Artificial Intelligence-Assisted Distributed MPC for Frequency Regulation in Networked Microgrid Systems

This thesis addresses the challenge of stabilizing electrical frequency in networks of microgrids powered by renewable energy, where sudden changes in wind and solar power can cause blackouts. It replaces advanced controllers with efficient neural networks that mimic optimal performance. This approach enables real-time, reliable frequency control using low-cost hardware, making resilient renewable energy grids practically achievable.