IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i3p208-d65945.html
   My bibliography  Save this article

Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm

Author

Listed:
  • Fei Lin

    (School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China)

  • Shihui Liu

    (School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China)

  • Zhihong Yang

    (School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China)

  • Yingying Zhao

    (School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China)

  • Zhongping Yang

    (School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China)

  • Hu Sun

    (School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China)

Abstract

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.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:3:p:208-:d:65945
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/3/208/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/3/208/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bin Wang & Zhongping Yang & Fei Lin & Wei Zhao, 2014. "An Improved Genetic Algorithm for Optimal Stationary Energy Storage System Locating and Sizing," Energies, MDPI, vol. 7(10), pages 1-25, October.
    2. Suzuki, Y. & Koyanagi, A. & Kobayashi, M. & Shimada, R., 2005. "Novel applications of the flywheel energy storage system," Energy, Elsevier, vol. 30(11), pages 2128-2143.
    3. 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.
    4. Bolund, Björn & Bernhoff, Hans & Leijon, Mats, 2007. "Flywheel energy and power storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(2), pages 235-258, February.
    5. Huan Xia & Huaixin Chen & Zhongping Yang & Fei Lin & Bin Wang, 2015. "Optimal Energy Management, Location and Size for Stationary Energy Storage System in a Metro Line Based on Genetic Algorithm," Energies, MDPI, vol. 8(10), pages 1-23, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Janusz Szkopiński & Andrzej Kochan, 2021. "Energy Efficiency and Smooth Running of a Train on the Route While Approaching Another Train," Energies, MDPI, vol. 14(22), pages 1-27, November.
    2. Zhang, Huan & Zhu, Chunguang & Zheng, Wandong & You, Shijun & Ye, Tianzhen & Xue, Peng, 2016. "Experimental and numerical investigation of braking energy on thermal environment of underground subway station in China's northern severe cold regions," Energy, Elsevier, vol. 116(P1), pages 880-893.
    3. Mo Chen & Zhuang Xiao & Pengfei Sun & Qingyuan Wang & Bo Jin & Xiaoyun Feng, 2019. "Energy-Efficient Driving Strategies for Multi-Train by Optimization and Update Speed Profiles Considering Transmission Losses of Regenerative Energy," Energies, MDPI, vol. 12(18), pages 1-25, September.
    4. Guifu Du & Dongliang Zhang & Guoxin Li & Chonglin Wang & Jianhua Liu, 2016. "Evaluation of Rail Potential Based on Power Distribution in DC Traction Power Systems," Energies, MDPI, vol. 9(9), pages 1-20, September.
    5. Shen, Xiaojun & Wei, Hongyang & Wei, Li, 2020. "Study of trackside photovoltaic power integration into the traction power system of suburban elevated urban rail transit line," Applied Energy, Elsevier, vol. 260(C).
    6. Marcin Szott & Marcin Jarnut & Jacek Kaniewski & Łukasz Pilimon & Szymon Wermiński, 2021. "Fault-Tolerant Control in a Peak-Power Reduction System of a Traction Substation with Multi-String Battery Energy Storage System," Energies, MDPI, vol. 14(15), pages 1-23, July.
    7. Jie Yang & Limin Jia & Shaofeng Lu & Yunxiao Fu & Ji Ge, 2016. "Energy-Efficient Speed Profile Approximation: An Optimal Switching Region-Based Approach with Adaptive Resolution," Energies, MDPI, vol. 9(10), pages 1-27, September.
    8. Jiang Liu & Tian-tian Li & Bai-gen Cai & Jiao Zhang, 2020. "Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line," Energies, MDPI, vol. 13(8), pages 1-25, April.
    9. Pan, Deng & Zhao, Liting & Luo, Qing & Zhang, Chuansheng & Chen, Zejun, 2018. "Study on the performance improvement of urban rail transit system," Energy, Elsevier, vol. 161(C), pages 1154-1171.
    10. 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.
    11. Yinggui Zhang & Qianying Xu & Runchuan Yu & Minghui Zhao & Jiachen Liu, 2023. "Receiving Routing Approach for Virtually Coupled Train Sets at a Railway Station," Mathematics, MDPI, vol. 11(9), pages 1-21, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Petru Valentin Radu & Adam Szelag & Marcin Steczek, 2019. "On-Board Energy Storage Devices with Supercapacitors for Metro Trains—Case Study Analysis of Application Effectiveness," Energies, MDPI, vol. 12(7), pages 1-22, April.
    2. Shuai Su & Tao Tang & Yihui Wang, 2016. "Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model," Energies, MDPI, vol. 9(2), pages 1-19, February.
    3. David Roch-Dupré & Carlos Camacho-Gómez & Asunción P. Cucala & Silvia Jiménez-Fernández & Álvaro López-López & Antonio Portilla-Figueras & Ramón R. Pecharromán & Antonio Fernández-Cardador & Sancho Sa, 2021. "Optimal Location and Sizing of Energy Storage Systems in DC-Electrified Railway Lines Using a Coral Reefs Optimization Algorithm with Substrate Layers," Energies, MDPI, vol. 14(16), pages 1-19, August.
    4. 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.
    5. Abdul Ghani Olabi & Tabbi Wilberforce & Mohammad Ali Abdelkareem & Mohamad Ramadan, 2021. "Critical Review of Flywheel Energy Storage System," Energies, MDPI, vol. 14(8), pages 1-33, April.
    6. Regina Lamedica & Alessandro Ruvio & Laura Palagi & Nicola Mortelliti, 2020. "Optimal Siting and Sizing of Wayside Energy Storage Systems in a D.C. Railway Line," Energies, MDPI, vol. 13(23), pages 1-22, November.
    7. Rastegarzadeh, Sina & Mahzoon, Mojtaba & Mohammadi, Hossein, 2020. "A novel modular designing for multi-ring flywheel rotor to optimize energy consumption in light metro trains," Energy, Elsevier, vol. 206(C).
    8. Arani, A.A. Khodadoost & Karami, H. & Gharehpetian, G.B. & Hejazi, M.S.A., 2017. "Review of Flywheel Energy Storage Systems structures and applications in power systems and microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 9-18.
    9. Álvaro J. López-López & Ramón R. Pecharromán & Antonio Fernández-Cardador & Asunción P. Cucala, 2017. "Improving the Traffic Model to Be Used in the Optimisation of Mass Transit System Electrical Infrastructure," Energies, MDPI, vol. 10(8), pages 1-18, August.
    10. Okou, R. & Sebitosi, A.B. & Pillay, P., 2011. "Flywheel rotor manufacture for rural energy storage in sub-Saharan Africa," Energy, Elsevier, vol. 36(10), pages 6138-6145.
    11. Albrecht, Amie & Howlett, Phil & Pudney, Peter & Vu, Xuan & Zhou, Peng, 2016. "The key principles of optimal train control—Part 1: Formulation of the model, strategies of optimal type, evolutionary lines, location of optimal switching points," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 482-508.
    12. Díaz-González, Francisco & Sumper, Andreas & Gomis-Bellmunt, Oriol & Villafáfila-Robles, Roberto, 2012. "A review of energy storage technologies for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2154-2171.
    13. Mehrabankhomartash, Mahmoud & Rayati, Mohammad & Sheikhi, Aras & Ranjbar, Ali Mohammad, 2017. "Practical battery size optimization of a PV system by considering individual customer damage function," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 36-50.
    14. Rabiee, Abdorreza & Khorramdel, Hossein & Aghaei, Jamshid, 2013. "A review of energy storage systems in microgrids with wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 316-326.
    15. Jin, Rui & Li, Lei & Liang, Xiaoling & Zou, Xiang & Yang, Zeyuan & Ge, Shuzhi Sam & Huang, Haihong, 2024. "Energy-efficient design of the powertrain for mechanical-electro-hydraulic equipment via configuring multidimensional controllable variables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
    16. Hegazy, Omar & Barrero, Ricardo & Van den Bossche, Peter & El Baghdadi, Mohamed & Smekens, Jelle & Van Mierlo, Joeri & Vriens, Wouter & Bogaerts, Bruno, 2016. "Modeling, analysis and feasibility study of new drivetrain architectures for off-highway vehicles," Energy, Elsevier, vol. 109(C), pages 1056-1074.
    17. Brenda Rojas-Delgado & Monica Alonso & Hortensia Amaris & Juan de Santiago, 2019. "Wave Power Output Smoothing through the Use of a High-Speed Kinetic Buffer," Energies, MDPI, vol. 12(11), pages 1-28, June.
    18. Rupp, A. & Baier, H. & Mertiny, P. & Secanell, M., 2016. "Analysis of a flywheel energy storage system for light rail transit," Energy, Elsevier, vol. 107(C), pages 625-638.
    19. Mahto, Tarkeshwar & Mukherjee, V., 2015. "Energy storage systems for mitigating the variability of isolated hybrid power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1564-1577.
    20. Hammad Alnuman & Daniel Gladwin & Martin Foster, 2018. "Electrical Modelling of a DC Railway System with Multiple Trains," Energies, MDPI, vol. 11(11), pages 1-20, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:9:y:2016:i:3:p:208-:d:65945. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.