一种基于改进K-means++算法的分布式光储聚合调峰方法  

A Peak Shaving Method of Aggregating the Distributed Photovoltaics and Energy Storages Based on the Improved K-means++Algorithm

作　　者：武艺 姚良忠 廖思阳[1] 刘运鑫 李健 王新迎 WU Yi;YAO Liangzhong;LIAO Siyang;LIU Yunxin;LI Jian;WANG Xinying(School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,Hubei Province,China;China Electric Power Research Institute,Haidian District,Beijing 100192,China)

机构地区：[1]武汉大学电气与自动化学院,湖北省武汉市430072 [2]中国电力科学研究院有限公司,北京市海淀区100192

出　　处：《电网技术》2022年第10期3923-3931,共9页Power System Technology

基　　金：国家重点研发计划项目(2020YFB0905900)。

摘　　要：近年来,随着光伏装机容量占比迅速提升,日益突出的光伏出力波动性及反调峰特性加剧了电力系统调峰的压力。利用光储资源参与调峰成为缓解电力系统调峰压力的一种有效措施。但是,高比例分布式光储(photovoltaic-energy storage,PV-ES)直接参与调峰会带来决策变量维数爆炸、求解结果难以收敛等诸多问题。对此,基于典型调峰特征量将高比例分布式光储聚合为数量较少的特征集群,建立聚合-调峰-分解模型,有效解决了高比例分布式光储参与电力系统调峰优化的问题。算例结果表明,所提方法有效降低了光储变量维数和求解难度,提升了其参与电力系统调峰优化的可行性,并在保证调峰能力的同时具备一定的经济优势。In recent years,with the rapid increase in the proportion of photovoltaic installation capacity,the more and more prominent photovoltaics output volatility and anti-peak regulation characteristics have intensified the pressure on the power system peak regulation.The application of distributed photovoltaics and energy storages resources to participate in the peak shaving has become an effective measure to alleviate the pressure of peak shaving in the power system.However,the high proportion of distributed photovoltaics and energy storages directly participating in peak shaving will lead to the explosion of the dimension of decision variables and the difficult convergence of solution results.In this regard,this paper aggregates the high proportion of distributed photovoltaics and energy storages into a smaller number of feature clusters based on the typical peak shaving characteristics.An aggregation-peak shaving-decomposition model is established,which effectively solves the problem of high proportion distributed photovoltaics and energy storages participating in the power system peak shaving optimization.The results of calculation examples show that the proposed method effectively reduces the dimension of variables and the solution difficulty.The feasibility of its participation in the optimization of power system peak shaving is improved,and it has certain economic advantages while ensuring the peak shaving capability.

关 键 词：高比例分布式光储聚合 K-means++算法 电力系统调峰 任务分解 

分 类 号：TM721[电气工程—电力系统及自动化]