Volume 3, issue 2 | Copyright
Wind Energ. Sci., 3, 845-868, 2018
https://doi.org/10.5194/wes-3-845-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research articles 05 Nov 2018

Research articles | 05 Nov 2018

Assessing variability of wind speed: comparison and validation of 27 methodologies

Joseph C. Y. Lee1,2, M. Jason Fields1, and Julie K. Lundquist1,2 Joseph C. Y. Lee et al.
  • 1National Renewable Energy Laboratory, Golden, CO 80401, USA
  • 2Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO 80309, USA

Abstract. Because wind resources vary from year to year, the intermonthly and interannual variability (IAV) of wind speed is a key component of the overall uncertainty in the wind resource assessment process, thereby creating challenges for wind farm operators and owners. We present a critical assessment of several common approaches for calculating variability by applying each of the methods to the same 37-year monthly wind-speed and energy-production time series to highlight the differences between these methods. We then assess the accuracy of the variability calculations by correlating the wind-speed variability estimates to the variabilities of actual wind farm energy production. We recommend the robust coefficient of variation (RCoV) for systematically estimating variability, and we underscore its advantages as well as the importance of using a statistically robust and resistant method. Using normalized spread metrics, including RCoV, high variability of monthly mean wind speeds at a location effectively denotes strong fluctuations of monthly total energy generation, and vice versa. Meanwhile, the wind-speed IAVs computed with annual-mean data fail to adequately represent energy-production IAVs of wind farms. Finally, we find that estimates of energy-generation variability require 10±3 years of monthly mean wind-speed records to achieve a 90% statistical confidence. This paper also provides guidance on the spatial distribution of wind-speed RCoV.

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To find the ideal way to quantify long-term wind-speed variability, we compare 27 metrics using 37 years of wind and energy data. We conclude that the robust coefficient of variation can effectively assess and correlate wind-speed and energy-production variabilities. We derive adequate results via monthly mean data, whereas uncertainty arises in interannual variability calculations. We find that reliable estimates of wind-speed variability require 10 ± 3 years of monthly mean wind data.
To find the ideal way to quantify long-term wind-speed variability, we compare 27 metrics using...
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