Siting and sizing of electric vehicle charging stations under the coupling of transport and power networks considering V2G potential

doi:10.3969/j.issn.2097-0706.2024.01.001

Abstract

Abstract:

Electric vehicles are both transportation means and mobile loads. Their large-scale and intensive charging will impact the transportation network and the power grid at the same time. In view of EVs' mobile attribute，their charging demands can be optimized in time and space dimensions through the vehicle-to-grid（V2G） technology. The technology can not only alleviate the aforementioned impact， but also provide energy storage capacity to the power grid，so as to promote the accommodation of renewable energy resources and peak load regulation of the power grid. Taking a charging station invested by State Grid Corporation as the example， a siting and sizing strategy for the charging station that considers transportation and power networks is proposed. On the premise of satisfying the charging demands of electric vehicles， the strategy optimizes the charging demand to maximize the energy storage capacity of the station. Firstly， based on the dynamic traffic network model， the driving paths of electric vehicles are accurately simulated by Floyd algorithm and regional characteristics，to predict the spatial and temporal characteristics of electric vehicle charging loads. Secondly，based on the prediction results， the preliminary station location is determined by three goals，maximizing the energy storage capacity，smoothing the charging load， and the extending the parking time in the station. Then， the optimal location and capacity of the charging station are adjusted according to the statistical results from the whole period. Taking the roads in a main urban area as an example， the spatial and temporal characteristics of the charging load are predicted， and the optimal location and capacity of the charging station aiming at maximizing its energy storage capacity is designed. The influence of this design on the transportation network and power grid is analyzed，which proves the effectiveness of the proposed strategy.

Key words: electric vehicle, V2G technology, traffic network, spatio-temporal distribution model, siting and sizing

CLC Number:

TK01

SUN Yule, QI Taoyi, ZHAO Yuming, YE Chengjin, HUI Hongxun. Siting and sizing of electric vehicle charging stations under the coupling of transport and power networks considering V2G potential[J]. Integrated Intelligent Energy, 2024, 46(1): 1-10.

Figures/Tables 13

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Table 1

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Table 2

References 27

[1]	刘勇, 李全优, 戴朝华. 电动汽车充电负荷时空分布建模研究综述[J]. 电测与仪表, 2022, 59(8): 1-9.
	LIU Yong, LI Quanyou, DAI Chaohua. Review on the spatio temporal distribution modeling of electric vehicle charging load[J]. Electrical Measurement & Instrumentation, 2022, 59(8): 1-9.
[2]	DU Z L, LIN B Q, GUAN C X. Development path of electric vehicles in China under environmental and energy security constraints[J]. Resources,Conservation and Recycling, 2019, 143: 17-26. doi: 10.1016/j.resconrec.2018.12.007
[3]	李科, 皇甫霄文, 李梦超, 等. 光-储一体电动汽车充电站储能规划[J/OL]. 电力系统及其自动化学报:1-12(2022-12-26)[2023-04-14].https://link.cnki.net/doi/10.19635/j.cnki.csu-epsa.001174.
	LI Ke, HUANGFU Xiaowen, LI Mengchao, et al. Energy storage configuration of integrated PV-storage electric vehicle charging station[J/OL]. Proceedings of the CSU-EPSA:1-12(2022-12-26)[2023-04-14].https://link.cnki.net/doi/10.19635/j.cnki.csu-epsa.001174.
[4]	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): 54-59.
	LIU Zhipeng, WEN Fushuan, XUE Yusheng, et al. Optimal siting and sizing of electric vehicle charging stations[J]. Automation of Electric Power Systems, 2012, 36(3): 54-59.
[5]	崔岩, 胡泽春, 段小宇. 考虑充电需求空间灵活性的电动汽车运行优化研究综述[J]. 电网技术, 2022, 46(3): 981-994.
	CUI Yan, HU Zechun, DUAN Xiaoyu. Review on the electric vehicles operation optimization considering the spatial flexibility of electric vehicles charging demands[J]. Power System Technology, 2022, 46(3): 981-994.
[6]	刘晓飞, 张千帆, 崔淑梅. 电动汽车V2G技术综述[J]. 电工技术学报, 2012, 27(2): 121-127.
	LIU Xiaofei, ZHANG Qianfan, CUI Shumei. Review of electric vehicle V2G technology[J]. Transactions of China Electrotechnical Society, 2012, 27(2): 121-127.
[7]	HABIB S, KAMRAN M. A novel vehicle-to-grid technology with constraint analysis—A review[C]// 2014 international conference on emerging technologies (ICET). IEEE, 2014: 69-74.
[8]	KEMPTON W, LETENDRE S E. Electric vehicles as a new power source for electric utilities[J]. Transportation Research Part D: Transport and Environment, 1997, 2(3): 157-175. doi: 10.1016/S1361-9209(97)00001-1
[9]	崔进, 张良力, 刘江, 等. 计及电能质量优化的V2G充电桩模糊控制建模[J]. 计算机仿真, 2023, 40(3): 116-121,245.
	CUI Jin, ZHANG Liangli, LIU Jiang, et al. Modeling for V2G charging pile fuzzy control considering power quality optimization[J]. Computer Simulation, 2023, 40(3): 116-121,245.
[10]	黄亮, 刘明, 张锐明. 双向充电桩V2G控制策略研究[J]. 电力电子技术, 2019, 53(8): 41-44,49.
	HUANG Liang, LIU Ming, ZHANG Ruiming. Research on V2G control strategy of bidirectional[J]. Power Electronics, 2019, 53(8): 41-44,49.
[11]	罗卓伟, 胡泽春, 宋永华, 等. 大规模电动汽车充放电优化控制及容量效益分析[J]. 电力系统自动化, 2012, 36(10): 19-26.
	LUO Zhuowei, HU Zechun, SONG Yonghua, et al. Coordinated charging and discharging of large-scale plug-in electric vehicles with cost and capacity benefit analysis[J]. Automation of Electric Power Systems, 2012, 36(10): 19-26.
[12]	韩海英. V2G参与电网调峰和调频控制策略研究[D]. 北京: 北京交通大学, 2011.
	HAN Haiying. The study on the control strategy of V2G participating peak regulation and frequency regulation of the grid[D]. Beijing: Beijing Jiaotong University, 2011.
[13]	周有为, 高忠江, 钟雨哲, 等. 基于改进向量序优化算法的V2G电动汽车充电站规划方法[J]. 智慧电力, 2022, 50(7): 104-110.
	ZHOU Youwei, GAO Zhongjiang, ZHONG Yuzhe, et al. V2G electric vehicle charging station planning method based on improved vector order optimization algorithm[J]. Smart Power, 2022, 50(7): 104-110.
[14]	吕媛, 何永秀, 王可蕙, 等. 需求响应下的电动汽车充电设施配置模型[J]. 现代电力, 2022, 39(4): 469-479.
	LÜ Yuan, HE Yongxiu, WANG Kehui, et al. Configuration model of electric vehicle charging facilities considering demand response[J]. Modern Electric Power, 2022, 39(4): 469-479.
[15]	李蓓, 赵松, 谢志佳, 等. 电动汽车虚拟储能可用容量建模[J]. 山东大学学报(工学版), 2020, 50(6): 101-111.
	LI Bei, ZHAO Song, XIE Zhijia, et al. Electric vehicle virtual energy storage available capacity modeling[J]. Journal of Shandong University(Engineering Science), 2020, 50(6): 101-111.
[16]	赵杨, 刘倩. 网约车冲击下出租车行业转型对策研究:以北京市为例[J]. 科学决策, 2022(12): 107-136.
	ZHAO Yang, LIU Qian. Taxi industry transformation under the impact of online car hailing: A case study of Beijing[J]. Scientific Decision Making, 2022(12): 107-136.
[17]	俞博. 基于RFID电子车牌数据的机动车出行行为研究[J]. 智能城市, 2021, 7(16): 10-12.
	YU Bo. A Study of motor vehicle travel behaviour based on RFID electronic license plate data[J]. Intelligent City, 2021, 7(16): 10-12.
[18]	张琳娟, 许长清, 王利利, 等. 基于OD矩阵的电动汽车充电负荷时空分布预测[J]. 电力系统保护与控制, 2021, 49(20): 82-91.
	ZHANG Linjuan, XU Changqing, WANG Lili, et al. OD matrix based spatiotemporal distribution of EV charging load prediction[J]. Power System Protection and Control, 2021, 49(20): 82-91.
[19]	杨鑫, 张启兆. 如何正确运用正态分布——例谈正态分布中的解题策略[J]. 新世纪智能, 2022(41): 12-14.
	YANG Xin, ZHANG Qizhao. How to use the normal distribution correctly—Example of a problem solving strategy in the normal distribution[J]. New Century Intelligence, 2022(41): 12-14.
[20]	郑远硕, 李峰, 董九玲, 等. “车-路-网”模式下电动汽车充放电时空灵活性优化调度策略[J]. 电力系统自动化, 2022, 46(12): 88-97.
	ZHENG Yuanshuo, LI Feng, DONG Jiuling, et al. Optimal dispatch strategy of spatio-temporal flexibility for electric vehicle charging and discharging in vehicle-road-grid mode[J]. Automation of Electric Power Systems, 2022, 46(12): 88-97.
[21]	邵尹池, 穆云飞, 余晓丹, 等. “车-路-网”模式下电动汽车充电负荷时空预测及其对配电网潮流的影响[J]. 中国电机工程学报, 2017, 37(18): 5207-5219,5519.
	SHAO Yinchi, MU Yunfei, YU Xiaodan, et al. A Spatial-temporal charging load forecast and impact analysis method for distribution network using EVs-traffic-distribution model[J]. Proceedings of the CSEE, 2017, 37(18): 5207-5219,5519.
[22]	卢立果, 刘立越, 鲁铁定, 等. 一种改进的Floyd算法[J]. 东华理工大学学报(自然科学版), 2019, 42(1): 78-81.
	LU Liguo, LIU Liyue, LU Tieding, et al. A modified floyd algorithm[J]. Journal of East China University of Technology, 2019, 42(1): 78-81.
[23]	吴海峰. 最短路径算法——Dijkstra及Floyd算法[J]. 中国新通信, 2019, 21(2): 32-33.
	WU Haifeng. Shortest path algorithm—Dijkstra and Floyd's algorithm[J]. Internet Communication, 2019, 21(2): 32-33.
[24]	黄杭. 浅谈Dijkstra算法与Floyd算法[J]. 中国新通信, 2019, 21(3): 162-163.
	HUANG Hang. An introduction to Dijkstra's algorithm and Floyd's algorithm[J]. Internet Security, 2019, 21(3): 162-163.
[25]	王志峰, 何雅玲, 康重庆, 等. 明确太阳能热发电战略定位促进技术发展[J]. 华电技术, 2021, 43(11): 1-4.
	WANG Zhifeng, HE Yaling, KANG Chongqing, et al. Strategic positioning of solar thermal power generation to promote technological progress[J]. Huadian Technology, 2021, 43(11): 1-4.
[26]	王义, 杨志伟, 吴坡, 等. 计及高比例分布式光伏能源接入的配电网状态估计[J]. 综合智慧能源, 2022, 44(10): 12-18. doi: 10.3969/j.issn.2097-0706.2022.10.002
	WANG Yi, YANG Zhiwei, WU Po, et al. State estimation for the distribution network with high-proportion distributed photovoltaic energy[J]. Integrated Intelligent Energy, 2022, 44(10): 12-18. doi: 10.3969/j.issn.2097-0706.2022.10.002
[27]	翁志鹏, 周京华, 李津, 等. 含风光接入的微电网可靠性影响分析[J]. 综合智慧能源, 2023, 45(1): 67-74. doi: 10.3969/j.issn.2097-0706.2023.01.008
	WENG Zhipeng, ZHOU Jinghua, LI Jin, et al. Impact of wind and solar power grid connection on microgrid reliability[J]. Integrated Intelligent Energy, 2023, 45(1): 67-74. doi: 10.3969/j.issn.2097-0706.2023.01.008

项目	节点编号
项目	14	20	26	8	25	9	23	其他
被选中次数	14	13	9	7	7	5	5	<5
占比/%	18.7	17.3	12.0	9.3	9.3	6.7	6.7	<6.7

项目	最优充电站分布节点编号
项目	8	14	20	25	26
每个充电站充电桩数量/个	200	93	89	89	135
最佳情况下储能容量/（kW·h）	14 270.28
最佳情况下可利用时长/h	888.52