Wang, X., Zhou, J., Qin, B. et al. (1 more author) (2023) Coordinated control of wind turbine and hybrid energy storage system based on multi-agent deep reinforcement learning for wind power smoothing. Journal of Energy Storage, 57. 106297. ISSN 2352-152X
Abstract
Due to the inherent fluctuation, wind power integration into the large-scale grid brings instability and other safety risks. In this study by using a multi-agent deep reinforcement learning, a new coordinated control strategy of a wind turbine (WT) and a hybrid energy storage system (HESS) is proposed for the purpose of wind power smoothing, where the HESS is combined with the rotor kinetic energy and pitch control of the wind turbine. Firstly, the wind power output is forecasted and decomposed into high, medium, and low-frequency components through an adaptive variational mode decomposition (VMD). The optimal secondary allocation of the reference power of the high-frequency and medium-frequency is then performed through a multi-agent twin-delay deep deterministic policy gradient algorithm (MATD3) to smooth the power output. To improve the exploration ability of the learning, a new type of α-“stable” Lévy noises is injected into the action space of the MATD3 and the noises are dynamically adjusted. Simulation and RT-LAB semi-physical real-time experimental results show that the proposed control strategy can make full use of the smoothing output power of the WT and HESS combined generation system reasonably, extend the life of the energy storage elements and reduce the wear of the WT.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Elsevier Ltd. This is an author produced version of a paper subsequently published in Journal of Energy Storage. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Wind power smoothing; Hybrid energy storage system (HESS); Pitch control; Rotor kinetic energy control; Coordinated control; Multi-agent deep reinforcement learning TD3 |
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: | 09 Dec 2022 17:47 |
Last Modified: | 09 Dec 2023 01:13 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.est.2022.106297 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194268 |