Definition: Commercial 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 commercial PV pricing in the 2020 ATB is represented in $/WDC, commercial 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), the bifaciality of the module, shading, 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. R&D could increase energy yield through bifaciality, better soil removal, improved cell temperature, lower system losses, O&M practices that improve uptime, and lower degradation rates of PV plant capacity factor; future projections assume energy yield gains of 0%–25% depending on the location and scenario For the 2020 ATB, commercial PV systems are modeled for a 200-kWDC fixed-tilt (5°), roof-mounted system.
Base Year: Click here and select Tech Detail = All to add filters to the initial figure on this page 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, California), high-mid (Los Angeles, California), mid (Kansas City, Missouri), low-mid (Chicago, Illinois), and low (Seattle, Washington) resource areas in the United States as implemented in the System Advisor Model using PV system characteristics from Fu, Feldman, and Margolis (2018).
Solar resources in the 2020 ATB are categorized according to the range of solar irradiance across the range of latitudes for five resource locations in the contiguous United States:
First-year operation capacity factors as modeled range from 13.2% to 20.6%, though these depend significantly on location and system configuration (e.g., fixed-tilt or single-axis tracking).
Over time, PV installation output is reduced due to degradation in module quality, which is accounted for 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 12.9% (Seattle, WA), 14.4% (Chicago, IL), 15.4% (Kansas City, MO), 18.0% (Los Angeles, CA), and 19.5% (Daggett, CA).
Future Years: Projections of capacity factors for plants installed in future years increase over time because of an increase in energy yield from the module, 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 and advanced technology innovation scenarios respectively. The conservative technology 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
We also developed and modeled a scenario one degree more aggressive than the Advanced Scenario to estimate its 2050 capacity factor. The 2050 Advances Scenario assumes: 23%-25% energy gain, depending on location, through a 20% energy yield gain at the module and lower system losses; and a 0.2%/year degradation rate. This is achieved through bifaciality, soil removal, improved cell temperature, and improved O&M uptime.
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.
Fu, Ran, Feldman, David, & Margolis, Robert. (2018). U.S. Solar Photovoltaic System Cost Benchmark: Q1 2018. National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy19osti/72399.pdf