Validation of wind resource and energy production simulations for small wind turbines in the United States

• Published in: Wind Energy Science Vol. 7; no. 2; pp. 659 - 676
• Main Authors: Sheridan, Lindsay M, Phillips, Caleb, Orrell, Alice C, Berg, Larry K, Tinnesand, Heidi, Rai, Raj K, Zisman, Sagi, Duplyakin, Dmitry, Flaherty, Julia E
• Format: Journal Article
• Language: English
• Published: Göttingen Copernicus GmbH 24-03-2022; Copernicus Publications
• Subjects: Alternative energy; Datasets; Energy; Laboratories; Performance evaluation; Renewable resources; Simulation; Turbines; Weather forecasting; Wind power; Wind speed; Denmark; United States > US
• ISSN: 2366-7443; 2366-7451
• DOI: 10.5194/wes-7-659-2022

Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce energy generation expectations. While efficient and user-friendly, these models and tools are subject to errors that have been insufficiently quantified at small wind turbine heights. This study leverages observations from meteorological towers and sodars across the United States to validate wind speed estimates from the Wind Integration National Dataset (WIND) Toolkit, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), revealing average biases within ±0.5 ms-1 at small wind hub heights. Observations from small wind turbines across the United States provide references for validating energy production estimates from the System Advisor Model (SAM), Wind Report, MyWindTurbine.com, and Global Wind Atlas 3 (GWA3), which are seen to overestimate actual annual capacity factors by 2.5, 4.2, 11.5, and 7.3 percentage points, respectively. In addition to quantifying the error metrics, this paper identifies sources of model and tool discrepancies, noting that interannual fluctuation in the wind resource, wind speed class, and loss assumptions produces more variability in estimates than different horizontal and vertical interpolation techniques. The results of this study provide small wind installers and owners with information about these challenges to consider when making performance estimates and thus possible adjustments accordingly. Looking to the future, recognizing these error metrics and sources of discrepancies provides model and tool researchers and developers with opportunities for product improvement that could positively impact small wind customer confidence and the ability to finance small wind projects.