Tao, Haochen; Wu, Jinhui; Casagrande, Vittorio; Boem, Francesca; (2025) A Distributed MPC-Guided Safe Reinforcement Learning for Load Frequency Control in Interconnected Microgrids. In: Proceedings of the 1st IFAC Joint Conference on Computers, Cognition, and Communication 2025. IFAC: Padova, Italy. (In press).

Text J3C2025_paper_final.pdf - Accepted Version Access restricted to UCL open access staff until 22 January 2026. Download (407kB)

Abstract

Learning-based controllers may offer advantageous performance in systems with model uncertainty but often lack safety guarantees. This paper introduces a distributed safe Reinforcement Learning (RL) architecture, where a decentralised learning-based controller is guided by a distributed Model Predictive Control (diMPC)-based safety checker. Each subsystem in the network of interconnected systems is controlled by a local reinforcement learning (RL) agent using local data only. The proposed framework exploits a distributed tube-based model predictive control logic to design local safety checkers, ensuring recursive feasibility and safety of the global system, as well as providing robustness with respect to external disturbances and coupling effects. Moreover, the local safety checker actively guides the local RL agent via a reward shaping technique, taking into account of safe or unsafe behaviour. Preliminary simulation analysis shows the effectiveness of the proposed approach for the load frequency control in a network of nonlinear interconnected microgrid systems.

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