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

Abstract

Abstract. 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 m s−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.

Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment

“…Ramon et al (2019) utilised 77 meteorological towers around the globe with measurement heights ranging from 10 m to 122 m and found median ERA5 seasonal wind speed biases between 0 and -1 m s -1 , though ERA5 had the best correlation with observations among five reanalyses. Across 62 sites in the continental US, Sheridan et al (2022) found that ERA5 underestimated the observed wind speeds by an average of 0.5 m s -1 but had higher correlations (average of 0.77) than two alternate reanalyses and wind models. At locations across Europe, Murcia et al (2022) determined that ERA5 slightly underestimated the observed wind speeds (average bias = -0.06 m s -1 ) and provided a high degree of correlation (average of https://doi.org/10.5194/wes-2024-37 Preprint.…”

“…To narrow down an effective training approach, different combinations of reference variables are explored for their impact on long-term wind resource assessment error metrics. The analysis of Phillips et al (2022) identified reanalysis wind speed, reanalysis wind direction, and time of day as the most important variables for wind speed bias correction using a variety of techniques including multivariable linear regressions and regression trees. Therefore, we explore progressively increasing variable combinations of ERA5 wind speeds at the provided output heights of 10 m and 100 m (uera5_10m and uera5_100m), power law-based wind speed estimates at the measurement height z (uera5_z) (Eq.…”

Resource Assessment for Distributed Wind Energy: An Evaluation of Best-Practice Methods in the Continental US

“…Wind source data from mesoscale atmospheric models are essential to both drive the obstacle deficit models and for understanding the long-term wind resource and temporal (seasonal and interannual) variability. Prior studies have shown significant differences in the performance of both publicly available and commercial models for DW siting and resource assessment [6]. In this study, we use and evaluate the popular, well understood, and publicly available Wind Integration National Dataset (WIND) Toolkit (WTK).…”

“…This involves fitting a multiple linear regression with the following form (Equation 1): Where 𝑤 ! "# is the observed wind speed at the meteorological tower, 𝑤 %&' is the WTK estimate for the wind speed, 𝑑 %&' is the direction of the model data in degrees, h is the hour of the day (0-23), and m is the month of the year (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12). Values for the coefficients x0, x1, x2, x3, and x4 are fitted with least squares regression.…”

Current status and grand challenges for small wind turbine technology

“…Development in highly complex areas, such as urban locations, is complicated due to the wind conditions in the city's canopy layer, which typically have low intensity, high variability, high levels of turbulence, and inclined or even reversed airflows. While several studies have shown a theoretically good potential for urban wind (Balduzzi et al, 2012;Toja-Silva et al, 2013), a number of challenges still need to be tackled to effectively fit wind energy converters to this environment, as recently discussed by Micallef and Bussel (2018) and Stathopoulos et al (2018). In the present study, the authors decided not to include a specific technical analysis of the needs for urban wind, although future work on the topic has to be encouraged (Battisti, 2018).…”