Non-Intrusive Load Monitoring and Controllability Evaluation of Electric Vehicle Charging Stations Based on K-Means Clustering Optimization Deep Learning

• Published in: World electric vehicle journal 2022-11, Vol.13 (11), p.198
• Main Authors: Lu, Shixiang, Feng, Xiaofeng, Lin, Guoying, Wang, Jiarui, Xu, Qingshan
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Appliances; Carbon; Cluster analysis; Clustering; Controllability; Data analysis; Decomposition; Deep learning; Electric vehicle charging; Electric vehicles; Energy efficiency; Energy industry; K-means clustering; Linear programming; Mathematical models; Monitoring; Neural networks; non-intrusive load monitoring; Optimization; Particle size; Perception; Random loads; Storage capacity; Teaching methods; Vector quantization
• ISSN: 2032-6653; 2032-6653
• DOI: 10.3390/wevj13110198

Electric vehicles have the advantages of zero emissions and high energy efficiency. They have a broad potential in today’s social life, especially in China where they have been widely used. In the current situation, whereby the storage capacity of electric vehicles is continually increasing and the requirements for grid stability are getting higher and higher, V2G technology emerges to keep up with the times. Since the electric vehicle charging station is a large-scale electric vehicle cluster charging terminal, it is necessary to pay attention to the status and controllability of each charging pile. In view of the lack of attention to the actual operation of the electric vehicle charging station in the existing vehicle–network interaction mode, the charging state of the current electric vehicle charging station is fixed. In this paper, deep learning is used to establish a load perception model for electric vehicle charging stations, and K-means clustering is used to optimize the load perception model to realize random load perception and non-intrusive load monitoring stations for electric vehicle charging. The calculation example results show that the proposed method has good performance in the load perception and controllability evaluation of electric vehicle charging stations, and it provides a feasible solution for the practical realization of electric vehicle auxiliary response.