A Carbon-Tax-based Pricing Scheme for Vehicle Scheduling in Coupled Power-Traffic Networks

The increasing penetration of electric vehicles (EVs) gradually couples the traffic network (TN) and the power distribution network (PDN), which brings both opportunities and challenges for decarbonization. In this research, a coupled power-traffic networks operation model is proposed to achieve the goals of cost reduction and low-carbon emissions, as well as minimize each entity's self-consumption. Thus, a bi-level game framework is adopted to model competitive behaviors. Specifically, the upper-level TN operator (TNO) determines monetary incentives (i.e., charging prices and carbon taxes) to guide vehicles, and the PDN operator (PDNO) cooperates with the TNO to optimize power flows. While the lower-level EV driver group (EVDG) and gasoline vehicle driver group (GVDG) make route choices with informed incentives to minimize consumption, respectively. A game theoretic approach is adopted to prove the existence of route selection solutions in the non-cooperative competitive behavior between EVDG and GVDG. Moreover, a carbon-tax-based pricing scheme is proposed to schedule vehicles in an economic and low-carbon manner. The bi-level model with four entities (i.e., PDNO, TNO, EVDG, and GVDG) is solved by a decentralized algorithm to identify the optimal operational state. Numerical results validate the effectiveness of the proposed scheme compared with several other schemes.