Vibrant Clean Energy Resource Adequacy Renewable Energy (RARE) Power Dataset

Source URL

https://vibrantcleanenergy.com/products/datasets/

Source Description

This dataset was produced by Vibrant Clean Energy and is licensed to the public under the Creative Commons Attribution 4.0 International license (CC-BY-4.0). The data consists of hourly, county-level renewable generation profiles in the continental United States and was compiled based on outputs from the NOAA HRRR weather model. Profiles are stated as a capacity factor (a percentage of nameplate capacity) and exist for onshore wind, offshore wind, and fixed-tilt solar generation types.

Sorce Format

Comma Separated Value (.csv)

Download Size

1937 MB

Temporal Coverage

2019-2023

PUDL Code

vcerare

Issues

Open Vibrant Clean Energy Resource Adequacy Renewable Energy (RARE) Power Dataset issues

PUDL Database Tables

We’ve segmented the processed data into the following normalized data tables. Clicking on the links will show you a description of the table as well as the names and descriptions of each of its fields.

Data Dictionary

Browse Online

out_vcerare__hourly_available_capacity_factor

Table not published to Datasette.

Background

The data in the Resource Adequacy Renewable Energy (RARE) Power Dataset was produced by Vibrant Clean Energy based on outputs from the NOA HRRR model and are licensed to the public under the Creative Commons Attribution 4.0 International license (CC-BY-4.0).

See the README archived on Zenodo for more detailed information.

Download additional documentation

Data available through PUDL

Hourly, county-level data from 2019 - 2023 is integrated into PUDL. There is a second release of data for the years 2014 - 2018 expected in Q1 of 2025, which will be integrated into PUDL pending funding availability.

You can explore the VCE RARE dataset with this Jupyter notebook on Kaggle.

Who submits this data?

This data does not come from a government agency, and is not the result of compulsory data reporting.

What does the original data look like?

The contents of the original CSVs are formatted so that Excel can display the data without crashing. There’s one file per year per generation type, and each file contains an index column for time (simply 1, 2, 3…8760 to represent the hours in a year) and columns for each county populated with capacity factor values as a percentage from 0-100.

Notable Irregularities

Non-county regions

The original data include capacity factors for some non-county areas including the Great Lakes and 2 small cities (Bedford City, VA and Clifton Forge City, VA). It associated “county” FIPS IDs with those areas, meaning that there was not a 1:1 relationship between the FIPS IDs and the named areas, and the geographic region implied by the FIPS IDs did not correspond to the named area. We’ve dropped the cities – one of which contained no data – and set the FIPS codes for the Great Lakes to NA. Note that lakes bordering multiple states will appear more than once in the data. VCE used a nearest neighbor technique to assign the state waters to the counties (this pertains to coastal areas as well).

Name and ID issues

  • The place name for Lake Huron is misspelled “Hurron” in the original data.

  • The place name for Lake Saint Claire in the original data uses the abbreviation “St.” including the period character – this is the only abbreviation and only non alphabetical or underscore character in any of the place names.

  • In 2015 Shannon County, South Dakota was renamed Oglala Lakota County and assigned a new FIPS code. The VCE data includes the new FIPS code (46102) but the old name, while the Census DP1 dataset included with PUDL is from 2010, and so uses both the old name and the old FIPS code (46113).

Capacity factors > 1

There are a couple of capacity factor values for the solar pv data that exceed the maximum value of 1 for capacity factor (or 100 for the raw data–PUDL converts the data from a percentage to a fraction to match other reported capacity factor data). This is 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).

8760-hour years

This data is primarily used for modeling purposes and conforms to the 8760 hour/year standard regardless of leap years. This means that 2020 is missing data for December 31st.

PUDL Data Transformations

To see the transformations applied to the data in each table, you can read the docstrings for pudl.transform.vcerare created for each table’s respective transform function.