An Orderly Charging and Discharging Strategy of Electric Vehicles Based on Space–Time Distributed Load Forecasting

Abstract

Given the widespread adoption of electric vehicles, their charging load is influenced not only by vehicle numbers but also by driving and parking behaviors. This paper proposes a method for forecasting electric vehicle charging load based on these behaviors, considering both spatial and temporal distribution. Initially, the parking generation rate model predicts parking demand, establishing the spatial and temporal distribution model for electric vehicle parking needs across various vehicle types and destinations. Subsequently, analyzing daily mileage and parking demand distributions of electric vehicles informs charging demand assessment. Using the Monte Carlo simulation method, large-scale electric vehicle behaviors in different spatial and temporal contexts—parking, driving, and charging—are simulated to predict charging load distributions. Optimization of electric vehicle charging and discharging enhances grid stability, cost management, charging efficiency, and user experience, supporting smart grid development. Furthermore, charging load forecasting examples under diverse scenarios validate the model’s feasibility and effectiveness.