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Predicting coastal impacts by wave farms: A comparison of wave-averaged and wave-resolving models

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  • David, Daniel R.
  • Rijnsdorp, Dirk P.
  • Hansen, Jeff E.
  • Lowe, Ryan J.
  • Buckley, Mark L.

Abstract

Wave energy converters (WECs) will have to be arranged into arrays of many devices to extract commercially viable amounts of energy. To understand the potential coastal impacts of WEC arrays, most research to date has relied on wave-averaged models given their computational efficiency. However, it is unknown how accurate wave-averaged model predictions are given a lack of validation data and their inherent simplifications of various hydrodynamic processes (e.g., diffraction). This paper compares the predictions of coastal wave farm impacts from a coupled wave-averaged and flow model (Delft3D-SNL-SWAN), to a wave-resolving wave-flow model (SWASH) that intrinsically accounts for more of the relevant physics. Model predictions were compared using an idealized coastal bathymetry over a range of wave conditions and wave farm geometries. Both models predicted the largest impacts (changes to the nearshore hydrodynamics) for large and dense wave farms located close to the shore (1 km) and the smallest impacts for the small and widely spaced farm at a greater offshore distance (3 km). However, the wave-resolving model generally predicted somewhat larger impacts (i.e., changes to the nearshore wave heights, mean velocities and mean water levels). We also found that coupling the wave-averaged model to a flow model resulted in more realistic downstream predictions than the stand-alone wave-averaged model.

Suggested Citation

  • David, Daniel R. & Rijnsdorp, Dirk P. & Hansen, Jeff E. & Lowe, Ryan J. & Buckley, Mark L., 2022. "Predicting coastal impacts by wave farms: A comparison of wave-averaged and wave-resolving models," Renewable Energy, Elsevier, vol. 183(C), pages 764-780.
    • Handle: RePEc:eee:renene:v:183:y:2022:i:c:p:764-780
    DOI: 10.1016/j.renene.2021.11.048
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    References listed on IDEAS

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    1. Tim Verbrugghe & Vicky Stratigaki & Peter Troch & Raphael Rabussier & Andreas Kortenhaus, 2017. "A Comparison Study of a Generic Coupling Methodology for Modeling Wake Effects of Wave Energy Converter Arrays," Energies, MDPI, vol. 10(11), pages 1-25, October.
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    4. Rijnsdorp, Dirk P. & Hansen, Jeff E. & Lowe, Ryan J., 2020. "Understanding coastal impacts by nearshore wave farms using a phase-resolving wave model," Renewable Energy, Elsevier, vol. 150(C), pages 637-648.
    5. Greenwood, Charles & Christie, David & Venugopal, Vengatesan & Morrison, James & Vogler, Arne, 2016. "Modelling performance of a small array of Wave Energy Converters: Comparison of Spectral and Boussinesq models," Energy, Elsevier, vol. 113(C), pages 258-266.
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    7. Craig Jones & Grace Chang & Kaustubha Raghukumar & Samuel McWilliams & Ann Dallman & Jesse Roberts, 2018. "Spatial Environmental Assessment Tool (SEAT): A Modeling Tool to Evaluate Potential Environmental Risks Associated with Wave Energy Converter Deployments," Energies, MDPI, vol. 11(8), pages 1-19, August.
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    Cited by:

    1. Cui, Lidong & Sergiienko, Nataliia Y. & Leontini, Justin S. & Cohen, Nadav & Bennetts, Luke G. & Cazzolato, Benjamin & Turner, Ian L. & Flocard, Francois & Westcott, Amy-Rose & Cheng, Fanrui & Manasse, 2024. "Protecting coastlines by offshore wave farms: On optimising array configurations using a corrected far-field approximation," Renewable Energy, Elsevier, vol. 224(C).
    2. Berrio, Y. & Rivillas-Ospina, G. & Ruiz-Martínez, G. & Arango-Manrique, A. & Ricaurte, C. & Mendoza, E. & Silva, R. & Casas, D. & Bolívar, M. & Díaz, K., 2023. "Energy conversion and beach protection: Numerical assessment of a dual-purpose WEC farm," Renewable Energy, Elsevier, vol. 219(P2).
    3. Mahdavi-Meymand, Amin & Sulisz, Wojciech, 2024. "Development of pyramid neural networks for prediction of significant wave height for renewable energy farms," Applied Energy, Elsevier, vol. 362(C).

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