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An Integrated Energy-Efficient Operation Methodology for Metro Systems Based on a Real Case of Shanghai Metro Line One

Author

Listed:
  • Cheng Gong

    (Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China)

  • Shiwen Zhang

    (Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China)

  • Feng Zhang

    (Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China)

  • Jianguo Jiang

    (Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China)

  • Xinheng Wang

    (School of Engineering and Computing, University of the West of Scotland, Paisley PA1 2BE, Scotland, UK)

Abstract

Metro systems are one of the most important transportation systems in people’s lives. Due to the huge amount of energy it consumes every day, highly-efficient operation of a metro system will lead to significant energy savings. In this paper, a new integrated Energy-efficient Operation Methodology (EOM) for metro systems is proposed and validated. Compared with other energy saving methods, EOM does not incur additional cost. In addition, it provides solutions to the frequent disturbance problems in the metro systems. EOM can be divided into two parts: Timetable Optimization (TO) and Compensational Driving Strategy Algorithm (CDSA). First, to get a basic energy-saving effect, a genetic algorithm is used to modify the dwell time of each stop to obtain the most optimal energy-efficient timetable. Then, in order to save additional energy when disturbances happen, a novel CDSA algorithm is formulated and proposed based on the foregoing method. To validate the correctness and effectiveness of the energy-savings possible with EOM, a real case of Shanghai Metro Line One (SMLO) is studied, where EOM was applied. The result shows that a significant amount of energy can be saved by using EOM.

Suggested Citation

  • Cheng Gong & Shiwen Zhang & Feng Zhang & Jianguo Jiang & Xinheng Wang, 2014. "An Integrated Energy-Efficient Operation Methodology for Metro Systems Based on a Real Case of Shanghai Metro Line One," Energies, MDPI, vol. 7(11), pages 1-25, November.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:11:p:7305-7329:d:42281
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    4. Mulder, J. & van Jaarsveld, W.L. & Dekker, R., 2016. "Simultaneous optimization of speed and buffer times for robust transportation systems," Econometric Institute Research Papers EI2016-36, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Andreas Bärmann & Alexander Martin & Oskar Schneider, 2017. "A comparison of performance metrics for balancing the power consumption of trains in a railway network by slight timetable adaptation," Public Transport, Springer, vol. 9(1), pages 95-113, July.
    6. Fei Lin & Shihui Liu & Zhihong Yang & Yingying Zhao & Zhongping Yang & Hu Sun, 2016. "Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm," Energies, MDPI, vol. 9(3), pages 1-21, March.
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    12. Yang, Xin & Chen, Anthony & Ning, Bin & Tang, Tao, 2016. "A stochastic model for the integrated optimization on metro timetable and speed profile with uncertain train mass," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 424-445.
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    15. Maria La Gennusa & Patrizia Ferrante & Barbara Lo Casto & Gianfranco Rizzo, 2015. "An Integrated Environmental Indicator for Urban Transportation Systems: Description and Application," Energies, MDPI, vol. 8(10), pages 1-19, October.
    16. Jia Feng & Xiamiao Li & Haidong Liu & Xing Gao & Baohua Mao, 2017. "Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands," Energies, MDPI, vol. 10(4), pages 1-20, March.
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