Optimal Approach to Improving the Utilization of Regenerative Energy Considering Power Profile

Abstract

Urban rail transit (URT) develops rapidly in modern cities, and its energy efficiency attracts extensive attention. The utilization of regenerative energy (URE) is an important method for energy-efficient operation of URT. Regenerative braking is an energy recovery mechanism that slows down a moving train by converting its kinetic energy into electric energy. The electric energy can be utilized for other trains to accelerate in a cooperative way. To take full advantage of the regenerative energy, an energy calculation method which considers regenerative braking power to optimize the timetable is proposed in this paper. First, four operating modes of URE are defined and an integer programming model is formulated. Second, a branch and bound algorithm is designed to solve the optimal timetable in different scenarios. Third, the model is evaluated based on the operation data from the Yanfang Line, Beijing Metro, China. For peak hours, the results illustrate that the proposed method can significantly improve URE by 73.7% compared with the original timetable. Also, URE can be improved by 46.3% for off-peak hours. Finally, the comparison between the proposed method and the method based on the kinetic energy theorem is given. The simulation results illustrate that the proposed method could increase URE by 29.7% and 9.9% for peak and off-peak hours scenarios, respectively, in comparison with the method based on the kinetic energy theorem.

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References

Han

B. M.

Yang

Z. X.

Y. R.

Qian

Chen

J. H.

Ran

J. L.

Sun

Y. J.

Statistical Analysis of Urban Rail Transit Operation in the World in 2020: A Review. Urban Rapid Rail Transit, 2020, Vol. 33, No.161, pp. 12–16.

Wang

C. C.

The Annual Power Consumption of the Subway will Reach 40 Billion kWh. China Youth Daily, December 29, 2018. https://baijiahao:baidu:com/s?id=1621159666822461397&wfr=spider&for=pc. Accessed November 26, 2020.

Ministry of Transport of the People’s Republic of China. Notice of the Ministry of Transport on Precise and Orderly Restoration of Transportation Services to Promote the Resumption of Production and Resumption of Production. 2020. http://xxgk.mot.gov.cn/jigou/ysfws/202003/t202003133345786.html. Accessed November 26, 2020.

Central People’s Government of the People’s Republic of China. Passenger Stations and Transportation of New Coronary Pneumonia Epidemic “Guidelines for Prevention and Control of Zoning and Classification”. 2020. http://www.gov.cn/zhengce/zhengceku/2020–06/15/content5519551.htm. Accessed November 26, 2020.

Yang

A Stochastic Timetable Optimization Model in Subway Systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 21, Supplement 1, 2013, pp. 1–15.

Yang

Ning

Tang

A Two-Objective Timetable Optimization Model in Subway Systems. IEEE Transactions on Intelligent Transportation Systems, Vol. 15, No. 5, 2014, pp. 1913–1921.

Yang

Ning

Tang

A Survey on Energy-Efficient Train Operation for Urban Rail Transit. IEEE Transactions on Intelligent Transportation Systems, Vol. 17, 2016, pp. 2–13.

Chen

Sun

W. Z.

An Energy-Efficient Operation Approach Based on the Utilization of Regenerative Braking Energy among Trains. Proc., 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Las Palmas de Gran Canaria, Spain, IEEE, New York, 2015. https://doi.org/10.1109/ITSC.2015.419.

Gonzlez-Gil

Palacin

Batty

Sustainable Urban Rail Systems: Strategies and Technologies for Optimal Management of Regenerative Braking Energy. Energy Conversion and Management, Vol. 75, 2013, pp. 374–388.

10.

Amit

Goldfarb

The Timetable Problem for Railways. In Development in Operations Research ( Eglese

R. W.

Rand

G. K.

, eds.), Gordon and Breach, New York, NY, 1971, pp: 379–387.

11.

Kroon

Maróti

Helmrich

M. R.

Vromans

Dekker

Stochastic Improvement of Cyclic Railway Timetables. Transportation Research Part B: Methodological, Vol. 42, No. 6, 2008, pp. 553–570.

12.

Jovanović

Kecman

Bojović

Mandić

Optimal Allocation of Buffer Times to Increase Train Schedule Robustness. European Journal of Operational Research, Vol. 256, No. 1, 2017, pp. 44–54.

13.

Adinolfi

Lamedica

Modesto

Prudenzi

Vimercati

Experimental Assessment of Energy Saving due to Regenerative Braking in an Electrified Subway Line. IEEE Transactions on Power Delivery, Vol. 13, No. 4, 1998, pp. 1536–1542.

14.

K. P.

J. J.

Optimizing the Train Timetable for a Subway System. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, Vol. 229, No. 8, 2015, pp. 852–862.

15.

Study on Energy Consumption Monitoring and Energy-saving Scheme of Urban Rail Transit Train. Railway Transport and Economy, Vol. 39, 2017, pp. 95–100.

16.

Gao

Bai

G. Z.

Chen

Timetable Optimization Based on Actual Timetable for Urban Rail Transit System. Proc., 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA), Xiangtan, China, 2019. https://doi.org/10.1109/ICICTA49267.

17.

Yang

Gao

Z. Y.

Wang

Tang

A Cooperative Scheduling Model for Timetable Optimization in Subway Systems. IEEE Transactions on Intelligent Transportation Systems, Vol. 14, 2013, pp. 438–447.

18.

Yang

Chen

Ning

Tang

An Energy-Efficient Scheduling Approach to Improve the Utilization of Regenerative Energy for Metro Systems. Transportation Research Part C: Emerging Technologies, Vol. 57, 2015, pp. 13–29.

19.

Liu

Yang

L. X.

Gao

Huang

Energy-Efficient Train Timetable Optimization in the Subway System with Energy Storage Devices. IEEE Transactions on Intelligent Transportation Systems, Vol. 19, No. 12, 2018, pp. 3947–3963.

20.

Xun

Liu

Ning

Liu

Using Approximate Dynamic Programming to Maximize Regenerative Energy Utilization for Metro. IEEE Transactions on Intelligent Transportation Systems, Vol. 21, No. 9, 2019, pp. 1–13.

21.

Cai

Xun

Xiao

Liu

Timetable Optimization Model with Considering Regenerative Braking Power for Urban Rail. Proc., 2020 Chinese Automation Congress (CAC), Shanghai, China, 2020, pp. 3018–3023. https://doi.org/10.1109/CAC51589.2020.9326888.

22.

Ghoseiri

Szidarovszky

Asgharpour

J. M.

A Multi-Objective Train Scheduling Model and Solution. Transportation Research Part B: Methodological, Vol. 38, 2004, pp. 927–952.

23.

A Common Model for Railway Train Timetable Optimization. Proc., 2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Qiqihar, China, 2019, pp. 115–119. https://doi.org/10.1109/ICMTMA.2019.00032.

24.

Ding

Y. Q.

Chien

Improving Transit Service Quality and Headway Regularity with Real-Time Control. Transportation Research Record: Journal of the Transportation Research Board, 2001. 1760: 161–170.

25.

Jiang

H. D.

Wang

A Novel TRUST-TECH Guided Branch-and-Bound Method for Nonlinear Integer Programming. IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 45, No. 10, 2015, pp. 1–11.

26.

Code of China. GB 501572013 Code for Design of Metro. China Construction Industry Press, Beijing, 2014.