The Little Network That Could: Adaptive Real-time Frequency Control in a 75+% Renewables Grid

The complications posed by renewable energy sources like wind and solar, paired with a growing influx of vehicle-to-grid batteries, have prompted enthusiasm for data-driven solutions surrounding frequency control, grid stabilization, and fault ride-through in modern power grids. The Matlabbased simulation landscape, however, lags far behind researcher interest. While many Simulink and Simscape models are available for grid scenarios with electronic converters that are PID controlled, it is nearly impossible to find even a single functioning model using ML/AI/Deep Learning architectures for converter or grid control of any sort – and equally difficult to find models suitable for adaptation to systems with 75+% RES penetration. This paper presents an entry point for future researchers: An AI-supported Virtual Inertia mechanism with continual realtime adaptation implemented in Simulink using the Matlab Deep Learning Toolbox. The data-driven component is designed to fill the role of PI and PID controllers, allowing all current failsafes and emergency shutoffs to remain in place. We demonstrate empirically in a scenario with varying wind speeds that an AI controller comprised of four neural network layers is able to outperform a classic PID controller in terms of battery SOC balanced against grid frequency. Critically, the AI controller and accompanying grid model are implemented without constant voltage or ideal current sources, making it one of the only open-source Matlab models suitable for forward research on adaptive grid control with high renewables penetration.