摘要
快速充电站选址是电动汽车运营的重要内容之一。本文考虑电动汽车用户会通过绕行一定距离对车辆进行充电这一特征,建立了一个以电动汽车快速充电站建站成本和旅客整体绕行成本之和最小的双层整数规划模型。本文首先给出了用于生成绕行路径集合的A*算法,然后设计了一种包含局部迭代搜索的自适应遗传算法对该模型进行求解。为了测试算法性能,通过两个不同规模的算例图与已有求解FPLM问题的遗传算法进行了比较,数值试验部分证明了算法的正确性和有效性。最后引入浙江省的高速路网图,从建站成本和截流量两方面对电池续航里程带来的影响进行了相关的灵敏度分析。
Fast recharging station location is one of the most important aspects in electric vehicle operations manage- ment. Considering the fact that the electric vehicle users will detour from their shortest paths to refuel the vehicles, this paper studies a battery fast recharging stations location problem and builds a bilevel integer programming model to minimize the sum of building cost and deviation cost. Firstly, an A-Star algorithm is presented to generate the path sets of all OD pairs, and then an adaptive-self genetic algorithm （AGA）including local iterative search is proposed to solve this problem. Compared with genetic algorithm（GA）in two networks with different size, simulation results indi- cate that AGA is effective especially in the large network. Furthermore, using the ZheJiang Province as the network, this paper also analyzes the impact of battery＇ s driving range on building cost and intercepting value.
出处
《运筹与管理》
CSSCI
CSCD
北大核心
2017年第1期8-17,共10页
Operations Research and Management Science
基金
国家自然科学基金国际交流重大项目资助(71320107001)
中央高校基本科研业务专项资金资助(HUST:2013QN101
2013ZZGH028)
关键词
电动汽车
快速充电站选址问题
绕行成本
自适应遗传算法
A^＊算法
electric vehicle
fast recharging station location problem
detour cost
adaptlve-self genetic algorithm
A-star algorithm
