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Add solar_potential constraint

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The constraint ensures the combined installed capacity of solar and solar-hsat does not exceed the total solar capacity of the node
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Parisra 2024-04-25 11:45:31 +02:00 committed by GitHub
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scripts/solve_network.py
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@ -43,6 +43,7 @@ from _helpers import (
set_scenario_config,
update_config_from_wildcards,
)
from functools import reduce
from pypsa.descriptors import get_activity_mask
from pypsa.descriptors import get_switchable_as_dense as get_as_dense
@ -198,6 +199,61 @@ def _add_land_use_constraint_m(n, planning_horizons, config):
n.generators.p_nom_max.clip(lower=0, inplace=True)
def add_solar_potential_constraints(n, config):
"""
Add constraint to make sure the sum capacity of all solar technologies (fixed, tracking, ets. ) is below the region potential.
Example:
ES1 0: total solar potential is 10 GW, meaning:
solar potential : 10 GW
solar-hsat potential : 8 GW (solar with single axis tracking is assumed to have higher land use)
The constraint ensures that:
solar_p_nom + solar_hsat_p_nom * 1.13 <= 10 GW
"""
land_use_factors= {
'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 = (
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)
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")
def add_co2_sequestration_limit(n, limit=200):
"""