Optimal pricing for vehicle-to-grid stations based on dynamic transportation network equilibrium

With the increasing prevalence of electric vehicles (EVs) and the widespread application of vehicle-to-grid (V2G) technology, transportation electrification has attracted surging attention. However, the spatiotemporal interactions between urban transportation network (UTN) and power distribution network (PDN) are complex and significant. Between UTN and PDN, a bi-level optimization model is designed to serve the V2G optimal pricing problem. EVs choose to charge or discharge according to the electricity prices declared by PDN. Under the dynamic pricing mechanism, PDN adjusts generation and electricity prices to guide EVs participating in V2G services. For UTN, the dynamic user equilibrium model is used to track the traffic assignment and flow propagation. For PDN, we introduce a linearized distribution flow (LinDistFlow) model to describe the grid. Then, the bi-level optimization model is transformed into a single-level mixed-integer quadratically constrained programming (MIQCP) based on the Karush-Kuhn-Tucker (KKT) conditions. Finally, we applied it to a coupled 13-node UTN and 18-bus PDN system to validate the proposed framework. Numerical results show that the impact of electricity price and power load on the coupled system can be efficiently captured.