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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (6): 1210-1217    DOI: 10.3785/j.issn.1008-973X.2020.06.019
Traf fic Engineering     
Configuration optimization of electric vehicle charging facilities in urban areas
Xia SHANG(),Mei-jia WANG,Liu-xiao XU,Li-hui ZHANG*()
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
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Abstract  

Considering the characteristics of public charging facilities and private charging facilities, the compositions of vehicle charging costs were first elaborated, including vehicle travel time, queuing time, waiting time and charging fee. A Logit model was employed to describe users’ choice behaviors of charging facilities, in order to obtain the charging distribution from each traffic analysis zone to public charging stations and private charging piles. To minimize users ’ total charging cost, an optimization model was built to optimize the configurations of charging facilities in urban areas, which could simultaneously optimize the locations and capacities of public and private charging facilities. The model was in form of a nonlinear program, which could be solved by existing solvers, such as the optimization toolbox provided in Matlab. Two numerical examples based on a portion of Hangzhou network were carried out to demonstrate the proposed approach, and to discuss the interactions of public and private facilities inside and outside of the study area. The numerical test results show that the proposed model has the potential to provide quantitative decision references to optimize the locations and capacities of electric charging facilities in urban areas.

Key words： electric vehicle      public charging station      private charging pile      Logit model     
Received: 21 May 2019      Published: 06 July 2020
CLC:  U 121  
Corresponding Authors: Li-hui ZHANG     E-mail: shangxia@zju.edu.cn;lihuizhang@zju.edu.cn
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Xia SHANG
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Li-hui ZHANG
Cite this article:

Xia SHANG,Mei-jia WANG,Liu-xiao XU,Li-hui ZHANG. Configuration optimization of electric vehicle charging facilities in urban areas. Journal of ZheJiang University (Engineering Science), 2020, 54(6): 1210-1217.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.06.019     OR     http://www.zjujournals.com/eng/Y2020/V54/I6/1210


城市区域电动汽车充电设施配置优化

综合考虑公共充电设施与私人充电设施特性，根据行程时间、排队时间、等待时间、充电费用等计算车辆充电成本. 采用Logit模型刻画用户的充电设施选择行为，得到各交通小区到各公共充电站、私人充电桩的出行分布量. 以充电总成本最小化为目标，建立城市区域充电设施网络配置优化模型，设计充电设施的布局位置和容量；所得模型为非线性优化模型，可采用现有的求解器（如：Matlab优化工具箱中的相关函数）直接进行求解. 基于杭州市区内的局部路网构建2套算例，来验证提出的充电设施配置优化方法的可行性，并讨论研究区域内外公共充电设施与私人充电设施之间的交互影响. 算例结果显示：所建立的模型可为城市电动汽车充电设施的选址布局和容量设计提供定量决策参考.


关键词： 电动汽车,  公共充电站,  私人充电桩,  Logit模型 
Fig.1 Traffic analysis zones and public charging stations
i Ti /（辆·d?1） i Ti /（辆·d?1）
Z1 580 Z9 380
Z2 270 Z10 290
Z3 380 Z11 250
Z4 290 Z12 300
Z5 310 Z13 820
Z6 370 Z14 540
Z7 320 Z15 270
Z8 190 Z16 310
Tab.1 Assumed charging demand from traffic analysis zones in study area
Fig.2 Fitting curve of walking time and driving time from survey in study area
辆
i A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 Pii
Z1 97 59 41 40 42 41 41 40 41 39 99
Z2 22 47 29 17 20 21 21 17 17 17 42
Z3 23 37 58 54 23 27 27 29 23 23 56
Z4 19 20 28 50 19 20 20 26 19 20 49
Z5 48 30 18 18 49 26 19 18 21 18 45
Z6 28 57 22 20 36 54 38 21 26 20 48
Z7 19 33 19 37 19 35 35 37 18 22 46
Z8 13 13 13 26 13 13 13 26 13 15 32
Z9 25 27 24 24 65 36 25 25 44 25 60
Z10 17 20 16 16 29 43 44 17 31 17 40
Z11 14 14 14 19 14 28 36 36 15 26 34
Z12 18 19 18 25 19 22 19 47 19 49 45
Z13 59 60 56 57 60 61 61 59 142 59 146
Z14 37 37 36 37 38 39 61 38 88 38 91
Z15 17 17 17 18 17 23 34 32 18 35 42
Z16 20 20 20 21 20 21 22 37 21 57 51
Tab.2 Number of charging vehicles from traffic analysis zones to charging stations
j Nj ${\lambda _j}$ /
（辆·min?1） 		$t_j^{\rm{q}}$ /
min 		j Nj ${\lambda _j}$ /
（辆·min?1） 		$t_j^{\rm{q}}$ /
min
A1 47 0.33 12.4 A6 52 0.35 12.17
A2 52 0.35 12.05 A7 53 0.36 12.01
A3 39 0.3 13.71 A8 52 0.35 12.11
A4 48 0.33 12.45 A9 61 0.39 11.22
A5 48 0.34 12.42 A10 48 0.33 12.36
Tab.3 Number of charging piles，EV arrival rates and waiting time of each public charging station
Fig.3 Sensitivity analysis results by changing installation cost of private charging pile
$m_i^{\rm{f}}$/元 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 $\sum\nolimits_j {{N_j}} $
0 47 52 39 48 48 52 53 52 61 48 500
30 48 52 39 48 48 52 53 51 61 48 500
60 48 52 40 48 48 51 53 51 61 48 500
90 48 52 40 48 48 51 53 51 61 48 500
120 48 52 40 48 48 51 53 51 61 48 500
150 48 52 40 48 48 51 53 51 61 48 500
180 48 52 40 48 48 51 53 51 61 48 500
Tab.4 Optimal pile configurations of public charging stations under different installation cost of private charging piles
Fig.4 Sensitivity analysis results by changing total number of public charging piles
${N_{{\rm{all}}}}$ A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 $\sum\nolimits_j {{N_j}} $
100 9 11 5 9 9 11 11 11 14 10 100
200 19 21 15 19 19 21 21 21 25 19 200
300 28 31 23 29 29 31 32 31 37 29 300
400 38 42 31 38 38 42 43 41 49 38 400
500 47 52 39 48 48 52 53 52 61 48 500
600 57 63 46 57 58 62 64 62 73 58 600
700 67 73 54 67 67 73 74 72 86 67 700
800 76 84 62 76 77 83 85 82 98 77 800
900 86 94 70 86 86 93 96 93 110 86 900
1000 95 105 78 95 96 104 106 103 122 96 1000
Tab.5 Optimal pile configurations of public charging stations with different total amount constraints
Fig.5 Public charging stations（B1 to B10）in extended example
$j$ ${N_j}$ ${\lambda _j}$ /
（辆·min?1） 		$t_j^{\rm{q}}$ /
min 		$j$ ${N_j}$ ${\lambda _j}$ /
（辆·min?1） 		$t_j^{\rm{q}}$ /
min
B1 76 0.42 10.03 B6 36 0.28 13.93
B2 74 0.42 10.13 B7 34 0.28 14.26
B3 81 0.44 9.78 B8 38 0.29 13.59
B4 38 0.29 13.68 B9 36 0.28 13.89
B5 43 0.31 12.79 B10 44 0.31 12.62
Tab.6 Number of charging piles，EV arrival rate and waiting time of each public charging station in extended example
$i$ ${P_{ii}}$ $i$ ${P_{ii}}$
Z1 114 Z9 72
Z2 47 Z10 53
Z3 70 Z11 44
Z4 57 Z12 53
Z5 59 Z13 145
Z6 62 Z14 98
Z7 56 Z15 50
Z8 35 Z16 57
Tab.7 Number of private charging piles in each traffic analysis zone in extended example
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