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Merge pull request #197 from Breakthrough-Energy/daniel/hifld_plant
feat: process plant data to create data frame
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prereise/gather/griddata/hifld/data_process/generators.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,136 @@ | ||
| import pandas as pd | ||
| from powersimdata.utility.distance import haversine | ||
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| from prereise.gather.griddata.hifld import const | ||
| from prereise.gather.griddata.hifld.data_access import load | ||
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| def floatify(value, default=float("nan")): | ||
| """Return a float if possible, otherwise return a default value. | ||
| :param object value: value to be coerced to float, if possible. | ||
| :param object default: value to be returned if float conversion isn't possible. | ||
| :return: (*float/object*) -- float or default value as applicable. | ||
| """ | ||
| try: | ||
| if isinstance(value, str): | ||
| value = value.replace(",", "") | ||
| return float(value) | ||
| except Exception: | ||
| return default | ||
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| def map_generator_to_sub_by_location(generator, substation_groupby): | ||
| """Determine a likely substation for a generator to be connected to. Priority order | ||
| of mapping is: 1) if location is available and one or more substations exist in that | ||
| ZIP code, map by location to closest substation within that ZIP code, 2) if location | ||
| is available but no substations exist in that ZIP code, map to the closest | ||
| substation within neighboring ZIP codes, 3) if only ZIP code is available | ||
| (no location), and one or more substations exist, map to an arbitrarily chosen | ||
| substation within that ZIP code, 4) if only ZIP code is available (no location) | ||
| but no substations exist in that ZIP code, return NA. | ||
| :param pandas.Series generator: one generating unit from data frame. | ||
| :param pandas.GroupBy substation_groupby: data frame of substations, grouped by | ||
| (interconnect, ZIP). | ||
| :return: (*int/pd.NA*) -- substation ID if the generator can be mapped successfully | ||
| to a substation, else pd.NA. | ||
| """ | ||
| lookup_params = tuple(generator.loc[["interconnect", "ZIP"]]) | ||
| if pd.isna(generator["lat"]) or pd.isna(generator["lon"]): | ||
| # No location available | ||
| try: | ||
| matching_subs = substation_groupby.get_group(lookup_params) | ||
| return matching_subs.index[0] | ||
| except KeyError: | ||
| return pd.NA | ||
| try: | ||
| # This ZIP code contains substations, this block will execute successfully | ||
| matching_subs = substation_groupby.get_group(lookup_params) | ||
| except KeyError: | ||
| # If this ZIP code does not contain substations, this block will execute, and | ||
| # we select a set of 'nearby' substations | ||
| zip_range = [int(generator.loc["ZIP"]) + offset for offset in range(-100, 101)] | ||
| zip_range_strings = [str(z).rjust(5, "0") for z in zip_range] | ||
| try: | ||
| matching_subs = pd.concat( | ||
| [ | ||
| substation_groupby.get_group((generator.loc["interconnect"], z)) | ||
| for z in zip_range_strings | ||
| if (generator.loc["interconnect"], z) in substation_groupby.groups | ||
| ] | ||
| ) | ||
| except ValueError: | ||
| # If no matching subs within the given interconnection and ZIPs, give up | ||
| return pd.NA | ||
| distance_to_subs = matching_subs.apply( | ||
| lambda x: haversine((x.LATITUDE, x.LONGITUDE), (generator.lat, generator.lon)), | ||
| axis=1, | ||
| ) | ||
| return distance_to_subs.idxmin() | ||
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| def map_generator_to_bus_by_sub(generator, bus_groupby): | ||
| """Determine a likely bus for a generator to be connected to, based on the bus with | ||
| the lowest voltage within the generator's specified substation. | ||
| :param pandas.Series generator: one generating unit from data frame. | ||
| :param pandas.GroupBy bus_groupby: data frame of buses, grouped by substation ID. | ||
| :return: (*int/pd.NA*) -- bus ID if the generator has a substation ID, else pd.NA. | ||
| """ | ||
| if pd.isna(generator.sub_id): | ||
| return pd.NA | ||
| else: | ||
| return bus_groupby.get_group(generator.sub_id)["baseKV"].idxmin() | ||
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| def build_plant(bus, substations): | ||
| """Use source data on generating units from EIA/EPA, along with transmission network | ||
| data, to produce a plant data frame. | ||
| :param pandas.DataFrame bus: data frame of buses, to be used within | ||
| :func:`map_generator_to_bus_by_sub`. | ||
| :param pandas.DataFrame substations: data frame of substations. | ||
| :return: (*pandas.DataFrame*) -- data frame of generator data. | ||
| """ | ||
| # Initial loading | ||
| generators = load.get_eia_form_860(const.blob_paths["eia_form860_2019_generator"]) | ||
| plants = load.get_eia_form_860(const.blob_paths["eia_form860_2019_plant"]) | ||
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| # Data interpretation | ||
| plants = plants.set_index("Plant Code") | ||
| plants["Latitude"] = plants["Latitude"].map(floatify) | ||
| plants["Longitude"] = plants["Longitude"].map(floatify) | ||
| for col in ["Summer Capacity (MW)", "Winter Capacity (MW)", "Minimum Load (MW)"]: | ||
| generators[col] = generators[col].map(floatify) | ||
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| # Filtering / Grouping | ||
| generators = generators.query( | ||
| "Technology not in @const.eia_storage_gen_types" | ||
| ).copy() | ||
| bus_groupby = bus.groupby(bus["sub_id"].astype(int)) | ||
| # Filter substations with no buses | ||
| substations = substations.loc[set(bus_groupby.groups.keys())] | ||
| substation_groupby = substations.groupby(["interconnect", "ZIP"]) | ||
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| # Add information | ||
| generators["interconnect"] = ( | ||
| generators["Plant Code"] | ||
| .map(plants["NERC Region"]) | ||
| .map(const.nercregion2interconnect) | ||
| ) | ||
| generators["lat"] = generators["Plant Code"].map(plants["Latitude"]) | ||
| generators["lon"] = generators["Plant Code"].map(plants["Longitude"]) | ||
| generators["ZIP"] = generators["Plant Code"].map(plants["Zip"]) | ||
| generators["sub_id"] = generators.apply( | ||
| lambda x: map_generator_to_sub_by_location(x, substation_groupby), axis=1 | ||
| ) | ||
| generators["bus_id"] = generators.apply( | ||
| lambda x: map_generator_to_bus_by_sub(x, bus_groupby), axis=1 | ||
| ) | ||
| generators["Pmax"] = generators[ | ||
| ["Summer Capacity (MW)", "Winter Capacity (MW)"] | ||
| ].max(axis=1) | ||
| generators.rename({"Minimum Load (MW)": "Pmin"}, inplace=True, axis=1) | ||
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| return generators |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,21 @@ | ||
| from prereise.gather.griddata.hifld import const | ||
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| def map_state_and_county_to_interconnect(state_abv, county): | ||
| """Map a state and a county to an assumed interconnection. | ||
| :param str state_abv: two-letter state abbreviation. | ||
| :param str county: county name. | ||
| :raises ValueError: if the provided state abbreviation isn't present in mappings. | ||
| :return: (*str*) -- interconnection name. | ||
| """ | ||
| state_upper = state_abv.upper() | ||
| for region in ("Eastern", "Western"): | ||
| if state_upper in const.interconnect2state[region]: | ||
| return region | ||
| if state_upper in const.interconnect2state["split"]: | ||
| for region in set(const.state_county_splits[state_upper].keys()) - {"default"}: | ||
| if county.upper() in const.state_county_splits[state_upper][region]: | ||
| return region | ||
| return const.state_county_splits[state_upper]["default"] | ||
| raise ValueError(f"Got an unexpected state: {state_abv}") |
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