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A Review of Inertia Prediction Methods for Power System with High Penetration Renewable Energy Sources
SHEN Fu, CAO Yang, XU Xiaoyuan, HUA Haochen, WANG Jian, ZENG Fang, QIU Gefei
Electric Power Construction ›› 2025, Vol. 46 ›› Issue (8) : 116-128.
PDF(4702 KB)
PDF(4702 KB)
A Review of Inertia Prediction Methods for Power System with High Penetration Renewable Energy Sources
[Objective] Inertia prediction is critical for frequency control，renewable energy penetration management，fast frequency response analysis，and integrated inertia design of ancillary service markets in the power sector. Predicting system inertia levels is increasingly complex and necessary because a high percentage of renewable power generation is connected to the grid，and the share of conventional controllable generating units is decreasing. [Methods] This paper provides a comprehensive overview of the necessity，challenges，and recent advances in inertia prediction in high-percentage renewable power systems. First，we review the inertia composition of power systems and the development of inertia prediction methods across different periods to analyze the necessity and difficulties of inertia prediction. Then，we present a research framework of inertia prediction methods in power systems with a high proportion of renewable energy，and categorize the inertia prediction methods into those based on statistical and data-driven methods according to their application scenarios and time scales. This classification is elaborated below. In addition，we propose optimization strategies for inertia prediction methods，focusing on accuracy enhancement and goal-oriented approaches，by combining them with existing research results. [Results] We identified key directions that require in-depth future research on power system inertia prediction to provide constructive ideas for advancing inertia management applications. [Conclusions] This study provides an important reference for the future theoretical development and practical application of power system inertia management. It promotes the establishment of a more accurate and practically relevant inertia prediction system to meet actual decision-making needs. This advancement is important for improving the frequency stability and regulation capabilities of the system.
inertia prediction / data-driven / unit commitment / prediction optimization
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