Spatiotemporal Coordination Optimization in Electric Vehicle Transportation-Energy Networks Based on Virtual Service Nodes

Electric vehicles (EVs) are essential for addressing dynamic path planning and charging scheduling problems in transportation-energy coupled networks. However, conventional traffic assignment models struggle to accurately capture EV charging behaviors due to limited temporal resolution, insufficient power-transportation integration, and inadequate representation of queue dynamics. This paper proposes a mixed-integer quadratic programming (MIQP) model based on Virtual Service Nodes (VSN), which decomposes the charging process into distinct waiting and service phases, thereby enabling fine-grained spatiotemporal capture of EV charging behaviors and their queue dynamics. We model each charging station as a logical triplet (entry, service, exit) connected by wait and charge arcs. Within the VSN framework, charging pile states and queue dynamics are characterized using discrete time steps and binary variables with linear constraints, which facilitates precise modeling of operational constraints. Furthermore, a grid-transportation collaborative equilibrium mechanism incorporates time-of-use pricing into the optimization objective, thereby incentivizing peak-valley load balancing. Experimental results demonstrate that the VSN strategy reduces average and maximum waiting times by 91.0% and 84.5%, respectively, while enhancing charging station utilization balance and smoothing grid load profiles, ultimately improving the overall service capacity of the coupled network.