Recent research has proposed a set of advanced Energy Management System (EMS) for microgrids, including Model Predictive Control (MPC), Mixed-Integer Linear Programming (MILP), decentralized methods like droop control, as well as metaheuristics such as ACO (Ant Colony. Recent research has proposed a set of advanced Energy Management System (EMS) for microgrids, including Model Predictive Control (MPC), Mixed-Integer Linear Programming (MILP), decentralized methods like droop control, as well as metaheuristics such as ACO (Ant Colony. Microgrids as the main building blocks of smart grids are small scale power systems that facilitate the effective integration of distributed energy resources (DERs). In normal operation, the microgrid is connected to the main grid. In the event of disturbances, the microgrid disconnects from the. This article proposes an Energy Management System (EMS) for smart microgrids with a decentralized multi-agent system (MAS) based on a bio-inspired T-Cell optimization algorithm. The proposed system allows real-time control and dynamic balancing of loads while addressing the challenges of. Microgrid (MG) technologies offer users attractive characteristics such as enhanced power quality, stability, sustainability, and environmentally friendly energy through a control and Energy Management System (EMS). Microgrids are enabled by integrating such distributed energy sources into the. Part of the book series: Sustainable Artificial Intelligence-Powered Applications ( (SAIPA)) This paper presents an optimal power flow management (OPFM) optimization approach for managing active and reactive energy in a low-voltage microgrid (MG) connected to the main grid that incorporates. This paper addresses the optimization of power flow management in a hybrid AC/DC microgrid through an energy management system driven by particle swarm optimization.