TY - JOUR
T1 - Timetable Optimization for Regenerative Energy Utilization in Subway Systems
AU - Liu, Hongjie
AU - Zhou, Meng Chu
AU - Guo, Xiwang
AU - Zhang, Zizhen
AU - Ning, Bin
AU - Tang, Tao
N1 - Funding Information:
Manuscript received June 11, 2018; revised August 25, 2018; accepted September 20, 2018. Date of publication December 11, 2018; date of current version August 27, 2019. This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1201501, in part by the Beijing Municipal Natural Science Foundation under Grant L161008, in part by the TCT Funding Program under Grant 9907006510, in part by the Chinese Railway Certification Center Funding Program under Grant 1852ZJ1303, in part by the Beijing Laboratory of Urban Rail Transit, in part by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant no. G-415-135-38, and in part by the China Scholarship Council. The Associate Editor for this paper was F. Wang. (Corresponding author: MengChu Zhou.) H. Liu is with the State Key Laboratory of Rail Traffic Control and Safety, School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China, and also with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA.
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
KW - Subway
KW - artificial bee colony
KW - dwell time
KW - headway time
KW - regenerative energy
KW - timetable optimization
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U2 - 10.1109/TITS.2018.2873145
DO - 10.1109/TITS.2018.2873145
M3 - Article
AN - SCOPUS:85058620672
SN - 1524-9050
VL - 20
SP - 3247
EP - 3257
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 9
M1 - 8573160
ER -