Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence-based methods

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Haochen Hua, Zhiqian Wei, Yuchao Qin, Tonghe Wang, Liuying Li, Junwei Cao
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引用次数: 23

Abstract

Energy internet (EI) can alleviate the arduous challenges brought about by the energy crisis and global warming and has aroused the concern of many scholars. In the research of EI control systems, the access of distributed energy causes the power system to exhibit complex nonlinearity, high uncertainty and strong coupling. Traditional control and optimization methods often have limited effectiveness in solving these problems. With the widespread application of distributed control technology and the maturity of artificial intelligence (AI) technology, the combination of distributed control and AI has become an effective method to break through current research bottlenecks. This study reviews the research progress of EI distributed control technologies based on AI in recent years. It can be found that AI-based distributed control methods have many advantages in maintaining EI stability and achieving optimal energy management. This combination of AI and distributed control makes EI control systems more intelligent, safe and efficient, which will be an important direction for future research. The purpose of this study is to provide a reference as well as useful research ideas for the study of EI control systems.

Abstract Image

能源互联网分布式控制与优化研究综述:从传统方法到基于人工智能的方法
能源互联网能够缓解能源危机和全球变暖带来的严峻挑战,引起了众多学者的关注。在EI控制系统的研究中,分布式能量的接入使电力系统呈现出复杂的非线性、高不确定性和强耦合。传统的控制和优化方法在解决这些问题时往往效果有限。随着分布式控制技术的广泛应用和人工智能(AI)技术的成熟,将分布式控制与AI相结合已成为突破当前研究瓶颈的有效方法。本文综述了近年来基于AI的EI分布式控制技术的研究进展。可以发现,基于人工智能的分布式控制方法在保持EI稳定性和实现最优能量管理方面具有诸多优势。这种人工智能与分布式控制的结合,使EI控制系统更加智能、安全、高效,将是未来研究的重要方向。本研究的目的是为EI控制系统的研究提供参考和有益的研究思路。
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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
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