Protecting privacy in microgrids using federated learning and deep reinforcement learning

Chen, Wenzhi, Sun, Hongjian, Jiang, Jing, You, Minglei and Piper, William J.S. (2022) Protecting privacy in microgrids using federated learning and deep reinforcement learning. In: 12th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM): Enabling high performance & resilience power grid: decarbonization, digitalization, automation, and beyond. IET, Stevenage. (In Press)

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Abstract

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.

Item Type: Book Section
Additional Information: 12th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM): Enabling high performance & resilience power grid: decarbonization, digitalization, automation, and beyond, APSCOM 2022, Hong Kong, 7-9 Nov 2022
Uncontrolled Keywords: Microgrids, Privacy, Deep learning, Multi-objective
Subjects: G500 Information Systems
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Related URLs:
Depositing User: John Coen
Date Deposited: 10 Jan 2023 09:26
Last Modified: 10 Jan 2023 09:30
URI: https://nrl.northumbria.ac.uk/id/eprint/51109

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