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A synergistic energy-efficient planning approach for urban rail transit operations

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  • Ning, Jingjie
  • Zhou, Yonghua
  • Long, Fengchu
  • Tao, Xin

Abstract

Large-scale development of urban rail transit has attracted attention owing to its sizable energy consumption. Energy-efficient planning can enhance the distribution and utilization of limited energy resources. This study proposes a two-stage urban rail transit operation planning approach comprising running time allocation and regenerative energy utilization to save energy consumption. The proposed models and algorithms holistically deal with inter-station running time synergy which utilizes surplus running time to achieve minimum energy consumption. They also implement the hauling and braking synergy of multiple trains in multiple trips, with adjustments to departure intervals and dwelling times, to maximize the regenerative energy real-time utilization rate. The algorithms utilize chromosomes in genetic algorithm to represent possible operation stage combinations, conduct feasible direction iterations to facilitate surplus-time effective allocations, and maximize the derived overlap time for operation synergy of trains under the precondition of energy-efficient train movements between stations. A case study of the metro line demonstrates that considerable energy saving is achievable through the proposed planning approach.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:151:y:2018:i:c:p:854-863
    DOI: 10.1016/j.energy.2018.03.111
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    Cited by:

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    5. 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).
    6. 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).
    7. 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).
    8. Lian, Deheng & Mo, Pengli & D’Ariano, Andrea & Gao, Ziyou & Yang, Lixing, 2024. "Energy-saving time allocation strategy with uncertain dwell times in urban rail transit: Two-stage stochastic model and nested dynamic programming framework," European Journal of Operational Research, Elsevier, vol. 317(1), pages 219-242.
    9. 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).

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