IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i2p105-d63760.html
   My bibliography  Save this article

Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model

Author

Listed:
  • Shuai Su

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, No.3, Shangyuncun, Haidian District, Beijing 100044, China)

  • Tao Tang

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, No.3, Shangyuncun, Haidian District, Beijing 100044, China)

  • Yihui Wang

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, No.3, Shangyuncun, Haidian District, Beijing 100044, China)

Abstract

Increasing attention is being paid to the energy efficiency in metro systems to reduce the operational cost and to advocate the sustainability of railway systems. Classical research has studied the energy-efficient operational strategy and the energy-efficient system design separately to reduce the traction energy consumption. This paper aims to combine the operational strategies and the system design by analyzing how the infrastructure and vehicle parameters of metro systems influence the operational traction energy consumption. Firstly, a solution approach to the optimal train control model is introduced, which is used to design the Optimal Train Control Simulator(OTCS). Then, based on the OTCS, the performance of some important energy-efficient system design strategies is investigated to reduce the trains’ traction energy consumption, including reduction of the train mass, improvement of the kinematic resistance, the design of the energy-saving gradient, increasing the maximum traction and braking forces, introducing regenerative braking and timetable optimization. As for these energy-efficient strategies, the performances are finally evaluated using the OTCS with the practical operational data of the Beijing Yizhuang metro line. The proposed approach gives an example to quantitatively analyze the energy reduction of different strategies in the system design procedure, which may help the decision makers to have an overview of the energy-efficient performances and then to make decisions by balancing the costs and the benefits.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:2:p:105-:d:63760
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/2/105/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/9/2/105/
    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. 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. 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.
    4. 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. Mikołaj Bartłomiejczyk, 2018. "Potential Application of Solar Energy Systems for Electrified Urban Transportation Systems," Energies, MDPI, vol. 11(4), pages 1-17, April.
    2. Arkadiusz Kampczyk & Wojciech Gamon & Katarzyna Gawlak, 2023. "Integration of Traction Electricity Consumption Determinants with Route Geometry and Vehicle Characteristics," Energies, MDPI, vol. 16(6), pages 1-23, March.
    3. Maryna Bulakh & Leszek Klich & Oleksandra Baranovska & Anastasiia Baida & Sergiy Myamlin, 2023. "Reducing Traction Energy Consumption with a Decrease in the Weight of an All-Metal Gondola Car," Energies, MDPI, vol. 16(18), pages 1-12, September.
    4. 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.
    5. 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.
    6. Hongjie Liu & Tao Tang & Jidong Lv & Ming Chai, 2019. "A Dual-Objective Substation Energy Consumption Optimization Problem in Subway Systems," Energies, MDPI, vol. 12(10), pages 1-28, May.
    7. 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.
    8. Petru Valentin Radu & Miroslaw Lewandowski & Adam Szelag, 2020. "On-Board and Wayside Energy Storage Devices Applications in Urban Transport Systems—Case Study Analysis for Power Applications," Energies, MDPI, vol. 13(8), pages 1-29, April.
    9. Napp, T.A. & Few, S. & Sood, A. & Bernie, D. & Hawkes, A. & Gambhir, A., 2019. "The role of advanced demand-sector technologies and energy demand reduction in achieving ambitious carbon budgets," Applied Energy, Elsevier, vol. 238(C), pages 351-367.
    10. 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.
    11. 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).
    12. Huang, Yu & Zhou, Wenliang & Qin, Jin & Deng, Lianbo, 2023. "Optimization of energy-efficiency train schedule considering passenger demand and rolling stock circulation plan of subway line," Energy, Elsevier, vol. 275(C).
    13. 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.
    14. Zhou, Wenliang & Huang, Yu & Deng, Lianbo & Qin, Jin, 2023. "Collaborative optimization of energy-efficient train schedule and train circulation plan for urban rail," Energy, Elsevier, vol. 263(PA).
    15. Feng, Jia & Li, Xiamiao & Mao, Baohua & Xu, Qi & Bai, Yun, 2017. "Weighted complex network analysis of the Beijing subway system: Train and passenger flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 213-223.
    16. 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.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Hongjie Liu & Tao Tang & Jidong Lv & Ming Chai, 2019. "A Dual-Objective Substation Energy Consumption Optimization Problem in Subway Systems," Energies, MDPI, vol. 12(10), pages 1-28, May.
    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. Felipe Jiménez & Wilmar Cabrera-Montiel, 2014. "System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information," Energies, MDPI, vol. 7(6), pages 1-23, June.
    11. Ziyu Wu & Chunhai Gao & Tao Tang, 2021. "An Optimal Train Speed Profile Planning Method for Induction Motor Traction System," Energies, MDPI, vol. 14(16), pages 1-14, August.
    12. 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).
    13. 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.
    14. 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.
    15. Mariano Gallo & Mario Marinelli, 2020. "Sustainable Mobility: A Review of Possible Actions and Policies," Sustainability, MDPI, vol. 12(18), pages 1-39, September.
    16. 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.
    17. Timur Yunusov & Maximilian J. Zangs & William Holderbaum, 2017. "Control of Energy Storage," Energies, MDPI, vol. 10(7), pages 1-5, July.
    18. 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.
    19. 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.
    20. 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.

    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:2:p:105-:d:63760. 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.