Monte Carlo-based Prediction of Electric Vehicle Charging Load and Coupling Mechanisms of Multiple Information Sources

The charging load of electric vehicles at a large scale exhibits spatiotemporal uncertainty and randomness. Its coupling with multiple sources of information, such as the road network, charging infrastructure, distribution network, and market, increases the complexity of charging load forecasting. This paper presents a comprehensive forecasting method that takes into account the coupling factors from multiple sources for electric vehicle ownership and charging load. The research investigates the coupling relationships between electric vehicles and various information sources. Addressing the inadequacy of traditional inventory prediction methods for predicting electric vehicle stock, this study introduces the Box-Cox-Dogit model to compute the maximum market share of electric vehicles. By integrating a time-varying Bass model, a comprehensive electric vehicle inventory prediction model is established, considering multiple factors. The study assesses factors impacting the scalability of electric vehicle charging loads and formulates a prediction function along with its associated parameters. Employing the Monte Carlo method to replicate urban electric vehicle users' travel patterns, the paper forecasts the unstructured charging of three types of electric vehicles in Beijing, designed for various purposes. The cumulative results provide the total charging load of electric vehicles in Beijing. The findings reveal that in 2030, the total charging load of electric vehicles in Beijing accounts for 10.30% of the total grid load. With the large-scale integration of electric vehicles into the grid, the peak charging loads may occur at certain times, potentially impacting some local distribution networks. Hence, Effective control measures are required for electric vehicle charging loads. This study analyzes the impact of various factors on charging load distribution and the electrical grid, thus validating the comprehensiveness and effectiveness of the proposed prediction model.