Smart City Charging Station allocation for electric vehicles using analytic hierarchy process and multiobjective goal-programming

In view of recent developments in urban transport systems, there has been a significant increase in the presence of electric vehicles on the market. One of the major challenges faced is the effective allocation of these electric vehicles to appropriate charging stations. The aim of this study is to tackle this challenge by leveraging various input parameter models within a smart city context. The primary objective is to assign electric vehicles to the most suitable charging stations, taking into account four objectives: maximizing energy requested by electric vehicles, minimizing total response time, reducing charging costs, and minimizing battery degradation. Several factors, such as travel distance, state of charge, grid-to-vehicle energy trading, time, road traffic, driver priorities, and charging station capacity, are taken into account to address this problem. In addition, we propose a novel methodology that combines the analytic hierarchy process with a multi-objective goal programming model to aid decision-making regarding the allocation of electric vehicles–charging stations. The aim of this methodology is to assist electric vehicle owners in selecting effective charging stations within a complex decision-making environment. In addition, practical examples of how these methodologies can be used to solve a real-life problem have been highlighted. We validated the effectiveness of the proposed method in terms of enhancing the satisfaction factor of electric vehicles by updating their energy levels and reducing range anxiety through simulation results using SUMO and GAMS with the BARON solver. These results confirm the efficacy of the proposed method.