Optimal Joint Scheduling of Distribution Network and Electric Vehicle Tracks Considering Actual Carbon Emission Responsibility

With the increasing popularity of electric vehicles (EVs), on the energy supply side, large-scale grid-connected charging of EVs poses new challenges to the safe and economical operation of power distribution networks (PDNs). On the transportation side, EV tracks also have a significant impact on traffic congestion. Moreover, the division of carbon responsibilities for EVs is unclear, which seriously affects the long-term healthy development of carbon reduction strategies. Therefore, this paper comprehensively considers EV charging scheduling and traffic trajectories and proposes a new joint optimization method for PDNs and EV tracks. Additionally, the method also takes into account the allocation of carbon emission responsibilities. Specifically, a three-layer iterative optimization framework is established: In the first layer, a carbon emission flow model is established based on the alternating current optimal power flow (ACOPF) framework, and price signals reflecting the carbon responsibility of each node are obtained. Then, using these price signals, the second layer employs evolutionary game theory to determine the charging prices at different nodes. In the third layer, a two-objective track optimization model is proposed to minimize operation cost and maximize traffic flow, and the charging demand of each node is fed back to the first layer until the iteration converges. Furthermore, an iterative algorithm is designed, driven by the Lagrangian function, the Bellman-Ford augmented path algorithm, and the Ford-Fulkerson maximum flow algorithm, to efficiently solve this problem. Numerical tests verify the effectiveness of the proposed methods in ensuring the safe and economical operation of PDNs and reducing carbon emissions.