考虑路网随机特性的高速公路换电站选址

doi:10.13229/j.cnki.jdxbgxb.20220767

摘要/Abstract

摘要：

综合考虑路径上电动汽车流量和初始剩余电量的随机特性，引入扩张网络理论，构建了多路径条件下潜在的换电站选址数学模型。并结合“精英主义”，利用遗传算法对选址模型进行求解，获取选址初始解。最后，利用统计分析的方法对初始解进行筛选得到最终的最优选址点。在此基础上对电动车续航里程与建站成本关系及换电站数量与服务流量关系进行了分析，通过比较，“精英主义”的引进可明显降低换电站初始建设成本及求解时间，而统计分析法的运用使换电站既可满足高速路网中绝大多数车主的用电需求，又能有效降低建站所需初始成本。

关键词: 交通运输规划与管理, 换电站选址, 遗传算法, 电动汽车, 高速公路

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

中图分类号:

李艳波,柳柏松,姚博彬,陈俊硕,渠开发,武奇生,曹洁宁. 考虑路网随机特性的高速公路换电站选址[J]. 吉林大学学报(工学版), 2023, 53(5): 1364-1371.

Yan-bo LI,Bai-song LIU,Bo-bin YAO,Jun-shuo CHEN,Kai-fa QU,Qi-sheng WU,Jie-ning CAO. Location of electrical changing station of expressway considering stochastic characteristics of road network[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1364-1371.

图/表 12

图1

图2

表1

模型符号定义"

符号	定义
$P$	路径集合
$D$	路径节点集合
$b m$	在节点 $m$ 单位电池库存成本
$f m$	节点 $m$ 处建立换电站固定成本
$k$	换电站建设节点
$t p$	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
$u m n p$	扩张网络中边 $(m, n)$ 的随机流量

表1

表2

图3

图4

表3

图5

图6

图7

图8

图9

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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

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
续航里程	最优目标值 /千元	运算时间 /s	最优目标值 /千元	运算时间 /s
200	60 367	2166.78	70 642	2211.36
240	43 213	2461.26	51 288	2373.24
280	36 763	1587.12	43 704	2465.36
320	28 160	1898.22	35 549	2320.76
360	16 625	1714.62	25 226	2584.48
400	13 545	1190.52	18 624	1214.82
440	13 245	1409.64	16 103	2194.44
480	11 273	1934.94	14 193	1997.62
500	10 973	1788.28	13 459	1820.70