import numpy as np
import pandas as pd
[docs]def match_gen_load_names(list1, list2):
"""Match generator and load names"""
list1.sort()
list2.sort()
prefix_dict = {}
for item in list2:
prefix = item.split("_")[0]
if prefix not in prefix_dict:
prefix_dict[prefix] = []
prefix_dict[prefix].append(item)
result = {}
for item in list1:
prefix = item.split("_")[0]
if prefix in prefix_dict and prefix_dict[prefix]:
result[item] = prefix_dict[prefix].pop(0)
else:
print(f"No match found for {item}")
return result
[docs]def make_storage_resources(data):
sced_gen = data["sced_gen_resource"][
["Resource Name", "QSE", "DME", "Resource Type"]
].drop_duplicates()
sced_gen_storage_names = sced_gen[sced_gen["Resource Type"] == "PWRSTR"][
"Resource Name"
].unique()
sced_load_all = data["sced_load_resource"]["Resource Name"].unique()
matched_load_gen_names = match_gen_load_names(sced_gen_storage_names, sced_load_all)
storage_resources = (
pd.DataFrame(
{
"gridstatus_id": list(matched_load_gen_names.keys()),
"ercot_gen_resource_name": list(matched_load_gen_names.keys()),
"ercot_load_resource_name": list(matched_load_gen_names.values()),
},
)
.merge(
data["settlement_point_mapping"],
how="left",
left_on="ercot_gen_resource_name",
right_on="Resource Name",
)
.drop(columns=["Resource Name"])
.merge(
sced_gen,
how="left",
left_on="ercot_gen_resource_name",
right_on="Resource Name",
)
.drop(columns=["Resource Name"])
.rename(columns={"Settlement Point Name": "settlement_point_name"})
)
# Fill missing settlement point names with manual matches
manual_matches = {
"ESTONIAN_BES1": "ESTONIAN_ALL",
"FENCESLR_BESS1": "FENCESLR_ALL",
"MV_VALV4_BESS": "MV_VALV4_RN",
"RVRVLYS_ESS1": "RVRVLYS_ALL",
"RVRVLYS_ESS2": "RVRVLYS_ALL",
"WFTANK_ESS1": "WFTANK_ESS1",
"LONESTAR_BESS": "LONESTAR_RN",
}
storage_resources["settlement_point_name"] = storage_resources[
"settlement_point_name"
].fillna(storage_resources["ercot_gen_resource_name"].map(manual_matches))
# Get SARA data and merge with storage_resources
cols = [
"gridstatus_id",
"unit_name",
"ercot_gen_resource_name",
"ercot_load_resource_name",
"settlement_point_name",
"qse",
"dme",
"resource_type",
"county",
"zone",
"in_service_year",
"installed_capacity_rating",
"summer_capacity_mw",
"generation_interconnection_project_code",
]
storage_resources = (
storage_resources.merge(
data["sara"],
how="left",
left_on="ercot_gen_resource_name",
right_on="Unit Code",
)
.drop(columns=["Unit Code", "Fuel", "New Planned Project Additions to Report"])
.rename(
columns={
"Unit Name": "unit_name",
"County": "county",
"Zone": "zone",
"Generation Interconnection Project Code": "generation_interconnection_project_code", # noqa
"In Service Year": "in_service_year",
"Installed Capacity Rating": "installed_capacity_rating",
"Summer Capacity (MW)": "summer_capacity_mw",
"QSE": "qse",
"DME": "dme",
"Resource Type": "resource_type",
},
)[cols]
)
return storage_resources
[docs]def process_dam_gen(df):
time_cols = [
"Interval Start",
"Interval End",
]
resource_cols = [
"QSE",
"DME",
"Resource Name",
"Resource Type",
"Settlement Point Name",
]
telemetry_cols = [
"Resource Status",
"HSL",
"LSL",
"Start Up Hot",
"Start Up Inter",
"Start Up Cold",
"Min Gen Cost",
]
energy_award_cols = [
"Awarded Quantity",
"Energy Settlement Point Price",
]
as_cols = [
"RegUp Awarded",
"RegUp MCPC",
"RegDown Awarded",
"RegDown MCPC",
"RRSPFR Awarded",
"RRSFFR Awarded",
"RRSUFR Awarded",
"RRS MCPC",
"ECRSSD Awarded",
"ECRS MCPC",
"NonSpin Awarded",
"NonSpin MCPC",
]
curve = "QSE submitted Curve"
df[curve] = extract_curve(df, "QSE submitted Curve")
all_cols = resource_cols + telemetry_cols + energy_award_cols + as_cols + [curve]
for col in all_cols:
if col not in df.columns:
df[col] = np.nan
df = df[time_cols + all_cols]
return df
[docs]def process_dam_load(df):
time_cols = [
"Time",
"Interval Start",
"Interval End",
]
resource_cols = ["Load Resource Name"]
telemetry_cols = [
"Max Power Consumption for Load Resource",
"Low Power Consumption for Load Resource",
]
as_cols = [
"RegUp Awarded",
"RegUp MCPC",
"RegDown Awarded",
"RegDown MCPC",
"RRSPFR Awarded",
"RRSFFR Awarded",
"RRSUFR Awarded",
"RRS MCPC",
"ECRSSD Awarded",
"ECRSMD Awarded",
"ECRS MCPC",
"NonSpin Awarded",
"NonSpin MCPC",
]
all_cols = resource_cols + telemetry_cols + as_cols
for col in all_cols:
if col not in df.columns:
df[col] = np.nan
df = df[time_cols + all_cols]
# rename for consistency
# with gen columns
df = df.rename(
columns={
"Load Resource Name": "Resource Name",
},
)
return df
[docs]def process_dam_load_as_offers(df):
if "QSE" not in df.columns:
# after Interval End
index = df.columns.tolist().index("Interval End") + 1
df.insert(index, "QSE", np.nan)
if "DME" not in df.columns:
# after QSE
index = df.columns.tolist().index("QSE") + 1
df.insert(index, "DME", np.nan)
df = df.rename(
columns={
"Load Resource Name": "Resource Name",
},
)
return df
[docs]def process_sced_gen(df):
time_cols = [
"Interval Start",
"Interval End",
"SCED Time Stamp",
]
resource_cols = ["QSE", "DME", "Resource Name", "Resource Type"]
telemetry_cols = [
"Telemetered Resource Status",
"Output Schedule",
"HSL",
"HASL",
"HDL",
"LSL",
"LASL",
"LDL",
"Base Point",
"Telemetered Net Output ",
]
as_cols = [
"Ancillary Service REGUP",
"Ancillary Service REGDN",
"Ancillary Service RRS",
"Ancillary Service RRSFFR",
"Ancillary Service NSRS",
"Ancillary Service ECRS",
]
tpo_cols = [
"Start Up Cold Offer",
"Start Up Hot Offer",
"Start Up Inter Offer",
"Min Gen Cost",
"SCED TPO Offer Curve",
]
sced1_offer_col = "SCED1 Offer Curve"
df[sced1_offer_col] = extract_curve(df, "SCED1 Curve")
df[tpo_cols[-1]] = extract_curve(df, "Submitted TPO")
all_cols = resource_cols + telemetry_cols + as_cols + [sced1_offer_col] + tpo_cols
for col in all_cols:
if col not in df.columns:
df[col] = np.nan
df = df[time_cols + all_cols]
# standardized to same naming as load
# clean up column names
df = df.rename(
columns={
"Ancillary Service RRS": "AS Responsibility for RRS",
"Ancillary Service RRSFFR": "AS Responsibility for RRSFFR",
"Ancillary Service NSRS": "AS Responsibility for NonSpin",
"Ancillary Service REGUP": "AS Responsibility for RegUp",
"Ancillary Service REGDN": "AS Responsibility for RegDown",
"Ancillary Service ECRS": "AS Responsibility for ECRS",
# remove space
"Telemetered Net Output ": "Telemetered Net Output",
},
)
return df
[docs]def process_sced_load(df):
time_cols = [
"Interval Start",
"Interval End",
"SCED Time Stamp",
]
resource_cols = ["QSE", "DME", "Resource Name"]
telemetry_cols = [
"Telemetered Resource Status",
"Max Power Consumption",
"Low Power Consumption",
"Real Power Consumption",
"HASL",
"HDL",
"LASL",
"LDL",
"Base Point",
]
as_cols = [
"AS Responsibility for RRS",
"AS Responsibility for RRSFFR",
"AS Responsibility for NonSpin",
"AS Responsibility for RegUp",
"AS Responsibility for RegDown",
"AS Responsibility for ECRS",
]
bid_curve_col = "SCED Bid to Buy Curve"
df[bid_curve_col] = extract_curve(df, "SCED Bid to Buy Curve")
all_cols = resource_cols + telemetry_cols + as_cols + [bid_curve_col]
for col in all_cols:
if col not in df.columns:
df[col] = np.nan
df = df[time_cols + all_cols]
return df
# # backup for more node names
# pd.read_html("https://www.ercot.com/content/cdr/html/current_np6788.html", skiprows=3)[0][0] # noqa
# # todo add in QSE
# # todo prefix and county match
