Dynamic Energy-Aware EV Charging Navigation in Interacting Transportation and Distribution Networks

With electric vehicles (EVs) and charging facilities as a bridge, the coupling of transportation network (TN) and distribution network (DN) is getting closer, and EV charging navigation considering TN-DN convergence is a current research hotspot. In order to reduce the total cost of EV charging navigation and the impact of charging load on the grid, this paper proposes a novel bi-layer coordinated charging navigation model (Bi-CCNM). The upper layer model is to minimize the total cost of EV charging navigation. To precisely estimate travel costs, a dynamic spatio-temporal energy consumption estimation model is established, which considers the impact of dynamic traffic flow on EV travel resistance. The lower layer model aims to reduce energy exchange between the charging station (CS) and the main grid, and maximize the utilization of local renewable energy sources. To cope with the intermittent nature of renewable energy generation, this paper utilizes Vehicle-to-Grid (V2G) technology to effectively mitigate the impact of EV charging loads on the grid. To efficiently tackle the Bi-CCNM, the Joint Optimization algorithm combining Generalized Benders Decomposition and Logarithmic Barrier Function Method (JO-GBLB) is developed. Ultimately, an optimal solution can be obtained through the interaction of information between the two layers. Real-world case validates the effectiveness of the proposed the Bi-CCNM, energy consumption estimation model, and JO-GBLB algorithm. The results indicate which it provides a low-cost charging navigation solution while significantly reducing the power exchange between the CS and the main grid, effectively preventing safety issues caused by load fluctuations. Besides, the accuracy of energy consumption estimation of EVs increases by 5.1% - 6.7%.