The increasing penetration of renewable energy technologies such as wind and solar photovoltaics has displaced conventional synchronous generation, resulting in reduced system inertia. Low-inertia conditions degrade frequency stability and limit the power system’s disturbance response capability. Accurate inertia estimation is therefore essential to prevent over-frequency events, unintended protection actions, load shedding, and cascading failures. Moreover, the variability of renewable energy sources complicates inertia dynamics, rendering traditional approximation-based estimation methods inadequate for modern power systems. This paper proposes a novel hybrid optimization-based inertia estimation method that combines particle swarm optimization (PSO) and genetic algorithm (GA) for grid-scale energy storage systems. Unlike conventional approaches, the proposed framework systematically integrates inertia formulations applicable to both synchronous generators and converter-interfaced resources within a unified estimation structure. The performance of the proposed method is evaluated and compared with PSO and GA using the IEEE 39 bus system in two disturbance scenarios. The results demonstrate that the proposed hybrid PSO–GA approach achieves superior robustness, estimation accuracy, and adaptability for operational inertia awareness and near real-time inertia applications. The results confirm that the proposed method provides an effective and reliable inertia estimation tool to support frequency regulation, enhance disturbance response, and ensure secure operation of low-inertia power systems.
Abdilahi et al. (2026) studied this question.