out_vcerare__hourly_available_capacity_factor
Hourly time series of estimated county-averaged 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.
- Most-recent data:
2023
- Processing:
Data is ready for use in analyses, but for practical reasons has not been denormalized and remains in narrow format.
- Source:
Vibrant Clean Energy Resource Adequacy Renewable Energy (RARE) Power Dataset
- Primary key:
state, place_name, datetime_utc
Usage Warnings
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.
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.
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).
Additional Details
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 technologies provided are:
Onshore wind assuming a 100m hub height and 120m rotor diameter;
Offshore wind assuming a 140m hub height and 120m rotor diameter;
Utility solar assuming a fixed axis panel tilted at latitude.
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 initialized 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.
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.
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.
Columns
Two letter US state abbreviation.
County or lake name, sourced from the latest Census PEP vintage based on county FIPS ID. Lake names originate from VCE RARE directly, and may also appear several times--once for each state it touches. FIPS ID values for lakes have been nulled.
Date and time converted to Coordinated Universal Time (UTC).
Four-digit year in which the data was reported.
Integer between 1 and 8670 representing the hour in a given year.
County ID from the Federal Information Processing Standard Publication 6-4.
Latitude of the place centroid (e.g., county centroid).
Longitude of the place centroid (e.g., county centroid).
Estimated capacity factor (0-1) calculated for solar PV assuming a fixed axis panel tilted at latitude and DC power outputs. 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).All values are based on outputs from the NOAA HRRR operational numerical weather prediction model. Capacity factors are normalized to unity for maximal power output. 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.
Estimated capacity factor (0-1) calculated for onshore wind assuming a 100m hub height and 120m rotor diameter.Based on outputs from the NOAA HRRR operational numerical weather prediction model. Capacity factors are normalized to unity for maximal power output. 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.
Estimated capacity factor (0-1) calculated for offshore wind assuming a 140m hub height and 120m rotor diameter.Based on outputs from the NOAA HRRR operational numerical weather prediction model. Capacity factors are normalized to unity for maximal power output. 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.