Intelligent modeling plays a crucial role in modern power systems, particularly in the planning, operation, and control of microgrids. Microgrids are local, low-voltage distribution systems that facilitate the integration of renewable energy sources and storage systems. Equipped with advanced control systems, microgrids enhance the reliability and stability of the power system. Intelligent modeling encompasses various techniques, including machine learning, data analytics, and optimization algorithms, developed to design, operate, and manage microgrids effectively.

This approach enables system operators to predict renewable power generation, load demand, and energy prices, optimize unit dispatch, facilitate the integration of renewable energy sources, and address environmental pollution concerns. Additionally, intelligent modeling aids in fault detection, which enhances self-healing mechanisms and improves microgrid resilience. In summary, intelligent modeling empowers microgrids to become intelligent, adaptive, and sustainable energy solutions, paving the way for a more resilient and decarbonized power system that harnesses the full potential of renewable energy sources.

This Special Issue aims to bring together various intelligent modeling approaches, advances in analytical techniques, integration of artificial intelligence, the Internet of Things (IoT), 5G/6G applications in energy systems, and other advanced methods and mechanisms to address microgrid challenges. These challenges encompass technical, environmental, and economic aspects related to the planning, operation, and control of microgrids. Furthermore, we encourage submissions focusing on energy management systems, self-healing mechanisms, and multi-carrier energy hubs. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Energy management in microgrids
• Enhancing resilience in microgrids
• Operation and planning of islanded microgrids
• Islanding detection in microgrids
• Multi-carrier energy systems
• Operation and control of multi-microgrids
• Community microgrids
• Intentional islanding of microgrids
• Integration of renewable energy sources into microgrids
• Environmental impacts of advanced microgrids
• Uncertainty modeling in microgrid operation
• Enhancements in hosting capacity of microgrids
• Impact of electric vehicle integration on microgrids
• 5G/6G applications in energy systems
• Forecasting renewable power generation, load demand, and market prices
• Operation and control of storage systems in microgrids
• Applications of machine learning, deep learning, and deep reinforcement learning in microgrid modeling