Two-Stage Integrated Planning of Energy-Saving Operations of Metro Trains Using MOJS and GWO Algorithms

In recent years, with a remarkable increase in urban rail transit operations, the issue of energy efficiency in train operations has attained increasing attention. In this study, a two-stage optimization model is proposed to optimize driving strategies and schedules. We comprehensively consider the optimization of train running curves, running time allocations to a whole line, and utilization of regenerative braking energy, to reduce the net energy consumption of train operations. In the first stage, a multi-objective jellyfish search (MOJS) optimization algorithm is used to optimize a switching sequence at each inter-station, and Pareto fronts are obtained corresponding to energy-saving train running curves. In the second stage, a grey wolf optimizer (GWO) is adopted to optimize running times between adjacent stations, dwell times at stations, and headway time. This stage aims to coordinate the operations of multiple trains, to reduce the traction energy consumption of a whole line, and to increase the utilization of regenerative braking energy. The optimality is discussed for the proposed two-stage optimization processes. Numerical experiments are conducted based on train and infrastructure data of the Beijing Yizhuang metro line. The results show that the proposed optimization model and solution algorithms have a considerable energy-saving effect.