A Cost-Oriented Methodology for Predicting Inertia in Power Systems

With the large-scale integration of renewable energy sources such as wind and photovoltaic power, the inertia level of power systems has been continuously declining, leading to deteriorated frequency response characteristics and posing significant challenges to the safe and stable operation of the grid. Therefore, assessing and predicting the inertia level of modern power systems is crucial for optimizing grid dispatch and operation. Traditional inertia prediction methods primarily focus on minimizing statistical error metrics; however, studies have shown that more precise predictions do not necessarily lead to better decision-making outcomes. Based on this insight, this paper proposes a cost-oriented inertia prediction methodology designed to enhance the practical contribution of prediction results to both economic efficiency and system security. The results demonstrate that, compared to conventional methods that solely minimize statistical errors, the proposed approach exhibits superior performance in real-world applications by effectively reducing system operating costs and improving the reliability of dispatch decisions.