A Two-Step Method for Energy-Efficient Train Operation, Timetabling, and Onboard Energy Storage Device Management

• Published in: IEEE transactions on transportation electrification Vol. 7; no. 3; pp. 1822 - 1833
• Main Authors: Wu, Chaoxian, Lu, Shaofeng, Xue, Fei, Jiang, Lin, Chen, Minwu, Yang, Jie
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Approximation; Computational efficiency; Computational geometry; Computing time; Convex optimization; Convexity; data fitting; Energy consumption; Energy management; Energy storage; onboard energy storage device (OESD); Optimization; Rail transportation; Subways; Supercapacitors; timetable; Timetables; train operation; Transportation
• ISSN: 2332-7782; 2577-4212; 2332-7782
• DOI: 10.1109/TTE.2021.3059111

This article proposes a novel two-step approach to concurrently optimize the train operation, timetable, and energy management strategy of the onboard energy storage device (OESD) to minimize the net energy consumption for a whole urban railway line. In Step 1, approximating functions representing the minimum net energy consumption of each specific interstation operation is obtained by data fitting based on the previous research outcomes. In Step 2, the optimal running time, initial state of energy (ISOE) of OESD, train speed profiles, discharge/charge management of the OESD during each interstation journey and at each station are obtained by applying convex optimization formulated by approximating functions gained in Step 1. The method is first tested with several general train cases to show its robustness and adaptability. Then, a real-world case based on Beijing metro Yizhuang line is studied and the optimal solution is found to reduce the net energy consumption 1.04%, 2.09%, and 23.77% for a service cycle of a single train when compared to other operation scenarios, i.e., fully charged, no management, and no OESD scenario, respectively. The approach is also computationally efficient with a computational time less than 1 min, namely 38.86 s for the upline and 48.38 s for the downline, spent on finding the optimal solution.