The Effect of Using the Tabu Search Algorithm on the Speed of Achieving the Optimal Train Speed Profile (in order to Reduce Energy Consumption)

Document Type : Original Article

Authors

1 Associate Professor, School of Railway Engineering, Iran University of Science, Technology (IUST), Tehran, Iran.

2 M.Sc. Grad., School of Railway Engineering, Iran University of Science, Technology (IUST), Tehran, Iran.

3 Ph.D. Student, School of Railway Engineering, Iran University of Science, Technology (IUST), Tehran, Iran.

Abstract

Rail transit plays an increasingly important role in the public transportation system, and effectively reducing its huge energy consumption is of great practical significance. Wider use of public transport, particularly rail and metro, is one way to save energy. A growing trend of applying the rail network and metro by governments on one hand and the considerable energy consumption of a train during one year, on the other hand, demonstrate the necessity of considering the consumed energy by train. Railway transportation consumes amounts of energy. Direct energy consumption to complete the transport tasks is the main part of energy consumption of rail transportation, especially the traction system, which leads to the railway transportation costly. Optimization of the train speed curve plays an important role in minimizing train energy consumption. In this paper, first, there was a review on models of train energy optimization with different characteristics and corresponding other algorithms to find the optimum speed profile and accuracy of them, Second Tabu Search (TS) algorithm as a new approach for optimizing the train speed profile to save energy will be investigated. In this approach, after determining the appropriate points of acceleration, neural and braking, a speed profile in which train could cover its route with minimum energy consumption will be achieved. We call these points "the variables for changing the training strategy. The algorithm was implemented in alternative routes. In this study, the simulations of the proposed method are implemented in MATLAB software and are finally compared with the Genetic Algorithm method.

Keywords

References

-صندیدزاده م.ع.، پورانیان ز.، موحدی، م.، (2016)، "کاهش مصرف انرژی در حمل و نقل ریلی با استفاده از الگوریتم بهینه سازی ازدحام ذرات" فصلنامه علمی - پژوهشی مهندسی حمل و نقل،6610 -6598.
 
-Allen, L. A., & Chien, S. I. J., (2017), “Reducing Rail Energy Consumption through Coasting and Regenerative Braking”
(No. 17-06235).
 
-Chang, S. H., Byen, Y. S., Baek, J. H., An, T. K., Lee, S. G., & Park, H. J., (1999), "An optimal automatic train operation (ATO) control using genetic algorithms (GA)", In TENCON 99. Proceedings of the IEEE Region 10 Conference, Vol. 1, pp. 360-362.
 
-Dalla Chiara, B., De Franco, D., Coviello, N., & Pastrone, D., (2017), “Comparative specific energy consumption between air transport and high-speed rail transport: A practical assessment”. Transportation Research Part D: Transport and Environment, 52, pp.227-243.
-Glover, F., & Laguna, M., (2002), "Handbook of Applied Optimization", chapter 3.6. 7: Tabu Search.
 
-Huang, Y., Yang, L., Tang, T., GAO, Z., & Cao, F., (2017), “Joint train scheduling optimization with service quality and energy efficiency in urban rail transit networks”, Energy, 138, pp.1124-1147.
 
-Kampeerawat, W., & Koseki, T., (2017), “A strategy for utilization of regenerative energy in urban railway system by application of smart train scheduling and wayside energy storage system”. Energy Procedia, 138, pp.795-800.
 
-Keskin, K., & Karamancioglu, A., (2017), “Energy-efficient train operation using nature-inspired algorithms”, Journal of Advanced Transportation.
 
-Li, X., & Lo, H. K., (2014), "An energy-efficient scheduling and speed control approach for metro rail operations". Transportation Research Part B: Methodological, 64, pp.73-89.
 
-Liu, J., Guo, H., & Yu, Y., (2016). Research on the cooperative train control strategy to reduce energy consumption. IEEE Transactions on Intelligent Transportation Systems, 18(5), pp.1134-1142.
 
-Scheepmaker, G. M., Goverde, R. M., & Kroon, L. G., (2017), “Review of energy-efficient train control and timetabling. European Journal of Operational Research”, 257(2), pp.355-376.
 
-Su, S., Tang, T., & Roberts, C., (2015), "A cooperative train control model for energy saving" IEEE transactions on intelligent transportation systems, 16(2), pp.622-631.
-Su, S., Tang, T., Chen, L., & Liu, B., (2015), "Energy-efficient train control in urban rail transit systems" Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 229(4), pp.446-454.
 
-Wang, J., & Rakha, H. A., (2017), “Electric train energy consumption modeling”, Applied energy, 193, pp.346-355.
 
-Watanabe, K., (2018), “Reduction of energy consumption for running in pre-mass-production train set of Series E235: the verification of energy consumption for running and traction control conditions in regenerative braking due to expansion of regenerative brake region”. Japanese Railway Engineering.
 
-Xu, X., Li, K., & Li, X., (2016),
“A multi‐objective subway timetable optimization approach with minimum passenger time and energy consumption”. Journal of Advanced Transportation, 50(1), pp.69-95.
 
-Yang, X., Li, X., Ning, B., & Tang, T., (2015), "An optimisation method for train scheduling with minimum energy consumption and travel time in metro rail systems" Transportmetrica B: Transport Dynamics, 3(2), pp.79-98.
 
-Yin, J., Yang, L., Tang, T., Gao, Z., & Ran, B., (2017), “Dynamic passenger demand oriented metro train scheduling with
energy-efficiency and waiting time minimization: Mixed-integer linear programming approaches”. Transportation Research Part B: Methodological, 97,
pp.182-213.
 
-Zhao, N., Roberts, C., Hillmansen, S., & Nicholson, G., (2015), "A multiple train trajectory optimization to minimize energy consumption and delay". IEEE Transactions on Intelligent Transportation Systems, 16(5), pp.2363-2372.
 
-Zhao, N., Roberts, C., Hillmansen, S., Tian, Z., Weston, P., & Chen, L., (2017), “An integrated metro operation optimization to minimize energy consumption”. Transportation Research Part C: Emerging Technologies, 75, pp.168-182.

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