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A New Approach for Real Time Train Energy Efficiency Optimization

Author

Listed:
  • Agostinho Rocha

    (Department of Electrical and Computer Engineering, Engineering Faculty, University of Porto, 4200-465 Porto, Portugal)

  • Armando Araújo

    (Department of Electrical and Computer Engineering, Engineering Faculty, University of Porto, 4200-465 Porto, Portugal)

  • Adriano Carvalho

    (Department of Electrical and Computer Engineering, Engineering Faculty, University of Porto, 4200-465 Porto, Portugal)

  • João Sepulveda

    (Department of Industrial Electronics, University of Minho, 4800-058 Braga, Portugal)

Abstract

Efficient use of energy is currently a very important issue. As conventional energy resources are limited, improving energy efficiency is, nowadays, present in any government policy. Railway systems consume a huge amount of energy, during normal operation, some routes working near maximum energy capacity. Therefore, maximizing energy efficiency in railway systems has, recently, received attention from railway operators, leading to research for new solutions that are able to reduce energy consumption without timetable constraints. In line with these goals, this paper proposes a Simulated Annealing optimization algorithm that minimizes train traction energy, constrained to existing timetable. For computational effort minimization, re-annealing is not used, the maximum number of iterations is one hundred, and generation of cruising and braking velocities is carefully made. A Matlab implementation of the Simulated Annealing optimization algorithm determines the best solution for the optimal speed profile between stations. It uses a dynamic model of the train for energy consumption calculations. Searching for optimal speed profile, as well as scheduling constraints, also uses line shape and velocity limits. As results are obtained in seconds, this new algorithm can be used as a real-time driver advisory system for energy saving and railway capacity increase. For now, a standalone version, with line data previously loaded, was developed. Comparison between algorithm results and real data, acquired in a railway line, proves its success. An implementation of the developed work as a connected driver advisory system, enabling scheduling and speed constraint updates in real time, is currently under development.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2660-:d:173906
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    References listed on IDEAS

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    1. Youneng Huang & Chen Yang & Shaofeng Gong, 2016. "Energy Optimization for Train Operation Based on an Improved Ant Colony Optimization Methodology," Energies, MDPI, vol. 9(8), pages 1-18, August.
    2. 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.
    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. 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.
    5. Rui Marques Chibante (ed.), 2010. "Simulated Annealing, Theory with Applications," Books, IntechOpen, number 780, January-J.
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    5. Szymon Haładyn, 2021. "The Problem of Train Scheduling in the Context of the Load on the Power Supply Infrastructure. A Case Study," Energies, MDPI, vol. 14(16), pages 1-19, August.
    6. Franciszek Restel & Szymon Mateusz Haładyn, 2022. "The Railway Timetable Evaluation Method in Terms of Operational Robustness against Overloads of the Power Supply System," Energies, MDPI, vol. 15(17), pages 1-17, September.
    7. Artur Kierzkowski & Szymon Haładyn, 2022. "Method for Reconfiguring Train Schedules Taking into Account the Global Reduction of Railway Energy Consumption," Energies, MDPI, vol. 15(5), pages 1-18, March.
    8. Guang Yang & Feng Zhang & Cheng Gong & Shiwen Zhang, 2019. "Application of a Deep Deterministic Policy Gradient Algorithm for Energy-Aimed Timetable Rescheduling Problem," Energies, MDPI, vol. 12(18), pages 1-19, September.
    9. Iosvany López-Sandin & Guadalupe Gutiérrez-Soto & Adriana Gutiérrez-Díez & Nancy Medina-Herrera & Edgar Gutiérrez-Castorena & Francisco Zavala-García, 2019. "Evaluation of the Use of Energy in the Production of Sweet Sorghum ( Sorghum Bicolor (L.) Moench) under Different Production Systems," Energies, MDPI, vol. 12(9), pages 1-13, May.

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