Dynamic estimation of frequency regulation capability in power systems across multiple time scales

The escalation in the integration of new energy sources has resulted in a continual decrease in the overall inertia level within the power system, accentuating the uneven distribution of frequency regulation capacity. Assessing the inertia level and quantifying primary frequency regulation ability in power systems using synchronized Phasor Measurement Units (PMUs) data online holds significant importance. This study investigates the feasibility of online assessment of inertia and primary frequency regulation ability on a regional basis by partitioning the large system into multiple areas, taking into account the spatiotemporal distribution characteristics of the power system's frequency dynamic response. The demand for swift online frequency regulation ability assessment algorithms is heightened within the context of dynamic system partitioning. This paper scrutinizes the principles of inertia and primary frequency regulation ability assessment in power systems, outlining the prerequisites for dynamic frequency response partitioning within the system. It suggests a real-time power system partitioning approach that amalgamates Pearson distance with the K-means clustering algorithm. Furthermore, the paper introduces an online evaluation algorithm for power system inertia and primary frequency regulation ability, leveraging quantum particle swarm optimization technique, enabling dynamic estimation of frequency response capability across various time scales in power systems. Ultimately, the efficacy of the proposed method, its capacity to promptly track and quantify power system frequency response capability across multiple time scales, and its adaptability to diverse scenarios are corroborated using the IEEE 39-bus system.