Advanced Applications of Artificial Intelligence for Planning, Control, and Operation Management of Networked Microgrids

Dear Colleagues,

The integration of renewable energy resources, decentralized energy systems, and advanced communication and monitoring technologies have together led to the emergence of networked microgrids as a pivotal solution making it possible to achieve efficient, sustainable, and resilient energy systems. However, the presence of uncertainty and the complex interactions within these systems require advanced methodologies for planning, control, and operation management to ensure reliability, efficiency, and adaptability. Artificial intelligence (AI) offers transformative potential in addressing these challenges by enabling innovative solutions for real-time decision-making, forecasting, fault detection, demand response, predictive maintenance, and adaptive control and operation management within microgrid networks. This Special Issue aims to bring together cutting-edge research and novel applications of AI to advance the planning and learning-based control and operation management of networked microgrids.

We invite researchers and practitioners to contribute original research articles, review papers, and case studies that explore, but are not limited to, the following topics:

• AI-based optimization techniques for microgrid design and operation;
• Machine learning and deep learning applications in load forecasting, predictive maintenance, and fault management;
• AI-driven and learning-based strategies for control, energy management, and demand response in interconnected microgrids;
• Learning-based decentralized control strategies for networked microgrids;
• Brain-inspired learning methods for adaptive control and energy management in microgrid networks;
• Data-driven cybersecurity measures for microgrid networks;
• Integration of IoT and AI for real-time monitoring and adaptive control;
• Multi-agent systems for coordinated control and operation management of networked microgrids;
• Case studies and real-world implementations of AI-driven networked microgrids.

Dr. Najmeh Bazmohammadi
Prof. Dr. Juan C. Vasquez
Dr. Mahshid Javidsharifi
Guest Editors

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