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.