Content displaying: Capacity Factor

Land-Based Wind

Capacity Factor

Definition: The capacity factor is influenced by hourly wind profile, expected downtime, and energy losses within the wind plant. The specific power (i.e., ratio of machine rating to rotor swept area) and hub height are design choices that influence the capacity factor.

Base Year: Most installed U.S. wind plants generally align with ATB estimates for performance in wind speed classes 2–7. High wind resource sites associated with wind speed class 1 as well as very low wind resource sites associated with wind speed classes 8–10 are not as common in the historical data, but the range of observed data encompasses ATB estimates.

The following chart shows a range of capacity factors based on variation in the resource for wind plants in the contiguous United States. Historical data from wind plants operating in the United States in 2015, according to the year in which plants were installed, are shown for comparison to the 2020 ATB Base Year estimates. The range of Base Year estimates illustrate the effect of locating a wind plant in sites with high wind speeds (wind speed class 1) or low wind speeds (wind speed class 10). Future projections are shown for the Conservative, Moderate, and Advanced Scenarios.

To calculate the Base Year capacity factors, the 2018 turbine characteristics (Wiser & Bolinger, 2019) are input into the System Advisor Model (SAM) and run for each of the weighted average wind speeds in each wind speed class.

The capacity factor is referenced to a 100-m, above-ground-level, long-term average hourly wind resource data from the Wind Integration National Dataset (WIND) Toolkit.

Future Years: The technology innovations described above are expected to increase capacity factor for all wind speed classes, with a more rapid rate of increases in capacity factor through 2030 and a slower rate of increase through 2050. This analysis illustrates one of many capacity factor improvement pathways for LCOE reduction. Of course, as is the case for CAPEX, there are many different pathways to a given capacity factor. Turbine rotor diameter, specific power, and hub height can each be traded-off to achieve a given capacity factor, depending on site conditions and relative costs for pursuing one approach or the other; plant layout and operational strategies that impact losses are additional levers that may be used to achieve a given capacity factor.

  • Moderate Scenario: The projected capacity factors in 2030 are calculated in SAM using the inputs of the predicted turbine technology in 2030 that are specific to the Moderate Scenario for each of the wind speed classes; additional wind plant performance and availability are also applied through technology innovations assessed in the SMART wind power plant of the future work (Dykes et al., 2017); analysts predict that beyond 2030 generally modest improvements in wind plant performance through 2050 for resource-rich sites (i.e., wind speed classes 1 and 2) and slight increases for less-favorable resource sites (i.e., wind speed classes 8–10).
  • Advanced Scenario: The projected capacity factors in 2030 are calculated in SAM using the inputs of the predicted turbine technology in 2030 that are specific to the Advanced Scenario for each of the wind speed classes; additional wind plant performance and availability are also applied through technology innovations assessed in the SMART wind power plant of the future work (Dykes et al., 2017), but they assume greater reduction in losses than in the Moderate case; beyond 2030, analysts predict slightly higher improvements in wind plant performance than in the Moderate case through 2050 for resource-rich sites (i.e., wind speed classes 1 and 2) and slight increases for less-favorable resource sites (i.e., wind speed classes 8–10).

References

The following references are specific to this page; for all references in this ATB, see References.

Dykes, Katherine, Hand, Maureen, Stehly, Tyler, Veers, Paul, Robinson, Mike, Lantz, Eric, & Tusing, Richard. (2017). Enabling the SMART Wind Power Plant of the Future Through Science-Based Innovation. (No. NREL/TP-5000-68123). National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy17osti/68123.pdf

Wiser, Ryan, & Bolinger, Mark. (2019). 2018 Wind Technologies Market Report. (No. DOE/GO-102019-5191). Lawrence Berkeley National Laboratory. https://emp.lbl.gov/sites/default/files/wtmr_final_for_posting_8-9-19.pdf


Developed with funding from the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy.