Two-stage Optimization Strategy for Coordinated Charging and Discharging of EVs Based on PSO Algorithm

被引：0

作者：

Zhang L. ^{[1
]}

Sun C. ^{[1
]}

Cai G. ^{[1
]}

Huang N. ^{[1
]}

Lyu L. ^{[1
]}

机构：

[1] Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2022年 / 42卷 / 05期

关键词：

Coordinated charging and discharging; Electric vehicle; Optimization strategy; PSO algorithm;

D O I：

10.13334/j.0258-8013.pcsee.211150

中图分类号：

学科分类号：

摘要：

Both the traditional time-of-use tariff (TOUT) and the real-time price (RTP) demand response mechanism will generate new load peaks during the low load period. To solve this problem, considering the demand for peak load regulating on the grid side and the different needs and willingness of different users for charging capacity and charging costs, a dynamic optimization method for TOUT was proposed. The proposed method dynamically updateed the peak-to-valley price of each electric vehicle (EV) based on the load information when the EV was connected to the grid, which made up for the shortcomings of the TOUT and RTP charging methods. Based on the proposed dynamic optimization method of TOUT, by establishing a multi-objective function with the most charging capacity and the least charging cost, the particle swarm optimization (PSO) was used to optimize the charging (discharging) behavior of each EV in two stages. And by introducing a virtual state of charge (SOC) to modify the optimized charging (discharging) behavior, each user autonomously responded to realize the coordinated charging (discharging) of the EV. To verify the effectiveness of the proposed method, based on the results of the 2017 National Household Vehicle Survey (NHTS2017), the Monte Carlo (MC) method was used to simulate the charging demand of 1, 000 EVs in a residential area. And the charging demand under different charging strategies, different optimization weights, different participation levels and different V2G (vehicle to grid) responsiveness was simulated and analyzed. The results show that compared with other charging strategies, the proposed optimization strategy can significantly reduce the user's charging cost and the peak-to-valley difference of the load curve. © 2022 Chin. Soc. for Elec. Eng.

引用

页码：1837 / 1851

页数：14

共 32 条

[1] ZHANG Qian, ZHU Yi, WANG Zhong, Et al., Reliability assessment of distribution network and electric vehicle considering quasi-dynamic traffic flow and vehicle-to-grid, IEEE Access, 7, pp. 131201-131213, (2019)
[2] JAHANGIR H, GOUGHERI S S, VATANDOUST B, Et al., Plug-in electric vehicle behavior modeling in energy market: a novel deep learning-based approach with clustering technique, IEEE Transactions on Smart Grid, 11, 6, pp. 4738-4748, (2020)
[3] YANG Jianwei, YANG He, ZHANG Xialin, Et al., A charging optimization strategy on charging and swapping station for electric buses based on optimization of switching rules and matching of buses and batteries, Proceedings of the CSEE, 39, 8, pp. 2337-2347, (2019)
[4] YANG Xiaodong, ZHANG Youbing, ZHAO Bo, Et al., Automated demand response method for electric vehicles charging and discharging to achieve supply-demand Coordinated Optimization, Proceedings of the CSEE, 37, 1, pp. 120-129, (2017)
[5] HOU Hui, XU Tao, KE Xianbin, Et al., Research on risks of electric vehicle charging to distribution network, Power System Protection and Control, 47, 16, pp. 87-93, (2019)
[6] GAN Lei, CHEN Xingying, YU Kun, Et al., A probabilistic evaluation method of household EVs dispatching potential considering users’ multiple travel needs, IEEE Transactions on Industry Applications, 56, 5, pp. 5858-5867, (2020)
[7] AL-OGAILI A S, JUHANA T, RAHMAT N A, Et al., Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: challenges and recommendations, IEEE Access, 7, 1, pp. 128353-128371, (2019)
[8] SHAFIQ S, IRSHAD U B, AL-MUHAINI M, Et al., Reliability evaluation of composite power systems: evaluating the impact of full and plug-in hybrid electric vehicles, IEEE Access, 8, pp. 114305-114314, (2020)
[9] HABIB S, KHAN M M, ABBAS F, Et al., A comprehensive study of implemented international standards, technical challenges, impacts and prospects for electric vehicles, IEEE Access, 6, pp. 13866-13890, (2018)
[10] KAUR K, KUMAR N, SINGH M., Coordinated power control of electric vehicles for grid frequency support: MILP-based hierarchical control design, IEEE Transactions on Smart Grid, 10, 3, pp. 3364-3373, (2019)

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