Optimal active power control of wind turbines for grid stability support

This dissertation addresses the critical challenge of grid frequency stability in the context of increasing reliance on renewable energy sources, particularly wind power. As the integration of wind turbines into power systems grows, ensuring their effective contribution to frequency regulation becomes essential. This research proposes a novel approach that employs data-enabled predictive control to enhance the frequency control of the future heterogeneous power grid and hence improve the overall grid stability. The study begins with a comprehensive analysis of the dynamic interactions between wind turbines and the power grid, identifying key factors that impact frequency control. A predictive control framework is developed to anticipate grid frequency fluctuations and optimize turbine responses. Through rigorous simulations and practical case studies, the author demonstrates the effectiveness of the proposed strategies in mitigating frequency deviations and improving overall system resilience. The findings highlight that data-enabled control not only enhances the responsiveness of wind turbines, among other generating units, to frequency support but also contributes significantly to a more stable and reliable power grid.