Applications of Artificial Intelligence Techniques in Microgrid

Dear Colleagues,

The microgrid concept represents an advanced approach for integrating multiple distributed energy resources (DERs) into power distribution systems. One defining characteristic of microgrids is their ability to seamlessly transition between grid-connected and autonomous modes during utility grid disturbances, minimizing disruption to facilities within the microgrid. Additionally, microgrids serve as significant applications, offering platforms for integrating renewable energy sources and enabling the local generation of green energy, thus representing environmentally sustainable, cost-effective, and operationally simpler power and energy systems. Over the past decade, microgrids have seen extensive development and deployment, providing valuable supplementary support to the main grid. However, despite these advancements, there remains a significant effort among researchers and developers to enhance microgrid performance to meet the evolving requirements of modern power grids.

Artificial intelligence (AI) is utilized across various domains to achieve objectives like optimization, estimation, prediction, and control, often functioning solely on data and treating the studied system as a black box, requiring minimal prior knowledge. This characteristic is advantageous, making AI-based methods attractive for deployment in diverse systems. In recent years, there has been widespread attention towards the application of AI techniques in smart-grid and microgrid studies. With their wide applicability, AI-based methods are increasingly integrated into power and energy applications, enhancing system performance and offering innovative solutions.

Recognizing the importance of microgrids and the capabilities of AI, there is an opportunity for synergistic collaboration between the two to enhance microgrid performance. This Special Issue aims to provide a platform for researchers, practicing engineers, and other stakeholders to share their latest findings in microgrid control, protection, energy management, communication, cyber-security, energy trading, and related areas within the context of AI solutions. It aims to explore various topics related to the integration of AI and microgrids, including, but not limited to, the following topics:

• The optimization of microgrid design/planning using AI;
• AI-driven energy and power management in microgrids;
• The hierarchical control of microgrids employing AI;
• The AI-based control of power converters in microgrids;
• AI-driven cyber-attack detection in microgrids;
• Multi-agent deep reinforcement learning (MADRL) for energy management;
• The enhancement of microgrid stability through AI-based methods;
• The AI-based protection of microgrids;
• The optimal operation of residential microgrids utilizing AI;
• The integration of AI-based flexibility and reliability in microgrids;
• The real-time implementation of machine learning-based converter control in microgrids;
• Deep learning-based forecasting for electric vehicle integration into microgrids;
• The implementation of machine learning-based virtual inertia control for microgrids;
• Assessments of microgrid reliability using machine learning;
• AI-based approaches for low-latency communications in microgrids;
• Data-driven approaches for load and generation forecasting in microgrids;
• Edge computing in microgrids using deep learning techniques.

Dr. Yajie Jiang
Dr. Yun Yang
Dr. Wei Xu
Guest Editors

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• material design
• microgrid modeling
• artificial intelligence
• electric vehicle
• battery management system
• energy management
• power electronics
• machine learning