A tri-level planning strategy for integrated power and transport systems incorporating EV elastic charging behaviors

With the rapid increase in electric vehicle (EV) adoption, there is an escalating need for coordinated planning between power and transportation systems to address the additional stress on urban infrastructure. This study tackles the critical challenge of aligning investment and planning between these systems, specifically considering the impact of EV charging behaviors. To address this issue, we propose a novel tri-level optimization framework that incorporates a quasi-variational inequality (QVI) model to effectively capture the interactions between user equilibrium (UE) in transportation networks and elastic EV charging demand. The upper level of the model optimizes investments in power systems, including distributed generation and charging infrastructure. The middle level concentrates on expanding road capacity, while the lower level resolves the traffic flow patterns under user equilibrium with elastic charging behaviors (UE-ECB) using the QVI approach. By transforming the tri-level model into a bi-level optimization program with quasi-variational inequality (OP-QVI) formulation, we achieve computational tractability and provide efficient solutions. Results show that the elastic charging demand model outperforms the non-elastic assumption, reducing total costs by 22.2 %, with power system investments down by 19.1 % and transportation system investments down by 39.8 %. These findings demonstrate the model's superior accuracy, avoiding the overestimation of infrastructure needs. Furthermore, the proposed algorithm efficiently solves complex integrated planning problems, ensuring both computational effectiveness and economic viability in system-wide optimization.