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https://doi.org/10.5194/wes-2018-49
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Research articles 30 Jul 2018

Research articles | 30 Jul 2018

Review status
This discussion paper is a preprint. A revision of the manuscript was accepted for the journal Wind Energy Science (WES).

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, Colorado 80401, USA
  • 2Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado 80309, USA

Abstract. Because wind resources vary from year to year, the inter-monthly and inter-annual variability (IAV) of wind speed is a key component of the overall uncertainty in the wind resource assessment process thereby causing challenges to 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 generations, 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 90% statistical confidence. This paper also provides guidance on the spatial distribution of wind-speed RCoV.

Joseph C. Y. Lee et al.
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Joseph C. Y. Lee et al.
Joseph C. Y. Lee et al.
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Short summary
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 reliable results in inter-monthly variabilities, whereas uncertainty arises in inter-annual variability calculations. We find that estimates of energy-generation 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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