Abstract
In order to reduce the impact of electric vehicles on the power grid, the charging behavior of electric vehicles is analyzed, which can improve the reliability of power grid operation. Understanding the charging behavior of electric vehicle users is helpful to improve charging service and guide charging behavior. Aiming at the problem of privacy disclosure in the process of data analysis, this paper proposes an electric vehicle charging data clustering algorithm under differential privacy protection. This method enhances the availability of clustering result by improving the selection of initial cluster center points and detecting outliers. The classification of electric vehicle users is realized through experiment, and the characteristics of charging mode corresponding to each user are analyzed.
Highlights
Electric Vehicles (EVs) have the advantages of energy saving and environmental protection, so they have been paid more and more attention in the world
In order to reduce the impact of electric vehicles on the power grid, the charging behavior of electric vehicles is analyzed, which can improve the reliability of power grid operation
Aiming at the problem of privacy disclosure in the process of data analysis, this paper proposes an electric vehicle charging data clustering algorithm under differential privacy protection
Summary
Electric Vehicles (EVs) have the advantages of energy saving and environmental protection, so they have been paid more and more attention in the world. Literature [8] proposes a method of power consumption data clustering analysis for mass users under differential privacy protection. In this paper, noise is added to the cluster center point, and differential privacy protection is used to reduce the risk of privacy leakage. This paper improves the k-means algorithm by detecting outliers and selecting the initial cluster center point. This paper designs a method for the clustering analysis of EV charging data under differential privacy protection. The model proposed in this paper is helpful to guide the charging behavior and reduce the influence of EV charging on the power grid It provides decision support for off-peak charging, and helps to provide precise marketing services for different types of EV users
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