Intelligent Charging Navigation for Electric Vehicles Based on Real-time Electricity Price

doi:10.11930/j.issn.1004-9649.201904106

Abstract

Abstract: A large number of electric vehicles (EVs) connected to the grid will affect the planning and operation of the power system. Hence it is of great significance to guide the charging behavior of electric vehicles in an orderly manner. Regarding this kind of problem, an electric vehicle intelligent charging navigation strategy based on real-time electricity price is proposed, where the PSO-GA algorithm is used to solve the optimal scheme. The motivation is to guide the EV charging toward an orderly way by virtue of the real-time electricity price mechanism. Furthermore, according to the different requirements of users, three charging navigation strategies are designed for different objectives, i.e., the minimization of the user's time cost, expense cost or overall cost respectively. Finally, the IEEE-33 bus system model is established in Matlab. By utilizing the Monte Carlo method, the road condition and EV space-time distribution information is simulated in a real traffic area on the map so as to verify the effectiveness of the method. The simulation results show that the method can reduce the travel cost of EV users and improve the node voltage quality of the power distribution system.

Key words: charging navigation, real-time electricity prices, electric vehicles, charging station, distribution system

WU Rui, ZHOU Liangsong, YAO Zhandong. Intelligent Charging Navigation for Electric Vehicles Based on Real-time Electricity Price[J]. Electric Power, 2020, 53(4): 131-138,146.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201904106

https://www.electricpower.com.cn/EN/Y2020/V53/I4/131

References

[1] NI W, COLLINGS I B, WANG X, et al. Radio alignment for inductive charging of electric vehicles[J]. IEEE Transactions on Industrial Informatics, 2015, 11(2): 427–440.
[2] 程杉, 王贤宁, 冯毅煁. 电动汽车充电站有序充电调度的分散式优化[J]. 电力系统自动化, 2018, 42(1): 39–46
CHENG Shan, WANG Xianning, FENG Yichen. Decentralized optimization of ordered charging scheduling in electric vehicle charging station[J]. Automation of Electric Power Systems, 2018, 42(1): 39–46
[3] 苏舒, 孙近文, 林湘宁, 等. 电动汽车智能充电导航[J]. 中国电机工程学报, 2013, 33(增刊1): 59–67
SU Shu, SUN Jinwen, LIN Xiangning, et al. Electric vehicle smart charging navigation[J]. Proceedings of the CSEE, 2013, 33(S1): 59–67
[4] CAO Y J, TANG S W, LI C B, et al. An optimized EV charging model considering TOU price and SOC curve[J]. IEEE Transactions on Smart Grid, 2012, 3(1): 388–393.
[5] 孙晓明, 王玮, 苏粟, 等. 基于分时电价的电动汽车有序充电控制策略设计[J]. 电力系统自动化, 2013, 37(1): 191–195
SUN Xiaoming, WANG Wei, SU Su, et al. Coordinated charging strategy for electric vehicles based on time-of-use price[J]. Automation of Electric Power Systems, 2013, 37(1): 191–195
[6] 郑培文. 考虑配电系统电压可承受度的电动汽车充电导航及有序快速充电策略研究[D]. 武汉: 华中科技大学, 2016.
ZHENG Peiwen. Study of the charging navigation of electric vehicle considering the voltage endurance of urban distribution system and its coordinated fast charging strategy[D] Wuhan: Huazhong University of Science and Technology, 2016.
[7] 黄晶, 杨健维, 王湘, 等. 下一目的地导向下的电动汽车充电引导策略[J]. 电网技术, 2017, 41(7): 2173–2181
HUANG Jing, YANG Jianwei, WANG Xiang, et al. Destination oriented electric vehicle charging guiding strategy[J]. Power System Technology, 2017, 41(7): 2173–2181
[8] 宋汶秦, 靳攀润, 孙亚璐, 等. 基于电动汽车充电导航的快充负荷有序接入研究[J]. 电工电能新技术, 2018, 37(6): 79–87
SONG Wenqin, JIN Panrun, SUN Yalu, et al. Study on coordinated access of fast load based on electric vehicle charging navigation[J]. Advanced Technology of Electrical Engineering and Energy, 2018, 37(6): 79–87
[9] 牛利勇, 张帝, 王晓峰, 等. 基于自适应变异粒子群算法的电动出租车充电引导[J]. 电网技术, 2015, 39(1): 63–68
NIU Liyong, ZHANG Di, WANG Xiaofeng, et al. An adaptive particle mutation swarm optimization based electric taxi charging guidance[J]. Power System Technology, 2015, 39(1): 63–68
[10] 徐娇. 基于物联网的电动汽车智能充电导航方法研究[D]. 成都: 西南交通大学, 2017.
XU Jiao. Research on intelligent charging navigation method for electric vehicles based on internet of things [D]. Chengdu: Southwest Jiaotong University, 2017.
[11] 杨洪明, 李明, 文福拴, 等. 利用实时交通信息感知的电动汽车路径选择和充电导航策略[J]. 电力系统自动化, 2017, 41(11): 106–113
YANG Hongming, LI Ming, WEN Fushuan, et al. Route selection and charging navigation strategy for electric vehicles employing real-time traffic information perception[J]. Automation of Electric Power Systems, 2017, 41(11): 106–113
[12] YANG H M, DENG Y J, QIU J, et al. Electric vehicle route selection and charging navigation strategy based on crowd sensing[J]. IEEE Transactions on Industrial Informatics, 2017, 13(5): 2214–2226.
[13] 邓友均, 李明, 余千, 等. 基于实时信息感知的电动汽车物流配送路径优化与充电导航[J]. 南方电网技术, 2017, 11(2): 41–49
DENG Youjun, LI Ming, YU Qian, et al. Logistics distribution route optimization and charging navigation of electric vehicle based on real-time information sensing[J]. Southern Power System Technology, 2017, 11(2): 41–49
[14] GUO Q L, WANG Y, SUN H B, et al. Research on architecture of ITS based smart charging guide system[C]//2011 IEEE Power and Energy Society General Meeting, July 24-29, 2011. San Diego, CA. IEEE, 2011.
[15] 侯慧, 樊浩, 谢俊, 等. 分时电价下价格理性用户最优充电策略[J]. 中国电力, 2018, 51(1): 171–178
HOU Hui, FAN Hao, XIE Jun, et al. The optimal charging strategy of EV rational user based on TOU Power Price[J]. Electric Power, 2018, 51(1): 171–178
[16] LEE T K, BAREKET Z, GORDON T, et al. Stochastic modeling for studies of real-world PHEV usage: driving schedule and daily temporal distributions[J]. IEEE Transactions on Vehicular Technology, 2012, 61(4): 1493–1502.
[17] HE Y F, VENKATESH B, GUAN L. Optimal scheduling for charging and discharging of electric vehicles[J]. IEEE Transactions on Smart Grid, 2012, 3(3): 1095–1105.
[18] 周强, 闫欣欣, 袁振洲. 考虑道路等级和拥堵惩罚的城市路网平均负荷度计算[J]. 公路与汽运, 2017(1): 33–36
ZHOU Qiang, YAN Xinxin, YUAN Zhenzhou. Influence of pricing parameters on transmission and distribution price and its regulation model[J]. Highways & Automotive Applications, 2017(1): 33–36
[19] 刘思强, 叶泽, 范先国, 等. 定价参数对输配电价的影响及调控模型[J]. 电力系统自动化, 2017, 41(24): 58–66
LIU Siqiang, YE Ze, FAN Xianguo, et al. Influence of pricing parameters on transmission and distribution price and its regulation model[J]. Automation of Electric Power Systems, 2017, 41(24): 58–66
[20] 罗纯坚, 李姚旺, 许汉平, 等. 需求响应不确定性对日前优化调度的影响分析[J]. 电力系统自动化, 2017, 41(5): 22–29
LUO Chunjian, LI Yaowang, XU Hanping, et al. Influence of demand response uncertainty on day-ahead optimization dispatching[J]. Automation of Electric Power Systems, 2017, 41(5): 22–29
[21] HUANG Q R, CAO M. Study on the improvement of floyd algorithm and its application in network plan[J]. Applied Mechanics and Materials, 2014, 641/642: 1312–1315.
[22] 李如琦, 苏浩益. 基于排队论的电动汽车充电设施优化配置[J]. 电力系统自动化, 2011, 35(14): 58–61
LI Ruqi, SU Haoyi. Optimal allocation of charging facilities for electric vehicles based on queuing theory[J]. Automation of Electric Power Systems, 2011, 35(14): 58–61
[23] 胡林静, 刘凯, 杨明文. 基于粒子群遗传算法的光伏MPPT控制研究[J]. 电测与仪表, 2019, 56(14): 23–27.
HU Linjing, LIU Kai, YANG Mingwen. Research on PV MPPT control based on particle swarm genetic algorithm [J]. Electric Measurement & Instrumentation, 2019, 56(14):23–27.
[24] 陈蓉珺, 何永秀, 陈奋开, 等. 基于系统动力学和蒙特卡洛模拟的电动汽车日负荷远期预测[J]. 中国电力, 2018, 51(9): 126–134
CHEN Rongjun, HE Yongxiu, CHEN Fenkai, et al. Long-term daily load forecast of electric vehicle based on system dynamics and monte carlo simulation[J]. Electric Power, 2018, 51(9): 126–134
[25] 田立亭, 史双龙, 贾卓. 电动汽车充电功率需求的统计学建模方法[J]. 电网技术, 2010, 34(11): 126–130
TIAN Liting, SHI Shuanglong, JIA Zhuo. A statistical model for charging power demand of electric vehicles[J]. Power System Technology, 2010, 34(11): 126–130
[26] 舒隽, 关睿, 韩冰, 等. 考虑电热耦合效应的配电网潮流计算[J]. 电力系统自动化, 2015, 39(19): 48–53
SHU Jun, GUAN Rui, HAN Bing, et al. Calculation of distribution network power flow considering electrothermal coupling effect[J]. Automation of Electric Power Systems, 2015, 39(19): 48–53