资金约束条件下高速公路充电桩选址与定容优化
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  • 英文篇名:Location and Sizing of Fast Charging Stations in Freeway Network with Limited Investment
  • 作者:何敏藩 ; 王玥
  • 英文作者:HE Minfan;WANG Yue;Mathematics and Big Data,Foshan University;School of System Engineering,National University of Defense Technology;
  • 关键词:充电站 ; 纯电动汽车 ; 遗传算法 ; 流捕获选址
  • 英文关键词:charging station;;plug-in electric vehicles;;genetic algorithm;;flow capturing location
  • 中文刊名:CQSF
  • 英文刊名:Journal of Chongqing Normal University(Natural Science)
  • 机构:佛山科技学院数学与大数据学院;国防科技大学系统工程学院;
  • 出版日期:2018-09-26 13:25
  • 出版单位:重庆师范大学学报(自然科学版)
  • 年:2018
  • 期:v.35;No.163
  • 基金:国家自然科学基金面上项目(No.71771215);; 广东省高等学校国际暨港澳台科技合作创新平台项目(No.2015KGJHZ023)
  • 语种:中文;
  • 页:CQSF201805003
  • 页数:9
  • CN:05
  • ISSN:50-1165/N
  • 分类号:23-31
摘要
【目的】讨论带有资金约束和服务能力约束的高速公路快速充电站选址与定容规划问题。【方法】应用效用理论分析了电动汽车驾驶员在高速公路上选择充电站的策略,给出了充电设施不充足情况下的车流平衡状态迭代计算方法。建立了充电站选址定容问题的数学优化模型,并设计了一种改进遗传算法对问题进行求解。最后,提出了模拟实际高速公路网的随机网络生成方法,并通过实例对算法进行验证。【结果】测试结果表明:充电桩充电能力的提高可以有效增加高速路网上服务的车流量。【结论】改进后的遗传算法在求解不同规模网络下的选址与定容问题时,都能给出较为稳定的结果。随着网络规模扩大,算法求解的稳定性越好。
        [Purposes]Investigate the location and sizing problem for fast charging stations in freeway network with both capacities on budget and service ability.[Methods]The utility theory is used to analyze the driverscharging strategies,and an iterative approach is proposed to compute the equilibrium distribution of PEV flows when the charging capacity is not enough.A mathematical model is developed to formulate the problem.An improved genetic algorithm is developed to maximize the overall valid PEV flows in the network,subject to the limited budget.An approach for designing random networks of different sizes is also presented,which are used to test the proposed algorithm.[Findings]The volume of serviced PEV flows in the freeway network can be increased efficiently through improving the charging capacity of fast charging spots.[Conclusions]The improved genetic algorithm can present robust solutions for all location and sizing instances with different scales.The robustness of the algorithm becomes better as the scale of the network becomes larger.
引文
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