])
for key in land_use_factors.keys():
land_use = land_use.apply(
lambda x: (x * land_use_factors[key]) if key in x.name else x, axis=1
)
rename = {"Generator-ext": "Generator"}
if "m" in snakemake.wildcards.clusters:
location = pd.Series(
[" ".join(i.split(" ")[:2]) for i in n.generators.index],
index=n.generators.index,
)
ggrouper = pd.Series(
n.generators.loc[solar].index.rename("bus").map(location),
index=n.generators.loc[solar].index,
).to_xarray()
rhs = (
n.generators.loc[solar_today, "p_nom_max"]
.groupby(n.generators.loc[solar_today].index.rename("bus").map(location))
.sum()
- n.generators.loc[solar_hsat, "p_nom_opt"]
.groupby(n.generators.loc[solar_hsat].index.rename("bus").map(location))
.sum()
* land_use_factors["solar-hsat"]
).clip(lower=0)
else:
location = (
gen_index = n.generators[n.generators.p_nom_extendable].index
filters = [("solar", True), ("thermal", False), ("rooftop", False)] ## filter all utility solar generation except solar thermal
solar = reduce(lambda gen_index, f: gen_index[gen_index.str.contains(f[0]) == f[1]], filters, gen_index)
solar_today = n.generators[(n.generators.carrier=='solar') & (n.generators.p_nom_extendable)].index
solar_hsat = n.generators[(n.generators.carrier=='solar-hsat') ].index
if not solar.empty :
land_use = pd.DataFrame(1, index=solar, columns=['land_use_factor'])
for key in land_use_factors.keys():
land_use = land_use.apply(lambda x: (x*land_use_factors[key]) if key in x.name else x, axis=1)
if "m" in snakemake.wildcards.clusters:
location = (
pd.Series([' '.join(i.split(' ')[:2]) for i in n.generators.index], index=n.generators.index)
)
ggrouper= pd.Series(n.generators.loc[solar].index.rename('bus').map(location), index=n.generators.loc[solar].index,).to_xarray()
rhs = (n.generators.loc[solar_today,"p_nom_max"]
.groupby(n.generators.loc[solar_today].index.rename('bus').map(location)).sum() -
n.generators.loc[solar_hsat,"p_nom_opt"]
.groupby(n.generators.loc[solar_hsat].index.rename('bus').map(location)).sum() * land_use_factors['solar-hsat'] ).clip(lower=0)
else :
location = (
n.buses.location
if "location" in n.buses.columns
else pd.Series(n.buses.index, index=n.buses.index)
)
ggrouper = n.generators.loc[solar].bus
rhs = (
n.generators.loc[solar_today, "p_nom_max"]
.groupby(n.generators.loc[solar_today].bus.map(location))
.sum()
- n.generators.loc[solar_hsat, "p_nom_opt"]
.groupby(n.generators.loc[solar_hsat].bus.map(location))
.sum()
* land_use_factors["solar-hsat"]
).clip(lower=0)
)
ggrouper= (n.generators.loc[solar].bus)
rhs = (n.generators.loc[solar_today,"p_nom_max"]
.groupby(n.generators.loc[solar_today].bus.map(location)).sum() -
n.generators.loc[solar_hsat,"p_nom_opt"]
.groupby(n.generators.loc[solar_hsat].bus.map(location)).sum() * land_use_factors['solar-hsat'] ).clip(lower=0)
lhs = (
(
n.model["Generator-p_nom"].rename(rename).loc[solar]
* land_use.squeeze().values
)
.groupby(ggrouper)
.sum()
)
lhs = (
(n.model["Generator-p_nom"].rename(rename).loc[solar]
*land_use.squeeze().values)
.groupby(ggrouper)
.sum()
)
print("adding solar rooftop constraints...")
n.model.add_constraints(lhs <= rhs, name="solar_potential")
print('adding solar rooftop constraints...')
n.model.add_constraints(lhs <= rhs, name="solar_potential")
def add_co2_sequestration_limit(n, limit=200):