A Coordination Optimization for Train Operation and Energy Infrastructure Control in a Metro System

An advanced metro system becomes imperative towards efficient and sustainable operations with the rapid growth of urban construction. As the essential factors to implement transport sustainability, a referred energy-efficient trajectory and an energy-saving infrastructure for train operations could guide trains running through with conquering complicated environmental impacts and help to control the energy flows in traction processes. Thus, we attempt to discover the optimal way of adapting to the track changes and resistances in train running and evaluate the energy exchanges with Energy Storage System (ESS) in an extended power supply network. In this study, we firstly optimize a train trajectory with consideration of air and track resistances by using a modified Q-Learning (QL) method. Secondly, for coordinated application between ESS and trains, an integration module is designed by implanting train trajectories. Specifically, based on the working properties of ESS, a deceleration-acceleration time separation is matched for supplementing the charging/discharging times of the devices. The space-time presentation is introduced to portray train running phases for connections with control processes of ESS. Then, a Mixed Integer Linear Programming (MILP) model is proposed for minimizing deceleration-acceleration overlaps. A Tabu Search (TS) algorithm is introduced to solve such complex problems. Finally, a numerical test is conducted to demonstrate the feasibility of the model in Beijing, China.