The Role of Digital Twins in Power System Inertia Estimation

Abstract

Modern power systems are experiencing a deep transformation phase, as a result of the increasing penetration of renewable power generators, which causes many consequences on grid stability. In this scenario, power system inertia is rapidly decreasing and extremely variable, pushing system operators to develop reliable tools enabling online inertia estimation. To effectively address this challenge, system operators could develop a mirrored copy of the system, called Digital Twin, which allows performing advanced online analyses aimed at studying the dynamic behavior of the grid. To outline the potential role of this emerging computing paradigm in the context of power system dynamics, this paper analyzes the performance of adaptive data-driven models in online grid parameter estimation. A two-area model is considered, where the experimental results showed the effectiveness of the analyzed methods in reliably reproducing the frequency evolution under different operation scenarios.