What is it about?
This study introduces a novel AI-based method to help manage multiple interconnected microgrids more safely and reliably. Microgrids are small, local power systems that include renewable energy sources like solar and wind. While they are key to a cleaner energy future, they can be vulnerable to disruptions such as cyberattacks or equipment failures. To address this, the authors developed a new algorithm using robust reinforcement learning, which is a type of AI that learns to make decisions even under uncertain and risky conditions. Specifically, they propose a method called Safe-RPO (Robust Policy Optimization) that learns how to operate these microgrids efficiently while still respecting safety limits, even when facing unexpected situations. The approach combines two major innovations: 1 Risk-aware learning that penalizes unsafe actions. 2 Multi-agent coordination, so each microgrid agent learns while interacting with others in the network.
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Why is it important?
energy and microgrids. However, existing AI decision-making systems can behave unpredictably under real-world risks, which could endanger grid safety and stability. This work is significant because it: 1 Improves energy system resilience, which is essential for reliable power supply during extreme events or attacks. 2 Bridges the gap between AI safety and power systems, ensuring decisions made by AI don’t violate operational constraints. 3 Enables real-world scalability, as it supports decentralized control across many microgrids, not just one. The proposed method shows superior performance in simulations when compared to standard reinforcement learning approaches, especially under uncertainty.
Perspectives
From the authors’ viewpoint, this work represents a step toward trustworthy AI in smart grid applications. While traditional control methods struggle with the dynamic and uncertain environment of modern energy systems, robust reinforcement learning offers a path forward—but only if safety can be guaranteed. This study not only proves that safe and robust AI can work in complex energy networks but also lays the groundwork for future field deployment. The authors believe that with further testing and collaboration between AI experts and power engineers, these algorithms can be deployed in real-world microgrid clusters, enhancing sustainability and reliability globally.
Professor/Clarivate Highly Cited Researcher/Associate Editor of IEEE TSG/TII/TSTE Yang Li
Northeast Electric Power University
Read the Original
This page is a summary of: Robust Reinforcement Learning-Based Resilient Operation of Networked Microgrids, IEEE Transactions on Industry Applications, January 2025, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/tia.2025.3619015.
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