Source code for pudl.metadata.resources.vcerare

"""Table definitions for data coming from the Vibrant Clean Energy Renewable Generation Profiles."""

from typing import Any



RESOURCE_METADATA: dict[str, dict[str, Any]] = {
    "out_vcerare__hourly_available_capacity_factor": {
        "description": (
            "The data in this table were produced by Vibrant Clean Energy, and are "
            "licensed to the public under the Creative Commons Attribution 4.0 International "
            "license (CC-BY-4.0). The table consists of estimated county-averaged hourly "
            "capacity factors for wind and solar generating facilities across the contiguous "
            "United States (US) to be used as a tool and input for resource adequacy modeling "
            "and planning. The hourly capacity factors are normalized to unity for maximal power "
            "output. To convert to units of power, the user must multiply by the installed capacity "
            "within the county.\n\n"
            "The technologies provided are:\n"
            "1) Onshore wind assuming a 100m hub height and 120m rotor diameter;\n"
            "2) Offshore wind assuming a 140m hub height and 120m rotor diameter;\n"
            "3) Utility solar assuming a fixed axis panel tilted at latitude.\n\n"
            "The foundation of the capacity factors provided here is the NOAA HRRR "
            "operational numerical weather prediction model. The HRRR covers the entire "
            "contiguous US at a horizontal resolution of 3 km. Forecasts are intialized each "
            "hour of the year. Forecast hour two (2) is used as the input data for the power "
            "algorithms. This forecast hour is chosen to trade-off the impact of the measurement "
            "and data assimilation procedure of the HRRR with the physics of the model to derive "
            "the most complete picture of the atmosphere at the forecast time horizon. "
            "Hourly capacity factors are spatially averaged across each county over the contiguous "
            "USA. There are a handful of counties that are too small to pick up representation on "
            "the HRRR operational forecast grid. As such, these counties will have no wind or solar "
            "power production curves.\n\n"
            "For wind capacity factors: vertical slices of the atmosphere are considered across "
            "the defined rotor swept area. Bringing together wind speed, density, temperature and "
            "icing information, a power capacity is estimated using a representative power coefficient "
            "(Cp) curve to determine the power from a given wind speed, atmospheric density and "
            "temperature. There is no wake modeling included in the dataset.\n\n"
            "For solar capacity factors: pertinent surface weather variables are pulled such as "
            "incoming short wave radiation, direct normal irradiance (calculated in the HRRR 2016 "
            "forward), surface temperature and other parameters. These are used in a non-linear "
            "I-V curve translation to power capacity factors. Due to power production "
            "performance being correlated with panel temperatures, during cold sunny periods, "
            "some solar capacity factor values are greater than 1 (but less that 1.1)."
        ),
        "schema": {
            "fields": [
                "state",
                "county_or_lake_name",
                "datetime_utc",
                "report_year",
                "hour_of_year",
                "county_id_fips",
                "latitude",
                "longitude",
                "capacity_factor_solar_pv",
                "capacity_factor_onshore_wind",
                "capacity_factor_offshore_wind",
            ],
            "primary_key": ["state", "county_or_lake_name", "datetime_utc"],
        },
        "sources": ["vcerare"],
        "field_namespace": "vcerare",
        "etl_group": "vcerare",
        "create_database_schema": False,
    },
}