Control of Low-Inertia Power Systems

IF 11.2 1区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Annual Review of Control Robotics and Autonomous Systems Pub Date : 2022-10-06 DOI:10.1146/annurev-control-052622-032657

F. Dörfler, Dominic Gross

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引用次数: 34

Abstract

Electric power systems are undergoing an unprecedented transition from fossil fuel–based power plants to low-inertia systems that rely predominantly on power electronics and renewable energy resources. This article reviews the resulting control challenges and modeling fallacies, at both the device and system level, and focuses on novel aspects or classical concepts that need to be revised in light of the transition to low-inertia systems. To this end, we survey the literature on modeling of low-inertia systems, review research on the control of grid-connected power converters, and discuss the frequency dynamics of low-inertia systems. Moreover, we discuss system-level services from a control perspective. Overall, we conclude that the system-theoretic mindset is essential to bridge different research communities and understand the complex interactions of power electronics, electric machines, and their controls in large-scale low-inertia power systems. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 14 is May 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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低惯量电力系统控制

电力系统正在经历一场前所未有的转变，从以化石燃料为基础的发电厂到主要依靠电力电子和可再生能源的低惯性系统。本文在设备和系统层面回顾了由此产生的控制挑战和建模谬误，并重点介绍了在向低惯性系统过渡的过程中需要修改的新方面或经典概念。为此，我们回顾了低惯量系统建模方面的文献，回顾了并网变流器控制方面的研究，并讨论了低惯量系统的频率动力学。此外，我们从控制的角度讨论系统级服务。总的来说，我们得出结论，系统理论思维对于弥合不同的研究社区和理解大规模低惯性电力系统中电力电子，电机及其控制的复杂相互作用至关重要。预计《控制、机器人和自主系统年度评论》第14卷的最终在线出版日期是2023年5月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。

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来源期刊

Annual Review of Control Robotics and Autonomous Systems Multiple-

CiteScore

28.30

自引率

2.20%

发文量

期刊介绍： The Annual Review of Control, Robotics, and Autonomous Systems offers comprehensive reviews on theoretical and applied developments influencing autonomous and semiautonomous systems engineering. Major areas covered include control, robotics, mechanics, optimization, communication, information theory, machine learning, computing, and signal processing. The journal extends its reach beyond engineering to intersect with fields like biology, neuroscience, and human behavioral sciences. The current volume has transitioned to open access through the Subscribe to Open program, with all articles published under a CC BY license.

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