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A wave model test bed study for wave energy resource characterization

Author

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  • Yang, Zhaoqing
  • Neary, Vincent S.
  • Wang, Taiping
  • Gunawan, Budi
  • Dallman, Annie R.
  • Wu, Wei-Cheng

Abstract

This paper presents a test bed study conducted to evaluate best practices in wave modeling to characterize energy resources. The model test bed off the central Oregon Coast was selected because of the high wave energy and available measured data at the site. Two third-generation spectral wave models, SWAN and WWIII, were evaluated. A four-level nested-grid approach—from global to test bed scale—was employed. Model skills were assessed using a set of model performance metrics based on comparison of six simulated wave resource parameters and observations from a wave buoy inside the test bed. Both WWIII and SWAN performed well at the test bed site and exhibited similar modeling skills. The ST4 physics package with WWIII, which represents better physics for wave growth and dissipation, out-performed ST2 physics and improved wave power density and significant wave height predictions. However, ST4 physics tended to over-predict the wave energy period. The newly developed ST6 physics did not improve the overall model skill for predicting the six wave resource parameters. Sensitivity analysis using different wave frequencies and direction resolutions indicated the model results were not sensitive to spectral resolutions at the test bed site, likely due to the absence of complex bathymetric and geometric features.

Suggested Citation

  • Yang, Zhaoqing & Neary, Vincent S. & Wang, Taiping & Gunawan, Budi & Dallman, Annie R. & Wu, Wei-Cheng, 2017. "A wave model test bed study for wave energy resource characterization," Renewable Energy, Elsevier, vol. 114(PA), pages 132-144.
  • Handle: RePEc:eee:renene:v:114:y:2017:i:pa:p:132-144
    DOI: 10.1016/j.renene.2016.12.057
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    References listed on IDEAS

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    Cited by:

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    2. Yang, Zhaoqing & García-Medina, Gabriel & Wu, Wei-Cheng & Wang, Taiping, 2020. "Characteristics and variability of the nearshore wave resource on the U.S. West Coast," Energy, Elsevier, vol. 203(C).
    3. 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).
    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. 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.
    6. Li, Ning & García-Medina, Gabriel & Cheung, Kwok Fai & Yang, Zhaoqing, 2021. "Wave energy resources assessment for the multi-modal sea state of Hawaii," Renewable Energy, Elsevier, vol. 174(C), pages 1036-1055.
    7. Wu, Wei-Cheng & Wang, Taiping & Yang, Zhaoqing & García-Medina, Gabriel, 2020. "Development and validation of a high-resolution regional wave hindcast model for U.S. West Coast wave resource characterization," Renewable Energy, Elsevier, vol. 152(C), pages 736-753.
    8. Wei-Cheng Wu & Zhaoqing Yang & Taiping Wang, 2018. "Wave Resource Characterization Using an Unstructured Grid Modeling Approach," Energies, MDPI, vol. 11(3), pages 1-15, March.
    9. Allahdadi, M. Nabi & Gunawan, Budi & Lai, Jonathan & He, Ruoying & Neary, Vincent S., 2019. "Development and validation of a regional-scale high-resolution unstructured model for wave energy resource characterization along the US East Coast," Renewable Energy, Elsevier, vol. 136(C), pages 500-511.
    10. 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.

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