Introduction: To address the issue of unreasonable capacity allocation of charging stations in service areas caused by the lack of consideration of the probabilistic impact of charging stations on users' charging choices, and to enable the highway grid to accommodate a large number of Electric Vehicle (EV) loads as well as the integration of wind, solar, and energy storage, it is necessary to rationally plan charging station capacity while considering the constraints of the highway road-electricity coupling network. This paper proposes a charging station capacity planning method within the context of a highway electric-traffic coupling network. The impact of EVs on the existing highway energy-traffic integration system is summarized, and the limitations of existing traffic flow distribution models and charging station planning methods are analyzed. Method: A logit-based stochastic user equilibrium traffic flow distribution model is established based on the highway network structure. The impact of charging stations on user charging behavior is analyzed using the Huff model, and the charging demand of EV users in service areas is evaluated based on queuing theory. A long-chain microgrid cluster model incorporating a DC Power Flow Controller (DCPFC) is constructed to derive the highway road-electricity coupling network model. Subsequently, a multi-objective optimization model is developed, considering user travel costs, charging station construction and operation costs, and the tripartite total cost of grid network losses. The multi-objective particle swarm optimization algorithm is employed to obtain the optimal charging station capacity planning scheme. Results: A case study is conducted using the road network structure data of the Wuwei-Zhangye section of the G30 Lianhuo Expressway and typical daily wind, solar, and load profiles of the highway service area. Discussion: This paper lays the foundation for research on capacity planning for highway charging stations, but does not consider the impact of long-chain DC microgrid cluster topologies on highways. Future research could explore bidirectional coupling and coordinated optimization of highway traffic and energy, constructing a two-layer optimization model to enhance the utilization of green energy. Conclusion: The simulation results demonstrate that the proposed model effectively balances the economic efficiency of the three-party operational costs, achieving an optimal charging station capacity planning scheme.
Chen et al. (Tue,) studied this question.