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Protecting privacy in microgrids using federated learning and deep reinforcement learning

Chen, Wenzhi; Sun, Hongjian; Jiang, Jing; You, Minglei; Piper, William J.S.


Wenzhi Chen

Hongjian Sun

Jing Jiang

William J.S. Piper


This paper aims to improve the energy management efficiency of home microgrids while preserving privacy. The proposed microgrid model includes energy storage systems, PV panels, loads, and the connection to the main grid. A federated multi-objective deep reinforcement learning architecture with Pareto fronts is proposed for total carbon emission and electricity bills optimization. The privacy of data is protected by federated learning, by which the original data will not be uploaded to the server. Numerical results show that compared with the traditional single Deep-Q network, using the proposed method the accumulated carbon emission decreased by 3% and the electricity bills decreased by 21%.


Chen, W., Sun, H., Jiang, J., You, M., & Piper, W. J. (2022). Protecting privacy in microgrids using federated learning and deep reinforcement learning.

Conference Name 12th IET International Conference on Advances in Power System Control, Operation and Management
Conference Location Hyatt Regency Tsim Sha Tsui, Hong Kong and Online
Start Date Nov 7, 2022
End Date Nov 9, 2022
Acceptance Date Oct 1, 2022
Online Publication Date Nov 7, 2022
Publication Date Nov 7, 2022
Deposit Date Feb 24, 2023
Publicly Available Date Mar 6, 2023
Publisher Institution of Engineering and Technology (IET)
Keywords Microgrids; Privacy; Deep learning; Multi- objective
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