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Wave energy resource classification system for US coastal waters

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  • Ahn, Seongho
  • Haas, Kevin A.
  • Neary, Vincent S.

Abstract

Energy resource classification systems are useful assessment tools that support energy planning and project development, e.g., siting and feasibility studies. They typically establish standard classes of power, a measure of the opportunity for energy resource capture. In this study, we develop wave energy resource classification systems for the US based on wave power (J, kW/m) and its distribution with peak period (Tp,s). These metrics are calculated for 70,386 sites from partitioned bulk wave parameters generated from a validated 30-year WaveWatch III model hindcast. As the operating resonant period bandwidth of a wave energy converter (WEC) technology is an important design characteristic, the dominant period band containing the largest energy content is identified among three peak period band classes. These classification systems, comprised of four power classes and three peak period band classes, are based on the total wave power or the partitioned wave power in the dominant peak period band. They discriminate distinct trends in wave energy resource among five regions within the US, and provide useful information for energy planners, project developers, and technology designers. They also establish a framework for investigating the feasibility of a compatible wave climate (design load) conditions and WEC technology classification system to reduce design and manufacturing costs.

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  • Ahn, Seongho & Haas, Kevin A. & Neary, Vincent S., 2019. "Wave energy resource classification system for US coastal waters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 54-68.
    • Handle: RePEc:eee:rensus:v:104:y:2019:i:c:p:54-68
    DOI: 10.1016/j.rser.2019.01.017
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    Cited by:

    1. Ahn, Seongho & Neary, Vincent S. & Haas, Kevin A., 2022. "Global wave energy resource classification system for regional energy planning and project development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    2. Tunde Aderinto & Hua Li, 2020. "Effect of Spatial and Temporal Resolution Data on Design and Power Capture of a Heaving Point Absorber," Sustainability, MDPI, vol. 12(22), pages 1-17, November.
    3. Seongho Ahn & Kevin A. Haas & Vincent S. Neary, 2020. "Dominant Wave Energy Systems and Conditional Wave Resource Characterization for Coastal Waters of the United States," Energies, MDPI, vol. 13(12), pages 1-26, June.
    4. Milad Shadman & Corbiniano Silva & Daiane Faller & Zhijia Wu & Luiz Paulo de Freitas Assad & Luiz Landau & Carlos Levi & Segen F. Estefen, 2019. "Ocean Renewable Energy Potential, Technology, and Deployments: A Case Study of Brazil," Energies, MDPI, vol. 12(19), pages 1-37, September.
    5. Fairley, Iain & Lewis, Matthew & Robertson, Bryson & Hemer, Mark & Masters, Ian & Horrillo-Caraballo, Jose & Karunarathna, Harshinie & Reeve, Dominic E., 2020. "A classification system for global wave energy resources based on multivariate clustering," Applied Energy, Elsevier, vol. 262(C).
    6. Ahn, Seongho & Neary, Vincent S., 2021. "Wave energy resource characterization employing joint distributions in frequency-direction-time domain," Applied Energy, Elsevier, vol. 285(C).
    7. Ribeiro, A.S. & deCastro, M. & Costoya, X. & Rusu, Liliana & Dias, J.M. & Gomez-Gesteira, M., 2021. "A Delphi method to classify wave energy resource for the 21st century: Application to the NW Iberian Peninsula," Energy, Elsevier, vol. 235(C).
    8. Coe, Ryan G. & Ahn, Seongho & Neary, Vincent S. & Kobos, Peter H. & Bacelli, Giorgio, 2021. "Maybe less is more: Considering capacity factor, saturation, variability, and filtering effects of wave energy devices," Applied Energy, Elsevier, vol. 291(C).
    9. Ahn, Seongho & Haas, Kevin A. & Neary, Vincent S., 2020. "Wave energy resource characterization and assessment for coastal waters of the United States," Applied Energy, Elsevier, vol. 267(C).
    10. Neary, Vincent S. & Ahn, Seongho, 2023. "Global atlas of extreme significant wave heights and relative risk ratios," Renewable Energy, Elsevier, vol. 208(C), pages 130-140.
    11. Robertson, Bryson & Bekker, Jessica & Buckham, Bradley, 2020. "Renewable integration for remote communities: Comparative allowable cost analyses for hydro, solar and wave energy," Applied Energy, Elsevier, vol. 264(C).
    12. Ahn, Seongho & Neary, Vincent S. & Ha, Taemin, 2023. "A practical method for modeling temporally-averaged ocean wave frequency-directional spectra for characterizing wave energy climates," Renewable Energy, Elsevier, vol. 207(C), pages 499-511.
    13. Ahn, Seongho & Neary, Vincent S. & Allahdadi, Mohammad Nabi & He, Ruoying, 2021. "Nearshore wave energy resource characterization along the East Coast of the United States," Renewable Energy, Elsevier, vol. 172(C), pages 1212-1224.
    14. Chongwei Zheng, 2023. "An Overview and Countermeasure of Global Wave Energy Classification," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    15. Guillou, Nicolas, 2020. "Estimating wave energy flux from significant wave height and peak period," Renewable Energy, Elsevier, vol. 155(C), pages 1383-1393.
    16. 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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