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Emerging Challenges and Solutions in Cyber–Physical Security of Smart Grids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 3558

Special Issue Editors


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Guest Editor
School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China
Interests: smart grids; electricity markets and their cyber–physical security; machine learning and its cyber security in smart grids

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Guest Editor
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: smart grid; cyber–physical security; demand-side management; artificial intelligence; climate change
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Interests: cyber–physical power system; power system stability; integrated energy system; artificial intelligence in power system; smart grid

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Guest Editor
1. College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410004, China
2. National Key Laboratory of Power Grid Disaster Prevention and Mitigation, Changsha 410004, China
Interests: vehicle-to-grid; integrated energy system; stochastic optimization; robust optimization; distributionally robust optimization; distributed optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of digital transformation, smart grids have emerged as pivotal elements in modern energy systems, integrating advanced information and communication technologies to enhance efficiency and reliability. However, this integration also introduces complex vulnerabilities, making smart grids a prime target for cyber–physical threats. These threats not only compromise the security of the data and control systems but also pose significant risks to the physical infrastructure and the reliability of the power supply. The security of smart grids is not only a technical necessity but also a strategic imperative for national security and public safety. Ensuring the integrity, confidentiality, and availability of these critical systems is paramount to preventing disruptions that can lead to economic losses and societal harm. As cyberattacks become more sophisticated, the need for advanced cyber–physical security measures becomes increasingly critical to defend against and mitigate these evolving threats. This Special Issue will investigate emerging challenges and advance the cyber–physical security of smart grids. We invite contributions from both academic researchers and industry professionals who are at the forefront of cyber–physical security for smart grids. We seek high-quality, original research papers, reviews, and case studies that offer innovative insights and practical solutions to the complex challenges facing the cyber–physical security of smart grids.

This Special Issue covers, but is not limited to, the following areas:

  • Development of threat models that can reflect potential attack strategies and their impacts on smart grid operations;
  • Exploration of cutting-edge security technologies, including blockchain, artificial intelligence, and machine learning, tailored for smart grid applications;
  • Methods to enhance the resilience of smart grids against cyber–physical attacks;
  • Analysis of current policies and the formulation of new regulations to foster robust security standards and practices;
  • Documentation and discussion of real-world scenarios, successful implementations, and lessons learned from securing smart grids.

Prof. Dr. Gaoqi Liang
Dr. Jiaqi Ruan
Dr. Haixin Wang
Dr. Junjie Zhong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart grid
  • cyber–physical system
  • cyber–physical security
  • false data injection attack
  • AI solutions for security

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Published Papers (3 papers)

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Research

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18 pages, 9335 KiB  
Article
Power Allocation Control Strategy of DC/DC Converters Based on Sliding Mode Control
by Wenwen Li, Jianwei Ji, Henan Dong, Miao He and Shubo Hu
Energies 2024, 17(18), 4628; https://doi.org/10.3390/en17184628 - 15 Sep 2024
Cited by 1 | Viewed by 882
Abstract
In the DC microgrid system, the bidirectional DC/DC converter is one of the most important components; thus, research on its control strategy has attracted widespread attention. Firstly, the single bidirectional DC/DC converter based on a sliding mode (variable structure) control (SMC) strategy exhibits [...] Read more.
In the DC microgrid system, the bidirectional DC/DC converter is one of the most important components; thus, research on its control strategy has attracted widespread attention. Firstly, the single bidirectional DC/DC converter based on a sliding mode (variable structure) control (SMC) strategy exhibits an inherent contradiction between the reaching time and the chattering phenomenon. In order to address this problem, an SMC strategy based on the improved exponential reaching law was designed. This control strategy modifies the constant-speed reaching term and introduces the system state variable to indicate the chattering level, which not only improves the dynamic performance of the bidirectional DC/DC converter but also suppresses the chattering problem. Secondly, the bidirectional DC/DC converter group based on the traditional droop control strategy exhibits an inherent contradiction between load power allocation and bus voltage stabilization. In order to address this problem, an improved droop control strategy that takes the line impedance characteristics into account is proposed. This control strategy modifies the traditional droop control strategy by introducing virtual resistance and uses DC bus voltage information to replace the line impedance value. This ensures the accuracy of power allocation and stability of the DC bus voltage simultaneously. Finally, the stability of each designed strategy is verified individually. The combination of the two control strategies is applied to a group of bidirectional DC/DC converters group to conduct a semi-physical simulation experiment, and the results verify that the proposed control strategies are effective and feasible. Full article
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17 pages, 2808 KiB  
Article
Power Supply Risk Identification Method of Active Distribution Network Based on Transfer Learning and CBAM-CNN
by Hengyu Liu, Jiazheng Sun, Yongchao Pan, Dawei Hu, Lei Song, Zishang Xu, Hailong Yu and Yang Liu
Energies 2024, 17(17), 4438; https://doi.org/10.3390/en17174438 - 4 Sep 2024
Cited by 1 | Viewed by 804
Abstract
With the development of the power system, power users begin to use their own power supply in order to improve the power economy, but this also leads to the occurrence of the risk of self-provided power supply. The actual distribution network has few [...] Read more.
With the development of the power system, power users begin to use their own power supply in order to improve the power economy, but this also leads to the occurrence of the risk of self-provided power supply. The actual distribution network has few samples of power supply risk and it is difficult to identify the power supply risk by using conventional deep learning methods. In order to achieve high accuracy of self-provided power supply risk identification with small samples, this paper proposes a combination of transfer learning, convolutional block attention module (CBAM), and convolutional neural network (CNN) to identify the risk of self-provided power supply in an active distribution network. Firstly, in order to be able to further identify whether or not a risk will be caused based on completing the identification of the faulty line, we propose that it is necessary to identify whether or not the captive power supply on the faulty line is in operation. Second, in order to achieve high-precision identification and high-efficiency feature extraction, we propose to embed the CBAM into a CNN to form a CBAM-CNN model, so as to achieve high-efficiency feature extraction and high-precision risk identification. Finally, the use of transfer learning is proposed to solve the problem of low risk identification accuracy due to the small number of actual fault samples. Simulation experiments show that compared with other methods, the proposed method has the highest recognition accuracy and the best effect, and the risk recognition accuracy of active distribution network backup power is high in the case of fewer samples. Full article
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Review

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21 pages, 2526 KiB  
Review
A Review and Prospective Study on Modeling Approaches and Applications of Virtual Energy Storage in Integrated Electric–Thermal Energy Systems
by Qitong Fu, Zuoxia Xing, Chao Zhang and Jian Xu
Energies 2024, 17(16), 4099; https://doi.org/10.3390/en17164099 - 18 Aug 2024
Cited by 1 | Viewed by 1366
Abstract
The increasing use of renewable energy sources introduces significant fluctuations in power generation, demanding enhanced regulatory capabilities to maintain the balance between power supply and demand. To promote multi-energy coupling and the local consumption of renewable energy, integrated energy systems have become a [...] Read more.
The increasing use of renewable energy sources introduces significant fluctuations in power generation, demanding enhanced regulatory capabilities to maintain the balance between power supply and demand. To promote multi-energy coupling and the local consumption of renewable energy, integrated energy systems have become a focal point of multidisciplinary research. This study models adjustable sources, networks, and loads within electric–thermal integrated energy systems as energy storage entities, forming virtual energy storage systems to participate in the optimization and scheduling of integrated energy systems. This paper investigates the modeling and control strategies of virtual energy storage systems within electric–thermal integrated energy systems. Initially, it introduces the definition, logical architecture, and technical connotations of virtual energy storage. Next, it models temperature-controlled loads as virtual energy storage systems and compares them with traditional energy storage systems, analyzing their characteristic differences and summarizing virtual energy storage system modeling methods and characteristic indicators. This paper then focuses on the specific applications of virtual energy storage systems in four typical scenarios. Finally, it explores the future development directions of virtual energy storage. Full article
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