Data-Driven Approach to Synthetic Inertia and Droop Estimation in Behind-the-Meter Renewable Energy Sources

Renewable energy sources (RESs) are expected to play a key role in supporting frequency stability in modern power systems, and thus, a variety of inertia-control methods have been developed. However, many RESs are installed behind the meter (BTM), and their data often remain inaccessible to the energy management systems (EMS) operated by utilities. As a result, their contributions to system inertia are frequently overlooked in stability evaluations. Although several inertia estimation methods have been proposed, most assume full availability of RES data. In practice, however, access to some data is often restricted to utilities, which limits the applicability of conventional methods, particularly for BTM-installed RESs. This article presents two data driven approaches for estimating the synthetic inertia and droop coefficients of BTM RESs, explicitly considering data availability under both normal and dynamic conditions. The first approach uses steady state EMS and RES data with different sampling intervals, while the second relies only on EMS dynamic data recorded during contingencies, avoiding dependence on RES measurements. Verification on the Jeju Island power system with practical measured EMS and RES data validate the proposed approaches and emphasize the influence of sampling intervals on estimation accuracy. The results show that the proposed methods provide an effective solution for assessing inertia and droop of BTM RESs with only limited data access, enabling utilities to conduct more reliable frequency stability analysis in low inertia grids.