Definition: The capacity factor represents the expected annual average energy production divided by the annual energy production, assuming the plant operates at rated capacity for every hour of the year. It is intended to represent a long-term average over the lifetime of the plant; it does not represent interannual variation in energy production. Future year estimates represent the estimated annual average capacity factor over the technical lifetime of a new plant installed in a given year.
Residential PV system capacity is not directly comparable to other technologies' capacity factors. Other technologies' capacity factors (including utility-scale PV) are represented exclusively in AC units (see Solar PV AC-DC Translation). However, because residential PV pricing in the 2020 ATB is represented in $/WDC, residential PV system capacity is a DC rating. Because each technology uses consistent capacity ratings, the LCOEs are comparable.
The capacity factor is influenced by the hourly solar profile, technology (e.g., thin-film or crystalline silicon), expected downtime, and inverter losses to transform from DC to AC power. The DC-to-AC ratio is a design choice that influences the capacity factor.
PV plant capacity factor incorporates an assumed degradation rate of 0.7%/yr (Feldman et al. Forthcoming) in the annual average calculation.
Base Year: R&D could lower degradation rates of PV plant capacity factor; future projections for the Moderate Scenario and the Advanced Scenario reduce degradation rates by 2030, using a straight-line basis, to 0.5%/yr, and 0.2%/yr respectively. The Conservative Scenario assumes no improvement in degradation rates through 2030.
Click here and select Tech Detail = All to add filters to the initial figure on to show a range of capacity factors based on variation in solar resource in the contiguous United States. The range of the Base Year estimates illustrate the effect of locating a utility-scale PV plant in places with lower or higher solar irradiance. These five values use specific locations as examples of high (Daggett, CA), high-mid (Los Angeles, CA), mid (Kansas City, MO), low-mid (Chicago, IL), and low (Seattle, WA) resource areas in the United States as implemented in the System Advisor Model using PV system characteristics from Feldman et al. (Forthcoming).
First-year operation capacity factors as modeled range from 14.2% to 22.0%, though these depend significantly on location and system configuration (e.g., south-facing or west-facing).
Over time, PV installation output is reduced due to degradation in module quality, which is accounted in ATB estimates of capacity factor over the 30-year lifetime of the plant. The adjusted average capacity factor values in the 2020 ATB Base Year are 13.4% (Seattle, WA), 15.4% (Chicago, IL), 16.5% (Kansas City, MO), 19.0% (Los Angeles, CA), and 20.8% (Daggett, CA).
Future Years: Projections of capacity factors for plants installed in future years increase over time because of reduced system losses, and a straight-line reduction in PV plant capacity degradation rates from 0.7%/yr that reach 0.5%/yr and 0.2%/yr by 2030 for the Moderate Scenario and the Advanced Scenario respectively. The conservative innovation scenario assumes no improvement in degradation rates through 2030. The following table summarizes the difference in average capacity factor in 2030 caused by these changes in the technology innovation scenarios. Similar to our CAPEX assumptions, we assume each scenario's 2050 capacity factor is the equivalent of the 2030 capacity factor of the scenario but one degree more aggressive, with a straight-line change in price in the intermediate years between 2030 and 2050.
Kansas City, MO
Los Angeles, CA
Advanced Scenario (0.20%/yr Degradation Rate)
Moderate Scenario (0.50%/yr Degradation Rate)
Conservative Scenario (0.7%/yr Degradation Rate)
Solar PV plants have very little downtime, and inverter efficiency is already optimized. Even so, there is potential for future increases in capacity factors through technological improvements beyond lower degradation rates, such as less panel reflectivity and improved performance in low-light conditions.
The following references are specific to this page; for all references in this ATB, see References.
Feldman, David, Vignesh Ramasamy, Ran Fu, Ashwin Ramdas, Jal Desai, and Robert Margolis. (Forthcoming). U.S. Solar Photovoltaic System and Energy Storage Cost Benchmark: Q1 2020. Golden, CO: National Renewable Energy Laboratory.
Developed with funding from the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy.