Intelligent Control Framework for Optimal Energy Management of a University Campus Microgrid

This study presents the development of a smart microgrid control framework. The goal is to achieve optimal energy management and maximize photovoltaic (PV) generation utilization through a combination of optimization and reinforcement learning techniques. A detailed Simulink model is developed in MATLAB to represent the dynamic behavior of the microgrid, including load variability, temperature profiles, and solar radiation. Initially, a genetic algorithm (GA) is used to perform static optimization and parameter tuning – identifying optimal battery charging/discharging schedules and balancing power flow between buildings in the microgrid to minimize main grid dependency. After that a Soft Actor-Critic (SAC) reinforcement learning agent is trained to perform real-time maximum power point tracking (MPPT) for the PV system under different environmental (weather) and load conditions. The SAC agent learns from multiple (eight) simulated PV generation scenarios and demand profiles, optimizing the duty cycle of the DC-DC converter to adaptively maintain maximum energy yield. The combined GA-SAC approach is validated on a university campus microgrid consisting of four interconnected buildings with heterogeneous loads, including computer labs that generate both active and reactive power demands. The results show improved efficiency, reduced power losses, and improved energy autonomy of the microgrid, illustrating the potential of AI-driven control strategies for sustainable smart energy systems.