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面向“削峰填谷”的电力客户用电行为分析
引用本文:王炳鑫,侯岩,方红旺,陈雨泽,刘建. 面向“削峰填谷”的电力客户用电行为分析[J]. 电信科学, 2017, 33(5). DOI: 10.11959/j.issn.1000-0801.2017067
作者姓名:王炳鑫  侯岩  方红旺  陈雨泽  刘建
作者单位:1. 国网福建省电力有限公司泉州供电公司,福建泉州,362000;2. 北京中电普华信息技术有限公司,北京,100085
基金项目:国网信息通信产业集团科技项目“支持售电云业务的大数据应用”(No.52680016007G)Science & Technology Project of State Grid Information and Telecommunication Group "Big Data Applications for Supporting Electricity Sales Business over Grid"
摘    要:为对海量电力客户实施有针对性的“削峰填谷”措施,提出了一种面向“削峰填谷”的海量电力客户用电行为分析方法.首先,利用聚类算法对国网某省公司主网一年的日负荷曲线数据进行聚类分析,得到不同时期主网的负荷特征.然后,分别对每个时期下所有电力客户的日负荷曲线数据进行聚类分析,得到不同主网特征下用户群体的负荷特征,对比主网和用户的负荷特征得到用户群体的“削峰填谷”模式.最后,利用动态时间规整算法将未来日期与历史日期进行匹配,得到未来日期用户群体的“削峰填谷”模式.实证研究表明,分析结果可以为有序用电、峰谷电价等公司决策提供更有针对性的参考依据,以更进一步实现配电网负荷的“削峰填谷”和平稳运行.

关 键 词:用电行为分析  削峰填谷  聚类分析  动态时间规整

Analysis of customers' electricity consumption behavior for peak load shifting
WANG Bingxin,HOU Yan,FANG Hongwang,CHEN Yuze,LIU Jian. Analysis of customers' electricity consumption behavior for peak load shifting[J]. Telecommunications Science, 2017, 33(5). DOI: 10.11959/j.issn.1000-0801.2017067
Authors:WANG Bingxin  HOU Yan  FANG Hongwang  CHEN Yuze  LIU Jian
Abstract:In order to implement well-directed peak load shifting for massive customers,a method for analyzing massive customers' electricity consumption behavior for peak load shifting was proposed.Firstly,clustering algorithm was used to cluster daily load curves of the main power grid in the previous year and get load characteristics in different dates.Then the load curves of all customers under every date cluster were clustered,the peak load shifting method was derived by comparing the load characteristics of the main power grid and customers.Finally,the peak load shifting method in a future day was given by date matching between the future day with a historical day using dynamic time warping (DTW).Empirical study shows that the method is conductive to peak-valley electricity pricing and orderly electricity consumption and can further achieve peak load shifting and stable operation of the main power grid.
Keywords:electricity consumption behavior analysis  peak load shifting  clustering analysis  dynamic time warping
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