IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i10p1876-d231858.html
   My bibliography  Save this article

A Dual-Objective Substation Energy Consumption Optimization Problem in Subway Systems

Author

Listed:
  • Hongjie Liu

    (School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
    State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

  • Tao Tang

    (School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
    State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

  • Jidong Lv

    (School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
    State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

  • Ming Chai

    (School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
    State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Maximizing regenerative energy utilization is an important way to reduce substation energy consumption in subway systems. Timetable optimization and energy storage systems are two main ways to improve improve regenerative energy utilization, but they were studied separately in the past. To further improve energy conservation while maintaining a low cost, this paper presents a strategy to improve regenerative energy utilization by an integration of them, which determines the capacity of each Wayside Energy Storage System (WESS) and correspondingly optimizes the timetable at the same time. We first propose a dual-objective optimization problem to simultaneously minimize substation energy consumption and the total cost of WESS. Then, a mathematical model is formulated with the decision variables as the configuration of WESS and timetable. Afterwards, we design an ϵ -constraint method to transform the dual-objective optimization problem into several single-objective optimization problems, and accordingly design an improved artificial bee colony algorithm to solve them sequentially. Finally, numerical examples based on the actual data from a subway system in China are conducted to show the effectiveness of the proposed method. Experimental results indicate that substation energy consumption is effectively reduced by using WESS together with a correspondingly optimized timetable. Note that substation energy consumption becomes lower when the total size of WESS is larger, and timetable optimization further reduces it. A set of Pareto optimal solutions is obtained for the experimental subway line—based on which, decision makers can make a sensible trade-off between energy conservation and WESS investment accordingly to their preferences.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1876-:d:231858
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/10/1876/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/10/1876/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bérubé, Jean-François & Gendreau, Michel & Potvin, Jean-Yves, 2009. "An exact [epsilon]-constraint method for bi-objective combinatorial optimization problems: Application to the Traveling Salesman Problem with Profits," European Journal of Operational Research, Elsevier, vol. 194(1), pages 39-50, 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. 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. Peter Reiter & Walter Gutjahr, 2012. "Exact hybrid algorithms for solving a bi-objective vehicle routing problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(1), pages 19-43, March.
    5. Li, Xiang & Lo, Hong K., 2014. "Energy minimization in dynamic train scheduling and control for metro rail operations," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 269-284.
    6. Iver Bakken Sperstad & Magnus Korpås, 2019. "Energy Storage Scheduling in Distribution Systems Considering Wind and Photovoltaic Generation Uncertainties," Energies, MDPI, vol. 12(7), pages 1-24, March.
    7. 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.
    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. Seunghyun Park & Surender Reddy Salkuti, 2019. "Optimal Energy Management of Railroad Electrical Systems with Renewable Energy and Energy Storage Systems," Sustainability, MDPI, vol. 11(22), pages 1-16, 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. 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.
    2. 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.
    3. 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.
    4. 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.
    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. 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.
    7. Andreas Bärmann & Alexander Martin & Oskar Schneider, 2021. "Efficient Formulations and Decomposition Approaches for Power Peak Reduction in Railway Traffic via Timetabling," Transportation Science, INFORMS, vol. 55(3), pages 747-767, May.
    8. 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.
    9. Ramos, Tânia Rodrigues Pereira & Gomes, Maria Isabel & Barbosa-Póvoa, Ana Paula, 2014. "Planning a sustainable reverse logistics system: Balancing costs with environmental and social concerns," Omega, Elsevier, vol. 48(C), pages 60-74.
    10. 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).
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Luca D’Acierno & Marilisa Botte, 2018. "A Passenger-Oriented Optimization Model for Implementing Energy-Saving Strategies in Railway Contexts," Energies, MDPI, vol. 11(11), pages 1-25, October.
    17. 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.
    18. 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.
    19. 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.
    20. De Martinis, Valerio & Weidmann, Ulrich A., 2015. "Definition of energy-efficient speed profiles within rail traffic by means of supply design models," Research in Transportation Economics, Elsevier, vol. 54(C), pages 41-50.

    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:12:y:2019:i:10:p:1876-:d:231858. 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.