Coordinated routing optimization and charging scheduling in a multiple-charging station system: A strategic bilevel multi-objective programming

Abstract

Effectively managing EV charging queues not only alleviates traffic congestion in high-demand areas but also improves user satisfaction by minimizing waiting times. This framework enhances overall system efficiency by better distributing the concentration of EVs during peak periods. This research investigates collaborative mechanisms from the perspectives of various stakeholders, including charging stations (CSs) and EVs, to optimize the charging process. A route optimization model is employed to direct EVs toward the most suitable CSs, followed by the introduction of two scheduling models: (1) a social welfare maximization model and (2) a game-theoretic iterative framework. These models aim to optimize EV charging locations while increasing CS profitability. EVs scheduling is performed using a mixed-integer non-linear programming (MINLP) approach, offering critical insights into its applicability across different scenarios. The numerical results demonstrate that coordinated EV scheduling substantially enhances the operational efficiency of E-mobility systems in both centralized and decentralized configurations. Compared to uncoordinated scheduling, total profits for CSs are 42 % higher for Test System 1 and 39 % higher for Test System 2. EV owners’ costs decrease by 47 % in the social welfare model and 32 % in the game-based model for Test System 1. In Test System 2, cost reductions are 12 % and 7 % for the social welfare and game-based models, respectively. Although power transactions with the market are slightly higher in the social welfare model, the game-based model demonstrates a more efficient distribution of EVs across charging stations, especially in Test System 2, resulting in a more balanced system and optimized resource allocation.