Abstract
As power systems around the world shift to incorporate more renewable energy sources, particularly wind power, maintaining grid stability becomes increasingly challenging due to the inherent variability of these sources. This paper introduces a novel bi-level robust optimization framework that enhances the capabilities of adaptive Under-Frequency Load Shedding (AUFLS) in managing the uncertainties brought by high penetration of wind energy and dynamic participation of electric vehicles (EVs). Central to this framework is an innovative adaptive non-parametric Kernel Density Estimation (AAKDE) technique, which sharpens the accuracy of wind power fluctuation predictions. This method enables more precise and efficient control of load-shedding events, which is crucial for preventing frequency drops that can lead to grid instability. This research proposes a strategic shedding queue mechanism that systematically prioritizes the discharge of EVs based on their real-time state-of-charge and charging behavior. This prioritization minimizes user discomfort and taps into the potential of EVs as flexible energy resources, thus providing substantial support to grid operations. To enhance the responsiveness of our AUFLS approach, we integrate a reinforcement learning model that adjusts in real time to grid conditions, optimizing decision-making for frequency stabilization. Our extensive MATLAB/SIMULINK simulations on an upgraded IEEE 39 bus test system demonstrate a significant reduction in load shedding requirements. Compared to traditional AUFLS methods, our approach cuts load shedding by over 50%, effectively maintains system frequency within safe operational limits, and shows superior performance in scenarios of high renewable variability and EV integration. This research highlights the potential of adaptive non-parametric methods in transforming AUFLS strategies, paving the way for smarter, more resilient power systems equipped to handle the complexities of modern energy landscapes.