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Correct Total System Costs Calculation #40

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Correct Total System Costs Calculation #40

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6f6a1b0
include interconnector and links/line costs into total system costs
toniseibold Feb 4, 2025
11b9263
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Feb 4, 2025
a2fb6e3
take care of deprecation warnings
toniseibold Feb 4, 2025
574ec76
include energy trade costs
toniseibold Feb 4, 2025
b66c2f5
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Feb 4, 2025
d3e327f
Merge branch 'main' into total_system_costs
toniseibold Feb 4, 2025
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211 changes: 192 additions & 19 deletions scripts/pypsa-de/export_ariadne_variables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
import pandas as pd
import pypsa
from numpy import isclose
from pypsa.statistics import get_transmission_carriers

from scripts._helpers import configure_logging, mock_snakemake
from scripts.add_electricity import calculate_annuity
Expand Down Expand Up @@ -106,7 +107,7 @@ def domestic_length_factor(n, carriers, region="DE"):

def _get_fuel_fractions(n, region, fuel):
kwargs = {
"groupby": n.statistics.groupers.get_name_bus_and_carrier,
"groupby": ["name", "bus", "carrier"],
"at_port": True,
"nice_names": False,
}
Expand Down Expand Up @@ -242,7 +243,7 @@ def _get_fuel_fractions(n, region, fuel):

def _get_h2_fossil_fraction(n):
kwargs = {
"groupby": n.statistics.groupers.get_name_bus_and_carrier,
"groupby": ["name", "bus", "carrier"],
"at_port": True,
"nice_names": False,
}
Expand Down Expand Up @@ -561,7 +562,7 @@ def _f(*args, **kwargs):

def _get_capacities(n, region, cap_func, cap_string="Capacity|"):
kwargs = {
"groupby": n.statistics.groupers.get_bus_and_carrier,
"groupby": ["bus", "carrier"],
"at_port": True,
"nice_names": False,
}
Expand Down Expand Up @@ -1118,7 +1119,7 @@ def _get_capacities(n, region, cap_func, cap_string="Capacity|"):

def get_CHP_E_and_H_usage(n, bus_carrier, region, fossil_fraction=1):
kwargs = {
"groupby": n.statistics.groupers.get_name_bus_and_carrier,
"groupby": ["name", "bus", "carrier"],
"nice_names": False,
}

Expand Down Expand Up @@ -1148,7 +1149,7 @@ def get_CHP_E_and_H_usage(n, bus_carrier, region, fossil_fraction=1):

def get_primary_energy(n, region):
kwargs = {
"groupby": n.statistics.groupers.get_name_bus_and_carrier,
"groupby": ["name", "bus", "carrier"],
"nice_names": False,
}

Expand Down Expand Up @@ -1480,7 +1481,7 @@ def get_primary_energy(n, region):

def get_secondary_energy(n, region, _industry_demand):
kwargs = {
"groupby": n.statistics.groupers.get_name_bus_and_carrier,
"groupby": ["name", "bus", "carrier"],
"nice_names": False,
}
var = pd.Series()
Expand Down Expand Up @@ -1922,7 +1923,7 @@ def get_final_energy(
var = pd.Series()

kwargs = {
"groupby": n.statistics.groupers.get_name_bus_and_carrier,
"groupby": ["name", "bus", "carrier"],
"nice_names": False,
}
h2_fossil_fraction = _get_h2_fossil_fraction(n)
Expand Down Expand Up @@ -2607,7 +2608,7 @@ def get_emissions(n, region, _energy_totals, industry_demand):
).sum()

kwargs = {
"groupby": n.statistics.groupers.get_name_bus_and_carrier,
"groupby": ["name", "bus", "carrier"],
"nice_names": False,
}

Expand Down Expand Up @@ -3102,7 +3103,7 @@ def get_nodal_flows(n, bus_carrier, region, query="index == index or index != in
pandas.DataFrame: The nodal flows for the specified bus carrier and region.
"""

groupby = n.statistics.groupers.get_name_bus_and_carrier
groupby = ["name", "bus", "carrier"]

result = (
n.statistics.withdrawal(
Expand Down Expand Up @@ -3136,7 +3137,7 @@ def get_nodal_supply(n, bus_carrier, query="index == index or index != index"):
pandas.DataFrame: The nodal flows for the specified bus carrier and region.
"""

groupby = n.statistics.groupers.get_name_bus_and_carrier
groupby = ["name", "bus", "carrier"]

result = (
n.statistics.supply(
Expand Down Expand Up @@ -3330,7 +3331,7 @@ def get_prices(n, region):
var = pd.Series()

kwargs = {
"groupby": n.statistics.groupers.get_name_bus_and_carrier,
"groupby": ["name", "bus", "carrier"],
"nice_names": False,
}
try:
Expand Down Expand Up @@ -4305,16 +4306,188 @@ def get_policy(n, investment_year):
def get_economy(n, region):
var = pd.Series()

s = n.statistics
g = s.groupers
grouper = g.get_country_and_carrier
system_cost = s.capex(groupby=grouper).add(s.opex(groupby=grouper))
def get_tsc(n, country):
n.statistics.set_parameters(drop_zero=False)
capex = n.statistics.capex(
groupby=pypsa.statistics.groupers["name", "carrier"], nice_names=False
)

# Cost|Total Energy System Cost in billion EUR2020/yr
var["Cost|Total Energy System Cost"] = round(
system_cost.groupby("country").sum()[region] / 1e9, 4
opex = n.statistics.opex(
groupby=pypsa.statistics.groupers["name", "carrier"], nice_names=False
)

# filter inter country transmission lines and links
inter_country_lines = n.lines.bus0.map(n.buses.country) != n.lines.bus1.map(
n.buses.country
)
inter_country_links = n.links.bus0.map(n.buses.country) != n.links.bus1.map(
n.buses.country
)
#
transmission_carriers = get_transmission_carriers(n).get_level_values("carrier")
transmission_lines = n.lines.carrier.isin(transmission_carriers)
transmission_links = n.links.carrier.isin(transmission_carriers)
#
country_transmission_lines = (
(n.lines.bus0.str.contains(country)) & ~(n.lines.bus1.str.contains(country))
) | (
~(n.lines.bus0.str.contains(country)) & (n.lines.bus1.str.contains(country))
)
country_tranmission_links = (
(n.links.bus0.str.contains(country)) & ~(n.links.bus1.str.contains(country))
) | (
~(n.links.bus0.str.contains(country)) & (n.links.bus1.str.contains(country))
)
#
inter_country_transmission_lines = (
inter_country_lines & transmission_lines & country_transmission_lines
)
inter_country_transmission_links = (
inter_country_links & transmission_links & country_tranmission_links
)
inter_country_transmission_lines_i = inter_country_transmission_lines[
inter_country_transmission_lines
].index
inter_country_transmission_links_i = inter_country_transmission_links[
inter_country_transmission_links
].index
inter_country_transmission_i = inter_country_transmission_lines_i.union(
inter_country_transmission_links_i
)

#
tsc = pd.concat([capex, opex], axis=1, keys=["capex", "opex"])
tsc = tsc.reset_index().set_index("name")
tsc.loc[inter_country_transmission_i, ["capex", "opex"]] = (
tsc.loc[inter_country_transmission_i, ["capex", "opex"]] / 2
)
tsc.rename(
index={
index: index + " " + country for index in inter_country_transmission_i
},
inplace=True,
)
# rename inter region links and lines
to_rename_links = n.links[
(n.links.bus0.str.contains(region))
& (n.links.bus1.str.contains(region))
& ~(n.links.index.str.contains(region))
].index
to_rename_lines = n.lines[
(n.lines.bus0.str.contains(region))
& (n.lines.bus1.str.contains(region))
& ~(n.lines.index.str.contains(region))
].index
tsc.rename(
index={index: index + " " + region for index in to_rename_links},
inplace=True,
)
tsc.rename(
index={index: index + " " + region for index in to_rename_lines},
inplace=True,
)

tsc = (
tsc.filter(like=country, axis=0)
.drop("component", axis=1)
.groupby("carrier")
.sum()
)

return tsc

def get_link_opex(n, carriers, region, sw):
# get flow of electricity/hydrogen...
# multiply it with the marginal costs
supplying = n.links[
(n.links.carrier.isin(carriers))
& (n.links.bus0.str.startswith(region))
& (~n.links.bus1.str.startswith(region))
].index

receiving = n.links[
(n.links.carrier.isin(carriers))
& (~n.links.bus0.str.startswith(region))
& (n.links.bus1.str.startswith(region))
].index

trade_out = 0
for index in supplying:
# price of energy in trade country
marg_price = n.buses_t.marginal_price[n.links.loc[index].bus0]
trade = n.links_t.p1[index].mul(sw)
trade_out += marg_price.mul(trade).sum()

trade_in = 0
for index in receiving:
# price of energy in Germany
marg_price = n.buses_t.marginal_price[n.links.loc[index].bus0]
trade = n.links_t.p1[index].mul(sw)
trade_in += marg_price.mul(trade).sum()
return abs(trade_in) - abs(trade_out)
# > 0: costs for Germany
# < 0: profit for Germany

def get_line_opex(n, region, sw):
supplying = n.lines[
(n.lines.carrier.isin(["AC"]))
& (n.lines.bus0.str.startswith(region))
& (~n.lines.bus1.str.startswith(region))
].index
receiving = n.lines[
(n.lines.carrier.isin(["AC"]))
& (~n.lines.bus0.str.startswith(region))
& (n.lines.bus1.str.startswith(region))
].index

# i have to clip the trade
net_out = 0
for index in supplying:
trade = n.lines_t.p1[index].mul(sw)
trade_out = trade.clip(lower=0) # positive
trade_in = trade.clip(upper=0) # negative
marg_price_DE = n.buses_t.marginal_price[n.lines.loc[index].bus0]
marg_price_EU = n.buses_t.marginal_price[n.lines.loc[index].bus1]
net_out += (
trade_out.mul(marg_price_DE).sum() + trade_in.mul(marg_price_EU).sum()
)
# net_out > 0: Germany is exporting more electricity
# net_out < 0: Germany is importing more electricity

net_in = 0
for index in receiving:
trade = n.lines_t.p1[index].mul(sw)
trade_in = trade.clip(lower=0) # positive
trade_out = trade.clip(upper=0) # negative
trade_out = trade_out.clip(upper=0)
marg_price_EU = n.buses_t.marginal_price[n.lines.loc[index].bus0]
marg_price_DE = n.buses_t.marginal_price[n.lines.loc[index].bus1]
net_in += (
trade_in.mul(marg_price_EU).sum() + trade_out.mul(marg_price_DE).sum()
)
# net_in > 0: Germany is importing more electricity
# net_in < 0: Germany is exporting more electricity

return -net_out + net_in

trade_carriers = [
"DC",
"H2 pipeline",
"H2 pipeline (Kernnetz)",
"H2 pipeline retrofitted" "renewable oil",
"renewable gas",
"methanol",
]

sw = n.snapshot_weightings.generators
tsc = get_tsc(n, region).sum().sum()
trade_costs = get_link_opex(n, trade_carriers, region, sw) + get_line_opex(
n, region, sw
)

# Cost|Total Energy System Cost in billion EUR2020/yr
var["Cost|Total Energy System Cost"] = round((tsc + trade_costs) / 1e9, 4)

return var


Expand Down Expand Up @@ -5468,7 +5641,7 @@ def get_data(
cap_func = n.statistics.optimal_capacity
cap_string = "Optimal Capacity|"
kwargs = {
"groupby": n.statistics.groupers.get_bus_and_carrier,
"groupby": ["bus", "carrier"],
"at_port": True,
"nice_names": False,
}
Expand Down