5G-V2X-Based Vehicle Charging Station Planning Method Considering Different Land-Use Types

doi:10.11930/j.issn.1004-9649.202005060

Abstract

Abstract: With the development of the 5G technology, much progress has been made in vehicle to everything (V2X) and the Internet of Vehicle. Mobile users could quickly obtain relevant physical facility information, and facility operators could better allocate scheduling resources based on the road network information. In the city planning, different area has different functions. From the view of electrical vehicle, two area will own the same characteristic in the arriving distribution if they belong to the same function. In addition, the grid capacity, land restriction, cost and price will have similar patterns. Based on the area characteristics and the V2X assistance, the station network model was proposed in this paper. Concerning about the aim of commercial charging network, we optimize the charging station network within a giving area with the objective of retained profits and the constraints of grid capacity and land. A charging network strategy which is on the basis of station parameter and parking distribution is devised. The strategy includes two parts, the first part calculates the optimal charger number in the given station; the second part removes and aggregates some candidate station to optimal the charging network station. The M/M/S/K queue model and user charging intention curve are introduced to picture the user behavior in charging station. The impacts of critical parameters of charging network on the system performance is provided.

Key words: 5G, vehicle to everything (V2X), electric vehicle, charging station, land-use type, M/M/S/K queue

XIONG Ke, WU Siyu, ZHENG Haina, WANG Rui, ZHANG Yu. 5G-V2X-Based Vehicle Charging Station Planning Method Considering Different Land-Use Types[J]. Electric Power, 2021, 54(3): 89-98.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202005060

https://www.electricpower.com.cn/EN/Y2021/V54/I3/89

References

[1] CHEN S Z, HU J L, SHI Y, et al. Vehicle-to-everything (v2x) services supported by LTE-based systems and 5G[J]. IEEE Communications Standards Magazine, 2017, 1(2): 70-76.
[2] VARGA N, BOKOR L, TAKÁCS A, et al. An architecture proposal for V2X communication-centric traffic light controller systems[C]//2017 15th International Conference on ITS Telecommunications (ITST). Warsaw, Poland. IEEE, 2017: 1-7.
[3] ZHOU H B, XU W C, CHEN J C, et al. Evolutionary V2X technologies toward the Internet of vehicles: challenges and opportunities[J]. Proceedings of the IEEE, 2020, 108(2): 308-323.
[4] HU Y, FENG J J, CHEN W L. A LTE-cellular-based V2X solution to future vehicular network[C]//2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). Xi'an, China. IEEE, 2018: 2658-2662.
[5] MOUBAYED A, SHAMI A, HEIDARI P, et al. Edge-enabled V2X service placement for intelligent transportation systems[J]. IEEE Transactions on Mobile Computing, DOI: 10.1109/TMC.2020.2965929.
[6] 缪立新, 王发平. V2X车联网关键技术研究及应用综述[J]. 汽车工程学报, 2020, 10(1): 1-12
MIAO Lixin, WANG Faping. Review on research and applications of V2X key technologies[J]. Chinese Journal of Automotive Engineering, 2020, 10(1): 1-12
[7] CHEN S Z, HU J L, SHI Y, et al. A vision of C-V2X: technologies, field testing, and challenges with Chinese development[J]. IEEE Internet of Things Journal, 2020, 7(5): 3872-3881.
[8] 魏秀岭, 杜传祥. 基于粒子群算法加权的Voronoi图电动汽车充网络优化规划[J]. 数字技术与应用, 2018, 36(2): 119-121
WEI Xiuling, DU Chuanxiang. Weighted Voronoi diagram electric vehicle charging network optimization planning based on particle swarm optimization[J]. Digital Technology and Application, 2018, 36(2): 119-121
[9] ZHANG W G, ZHANG D, MU B Q, et al. Decentralized electric vehicle charging strategies for reduced load variation and guaranteed charge completion in regional distribution grids[J]. Energies, 2017, 10(2): 147.
[10] CAPAR I, KUBY M, LEON V J, et al. An arc cover-path-cover formulation and strategic analysis of alternative-fuel station locations[J]. European Journal of Operational Research, 2013, 227(1): 142-151.
[11] CHEN L X, HUANG X L, CHEN Z, et al. Study of a new quick-charging strategy for electric vehicles in highway charging stations[J]. Energies, 2016, 9(9): 744.
[12] 葛少云, 冯亮, 刘洪, 等. 电动汽车充电站规划布局与选址方案的优化方法[J]. 中国电力, 2012, 45(11): 96-101
GE Shaoyun, FENG Liang, LIU Hong, et al. An optimization approach for the layout and location of electric vehicle charging stations[J]. Electric Power, 2012, 45(11): 96-101
[13] 高建树, 王明强, 宋兆康, 等. 基于遗传算法的机场充电桩布局选址研究[J]. 计算机工程与应用, 2018, 54(23): 210-216
GAO Jianshu, WANG Mingqiang, SONG Zhaokang, et al. Study on site selection of airport charging pile based ongenetic algorithm[J]. Computer Engineering and Applications, 2018, 54(23): 210-216
[14] YOU P C, YANG Z Y, ZHANG Y M, et al. Optimal charging schedule for a battery switching station serving electric buses[J]. IEEE Transactions on Power Systems, 2016, 31(5): 3473-3483.
[15] ZHANG Y M, WEI Z, LI H, et al. Optimal charging scheduling for catenary-free trams in public transportation systems[J]. IEEE Transactions on Smart Grid, 2019, 10(1): 227-237.
[16] ZHANG Y M, YOU P C, CAI L. Optimal charging scheduling by pricing for EV charging station with dual charging modes[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(9): 3386-3396.
[17] 黄碧斌, 孔维政, 李琼慧. 中国典型城市电网电动汽车容纳能力研究[J]. 中国电力, 2013, 46(10): 91-95
HUANG Bibin, KONG Weizheng, LI Qionghui. Capability study of typical power grid in accommodating electric vehicles in China[J]. Electric Power, 2013, 46(10): 91-95
[18] ZHANG Y M, CHEN J Y, CAI L, et al. Expanding EV charging networks considering transportation pattern and power supply limit[J]. IEEE Transactions on Smart Grid, 2019, 10(6): 6332-6342.
[19] 美国交通部调查统计数据 (National Household Travel Survey, NHTS): U. S. Department of transportation, federal highway administration, 2009 national household travel survey [EB/OL].[2020-3-2]. http://nhts.ornl.gov.
[20] TOPRAK S, YALMAN Y. A new full-reference image quality metric based on just noticeable difference[J]. Computer Standards & Interfaces, 2017, 50: 18-25.
[21] ARREOLA E V, WILSON J R. Bayesian multiple membership multiple classification logistic regression model on student performance with random effects in university instructors and majors[J]. PLoS One, 2020, 15(1): 1-19.
[22] 李松. 多服务窗混合制M/M/C/N排队模型研究[D]. 重庆: 重庆师范大学, 2013.
LI Song. Variable input rate with impatient customers M/M/C/N queueing model[D]. Chongqing: Chongqing Normal University, 2013.
[23] JIAO D K. Study on a call model based on the M/M/ S/K queuing system[C]//Proceedings of 2011 International Conference on Electronics and Optoelectronics. Dalian, Dalian. IEEE, 2011: V3-165-V3-168.
[24] 孙荣恒, 李建平. 排队论基础[M]. 北京: 科学出版社, 2015.
[25] XIAO D, AN S, CAI H, et al. An optimization model for electric vehicle charging infrastructure planning considering queuing behavior with finite queue length[J]. Journal of Energy Storage, 2020, 29: 101317.
[26] ZHANG Y M, CAI L. Poster: dynamic charging scheduling for EV parking lots with renewable energy[C]//2017 IEEE 86th Vehicular Technology Conference (VTC-Fall). Toronto, ON, Canada. IEEE, 2017: 1-2.
[27] 张宁, 刘晓波, 黄少华, 等. 考虑电网-用户多目标的V2G模式研究[J]. 电力科学与工程, 2020, 36(4): 32-37
ZHANG Ning, LIU Xiaobo, HUANG Shaohua, et al. Research on V2G model considering grid-user multi-objective[J]. Electric Power Science and Engineering, 2020, 36(4): 32-37
[28] LIN Y N. Necessary/sufficient conditions for Pareto optimality in finite horizon mean-field type stochastic differential game[J]. Automatica, 2020, 119: 108951.