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access icon free Improving energy-efficient train operation in urban railways: employing the variation of regenerative energy recovery rate

© The Institution of Engineering and Technology
Received 11/10/2016, Accepted 20/05/2017, Revised 10/05/2017, Published 25/05/2017

Energy-efficient train operation is of high importance in urban railways. It takes into account both energy saving and punctuality at the same time, which is the goal of the most operators. In this study, the effect of employing variable regenerative energy recovery rate (RERR) for each inter-station was shown in energy-efficient operation improvement. For this purpose, a two-stage optimisation was proposed. In the first stage, which is a mechanical optimisation, optimal speed profiles for a single train were determined by a bi-objective optimisation through a non-dominated sorting algorithm. In this stage, a simulation model for determination of train's performance was developed and utilised. It was shown that variable RERRs eventually led to different energy-time Pareto fronts. In the second stage, the total input energy for a multi-train system was minimised by using obtained optimal speed profiles from the first stage and distributing total travelling time among inter-stations (electrical/electromechanical optimisation). It was shown that optimisation of the net energy – with different values of RERR – instead of consumed energy, can reduce the total input energy of the network. The simulation results, which are based on actual operating data of Mashhad urban railway system, confirm the feasibility and effectiveness of the proposed method.

Inspec keywords: railway electrification; energy conservation; sorting; Pareto optimisation

Other keywords: energy-time Pareto fronts; electrical optimisation; Mashhad urban railway system; two-stage optimisation; energy-efficient train operation; nondominated sorting algorithm; RERR; train performance determination; optimal speed profiles; regenerative energy recovery rate variation; bi-objective optimisation; net energy optimisation; multitrain system; energy saving; electromechanical optimisation; mechanical optimisation

Subjects: Rail transportation (energy utilisation); Transportation; Optimisation techniques; Combinatorial mathematics

References

    1. 1)
      • 9. Bocharnikov, Y.V., Tobias, A.M., Roberts, C., et al: ‘Optimal driving strategy for traction energy saving on DC suburban railways’, IET Elect. Power Appl., 2007, 1, (5), pp. 675–682.
    2. 2)
      • 31. Scheepmaker, G.M., Goverde, R.M., Kroon, L.G.: ‘Review of energy-efficient train control and timetabling’, Eur. J. Oper. Res., 2017, 257, (2), pp. 355–376.
    3. 3)
      • 17. Yin, J., Chen, D., Li, L.: ‘Intelligent train operation algorithms for subway by expert system and reinforcement learning’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (6), pp. 2561–2571.
    4. 4)
      • 15. Domínguez, M., Fernández-Cardador, A., Cucala, A.P., et al: ‘Energy savings in metropolitan railway substations through regenerative energy recovery and optimal design of ATO speed profiles’, IEEE Trans. Autom. Sci. Eng., 2012, 9, (3), pp. 496–504.
    5. 5)
      • 5. Su, S., Tang, T., Li, X., et al: ‘Optimization on multi-train operation in subway system’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (2), pp. 673–684.
    6. 6)
      • 13. Ke, B.R., Lin, C.L., Yang, C.C.: ‘Optimisation of train energy-efficient operation for mass rapid transit systems’, IET Intell. Transp. Syst., 2012, 6, (1), pp. 58–66.
    7. 7)
      • 23. Yang, X., Chen, A., Ning, B., et al: ‘Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty’, Transp. Res. E Logist. Transp. Rev., 2017, 97, pp. 22–37.
    8. 8)
      • 2. Miyatake, M., Ko, H.: ‘Optimization of train speed profile for minimum energy consumption’, IEEJ Trans. Elect. Electron., 2010, 5, (3), pp. 263–269.
    9. 9)
      • 26. Fernández-Rodríguez, A., Fernández-Cardador, A., Cucala, A.P., et al: ‘Design of robust and energy-efficient ATO speed profiles of metropolitan lines considering train load variations and delays’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (4), pp. 2061–2071.
    10. 10)
      • 18. Chevrier, R., Pellegrini, P., Rodriguez, J.: ‘Energy saving in railway timetabling: a bi-objective evolutionary approach for computing alternative running times’, Transp. Res. C, 2013, 37, pp. 20–41.
    11. 11)
      • 6. Scheepmaker, G.M., Goverde, R.M.P.: ‘The interplay between energy-efficient train control and scheduled running time supplements’, J. Rail Transp. Plan. Manag., 2015, 5, (4), pp. 225–239.
    12. 12)
      • 35. Wang, Y., Ning, B., van den Boom, T., et al: ‘Optimal trajectory planning and train scheduling for urban rail transit systems’, in Grimble, M.J., Johnson, M.A. (Eds.): ‘Advances in industrial control’ (Springer, 2016).
    13. 13)
      • 30. Yang, X., Chen, A., Li, X., et al: ‘An energy-efficient scheduling approach to improve the utilization of regenerative energy for metro systems’, Transp. Res. C Emerg. Technol., 2015, 57, pp. 13–29.
    14. 14)
      • 37. Ahmadi, S., Dastfan, A.: ‘Energy saving in urban railway using speed profile optimization’. 24th Iranian Conf. Electrical Engineering (ICEE), Tehran, Iran, May 2016, pp. 1076–1081.
    15. 15)
      • 10. Acikbas, S., Soylemez, M.: ‘Coasting point optimisation for mass rail transit lines using artificial neural networks and genetic algorithms’, IET Elect. Power Appl., 2008, 2, (3), pp. 172–182.
    16. 16)
      • 11. Goodwin, J.C., Fletcher, D.I., Harrison, R.F.: ‘Multi-train trajectory optimisation to maximise rail network energy efficiency under travel-time constraints’, Proc. Inst. Mech. Eng. F J. Rail Rapid Transit., 2015, 230, (4), pp. 1318–1335.
    17. 17)
      • 28. ShangGuan, W., Yan, X.H., Cai, B.G., et al: ‘Multi-objective optimization for train speed trajectory in CTCS high-speed railway with hybrid evolutionary algorithm’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (4), pp. 2215–2225.
    18. 18)
      • 16. Lu, S., Hillmansen, S., Kin Ho, T., et al: ‘Single-train trajectory optimization’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (2), pp. 743–750.
    19. 19)
      • 7. Su, S., Tang, T., Roberts, C.: ‘A cooperative train control model for energy saving’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (2), pp. 622–631.
    20. 20)
      • 1. Howlett, P.G., Pudney, P.J., Vu, X.: ‘Local energy minimization in optimal train control’, Automatica, 2009, 45, (11), pp. 2692–2698.
    21. 21)
      • 8. Albrecht, A., Howlett, P., Pudney, P., et al: ‘The key principles of optimal train control- Part 1: formulation of the model, strategies of optimal type, evolutionary lines, location of optimal switching points’, Transp. Res B Methodol., 2015, 94, pp. 482–508.
    22. 22)
      • 36. Wolf, K.: ‘Dynamic performance LRV Mashhad’. Rep. Doc: 4046228.00.07, ELIN EBG Traction GmbH, 2009.
    23. 23)
      • 14. Kim, K., Chien, S.: ‘Optimal train operation for minimum energy consumption considering track alignment, speed limit, and schedule adherence’, J. Transp. Eng., 2011, 137, (9), pp. 665–674.
    24. 24)
      • 27. Huang, J., Deng, Y., Yang, Q., et al: ‘An energy-efficient train control framework for smart railway transportation’, IEEE Trans. Comput., 2016, 65, (5), pp. 1407–1417.
    25. 25)
      • 33. Lejeune, A., Chevrier, R., Vandanjon, P.O., et al: ‘Towards eco-aware timetabling: evolutionary approach and cascading initialisation strategy for the bi-objective optimisation of train running times’, IET Intell. Transp. Syst., 2016, 10, (7), pp. 483–494.
    26. 26)
      • 3. Rodrigo, E., Tapia, S., Mera, J.M., et al: ‘Optimizing electric rail energy consumption using the Lagrange multiplier technique’, J. Transp. Eng., 2013, 139, (3), pp. 321–329.
    27. 27)
      • 32. Yang, X., Li, X., Ning, B., et al: ‘A survey on energy-efficient train operation for urban rail transit’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (1), pp. 2–13.
    28. 28)
      • 34. Vuchic, V.R.: ‘Urban transit systems and technology’ (John Wiley & Sons, 2007).
    29. 29)
      • 22. Yang, X., Ning, B., Li, X., et al: ‘A two-objective timetable optimization model in subway systems’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (5), pp. 1913–1921.
    30. 30)
      • 29. Yang, X., Li, X., Ning, B., et al: ‘An optimisation method for train scheduling with minimum energy consumption and travel time in metro rail systems’, Transportmetrica B: Transp. Dyn., 2015, 3, (2), pp. 79–98.
    31. 31)
      • 25. Liu, J., Cai, B., Xu, G., et al: ‘Energy consumption modeling and optimization of traction control for high-speed railway trains’, Int. J. Control Autom., 2015, 8, (10), pp. 109–124.
    32. 32)
      • 4. Gu, Q., Tang, T., Cao, F., et al: ‘Energy-efficient train operation in urban rail transit using real-time traffic information’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (3), pp. 1216–1233.
    33. 33)
      • 12. Yang, X., Li, X., Gao, Z., et al: ‘A cooperative scheduling model for timetable optimization in subway systems’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (1), pp. 438–447.
    34. 34)
      • 24. Domínguez, M., Fernández-Cardador, A., Cucala, A.P., et al: ‘Multi objective particle swarm optimization algorithm for the design of efficient ATO speed profiles in metro lines’, Eng. Appl. Artif. Intell., 2014, 29, pp. 43–53.
    35. 35)
      • 20. Zhao, N., Roberts, C., Hillmansen, S., et al: ‘An integrated metro operation optimization to minimize energy consumption’, Transp. Res. C Emerg. Technol., 2017, 75, pp. 168–182.
    36. 36)
      • 19. Li, X., Lo, H.K.: ‘An energy-efficient scheduling and speed control approach for metro rail operations’, Transp. Res. B, 2014, 64, pp. 73–89.
    37. 37)
      • 21. Yang, X., Chen, A., Ning, B., et al: ‘A stochastic model for the integrated optimization on metro timetable and speed profile with uncertain train mass’, Transp. Res. B Methodol., 2016, 91, pp. 424–445.
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