Cyber-Physical Security and Co-Simulation Technologies for Smart Grids

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (30 December 2025) | Viewed by 1560

Special Issue Editors


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Guest Editor
School of Electrical and Electronic Engineering, Imperial College London, London, UK
Interests: smart grid; cyber–physical security; cyber–physical co-simulation
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Guest Editor
School of Electrical and Electronic Engineering, University of Sheffield, Sheffield, UK
Interests: power systems; cyber-security; machine learning; digital twins

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Guest Editor
School of Electrical and Electronic Engineering, University of Sheffield, Sheffield, UK
Interests: cyber–physical system; integrated energy system; power system simulation and operation

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Guest Editor
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China
Interests: wind power; smart grid; cyber security; resilient control

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Guest Editor
College of Computer Science and Technology, Guizhou University, Guiyang, China
Interests: smart grid security; machine learning security; cybersecurity
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Special Issue Information

Dear Colleagues,

The smart grid represents a transformative leap in modern energy systems, integrating advanced communication, control, and physical infrastructure to enable efficient energy distribution, renewable integration, and consumer engagement. However, this enhanced interconnectivity between cyber and physical layers introduces significant vulnerabilities. Cyber–physical security has become a critical concern, as attacks targeting either layer can cascade through the system, disrupting operations, compromising data integrity, and threatening critical infrastructure. Ensuring the security of smart grids requires innovative strategies that bridge the gap between cyber and physical domains, protecting against an evolving array of sophisticated threats.

Co-simulation technologies offer a powerful approach to addressing these challenges. By modeling the interactions between cyber and physical components, co-simulation frameworks enable a comprehensive analysis of smart grid behavior under diverse scenarios, including potential cyber–physical attacks. These tools facilitate the evaluation of resilience strategies, the design of robust defenses, and the testing of adaptive mechanisms in a controlled and realistic environment. Co-simulation enhances our understanding of vulnerabilities and accelerates the development of integrated solutions that safeguard the smart grid from disruptions.

This call for papers focuses on advancing cyber–physical security and co-simulation technologies for the smart grid. We seek contributions that explore the intersection of these domains, proposing innovative frameworks, methodologies, and applications that enhance the resilience, reliability, and security of smart grids.

Topics of Interest:

  1. Threat modeling, detection, and prevention mechanisms.
  2. Cryptographic techniques and secure communication protocols.
  3. Role-based access control, attribute-based access control, and other access control mechanisms.
  4. Blockchain and software-defined networking technologies for grid security.
  5. AI and machine learning for anomaly detection, risk assessment, and adaptive cybersecurity measures.
  6. Cyber–physical coordinated detection and response strategies.
  7. Privacy-preserving data-sharing framework among multiple energy sectors.
  8. Real-time cyber–physical co-simulation synchronization schemes.
  9. Realistic cyber–physical co-simulation testbeds involving energy sectors.
  10. Resilient control and optimization algorithms for smart grids.

Dr. Mengxiang Liu
Prof. Dr. Xin Zhang
Dr. Suhan Zhang
Dr. Shiyi Zhao
Dr. Zhenyong Zhang
Guest Editors

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Keywords

  • smart grid
  • cyber–physical security
  • cyber–physical co-simulation
  • AI and machine learning
  • cryptographic and access control
  • blockchain and software-defined networking
  • privacy-preserving
  • resilient control and optimization

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Published Papers (2 papers)

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Research

22 pages, 861 KB  
Article
STD: Sensor-Oriented Temporal Detector Against Multi-Type Load Redistribution Attacks in Smart Grid
by Yunhao Yu, Boda Zhang, Mengxiang Liu and Xuguo Jiao
Electronics 2026, 15(4), 746; https://doi.org/10.3390/electronics15040746 - 10 Feb 2026
Viewed by 201
Abstract
The modern smart grid integrates information and communication technology (ICT) with electronic devices, but this integration introduces cybersecurity risks. Load measurements, crucial for grid operation, are vulnerable to attacks, particularly Load Redistribution Attacks (LRAs). LRAs maliciously alter load readings to mislead control systems [...] Read more.
The modern smart grid integrates information and communication technology (ICT) with electronic devices, but this integration introduces cybersecurity risks. Load measurements, crucial for grid operation, are vulnerable to attacks, particularly Load Redistribution Attacks (LRAs). LRAs maliciously alter load readings to mislead control systems without being detected by conventional methods. This paper first introduces two advanced LRA variants: a stealthy-enhanced LRA designed to bypass sophisticated data-driven detectors, and an impact-enhanced LRA engineered to cause significant operational disruptions, such as increased generation costs. To address these evolving threats, we propose a novel Sensor-oriented Temporal Detector (STD). Unlike existing methods that often rely on aggregate data or labeled attack examples, our STD focuses on the unique temporal patterns of individual sensor measurements. It achieves this by combining principal subspace projection to identify normal data subspaces with sequential change extraction to detect subtle deviations over time. This approach allows the STD to identify various LRA types effectively, even without prior knowledge of attack signatures. Extensive simulations validate the destructive impact of our proposed LRA variants and demonstrate the superior detection performance of the STD against these sophisticated attacks. Full article
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17 pages, 647 KB  
Article
Resilience Enhancement for Power System State Estimation Against FDIAs with Moving Target Defense
by Zeyuan Zhou, Jichao Bi and Zhenyong Zhang
Electronics 2025, 14(17), 3367; https://doi.org/10.3390/electronics14173367 - 25 Aug 2025
Cited by 1 | Viewed by 798
Abstract
False data injection attack (FDIA) by tampering with the sensor measurements is a big threat to the system’s observability. Power system state estimation (PSSE) is a critical observing function challenged by FDIAs in terms of its resiliency. Therefore, in this paper, we analyze [...] Read more.
False data injection attack (FDIA) by tampering with the sensor measurements is a big threat to the system’s observability. Power system state estimation (PSSE) is a critical observing function challenged by FDIAs in terms of its resiliency. Therefore, in this paper, we analyze the effectiveness of moving target defense (MTD) in enhancing the resiliency of PSSE against FDIAs. To begin with, the resiliency factor of PSSE against FDIAs is quantified using the relative estimation error against the injected measurement error. Then, the MTD is strategically designed to improve the resiliency factor by changing the line parameter and measurement case, for which the analytical results are provided. Furthermore, the infrastructure and operation costs caused by MTD are optimized to construct a cost-effective MTD. Finally, extensive simulations are conducted to validate the effectiveness of MTD in enhancing PSSE’s resiliency against FDIAs, which show that the MTD can improve the system’s resiliency factor by 10–20% and the generation cost can be reduced by about 10 USD/MWh after MTD. Full article
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