高比例可再生能源电力系统惯量预测方法研究综述

沈赋; 曹旸; 徐潇源; 华昊辰; 王健; 曾芳; 邱革非

doi:10.12204/j.issn.1000-7229.2025.08.011

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电力建设 ›› 2025, Vol. 46 ›› Issue (8) : 116-128. DOI: 10.12204/j.issn.1000-7229.2025.08.011

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高比例可再生能源电力系统惯量预测方法研究综述

沈赋 ¹ ,
曹旸 ¹ ,
徐潇源 ² ,
华昊辰 ³ ,
王健 ¹ ,
曾芳 ¹ ,
邱革非 ¹

作者信息 +

A Review of Inertia Prediction Methods for Power System with High Penetration Renewable Energy Sources

SHEN Fu ¹ ,
CAO Yang ¹ ,
XU Xiaoyuan ² ,
HUA Haochen ³ ,
WANG Jian ¹ ,
ZENG Fang ¹ ,
QIU Gefei ¹

Author information +

文章历史 +

摘要

【目的】惯量预测对于频率控制、管理可再生能源渗透、快速频率响应分析以及综合惯量设计电力辅助服务市场等都至关重要。随着高比例可再生能源发电的接入和传统可控发电机组占比的下降，对系统惯量水平进行预测变得更加复杂。文章对高比例可再生能源电力系统中惯量预测的必要性、挑战及最新进展进行了全面概述。【方法】首先，回顾了不同时期电力系统的惯量构成及惯量预测方法的发展，分析开展惯量预测工作的必要性和难点。进而，给出了高比例可再生能源电力系统惯量预测方法的研究框架，依据应用场景和时间尺度，将惯量预测方法划分为基于统计法的惯量预测和基于数据驱动法的惯量预测，并进行分类阐述。同时，结合现有方法，从准确性优化和目标导向性优化2个方面提出了惯量预测方法的优化策略。【结果】最后，对未来电力系统惯量预测领域须深入研究的方向进行展望，旨在为未来电力系统的惯量管理应用提供思路。【结论】文章为未来电力系统惯量管理的理论发展与实践应用提供了重要参考，推动建立更精确、更贴合实际决策需求的惯量预测体系，对提升系统频率稳定性与调节能力具有重要意义。

Abstract

[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.

导出引用

沈赋, 曹旸, 徐潇源, 等. 高比例可再生能源电力系统惯量预测方法研究综述[J]. 电力建设. 2025, 46(8): 116-128 https://doi.org/10.12204/j.issn.1000-7229.2025.08.011

SHEN Fu, CAO Yang, XU Xiaoyuan, et al. A Review of Inertia Prediction Methods for Power System with High Penetration Renewable Energy Sources[J]. Electric Power Construction. 2025, 46(8): 116-128 https://doi.org/10.12204/j.issn.1000-7229.2025.08.011

中图分类号： TM73

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摘要

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https://doi.org/10.12096/j.2096-4528.pgt.22118

本文引用 [1] 摘要

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https://doi.org/10.12204/j.issn.1000-7229.2023.08.003

摘要

对于新型电力系统,提前预测系统惯量水平是消除系统惯量薄弱风险的重要前提。而预测所使用的机器学习模型大多是“黑箱”模型,存在可解释性不足的问题。为此,提出了一种基于可解释性极端梯度提升算法 (extreme gradient boosting,XGBoost)的电力系统惯量短期预测方法。该方法根据系统惯量响应特性分析,选择电力系统运行数据和气象数据作为输入特征。基于SHAP归因方法构建可解释性XGBoost的解释机制,通过计算Shapley值来量化各特征的重要程度,从而对模型预测结果进行多维度解构分析。以实际系统为例进行实证分析,结果表明,所提方法能够有效地预测电力系统短期惯量以及解释特征对预测的影响。

ZHANG

Lei

, LI

Haitao

, XIONG

Zhizhi

, et al. Short-term prediction method based on interpretable XGBoost for power system inertia[J]. Electric Power Construction, 2023, 44(8): 22-30.

https://doi.org/10.12204/j.issn.1000-7229.2023.08.003

本文引用 [1] 摘要

Predicting the level of in advance in new power systems is essential to eliminate the risk of a weak system inertia, and black-box machine learning models, which have insufficient interpretability, are widely used for system-inertia predictions. Therefore, this paper introduces a short-term prediction method, based on interpretable extreme gradient boosting (XGBoost), for power system inertia. Based on the analysis of the system inertia response characteristics, the method selects the power system operation and meteorological data as input features. The interpretation mechanism of XGBoost was constructed based on Shapley additive explanation values. By calculating the Shapley value to quantify the importance of each feature, the model prediction results can be deconstructed into multiple dimensions. Simulations were performed using a realistic photovoltaic system, and the results showed that the proposed method can effectively predict the short-term inertia of a power system as well as elucidate the influence of the features on the predicted results.

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