IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i7p1836-d343771.html
   My bibliography  Save this article

Assessment of the Worthwhileness of Efficient Driving in Railway Systems with High-Receptivity Power Supplies

Author

Listed:
  • Alejandro Cunillera

    (Institute for Research in Technology, ICAI School of Engineering, Comillas Pontifical University, 23 Alberto Aguilera Street, 28015 Madrid, Spain)

  • Adrián Fernández-Rodríguez

    (Institute for Research in Technology, ICAI School of Engineering, Comillas Pontifical University, 23 Alberto Aguilera Street, 28015 Madrid, Spain)

  • Asunción P. Cucala

    (Institute for Research in Technology, ICAI School of Engineering, Comillas Pontifical University, 23 Alberto Aguilera Street, 28015 Madrid, Spain)

  • Antonio Fernández-Cardador

    (Institute for Research in Technology, ICAI School of Engineering, Comillas Pontifical University, 23 Alberto Aguilera Street, 28015 Madrid, Spain)

  • Maria Carmen Falvo

    (DIAEE—Electrical Engineering, University of Rome Sapienza, via delle Sette Sale 12b, 00184 Rome, Italy)

Abstract

Eco-driving is one of the most important strategies for significantly reducing the energy consumption of railways with low investments. It consists of designing a way of driving a train to fulfil a target running time, consuming the minimum amount of energy. Most eco-driving energy savings come from the substitution of some braking periods with coasting periods. Nowadays, modern trains can use regenerative braking to recover the kinetic energy during deceleration phases. Therefore, if the receptivity of the railway system to regenerate energy is high, a question arises: is it worth designing eco-driving speed profiles? This paper assesses the energy benefits that eco-driving can provide in different scenarios to answer this question. Eco-driving is obtained by means of a multi-objective particle swarm optimization algorithm, combined with a detailed train simulator, to obtain realistic results. Eco-driving speed profiles are compared with a standard driving that performs the same running time. Real data from Spanish high-speed lines have been used to analyze the results in two case studies. Stretches fed by 1 × 25 kV and 2 × 25 kV AC power supply systems have been considered, as they present high receptivity to regenerate energy. Furthermore, the variations of the two most important factors that affect the regenerative energy usage have been studied: train motors efficiency ratio and catenary resistance. Results indicate that the greater the catenary resistance, the more advantageous eco-driving is. Similarly, the lower the motor efficiency, the greater the energy savings provided by efficient driving. Despite the differences observed in energy savings, the main conclusion is that eco-driving always provides significant energy savings, even in the case of the most receptive power supply network. Therefore, this paper has demonstrated that efforts in improving regenerated energy usage must not neglect the role of eco-driving in railway efficiency.

Suggested Citation

  • Alejandro Cunillera & Adrián Fernández-Rodríguez & Asunción P. Cucala & Antonio Fernández-Cardador & Maria Carmen Falvo, 2020. "Assessment of the Worthwhileness of Efficient Driving in Railway Systems with High-Receptivity Power Supplies," Energies, MDPI, vol. 13(7), pages 1-24, April.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1836-:d:343771
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/7/1836/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/7/1836/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Diego Iannuzzi & Enrico Pagano & Pietro Tricoli, 2013. "The Use of Energy Storage Systems for Supporting the Voltage Needs of Urban and Suburban Railway Contact Lines," Energies, MDPI, vol. 6(4), pages 1-19, March.
    2. 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.
    3. Yang, Lixing & Li, Keping & Gao, Ziyou & Li, Xiang, 2012. "Optimizing trains movement on a railway network," Omega, Elsevier, vol. 40(5), pages 619-633.
    4. Liu, Rongfang (Rachel) & Golovitcher, Iakov M., 2003. "Energy-efficient operation of rail vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(10), pages 917-932, December.
    5. Youneng Huang & Xiao Ma & Shuai Su & Tao Tang, 2015. "Optimization of Train Operation in Multiple Interstations with Multi-Population Genetic Algorithm," Energies, MDPI, vol. 8(12), pages 1-19, December.
    6. Li, Xiang & Lo, Hong K., 2014. "An energy-efficient scheduling and speed control approach for metro rail operations," Transportation Research Part B: Methodological, Elsevier, vol. 64(C), pages 73-89.
    7. Albrecht, Amie & Howlett, Phil & Pudney, Peter & Vu, Xuan & Zhou, Peng, 2018. "The two-train separation problem on non-level track—driving strategies that minimize total required tractive energy subject to prescribed section clearance times," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 135-167.
    8. Adrián Fernández-Rodríguez & Antonio Fernández-Cardador & Asunción P. Cucala & Maria Carmen Falvo, 2019. "Energy Efficiency and Integration of Urban Electrical Transport Systems: EVs and Metro-Trains of Two Real European Lines," Energies, MDPI, vol. 12(3), pages 1-20, January.
    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. 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.
    2. 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.
    3. Mariano Gallo & Marilisa Botte & Antonio Ruggiero & Luca D’Acierno, 2020. "A Simulation Approach for Optimising Energy-Efficient Driving Speed Profiles in Metro Lines," Energies, MDPI, vol. 13(22), pages 1-17, November.

    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. Manuel Blanco-Castillo & Adrián Fernández-Rodríguez & Antonio Fernández-Cardador & Asunción P. Cucala, 2022. "Eco-Driving in Railway Lines Considering the Uncertainty Associated with Climatological Conditions," Sustainability, MDPI, vol. 14(14), pages 1-26, July.
    2. Zhou, Leishan & Tong, Lu (Carol) & Chen, Junhua & Tang, Jinjin & Zhou, Xuesong, 2017. "Joint optimization of high-speed train timetables and speed profiles: A unified modeling approach using space-time-speed grid networks," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 157-181.
    3. Ye, Hongbo & Liu, Ronghui, 2016. "A multiphase optimal control method for multi-train control and scheduling on railway lines," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 377-393.
    4. 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.
    5. Scheepmaker, Gerben M. & Goverde, Rob M.P. & Kroon, Leo G., 2017. "Review of energy-efficient train control and timetabling," European Journal of Operational Research, Elsevier, vol. 257(2), pages 355-376.
    6. Ning, Jingjie & Zhou, Yonghua & Long, Fengchu & Tao, Xin, 2018. "A synergistic energy-efficient planning approach for urban rail transit operations," Energy, Elsevier, vol. 151(C), pages 854-863.
    7. Wang, Pengling & Goverde, Rob M.P., 2019. "Multi-train trajectory optimization for energy-efficient timetabling," European Journal of Operational Research, Elsevier, vol. 272(2), pages 621-635.
    8. 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.
    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. 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).
    11. 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.
    12. Canca, David & Zarzo, Alejandro, 2017. "Design of energy-Efficient timetables in two-way railway rapid transit lines," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 142-161.
    13. 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.
    14. Luan, Xiaojie & Wang, Yihui & De Schutter, Bart & Meng, Lingyun & Lodewijks, Gabriel & Corman, Francesco, 2018. "Integration of real-time traffic management and train control for rail networks - Part 2: Extensions towards energy-efficient train operations," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 72-94.
    15. 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.
    16. Wang, Jinghui & Rakha, Hesham A., 2017. "Electric train energy consumption modeling," Applied Energy, Elsevier, vol. 193(C), pages 346-355.
    17. Yin, Jiateng & Yang, Lixing & Tang, Tao & Gao, Ziyou & Ran, Bin, 2017. "Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: Mixed-integer linear programming approaches," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 182-213.
    18. Wang, Xuekai & Tang, Tao & Su, Shuai & Yin, Jiateng & Gao, Ziyou & Lv, Nan, 2021. "An integrated energy-efficient train operation approach based on the space-time-speed network methodology," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    19. Gupta, Shuvomoy Das & Tobin, J. Kevin & Pavel, Lacra, 2016. "A two-step linear programming model for energy-efficient timetables in metro railway networks," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 57-74.
    20. Wang, Chao & Meng, Xin & Guo, Mingxue & Li, Hao & Hou, Zhiqiang, 2022. "An integrated energy-efficient and transfer-accessible model for the last train timetabling problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).

    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:13:y:2020:i:7:p:1836-:d:343771. 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.