Optimizing Electric Vehicle Clustering through Improved MVABC-GMM Algorithm Based on Gaussian Mixture Model and Monte Carlo Prediction
DOI:
https://doi.org/10.54097/g2mn1262Keywords:
Electric Vehicles; Clustering Algorithm; MVABC-GMM; Monte Carlo; Clustering Evaluation Metrics.Abstract
In the practical application of electric vehicle (EV) charging, significant differences in charging behaviors across different time periods lead to scheduling challenges, especially when dealing with a large number of vehicles, potentially causing the "curse of dimensionality" in scheduling. To address this issue, an improved MVABC-GMM algorithm based on the Gaussian Mixture Model (GMM) is proposed for clustering large-scale EVs, aiming to optimize EV scheduling strategies and enhance the utilization efficiency of grid resources. The charging data is fitted using a Gaussian distribution, and a Monte Carlo prediction model is built to simulate the charging behavior patterns of EVs. Clustering evaluation metrics are introduced to determine the optimal number of clusters. Comparisons show that the improved algorithm significantly enhances convergence speed and clustering performance. The clustering center at the position (17.79, 7.62) in the cross-shaped region visually reflects the charging patterns of EV users.
Downloads
References
[1] González L G, Siavichay E, Espinoza J L. Impact of EV fast charging stations on the power distribution network of alatin American intermediate city[J]. Renewable and Sustainable Energy Reviews, 2019, 144 (107): 309-318.
[2] Kempton W, Tomi J. Vehicle-to-grid power implementation: from stabilizing the grid to supporting large-scale renewable energy[J]. Journal of Power Sources, 2021, 144(1): 280-294.
[3] LIU Jinpeng, YANG Hao, WU Lan, et al. Evaluation of residential demand response potential under multiple confidence scenarios based on Gaussian mixture model[J]. Electric Power Engineering Technology, 2023, 42(2): 20-28
[4] Zhang Meixia, Li Li, Yang Xiu, et al. Load classification method based on Gaussian Mixture Model clustering and multidimensional scaling analysis[J]. Power System Technology, 2020, 44(11): 4283-4296.
[5] Wang Tonghui, Hou Yi, Yan Ying. China New Energy Passenger Car Big Data Research Report [M]. Beijing: China Machine Press, 2021.
[6] Wang Xin, Ding Yunfei, Lu Hongzhuang. Improved Kernel Extreme Learning Machine for Electric Vehicle Charging Load Prediction. Journal of Shanghai University of Electric Power, 2022, 25(1): 1-6.
[7] Bu Hui. Research on User Information Demand Aggregation and Application in Online Q&A Communities Based on the Integration of GMM and K-means [D]. Qufu Normal University, 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Academic Journal of Science and Technology

This work is licensed under a Creative Commons Attribution 4.0 International License.