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Wave energy assessment in the China East Adjacent Seas based on a 25-year wave-current interaction numerical simulation

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  • Shi, Xueli
  • Liang, Bingchen
  • Du, Shengtao
  • Shao, Zhuxiao
  • Li, Shaowu

Abstract

This study evaluates the wave energy in the China East Adjacent Seas based on a 25-year high temporal and spatial resolution wave-current interaction wave hindcast dataset from 1996 to 2020. The spatial distribution characteristics of the multi-year and monthly average wave energy flux in the China East Adjacent Seas are analyzed. The temporal variability characteristics of wave energy flux are quantified and analyzed by using a coefficient of variation, a seasonal variability index and a monthly variability index. The exploitable wave energy flux was assessed by excluding the waves with energy flux less than 2 kW/m and setting different significant wave height upper thresholds. Then, after considering factors such as wave energy resource abundance, temporal variability, offshore distance and water depth, 12 hotspots in the China East Adjacent Seas are believed relatively suitable for wave energy development. Four types of wave energy converters with different power capacities are selected to analyze their performance in different hotspots to provide a basis for design and selection of wave energy converters suitable to the China East Adjacent Seas. Due to the higher conversion efficiency when the significant wave height is small, Oyster performs best among the four selected wave energy converters.

Suggested Citation

  • Shi, Xueli & Liang, Bingchen & Du, Shengtao & Shao, Zhuxiao & Li, Shaowu, 2022. "Wave energy assessment in the China East Adjacent Seas based on a 25-year wave-current interaction numerical simulation," Renewable Energy, Elsevier, vol. 199(C), pages 1381-1407.
    • Handle: RePEc:eee:renene:v:199:y:2022:i:c:p:1381-1407
    DOI: 10.1016/j.renene.2022.09.094
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    2. Clemente, D. & Rosa-Santos, P. & Ferradosa, T. & Taveira-Pinto, F., 2023. "Wave energy conversion energizing offshore aquaculture: Prospects along the Portuguese coastline," Renewable Energy, Elsevier, vol. 204(C), pages 347-358.
    3. Uti, Mat Nizam & Md Din, Ami Hassan & Yusof, Norhakim & Yaakob, Omar, 2023. "A spatial-temporal clustering for low ocean renewable energy resources using K-means clustering," Renewable Energy, Elsevier, vol. 219(P2).
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    5. Zhao, Lingxiao & Li, Zhiyang & Pei, Yuguo & Qu, Leilei, 2024. "Disentangled Seasonal-Trend representation of improved CEEMD-GRU joint model with entropy-driven reconstruction to forecast significant wave height," Renewable Energy, Elsevier, vol. 226(C).
    6. Shi, Xueli & Li, Shaowu & Liang, Bingchen & Zhao, Jianchun & Liu, Ye & Wang, Zhenlu, 2023. "Numerical study on the impact of wave-current interaction on wave energy resource assessments in Zhoushan sea area, China," Renewable Energy, Elsevier, vol. 215(C).
    7. Chongwei Zheng, 2023. "An Overview and Countermeasure of Global Wave Energy Classification," Sustainability, MDPI, vol. 15(12), pages 1-21, June.

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