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.
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.
Fig.1Traffic 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.1Assumed charging demand from traffic analysis zones in study area
Fig.2Fitting 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.2Number 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.3Number of charging piles，EV arrival rates and waiting time of each public charging station
Fig.3Sensitivity 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.4Optimal pile configurations of public charging stations under different installation cost of private charging piles
Fig.4Sensitivity 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.5Optimal pile configurations of public charging stations with different total amount constraints
Fig.5Public 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.6Number 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.7Number of private charging piles in each traffic analysis zone in extended example
[1]
郑雪芹 2018车市回顾及2019展望[J]. 汽车纵横, 2019, 94 (1): 24- 28 ZHEN Xue-qin Review of car market in 2018 and prospect for 2019[J]. Auto Review, 2019, 94 (1): 24- 28
[2]
刘志鹏, 文福拴, 薛禹胜, 等 电动汽车充电站的最优选址和定容[J]. 电力系统自动化, 2012, 36 (3): 174- 175 LIU Zhi-peng, WEN Fu-shuan, XUE Yu-sheng, et al Optimal siting and sizing of electric vehicle charging stations[J]. Automation of Electric Power Systems, 2012, 36 (3): 174- 175
[3]
中国电动汽车充电基础设施促进联盟 充电基础设施月度数据追踪[J]. 汽车纵横, 2018, (8): 59- 59 EVCIPA Monthly data tracking of charging infrastructure[J]. Auto Review, 2018, (8): 59- 59
[4]
廖振宏. 基于目的地选择的电动出租车充电站规划研究[D]. 北京: 华北电力大学, 2016. LIAO Zhen-hong. Study on electric taxi charging station planning based on destination choice [D]. Beijing: North China Electric Power University, 2016.
[5]
宋艳慧 新能源汽车充电服务面临的发展困境及其商业模式研究[J]. 科技经济市场, 2019, (1): 121- 122 SONG Yan-hui Development dilemma and business model of new energy vehicle charging service[J]. Science and Technology Economy Market, 2019, (1): 121- 122
doi: 10.3969/j.issn.1009-3788.2019.01.060
[6]
任玉珑, 史乐峰, 张谦, 等 电动汽车充电站最优分布和规模研究[J]. 电力系统自动化, 2011, 35 (14): 53- 57 REN Yu-long, SHI Le-feng, ZHANG Qian, et al Optimal distribution and scale of charging stations for electric vehicles[J]. Automation of Electric Power Systems, 2011, 35 (14): 53- 57
[7]
宋阳. 电动汽车充电站布局优化研究[D]. 成都: 西南交通大学, 2016. SONG Yang. Optimization of electric vehicles’ charging station location [D]. Chengdu: Southwest Jiaotong University, 2016.
[8]
XIANG Y, ZHOU H, YANG W, et al Scale evolution of electric vehicles: a system dynamics approach[J]. IEEE Access, 2017, 5: 8859- 8868
doi: 10.1109/ACCESS.2017.2699318
[9]
李颖. 基于电动汽车出行随机模拟的充电桩需求研究[D]. 北京: 华北电力大学, 2017. LI Ying. Research on demand of charging piles based on stochastic simulation of EV trip chain [D]. Beijing: North China Electric Power University, 2017
[10]
DONG J, LIU C, LIN Z Charging infrastructure planning for promoting battery electric vehicles: an activity-based approach using multiday travel data[J]. Transportation Research Part C: Emerging Technologies, 2014, 38: 44- 55
doi: 10.1016/j.trc.2013.11.001
[11]
邱海. 基于Agent仿真的城市电动汽车充电设施布局优化研究[D]. 杭州: 浙江大学, 2019. QIU Hai. Research on optimization of charging facilities layout of urban electric vehicles based on agent simulation [D]. Hangzhou: Zhejiang University, 2019.
[12]
NI X, SUN D J, PENG Z An improved incremental assignment model for parking variable message sign location problem[J]. Journal of Advanced Transportation, 2015, 49 (7): 817- 828
doi: 10.1002/atr.1305
[13]
NI X, SUN D J An agent-based simulation model for parking variable message sign location problem[J]. Transportation Research Record, 2018, 2672 (19): 135- 144
doi: 10.1177/0361198118787387
[14]
CAI H, JIA X, CHIU A S F, et al Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet[J]. Transportation Research Part D: Transport and Environment, 2014, 33: 39- 46
doi: 10.1016/j.trd.2014.09.003
[15]
TU W, LI Q, FANG Z, et al Optimizing the locations of electric taxi charging stations: a spatial-temporal demand coverage approach[J]. Transportation Research Part C: Emerging Technologies, 2016, 65: 172- 189
doi: 10.1016/j.trc.2015.10.004
[16]
YUN B, SUN D J, ZHANG Y, et al A charging location choice model for plug-in hybrid electric vehicle users[J]. Sustainability, 2019, 11 (20): 5761
doi: 10.3390/su11205761
[17]
KANG Q, WANG J, ZHOU M, et al Centralized charging strategy and scheduling algorithm for electric vehicles under a battery swapping scenario[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17 (3): 659- 669
doi: 10.1109/TITS.2015.2487323
IMGBEMENA C E, MGBEMENA C O, CHINWUKO E C A regression analysis approach to queuing system modelling: a case of banks[J]. Journal of Applied Sciences Research, 2011, 7: 200- 212
