Optimization Method of Subway Timetables for Regenerative Braking Energy Utilization
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 5
Abstract
Reducing the energy consumption of trains plays an important role in sustainable development of the subway system. When timetables are generated mainly considering passenger satisfaction, there are still some margins to optimize them to improve the energy efficiency. This paper slightly reoptimizes the planned timetable to reduce the energy consumption by improving the utilization of regenerative braking energy, while not changing the number of the subway trains. To this purpose, the authors innovatively propose a periodic or off-peak subway timetable optimization method to coordinate the arrivals and departures of all trains located in the same power supply section, so that the regenerative energy from braking trains can be more effectively utilized by other trains. First, we built a timetable optimization model with constraints derived from the planned timetable, which takes dwell time, the time shift of downbound timetable, and headway as decision variables, aiming to minimize the total energy consumption. Second, we designed a timetable optimization algorithm with two levels and high computational efficiency. In the upper level, the multiresolution-based traversal algorithm (MBTA) is proposed to search the feasible sets of time shift of the downbound timetable and the headway. And in the lower level, the decomposition coordination algorithm (DCA) is designed, in which the dwell times of the trains in the same power supply section are optimized in an optimization subproblem. Simulations are given using the real data of Beijing Metro Line 4 to evaluate the proposed method, and the results show that the proposed timetable optimization method can reduce energy consumption by 11.12%. In the random disturbance simulations, the proposed method showed robustness, which makes it possible to apply this method to real subway operations.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work is supported by the National Natural Science Foundation of China (61304196) and Fundamental Research Funds for the Central Universities (2018JBM010).
References
Albrecht, A., P. Howlett, P. Pudney, X. Vu, and P. Zhou. 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 (Dec): 482–508. https://doi.org/10.1016/j.trb.2015.07.023.
Albrecht, T. 2004. “Reducing power peaks and energy consumption in rail transit systems by simultaneous train running time control.” In Computers in railways IX. Southampton, UK: WIT Press.
Carruthers, J. J., M. Calomfirescu, P. Ghys, and J. Prockat. 2009. “The application of a systematic approach to material selection for the lightweighting of metro vehicles.” Proc. Inst. Mech. Eng. Part F: J. Rail Rapid Transit. 223 (5): 427–437. https://doi.org/10.1243/09544097JRRT279.
Choi, M. E., S.-W. Kim, and S.-W. Seo. 2012. “Energy management optimization in a battery/supercapacitor hybrid energy storage system.” IEEE Trans. Smart Grid 3 (1): 463–472. https://doi.org/10.1109/TSG.2011.2164816.
Falvo, M. C., D. Sbordone, A. Fernandez-Cardador, A. P. Cucala, R. R. Pecharroman, and A. Lopez-Lopez. 2014. “Energy savings in metro-transit systems: A comparison between operational Italian and Spanish lines.” Proc. Inst. Mech. Eng. Part F: J. Rail Rapid Transit 230 (2): 345–359. https://doi.org/10.1177/0954409714542276.
Fiori, C., K. Ahn, and H. A. Rakha. 2016. “Power-based electric vehicle energy consumption model: Model development and validation.” Appl. Energy 168 (Apr): 257–268. https://doi.org/10.1016/j.apenergy.2016.01.097.
Galán, A. R., M. T. P. Alcaraz, A. F. Morales, and P. Cucala. 2008. “Mathematical programming approach to underground timetabling problem for maximizing time synchronization.” Dirección y Organización 35 (Jun): 88–95.
Gao, Z., J. Fang, Y. Zhang, L. Jiang, D. Sun, and W. Guo. 2015. “Control of urban rail transit equipped with ground-based supercapacitor for energy saving and reduction of power peak demand.” Int. J. Electr. Power Energy Syst. 67 (May): 439–447. https://doi.org/10.1016/j.ijepes.2014.11.019.
Ghoseiri, K., F. Szidarovszky, and M. J. Asgharpour. 2004. “A multi-objective train scheduling model and solution.” Transp. Res. Part B: Methodol. 38 (10): 927–952. https://doi.org/10.1016/j.trb.2004.02.004.
Gupta, S. D., J. K. Tobin, and L. Pavel. 2016. “A two-step linear programming model for energy-efficient timetables in metro railway networks.” Transp. Res. Part B: Methodol. 93 (Nov): 57–74. https://doi.org/10.1016/j.trb.2016.07.003.
Howlett, P. G., I. Milroy, and P. Pudney. 1995. “Energy-efficient train control.” Control Eng. Pract. 2 (2): 193–200. https://doi.org/10.1016/0967-0661(94)90198-8.
Ke, B. R., C. L. Lin, and C. C. Yang. 2012. “Optimisation of train energy-efficient operation for mass rapid transit systems.” IET Intell. Transport Syst. 6 (1): 58–66. https://doi.org/10.1049/iet-its.2010.0144.
Kumar, M., and I. N. Kar. 2010. “Design of model-based optimizing control scheme for an air-conditioning system.” HVAC&R Res. 16 (5): 565–597. https://doi.org/10.1080/10789669.2010.10390922.
Lee, H., S. Jung, Y. Cho, D. Yoon, and G. Jang. 2013. “Peak power reduction and energy efficiency improvement with the superconducting flywheel energy storage in electric railway system.” Physica C 494 (11): 246–249. https://doi.org/10.1016/j.physc.2013.04.033.
Li, X., and H. K. Lo. 2014. “An energy-efficient scheduling and speed control approach for metro rail operations.” Transp. Res. Part B: Methodol. 64 (Jun): 73–89. https://doi.org/10.1016/j.trb.2014.03.006.
Liu, H., T. Tang, X. Guo, and X. Xia. 2018. “A timetable optimization model and an improved artificial bee colony algorithm for maximizing regenerative energy utilization in a subway system.” Adv. Mech. Eng. 10 (9): 1687814018797034. https://doi.org/10.1177/1687814018797034.
Lu, Y., Z. Xiaohao, and Z. Yehui. 2014. “Application of regenerative braking energy absorption devices in Beijing subway.” [In Chinese.] Urban Rapid Railway Transit 27 (04): 105–108.
Nasri, A., M. F. Moghadam, and H. Mokhtari. 2010. “Timetable optimization for maximum usage of regenerative energy of braking in electrical railway systems.” In Proc., SPEEDAM 2010, 1218–1221. New York: IEEE. https://doi.org/10.1109/SPEEDAM.2010.5542099.
Pena-Alcaraz, M., A. Fernandez, A. P. Cucala, A. Ramos, and R. R. Pecharroman. 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. https://doi.org/10.1177/0954409711429411.
Scheepmaker, G. M., R. M. P. Goverde, and L. G. Kroon. 2017. “Review of energy-efficient train control and timetabling.” Eur. J. Oper. Res. 257 (2): 355–376. https://doi.org/10.1016/j.ejor.2016.09.044.
Shang, P., R. Li, and L. Yang. 2016. “Optimization of urban single-line metro timetable for total passenger travel time under dynamic passenger demand.” Procedia Eng. 137: 151–160. https://doi.org/10.1016/j.proeng.2016.01.245.
Solis, O., F. Castro, L. Bukhin, K. Pham, D. Turner, and G. Thompson. 2015. “Saving money every day: LA metro subway wayside energy storage substation.” In Proc., ASCE/IEEE Joint Rail Conf. New York: ASME.
Sun, L., J. G. Jin, D.-H. Lee, K. W. Axhausen, and A. Erath. 2014. “Demand-driven timetable design for metro services.” Transp. Res. Part C: Emerging Technol. 46 (Sep): 284–299. https://doi.org/10.1016/j.trc.2014.06.003.
Teymourfar, R., B. Asaei, and H. Iman-Eini. 2012. “Stationary super-capacitor energy storage system to save regenerative braking energy in a metro line.” Energy Convers. Manage. 56 (Apr): 206–214. https://doi.org/10.1016/j.enconman.2011.11.019.
Yang, S., F. Liao, J. Wu, H. J. P. Timmermans, H. Sun, and Z. Gao. 2020. “A bi-objective timetable optimization model incorporating energy allocation and passenger assignment in an energy-regenerative metro system.” Transp. Res. Part B: Methodol. 133 (Mar): 85–113. https://doi.org/10.1016/j.trb.2020.01.001.
Yang, X., A. Chen, X. Li, B. Ning, and T. Tang. 2015. “An energy-efficient scheduling approach to improve the utilization of regenerative energy for metro systems.” Transp. Res. Part C: Emerging Technol. 57 (Aug): 13–29. https://doi.org/10.1016/j.trc.2015.05.002.
Yang, X., A. Chen, B. Ning, and T. Tang. 2017. “Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty.” Transp. Res. Part E: Logistics Transp. Rev. 97 (Jan): 22–37. https://doi.org/10.1016/j.tre.2016.10.012.
Yang, X., A. Chen, J. Wu, Z. Gao, and T. Tang. 2019. “An energy-efficient rescheduling approach under delay perturbations for metro systems.” Transportmetrica B: Transp. Dyn. 7 (1): 386–400. https://doi.org/10.1080/21680566.2017.1421109.
Yang, X., X. Li, Z. Gao, H. Wang, and T. Tang. 2013. “A cooperative scheduling model for timetable optimization in subway systems.” IEEE Trans. Intell. Transp. Syst. 14 (1): 438–447. https://doi.org/10.1109/TITS.2012.2219620.
Yang, X., B. Ning, X. Li, and T. Tang. 2014. “A two-objective timetable optimization model in subway systems.” IEEE Trans. Intell. Transp. Syst. 15 (5): 1913–1921. https://doi.org/10.1109/TITS.2014.2303146.
Zhan, S., S. C. Wong, and S. M. Lo. 2020. “Social equity-based timetabling and ticket pricing for high-speed railways.” Transp. Res. Part A: Policy Pract. 137 (Jul): 165–186. https://doi.org/10.1016/j.tra.2020.04.018.
Zhang, H., S. Li, and L. Yang. 2019. “Real-time optimal train regulation design for metro lines with energy-saving.” Comput. Ind. Eng. 127 (Jan): 1282–1296. https://doi.org/10.1016/j.cie.2018.02.019.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 5 • May 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: May 26, 2020
Accepted: Dec 2, 2020
Published online: Mar 1, 2021
Published in print: May 1, 2021
Discussion open until: Aug 1, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.