Review of Hybrid Data-driven and Physics-based Modeling for the Operation of New-type Power Systems

被引：0
作者：
Ruan G. [1 ]
He Y. [1 ]
Tan Z. [1 ]
Zhong H. [1 ]
机构：
[1] Department of Electrical Engineering, Tsinghua University, Haidian District, Beijing
来源：
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2024年 / 44卷 / 13期
关键词：
artificial intelligence; data-driven; high renewable energy penetration; knowledge-driven; machine learning; new-type power system;
D O I：
10.13334/j.0258-8013.pcsee.230321
中图分类号：
学科分类号：
摘要：
Constructing new-generation power systems dominated by renewable energy is crucial to achieve China’s carbon neutrality goal, but this will inevitably bring significant changes and challenges to the existing power grids. Hybrid data-driven and physics-based modeling (hybrid modeling for short) is an emerging technique to combine the advantages of physic laws and data, showing great potential to serve as an important analysis tool for the new-generation power systems. To this end, this paper clarifies the relevant concepts and use cases at first, and then discusses the research trends and hotspots in recent literature. A general framework to evaluate the performance of hybrid modeling from an aspect of technical features and hybrid patterns is also proposed. In addition, this paper is focused on the operation of new-generation power systems, and has fully summarized the pros and cons of hybrid modeling in addressing the existing technical challenges. Further suggestions for future research work and pilot projects are discussed at last. ©2024 Chin.Soc.for Elec.Eng.
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页码：5021 / 5036
页数：15
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