Prediction of spatiotemporal distribution of electric vehicle charging load in complex environments

Regarding the problem of excessive reliance on historical data and lack of effective integration of real-time road conditions and environmental factors in traditional electric vehicle load forecasting modeling, which leads to inaccurate prediction results. On the basis of fully considering the coupling relationship between traffic flow and power grid flow, this article proposes an electric vehicle charging load prediction model that comprehensively considers complex environmental factors such as road conditions, temperature, and seasonal changes, and accurately calculates the unit mileage power consumption of electric vehicles in different environments. Secondly, based on the main parameters of the vehicle, a single electric vehicle charging model is established, and Monte Carlo method is used to simulate the travel behavior and charging process of electric vehicles, thereby obtaining the spatiotemporal distribution of electric vehicle charging load. Finally, simulation analysis was conducted on the actual urban road network in a certain region to verify the effectiveness of the model proposed in this paper.