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Fixes to pypsa-scripts #5

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Fixes to pypsa-scripts #5

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1fc5b2d
Minor error correction in line parameter calculation
May 22, 2025
475e405
Remove wrong investment costs
May 22, 2025
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8 changes: 4 additions & 4 deletions PypsaReader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,7 @@ def __init__(self, network: pypsa.Network):
"RampDown": lambda df: df["ramp_down"],
"pStartupCostEUR": lambda df: df["start_up_cost"],
"EnableInvest": lambda df: df["enable_invest"],
"InvestCost": lambda df: df["capital_cost"],
"InvestCost": lambda df: "check_pypsa_data",
"OMVarCost": lambda df: df["marginal_cost"],
},
"dPower_VRESProfiles": {
Expand All @@ -97,7 +97,7 @@ def __init__(self, network: pypsa.Network):
"MaxProd": lambda df: df["max_prod"],
"enableinvest": lambda df: df["enable_invest"],
"MaxInvest": lambda df: df["p_nom_max"],
"InvestCost": lambda df: df["capital_cost"],
"InvestCost": lambda df: "check_pypsa_data",
"OMVarCost": lambda df: df["marginal_cost"]
},
"dPower_RoR": {
Expand All @@ -112,7 +112,7 @@ def __init__(self, network: pypsa.Network):
"OMVarCost": lambda df: df["marginal_cost"],
"EnableInvest": lambda df: df["enable_invest"],
"MaxInvest": lambda df: df["p_nom_max"],
"InvestCostPerMW": lambda df: df["capital_cost"]
"InvestCostPerMW": lambda df: "check_pypsa_data"
},
"dPower_Storage": {
"source": lambda net: h.prepare_storage_units(net),
Expand All @@ -128,7 +128,7 @@ def __init__(self, network: pypsa.Network):
"OMVarCost": lambda df: df["marginal_cost"],
"EnableInvest": lambda df: df["enable_invest"],
"MaxInvest": lambda df: df["p_nom_max"],
"InvestCostPerMWh": lambda df: df["capital_cost"],
"InvestCostPerMWh": lambda df: "check_pypsa_data",
"Ene2PowRatio": lambda df: df["max_hours"],
"ShelfLife": lambda df: df["lifetime"]
},
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2 changes: 1 addition & 1 deletion pypsa_helper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ def prepare_ac_lines(net):
lines["x"] = lines.apply(
lambda row: row["x"] if row["x"] != 0 else types.loc[row["type"]].x_per_length * row["length"], axis=1)
lines["b"] = lines.apply(
lambda row: row["b"] if row["b"] != 0 else types.loc[row["type"]].x_per_length * row["length"], axis=1)
lambda row: row["b"] if row["b"] != 0 else types.loc[row["type"]].c_per_length * row["length"], axis=1)

lines["pmax"] = lines["s_nom"] * lines["s_max_pu"]
lines["id"] = lines.index
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