An Approach to Improving Regenerative Energy by Using Swarm Intelligence for Urban Rail Transit

Abstract

Urban rail transit (URT) develops rapidly in modern cities, and its energy efficiency attracts extensive attention. The utilization of regenerative braking energy (RBE) is an important method for energy-efficient operation of URT. In order to make full use of RBE, this paper proposed a method to maximize RBE by train group matching with swarm intelligence algorithm. First, four cases of overlapping time are defined for train group matching. A train group can be formed with energy matching between trains and the moments when energy matching occurs in train groups are found out based on the actual timetable. Then, a dual-objective optimization model is formulated to maximize RBE and reduce the deviation of departure times for all train groups. Specifically, the train dwell time is adjusted to increase the overlapping time of train traction and braking, by which the RBE can be improved. A Particle Swarm Optimization (PSO) algorithm and a Gray Wolf Optimization (GWO) algorithm are developed to solve the optimal RBE of train groups in different scenarios. Finally, the proposed method is evaluated based on the operation data from the Beijing YANFANG Line. The results show that the proposed method can clarify the energy matching situations in each train group. Compared with the original timetable, the optimized timetable improved RBE by 38.3% in average with acceptable influence on departure times.