计算机应用 ›› 2017, Vol. 37 ›› Issue (10): 2978-2982.DOI: 10.11772/j.issn.1001-9081.2017.10.2978

• 应用前沿、交叉与综合 • 上一篇    下一篇

基于二次聚类的大规模电动汽车有序充电调度策略优化

张洁1, 杨春玉1, 鞠非2, 徐小龙1   

  1. 1. 南京邮电大学 计算机学院, 南京 210023;
    2. 国家电网公司 常州供电公司, 江苏 常州 213017
  • 收稿日期:2017-05-11 修回日期:2017-07-20 出版日期:2017-10-10 发布日期:2017-10-16
  • 通讯作者: 张洁(1981-),女,江苏沛县人,高级工程师,博士,主要研究方向:电动汽车与电网互动、电力信息集成,E-mail:zhangjie@njupt.edu.cn
  • 作者简介:张洁(1981-),女,江苏沛县人,高级工程师,博士,主要研究方向:电动汽车与电网互动、电力信息集成;杨春玉(1992-),女,安徽安庆人,硕士研究生,主要研究方向:电动汽车充电站规划、电动汽车充电站运行;鞠非(1977-),男,江苏常州人,高级经济师,主要研究方向:电力系统的运行;徐小龙(1977-),男,江苏盐城人,教授,博士,主要研究方向:电力系统运行、信息网络、分布式计算、信息安全.
  • 基金资助:
    国家自然科学基金资助项目(61472129);国家电网公司科技项目(SGJSCZ00FZJS1600884);南京邮电大学引进人才科研启动基金资助项目(NY213036)。

Optimization of ordered charging strategy for large scale electric vehicles based on quadratic clustering

ZHANG Jie1, YANG Chunyu1, JU Fei2, XU Xiaolong1   

  1. 1. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing Jiangsu 210023, China;
    2. Changzhou Power Supply Company, State Grid Corporation of China, Changzhou Jiangsu 213017, China
  • Received:2017-05-11 Revised:2017-07-20 Online:2017-10-10 Published:2017-10-16
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61472129), the State Grid Corporation of China (SGJSCZ00FZJS1600884), NUPTSFC (NY2130306).

摘要: 针对大量电动汽车无序充电造成的充电站利用率不均衡问题,提出一种大规模电动汽车有序充电调度策略。首先,以电动汽车充电需求的位置为聚类指标,借助归一化相似度进行层次聚类和基于K-means算法的二次划分,以实现属性相似的电动汽车的汇聚。进一步地,通过Dijkstra算法获取电动汽车到达各个充电站的最优路径,以充电站内电动汽车的均匀分配和电动汽车充电路程最短作为目标函数,构建了基于电动汽车聚类的充电调度模型,通过遗传算法求取最优解。与未进行电动汽车聚类的充电调度策略进行的仿真对比实验结果表明,在车辆较多时所提方法的计算时间可减少一半以上,具有较高的实用性。

关键词: 电动汽车, 二次聚类, Dijkstra算法, 充电策略, K-means算法

Abstract: Aiming at the problem of unbalanced utilization rate distribution of charging station caused by disordered charging for a large number of electric vehicles, an orderly charging strategy for electric vehicles was proposed. Firstly, the location of the electric vehicle's charging demand was clustered, and the hierarchical clustering and quadratic division based on K-means were used to achieve the convergence of electric vehicles with similar properties. Furthermore, the optimized path to charging station was determined by Dijkstra algorithm, and by using the even distribution and the shortest charging distance of electric vehicles as objectives functions, the charging scheduling model based on electric vehicle clustering was constructed, and the genetic algorithm was used to solve the problem. The simulation results show that compared with the charging scheduling strategy without clustering of electric vehicles, the computation time of the proposed method can be reduced by more than a half for large scale vehicles, and it has higher practicability.

Key words: electric vehicle, quadratic clustering, Dijkstra algorithm, charging strategy, K-means algorithm

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