Optimal underground timetable design based on power flow for maximizing the use of regenerative-braking energy

Abstract

This paper deals with the design of underground rail system timetables that synchronize the movement of trains to allow the energy consumption from substations to be reduced by maximizing the use of regenerative-braking energy. Nowadays, most trains are equipped with regenerative-braking systems and any of this recovered energy not used by on-board auxiliary services can be consumed by other trains in the same rail section. A mathematical programming optimization model has been designed to synchronize the braking of trains arriving at station with the acceleration of trains exiting from the same or another station. In addition, a power flow model of the electrical network has been developed to calculate the power-saving factor for each synchronization event in order to encourage better synchronizations, particularly those which have fewer energy losses. These models were applied in the design of a schedule for line 3 of the Madrid underground system. This schedule was then trialled for a week. Energy savings were measured and a significant correlation with the synchronization of train movements was observed. It was concluded that a modification in the published timetables would result in energy savings, with no effect on the quality of service for passengers and low associated investment costs.

Keywords

Energy efficiency regenerative-braking system train timetabling problem underground scheduling

Get full access to this article

View all access options for this article.

References

Greatbanks

. Review of the discount for using regenerative braking. Report for the Office of Rail Regulation, London, UK. AEA Technology Rail Report no. ORR/CT/RDRB/192, June 2005.

Açikbas S and Söylemez MT. Parameters affecting braking energy recuperation rate in DC rail transit. In: ASME/IEEE joint rail conference and the ASME Internal Combustion Engine Division spring technical conference, Pueblo, Colorado, 13–16 March 2007, pp. 263–268.

Gunselmann W. Technologies for increased energy efficiency in railway systems. In IEEE 11th European conference on power electronics and applications, Dresden, Germany, 11–14 September 2005 (IEEE Press, Piscataway, New Jersey), pp. 1–10.

Steiner M and Scholten J. Energy storage on board of DC fed railway vehicles. In IEEE 35th Annual power electronics specialists conference, Aachen, Germany, 15 November 2004, vol. 1–6 (IEEE, Piscataway, New Jersey), pp. 666–671.

Adinolfi

Lamedica

Modesto

. Experimental assessment of energy saving due to trains regenerative braking in an electrified subway line. IEEE Trans Power Deliv 1998; 13(4): 1536–1542.

Destraz B, Barrade P, Rufer A and Klohr M. Study and simulation of the energy balance of an urban transportation network. In European conference on power electronics and applications, Aalborg, Denmark, 2–5 September 2007, vol. 1–10 (IEEE, Piscataway, New Jersey), pp. 5238–5247.

Kadhim

. Energy storage systems for railway applications. R&D Project T779 – Phase 1. Technical report. London: Rail Safety and Standards Board, 2009.

Bocharnikov

Tobias

Roberts

. Optimal driving strategy for traction energy saving on DC suburban railways. IET Electric Power Appl 2007; 1: 675–682.

Bocharnikov YV, Tobias AM and Roberts C. Reduction of train and net energy consumption using genetic algorithms for trajectory optimisation. In IET Conference on railway traction systems, Birmingham, UK, 13–15 April 2010 (Institution of Engineering and Technology), pp. 1–5.

10.

Miyatake M and Ko H. Numerical analyses of minimum energy operation of multiple trains under DC power feeding circuit. In European conference on power electronics and applications, Aalborg, Denmark, 2–5 September 2007, pp. 1–10.

11.

Malavasi

Palleschi

Ricci

. Driving and operation strategies for traction-energy saving in mass rapid transit systems. Proc IMechE, Part F: J Rail Rapid Transit 2011; 225(5): 475–482.

12.

Caprara

Fischetti

Toth

. Modeling and solving the train timetabling problem. Oper Res 2002; 50: 851–861.

13.

Takagi

Goodman

Roberts

. Optimization of train departure times at an interchange considering passenger flows. Proc IMechE, Part F: J Rail Rapid Transit 2006; 220(2): 113–120.

14.

Zhou

Zhong

. Single-track train timetabling with guaranteed optimality: branch-and-bound algorithms with enhanced lower bounds. Transp Res, Part B: Methodol 2007; 41: 320–341.

15.

Albrecht

. Reducing power peaks and energy consumption in rail transit systems by simultaneous train running time control. In Computers in railways IX, Dresden, Germany, 17–19 May 2004 (WIT Press, Wessex, UK); 885–894.

16.

Chen

Lin

. Optimization of an MRT train schedule: reducing maximum traction power by using genetic algorithms. IEEE Trans Power Syst 2005; 20(3): 1366–1372.

17.

Nasri

Fekri Moghadam

Mokhtari

. Timetable optimization for maximum usage of regenerative energy of braking in electrical railway systems. In International symposium on power electronics, electrical drives, automation and motion, Pisa, Italy, 14–16 June 2010 (IEEE, Piscataway, New Jersey); 1218–1221.

18.

Lindner

Zimmermann

. Cost optimal periodic train scheduling. Math Methods Oper Res 2005; 62: 281–295.

19.

Kim

Han

. A mathematical approach for reducing the maximum traction energy: the case of Korean MRT trains. In International multiconference of engineers and computer scientists. Hong Kong, 17–19 March 2010, vol. III (International Association of Engineers, Hong Kong), pp. 2169–2173.

20.

Tzeng

. A detailed R-L fed bridge converter model for power flow studies in industrial AC/DC power systems. IEEE Trans Ind Electron 1995; 42(5): 531–538.

21.

Higgins

Kozan

Ferreira

. Optimal scheduling of trains on a single line track. Transp Res, Part B: Methodol 1996; 30: 147–161.

22.

Bussieck

Winter

Zimmermann

. Discrete optimization in public rail transport. Math Program 1997; 79: 415–444.

23.

Brooke A, Kendrick D, Meeraus A and Raman R. GAMS – a users guide, available from http://www.gams.com (access date 15 January 2011).

24.

Lastusilta

Bussieck

Westerlund

. Comparison of some high-performance MINLP solvers. Chem Engng Trans 2007; 11: 125–130.

25.

Khan

Zhou

. Stochastic optimization model and solution algorithm for robust double-track train-timetabling problem. IEEE Trans Intell Transp Syst 2010; 11: 81–89.