Microgrid Intelligent Agent Control

Microgrid control is complex due to its need to accommodate the intermittence of renewables, balance generation with load, transit between grid-connected and islanded modes, and maintain reliable power supply to customers. Much research has addressed microgrid control complexity in both centralized and decentralized settings. This paper presents an intelligent software agent control with advanced autonomous capabilities to address the intermittent nature of renewables and their integration in real-world scenarios. Such capabilities include data acquisition, load and renewable generation forecasting, energy management, scheduling, optimal power flow, and real-time control to maintain generation-load balance in a secure and reliable microgrid environment. Accurate AI predictive models, machine learning algorithms, and non-linear optimization will be at the core function of the control agents.