Electric Vehicles Hierarchical Charging Method Considering Multiple Modes Coordination

doi:10.16183/j.cnki.jsjtu.2023.564

Abstract

Abstract:

To alleviate the adverse effect of large-scale electric vehicles (EVs) random charging, an EV hierarchical charging method considering multiple modes coordination is proposed in this paper, which avoids the large-scale charging load centralized in a certain period by coordinating diverse charging modes. Considering the load characteristics such as the output of renewable power generation, the power load curves of an area in Inner Mongolia Autonomous Region are clustered into five typical power load curves based on the K-means clustering algorithm and elbow method. According to the characteristics of EV charging and battery swapping (BS) modes, the charging station models and EV hierarchical power exchange model are established. At the upper level, the users’ requirements and charging costs are considered, and EVs are matched with the charging mode based on the particle swarm optimization algorithm. At the lower level, the electric price and charging station operation conditions are considered, and the charging schemes in the charging/BS station are optimized based on the optimization toolbox in the MATLAB software platform. Extensive case studies are conducted to validate the effectiveness of the proposed method, where a large number of EVs charge continuously with cost efficiency. However, relying solely on electricity price as the control signal for EV charging load may exacerbate the valley-to-peak disparity and instability of the local power load curve.

Key words: electric vehicle (EV), electric vehicle charger, battery swapping (BS), charging modes coordination, hierarchical optimization

CLC Number:

TM732

LIU Yongjiang, GUO Shan, JIA Junqing, LIU Xiaokai, CAI Wenchao, ZENG Long. Electric Vehicles Hierarchical Charging Method Considering Multiple Modes Coordination[J]. Journal of Shanghai Jiao Tong University, 2025, 59(9): 1304-1314.

Figures/Tables 16

Fig.1

Fig.2

Fig.3

Tab.1

Fig.4

Fig.5

Fig.6

Tab.2

Tab.3

Fig.7

Tab.4

Tab.5

Tab.6

Tab.7

Tab.8

Tab.9

References 23

[1]	李高俊杰, 杨军, 朱旭, 等. 计及电动汽车用户响应特性的充电站实时电能共享机制[J]. 电力系统自动化, 2022, 46(12): 56-66.
	LI Gaojunjie, YANG Jun, ZHU Xu, et al. Real-time energy sharing mechanism of charging stations considering user response characteristics of electric vehicles[J]. Automation of Electric Power Systems, 2022, 46(12): 56-66.
[2]	CAO Y, ZHOU B, CHUNG C Y, et al. Dynamic modelling and mutual coordination of electricity and watershed networks for spatio-temporal operational flexibility enhancement under rainy climates[J]. IEEE Transactions on Smart Grid, 2023, 14(5): 3450-3464.
[3]	LIU X, HAN W, LIU Y, et al. A coordinated voltage-frequency support method for VSC-MTDC integrated offshore wind farms system[J]. IEEE Transactions on Power Systems, 2024, 39(1): 1485-1502.
[4]	ZENG L, CHEN S, ZHONG C, et al. Hierarchical transactive power exchange method on expressways for EVs energy supplement[J]. Frontiers in Energy Research, 2023, 11: 1213883.
[5]	ZENG L, LI C, LI Z, et al. Hierarchical dispatching method based on Hungarian algorithm for reducing the battery degradation cost of EVs participating in frequency regulation[J]. IET Generation, Transmission and Distribution, 2020, 14(23): 5617-5625.
[6]	吴翠玉, 张美霞, 陈海燕, 等. 国内外电动汽车扶持政策比较与分析[J]. 上海电力学院学报, 2016, 32(2): 188-192.
	WU Cuiyu, ZHANG Meixia, CHEN Haiyan, et al. Comparative analysis of supportive policies for electric vehicles at home and abroad[J]. Journal of Shanghai University of Electric Power, 2016, 32(2): 188-192.
[7]	蒋菱, 王旭东, 庄剑, 等. 智能电网高适应性发展建设模式与政策建议[J]. 电力系统及其自动化学报, 2015, 27(Sup.1): 120-125.
	JIANG Ling, WANG Xudong, ZHUANG Jian, et al. Policy recommendations & development construction mode of high adaptability of smart grid[J]. Proceedings of the CSU-EPSA, 2015, 27(Sup.1): 120-125.
[8]	杨天宇, 郭庆来, 盛裕杰, 等. 系统互联视角下的城域电力-交通融合网络协同[J]. 电力系统自动化, 2020, 44(11): 1-9.
	YANG Tianyu, GUO Qinglai, SHENG Yujie, et al. Coordination of urban integrated electric power and traffic network from perspective of system interconnection[J]. Automation of Electric Power Systems, 2020, 44(11): 1-9.
[9]	ZHOU T, SUN W. Research on multi-objective optimisation coordination for large-scale V2G[J]. IET Renewable Power Generation, 2020, 14(3): 445-453.
[10]	王岩庆, 王骁, 丛若晨, 等. 考虑配电网运行安全的出行电动汽车充电引导策略[J]. 高电压技术, 2023, 49(5): 2131-2139.
	WANG Yanqing, WANG Xiao, CONG Ruochen, et al. Charging guidance strategy of traveling electric vehicle considering the operation safety of distribution network[J]. High Voltage Engineering, 2023, 49(5): 2131-2139.
[11]	朱磊, 黄河, 高松. 计及风电消纳的电动汽车负荷优化配置研究[J]. 中国电机工程学报, 2021, 41(Sup.1): 194-203.
	ZHU Lei, HUANG He, GAO Song, et al. Research on optimal load allocation of electric vehicle considering wind power consumption[J]. Proceeding of the CSEE, 2021, 41(Sup.1): 194-203.
[12]	姚丽娟, 蔡瑞天, 钱江, 等. 面向低碳园区供需平衡的混杂负荷系统聚合和控制模型构建[J]. 电网技术, 2023, 47(8): 3153-3166.
	YAO Lijuan, CAI Ruitian, QIAN Jiang, et al. Aggregation and control model construction of hybrid load system for supply and demand balance in low-carbon zone[J]. Power System Technology, 2023, 47(8): 3153-3166.
[13]	贾士铎, 康小宁, 黑皓杰, 等. 基于V2G负荷反馈修正的电热氢综合能源系统多层协调优化调度[J]. 电力系统自动化, 2023, 47(15): 100-110.
	JIA Shiduo, KANG Xiaoning, HEI Haojie, et al. Multi-layer coordinated optimal dispatch of electric-thermal-hydrogen integrated energy system based on V2G load feedback correction[J]. Automation of Electric Power Systems, 2023, 47(15): 100-110.
[14]	JOZI F, ABDALI A, MAZLUMI K, et al. Reliability improvement of the smart distribution grid incorporating EVs and BESS via optimal charging and discharging process scheduling[J]. Frontiers in Energy Research, 2022, 10: 920343.
[15]	LI D, ZHANG L, ZHANG Z, et al. Battery safety issue detection in real-world electric vehicles by integrated modeling and voltage abnormality[J]. Energy, 2023, 284: 128438.
[16]	孙欣, 江海林, 谢敬东, 等. 基于用户信用指数的园区电动汽车充放电调度机[J]. 上海交通大学学报, 2025, 59(2): 200-207. doi: 10.16183/j.cnki.jsjtu.2023.196
	SUN Xin, JIANG Hailin, XIE Jingdong, et al. Aggregation and control model construction of hybrid load system for supply and demand balance in low-carbon zone[J]. Journal of Shanghai Jiao Tong University, 2025, 59(2): 200-207.
[17]	GUO D, ZHOU C. Potential performance analysis and future trend prediction of electric vehicle with V2G/V2H/V2B capability[J]. AIMS Energy, 2016, 4(2): 331-346.
[18]	BORGE-DIEZ D, ICAZA D, ACIKKALP E, et al. Combined vehicle to building (V2B) and vehicle to home (V2H) strategy to increase electric vehicle market share[J]. Energy, 2021, 237: 121608.
[19]	LI S, ZHAO P, GU C, et al. Aging mitigation for battery energy storage system in electric vehicles[J]. IEEE Transactions on Smart Grid, 2023, 13(3): 2152-2163.
[20]	崔杨, 李翼成, 付小标, 等. 基于换电服务定价策略及动态调控方法的含充换电站微电网系统双层优化调度[J]. 电网技术, 2023, 47(5): 1998-2011.
	CUI Yang, LI Yicheng, FU Xiaobiao, et al. Double-layer optimal scheduling of micro-grid system with charging and swapping stations based on battery swap service pricing strategy and dynamic regulation[J]. Power System Technology, 2023, 47(5): 1998-2011.
[21]	ZENG L, LI C, LI Z, et al. Hierarchical bipartite graph matching method for transactive V2V power exchange in distribution power system[J]. IEEE Transactions on Smart Grid, 2021, 12(1): 301-311.
[22]	ZENG L, CHEN S, TANG Z, et al. An electric vehicle charging method considering multiple power exchange modes’ coordination[J]. Sustainability, 2023, 15: 10520.
[23]	DATTA U, SAIPRASAD N, KALAM A, et al. A price-regulated electric vehicle charge-discharge strategy for G2V, V2H, and V2G[J]. International Journal of Energy Research. 2019, 43: 1032-1042.

参数	取值	参数	取值
T/h	24	P_max/kW	70
Δt/h	1	P_min/kW	0
t₀/h	0	${F}_{n}^{ini}$	0.4
N_EV	100	${F}_{n}^{obj}$	0.8
M_B	100	Q_n/(kW·h)	70
α₁/(美元·kW⁵)	2.281×10^-7	F_max	0.8
α₂/(美元·kW²)	8.442×10^-7	F_min	0.2
β₁	5	${F}_{m}^{obj}$	0.8
β₂	2	${F}_{m}^{ini}$	0.4

方法	电力成本/美元	电池损耗成本/美元	总成本/美元
方法1	2 514.8	655.0	3 169.9
方法2	2 418.6	221.7	2 640.3
所提方法	2 260.5	106.9	2 367.4

EV	停车初始时刻	停车时长/h
1	7:00	1
2	12:00	2
3	19:00	4

EV	方法	电力成本/ 美元	电池损耗成本/美元	总成本/ 美元
1	方法1	2.125 2	3.927 0	6.052 2
	方法2	1.548 5	0.026 7	1.575 2
	所提方法	1.548 5	0.026 7	1.575 2
2	方法1	3.215 0	0.264 9	3.479 9
	方法2	3.744 4	0.245 8	3.990 2
	所提方法	3.215 0	0.264 9	3.479 9
3	方法1	3.249 2	0.078 0	3.327 2
	方法2	3.570 0	3.926 3	7.496 3
	所提方法	3.198 1	0.109 3	3.307 4

方法	峰谷差/ MW	平均负荷/ MW	方差/ kW²	风光出力消纳/MW
无叠加	0.008 4	-0.001 9	2.600	0
方法1	1.571 6	0.942 0	477.1	0.005
方法2	1.442 2	0.942 0	437.0	0.004
所提方法	1.866 3	0.942 0	513.6	0.004