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Fix bugs in add_energy_storage_database #285

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Fix bugs in add_energy_storage_database #285

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c021858
Fix bugs in add_energy_storage_database
millingermarkus Sep 7, 2026
639313c
Catch all fixed costs in add_energy_storage_database
millingermarkus Sep 7, 2026
fdd8f4a
Minor cleanup
millingermarkus Sep 7, 2026
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134 changes: 67 additions & 67 deletions outputs/US/costs_2020.csv
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134 changes: 67 additions & 67 deletions outputs/US/costs_2025.csv
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134 changes: 67 additions & 67 deletions outputs/US/costs_2030.csv
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134 changes: 67 additions & 67 deletions outputs/US/costs_2035.csv
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134 changes: 67 additions & 67 deletions outputs/US/costs_2040.csv
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134 changes: 67 additions & 67 deletions outputs/US/costs_2045.csv
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134 changes: 67 additions & 67 deletions outputs/US/costs_2050.csv
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134 changes: 67 additions & 67 deletions outputs/costs_2020.csv
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134 changes: 67 additions & 67 deletions outputs/costs_2025.csv
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134 changes: 67 additions & 67 deletions outputs/costs_2030.csv
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134 changes: 67 additions & 67 deletions outputs/costs_2035.csv
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134 changes: 67 additions & 67 deletions outputs/costs_2040.csv
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134 changes: 67 additions & 67 deletions outputs/costs_2045.csv
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134 changes: 67 additions & 67 deletions outputs/costs_2050.csv
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54 changes: 41 additions & 13 deletions scripts/compile_cost_assumptions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3735,18 +3735,43 @@ 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 @@ -3867,7 +3892,7 @@ 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 @@ -3880,7 +3905,7 @@ 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 @@ -3890,7 +3915,7 @@ 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 @@ -3901,7 +3926,7 @@ 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 @@ -3911,7 +3936,7 @@ 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 @@ -3922,7 +3947,7 @@ 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 @@ -3968,14 +3993,17 @@ 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"]

for i in df.index:
df.loc[i, "further description"] = str(
{
"carrier": df.loc[i, "carrier"],
"technology_type": [df.loc[i, "technology_type"]],
"type": [df.loc[i, "type"]],
"note": [df.loc[i, "note"]],
"note": [str(df.loc[i, "note"])],
}
)
# keep only relevant columns
Expand Down