Optimal Planning of Electric Vehicle Charging Stations Combined with Battery Energy Storage Systems Considering Driving Characteristics

doi:10.16183/j.cnki.jsjtu.2023.629

Abstract

Abstract:

With the continuous increase in the number of electric vehicles (EVs) in China, EV charging stations (EVCS) are becoming extensively connected to distribution networks to meet the growing charging demand, which poses unprecedented challenges to the stability, safety, and economy of distribution networks. To reduce the impact of EVCS on distribution networks while ensuring the interests of investors and EV users, this paper proposes a multi-objective planning model of EVCS combined battery energy storage system (BESS) which considers the behavioral characteristics of EV users, aiming to minimize the comprehensive cost of EVCS and BESS, waiting time of users, and system voltage fluctuations to achieve the best balance between the economy and stability by planning for EVCS and BESS. Meanwhile, the non-dominated sorting genetic algorithm III (NSGA-III) is used to verify the model on the extended IEEE-33 node testing system and the university town in Chenggong, Kunming. The simulation results show that in the IEEE-33 node test system, compared with the case without BESS configuration, the voltage fluctuation and system network loss decreases by 36.73% and 35.41%, respectively, effectively improving the stability and economy of the distribution network.

Key words: electric vehicles (EVs), energy storage systems, charging demand forecasting, site selection and capacity determination, non-dominated sorting genetic algorithm III (NSGA-III)

CLC Number:

TM721

HAN Yiming, HE Bin, YANG Bo, LI Jiale. Optimal Planning of Electric Vehicle Charging Stations Combined with Battery Energy Storage Systems Considering Driving Characteristics[J]. Journal of Shanghai Jiao Tong University, 2025, 59(11): 1720-1731.

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优化策略	成本/万元	系统网损/ (MW·h)	电压波动标幺值
初始状态		0.920	0.330
NSGA-III(无BESSs)		3.722	0.569
NSGA-III	1115.800	2.404	0.360
MOTEO	1282.264	3.229	0.434
MOMA	1059.716	2.919	0.444
MOPSO	611.643	2.973	0.498

优化策略	成本/万元	等待时间/h	电压波动标幺值
初始状态			0.306
NSGA-III(无BESSs)			0.729
NSGA-III	4596.216	0.297	0.375
MOTEO	4428.101	0.548	0.538
MOMA	4620.419	0.498	0.589
MOPSO	3705.245	0.603	0.825