Networked Microgrids: Artificial Intelligence (AI)-Based Control and Stability

Dear Colleagues,

The ongoing transition toward low-carbon and decentralized energy systems has accelerated the deployment of microgrids integrating renewable energy resources, energy storage systems, electric vehicles, and intelligent loads. Increasingly, multiple microgrids are being interconnected to form networked microgrids, enabling enhanced operational flexibility, resilience, energy sharing, and reliability. However, the growing complexity, uncertainty, and dynamic interactions within such systems present significant challenges for conventional model-based control and stability assessment techniques.

Recent advances in Artificial Intelligence (AI), including machine learning, deep learning, reinforcement learning, multi-agent systems, and data-driven optimization, offer transformative opportunities for the monitoring, control, and management of networked microgrids. AI-based approaches can improve adaptability to uncertain operating conditions, facilitate autonomous decision-making, enhance energy management, and support resilient operation under disturbances, communication failures, and cyber threats. Nevertheless, the integration of AI into critical power infrastructures raises fundamental questions regarding system stability, robustness, safety, interpretability, and real-time implementation.

This Special Issue, “Networked Microgrids: Artificial Intelligence (AI)-based Control and Stability,” aims to provide a dedicated platform for researchers, engineers, and practitioners to present the latest theoretical developments, methodologies, and practical applications in this rapidly evolving field. The issue focuses on innovative AI-enabled control architectures and advanced stability analysis techniques for interconnected microgrids and distributed energy systems.

Topics of interest include, but are not limited to, AI-based primary, secondary, and tertiary control; reinforcement learning and multi-agent learning for distributed energy management; data-driven and adaptive control strategies; stability analysis of learning-enabled controllers; resilient and secure control under cyber-physical disturbances; communication-aware and event-triggered control; cooperative energy sharing among networked microgrids; digital twins and intelligent monitoring; grid-forming inverter control; and hardware-in-the-loop and real-time validation of AI-based solutions.

While extensive literature exists on microgrid control, energy management, and AI applications in power systems, most studies focus on either standalone microgrids or AI algorithm development without comprehensive consideration of stability, resilience, and scalability in interconnected environments. This Special Issue seeks to bridge these research domains by bringing together contributions that integrate AI-driven decision-making with rigorous control-theoretic and stability-oriented frameworks. The collection will therefore provide a timely and valuable supplement to existing literature, fostering interdisciplinary advances that support the reliable, secure, and intelligent operation of future networked microgrid systems.

Dr. Thomas John
Prof. Dr. Marco Rivera
Dr. Arunachalam Sundaram
Guest Editors

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• networked microgrids
• hybrid multi-microgrid systems
• smart grids
• distributed energy resources (DERs)
• renewable energy integration
• grid-forming inverters
• artificial intelligence (AI)
• machine learning
• reinforcement learning
• multi-agent systems
• distributed control
• cooperative control
• intelligent energy management
• stability analysis
• data-driven control
• adaptive control
• resilient control
• AI-enhanced model predictive control (MPC)
• cyber-physical energy systems
• software-in-loop (SIL)
• hardware-in-loop (HIL)