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add sanitize_locations function and apply it

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address some deprecation warnings
This commit is contained in:
Fabian 2024-02-05 16:16:34 +01:00
parent 7e2ad98011
commit 76872fa0f1
8 changed files with 57 additions and 20 deletions
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2
config/test/config.perfect.yaml
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@ -18,7 +18,7 @@ scenario:
clusters:
- 5
sector_opts:
- 8760H-T-H-B-I-A-dist1
- 8760h-T-H-B-I-A-dist1
planning_horizons:
- 2030
- 2040

9
scripts/add_electricity.py
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@ -178,6 +178,15 @@ def sanitize_carriers(n, config):
n.carriers["color"] = n.carriers.color.where(n.carriers.color != "", colors)
def sanitize_locations(n):
n.buses["x"] = n.buses.x.where(n.buses.x != 0, n.buses.location.map(n.buses.x))
n.buses["y"] = n.buses.y.where(n.buses.y != 0, n.buses.location.map(n.buses.y))
n.buses["country"] = n.buses.country.where(
n.buses.country.ne("") & n.buses.country.notnull(),
n.buses.location.map(n.buses.country),
)
def add_co2_emissions(n, costs, carriers):
"""
Add CO2 emissions to the network's carriers attribute.

8
scripts/add_extra_components.py
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@ -56,7 +56,7 @@ import numpy as np
import pandas as pd
import pypsa
from _helpers import configure_logging
from add_electricity import load_costs, sanitize_carriers
from add_electricity import load_costs, sanitize_carriers, sanitize_locations
idx = pd.IndexSlice
@ -100,10 +100,9 @@ def attach_stores(n, costs, extendable_carriers):
n.madd("Carrier", carriers)
buses_i = n.buses.index
bus_sub_dict = {k: n.buses[k].values for k in ["x", "y", "country"]}
if "H2" in carriers:
h2_buses_i = n.madd("Bus", buses_i + " H2", carrier="H2", **bus_sub_dict)
h2_buses_i = n.madd("Bus", buses_i + " H2", carrier="H2", location=buses_i)
n.madd(
"Store",
@ -143,7 +142,7 @@ def attach_stores(n, costs, extendable_carriers):
if "battery" in carriers:
b_buses_i = n.madd(
"Bus", buses_i + " battery", carrier="battery", **bus_sub_dict
"Bus", buses_i + " battery", carrier="battery", location=buses_i
)
n.madd(
@ -246,6 +245,7 @@ if __name__ == "__main__":
attach_hydrogen_pipelines(n, costs, extendable_carriers)
sanitize_carriers(n, snakemake.config)
sanitize_locations(n)
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])

12
scripts/make_summary.py
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@ -507,7 +507,7 @@ def calculate_weighted_prices(n, label, weighted_prices):
if carrier in ["H2", "gas"]:
load = pd.DataFrame(index=n.snapshots, columns=buses, data=0.0)
else:
load = n.loads_t.p_set[buses]
load = n.loads_t.p_set[buses.intersection(n.loads.index)]
for tech in value:
names = n.links.index[n.links.index.to_series().str[-len(tech) :] == tech]
@ -560,7 +560,10 @@ def calculate_market_values(n, label, market_values):
)
revenue = dispatch * n.buses_t.marginal_price[buses]
market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
if total_dispatch := dispatch.sum().sum():
market_values.at[tech, label] = revenue.sum().sum() / total_dispatch
else:
market_values.at[tech, label] = np.nan
## Now do market value of links ##
@ -583,7 +586,10 @@ def calculate_market_values(n, label, market_values):
revenue = dispatch * n.buses_t.marginal_price[buses]
market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
if total_dispatch := dispatch.sum().sum():
market_values.at[tech, label] = revenue.sum().sum() / total_dispatch
else:
market_values.at[tech, label] = np.nan
return market_values

24
scripts/make_summary_perfect.py
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@ -265,7 +265,7 @@ def calculate_energy(n, label, energy):
totals[no_bus] = float(
n.component_attrs[c.name].loc["p" + port, "default"]
)
c_energies -= totals.groupby(c.df.carrier, axis=1).sum()
c_energies -= totals.T.groupby(c.df.carrier).sum().T
c_energies = pd.concat([c_energies.T], keys=[c.list_name])
@ -376,9 +376,8 @@ def calculate_supply_energy(n, label, supply_energy):
.groupby(level=0)
.sum()
.multiply(c.df.loc[items, "sign"])
.groupby(c.df.loc[items, "carrier"], axis=1)
.T.groupby(c.df.loc[items, "carrier"])
.sum()
.T
)
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
@ -525,9 +524,12 @@ def calculate_weighted_prices(n, label, weighted_prices):
# stores[stores > 0.] = 0.
# load += -stores
weighted_prices.loc[carrier, label] = (
load * n.buses_t.marginal_price[buses]
).sum().sum() / load.sum().sum()
if total_load := load.sum().sum():
weighted_prices.loc[carrier, label] = (
load * n.buses_t.marginal_price[buses]
).sum().sum() / total_load
else:
weighted_prices.loc[carrier, label] = np.nan
if carrier[:5] == "space":
print(load * n.buses_t.marginal_price[buses])
@ -562,7 +564,10 @@ def calculate_market_values(n, label, market_values):
revenue = dispatch * n.buses_t.marginal_price[buses]
market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
if total_dispatch := dispatch.sum().sum():
market_values.at[tech, label] = revenue.sum().sum() / total_dispatch
else:
market_values.at[tech, label] = np.nan
## Now do market value of links ##
@ -585,7 +590,10 @@ def calculate_market_values(n, label, market_values):
revenue = dispatch * n.buses_t.marginal_price[buses]
market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
if total_dispatch := dispatch.sum().sum():
market_values.at[tech, label] = revenue.sum().sum() / total_dispatch
else:
market_values.at[tech, label] = np.nan
return market_values

4
scripts/plot_hydrogen_network.py
View File

@ -36,7 +36,9 @@ def group_pipes(df, drop_direction=False):
lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
axis=1,
)
return df.groupby(level=0).agg({"p_nom_opt": sum, "bus0": "first", "bus1": "first"})
return df.groupby(level=0).agg(
{"p_nom_opt": "sum", "bus0": "first", "bus1": "first"}
)
def plot_h2_map(n, regions):

2
scripts/prepare_perfect_foresight.py
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@ -221,7 +221,7 @@ def concat_networks(years):
# set investment periods
n.investment_periods = n.snapshots.levels[0]
# weighting of the investment period -> assuming last period same weighting as the period before
time_w = n.investment_periods.to_series().diff().shift(-1).fillna(method="ffill")
time_w = n.investment_periods.to_series().diff().shift(-1).ffill()
n.investment_period_weightings["years"] = time_w
# set objective weightings
objective_w = get_investment_weighting(

16
scripts/prepare_sector_network.py
View File

@ -19,7 +19,7 @@ import pandas as pd
import pypsa
import xarray as xr
from _helpers import update_config_with_sector_opts
from add_electricity import calculate_annuity, sanitize_carriers
from add_electricity import calculate_annuity, sanitize_carriers, sanitize_locations
from build_energy_totals import build_co2_totals, build_eea_co2, build_eurostat_co2
from networkx.algorithms import complement
from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation
@ -546,6 +546,14 @@ def patch_electricity_network(n):
n.loads_t.p_set.rename(lambda x: x.strip(), axis=1, inplace=True)
def add_eu_bus(n, x=-5.5, y=46):
"""
Add EU bus to the network.
"""
n.add("Bus", "EU", location="EU", x=x, y=y, carrier="none")
n.add("Carrier", "none")
def add_co2_tracking(n, costs, options):
# minus sign because opposite to how fossil fuels used:
# CH4 burning puts CH4 down, atmosphere up
@ -1005,6 +1013,7 @@ def insert_electricity_distribution_grid(n, costs):
"Store",
nodes + " home battery",
bus=nodes + " home battery",
location=nodes,
e_cyclic=True,
e_nom_extendable=True,
carrier="home battery",
@ -3527,8 +3536,8 @@ def lossy_bidirectional_links(n, carrier, efficiencies={}):
rev_links["reversed"] = True
rev_links.index = rev_links.index.map(lambda x: x + "-reversed")
n.links["reversed"] = False
n.links = pd.concat([n.links, rev_links], sort=False)
n.links["reversed"] = n.links["reversed"].fillna(False)
n.links["length_original"] = n.links["length_original"].fillna(n.links.length)
# do compression losses after concatenation to take electricity consumption at bus0 in either direction
@ -3594,6 +3603,8 @@ if __name__ == "__main__":
for carrier in conventional:
add_carrier_buses(n, carrier)
add_eu_bus(n)
add_co2_tracking(n, costs, options)
add_generation(n, costs)
@ -3733,5 +3744,6 @@ if __name__ == "__main__":
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
sanitize_carriers(n, snakemake.config)
sanitize_locations(n)
n.export_to_netcdf(snakemake.output[0])