Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

被引:0
|
作者
Sun J. [1 ]
Che Y. [1 ]
Yang T. [1 ]
Zhang J. [2 ]
Cai Y. [1 ]
机构
[1] Key Laboratory of Smart Grid of Education Ministry, Tianjin University, Tianjin
[2] Tianjin Electric Power Company, State Grid, Tianjin
关键词
Electric vehicle charging station; location selection and capacity configuration; loss of distribution systemsimulated annealing immune particle swarm optimization; Voronoi diagram;
D O I
10.32604/ee.2023.023661
中图分类号
学科分类号
摘要
As the number of electric vehicles (EVs) continues to grow and the demand for charging infrastructure is also increasing, how to improve the charging infrastructure has become a bottleneck restricting the development of EVs. In other words, reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system. Considering the construction and maintenance of the charging station, the distribution network loss of the charging station, and the economic loss on the user side of the EV, this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function, and thus proposes a location and capacity planning model for the EV charging station. Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm, the simulated annealing immune particle swarm optimization algorithm (SA-IPSO) is adopted in this paper. The simulated annealing algorithm is used in the global update of the particle swarm optimization (PSO), and the immune mechanism is introduced to participate in the iterative update of the particles, so as to improve the speed and efficiency of PSO. Voronoi diagram is used to divide service area of the charging station, and a joint solution process of Voronoi diagram and SA-IPSO is proposed. By example analysis, the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost, while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%. The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1% and basically does not fall into local convergence. © 2023, Tech Science Press. All rights reserved.
引用
收藏
页码:367 / 384
页数:17
相关论文
共 50 条
  • [1] Siting and Capacity Planning Method for Electric Vehicle Charging Station Based on Chaotic Simulated Annealing Particle Swarm Optimization
    Li, Shangze
    Kong, Xiangyu
    Gao, Bixuan
    Liu, Ziyu
    Shen, Yu
    Hu, Wei
    [J]. 2022 25TH INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES AND SYSTEMS (ICEMS 2022), 2022,
  • [2] The location model of the electric vehicle charging station based on simulated annealing algorithm
    Jia, Heping
    Liu, Shugang
    Xe, Shengli
    Huang, Shilong
    Pei, Shaotong
    [J]. ENERGY AND POWER TECHNOLOGY, PTS 1 AND 2, 2013, 805-806 : 1895 - +
  • [3] Location and capacity determination of charging station based on electric vehicle charging behavior analysis
    Cao, Weitao
    Wan, Youhong
    Wang, Lu
    Wu, Yue
    [J]. IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2021, 16 (06) : 827 - 834
  • [4] New energy vehicle charging station location method based on improved particle swarm optimization algorithm
    Zhang, Liang-Li
    Ma, Xiao-Feng
    [J]. Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), 2024, 54 (08): : 2275 - 2281
  • [5] Optimal Planning of Charging Station for Electric Vehicle Based on Simulated Annealing Genetic Optimization Algorithm
    Wang, Jiao
    Bai, Jie-yin
    Ding, Ning
    Chen, Ji-huan
    Zhang, Jin-yang
    [J]. 2019 INTERNATIONAL CONFERENCE ON ENERGY, POWER, ENVIRONMENT AND COMPUTER APPLICATION (ICEPECA 2019), 2019, 334 : 100 - 105
  • [6] ELECTRIC VEHICLE CHARGING STATION LAYOUT BASED ON PARTICLE SWARM SIMULATION
    Liu, J-Y
    Liu, S-F
    Gong, D-Q
    [J]. INTERNATIONAL JOURNAL OF SIMULATION MODELLING, 2021, 20 (04) : 754 - 765
  • [7] Research on Location of Electric Vehicle Charging Station Based on Voronoi Diagram and Improved Particle Swarm Algorithm
    Jiang, Yong
    Wan, Jiangping
    [J]. PROCEEDINGS OF NINETEENTH WUHAN INTERNATIONAL CONFERENCE ON E-BUSINESS, 2020, : 450 - 460
  • [8] Optimal Planning of Electric Vehicle Charging Stations Location Based on Hybrid Particle Swarm Optimization
    Tang, Zheci
    Guo, Chunlin
    Hou, Pengxin
    Fan, Yubo
    Jia, Dongming
    [J]. APPLIED ENERGY TECHNOLOGY, PTS 1 AND 2, 2013, 724-725 : 1355 - +
  • [9] The Location of Electric Vehicle Charging Station Based on TLBO Optimization Algorithm
    Hu Jinlei
    Sun Yunlian
    Yu Junwei
    Lu Jue
    Zou Qiwu
    Xie Xinlin
    Fu Bin
    [J]. 2019 THE 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, CONTROL AND ROBOTICS (EECR 2019), 2019, 533
  • [10] Optimum placement of Electric Vehicle Charging Station using Particle Swarm Optimization Algorithm
    Sriabisha, R.
    Yuvaraj, T.
    [J]. 2023 9TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENERGY SYSTEMS, ICEES, 2023, : 283 - 288