Abstract
Electric vehicles (EV) are growing fast in recent years with the widespread concern about carbon neutrality. The development of charging infrastructures needs to be in phase with EV both in terms of quantity and charging time to decrease the range anxiety of EV users and resource waste. This paper proposed a multistage and dynamic layout optimization model based on mixed integer linear programming (MILP) for EV charging stations (CSs) to minimize the total social costs (TSC) consisting of the detour cost of EV users and the construction, relocation, and operating cost of CSs. The charging satisfaction coefficient and M/M/S/K model of queuing theory has been introduced to determine the desirable charging supply. The spatial-temporal distribution of charging demand was modeled based on the behavior analysis of travelers and over the discrete-time intervals for a day. Comparison studies based on the Sioux Falls network reveal that TSC with a multistage optimization strategy will drop 8.79% from that with a one-time optimization strategy. Charging service quality, relocation cost, and road network scales have a significant impact on the optimization results according to the sensitivity analysis.
Highlights
The deployment of electric vehicles (EVs) has been growing rapidly in recent years, with the global stock of electric passenger cars reaching 7.2 million units in 2019, 40% higher than that in 2018 [1]
This paper proposes a model aimed at minimizing the total social cost (TSC) to balance the interests of EV users and charging stations (CSs) investors
This paper proposed a multistage layout optimization model for CSs aimed at minimizing the total social costs (TSC) and solving with mixed integer linear programming (MILP) with a particular consideration of EV users’ behavior
Summary
The deployment of electric vehicles (EVs) has been growing rapidly in recent years, with the global stock of electric passenger cars reaching 7.2 million units in 2019, 40% higher than that in 2018 [1]. Based on the above analysis, it could be known that the considerations about the behavior of EV users and the fluctuation of the charging demand in a day are not enough in the multistage layout optimization of CSs. This paper proposed a multistage layout optimization model for CSs aimed at minimizing the TSC and solving with MILP with a particular consideration of EV users’ behavior. The proposed method cannot only optimize the layout of CSs considering the numbers of EVs at different stages and ensure the service quality by satisfying the charging demand at different time intervals in a day. This paper proposes a model based on the Monte Carlo method to generate the spatial-temporal distribution of charging demand with the information of the road network, UFZs, the temporal travel patterns of EVs and the population scale
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