LAPSE:2018.0941
Published Article
LAPSE:2018.0941
Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm
Fei Lin, Shihui Liu, Zhihong Yang, Yingying Zhao, Zhongping Yang, Hu Sun
November 27, 2018
With its large capacity, the total urban rail transit energy consumption is very high; thus, energy saving operations are quite meaningful. The effective use of regenerative braking energy is the mainstream method for improving the efficiency of energy saving. This paper examines the optimization of train dwell time and builds a multiple train operation model for energy conservation of a power supply system. By changing the dwell time, the braking energy can be absorbed and utilized by other traction trains as efficiently as possible. The application of genetic algorithms is proposed for the optimization, based on the current schedule. Next, to validate the correctness and effectiveness of the optimization, a real case is studied. Actual data from the Beijing subway Yizhuang Line are employed to perform the simulation, and the results indicate that the optimization method of the dwell time is effective.
Keywords
braking energy, dwell time, energy saving, Genetic Algorithm, multi-train, urban rail transit
Suggested Citation
Lin F, Liu S, Yang Z, Zhao Y, Yang Z, Sun H. Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm. (2018). LAPSE:2018.0941
Author Affiliations
Lin F: School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China
Liu S: School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China
Yang Z: School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China [ORCID]
Zhao Y: School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China
Yang Z: School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China
Sun H: School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China
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Journal Name
Energies
Volume
9
Issue
3
Article Number
E208
Year
2016
Publication Date
2016-03-17
Published Version
ISSN
1996-1073
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PII: en9030208, Publication Type: Journal Article
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LAPSE:2018.0941
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doi:10.3390/en9030208
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Nov 27, 2018
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Nov 27, 2018
 
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Calvin Tsay
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