ReEDS: Land-based wind profiles

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 Coordinated Universal Time (UTC) 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 up to ~50,000 11.5 km × 11.5 km resource sites for the three siting access assumptions described by Lopez et al. (2025) ("limited", "reference", and "open"). Additional siting data are available at https://data.openei.org/siting_lab. File structure sitemap.csv: Site locations (latitudes and longitudes). The sc_point_gid column contains the identifier for each site. cf_wind-ons_{access scenario}.h5: Hourly capacity factor profiles. Each column name is the identifier for the corresponding site. Should be multiplied by the "scale" attribute (a factor of 0.0001) to convert to fractional values in the range [0-1]. sc_wind-ons_{access scenario}.csv: Available capacity [MW] and average capacity factor for each site. 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, year): """ Read a CF profile from `filepath` for `year` and return a pandas dataframe. Usage: `df = read_cf_profile('/path/to/filename.h5', 2023)` """ with h5py.File(filepath, 'r') as f: time_index = pd.to_datetime( pd.Series(f[f"time_index_{year}"][:]).str.decode('utf-8') ) cf_values = ( f[f"cf_profile_{year}"][:] * f[f"cf_profile_{year}"].attrs["scale"] ) df = pd.DataFrame( index=time_index, columns=f["columns"], data=cf_values ) return df