IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i17p6458-d906360.html
   My bibliography  Save this article

The Railway Timetable Evaluation Method in Terms of Operational Robustness against Overloads of the Power Supply System

Author

Listed:
  • Franciszek Restel

    (Department of Technical Systems Operation and Maintenance, Faculty of Mechanical Engineering, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

  • Szymon Mateusz Haładyn

    (Department of Technical Systems Operation and Maintenance, Faculty of Mechanical Engineering, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

Abstract

The main aim of this study was to develop a method for assessing the level of robustness of timetabled transport performance in rail transport. When the railway lines are supplied by DC networks, lower voltages are observed, and consequently, current values are often ten times higher than in AC networks. This is an operational problem, as high currents make it easier to overload the supply network. Based on a literature review, the authors show that the problem of running railway traffic when the capacity of the power supply network is limited (by the size of the permitted currents) is not well studied. The authors propose a method based on the Markov approach supplemented by classical theoretical vehicle traffic dynamics to improve the operational robustness of the rail transport system using DC power supply system. Each train run was parameterised in such a way that it is possible to determine the state that the train is in during the run, the transitions between states, and the determination of the probabilities of occurrence of such states. On the other hand, classical vehicle dynamics was used to assess the load generated by the train on the power grid. The proposed method—reduced to a function—was verified using a case study. The method of timetable reconfiguration proposed by the authors increased the operational robustness from 0.9454 to 0.9774.

Suggested Citation

  • Franciszek Restel & Szymon Mateusz Haładyn, 2022. "The Railway Timetable Evaluation Method in Terms of Operational Robustness against Overloads of the Power Supply System," Energies, MDPI, vol. 15(17), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6458-:d:906360
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/17/6458/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/17/6458/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Giuliano Cipolletta & Antonio Delle Femine & Daniele Gallo & Mario Luiso & Carmine Landi, 2021. "Design of a Stationary Energy Recovery System in Rail Transport," Energies, MDPI, vol. 14(9), pages 1-16, April.
    2. 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.
    3. Mihaela Popescu & Alexandru Bitoleanu, 2019. "A Review of the Energy Efficiency Improvement in DC Railway Systems," Energies, MDPI, vol. 12(6), pages 1-25, March.
    4. Donato Morea & Stefano Elia & Chiara Boccaletti & Pasquale Buonadonna, 2021. "Improvement of Energy Savings in Electric Railways Using Coasting Technique," Energies, MDPI, vol. 14(23), pages 1-15, December.
    5. Xuan Lin & Qingyuan Wang & Pengling Wang & Pengfei Sun & Xiaoyun Feng, 2017. "The Energy-Efficient Operation Problem of a Freight Train Considering Long-Distance Steep Downhill Sections," Energies, MDPI, vol. 10(6), pages 1-26, June.
    6. Burggraeve, Sofie & Vansteenwegen, Pieter, 2017. "Robust routing and timetabling in complex railway stations," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 228-244.
    7. Franciszek Restel & Łukasz Wolniewicz & Matea Mikulčić, 2021. "Method for Designing Robust and Energy Efficient Railway Schedules," Energies, MDPI, vol. 14(24), pages 1-12, December.
    8. Longda Wang & Xingcheng Wang & Kaiwei Liu & Zhao Sheng, 2019. "Multi-Objective Hybrid Optimization Algorithm Using a Comprehensive Learning Strategy for Automatic Train Operation," Energies, MDPI, vol. 12(10), pages 1-33, May.
    9. Qiwei Lu & Bangbang He & Mingzhe Wu & Zhichun Zhang & Jiantao Luo & Yankui Zhang & Runkai He & Kunyu Wang, 2018. "Establishment and Analysis of Energy Consumption Model of Heavy-Haul Train on Large Long Slope," Energies, MDPI, vol. 11(4), pages 1-20, April.
    10. Hassini, Elkafi & Verma, Manish, 2016. "Disruption risk management in railroad networks: An optimization-based methodology and a case studyAuthor-Name: Azad, Nader," Transportation Research Part B: Methodological, Elsevier, vol. 85(C), pages 70-88.
    11. 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.
    12. Bing Bu & Guoying Qin & Ling Li & Guojie Li, 2018. "An Energy Efficient Train Dispatch and Control Integrated Method in Urban Rail Transit," Energies, MDPI, vol. 11(5), pages 1-23, May.
    13. Yang, Lixing & Li, Keping & Gao, Ziyou & Li, Xiang, 2012. "Optimizing trains movement on a railway network," Omega, Elsevier, vol. 40(5), pages 619-633.
    14. Agostinho Rocha & Armando Araújo & Adriano Carvalho & João Sepulveda, 2018. "A New Approach for Real Time Train Energy Efficiency Optimization," Energies, MDPI, vol. 11(10), pages 1-21, 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. Artur Kierzkowski & Agnieszka A. Tubis, 2023. "Transportation Systems Modeling, Simulation and Analysis with Reference to Energy Supplying," Energies, MDPI, vol. 16(8), pages 1-6, April.
    2. Antonio Gabaldón & Ana García-Garre & María Carmen Ruiz-Abellón & Antonio Guillamón & Roque Molina & Juan Medina, 2023. "Management of Railway Power System Peaks with Demand-Side Resources: An Application to Periodic Timetables," Sustainability, MDPI, vol. 15(3), pages 1-27, February.

    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. Artur Kierzkowski & Szymon Haładyn, 2022. "Method for Reconfiguring Train Schedules Taking into Account the Global Reduction of Railway Energy Consumption," Energies, MDPI, vol. 15(5), pages 1-18, March.
    2. Mihaela Popescu, 2022. "Energy Efficiency in Electric Transportation Systems," Energies, MDPI, vol. 15(21), pages 1-5, November.
    3. Szymon Haładyn, 2021. "The Problem of Train Scheduling in the Context of the Load on the Power Supply Infrastructure. A Case Study," Energies, MDPI, vol. 14(16), pages 1-19, August.
    4. Guang Yang & Feng Zhang & Cheng Gong & Shiwen Zhang, 2019. "Application of a Deep Deterministic Policy Gradient Algorithm for Energy-Aimed Timetable Rescheduling Problem," Energies, MDPI, vol. 12(18), pages 1-19, September.
    5. Sahil Bhagat & Jacopo Bongiorno & Andrea Mariscotti, 2023. "Influence of Infrastructure and Operating Conditions on Energy Performance of DC Transit Systems," Energies, MDPI, vol. 16(10), pages 1-26, May.
    6. Franciszek Restel & Łukasz Wolniewicz & Matea Mikulčić, 2021. "Method for Designing Robust and Energy Efficient Railway Schedules," Energies, MDPI, vol. 14(24), pages 1-12, December.
    7. Ivan Radaš & Ivan Župan & Viktor Šunde & Željko Ban, 2021. "Route Profile Dependent Tram Regenerative Braking Algorithm with Reduced Impact on the Supply Network," Energies, MDPI, vol. 14(9), pages 1-22, April.
    8. Mihaela Popescu & Alexandru Bitoleanu & Mihaita Linca & Constantin Vlad Suru, 2021. "Improving Power Quality by a Four-Wire Shunt Active Power Filter: A Case Study," Energies, MDPI, vol. 14(7), pages 1-20, April.
    9. 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.
    10. M. Shakibayifar & A. Sheikholeslami & F. Corman & E. Hassannayebi, 2020. "An integrated rescheduling model for minimizing train delays in the case of line blockage," Operational Research, Springer, vol. 20(1), pages 59-87, March.
    11. Maiyar, Lohithaksha M. & Thakkar, Jitesh J., 2019. "Modelling and analysis of intermodal food grain transportation under hub disruption towards sustainability," International Journal of Production Economics, Elsevier, vol. 217(C), pages 281-297.
    12. Canca, David & Barrena, Eva, 2018. "The integrated rolling stock circulation and depot location problem in railway rapid transit systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 115-138.
    13. Aleksandra Kuzior & Marek Staszek, 2021. "Energy Management in the Railway Industry: A Case Study of Rail Freight Carrier in Poland," Energies, MDPI, vol. 14(21), pages 1-21, October.
    14. Svetla Stoilova, 2020. "An Integrated Multi-Criteria and Multi-Objective Optimization Approach for Establishing the Transport Plan of Intercity Trains," Sustainability, MDPI, vol. 12(2), pages 1-24, January.
    15. Li, Jiajie & Bai, Yun & Chen, Yao & Yang, Lingling & Wang, Qian, 2022. "A two-stage stochastic optimization model for integrated tram timetable and speed control with uncertain dwell times," Energy, Elsevier, vol. 260(C).
    16. Oleg Bazaluk & Valerii Havrysh & Mykhailo Fedorchuk & Vitalii Nitsenko, 2021. "Energy Assessment of Sorghum Cultivation in Southern Ukraine," Agriculture, MDPI, vol. 11(8), pages 1-22, July.
    17. Huang, Yeran & Yang, Lixing & Tang, Tao & Gao, Ziyou & Cao, Fang, 2017. "Joint train scheduling optimization with service quality and energy efficiency in urban rail transit networks," Energy, Elsevier, vol. 138(C), pages 1124-1147.
    18. Marcin Steczek & Piotr Chudzik & Adam Szeląg, 2020. "Application of a Non-carrier-Based Modulation for Current Harmonics Spectrum Control during Regenerative Braking of the Electric Vehicle," Energies, MDPI, vol. 13(24), pages 1-21, December.
    19. 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.
    20. Xiaoming Xu & Keping Li & Lixing Yang & Ziyou Gao, 2019. "An efficient train scheduling algorithm on a single-track railway system," Journal of Scheduling, Springer, vol. 22(1), pages 85-105, February.

    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:15:y:2022:i:17:p:6458-:d:906360. 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.