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Evaluation of the wave energy conditions along the coastal waters of Beibu Gulf, China

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  • Zhou, Guoqing
  • Huang, Jingjin
  • Zhang, Guangyun

Abstract

To evaluate the wave energy conditions along the coastal waters of Beibu Gulf, China, the SWAN (Simulated WAve Nearshore) model is used to estimate the wave field using 20 years of wind field data. The results show that (1) the wave field conditions are promising, for example, in the coastal waters off Guangxi, where the annually averaged significant wave height and peak wave period are approximately 0.45 m and 3.0 s, respectively, and especially along the coast of Hainan Island, where the annually averaged significant wave height and peak wave period are approximately 0.60 m and 3.7 s, respectively; (2) The major direction of wave energy is west or southwest of Guangxi and northeast or northeast by north of Hainan Island; and (3) The wave energy conditions show clear seasonal variations. The maximum storages are approximately 4.48 × 103 kWh/m off Guangxi and approximately 8.02 × 103 kWh/m off Hainan Island. In addition, the wave energy is more stable in the coastal waters off Guangxi than off Hainan Island. Finally, Weizhou Island and the southern coast of Hainan Island are suggested as potential wave power sites.

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  • Zhou, Guoqing & Huang, Jingjin & Zhang, Guangyun, 2015. "Evaluation of the wave energy conditions along the coastal waters of Beibu Gulf, China," Energy, Elsevier, vol. 85(C), pages 449-457.
    • Handle: RePEc:eee:energy:v:85:y:2015:i:c:p:449-457
    DOI: 10.1016/j.energy.2015.03.094
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    7. Kamranzad, Bahareh & Etemad-Shahidi, Amir & Chegini, Vahid, 2017. "Developing an optimum hotspot identifier for wave energy extracting in the northern Persian Gulf," Renewable Energy, Elsevier, vol. 114(PA), pages 59-71.
    8. Shih-Chun Hsiao & Chao-Tzuen Cheng & Tzu-Yin Chang & Wei-Bo Chen & Han-Lun Wu & Jiun-Huei Jang & Lee-Yaw Lin, 2021. "Assessment of Offshore Wave Energy Resources in Taiwan Using Long-Term Dynamically Downscaled Winds from a Third-Generation Reanalysis Product," Energies, MDPI, vol. 14(3), pages 1-25, January.
    9. Cuadra, L. & Salcedo-Sanz, S. & Nieto-Borge, J.C. & Alexandre, E. & Rodríguez, G., 2016. "Computational intelligence in wave energy: Comprehensive review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1223-1246.
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