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Energy penalty for carbon capture #287

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aea0ef0
Fix bug preventing add_energy_storage_database to run through
millingermarkus Sep 8, 2026
9cc4292
Release note: fix bug preventing add_energy_storage_database to run t…
millingermarkus Sep 8, 2026
213f171
Fix bug preventing energy penalty of carbon capture
millingermarkus Sep 8, 2026
95e8147
Energy penalty for central gas CHP CC
millingermarkus Sep 8, 2026
8769fb8
Energy penalty for CHP with power boost
millingermarkus Sep 8, 2026
6a67ff7
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 8, 2026
05a0b6a
Remove double calculations of energy penalty
millingermarkus Sep 8, 2026
fce7aae
Minor code cleanup
millingermarkus Sep 8, 2026
fcd6e27
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 8, 2026
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4 changes: 4 additions & 0 deletions docs/release_notes.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,10 @@ Upcoming Release

* Add cost assumptions for water network infrastructure: water pipeline HDPE and water pipeline booster pump (https://github.com/PyPSA/technology-data/pull/277)

* Fix bug preventing add_energy_storage_database to run through

* Fix bug preventing energy penalty for carbon capture technologies

`v0.15.0 <https://github.com/PyPSA/technology-data/releases/tag/v0.15.0>`__ (9th June 2026)
================================================================================================

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187 changes: 95 additions & 92 deletions outputs/US/costs_2020.csv
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187 changes: 95 additions & 92 deletions outputs/US/costs_2025.csv
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187 changes: 95 additions & 92 deletions outputs/US/costs_2030.csv
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187 changes: 95 additions & 92 deletions outputs/US/costs_2035.csv
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187 changes: 95 additions & 92 deletions outputs/US/costs_2040.csv
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187 changes: 95 additions & 92 deletions outputs/US/costs_2045.csv
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187 changes: 95 additions & 92 deletions outputs/US/costs_2050.csv
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187 changes: 95 additions & 92 deletions outputs/costs_2020.csv
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187 changes: 95 additions & 92 deletions outputs/costs_2025.csv
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187 changes: 95 additions & 92 deletions outputs/costs_2050.csv
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104 changes: 58 additions & 46 deletions scripts/compile_cost_assumptions.py
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Expand Up @@ -3266,23 +3266,46 @@ def energy_penalty(cost_dataframe: pd.DataFrame) -> pd.DataFrame:
"direct firing solid fuels CC",
"direct firing gas CC",
"biogas CC",
"central gas CHP CC",
"central solid biomass CHP powerboost CC",
]:
if "powerboost" in tech_name:
boiler = "electric boiler steam"
feedstock = "solid biomass"
co2_capture = cost_dataframe.loc[(feedstock, "CO2 intensity"), "value"]
elif "biogas" in tech_name:
boiler = "gas boiler steam"
co2_capture = cost_dataframe.loc[(tech_name, "CO2 stored"), "value"]
cost_dataframe.loc[(tech_name, "VOM"), "unit"] = "EUR/MWh"
elif "gas" in tech_name:
boiler = "gas boiler steam"
feedstock = "gas"
co2_capture = cost_dataframe.loc[(feedstock, "CO2 intensity"), "value"]
elif "biogas" in tech_name:
boiler = "gas boiler steam"
co2_capture = cost_dataframe.loc[(tech_name, "CO2 stored"), "value"]
else:
boiler = "solid biomass boiler steam"
feedstock = "solid biomass"
co2_capture = cost_dataframe.loc[(feedstock, "CO2 intensity"), "value"]

if "gas CHP" in tech_name:
base_tech = "central gas CHP"
cost_dataframe.loc[(base_tech, "efficiency-heat"), "value"] = (
cost_dataframe.loc[(base_tech, "efficiency"), "value"]
/ cost_dataframe.loc[(base_tech, "c_b"), "value"]
)
cost_dataframe.loc[(base_tech, "efficiency-heat"), "source"] = (
"Calculated based on electric efficiency and back pressure ratio"
)
cost_dataframe.loc[(base_tech, "efficiency-heat"), "unit"] = "per unit"

cost_dataframe.loc[(tech_name, "efficiency-heat"), "value"] = (
cost_dataframe.loc[(tech_name, "efficiency"), "value"]
/ cost_dataframe.loc[(tech_name, "c_b"), "value"]
)
cost_dataframe.loc[(tech_name, "efficiency-heat"), "source"] = (
"Calculated based on electric efficiency and back pressure ratio"
)
cost_dataframe.loc[(tech_name, "efficiency-heat"), "unit"] = "per unit"

# Scaling biomass input to account for heat demand of carbon capture
scalingFactor = 1 / (
1
Expand All @@ -3295,73 +3318,49 @@ def energy_penalty(cost_dataframe: pd.DataFrame) -> pd.DataFrame:
(boiler, "efficiency"), "value"
]
eta_old = cost_dataframe.loc[(tech_name, "efficiency"), "value"]
eta_main = eta_old * scalingFactor

eta_main = (
cost_dataframe.loc[(tech_name, "efficiency"), "value"] * scalingFactor
)
source = f"Combination of {tech_name} and {boiler}"

# Adapting investment share of tech due to steam boiler addition. Investment per MW_el.
cost_dataframe.loc[(tech_name, "investment"), "value"] = (
cost_dataframe.loc[(tech_name, "investment"), "value"] * eta_old / eta_main
+ cost_dataframe.loc[(boiler, "investment"), "value"] * eta_steam / eta_main
)
cost_dataframe.loc[(tech_name, "investment"), "source"] = (
"Combination of " + tech_name + " and " + boiler
)
cost_dataframe.loc[(tech_name, "investment"), "source"] = source
cost_dataframe.loc[(tech_name, "investment"), "further description"] = ""

if cost_dataframe.loc[(tech_name, "VOM"), "value"]:
break
else:
if (tech_name, "VOM") not in cost_dataframe.index:
cost_dataframe.loc[(tech_name, "VOM"), "value"] = 0.0

cost_dataframe.loc[(tech_name, "VOM"), "value"] = (
cost_dataframe.loc[(tech_name, "VOM"), "value"] * eta_old / eta_main
+ cost_dataframe.loc[(boiler, "VOM"), "value"] * eta_steam / eta_main
)
cost_dataframe.loc[(tech_name, "VOM"), "source"] = (
"Combination of " + tech_name + " and " + boiler
)
cost_dataframe.loc[(tech_name, "VOM"), "source"] = source
cost_dataframe.loc[(tech_name, "VOM"), "further description"] = ""

cost_dataframe.loc[(tech_name, "efficiency"), "value"] = eta_main
cost_dataframe.loc[(tech_name, "efficiency"), "source"] = (
"Combination of " + tech_name + " and " + boiler
)
cost_dataframe.loc[(tech_name, "efficiency"), "source"] = source
cost_dataframe.loc[(tech_name, "efficiency"), "further description"] = ""

if "CHP" in tech_name:
cost_dataframe.loc[(tech_name, "efficiency-heat"), "value"] = (
cost_dataframe.loc[(tech_name, "efficiency-heat"), "value"]
* scalingFactor
+ cost_dataframe.loc[("solid biomass", "CO2 intensity"), "value"]
+ co2_capture
* (
cost_dataframe.loc[("biomass CHP capture", "heat-output"), "value"]
+ cost_dataframe.loc[
("biomass CHP capture", "compression-heat-output"), "value"
]
)
)
cost_dataframe.loc[(tech_name, "efficiency-heat"), "source"] = (
"Combination of " + tech_name + " and " + boiler
)
cost_dataframe.loc[(tech_name, "efficiency-heat"), "source"] = source
cost_dataframe.loc[
(tech_name, "efficiency-heat"), "further description"
] = ""

if "biogas CC" in tech_name:
cost_dataframe.loc[(tech_name, "VOM"), "value"] = 0
cost_dataframe.loc[(tech_name, "VOM"), "unit"] = "EUR/MWh"

cost_dataframe.loc[(tech_name, "VOM"), "value"] = (
cost_dataframe.loc[(tech_name, "VOM"), "value"] * eta_old / eta_main
+ cost_dataframe.loc[(boiler, "VOM"), "value"] * eta_steam / eta_main
)
cost_dataframe.loc[(tech_name, "VOM"), "source"] = (
"Combination of " + tech_name + " and " + boiler
)
cost_dataframe.loc[(tech_name, "VOM"), "further description"] = ""

return cost_dataframe


Expand Down Expand Up @@ -3806,17 +3805,24 @@ def add_energy_storage_database(
df = df.drop(columns=["ref_size_MW", "EP_ratio_h"])
df = df.fillna(df.dtypes.replace({"float64": 0.0, "O": "NULL"}))
df.loc[:, "unit"] = df.unit.str.replace("NULL", "per unit")
df.loc[(df["parameter"] == "efficiency") & (df["unit"].isna()), "unit"] = "per unit"

# b) Change data to PyPSA format (aggregation of components, units, currency, etc.)
df = clean_up_units(df, "value") # base clean up

# rewrite technology to be charger, store, discharger, bidirectional-charger
df.loc[:, "carrier"] = df.carrier.str.replace("NULL", "")
df.loc[:, "carrier"] = df["carrier"].apply(lambda x: x.split("-"))
carrier_list_len = df["carrier"].apply(lambda x: len(x))
carrier_str_len = df["carrier"].apply(lambda x: len(x[0]))
carrier_first_item = df["carrier"].apply(lambda x: x[0])
carrier_last_item = df["carrier"].apply(lambda x: x[-1])
df["carrier"] = (
df["carrier"]
.fillna("")
.astype(str)
.str.strip()
.apply(lambda x: x.split("-") if x else [])
)
carrier_list_len = df["carrier"].apply(len)
carrier_str_len = df["carrier"].apply(lambda x: len(x[0]) if len(x) > 0 else 0)
carrier_first_item = df["carrier"].apply(lambda x: x[0] if len(x) > 0 else "")
carrier_last_item = df["carrier"].apply(lambda x: x[-1] if len(x) > 0 else "")
bicharger_filter = carrier_list_len == 3
charger_filter = (carrier_list_len == 2) & (carrier_first_item == "elec")
discharger_filter = (carrier_list_len == 2) & (carrier_last_item == "elec")
Expand Down Expand Up @@ -3937,7 +3943,8 @@ def add_energy_storage_database(
or tech_name == "Pumped-Heat-store"
):
x1 = pd.concat(
[x, pd.DataFrame(other_segments_points)], ignore_index=True
[x.reset_index(drop=True), pd.Series(other_segments_points)],
ignore_index=True,
)
y1 = y
factor = 5
Expand All @@ -3950,7 +3957,7 @@ def add_energy_storage_database(
number_of_terms=i + 1,
)
y1 = pd.concat(
[y1, pd.DataFrame([cost_at_year])], ignore_index=True
[y1, pd.Series([cost_at_year])], ignore_index=True
)
f = interpolate.interp1d(
x1.squeeze(),
Expand All @@ -3960,7 +3967,8 @@ def add_energy_storage_database(
)
elif tech_name == "Hydrogen-charger":
x2 = pd.concat(
[x, pd.DataFrame(other_segments_points)], ignore_index=True
[x.reset_index(drop=True), pd.Series(other_segments_points)],
ignore_index=True,
)
y2 = y
factor = 6.5
Expand All @@ -3971,7 +3979,7 @@ def add_energy_storage_database(
number_of_terms=i + 1,
)
y2 = pd.concat(
[y2, pd.DataFrame([cost_at_year])], ignore_index=True
[y2, pd.Series([cost_at_year])], ignore_index=True
)
f = interpolate.interp1d(
x2.squeeze(),
Expand All @@ -3981,7 +3989,8 @@ def add_energy_storage_database(
)
else:
x3 = pd.concat(
[x, pd.DataFrame(other_segments_points)], ignore_index=True
[x.reset_index(drop=True), pd.Series(other_segments_points)],
ignore_index=True,
)
y3 = y
factor = 2
Expand All @@ -3992,7 +4001,7 @@ def add_energy_storage_database(
number_of_terms=i + 1,
)
y3 = pd.concat(
[y3, pd.DataFrame([cost_at_year])], ignore_index=True
[y3, pd.Series([cost_at_year])], ignore_index=True
)
f = interpolate.interp1d(
x3.squeeze(),
Expand Down Expand Up @@ -4038,7 +4047,10 @@ def add_energy_storage_database(
df = pd.concat([df, df_new], ignore_index=True)

# d) Combine metadata and add to cost database
df["source"] = df["source"].fillna("").astype(str)
df["reference"] = df["reference"].fillna("").astype(str)
df.loc[:, "source"] = df["source"] + ", " + df["reference"]
df["note"] = df["note"].fillna("NULL").astype(str)
for i in df.index:
df.loc[i, "further description"] = str(
{
Expand Down
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