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Toward Resilient Modern Power Systems: From Single-Domain to Cross-Domain Resilience Enhancement

Authors :
Huang, Hao
Vincent Poor, H.
Davis, Katherine R.
Overbye, Thomas J.
Layton, Astrid
Goulart, Ana E.
Zonouz, Saman
Source :
Proceedings of the IEEE; 2024, Vol. 112 Issue: 4 p365-398, 34p
Publication Year :
2024

Abstract

Modern power systems are the backbone of our society, supplying electric energy for daily activities. With the integration of communication networks and high penetration of renewable energy sources (RESs), modern power systems have evolved into a cross-domain multilayer complex system of systems with improved efficiency, controllability, and sustainability. However, increasing numbers of unexpected events, including natural disasters, extreme weather, and cyberattacks, are compromising the functionality of modern power systems and causing tremendous societal and economic losses. Resilience, a desirable property, is needed in modern power systems to ensure their capability to withstand all kinds of hazards while maintaining their functions. This article presents a systematic review of recent power system resilience enhancement techniques and proposes new directions for enhancing modern power systems’ resilience considering their cross-domain multilayer features. We first answer the question, “what is power system resilience?” from the perspectives of its definition, constituents, and categorization. It is important to recognize that power system resilience depends on two interdependent factors: network design and system operation. Following that, we present a review of articles published since 2016 that have developed innovative methodologies to improve power system resilience and categorize them into infrastructural resilience enhancement and operational resilience enhancement. We discuss their problem formulations and proposed quantifiable resilience measures, as well as point out their merits and limitations. Finally, we argue that it is paramount to leverage higher order subgraph studies and scientific machine learning (SciML) for modern power systems to capture the interdependence and interactions across heterogeneous networks and data for holistically enhancing their infrastructural and operational resilience.

Details

Language :
English
ISSN :
00189219
Volume :
112
Issue :
4
Database :
Supplemental Index
Journal :
Proceedings of the IEEE
Publication Type :
Periodical
Accession number :
ejs66690367
Full Text :
https://doi.org/10.1109/JPROC.2024.3405709