Frequency Control Capability Estimation for Renewable Energy Stations Accounting for Dynamic Response Variations and Power Decoupling

The large-scale integration of converter-interfaced renewable energy sources has significantly reduced power system inertia, posing challenges to frequency stability. Although virtual inertia and primary frequency control can enhance the frequency support capability of renewable energy units, their actual performance often deviates from set values due to dynamic response differences among various energy sources (e.g., energy storage, photovoltaic, and wind power) and coupling between inertia and primary regulation power. Existing evaluation methods fail to accurately decouple these components or account for unit-specific dynamic characteristics, leading to considerable estimation errors. To address these issues, this paper proposes a novel estimation method for the frequency regulation capability of renewable energy stations. First, the dynamic frequency response characteristics of synchronous and renewable generators are compared. Then, a decoupling method is developed to separate virtual inertia power from primary frequency regulation power by leveraging their distinct response features. A first-order plus delay time (FOPDT) model is employed to characterize the external frequency response of different renewable energy units. The primary frequency regulation coefficient is estimated using a sliding window integration method, and the virtual inertia time constant is identified via a gradient descent algorithm based on the decoupled inertia power. A hardware-in-the-loop experimental platform is constructed using a real-time digital simulator (RTDS) and phasor measurement units (PMUs) to validate the proposed method. Simulation results show that the estimation errors for energy storage, photovoltaic, and wind power units are 0.63%, 6.38%, and 8.38% for the virtual inertia time constant and 0.45%, 0.72%, and 3.81% for the primary frequency regulation coefficient, respectively. Field test data further confirm the practical applicability and accuracy of the approach. The proposed method enables precise frequency control capability estimation, providing a reliable basis for parameter setting and capacity configuration of frequency regulation resources in low-inertia power systems.