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Nearshore wave energy resource characterization along the East Coast of the United States

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  • Ahn, Seongho
  • Neary, Vincent S.
  • Allahdadi, Mohammad Nabi
  • He, Ruoying

Abstract

A feasibility level nearshore wave energy resource characterization is conducted for the East Coast of the United States using a 32-year (1979–2010) hindcast from a high-resolution unstructured-grid Simulating Waves Nearshore (SWAN) model with a spatial resolution of 200 m along the coastline. Wave energy resource attributes including wave energy potentials, seasonal variability, frequency and directional spreading, and extreme sea states are characterized using a broad range of resource parameters from which opportunities, risks, and constraints for wave energy conversion (WEC) projects are assessed. Cross-shore and alongshore variations of these parameters due to varying wave energy climate and coastline orientation relative to the dominant wave systems are examined. The present study also introduces a zero-crossing method for delineating wave energy climate regions based on a broad range of resource attributes beyond just wave power. Applying this method, eight nearshore wave energy climate regions are delineated for the East Coast; each region with a unique set of resource attributes to inform regional energy planning, WEC project development, conceptual WEC design, and the operation and maintenance of WEC projects.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:172:y:2021:i:c:p:1212-1224
    DOI: 10.1016/j.renene.2021.03.037
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    References listed on IDEAS

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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. Yang, Zhaoqing & García Medina, Gabriel & Neary, Vincent S. & Ahn, Seongho & Kilcher, Levi & Bharath, Aidan, 2023. "Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters," Renewable Energy, Elsevier, vol. 212(C), pages 803-817.
    3. García Medina, Gabriel & Yang, Zhaoqing & Li, Ning & Cheung, Kwok Fai & Lutu-McMoore, Elinor, 2023. "Wave climate and energy resources in American Samoa from a 42-year high-resolution hindcast," Renewable Energy, Elsevier, vol. 210(C), pages 604-617.
    4. Kilcher, Levi & García Medina, Gabriel & Yang, Zhaoqing, 2023. "A scalable wave resource assessment methodology: Application to U.S. waters," Renewable Energy, Elsevier, vol. 217(C).
    5. 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.
    6. Rusu, Liliana, 2022. "The near future expected wave power in the coastal environment of the Iberian Peninsula," Renewable Energy, Elsevier, vol. 195(C), pages 657-669.
    7. Kong, Weihua & He, Liujin & Hao, Daning & Wu, Xiaoping & Xiao, Luo & Zhang, Zutao & Xu, Yongsheng & Azam, Ali, 2023. "A wave energy harvester based on an ultra-low frequency synergistic PTO for intelligent fisheries," Renewable Energy, Elsevier, vol. 217(C).
    8. Ruth Branch & Gabriel García-Medina & Zhaoqing Yang & Taiping Wang & Fadia Ticona Rollano & Lucia Hosekova, 2021. "Modeling Sea Ice Effects for Wave Energy Resource Assessments," Energies, MDPI, vol. 14(12), pages 1-15, June.

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