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The assessment of extreme wave analysis methods applied to potential marine energy sites using numerical model data

Author

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  • Agarwal, Atul
  • Venugopal, Vengatesan
  • Harrison, Gareth P.

Abstract

The accurate estimation of extreme conditions, such as 100-yr return levels of significant wave height is an important aspect in the design of marine energy converters, offshore and coastal structures. This study investigates the different approaches for the estimation of extreme waves that have been applied in the past, and determines the 100-yr return levels using the high resolution ERA-Interim dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). It is demonstrated in the paper that fitting a Generalized Pareto Distribution to all exceedances over a high threshold is the most suitable approach. The estimates thus obtained are compared with previously computed estimates for buoys and offshore platforms. The effect of duration of data on the estimates is also investigated. Finally, a 100-yr return level map for the North Atlantic region is presented.

Suggested Citation

  • Agarwal, Atul & Venugopal, Vengatesan & Harrison, Gareth P., 2013. "The assessment of extreme wave analysis methods applied to potential marine energy sites using numerical model data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 244-257.
    • Handle: RePEc:eee:rensus:v:27:y:2013:i:c:p:244-257
    DOI: 10.1016/j.rser.2013.06.049
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    References listed on IDEAS

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    1. Wimmer, Werenfrid & Challenor, Peter & Retzler, Chris, 2006. "Extreme wave heights in the North Atlantic from Altimeter Data," Renewable Energy, Elsevier, vol. 31(2), pages 241-248.
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    Cited by:

    1. Lavidas, George, 2020. "Selection index for Wave Energy Deployments (SIWED): A near-deterministic index for wave energy converters," Energy, Elsevier, vol. 196(C).
    2. Jessica Borges Posterari & Takuji Waseda, 2022. "Wave Energy in the Pacific Island Countries: A New Integrative Conceptual Framework for Potential Challenges in Harnessing Wave Energy," Energies, MDPI, vol. 15(7), pages 1-24, April.
    3. Hashim, Roslan & Roy, Chandrabhushan & Motamedi, Shervin & Shamshirband, Shahaboddin & Petković, Dalibor, 2016. "Selection of climatic parameters affecting wave height prediction using an enhanced Takagi-Sugeno-based fuzzy methodology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 246-257.
    4. Zheng, Chong Wei & Wang, Qing & Li, Chong Yin, 2017. "An overview of medium- to long-term predictions of global wave energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1492-1502.
    5. Penalba, Markel & Aizpurua, Jose Ignacio & Martinez-Perurena, Ander & Iglesias, Gregorio, 2022. "A data-driven long-term metocean data forecasting approach for the design of marine renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).

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