Aggregated optimization scheduling of electric vehicles in high-speed railway station areas considering carbon trading

High-speed rail (HSR) station areas are evolving into integrated energy systems with significant energy demands and diverse resources, necessitating coordinated optimization to achieve sustainable development. However, the integration of electric vehicles (EVs) and carbon trading mechanisms in these areas introduces complexities in balancing multiple stakeholders’ interests. This study proposes an aggregated optimization scheduling strategy for electric vehicles in high-speed railway station areas, taking into account carbon trading. A dual-layer Stackelberg game model is developed to ensure mutual benefits for both HSR station areas and EVs. In the lower layer, the EV model predicts peak passenger flow based on daily averages and characterizes travel behavior to minimize user charging/discharging costs, feeding these behaviors to the upper layer. The upper-layer station model minimizes operational costs by leveraging EV scheduling data and achieves optimal pricing decisions within a carbon trading framework through iterative game-theoretic strategies. A case study at Neijiang North Station demonstrates that the proposed model reduces station operating costs by 4.6%, decreases EV user scheduling costs by 3.5%, and increases carbon reduction by 5.6% compared to conventional approaches, effectively promoting economic and low-carbon operations. • The travel behavior characteristics of HSR station area were depicted to determine the distribution of EV travel characteristics within them. • Interval possibility is introduced into the model to effectively handle the uncertainty of PV and load. • A Stackelberg game optimization model is established to balance the interests of HSR station areas and EVs. • Optimal pricing considering carbon trading is proposed to achieve economic and low-carbon operations.