Flexibility Assessment of Grid Controllable Resources Based on RBF Flow Calculation

As the global energy transition progresses, the integration of various distributed renewable energy sources has steadily increased. However, the inherent uncertainties associated with renewable energy outputs, such as wind and solar power, present significant challenges to the safe, stable, and reliable operation of power grids. The flexibility of power systems, particularly in managing active power regulation resources to meet varying load demands, is crucial for ensuring grid resilience. This paper proposes a methodology for evaluating grid flexibility based on RBF power flow calculations. Specifically, we develop a framework that incorporates Radial Basis Function (RBF) neural networks to compute power flow, combined with flexibility indicators such as up-regulation and down-regulation insufficiency probabilities, transformer flexibility margin expectations, and other key metrics reflecting the grid's capacity to absorb renewable energy. Case studies based on the IEEE 34node system demonstrate the effectiveness of the proposed approach, offering a high-precision and reliable method for assessing grid flexibility in modern power systems.