Abstract
At present, with the rapid development of urban rail transit, the energy consumption of the urban rail transit system has become a hot spot for many scholars. In order to effectively reduce the traction energy consumption of the urban rail transit system and improve the utilization of regenerative braking energy, this paper proposes a collaborative optimization strategy for multi-train operation curve. Firstly, this paper builds the simulation model of multi-train operation for urban rail power supply, it uses the improved Rosenbrock algorithm to calculate and solve, and analyzes the energy flow and utilization of the system energy. On this basis, it establishes the optimization model, proposes a collaborative optimization strategy for multi-train operation curve under the different operational scenarios by using the particle swarm optimization for the optimization solution. Finally, based on the actual line and train data of Bei**g Metro Batong Line, the effectiveness verification of the optimization strategy under multiple scenarios is realized.
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
Similar content being viewed by others
References
Zhao, Y.: Research on Multi-train Energy Saving Operation of Urban Rail Transit Considering Regenerative Braking Energy Utilization. Bei**g Jiaotong University, Bei**g (2015). (in Chinese)
Su, S.: Integrated Energy Saving Method of Urban Rail Train Diagram and Speed Curve. Bei**g Jiaotong University, Bei**g (2016). (in Chinese)
Zhu, M.: Research on Optimization Method of Train Energy Saving Operation Based on Speed Curve Adjustment. Nan**g University of technology, Nan**g (2018). (in Chinese)
Yang, Z.: Research on Train Energy Saving Operation Based on Multi Train Energy Interaction and Optimal Control of Energy Storage System. Bei**g Jiaotong University, Bei**g (2018). (in Chinese)
Tang, H.: Energy Saving Optimal Control of Multi-train Operation in Urban Rail Transit. Southwest Jiaotong University, Chengdu (2015). (in Chinese)
Yang, X., Ning, B., Li, X., et al.: A two-objective timetable optimization model in subway systems. IEEE Trans. Intell. Transp. Syst. 15(5), 1913–1921 (2014)
Yang, X., Ning, B., Li, X.: A survey on energy-efficient train operation for urban rail transit. IEEE Trans. Intell. Transp. Syst. 17(1), 2–13 (2016)
Nasri, A., Fekri Moghadam, M., Mokhtari, H..: Timetable optimization for maximum usage of regenerative energy of braking in electrical railway systems. In: International Symposium on Power Electronics, Electrical Drives, Automation and Motion, pp. 1218–1221. IEEE (2010)
Su, S., Tang, T., Li, X., et al.: Optimization of multitrain operations in a subway system. IEEE Trans. Intell. Transp. Syst. 15(2), 673–684 (2014)
Gu, Q., Tang, T., Cao, F., et al.: Energy-efficient train operation in urban rail transit using real-time traffic information. IEEE Trans. Intell. Transp. Syst. 15(3), 216–1233 (2014)
Zhang, J.: Modeling and Simulation of Urban Rail Transit AC/DC Power Supply System Based on MATLAB/Simulink. Bei**g Jiaotong University, Bei**g (2017). (in Chinese)
Steihaug, T., Wolfbrandt, A.: An attempt to avoid exact Jacobian and nonlinear equations in the numerical solution of stiff differential equations. Math. Comp 33(146), 521–534 (1979)
Xu, L., Liu, W., Liao, J., et al.: Measurement and analysis of traction and braking energy consumption of urban rail transit. J. Rail. Sci. Eng. 13(9), 1819–1824 (2016). (in Chinese)
Zhang, S.: Multi Objective Optimization of High Speed Train Tracking Based on Particle Swarm Optimization. Lanzhou Jiaotong University, Lanzhou (2020). (in Chinese)
Acknowledgment
This research is funded by the National Innovation Center of High Speed Train. Thanks for the support of National Innovation Center of High Speed Train.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Song, M., Wu, X., **, T., Liu, Y., Lin, F. (2022). Collaborative Optimization Design of Multi-train Operation Curve Based on Utilization of Regenerative Braking Energy in Urban Rail Transit. In: Liang, J., Jia, L., Qin, Y., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021. EITRT 2021. Lecture Notes in Electrical Engineering, vol 867. Springer, Singapore. https://doi.org/10.1007/978-981-16-9909-2_32
Download citation
DOI: https://doi.org/10.1007/978-981-16-9909-2_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9908-5
Online ISBN: 978-981-16-9909-2
eBook Packages: EngineeringEngineering (R0)