Wang, X., Luo, Y., Qin, B. et al. (1 more author) (2023) Power allocation strategy for urban rail HESS based on deep reinforcement learning sequential decision optimization. IEEE Transactions on Transportation Electrification, 9 (2). pp. 2693-2710. ISSN 2332-7782
Abstract
A hybrid energy storage system (HESS) is adopted to tackle the traction network voltage fluctuation problem caused by high power and large energy demand during the starting and braking of urban rail trains. The system is composed of on-board ultracapacitors and ground lithium batteries, aiming to smooth out the power fluctuation to realize “peak-shaving and valley-filling”. Based on deep reinforcement learning (DRL) online sequence decision, a dynamic power allocation strategy is proposed to improve the energy-saving and voltage stabilization of DC traction networks as well as HESS life protection. Furthermore, to enhance the DRL’s efficiency under time-varying operating conditions, an annealing bias - priority experience replay twin delayed deep deterministic policy gradient algorithm (A-TD3) is proposed to train the replay buffer in DRL. The online learning and optimization strategy is implemented via the mechanism of “trial and error” and “feedback” of the agent. RT-LAB semi-physical real-time simulation systems are adopted to verify the effectiveness of the proposed strategy. Compared with the traditional filtering algorithms and DRL algorithms, the results show that the proposed method converges faster and is more energy saving and stable while effectively protecting the energy storage components.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Regenerative braking energy; HESS; Power dynamic allocation; Deep reinforcement learning; SOC |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Dec 2022 10:35 |
Last Modified: | 26 Sep 2024 13:02 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/tte.2022.3227900 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194308 |