Multi-type electric vehicle load prediction based on Monte Carlo simulation

In the paper, an electric vehicle (EV) charging load prediction approach considering vehicle types is developed. The GM (1,1) model is initially introduced to estimate the ownership of electric vehicles (EVs), and the model is validated by examining the difference between the forecast results and the actual situation. The Monte Carlo algorithm is also used to build the probability distribution model of travel laws and charging characteristics to predict the load demand when the scale of EVs is connected to the grid. For various types of EVs, the charging behavior is analyzed by considering charging start moment, charging duration, the initial state of charge (SOC), daily mileage, and charging mode. The charging load curves for each type of EV are superimposed to obtain the total load demand curve. Then it is compared with the original load curve in a certain region to analyze the impact of EV charging load on the original load curve of the grid, and to provide theoretical guidance for grid planning and optimal dispatching. (C) 2022 The Author(s). Published by Elsevier Ltd.

Automated summary

This paper presents an electric vehicle (EV) charging load prediction approach that considers vehicle types. It utilizes the GM (1,1) model to estimate EV ownership and employs the Monte Carlo algorithm to build a probability distribution model for travel laws and charging characteristics. The analysis of charging behavior for different types of EVs allows for the comparison of load demand with the original load curve, providing theoretical guidance for grid planning and optimal dispatching.