基于高斯混合模型的海洋能发电资源优化配置研究
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TM715

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国家电网有限公司总部科技项目“分布式海洋可再生能源发电并网控制关键技术研究及示范”


Gaussian mixture model based optimal allocation of marine energy power generation resources
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Research and demonstration on the key technologies of grid connection control for distributed Marine renewable energy generation

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    摘要:

    为了充分利用波浪、潮流等海洋可再生能源,针对海洋能资源丰富的海岛电网,文中提出了以消纳率最优为目标的海洋能发电资源优化配置方法。首先,采用高斯混合模型(GMM)描述海洋能发电的随机波动性,并生成出力场景;其次,以海洋能消纳率最优为目标构建了海岛电网的海洋能发电资源优化配置模型;然后,对渗透率、消纳率及源荷匹配度3个指标进行综合分析,获得最优配置方案;最后,基于实际海岛负荷、波浪和潮流数据构建了海洋能发电资源优化配置算例,并重点分析了资源优化配置过程中渗透率约束的取值范围。结果表明:在不同的渗透率约束下,源荷匹配度呈现先升后降的趋势;在最优渗透率约束取值范围内,GMM出力场景与实测数据场景的优化配置结果相差不大且趋势一致,验证了GMM生成海洋能出力场景的有效性。

    Abstract:

    In order to make full use of marine renewable energy such as waves and tidal power generations,an optimal allocation method of marine energy power generation resources with the objective of optimal absorption rate is proposed for the island power grid. Firstly,Gaussian mixture model (GMM) is used to describe the random fluctuation of marine energy generation and generate the output scene. Secondly,the optimal allocation model of marine energy power generation resources for island power grid is constructed with the objective of optimal marine energy absorption rate. Then,the permeability,absorption rate and source-load matching degree are comprehensively analyzed to obtain the optimal allocation scheme. Finally,based on actual island load,wave and tidal current data,an example of optimal allocation of marine energy power resources is built,and the value range of permeability constraint in optimal allocation of resources is analyzed. The results show that the source-load matching degree increases at first and then decreases under different permeability constraints. Within the range of the optimal permeability constraint,there are little difference and same trend between the optimal configuration results of the GMM and the measured data scenes,which verify the effectiveness of the GMM to generate marine energy output scenes.

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引用本文

唐成虹,余良辉,孙树敏,于芃,赵紫璇,秦川.基于高斯混合模型的海洋能发电资源优化配置研究[J].电力工程技术,2022,41(2):97-104,127

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历史
  • 收稿日期:2021-11-07
  • 最后修改日期:2022-01-23
  • 录用日期:2021-05-16
  • 在线发布日期: 2022-03-24
  • 出版日期: 2022-03-28