DOI: 10.5281/zenodo.18422200
title: ReEDS: Land-based wind profiles

description: This record provides modeled hourly land-based wind capacity factor profiles for the contiguous United States (U.S.), generated using the Wind Integration National Dataset Toolkit (WTK), High-Resolution Rapid Refresh (HRRR) dataset, System Advisor Model (SAM), and Renewable Energy Potential (reV) model. These profiles are used in the Regional Energy Deployment System (ReEDS) model.

 

Additional details are provided in the ReEDS documentation.

Technology assumptions

Land-based wind capacity factor profiles use assumptions consistent with the 2024 Annual Technology Baseline (ATB) and Lopez et al. (2025) assumptions for 2035, namely:






Turbine rating: 6 MW

Rotor diameter: 170 m

Hub height: 115 m

Specific power: 264 $W/m^2$

Static losses: 11%

Additional losses: Hourly wake losses are determined endogenously for each site, with average values ranging from 0.05%–25%. Each profile also includes hourly outages during extreme cold (hub height air temperature ≤–20°C) and icing (air temperature ≤0 °C and relative humidity ≥95%) conditions.


Temporal resolution



All capacity factor profiles are at hourly resolution in U.S. Central Standard Time (UTC–06:00) and represent instantaneous values on the hour. The profiles span 15 weather years (2007–2013 + 2016–2023); profiles for 2007–2013 use WTK data and profiles for 2016–2023 use bias-corrected HRRR data, with further information available in the Wind Resource Database (WRDB). For leap years, the final day of the year (December 31) is dropped, such that each year contains 8760 hours.

Spatial resolution



Capacity factor profiles are provided for three siting access assumptions described by Lopez et al. (2025) ("limited", "reference", and "open") and two spatial resolutions. Files with names ending in "county" are resolved for individual U.S. counties; files with names ending in "ba" are resolved for the 134 ReEDS model zones. Profiles are further differentiated into ten resource classes, with higher class numbers indicating higher average capacity factor.




File structure





county2zone.csv: U.S. counties comprising each of the 134 ReEDS model zones

cf_wind-ons_{access scenario}_{ba or county}.h5: Hourly capacity factor profiles. Columns are labeled as {resource class}|p{zone number or 5-digit county FIPS code}.

sc_wind-ons_{access scenario}_{ba or county}.csv: Available capacity [MW] associated with each class/region capacity factor profile



Capacity factor profiles are saved as hierarchical Data Format (HDF5) files. The following Python function can be used to read a capacity factor .h5 file into a pandas dataframe:

 



import h5py

import pandas as pd

def read_cf_profile(filepath):

    """

    Read a CF profile from `filepath` and return a pandas dataframe.

    Usage:

    `df = read_profile('/path/to/filename.h5')`

    """

    encoding = 'utf-8'

    with h5py.File(filepath, 'r') as f:

        df = pd.DataFrame(

            f['data'][:],

            columns=pd.Series(f['columns']).str.decode(encoding),

            index=f['index_0'],

        )

        df.index = pd.to_datetime(

            pd.Series(df.index, name='datetime').str.decode(encoding)

        )

    return df