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Pareto multi-objective optimization of metro train energy-saving operation using improved NSGA-II algorithms

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  • Zhang, Zhenyu
  • Cheng, Xiaoqing
  • Xing, Zongyi
  • Gui, Xingdong

Abstract

Quantitative analysis of the factors affecting the energy consumption of metro trains and finding out the breakthrough points of energy conservation is of great practical significance for reducing transport costs and improving energy utilization. Train operation curve optimization is one of the main methods to reduce the energy consumption of train operation. This paper introduces the metro train energy-saving operation optimization model with fixed travel time. Firstly, taking the energy consumption index and punctuality index as the objectives, a train energy-saving operation optimization model is established. Then, the solution method based on the improved NSGA-II algorithm is developed. Finally, the developed optimization model is applied to Guangzhou Metro Line 7 to verify the performance. The results show that the energy consumption of the developed operation strategy is 24.4 % less than that of the minimum travel time strategy, and the total operation time meets the punctuality requirements. Meanwhile, the improved NSGA-II algorithm has less energy consumption than GA and PSO algorithms in the application of Guangzhou Metro Line 7.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923010858
    DOI: 10.1016/j.chaos.2023.114183
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    References listed on IDEAS

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    1. 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).
    2. Kai-wei Liu & Xing-Cheng Wang & Zhi-hui Qu, 2019. "Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation," Energies, MDPI, vol. 12(20), pages 1-22, October.
    3. Kasin Ransikarbum & Scott J. Mason, 2022. "A bi-objective optimisation of post-disaster relief distribution and short-term network restoration using hybrid NSGA-II algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 60(19), pages 5769-5793, October.
    4. Aminmahalati, Alireza & Fazlali, Alireza & Safikhani, Hamed, 2021. "Multi-objective optimization of CO boiler combustion chamber in the RFCC unit using NSGA II algorithm," Energy, Elsevier, vol. 221(C).
    5. Donato Morea & Stefano Elia & Chiara Boccaletti & Pasquale Buonadonna, 2021. "Improvement of Energy Savings in Electric Railways Using Coasting Technique," Energies, MDPI, vol. 14(23), pages 1-15, December.
    6. 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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