Analysis of charging load characteristics of electric vehicles based on improved K-Means algorithm
The charging behavior of electric vehicles has significant randomness,which to some extent affects the stable operation and planning of the power grid.To more accurately analyze the characteristics of electric vehicle charging load,a clustering analysis method based on improved K-Means algorithm is proposed.In response to the randomness and instability of the initial cluster center selection in the K-Means algorithm,the Mini Batch K-Means algorithm's random sampling ability is first used to optimize the determination of the initial cluster center.Then,the K-Means algorithm is combined for iterative optimization,effectively solving the problem of unstable clustering results in the K-Means algorithm.Taking the load data of charging stations in a city in Yunnan as an example,the results show that the proposed algorithm can more accurately classify users with different load characteristics compared to traditional methods,thus more effectively guiding the formulation of orderly electricity management strategies.
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