ReEDS: Utility-scale PV profiles

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 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). 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 five 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_upv_{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_upv_{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