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ISSN 0254-0096 CN 11-2082/K

太阳能学报 ›› 2022, Vol. 43 ›› Issue (9): 416-423.DOI: 10.19912/j.0254-0096.tynxb.2021-0060

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南海岛礁海域波浪能资源分析及总量评估

武贺1, 方舣洲1, 张松1, 马勇2   

  1. 1.国家海洋技术中心,天津 300112;
    2.南方海洋科学与工程广东省实验室(珠海),珠海 519082
  • 收稿日期:2021-01-13 出版日期:2022-09-28 发布日期:2023-03-28
  • 通讯作者: 武 贺(1981—),男,博士、研究员,主要从事海洋能资源开发利用方面的研究。wh_crane@163.com
  • 基金资助:
    国家重点研发计划(2019YFE0102500; 2019YFB1504401; 2016YFC1401800)

WAVE ENERGY CHARACTERIZATION AND POTENTIAL ESTIMATION FOR ISLANDS OF SOUTH CHINA SEA

Wu He1, Fang Yizhou1, Zhang Song1, Ma Yong2   

  1. 1. National Ocean Technology Center, Tianjin 300112, China;
    2. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
  • Received:2021-01-13 Online:2022-09-28 Published:2023-03-28

摘要: 基于波浪能的能通量原理,建立代表区段长度的概念,提出针对于海岛海域的波浪能资源蕴藏量评估方法及具体公式。通过第3代波浪谱模型SWAN对南海海域近10年的波浪场进行数值模拟,并利用实测波浪资料进行验证。在此基础上重点刻画该海域波高、周期、能流密度等波浪能资源时空分布特征,利用新方法对南海岛礁海域波浪能资源蕴藏量进行估算。结果表明,以离岸50 km等值线为波浪能量输入线时,南海群岛波浪能理论蕴藏量约为18300 MW。其中,西沙群岛海域约为2600 MW,东沙群岛海域为2120 MW,中沙群岛海域约为6720 MW,南沙群岛海域约为6860 MW。

关键词: 可再生能源, 波浪能, 资源评估, 南海海域, 数值模拟

Abstract: Based on the principles of energy flux, the concept of representative section and preferred method for assessment of wave energy resources of islands potential has been proposed for the first time. The third-generation wave spectrum model SWAN is used to numerically simulate the wave field in the South China Sea over the past 10 years and validated against observations. On this basis, the spatial and temporal distribution characteristics of wave energy resources, such as wave height, period and power density, were characterized. And a new method was used to estimate the wave energy resource reserves in the South China Sea. The results show that the theoretical wave energy reserves of the South China Sea Islands are about 18300 MW when the 50 km offshore contour is used as the wave energy input line, of which about 2600 MW are in the Xisha Islands, 2120 MW in the Dongsha Islands, 6720 MW in the Zhongsha Islands and 6860 MW in the Nansha Islands.

Key words: renewable energy resource, wave power, resource assessment, South China Sea, numerical modelling

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