NREL/ReEDS
Published March 2, 2026 | Version v3
Dataset Open

ReEDS: Utility-scale PV profiles

Authors/Creators

  • 1. National Renewable Energy Laboratory
  • 2. Matt Mowers LLC

Description

This record provides modeled hourly utility-scale photovoltaic (PV) capacity factor profiles for the contiguous United States (U.S.), generated using the National Solar Radiation Database (NSRDB), 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

Utility-scale PV capacity factor profiles use assumptions consistent with the 2024 Annual Technology Baseline (ATB) and Lopez et al. (2025) assumptions for 2035, namely:
  • 1-axis tracking with 0° tilt
  • Inverter loading ratio: 1.34
  • DC losses: 10.4%
  • Ground cover ratio: 0.4
Values are given in units of  $[MW_{AC,grid}/MW_{DC,array}]$, where  $MW_{AC,grid}$ is the post-inverter output to the grid and  $MW_{DC,array}$ is the pre-inverter PV array capacity. To convert to units of  $[MW_{AC,grid}/MW_{AC,nameplate}]$, the profiles should be multiplied by the inverter loading ratio of 1.34  $[MW_{DC,array}/MW_{AC,nameplate}]$.

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). 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 ~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_upv_{access scenario}.h5: Hourly capacity factor profiles. The 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_upv_{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

Files

sc_upv_limited.csv

Files (17.4 GB)

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