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Wave Climate Resource Analysis Based on a Revised Gamma Spectrum for Wave Energy Conversion Technology

Author

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  • Jeremiah Pastor

    (Department of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA)

  • Yucheng Liu

    (Department of Mechanical Engineering, Mississippi State University, Mississippi State, MS 39760, USA)

Abstract

In order to correctly predict and evaluate the response of wave energy converters (WECs), an accurate representation of wave climate resource is crucial. This paper gives an overview of wave resource modeling techniques and applies a methodology to estimate the naturally available and technically recoverable resource in a given deployment site. The methodology was initially developed by the Electric Power Research Institute (EPRI), which uses a modified gamma spectrum to interpret sea state hindcast parameter data produced by National Oceanic and Atmospheric Administration’s (NOAA’s) WaveWatch III. This gamma spectrum is dependent on the calibration of two variables relating to the spectral width parameter and spectral peakedness parameter. In this study, this methodology was revised by the authors to increase its accuracy in formulating wavelength. The revised methodology shows how to assess a given geographic area’s wave resource based on its wave power density and total annual wave energy flux.

Suggested Citation

  • Jeremiah Pastor & Yucheng Liu, 2016. "Wave Climate Resource Analysis Based on a Revised Gamma Spectrum for Wave Energy Conversion Technology," Sustainability, MDPI, vol. 8(12), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:12:p:1321-:d:85166
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    References listed on IDEAS

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    1. Fusco, Francesco & Nolan, Gary & Ringwood, John V., 2010. "Variability reduction through optimal combination of wind/wave resources – An Irish case study," Energy, Elsevier, vol. 35(1), pages 314-325.
    2. Folley, M. & Whittaker, T.J.T., 2009. "Analysis of the nearshore wave energy resource," Renewable Energy, Elsevier, vol. 34(7), pages 1709-1715.
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    Cited by:

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    3. Guillou, Nicolas & Chapalain, Georges, 2020. "Assessment of wave power variability and exploitation with a long-term hindcast database," Renewable Energy, Elsevier, vol. 154(C), pages 1272-1282.
    4. 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).
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    8. Yingjie Cui & Fei Zhang & Zhongxian Chen, 2023. "Predication of Ocean Wave Height for Ocean Wave Energy Conversion System," Energies, MDPI, vol. 16(9), pages 1-13, April.
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    14. Vázquez, Rubén & Cabos, William & Nieto-Borge, José Carlos & Gutiérrez, Claudia, 2024. "Complementarity of offshore energy resources on the Spanish coasts: Wind, wave, and photovoltaic energy," Renewable Energy, Elsevier, vol. 224(C).
    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. Egidijus Kasiulis & Jens Peter Kofoed & Arvydas Povilaitis & Algirdas Radzevičius, 2017. "Spatial Distribution of the Baltic Sea Near-Shore Wave Power Potential along the Coast of Klaipėda, Lithuania," Energies, MDPI, vol. 10(12), pages 1-18, December.
    17. Joan Pau Sierra & Ricard Castrillo & Marc Mestres & César Mösso & Piero Lionello & Luigi Marzo, 2020. "Impact of Climate Change on Wave Energy Resource in the Mediterranean Coast of Morocco," Energies, MDPI, vol. 13(11), pages 1-19, June.
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