A Cluster-based Optimal Scheduling Strategy for Electric Vehicles Considering User Participation - HAN Yan; DING Xiying; CHENG Kun; LI Xiaodong (Kongzhi Yu Xinxi Jishu)
To solve the scheduling problem of large-scale electric vehicles participating in peak shaving, this paper presents a cluster-based optimal scheduling strategy for electric vehicles considering user participation. Multilayer perceptron neural network is adopted to predict power load and obtain peak difference. The electric vehicle cluster classification network based on convolutional neural network is trained by using a large number of vehicle information and considering vehicle owner intention to classify the vehicle peak shaving participation, and quickly determine the total electricity involved in peak shaving. Considering both peak shaving effect and user economic benefits, an improved particle swarm optimization algorithm is proposed to optimize the power participating in peak shaving. Taking the county area as an example, the proposed method is verified on Matlab platform. The accuracy of all kinds of electric vehicle clusters is higher than 90% and the peak difference between peak and valey load is reduced through the charge and discharge of electric vehicle clusters, which can verify the effectiveness of the electric vehicle clustering method and the scheduling scheme of peak shaving and valley filling