IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i19p6935-d1252908.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/19/6935/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/19/6935/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Bali, Turan G., 2003. "The generalized extreme value distribution," Economics Letters, Elsevier, vol. 79(3), pages 423-427, June.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Héctor J. Gómez & Karol I. Santoro & Diego Ayma & Isaac E. Cortés & Diego I. Gallardo & Tiago M. Magalhães, 2024. "A New Generalization of the Truncated Gumbel Distribution with Quantile Regression and Applications," Mathematics, MDPI, vol. 12(11), pages 1-20, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christopher Jung & Dirk Schindler & Alexander Buchholz & Jessica Laible, 2017. "Global Gust Climate Evaluation and Its Influence on Wind Turbines," Energies, MDPI, vol. 10(10), pages 1-18, September.
    2. Kresning, Boma & Hashemi, M. Reza & Shirvani, Amin & Hashemi, Javad, 2024. "Uncertainty of extreme wind and wave loads for marine renewable energy farms in hurricane-prone regions," Renewable Energy, Elsevier, vol. 220(C).
    3. Mehr Gul & Nengling Tai & Wentao Huang & Muhammad Haroon Nadeem & Moduo Yu, 2020. "Evaluation of Wind Energy Potential Using an Optimum Approach based on Maximum Distance Metric," Sustainability, MDPI, vol. 12(5), pages 1-23, March.
    4. Yuyang Gao & Chao Qu & Kequan Zhang, 2016. "A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(10), pages 1-28, September.
    5. Gaige Hunter Kerr & Michelle Meyer & Daniel L. Goldberg & Joshua Miller & Susan C. Anenberg, 2024. "Air pollution impacts from warehousing in the United States uncovered with satellite data," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    6. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    7. Jia, Junmei & Yan, Zaizai & Peng, Xiuyun & An, Xiaoyan, 2020. "A new distribution for modeling the wind speed data in Inner Mongolia of China," Renewable Energy, Elsevier, vol. 162(C), pages 1979-1991.
    8. Wang, Jianzhou & Huang, Xiaojia & Li, Qiwei & Ma, Xuejiao, 2018. "Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China," Energy, Elsevier, vol. 164(C), pages 432-448.
    9. Chen, Lei & Kou, Yingxin & Li, Zhanwu & Xu, An & Wu, Cheng, 2018. "Empirical research on complex networks modeling of combat SoS based on data from real war-game, Part I: Statistical characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 754-773.
    10. Gerald A. Abantao & Jessa A. Ibañez & Paul Eugene Delfin C. Bundoc & Lean Lorenzo F. Blas & Xaviery N. Penisa & Eugene A. Esparcia & Michael T. Castro & Roger Victor E. Buendia & Karl Ezra S. Pilario , 2024. "Reconceptualizing Reliability Indices as Metrics to Quantify Power Distribution System Resilience," Energies, MDPI, vol. 17(8), pages 1-13, April.
    11. Li, Delei & Geyer, Beate & Bisling, Peter, 2016. "A model-based climatology analysis of wind power resources at 100-m height over the Bohai Sea and the Yellow Sea," Applied Energy, Elsevier, vol. 179(C), pages 575-589.
    12. Wang, Hao & Wang, Tongguang & Ke, Shitang & Hu, Liang & Xie, Jiaojie & Cai, Xin & Cao, Jiufa & Ren, Yuxin, 2023. "Assessing code-based design wind loads for offshore wind turbines in China against typhoons," Renewable Energy, Elsevier, vol. 212(C), pages 669-682.
    13. 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.
    14. Pryor, Sara C. & Barthelmie, Rebecca J., 2024. "Wind shadows impact planning of large offshore wind farms," Applied Energy, Elsevier, vol. 359(C).
    15. Zhao, Xuejing & Wang, Chen & Su, Jinxia & Wang, Jianzhou, 2019. "Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system," Renewable Energy, Elsevier, vol. 134(C), pages 681-697.
    16. Kang, Dongbum & Ko, Kyungnam & Huh, Jongchul, 2015. "Determination of extreme wind values using the Gumbel distribution," Energy, Elsevier, vol. 86(C), pages 51-58.
    17. Wang, Junling & Cheng, Siyu & Rong, Xueyun & Xu, Xin, 2024. "Nonlinear impact of climate transition risks on green stock performance: Perspectives from multiscale and lag effects," International Review of Financial Analysis, Elsevier, vol. 94(C).
    18. Hocaoglu, Fatih Onur & Serttas, Fatih, 2017. "A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 635-643.
    19. Han, Wanlong & Yan, Peigang & Han, Wanjin & He, Yurong, 2015. "Design of wind turbines with shroud and lobed ejectors for efficient utilization of low-grade wind energy," Energy, Elsevier, vol. 89(C), pages 687-701.
    20. Jiang, Haiyan & Wang, Jianzhou & Wu, Jie & Geng, Wei, 2017. "Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1199-1217.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6935-:d:1252908. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.