Timetable Optimization for Regenerative Energy Utilization in Subway Systems
Document Type
Article
Publication Date
9-1-2019
Abstract
In subway systems, kinetic energy can be converted into electrical one by using regenerative braking systems. If regenerative energy (RE) is fully used, the energy demands from power grid can be dramatically reduced. Since energy storage systems usually have a high cost, they are not considered in this work. Thus, RE has to be immediately utilized by accelerating trains; otherwise, it is wasted into heat via resistors. Timetable optimization methods are often used to coordinate accelerating and braking trains at a station, such that RE can be optimally used by the former. To improve RE utilization (REU) in a subway line, we propose a timetable optimization problem and establish its mathematical model. Many realistic constraints with the decision variables, i.e., headway time and dwell time, are considered. Then we design an improved artificial bee colony (IABC) algorithm to solve the problem. Several numerical experiments are conducted based on the actual data from a subway line in Beijing, China. The correctness of the mathematical model and effectiveness of IABC are shown by comparing it with commercial software CPLEX and a genetic algorithm, respectively. The impact of the decision variables on REU is analyzed, which helps to improve the timetable currently used in this subway line. We also test the robustness of the optimized timetable when certain disturbance takes place.
Identifier
85058620672 (Scopus)
Publication Title
IEEE Transactions on Intelligent Transportation Systems
External Full Text Location
https://doi.org/10.1109/TITS.2018.2873145
e-ISSN
15580016
ISSN
15249050
First Page
3247
Last Page
3257
Issue
9
Volume
20
Grant
1852ZJ1303
Fund Ref
King Abdulaziz University
Recommended Citation
Liu, Hongjie; Zhou, Meng Chu; Guo, Xiwang; Zhang, Zizhen; Ning, Bin; and Tang, Tao, "Timetable Optimization for Regenerative Energy Utilization in Subway Systems" (2019). Faculty Publications. 7379.
https://digitalcommons.njit.edu/fac_pubs/7379
