Real-time energy saving optimization method for urban rail transit train timetable under delay condition. (1st November 2022)

• Record Type: Journal Article
• Authors: Zhang, Lang; He, Deqiang; He, Yan; Liu, Bin; Chen, Yanjun; Shan, Sheng
• Is Part Of: Energy. Volume 258(2022)
• Publication Date: 2022-11-01
• Subjects: Energy-efficient train timetable -- Urban rail transit -- Delay disturbance -- Rapid iterative method; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.124853
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The global energy crunch and rising demand for electricity make it all the more important to develop energy-efficient timetable for urban rail transit trains. Under normal circumstances, the train can operate normally according to the energy-saving timetable that has been formulated. However, trains may deviate from the original schedule and increase energy consumption due to station delays. For this, how to quickly restore the train to an efficient state and effectively transport the delayed passengers to the destination as soon as possible is a challenging problem. Therefore, this paper proposed a two-stage optimization method to solve the above problem. In the first stage, an improved differential evolution algorithm was used to optimize the energy-saving operation strategy of the train without delay, and the optimal running time-energy consumption solution set of the train in the redundant running time of each section on the entire line was obtained. Then, based on this, the second stage proposed a rapid iterative method to optimize and reschedule the timetable of the delayed train in the remaining sections, and an efficient solution was obtained. Finally, Nanning Rail Transit (NNRT) Line 5 is selected for a case study, and the proposed model and method are verified. Compared with the existing solution that temporarily shortens the running time of the next section in the delayed station, the proposed method reduces the energy consumption by 3.32% on average, and the average calculation time is about 0.36 s, which showed that the method is effective and meets the real-time requirements.