Wang, X. orcid.org/0000-0002-9075-2833, Zhou, J., Qin, B. orcid.org/0000-0001-9727-8989 et al. (1 more author) (2023) Coordinated power smoothing control strategy of multi-wind turbines and energy storage systems in wind farm based on MADRL. IEEE Transactions on Sustainable Energy. ISSN 1949-3029
Abstract
The randomness and volatility of wind power greatly affect the safety and economy of the power systems, and the wake effect of the wind farm aggravates the wind energy loss and the wind power fluctuation. Taking into consideration the wake effect of the wind farm, a new coordinated wind power smoothing control strategy for multi-wind turbines (M-WT) and energy storage systems (ESS) is proposed. The proposed method is based on a multi-agent deep reinforcement learning (MADRL), in which the relationship between output power and wake effect is firstly analyzed, and a power smoothing control model of the M-WT and ESS is established. MADRL is then introduced to optimize the power control of M-WT and ESS. In order to further increase the learning and training efficiency, an improved MADRL algorithm based on the partitioned experience buffer and prioritized experience replay is proposed, where the experience buffer is divided into positive, negative, and neutral experiences, and the experiences are sampled according to experience priority. The effectiveness of the proposed strategy is verified on the SimWindFarm platform. The results show that the proposed control strategy can maximize the economic benefits while further smoothing wind power fluctuations and increasing power generation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Sustainable Energy is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | wind farm; energy storage systems; power control; wake effect; multi-agent deep reinforcement learning |
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: | 21 Jun 2023 08:30 |
Last Modified: | 04 Sep 2023 12:43 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/tste.2023.3287871 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:200675 |