Assessing variability of wind speed: comparison and validation of 27 methodologies
Abstract
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. Lastly, 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 papermore »
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
- OSTI Identifier:
- 1480875
- Alternate Identifier(s):
- OSTI ID: 1481842
- Report Number(s):
- NREL/JA-5000-72768
Journal ID: ISSN 2366-7451
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Published Article
- Journal Name:
- Wind Energy Science (Online)
- Additional Journal Information:
- Journal Name: Wind Energy Science (Online) Journal Volume: 3 Journal Issue: 2; Journal ID: ISSN 2366-7451
- Publisher:
- European Wind Energy Association - Copernicus
- Country of Publication:
- Germany
- Language:
- English
- Subject:
- 17 WIND ENERGY; inter-annual variability; statistics; uncertainty quantification; variability; wind resource assessment
Citation Formats
Lee, Joseph C. Y., Fields, M. Jason, and Lundquist, Julie K. Assessing variability of wind speed: comparison and validation of 27 methodologies. Germany: N. p., 2018.
Web. doi:10.5194/wes-3-845-2018.
Lee, Joseph C. Y., Fields, M. Jason, & Lundquist, Julie K. Assessing variability of wind speed: comparison and validation of 27 methodologies. Germany. https://doi.org/10.5194/wes-3-845-2018
Lee, Joseph C. Y., Fields, M. Jason, and Lundquist, Julie K. Mon .
"Assessing variability of wind speed: comparison and validation of 27 methodologies". Germany. https://doi.org/10.5194/wes-3-845-2018.
@article{osti_1480875,
title = {Assessing variability of wind speed: comparison and validation of 27 methodologies},
author = {Lee, Joseph C. Y. and Fields, M. Jason and Lundquist, Julie K.},
abstractNote = {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. Lastly, 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.},
doi = {10.5194/wes-3-845-2018},
journal = {Wind Energy Science (Online)},
number = 2,
volume = 3,
place = {Germany},
year = {Mon Nov 05 00:00:00 EST 2018},
month = {Mon Nov 05 00:00:00 EST 2018}
}
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