An Innovative Real-Time Recursive Framework for Techno-Economical Self-Healing in Large Power Microgrids Against Cyber–Physical Attacks Using Large Change Sensitivity Analysis

Mehdi Zareian Jahromi, Elnaz Yaghoubi, Elaheh Yaghoubi, Mohammad Reza Maghami and Harold R. Chamorro ()
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Mehdi Zareian Jahromi: Department of Electrical and Electronics Engineering, Amirkabir University, Tehran 1591634311, Iran
Elnaz Yaghoubi: Department of Electrical and Electronics Engineering, Karabuk University, Karabuk 78050, Turkey
Elaheh Yaghoubi: Department of Electrical and Electronics Engineering, Karabuk University, Karabuk 78050, Turkey
Mohammad Reza Maghami: Strategic Research Institute (SRI), Asia Pacific University of Technology and Innovation (APU), Jalan Teknologi 5, Kuala Lumpur 57000, Malaysia
Harold R. Chamorro: Department of Electric Power Systems, KTH Royal Institute of Technology, 11428 Stockholm, Sweden

Energies, 2025, vol. 18, issue 1, 1-41

Abstract: In the past, providing an online and real-time response to cyber–physical attacks in large-scale power microgrids was considered a fundamental challenge by operators and managers of power distribution networks. To address this issue, an innovative framework is proposed in this paper, enabling real-time responsiveness to cyberattacks while focusing on the techno-economic energy management of large-scale power microgrids. This framework leverages the large change sensitivity (LCS) method to receive immediate updates to the system’s optimal state under disturbances, eliminating the need for the full recalculation of power flow equations. This significantly reduces computational complexity and enhances real-time adaptability compared to traditional approaches. Additionally, this framework optimizes operational points, including resource generation and network reconfiguration, by simultaneously considering technical, economic, and reliability parameters—a comprehensive integration often overlooked in recent studies. Performance evaluation on large-scale systems, such as IEEE 33-bus, 69-bus, and 118-bus networks, demonstrates that the proposed method achieves optimization in less than 2 s, ensuring superior computational efficiency, scalability, and resilience. The results highlight significant improvements over state-of-the-art methods, establishing the proposed framework as a robust solution for real-time, cost-effective, and resilient energy management in large-scale power microgrids under cyber–physical disturbances.

Keywords: real-time self-healing; large power microgrids; large change sensitivity analysis; cyber–physical attacks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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