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董彦君,闫志杰,马喜平,刘丽娟,张蕊萍,董海鹰.基于加权Voronoi图和自适应PSO算法的电动汽车充换电站联合规划[J].电源学报,2018,16(4):71-79
基于加权Voronoi图和自适应PSO算法的电动汽车充换电站联合规划
Joint Planning of EV Charging and Battery Swapping Stations Based on Weighted Voronoi Diagram and Adaptive PSO Algorithm
投稿时间:2018-01-30  修订日期:2018-05-15
DOI:10.13234/j.issn.2095-2805.2018.4.71
中文关键词:  电动汽车  换电需求  加权Voronoi图  充、换电站规划
英文关键词:electric vehicle(EV)  battery swapping demand  weighted Voronoi diagram  planning of charging and batt-ery swapping stations
基金项目:国网甘肃省电力公司科技支撑资助项目(522722160021)
作者单位E-mail
董彦君 中国农业大学信息与电气工程学院, 北京 100083  
闫志杰 兰州交通大学自动化与电气工程学院, 兰州 730070  
马喜平 国网甘肃省电力公司电力科学研究院, 兰州 730050  
刘丽娟 国网甘肃省电力公司电力科学研究院, 兰州 730050  
张蕊萍 兰州交通大学自动化与电气工程学院, 兰州 730070  
董海鹰 兰州交通大学自动化与电气工程学院, 兰州 730070 hydong@mail.lzjtu.cn 
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中文摘要:
      充、换电站是为电动汽车提供电能补给的基础设施,合理规划充、换电站对推动电动汽车普及具有重要意义。首先,预测规划区域电动汽车保有量,根据保有量及各类型电动汽车行驶特性数据,得到电动汽车总换电需求和各路网节点换电需求;其次,以充、换电站建设运维成本、用户损耗成本和电池配送成本之和最小为目标,建立了充、换电站联合规划模型;最后,采用加权Voronoi图与自适应粒子群算法对模型进行求解。算例结果表明,在综合考虑电动汽车用户和充、换电站运营商的利益下,所提方法既能满足用户电能补给需求,又能使充、换电站年均综合成本达到最优。
英文摘要:
      Charging and battery swapping stations are the basic infrastructure to provide power supply for electric vehicles(EVs), thus a rational planning of stations is of great significance to promoting the popularization of EVs. Firstly, the number of EVs is predicted in the planning area; afterwards, according to the predicted number and the driving characteristics of various types of EVs, the total battery swapping demand of EVs and each node in the road network is obtained. Secondly, a joint planning model is established, with the objective of minimizing the sum of the construction and operation cost of stations, users' loss cost, and battery delivery cost. Finally, weighted Voronoi diagram and adaptive particle swarm optimization(APSO) algorithm are used to solve the model. The analysis result of a numerical example shows that in consideration of the benefits of EV users and station operators, the proposed method can not only meet the users'power supply demand, but also optimize the annual comprehensive cost of stations.
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