Defining Agentic AI in Microgrid Management for Sustainable Distributed Energy Generation

Background: Microgrids, which are pivotal in sustainable energy distribution, are increasingly integrating renewable energy sources. However, renewable;sources like solar and wind introduce variability and unpredictability into the energy supply, challenging stable microgrid management. Traditional;centralized models struggle to handle these fluctuations effectively, making more autonomous approaches essential. Research Gap: Current centralized;models do not offer the agility required to balance supply and demand in real time, given the rapid, unpredictable shifts inherent to renewable energy;sources. This gap necessitates the exploration of decentralized, autonomous systems, particularly in the context of agentic AI, to handle these;challenges. Aim: This project aims to investigate the optimal level of autonomy required for agentic AI to maintain microgrid stability effectively. The goal;is to identify a balance between decentralized, autonomous decision-making and centralized oversight to manage the variability of renewable energy;sources while minimizing disruptions. Objectives: To assess the trade-offs between autonomy and centralized control in microgrid management. To;evaluate how agentic AI’s predictive and real-time capabilities can mitigate the instability introduced by renewable energy variability. To explore a multiagent system (MAS) architecture in which autonomous agents collaborate to monitor and regulate various microgrid components, such as battery;storage and connectivity. To optimize microgrid performance through a distributed, agent-based decision-making framework that maintains a balanced;energy supply-demand relationship.