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Fix_Add_electricity
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@Margherita-Capitani
Margherita-Capitani committed Jan 13, 2025
1 parent 19f2742 commit bf64f79
Showing 1 changed file with 96 additions and 110 deletions.
206 changes: 96 additions & 110 deletions scripts/add_electricity.py
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Original file line number Diff line number Diff line change
Expand Up @@ -190,41 +190,45 @@ def attach_wind_and_solar(
# Add any missing carriers from the costs data to the tech_modelling variable
_add_missing_carriers_from_costs(n, costs, tech_modelling)

microgrid_ids = [f"microgrid_{i+1}" for i in range(len(number_microgrids.keys()))]
number_microgrids = len(number_microgrids.keys())
microgrid_ids = [f"microgrid_{i+1}" for i in range(number_microgrids)]

# Iterate over each technology
for tech in tech_modelling:
# Iterate through each microgrid
# for microgrid in microgrid_ids: #TODO: review this function

# Open the dataset for the current technology from the input_profiles
with xr.open_dataset(getattr(snakemake.input, "profile_" + tech)) as ds:
# If the dataset's "bus" index is empty, skip to the next technology
if ds.indexes["bus"].empty:
continue

suptech = tech.split("-", 2)[0]
# Add the wind and solar generators to the power network

for bus_index in ds.indexes["bus"]:
gen_bus = f"{bus_index}"
n.madd(
"Generator",
[f"{tech}_{bus_index}"],
carrier=tech,
bus=gen_bus,
p_nom_extendable=tech in extendable_carriers["Generator"],
p_nom_max=ds["p_nom_max"].to_pandas().loc[bus_index],
weight=ds["weight"].to_pandas().loc[bus_index],
marginal_cost=costs.at[suptech, "marginal_cost"],
capital_cost=costs.at[tech, "capital_cost"],
efficiency=costs.at[suptech, "efficiency"],
p_set=ds["profile"]
.to_pandas()
.reindex(n.snapshots)
.loc[:, bus_index],
p_max_pu=ds["profile"]
.to_pandas()
.reindex(n.snapshots)
.loc[:, bus_index],
)
suptech = tech.split("-", 2)[0]
# Add the wind and solar generators to the power network
n.madd(
"Generator",
ds.indexes["bus"],
# {microgrid},
" " + tech, # TODO: review indexes
# bus=f"new_bus_{microgrid}",
bus=ds.indexes["bus"],
carrier=tech,
p_nom_extendable=tech in extendable_carriers["Generator"],
p_nom_max=ds["p_nom_max"].to_pandas(), # look at the config
weight=ds["weight"].to_pandas(),
marginal_cost=costs.at[suptech, "marginal_cost"],
capital_cost=costs.at[tech, "capital_cost"],
efficiency=costs.at[suptech, "efficiency"],
p_set=ds["profile"]
.transpose("time", "bus")
.to_pandas()
.reindex(n.snapshots),
p_max_pu=ds["profile"]
.transpose("time", "bus")
.to_pandas()
.reindex(n.snapshots),
)


def load_powerplants(ppl_fn):
Expand All @@ -246,70 +250,58 @@ def load_powerplants(ppl_fn):
.replace({"carrier": carrier_dict})
)


def attach_conventional_generators(
n,
costs,
ppl,
conventional_carriers,
extendable_carriers,
conventional_config,
conventional_inputs,
number_microgrids,
):
# Create a set of all conventional and extendable carriers
carriers = set(conventional_carriers) | set(extendable_carriers["Generator"])

# Add any missing carriers from the costs data to the "carriers" variable
_add_missing_carriers_from_costs(n, costs, carriers)

# Filter the ppl dataframe to only include the relevant 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)

# Get the index of the buses in the power network
microgrid_ids = [f"microgrid_{i+1}" for i in range(len(number_microgrids.keys()))]
for microgrid in microgrid_ids:
gen_bus = f"{microgrid}_gen_bus"
gen_name = [f"{microgrid}_gen"]

# Add conventional generators to each bus in the power network (one for microgrid)
n.madd(
"Generator",
gen_name,
carrier=ppl.carrier,
bus=gen_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,
build_year=1984, # TODO: review this
lifetime=(ppl.dateout - ppl.datein).fillna(np.inf),
n,
costs,
ppl,
conventional_carriers,
extendable_carriers,
conventional_config,
conventional_inputs,
number_microgrids,
):
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)
microgrid_ids = [f"microgrid_{i+1}" for i in range(len(number_microgrids))]
for microgrid in microgrid_ids:
ppl.index = ppl.index + "_" + microgrid #TODO: review this
ppl["bus"] = microgrid + "_gen_bus"
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(pd.Int64Dtype()),
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 carrier in conventional_config:
idx = n.generators.query("carrier == @carrier").index

for attr in list(set(conventional_config[carrier]) & set(n.generators)):
values = conventional_config[carrier][attr]
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 indiscriminately of country
n.generators.loc[idx, attr] = values
if f"conventional_{carrier}_{attr}" in conventional_inputs:
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:
n.generators.loc[idx, attr] = values


def attach_storageunits(n, costs, number_microgrids, technologies, extendable_carriers):
Expand All @@ -326,31 +318,25 @@ def attach_storageunits(n, costs, number_microgrids, technologies, extendable_ca
microgrid_ids = [f"microgrid_{i+1}" for i in range(len(number_microgrids))]

# Add the storage units to the power network
for tech in technologies:
microgrid_ids = [
f"microgrid_{i+1}" for i in range(len(number_microgrids.keys()))
]
for microgrid in microgrid_ids:
gen_bus = f"{microgrid}_gen_bus"
n.madd(
"StorageUnit",
[f"{microgrid}_{tech}"],
bus=gen_bus,
carrier=tech,
p_nom_extendable=True,
capital_cost=costs.at[tech, "capital_cost"],
marginal_cost=costs.at[tech, "marginal_cost"],
efficiency_store=costs.at[
lookup_store["battery"], "efficiency"
], # Lead_acid and lithium have the same value
efficiency_dispatch=costs.at[
lookup_dispatch["battery"], "efficiency"
], # Lead_acid and lithium have the same value
max_hours=max_hours[
"battery"
], # Lead_acid and lithium have the same value
cyclic_state_of_charge=True,
)
for i, tech in enumerate(technologies):
n.madd(
"StorageUnit",
[microgrid_ids[i]],
" " + tech,
bus=[f"microgrid_{i+1}_gen_bus"],
carrier=tech,
p_nom_extendable=True,
capital_cost=costs.at[tech, "capital_cost"],
marginal_cost=costs.at[tech, "marginal_cost"],
efficiency_store=costs.at[
lookup_store["battery"], "efficiency"
], # Lead_acid and lithium have the same value
efficiency_dispatch=costs.at[
lookup_dispatch["battery"], "efficiency"
], # Lead_acid and lithium have the same value
max_hours=max_hours["battery"], # Lead_acid and lithium have the same value
cyclic_state_of_charge=True,
)


def attach_load(n, load_file, tech_modelling):
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