Distributed Predictive Cooperative Control of Smart Power Converters in Microgrids

Microgrids with power converter-based distributed energy resources are modular electrical networks that make our power systems more sustainable, reliable, and resilient. However, traditional model predictive control of power converters suffers from parameter uncertainties and limited distributed energy resource coordination, while existing solutions inadequately address measurement noise and dynamic response limitations. This talk presents novel control strategies for smart power converters in microgrids to enhance sustainability, reliability, and resilience. Two main contributions are presented. First, a model-free predictive control approach using a multifrequency extended state observer (ESO) framework that estimates sub-frequency harmonics of lumped disturbances, achieving 55% better measurement noise suppression than conventional ESOs without compromising disturbance rejection or parameter robustness. Second, distributed cooperative secondary control with virtual synchronous generator inertia emulation and novel virtual capacitance algorithms for accurate reactive power sharing, delivering 60% improvement over state-of-the-art methods despite line parameter mismatches. Real-time hardware-in-the-loop tests and hardware experiments validate performance across islanded and grid-connected microgrid scenarios.