Energy-saving operation approaches for urban rail transit systems

doi:10.1007/s42524-019-0030-7

Front. Eng

2019, Vol. 6

Issue (2) : 139-151 https://doi.org/10.1007/s42524-019-0030-7

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Energy-saving operation approaches for urban rail transit systems

Ziyou GAO, Lixing YANG(

)

State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

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Abstract

With the accelerated urbanization in China, passenger demand has dramatically increased in large cities, and traffic congestion has become serious in recent years. Developing public urban rail transit systems is an indispensable approach to overcome these problems. However, the high energy consumption of daily operations is an emerging issue due to increased rail transit networks and passenger demands. Thus, reducing the energy consumption and operational cost by using advanced optimization methodologies is an urgent task for operation managers. This work systematically introduces energy-saving approaches for urban rail transit systems in three aspects, namely, train speed profile optimization, utilization of regenerative energy, and integrated optimization of train timetable and speed profile. Future research directions in this field are also proposed to meet increasing passenger demands and network-based urban rail transit systems.

Keywords urban rail transit energy saving speed profile optimization regenerative energy train timetable

Corresponding Author(s): Lixing YANG

Just Accepted Date: 29 March 2019 Online First Date: 29 April 2019 Issue Date: 17 May 2019

Cite this article:

Ziyou GAO,Lixing YANG. Energy-saving operation approaches for urban rail transit systems[J]. Front. Eng, 2019, 6(2): 139-151.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-019-0030-7
https://academic.hep.com.cn/fem/EN/Y2019/V6/I2/139

Tab.1 Composition of traction energy consumption

Fig.1 Recommended and actual speed profiles

Fig.2 GA for optimization of train speed profile

Tab.2 Comparison of three energy storage methods

Fig.3 Schematic of the utilization of regenerative braking energy

Fig.4 Storage and utilization of regenerative braking energy

Fig.5 Integrated optimization of train scheduling and operations

Fig.6 Improving the utilization of regenerative energy by matching the departure times of trains

Fig.7 Speed profiles under different levels

Fig.8 Illustration of the space–time network

1	A L Allegre, A Bouscayrol, P Delarue, P Barrade, E Chattot, S El-Fassi (2010). Energy storage system with supercapacitor for an innovative subway. IEEE Transactions on Industrial Electronics, 57(12): 4001–4012 https://doi.org/10.1109/TIE.2010.2044124
2	C S Chang, S S Sim (1997). Optimizing train movements through coast control using genetic algorithms. IEE Proceedings-Electric Power Applications, 144(1): 65–73 https://doi.org/10.1049/ip-epa:19970797
3	Q Gu, T Tang, F Cao, Y D Song (2014). Energy-efficient train operation in urban rail transit using real-time traffic information. IEEE Transactions on Intelligent Transportation Systems, 15(3): 1216–1233 https://doi.org/10.1109/TITS.2013.2296655
4	P G Howlett, I P Milroy, P J Pudney (1994). Energy-efficient train control. Control Engineering Practice, 2(2): 193–200 https://doi.org/10.1016/0967-0661(94)90198-8
5	Y R Huang, L X Yang, T Tang, Z Y Gao, F Cao, K P Li (2018). Train speed profile optimization with on-board energy storage devices: A dynamic programming based approach. Computers & Industrial Engineering, 126: 149–164 https://doi.org/10.1016/j.cie.2018.09.024
6	K Ichikawa (1968). Application of optimization theory for bounded state variable problems to the operation of train. Bulletin of the JSME, 11(47): 857–865 https://doi.org/10.1299/jsme1958.11.857
7	B R Ke, M C Chen, C L Lin (2009). Block-layout design using MAX–MIN ant system for saving energy on mass rapid transit systems. IEEE Transactions on Intelligent Transportation Systems, 10(2): 226–235 https://doi.org/10.1109/TITS.2009.2018324
8	B R Ke, C L Lin, C C Yang (2012). Optimization of train energy-efficient operation for mass rapid transit systems. IET Intelligent Transport Systems, 6(1): 58–66 https://doi.org/10.1049/iet-its.2010.0144
9	X Li, H Lo (2014). An energy-efficient scheduling and speed control approach for metro rail operations. Transportation Research Part B: Methodological, 64: 73–89 https://doi.org/10.1016/j.trb.2014.03.006
10	P Liu, L X Yang, Z Y Gao, Y R Huang, S K Li, Y Gao (2018). Energy-efficient train timetable optimization in the subway system with energy storage devices. IEEE Transactions on Intelligent Transportation Systems, 19(12): 3947–3963 https://doi.org/10.1109/TITS.2018.2789910
11	R Liu, I M Golovitcher (2003). Energy-efficient operation of rail vehicles. Transportation Research Part A: Policy and Practice, 37(10): 917–932 https://doi.org/10.1016/j.tra.2003.07.001
12	C Y Ma, Y Ding, P Du, B H Mao (2010). Study on coast control of train movement for saving energy based-on genetic algorithm. Railway Computer Application, 19(6): 4–8
13	Z Rao (2006). Train Traction Calculation. Beijing: China Railway Press
14	Y Wang (2016). Calculation of Train Operations in Urban Metro Systems. Beijing: Science Press
15	L X Yang, K P Li, Z Y Gao, X Li (2012). Optimizing trains movement on a railway network. Omega, 40(5): 619–633 https://doi.org/10.1016/j.omega.2011.12.001
16	X Yang (2016). Research on train timetable optimization for energy-saving operations in urban rail transit. Dissertation for the Doctoral Degree. Beijing: Beijing Jiaotong University
17	X Yang, X Li, Z Y Gao, H W Wang, T Tang (2013). A cooperative scheduling model for timetable optimization in subway systems. IEEE Transactions on Intelligent Transportation Systems, 14(1): 438–447 https://doi.org/10.1109/TITS.2012.2219620
18	J T Yin, L X Yang, T Tang, Z Y Gao, B Ran (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: 182–213 https://doi.org/10.1016/j.trb.2017.01.001
19	L Zhao (2014). Research on metro timetable optimization model and algorithm based on regenerative braking. Dissertation for the Master Degree. Beijing: Beijing Jiaotong University
20	S Zhao (2014). Research and simulation of urban rail transit super capacitor energy storage system. Dissertation for the Master Degree. Changsha: Central South University of Forestry and Technology

[1]	Wen-wu Yang. Study of Sustainable Urban Rail Transit Development Model in China[J]. Front. Eng, 2014, 1(2): 195-201.
[2]	You-mei Lu,Cun-liang Shang. The Environmental Impact of the Three Gorges Project and the Countermeasures[J]. Front. Eng, 2014, 1(2): 120-128.
[3]	Zhi-huan Fu,Qing-zhong Luo,Guang-zhi Jia. The Construction of a Green Transportation System of China[J]. Front. Eng, 2014, 1(1): 3-12.

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