Energy-efficient Optimization Method for Train Operation Combining Driving Strategy and Timetabling

被引：0
|
作者：
Sheng Z. [1 ]
Cai B. [1 ]
Shangguan W. [1 ,2 ]
Wang J. [1 ,2 ]
机构：
[1] School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing
[2] State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing
来源：
Tiedao Xuebao/Journal of the China Railway Society | 2019年 / 41卷 / 10期
关键词：
High-speed train; KKT (Karush-Kuhn-Tucker) condition; Particle swarm optimization-simulated annealing algorithm; Timetable; Train operation optimization;
D O I：
10.3969/j.issn.1001-8360.2019.10.010
中图分类号：
学科分类号：
摘要：
In order to reduce energy consumption of high-speed train operation in the multi-sections, the train driving strategy optimization model was established with the crucial objectives of energy consumption and running time in each section. Particle swarm optimization algorithm based on simulated annealing algorithm (PSO-SA) was implemented in order to find the optimal energy consumption-time Pareto set of each section quickly and efficiently, and the corresponding Pareto curve was obtained by fitting based on the method of least-square. Under the conditions of the fixed running time from the departure station to terminal station and the slightly variable running time of each station, the optimal interstation running time and optimal operation strategy can be acquired based on KKT (Karush-Kuhn-Tucker) condition. Finally, a simulation model was applied based on the actual operational data from Zhenjiang South-Kunshan South high speed railway line of China to verify the effectiveness of the proposed approach. The results indicate that the total operation energy consumption of the train is reduced by 17.6% under the strategy of the Pareto set solution and optimization of interstation operation time. © 2019, Department of Journal of the China Railway Society. All right reserved.
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页码：68 / 75
页数：7
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