Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (5): 1364-1371.doi: 10.13229/j.cnki.jdxbgxb.20220767

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Location of electrical changing station of expressway considering stochastic characteristics of road network

Yan-bo LI1(),Bai-song LIU1,Bo-bin YAO1(),Jun-shuo CHEN1,Kai-fa QU2,Qi-sheng WU1,Jie-ning CAO1   

  1. 1.School of Energy and Electrical Engineering，Chang'an University，Xi'an 710064，China
    2.Shandong Transportation Planning and Design Institute Co. ，Ltd，Jinan 250031，China
  • Received:2022-06-20 Online:2023-05-01 Published:2023-05-25
  • Contact: Bo-bin YAO E-mail:ybl@chd.edu.cn;b.b.yao@chd.edu.cn

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Abstract:

In this paper， considering the stochastic characteristics of the electric vehicle flow and the initial residual electricity on the path， the extended network theory is introduced to build a mathematical model for the potential location of the power exchange station under the condition of multi-path. Combined with 'elitism'， the genetic algorithm is used to solve the location model and obtain the initial location solution. Finally， the statistical analysis method is used to screen the initial solution to get the final optimal location. On this basis， the relationship between electric vehicle mileage and station construction cost， and the relationship between the number of replacement stations and service flow are analyzed. By comparison， the introduction of 'elitism' can significantly reduce the initial construction cost and solution time of the charging pile， while the application of statistical analysis method can not only meet the power demand of the vast majority of vehicle owners in the expressway network， but also effectively reduce the initial cost required for the construction of the station.

Key words: transportation planning and management, the location of electrical changing station, genetic algorithm, electric vehicle, expressways

CLC Number: 

  • U49

Fig.1

Schematic constraint diagram of expressway single path"

Fig.2

Schematic diagram of expansion network of single path p"

Table 1

Symbol definition in model"

符号定 义
P路径集合
D路径节点集合
bm在节点m单位电池库存成本
fm节点m处建立换电站固定成本
k换电站建设节点
tp路径p上电动汽车的随机流量
M(m)节点m处电动汽车剩余行驶里程
umnp扩张网络中边(m,n)的随机流量

Table 2

Flow and implementation of genetic algorithm"

流程名称主要实现方法
编码与解码实数编码、符号编码、二进制编码
产生初始种群随机生成
求解适应度

转换目标函数，变换方式有动态线性变换、

线性变换、对数变换、幂律变换等

选择锦标赛、轮盘赌、随机遍历抽样
交叉多点交叉、单点交叉、均匀交叉
变异离散变异、实值变异
终止条件

最优个体适应度满足预期、适应度不再改变、

迭代次数达到给定值

Fig.3

Complex path network diagram"

Fig.4

Path network diagram after adding alternative points"

Table 3

Numerical results"

续航

里程

引入“精英主义” 遗传算法求解未引入“精英主义” 遗传算法求解
最优目标值 /千元运算时间 /s最优目标值 /千元运算时间 /s
20060 3672166.7870 6422211.36
24043 2132461.2651 2882373.24
28036 7631587.1243 7042465.36
32028 1601898.2235 5492320.76
36016 6251714.6225 2262584.48
40013 5451190.5218 6241214.82
44013 2451409.6416 1032194.44
48011 2731934.9414 1931997.62
50010 9731788.2813 4591820.70

Fig.5

20 random times site selection scheme"

Fig.6

50 random times site selection scheme"

Fig.7

100 random times site selection scheme"

Fig.8

Relationship between mileage and cost"

Fig.9

Construction quantity and service flow of exchange station"

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