Advancing microgrid cyber resilience: Fundamentals, trends and case study on data-driven practices

Sarker, SK; Shafei, H; Li, L; Aguilera, RP; Hossain, MJ; Muyeen, SM

Advancing microgrid cyber resilience: Fundamentals, trends and case study on data-driven practices

Sarker, SK Shafei, H Li, L

Aguilera, RP Hossain, MJ Muyeen, SM

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Applied Energy, 2025, 401
Issue Date:: 2025-12-15

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Field	Value	Language
dc.contributor.author	Sarker, SK
dc.contributor.author	Shafei, H
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.contributor.author	Aguilera, RP
dc.contributor.author	Hossain, MJ
dc.contributor.author	Muyeen, SM
dc.date.accessioned	2025-12-19T04:48:46Z
dc.date.available	2025-12-19T04:48:46Z
dc.date.issued	2025-12-15
dc.identifier.citation	Applied Energy, 2025, 401
dc.identifier.issn	0306-2619
dc.identifier.issn	1872-9118
dc.identifier.uri	http://hdl.handle.net/10453/191033
dc.description.abstract	Microgrids (MGs) serve as the core components of the upcoming sustainable power systems, and ensuring their security against cyber threats presents a critical research challenge due to the widespread use of advanced energy technologies. This paper explores various strategies for maintaining the cyber-resilient operation of MGs, focusing on technical, economic, and regulatory frameworks, in addition to their operational essentials for seamless functionality. In this paper, cyber-resilient operation refers to the system's ability to withstand, respond to, and recover from cyber incidents, thereby ensuring the continuous and reliable operation of the MG. An outline of the various security challenges linked to different cyber-attacks and MG frameworks, highlighting the importance of developing effective and adaptable solutions, is also studied in this paper. While model-based approaches offer precise detection accuracy under steady-state conditions, they often struggle in real-time dynamic scenarios due to their complexity and dependence on accurate system modeling. Conversely, data-driven approaches offer enhanced flexibility and adaptability, enabling swift responses to emerging cyber threats. This makes them a compelling alternative to dynamic model-based methods for ensuring cyber-secure operations of MGs. This study focuses on data-driven techniques, acknowledging the comparative strengths and limitations of both paradigms. This paper also outlines crucial steps for crafting scalable and efficient data-driven cyber solutions, highlighting their key characteristics that enhance MG security. It provides a thorough overview of recent data-driven cyber solutions for MGs, offering careful analysis to evaluate the effectiveness of these methods in enhancing security while identifying operational and implementation challenges. A case study on a two-area isolated microgrid is presented, where a data-driven framework optimized by Bayesian learning approximation is examined. This case study demonstrates the capability of the studied data-driven framework in enhancing the resilience of IMGs against cyber threats. Ultimately, the paper concludes with recommendations for the field of data-driven cyber solutions and MGs, aiming to foster further advancements in sustainable and reliable cybersecurity measures for MG frameworks.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Applied Energy
dc.relation.isbasedon	10.1016/j.apenergy.2025.126753
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	09 Engineering, 14 Economics
dc.subject.classification	Energy
dc.subject.classification	33 Built environment and design
dc.subject.classification	38 Economics
dc.subject.classification	40 Engineering
dc.title	Advancing microgrid cyber resilience: Fundamentals, trends and case study on data-driven practices
dc.type	Journal Article
utslib.citation.volume	401
utslib.for	09 Engineering
utslib.for	14 Economics
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-12-19T04:48:44Z
pubs.publication-status	Accepted
pubs.volume	401

Abstract:

Microgrids (MGs) serve as the core components of the upcoming sustainable power systems, and ensuring their security against cyber threats presents a critical research challenge due to the widespread use of advanced energy technologies. This paper explores various strategies for maintaining the cyber-resilient operation of MGs, focusing on technical, economic, and regulatory frameworks, in addition to their operational essentials for seamless functionality. In this paper, cyber-resilient operation refers to the system's ability to withstand, respond to, and recover from cyber incidents, thereby ensuring the continuous and reliable operation of the MG. An outline of the various security challenges linked to different cyber-attacks and MG frameworks, highlighting the importance of developing effective and adaptable solutions, is also studied in this paper. While model-based approaches offer precise detection accuracy under steady-state conditions, they often struggle in real-time dynamic scenarios due to their complexity and dependence on accurate system modeling. Conversely, data-driven approaches offer enhanced flexibility and adaptability, enabling swift responses to emerging cyber threats. This makes them a compelling alternative to dynamic model-based methods for ensuring cyber-secure operations of MGs. This study focuses on data-driven techniques, acknowledging the comparative strengths and limitations of both paradigms. This paper also outlines crucial steps for crafting scalable and efficient data-driven cyber solutions, highlighting their key characteristics that enhance MG security. It provides a thorough overview of recent data-driven cyber solutions for MGs, offering careful analysis to evaluate the effectiveness of these methods in enhancing security while identifying operational and implementation challenges. A case study on a two-area isolated microgrid is presented, where a data-driven framework optimized by Bayesian learning approximation is examined. This case study demonstrates the capability of the studied data-driven framework in enhancing the resilience of IMGs against cyber threats. Ultimately, the paper concludes with recommendations for the field of data-driven cyber solutions and MGs, aiming to foster further advancements in sustainable and reliable cybersecurity measures for MG frameworks.

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http://hdl.handle.net/10453/191033