Tri-level collaborative optimization strategy for coupled power and transportation networks considering energy scheduling of fast charging stations

The development of electric vehicles (EVs) has accelerated the integration with transportation network (TN) and power distribution network (PDN). The coupled transportation and power distribution network (CPTN) is expected to play a significant role in the future energy internet. Fast charging stations (FCSs) are critical nodes of the CPTN and are progressively transforming into microgrids that integrate renewable energy sources (RES). How to effectively achieve coordinated optimization of EV scheduling, FCS energy scheduling, and the operation of PDN remains a significant challenge. This paper proposes a tri-level collaborative optimization scheduling model for CPTN. First, a tri-level optimization framework is proposed. At the upper level, EV scheduling center determines selection and route of FCS for EV users based on dynamic pricing. At the middle level, FCSs optimize the energy scheduling and set charging and discharging prices, while at the lower level, the PDN optimizes power flow and updates distribution locational marginal pricing (DLMP). Second, an EV FCS selection and route optimization model that accounts for temperature effects and queue waiting time is developed. Additionally, FCS energy scheduling and pricing models for FCSs are proposed, alongside an alternating current optimal power flow (ACOPF) model for integrating FCSs into the PDN. Then, an iterative solution method combining the sine-cosine greater cane rat algorithm (SCGCRA) with optimal condition decomposition is proposed to solve the tri-level model through iterative processes. Finally, four different strategies are analyzed using real world road network cases. The results indicate that the proposed strategy can reduce the total EV cost by 36.18 %, decrease total carbon emissions by 7.36 %, lower the total cost of the PDN by 12.35 %, and increase charging station revenue by 19.09 %, thereby validating the effectiveness of the proposed strategy.