Spatiotemporal charging load modeling for intelligent electric vehicle charging systems based on traffic–distribution network coupling

Abstract

With the rapid growth of electric vehicles (EVs), uncontrolled charging demand is creating new challenges for urban transportation systems and distribution networks. This study aims to characterize the spatiotemporal formation mechanism of EV charging loads and evaluate their impact on smart energy systems under coupled traffic–power network conditions. A transportation–distribution network coupling framework is developed by integrating EV travel behavior modeling, traffic network topology, state of charge (SOC)-based charging decisions, and charging-station service analysis. The shortest-path algorithm is used to simulate vehicle trajectories, while an M/M/c queuing model is introduced to describe waiting and service processes at charging stations. The resulting charging loads are mapped to the IEEE 33-bus distribution network, and power-flow analysis is performed to examine nodal load distribution and voltage responses. Simulation results show that EV charging demand presents clear spatiotemporal heterogeneity, with concentrated charging loads emerging at specific traffic nodes and time periods. As charging demand increases, voltage levels at several distribution nodes decline, indicating a strong coupling effect between charging behavior and grid operation. The proposed framework provides an effective analytical basis for charging facility planning, intelligent charging management, and coordinated energy dispatch in smart cities.