Abstract
Rising energy cost and environmental awareness make energy-efficient operation a key issue for metro management. The speed profile and timetable optimization are two significant ways to reduce total energy consumption for metro systems. This paper proposes an integrated speed profile and timetable optimization model to reduce the net energy consumption while incorporating with complex track conditions like undulate gradients, curves and tunnels. The net energy consumption is minimized by force coefficients and coast control for single train movement and accelerating and braking synchronization for multiple trains. An efficient hybrid particle swarm method based on the particle swarm optimization and genetic algorithm is designed to obtain a satisfactory solution. Finally, numerical case studies based on one metro line in Beijing are conducted to validate the energy-efficient performance of integrated model and the results show that the integrated model can achieve a better tradeoff between traction energy consumption and reused braking energy on comparison with individual speed profile and timetable optimization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Douglas H, Roberts C, Hillmansen S et al (2015) An assessment of available measures to reduce traction energy use in railway networks. Energy Convers Manag 106(12):1149–1165
Scheepmaker GM, Goverde RMP, Kroon LG (2017) Review of energy-efficient train control and timetabling. Eur J Oper Res 257(2):355–376
Howlett P (1990) An optimal strategy for the control of a train. Anziam J 31(4):454–471
Khmelnitsky E (2000) On an optimal control problem of train operation. Autom Control IEEE Trans 45(7):1257–1266
Liu R, Golovitcher IM (2003) Energy-efficient operation of rail vehicles. Transp Res Part A 37(10):917–932
Albrecht A, Howlett P, Pudney P et al (2016) 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 Part B Methodol 94(2):482–508
Albrecht A, Howlett P, Pudney P et al (2016) The key principles of optimal train control—part 2: existence of an optimal strategy, the local energy minimization principle, uniqueness, computational techniques. Transp Res Part B Methodol 94(2):509–538
Lu S, Hillmansen S, Ho TK et al (2013) Single-Train trajectory optimization. IEEE Trans Intell Transp Syst 14(2):743–750
Zhao N, Roberts C, Hillmansen S et al (2015) A multiple train trajectory optimization to minimize energy consumption and delay. IEEE Trans Intell Transp Syst 16(5):2363–2372
Chang CS, Sim SS (1997) Optimising train movements through coast control using genetic algorithms. Electric Power Appl IEE Proc 144(1):65–73
Wong KK, Ho TK (2004) Dynamic coast control of train movement with genetic algorithm. Int J Syst Sci 35(13–14):835–846
Ke BR, Chen MC, Lin CL (2009) Block-layout design using MAX–MIN ant system for saving energy on mass rapid transit systems. IEEE Trans Intell Transp Syst 10(2):226–235
Yang X, Li X, Ning B et al (2015) A survey on energy-efficient train operation for urban rail transit. IEEE Trans Intell Transp Syst 17(1):2–13
Yang X, Li X, Gao Z et al (2013) A cooperative scheduling model for timetable optimization in subway systems. IEEE Trans Intell Transp Syst 14(1):438–447
Pena-Alcaraz M, Fernandez A, Cucala AP et al (2011) Optimal underground timetable design based on power flow for maximizing the use of regenerative-braking energy. Proc Inst Mech Eng Part F J Rail & Rapid Transit 226(4):397–408
Li X, Hong KL (2014) An energy-efficient scheduling and speed control approach for metro rail operations. Transp Res Part B Methodol 64(4):73–89
Yang X, Chen A, Li X et al (2015) An energy-efficient scheduling approach to improve the utilization of regenerative energy for metro systems. Transp Res Part C Emerg Technol 57:13–29
Ye H, Liu R (2016) A multiphase optimal control method for multi-train control and scheduling on railway lines. Transp Res Part B Methodol 93:377–393
Zhao N, Roberts C, Hillmansen S et al (2017) An integrated metro operation optimization to minimize energy consumption ☆. Transp Res Part C Emerg Technol 75:168–182
Chevrier R, Pellegrini P, Rodriguez J (2013) Energy saving in railway timetabling: a bi-objective evolutionary approach for computing alternative running times. Transp Res Part C Emerg Technol 37(3):20–41
Ding Y, Liu H, Bai Y et al (2011) A two-level optimization model and algorithm for energy-efficient urban train operation. J Transp Syst Eng Inf Technol 11(1):96–101
Acknowledgements
The authors are grateful to the National Natural Science Foundation of China (71571015, 71621001) for their financial supports.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ran, X., Chen, S., Chen, L. (2020). An Integrated Energy-Efficient Scheduling and Train Control Model with Regenerative Braking for Metro System. In: Wang, W., Baumann, M., Jiang, X. (eds) Green, Smart and Connected Transportation Systems. Lecture Notes in Electrical Engineering, vol 617. Springer, Singapore. https://doi.org/10.1007/978-981-15-0644-4_22
Download citation
DOI: https://doi.org/10.1007/978-981-15-0644-4_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0643-7
Online ISBN: 978-981-15-0644-4
eBook Packages: EngineeringEngineering (R0)