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Comparison of Extreme Wind and Waves Using Different Statistical Methods in 40 Offshore Wind Energy Lease Areas Worldwide

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  • Saravanan Bhaskaran

    (Department of Mechanical Engineering, University of Maine, Orono, ME 04473, USA)

  • Amrit Shankar Verma

    (Department of Mechanical Engineering, University of Maine, Orono, ME 04473, USA)

  • Andrew J. Goupee

    (Department of Mechanical Engineering, University of Maine, Orono, ME 04473, USA)

  • Subhamoy Bhattacharya

    (Department of Civil and Environmental Engineering, University of Surrey, Guildford GU2 7XH, UK)

  • Amir R. Nejad

    (Department of Marine Technology, Norwegian University of Science and Technology, 7491 Trondheim, Norway)

  • Wei Shi

    (Deepwater Engineering Research Center, Dalian University of Technology, Dalian 116024, China)

Abstract

With the ongoing global drive towards renewable energy, several potential offshore wind energy lease areas worldwide have come into focus. This study aims to estimate the extreme wind and wave conditions across several newly designated offshore wind lease sites spanning six continents that are crucial for risk assessment and the design of offshore wind turbines. Firstly, the raw data of wind speeds and wave heights prevailing in these different lease areas were obtained. Following this, an in-depth extreme value analysis was performed over different return periods. Two principal methodologies were applied for this comparative study: the block-maxima and the peaks-over-threshold (POT) approaches. Various statistical techniques, including the Gumbel method of moments, Gumbel maximum likelihood, Gumbel least-squares, and the three-parameter GEV, were employed under the block-maxima approach to obtain the distribution parameters. The threshold for the POT approach was defined using the mean residual life method, and the distribution parameters were obtained using the maximum likelihood method. The Gumbel least-squares method emerged as the most conservative estimator of extreme values in the majority of cases, while the POT approach generally yielded lower extreme values compared to the block-maxima approach. However, the results from the POT approach showed large variations based on the selected threshold. This comprehensive study’s findings will provide valuable input for the efficient planning, design, and construction of future offshore wind farms.

Suggested Citation

  • Saravanan Bhaskaran & Amrit Shankar Verma & Andrew J. Goupee & Subhamoy Bhattacharya & Amir R. Nejad & Wei Shi, 2023. "Comparison of Extreme Wind and Waves Using Different Statistical Methods in 40 Offshore Wind Energy Lease Areas Worldwide," Energies, MDPI, vol. 16(19), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6935-:d:1252908
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    References listed on IDEAS

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    1. Bali, Turan G., 2003. "The generalized extreme value distribution," Economics Letters, Elsevier, vol. 79(3), pages 423-427, June.
    2. Lee, Bong-Hee & Ahn, Dong-Joon & Kim, Hyun-Goo & Ha, Young-Cheol, 2012. "An estimation of the extreme wind speed using the Korea wind map," Renewable Energy, Elsevier, vol. 42(C), pages 4-10.
    3. Sara C. Pryor & Rebecca J. Barthelmie, 2021. "A global assessment of extreme wind speeds for wind energy applications," Nature Energy, Nature, vol. 6(3), pages 268-276, March.
    4. Kang, Dongbum & Ko, Kyungnam & Huh, Jongchul, 2015. "Determination of extreme wind values using the Gumbel distribution," Energy, Elsevier, vol. 86(C), pages 51-58.
    5. Wang, Jianzhou & Qin, Shanshan & Jin, Shiqiang & Wu, Jie, 2015. "Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 26-42.
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