IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v258y2022ics0360544222017510.html
   My bibliography  Save this article

Energy consumption optimization of tramway operation based on improved PSO algorithm

Author

Listed:
  • Xing, Zongyi
  • Zhu, Junlin
  • Zhang, Zhenyu
  • Qin, Yong
  • Jia, Limin

Abstract

Tramway has the advantages of reliable operation, energy-saving, and environmental protection, which is an important way to alleviate traffic pressure. Energy consumption optimization of tramway operation is significant in reducing the operation cost. Therefore, the energy consumption optimization model of tramway operation based on the improved PSO algorithm is developed. Firstly, the energy consumption model of the tramway is established based on the energy-saving operation strategy. Then, the competition mechanism-based particle swarm optimization (CM-PSO) algorithm is developed to improve energy-saving performance. Finally, the Guangzhou Haizhu tramway is taken as an example to illustrate the effectiveness of the developed method. The results show that the developed model effectively reduces the tramway operation energy consumption and performs better than the previous methods.

Suggested Citation

  • Xing, Zongyi & Zhu, Junlin & Zhang, Zhenyu & Qin, Yong & Jia, Limin, 2022. "Energy consumption optimization of tramway operation based on improved PSO algorithm," Energy, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:energy:v:258:y:2022:i:c:s0360544222017510
    DOI: 10.1016/j.energy.2022.124848
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222017510
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124848?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. He, Deqiang & Yang, Yanjie & Chen, Yanjun & Deng, Jianxin & Shan, Sheng & Liu, Jianren & Li, Xianwang, 2020. "An integrated optimization model of metro energy consumption based on regenerative energy and passenger transfer," Applied Energy, Elsevier, vol. 264(C).
    2. Omar Hazem Mohammed & Mohammed Kharrich, 2021. "An Overview of the Performance of PSO Algorithm in Renewable Energy Systems," International Series in Operations Research & Management Science, in: Burcu Adıgüzel Mercangöz (ed.), Applying Particle Swarm Optimization, edition 1, chapter 0, pages 307-320, Springer.
    3. 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.
    4. 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.
    5. Canan G. Corlu & Rocio de la Torre & Adrian Serrano-Hernandez & Angel A. Juan & Javier Faulin, 2020. "Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities," Energies, MDPI, vol. 13(5), pages 1-33, March.
    6. Kangge Zou & Yanmin Liu & Shihua Wang & Nana Li & Yaowei Wu & Nan-Jing Huang, 2021. "A Multiobjective Particle Swarm Optimization Algorithm Based on Grid Technique and Multistrategy," Journal of Mathematics, Hindawi, vol. 2021, pages 1-17, December.
    7. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Zhang, Jiye & Song, Pengyun, 2019. "Modeling and optimal energy management strategy for a catenary-battery-ultracapacitor based hybrid tramway," Energy, Elsevier, vol. 183(C), pages 1123-1135.
    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. Namala Narasimhulu & Mohan Awasthy & Rocío Pérez de Prado & Parameshachari Bidare Divakarachari & Nadimapalli Himabindu, 2023. "Analysis and Impacts of Grid Integrated Photo-Voltaic and Electric Vehicle on Power Quality Issues," Energies, MDPI, vol. 16(2), pages 1-18, January.
    2. Marcin Relich & Arkadiusz Gola & Małgorzata Jasiulewicz-Kaczmarek, 2022. "Identifying Improvement Opportunities in Product Design for Reducing Energy Consumption," Energies, MDPI, vol. 15(24), pages 1-19, December.
    3. Xing, Zongyi & Zhang, Zhenyu & Guo, Jian & Qin, Yong & Jia, Limin, 2023. "Rail train operation energy-saving optimization based on improved brute-force search," Applied Energy, Elsevier, vol. 330(PA).
    4. Elnaz Ghorbani & Tristan Fluechter & Laura Calvet & Majsa Ammouriova & Javier Panadero & Angel A. Juan, 2023. "Optimizing Energy Consumption in Smart Cities’ Mobility: Electric Vehicles, Algorithms, and Collaborative Economy," Energies, MDPI, vol. 16(3), pages 1-19, January.
    5. Yingjie Zhu & Jiageng Ma & Fangqing Gu & Jie Wang & Zhijuan Li & Youyao Zhang & Jiani Xu & Yifan Li & Yiwen Wang & Xiangqun Yang, 2023. "Price Prediction of Bitcoin Based on Adaptive Feature Selection and Model Optimization," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    6. Arsalan Raza & Muhammad Umair Ali & Ubaid Ullah & Muhammad Fayaz & Muhammad Junaid Alvi & Karam Dad Kallu & Amad Zafar & Sarvar Hussain Nengroo, 2022. "Evaluation of a Sustainable Urban Transportation System in Terms of Traffic Congestion—A Case Study in Taxila, Pakistan," Sustainability, MDPI, vol. 14(19), pages 1-25, September.
    7. Zhang, Zhenyu & Cheng, Xiaoqing & Xing, Zongyi & Gui, Xingdong, 2023. "Pareto multi-objective optimization of metro train energy-saving operation using improved NSGA-II algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    8. Robert Pietracho & Christoph Wenge & Przemyslaw Komarnicki & Leszek Kasprzyk, 2022. "Multi-Criterial Assessment of Electric Vehicle Integration into the Commercial Sector—A Case Study," Energies, MDPI, vol. 16(1), pages 1-29, December.

    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. Zhang, Lang & He, Deqiang & He, Yan & Liu, Bin & Chen, Yanjun & Shan, Sheng, 2022. "Real-time energy saving optimization method for urban rail transit train timetable under delay condition," Energy, Elsevier, vol. 258(C).
    2. Xing, Zongyi & Zhang, Zhenyu & Guo, Jian & Qin, Yong & Jia, Limin, 2023. "Rail train operation energy-saving optimization based on improved brute-force search," Applied Energy, Elsevier, vol. 330(PA).
    3. Li, Wenxin & Peng, Qiyuan & Wen, Chao & Wang, Pengling & Lessan, Javad & Xu, Xinyue, 2020. "Joint optimization of delay-recovery and energy-saving in a metro system: A case study from China," Energy, Elsevier, vol. 202(C).
    4. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Deng, Pengyi & Wu, Xiaohua & Zhang, Jiye, 2022. "Hierarchical energy management of a hybrid propulsion system considering speed profile optimization," Energy, Elsevier, vol. 244(PB).
    5. Rafidah Md Noor & Nadia Bella Gustiani Rasyidi & Tarak Nandy & Raenu Kolandaisamy, 2020. "Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study," Sustainability, MDPI, vol. 13(1), pages 1-24, December.
    6. Yuan, Weichang & Frey, H. Christopher, 2020. "Potential for metro rail energy savings and emissions reduction via eco-driving," Applied Energy, Elsevier, vol. 268(C).
    7. Häckel, Björn & Pfosser, Stefan & Tränkler, Timm, 2017. "Explaining the energy efficiency gap - Expected Utility Theory versus Cumulative Prospect Theory," Energy Policy, Elsevier, vol. 111(C), pages 414-426.
    8. Kapetanović, Marko & Núñez, Alfredo & van Oort, Niels & Goverde, Rob M.P., 2021. "Reducing fuel consumption and related emissions through optimal sizing of energy storage systems for diesel-electric trains," Applied Energy, Elsevier, vol. 294(C).
    9. 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.
    10. 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.
    11. Zhan, Shuguang & Wang, Pengling & Wong, S.C. & Lo, S.M., 2022. "Energy-efficient high-speed train rescheduling during a major disruption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    12. Hartleb, Johann & Schmidt, Marie, 2022. "Railway timetabling with integrated passenger distribution," European Journal of Operational Research, Elsevier, vol. 298(3), pages 953-966.
    13. Adrian Serrano-Hernandez & Aitor Ballano & Javier Faulin, 2021. "Selecting Freight Transportation Modes in Last-Mile Urban Distribution in Pamplona (Spain): An Option for Drone Delivery in Smart Cities," Energies, MDPI, vol. 14(16), pages 1-17, August.
    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.
    15. 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.
    16. Mo, Pengli & D’Ariano, Andrea & Yang, Lixing & Veelenturf, Lucas P. & Gao, Ziyou, 2021. "An exact method for the integrated optimization of subway lines operation strategies with asymmetric passenger demand and operating costs," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 283-321.
    17. 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.
    18. Luijt, Ralph S. & van den Berge, Maarten P.F. & Willeboordse, Helen Y. & Hoogenraad, Jan H., 2017. "5years of Dutch eco-driving: Managing behavioural change," Transportation Research Part A: Policy and Practice, Elsevier, vol. 98(C), pages 46-63.
    19. Wang, Xuekai & D’Ariano, Andrea & Su, Shuai & Tang, Tao, 2023. "Cooperative train control during the power supply shortage in metro system: A multi-agent reinforcement learning approach," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 244-278.
    20. Yibo Deng & Chushan Li & Yan Deng & Ting Chen & Shaoyu Feng & Yujie Chu & Chengmin Li, 2023. "Energy Efficiency Optimization of Collaborative Power Supply System with Supercapacitor Storages," Energies, MDPI, vol. 16(3), pages 1-15, January.

    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:eee:energy:v:258:y:2022:i:c:s0360544222017510. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.