Abstract: [Objectives] With the widespread adoption of electric vehicles and the rapid advancement of vehicle-to-grid (V2G) technology, achieving synergistic optimization among path selection, power grid load regulation, and user satisfaction has become a critical research topic in intelligent transportation-energy systems. To address this, a synergistic optimization method for electric vehicle fleets is proposed, integrating heterogeneous behavior modeling and V2G scheduling. [Methods] At the transportation level, an urban road network is constructed based on graph theory, and a heterogeneous user behavior model that accounts for price-sensitive, time-sensitive, and range-anxious drivers is introduced. By integrating the dynamic Dijkstra algorithm, the optimal path planning is achieved, thereby enhancing user response accuracy and scheduling strategy flexibility. At the power grid level, considering the coupling mechanisms between electricity prices and loads, a convex optimization scheduling model is established. Additionally, the interior-point method is applied for real-time optimization of electric vehicle charging and discharging behavior. [Results] Simulation analysis indicates that the proposed method effectively achieves peak shaving and valley filling, reduces system operating costs by 14% to 23%, and significantly enhances overall user satisfaction. [Conclusions] The proposed method effectively improves the stability and economy of power grid operation while improving the user experience, providing a feasible theoretical basis and practical approach for the deep integration of intelligent transportation systems and smart grids.

Key words: electric vehicles, vehicle-to-grid, path planning, heterogeneous behavior modeling, optimization scheduling