Load Aggregation Method for Electric Vehicle Based on SOM Neural Network Clustering

被引：0
作者：
Wang, Liwen [1 ]
Yan, Zhaoyang [2 ]
Yan, Huixin [1 ]
Liu, Jun [1 ]
Liu, Jiantao [1 ]
Wang, Yuyang [2 ]
机构：
[1] China Elect Power Res Inst, Power Automat Dept, Nanjing, Peoples R China
[2] State Grid Jiangsu Elect Power Co Ltd, Dispatching Control Ctr, Nanjing, Peoples R China
来源：
2023 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA, I&CPS ASIA | 2023年
关键词：
electric vehicle; neural network; clustering; polymerization; CHARGING LOAD;
D O I：
10.1109/ICPSASIA58343.2023.10294770
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The large-scale development of electric vehicles will have a great impact on the power grid. However, individual electric vehicles cannot directly participate in the power grid operation, so it is really necessary to analyze the polymerization characteristics of EVs. The current polymerization method of EVs is to analyze the physical characteristics of a single EV, and then we aggregate. However, there are differences in the characteristics of different types of EV, so it is necessary to cluster electric vehicles first and then aggregate them. This paper presents a load aggregation method for EVs based on SOM neural network clustering. Firstly, SOM neural network is used to cluster different kinds of electric vehicles. Then, the clustering results are aggregated. In this way, the accuracy of aggregation model can be improved.
引用
收藏
页码：908 / 912
页数：5
相关论文
共 18 条
  • [1] [Anonymous], 2006, SURVEY CLUSTERING DA
  • [2] Rapid-Charge Electric-Vehicle Stations
    Etezadi-Amoli, Mehdi
    Choma, Kent
    Stefani, Jason
    [J]. IEEE TRANSACTIONS ON POWER DELIVERY, 2010, 25 (03) : 1883 - 1887
  • [3] FORGY EW, 1965, BIOMETRICS, V21, P768
  • [4] Fukui S, 2019, ASIAPAC SIGN INFO PR, P1411, DOI 10.1109/APSIPAASC47483.2019.9023120
  • [5] Using ICT-Controlled Plug-in Electric Vehicles to Supply Grid Regulation in California at Different Renewable Integration Levels
    Goebel, Christoph
    Callaway, Duncan S.
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2013, 4 (02) : 729 - 740
  • [6] Hartigan J. A., 1979, Applied Statistics, V28, P100, DOI 10.2307/2346830
  • [7] Ito Akinori, 2022, 2022 7th International Conference on Signal and Image Processing (ICSIP), P772, DOI 10.1109/ICSIP55141.2022.9886452
  • [8] Data clustering: 50 years beyond K-means
    Jain, Anil K.
    [J]. PATTERN RECOGNITION LETTERS, 2010, 31 (08) : 651 - 666
  • [9] Khan K, 2014, 2014 FIFTH INTERNATIONAL CONFERENCE ON THE APPLICATIONS OF DIGITAL INFORMATION AND WEB TECHNOLOGIES (ICADIWT), P232, DOI 10.1109/ICADIWT.2014.6814687
  • [10] Autoencoder Constrained Clustering With Adaptive Neighbors
    Li, Xuelong
    Zhang, Rui
    Wang, Qi
    Zhang, Hongyuan
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 32 (01) : 443 - 449
12