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Energies 2015, 8(12), 14311-14329; doi:10.3390/en81212433

Optimization of Train Operation in Multiple Interstations with Multi-Population Genetic Algorithm

1,2,4,* , 1,2
,
1,2
and
3,4
1
School of Electronics and Information Engineering, Beijing Jiaotong University, Haidian District, Beijing 100044, China
2
National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Haidian District, Beijing 100044, China
3
State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Haidian District, Beijing 100044, China
4
Beijing Lab of Rail Traffic, Beijing Jiaotong University, Haidian District, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Academic Editor: Susan Krumdieck
Received: 29 September 2015 / Revised: 30 November 2015 / Accepted: 7 December 2015 / Published: 18 December 2015
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Abstract

Subway systems consume a large amount of energy each year. How to reduce the energy consumption of subway systems has already become an issue of concern in recent years. This paper proposes an energy-efficient approach to reduce the traction energy by optimizing the train operation for multiple interstations. Both the trip time and driving strategy are considered in the proposed optimization approach. Firstly, a bi-level programming model of multiple interstations is developed for the energy-efficient train operation problem, which is then converted into an integrated model to calculate the driving strategy for multiple interstations. Additionally, the multi-population genetic algorithm (MPGA) is used to solve the problem, followed by calculating the energy-efficient trip times. Finally, the paper presents some examples based on the operation data of the Beijing Changping subway line. The simulation results show that the proposed approach presents a better energy-efficient performance than that with only optimizing the driving strategy for a single interstation. View Full-Text
Keywords: subway; driving strategy; energy-efficient operation; trip time subway; driving strategy; energy-efficient operation; trip time
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Huang, Y.; Ma, X.; Su, S.; Tang, T. Optimization of Train Operation in Multiple Interstations with Multi-Population Genetic Algorithm. Energies 2015, 8, 14311-14329.

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