Prediction of Electric Vehicle Charging Demand in Rural Areas Based on Driving Track Data

As the disorderly charging behavior of large-scale electric vehicles can affect the economic operation of the power grid. An electric vehicle charging demand prediction model driven by driving track data in rural areas based on Beidou system is proposed. According to the predicted charging situation of rural electric vehicles, a mathematical model with different objective functions of the scheduling strategy is established, and the Particle Swarm Algorithm is used to find to the optimal charging plan. Based on the prediction, the optimized electric vehicle charging scheduling strategy can not only smooth out the load fluctuation of the grid, but also effectively reduce the peak-to-valley load difference and maximize the economic benefits for the participating users.