Research on Real-Time Operation Optimization of the Urban Rail Traction Power Supply System Integrated Storage and Feedback

Authors

  • Haotian Deng School of Electrical Engineering, Southwest Jiaotong University, 611756, China
  • Wei Liu School of Electrical Engineering, Southwest Jiaotong University, 611756, China
  • Jun Dai School of Electrical Engineering, Southwest Jiaotong University, 611756, China

DOI:

https://doi.org/10.53469/jrse.2025.07(02).05

Keywords:

Urban rail transit, Power supply system, Energy feedback system, Energy storage system, Real-time operation optimization

Abstract

Urban rail transit plays an important role in the rapid economic and social development of China. The integrated storage and feedback urban rail traction power supply system is one of the strategies for the green and low-carbon development of urban rail transit. This paper establishes a real-time operation optimization model based on the Markov decision process in the context of the integrated storage and feedback urban rail traction power supply system. It uses offline training of a deep reinforcement learning agent to optimize the control parameters of the energy feedback systems and energy storage systems in real-time to reduce the traction energy consumption of the power supply system, providing a reference for energy-saving operation of subways.

References

Shang M, Zhou Y, Mei Y, et al. Energy-Saving Train Operation Synergy Based on Multi-Agent Deep Reinforcement Learning on Spark Cloud [J]. IEEE Transactions on Vehicular Technology, 2022, PP: 1-13.

Wei L, Jian Z, Hui W, Tuojian W, Ying L, Xiaowen Y, et al. Modified AC/DC Unified Power Flow and Energy-Saving Evaluation for Urban Rail Power Supply System With Energy Feedback Systems [J], IEEE Transactions on Vehicular Technology, 2021, 70(10): 9898-9909.

Zhang G, Tian Z, Tricoli P, et al. Inverter Operating Characteristics Optimization for DC Traction Power Supply Systems [J]. IEEE Transactions on Vehicular Technology, 2019, 68(4): 3400-10.

Qian X, Wei L, Qianfeng Y, Xiaodong Z, Haohao G, Haotian D, Peng X, et al. Bi-level optimal design for DC traction power supply system with reversible substations [J], IEEE Transactions on Transportation Electrification, 2023, PP(99): 1-1.

Liu Y, Yang Z, Wu X, et al. Adaptive Threshold Adjustment Strategy Based on Fuzzy Logic Control for Ground Energy Storage System in Urban Rail Transit [J]. IEEE Transactions on Vehicular Technology, 2021, 70(10): 9945-56.

Fujimoto S, Hoof H v, Meger D. Addressing Function Approximation Error in Actor-Critic Methods; proceedings of the International Conference on Machine Learning, F, 2018 [C].

Zhang J, Liu W, Tian Z, et al. Urban Rail Substation Parameter Optimization by Energy Audit and Modified Salp Swarm Algorithm [J]. IEEE Transactions on Power Delivery, 2022, 37(6): 4968-78.

Downloads

Published

2025-02-27

How to Cite

Deng, H., Liu, W., & Dai, J. (2025). Research on Real-Time Operation Optimization of the Urban Rail Traction Power Supply System Integrated Storage and Feedback. Journal of Research in Science and Engineering, 7(2), 23–27. https://doi.org/10.53469/jrse.2025.07(02).05

Issue

Vol. 7 No. 2 (2025)

Section

Articles

License

Copyright (c) 2025 Haotian Deng, Wei Liu, Jun Dai

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.