Reconfiguration of Distributed Cooperative Microgrid Control Systems via Deep Reinforcement Learning to Mitigate Synchronization Attacks

Microgrids represent a promising paradigm within smart grid systems, enabling efficient energy management through the integration of renewable energy sources via distributed generators (DGs). These DGs are interconnected through communication networks and managed by distributed, cooperative control systems that synchronize set points via information exchange. A critical challenge in these systems is the control network reconfiguration in response to synchronization attacks targeting communication links. In this paper, we propose a Deep Reinforcement Learning (DRL)-based reconfiguration approach that autonomously adjusts the control network among DGs, considering the microgrid’s stability constraints. The main idea is to enhance synchronization in our microgrid by connecting synchronized nodes to unsynchronized ones. Our objective is to construct a minimum spanning tree (MST) that enables the distributed control system to exchange synchronization information efficiently and in a timely manner, while avoiding compromised links and minimizing disruption to microgrid stability after reconfiguration. Our experimental results demonstrate that the DRL-based strategy outperforms a traditional greedy algorithm by achieving a more optimal reconfiguration of the control network.