As a promising way to achieve energy transition, electric vehicles (EVs) are rapidly advancing. Dynamic charging price of charging network operator (CNO) guides the owners to vary travel decisions, which in return affect the operation of power distribution networks (PDNs) and transportation networks (TNs). Therefore, an appropriate dynamic charging price can decrease the operating costs of PDN and TN, while increasing the profit of the CNO. This study proposes a tri-level dynamic pricing model. At the upper level, the CNO sets the charging price to maximize its profit considering the decision of EVs. And at the middle level, the gasoline vehicles (GVs) and EVs make their travel and charging decisions to minimize their travel cost by considering simultaneous charging, routing and departure time (SCRDT) choices, yielding a mixed dynamic user equilibrium (MDUE) pattern. Consequently, the charging loads are determined. At the lower level, the distribution network operator (DNO) calculates the power purchase price by solving a second-order cone ACOPF (SOC-ACOPF) problem with charging loads with temporal and geographical variations. Furthermore, a mixed genetic algorithm nested with the projection gradient method (PGM) and second-order cone programming (SOCP) is designed to solve the proposed model. Numerical examples validate the effectiveness of the proposed model, and analyze the impact of dynamic pricing on the operation of PDN and TN and CNO’s profit.

With the scale development of electric vehicles (EVs) exacerbating the peak-to-valley difference and congestion problem of the power grid, how to coordinate the charging behaviour of users with the operation of the power grid has become a key challenge in energy transition. Aiming at the limitations of the existing studies, which are insufficient to consider the heterogeneity and satisfaction of users, this paper proposes a cooperative optimization method based on the coupling of evolutionary game and master-slave game for electric vehicle charging behaviour. The dynamic equilibrium is achieved through the closed-loop feedback mechanism of "load-price-strategy" in the model. The proposed method can effectively smooth the load curve , while taking into account the optimisation of user cost and the improvement of grid revenue, providing theoretical support and practical path for the synergistic dispatch between electric vehicles and the grid under the goal of "dual-carbon".