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Energies
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Secure and Efficient Communication in Smart Grids
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Secure and Efficient Communication in 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 December 2022) | Viewed by 17363

Special Issue Editors

Dr. Mostafa Fouda

E-Mail Website1 Website2
Guest Editor
Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
Interests: cybersecurity; artificial intelligence (AI); internet of things (IoT); smart grids; 5G/6G networks; vehicular networks; communication networks; image processing; signal processing; smart healthcare
Special Issues, Collections and Topics in MDPI journals
Dr. Mohamed Ibrahem

E-Mail Website
Guest Editor
Department of Cyber Security Engineering, Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA
Interests: security and privacy in wireless networks including IoT and smart grid networks; privacy-preserving machine learning; machine learning for cyber-security; secure federated learning; traffic analysis attacks and countermeasures; cyber-physical systems security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is intended to present feature and scholarly papers that address some of the diverse array of topics related to Smart Grid Secure and Efficient Communication. These topics include, but are not limited to, secure and resilient communication; secure smart metering; key management; authorization and access control; attack detection, mitigation and attribution; trust and privacy; demand-response management; security design and verification tools; and simulation and performance analysis of security operations and services. Technological advances in all these areas have profoundly affected the realization of the secure and efficient communications and information management that are essential to all aspects of the smart grid. We invite scientists and researchers to submit papers for this important Special Issue, “Secure and Efficient Communication in Smart Grids”. Case studies, reviews, and research papers on all topics related to the cybersecurity and communication issues of smart grids are invited.

Dr. Mostafa Fouda
Dr. Mohamed Ibrahem
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

  • secure and resilient communication and control architectures in smart grids
  • demand-response management
  • secure and private demand-response management
  • secure and private smart metering
  • cryptography, key management, authorization and access control in smart grids
  • security threat and vulnerability assessment and measurement in smart grids
  • cyber-physical security information and event management in smart grids
  • trust and privacy in smart grids
  • security design and verification tools in smart grids
  • simulation and performance analysis of security operations and services in smart grids
  • big data analysis in smart grid security and privacy
  • security of integrating renewable energy resources in smart grids
  • security of electric vehicles in smart grids
  • security of energy management in smart grids
  • secure real-time communication in smart grids
  • machine learning for smart grid security and privacy
  • integrated simulation, testbed, and case studies for smart grid security and privacy
  • attack detection, mitigation and attribution in smart grids
  • security against data injection and falsification attacks in smart grids
  • intrusion detection in smart grids
  • privacy and security issues in AI applications in smart grids
  • key management and authorization in energy management systems
  • privacy and security issues in smart grid interoperability
  • game-theoretic study of smart grid security and privacy problems
  • information-theoretic models of privacy and security in smart grids
  • data-analytics-based approaches for privacy and security assessment and mitigation
  • secure integration of internet-of-things solutions to smart grids
  • data analysis, simulation and assessment studies from testbeds and real-time implementations
  • cyber-physical security of energy storage and management systems
  • wide area monitoring and control for attack detection, prevention, and mitigation
  • cyber-physical testbed development for risk analysis
  • cyber-physical security of distributed energy resources (DER) systems
  • cyber-physical security of micro-grids and DC grids
  • electricity theft detection and mitigation

Published Papers (6 papers)

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Editorial

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2 pages, 154 KiB  
Editorial
Secure and Efficient Communication in Smart Grids
by Mostafa M. Fouda and Mohamed I. Ibrahem
Energies 2023, 16(15), 5613; https://doi.org/10.3390/en16155613 - 26 Jul 2023
Viewed by 574
Abstract
This Special Issue on “Secure and Efficient Communication in Smart Grids” received a total of 11 submitted articles, of which 5 were accepted and published after each passing an independent peer-review process [...] Full article
(This article belongs to the Special Issue Secure and Efficient Communication in Smart Grids)

Research

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23 pages, 12655 KiB  
Article
Privacy-Preserving Charging Coordination Scheme for Smart Power Grids Using a Blockchain
by Hany Habbak, Mohamed Baza, Mohamed M. E. A. Mahmoud, Khaled Metwally, Ahmed Mattar and Gouda I. Salama
Energies 2022, 15(23), 8996; https://doi.org/10.3390/en15238996 - 28 Nov 2022
Cited by 6 | Viewed by 1646
Abstract
With the rapid emergence of smart grids, charging coordination is considered the intrinsic actor that merges energy storage units (ESUs) into the grid in addition to its substantial role in boosting the resiliency and efficiency of the grid. However, it suffers [...] Read more.
With the rapid emergence of smart grids, charging coordination is considered the intrinsic actor that merges energy storage units (ESUs) into the grid in addition to its substantial role in boosting the resiliency and efficiency of the grid. However, it suffers from several challenges beginning with dependency on the energy service provider (ESP) as a single entity to manage the charging process, which makes the grid susceptible to several types of attacks such as a single point of failure or a denial-of-service attack (DoS). In addition, to schedule charging, the ESUs should submit charging requests including time to complete charging (TCC) and battery state of charge (SoC), which may disclose serious information relevant to the consumers. The analysis of this data could reveal the daily activities of those consumers. In this paper, we propose a privacy-preservation charging coordination scheme using a blockchain. The blockchain achieves decentralization and transparency to defeat the security issues related to centralized architectures. The privacy preservation will be fulfilled using a verifiable aggregation mechanism integrated with an aggregated signing technique to identify the untrusted aggregator and assure the data source and the identity of the sender. Security and performance evaluations are performed, including off-chain and on-chain experiments and simulations, to assess the security and efficiency of the scheme. Full article
(This article belongs to the Special Issue Secure and Efficient Communication in Smart Grids)
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18 pages, 437 KiB  
Article
Real-Time Locational Detection of Stealthy False Data Injection Attack in Smart Grid: Using Multivariate-Based Multi-Label Classification Approach
by Hanem I. Hegazy, Adly S. Tag Eldien, Mohsen M. Tantawy, Mostafa M. Fouda and Heba A. TagElDien
Energies 2022, 15(14), 5312; https://doi.org/10.3390/en15145312 - 21 Jul 2022
Cited by 17 | Viewed by 2464
Abstract
Recently, false data injection attacks (FDIAs) have been identified as a significant category of cyber-attacks targeting smart grids’ state estimation and monitoring systems. These cyber-attacks aim to mislead control system operations by compromising the readings of various smart grid meters. The real-time and [...] Read more.
Recently, false data injection attacks (FDIAs) have been identified as a significant category of cyber-attacks targeting smart grids’ state estimation and monitoring systems. These cyber-attacks aim to mislead control system operations by compromising the readings of various smart grid meters. The real-time and precise locational identification of FDIAs is crucial for smart grid security and reliability. This paper proposes a multivariate-based multi-label locational detection (MMLD) mechanism to detect the presence and locations of FDIAs in real-time measurements with precise locational detection accuracy. The proposed architecture is a parallel structure that concatenates Long Short-Term Memory (LSTM) with Temporal Convolutional Neural Network (TCN). The proposed architecture is trained using Keras with Tensorflow libraries, and its performance is verified using an IEEE standard bus system in the MATPOWER package. Extensive testing has shown that the proposed approach effectively improves the presence-detection accuracy for locating stealthy FDIAs in small and large systems under various attack conditions. In addition, this work provides a customized loss function for handling the class imbalance problem. Simulation results reveal that our MMLD technique has a modest advantage in some aspects. First, our mechanism outperforms benchmark models because the problem is formulated as a multivariate-based multi-label classification problem. Second, it needs fewer iterations for training and reaching the optimal model. More specifically, our approach is less complex and more scalable than benchmark algorithms. Full article
(This article belongs to the Special Issue Secure and Efficient Communication in Smart Grids)
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15 pages, 1920 KiB  
Article
Data Mining-Based Cyber-Physical Attack Detection Tool for Attack-Resilient Adaptive Protective Relays
by Nancy Mohamed and Magdy M. A. Salama
Energies 2022, 15(12), 4328; https://doi.org/10.3390/en15124328 - 13 Jun 2022
Cited by 9 | Viewed by 1389
Abstract
Maintaining proper operation of adaptive protection schemes is one of the main challenges that must be considered for smart grid deployment. The use of reliable cyber detection and protection systems boosts the preparedness potential of the network as required by National Infrastructure Protection [...] Read more.
Maintaining proper operation of adaptive protection schemes is one of the main challenges that must be considered for smart grid deployment. The use of reliable cyber detection and protection systems boosts the preparedness potential of the network as required by National Infrastructure Protection Plans (NIPPS). In an effort to enhance grid cyber-physical resilience, this paper proposes a tool to enable attack detection in protective relays to tackle the problem of compromising their online settings by cyber attackers. Implementing the tool first involves an offline phase in which Monte Carlo simulation is used to generate a training dataset. Using rough set classification, a set of If-Then rules is obtained for each relay and loaded to the relays at the initialization stage. The second phase occurs during online operation, with each updated setting checked by the corresponding relay’s built-in tool to determine whether the settings received are genuine or compromised. A test dataset was generated to assess tool performance using the modified IEEE 34-bus test feeder. Several assessment measures have been used for performance evaluation and their results demonstrate the tool’s superior ability to classify settings efficiently using physical properties only. Full article
(This article belongs to the Special Issue Secure and Efficient Communication in Smart Grids)
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12 pages, 312 KiB  
Article
Application of Doubly Connected Dominating Sets to Safe Rectangular Smart Grids
by Joanna Cyman and Joanna Raczek
Energies 2022, 15(9), 2969; https://doi.org/10.3390/en15092969 - 19 Apr 2022
Cited by 2 | Viewed by 1436
Abstract
Smart grids, together with the Internet of Things, are considered to be the future of the electric energy world. This is possible through a two-way communication between nodes of the grids and computer processing. It is necessary that the communication is easy and [...] Read more.
Smart grids, together with the Internet of Things, are considered to be the future of the electric energy world. This is possible through a two-way communication between nodes of the grids and computer processing. It is necessary that the communication is easy and safe, and the distance between a point of demand and supply is short, to reduce the electricity loss. All these requirements should be met at the lowest possible cost. In this paper, we study a two-dimensional rectangular grid graph which is considered to be a model of a smart grid; nodes of the graph represent points and devices of the smart grid, while links represent possible ways of communication and energy transfer. We consider the problem of choosing the lowest possible number of locations (nodes, points) of the grid which could serve as energy sources (or a source of different resources) to other nodes in such a way that we ensure reduction in electricity loss and provide safe communication and resistance to failures and increases in energy demand.Therefore, we study minimum doubly connected dominating sets in grid graphs. We show that the proposed solutions are the best possible in terms of the number of source points for the case of narrow grid graphs and we give upper and lower bounds for the case of wide grid graphs. Full article
(This article belongs to the Special Issue Secure and Efficient Communication in Smart Grids)
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Review

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33 pages, 10124 KiB  
Review
Load Forecasting Techniques and Their Applications in Smart Grids
by Hany Habbak, Mohamed Mahmoud, Khaled Metwally, Mostafa M. Fouda and Mohamed I. Ibrahem
Energies 2023, 16(3), 1480; https://doi.org/10.3390/en16031480 - 02 Feb 2023
Cited by 38 | Viewed by 7560
Abstract
The growing success of smart grids (SGs) is driving increased interest in load forecasting (LF) as accurate predictions of energy demand are crucial for ensuring the reliability, stability, and efficiency of SGs. LF techniques aid SGs in making decisions related to power operation [...] Read more.
The growing success of smart grids (SGs) is driving increased interest in load forecasting (LF) as accurate predictions of energy demand are crucial for ensuring the reliability, stability, and efficiency of SGs. LF techniques aid SGs in making decisions related to power operation and planning upgrades, and can help provide efficient and reliable power services at fair prices. Advances in artificial intelligence (AI), specifically in machine learning (ML) and deep learning (DL), have also played a significant role in improving the precision of demand forecasting. It is important to evaluate different LF techniques to identify the most accurate and appropriate one for use in SGs. This paper conducts a systematic review of state-of-the-art forecasting techniques, including traditional techniques, clustering-based techniques, AI-based techniques, and time series-based techniques, and provides an analysis of their performance and results. The aim of this paper is to determine which LF technique is most suitable for specific applications in SGs. The findings indicate that AI-based LF techniques, using ML and neural network (NN) models, have shown the best forecast performance compared to other methods, achieving higher overall root mean squared (RMS) and mean absolute percentage error (MAPE) values. Full article
(This article belongs to the Special Issue Secure and Efficient Communication in Smart Grids)
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