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Abstract This paper provides a comprehensive review of inertia estimation methods, with a particular emphasis on the challenges posed by the integration of renewable energy sources (RESs). It examines a broad spectrum of inertia estimation methods, ranging from traditional swing equation-based methods to cutting-edge advancements such as machine learning and real-time analytics. These estimation methods are systematically categorised and evaluated based on key performance metrics including accuracy, simplicity, computational efficiency, and robustness against noise. The analytic hierarchy process (AHP) is used to identify the most suitable methods for low-inertia systems with high renewable energy penetration. The evaluation also includes an assessment of the temporal operational modes and the implementation requirements for the estimation methods. This leads to detailed recommendations on the most appropriate application environments for each method, considering factors such as system scale and generation mix. Existing challenges and future directions related to inertia estimation are also discussed. H ighlights • Comprehensive review of traditional and state-of-the-art inertia estimation methods for low-inertia power systems. • Rigorous evaluation of inertia estimation methods based on accuracy, computational efficiency, simplicity, and robustness. • Introduction of an Analytic Hierarchy Process (AHP) framework for ranking inertia estimation methods. • Detailed discussion of implementation challenges and operational modes in inertia estimation. • Identification of key research gaps and future directions for enhancing inertia estimation methodologies.
Ahmed et al. (Mon,) studied this question.