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

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...

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Bibliographic Details
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
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Summary: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.
ISSN:2032-6653
2032-6653
DOI:10.3390/wevj13110198