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Cyber Security in Microgrids and Smart Grids

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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 (25 March 2024) | Viewed by 4815

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Special Issue Editors

Dr. Sen Tan

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Guest Editor

The Faculty of Engineering and Science, Aalborg University, 9220 Aalborg, Denmark
Interests: distributed control; multiple microgrids; cyber; control; cyber attack
Special Issues, Collections and Topics in MDPI journals

Dr. José Matas

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Guest Editor

Electric Engineering Department, Barcelona East School of Engineering (EEBE), Polytechnic University of Catalonia (UPC), Barcelona, Spain
Interests: power electronics; power quality; grid monitoring; renewable energy systems; nonlinear control of power electronics and microgrids
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Dr. Jorge El Mariachet

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Guest Editor

Electrical Engineering Department, Polytechnic University of Catalonia (EEBE-UPC), Avinguda Eduard Maristany 16, Building A, Office A9.7, 08019 Barcelona, Spain
Interests: energy transition; energy management systems; renewable energies; biomass; hydrogen; microgrids; Smart Grid; power quality; power calculation; space microgrids; lunar microgrids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The modern electric grid heavily relies on the extensive deployment of communication and information technologies across multiple systems, connecting consumer premises to electricity distribution, transmission, and generation facilities. This transformation, known as grid digitization, has revolutionized grid automation and control systems, which enables better control, monitoring, and maintenance prediction of power grid components, facilitates the integration of distributed energy resources (DERs), and enhances customer services. However, cyberattacks pose significant risks to security and results in substantial economic losses.

Recent reports indicate a rise in the frequency and severity of cyberattacks targeting critical infrastructures, among which are conventional grid networks, smart grids and microgrids. This necessitates the development and implementation of innovative solutions by smart grid operators and electricity industry stakeholders to enhance grid resilience and the ability to detect, neutralize, and respond to cyberattacks.

This Special Issue is dedicated to presenting new concepts, methods, strategies, technologies, and implementation experiences in grid cybersecurity. The aim of this Issue is to establish clear procedures for cyberattack detection, mitigation, and correction, as well as cybersecurity in power systems planning, operation, and control.

The topics of interest include, but are not limited to:

Cyberattack detection and prevention techniques for microgrids or smart grids;
Mitigation and correction strategies for cyberattacks in microgrids or smart grids;
Cybersecurity considerations in microgrids or smart grids planning, operation, and control;
Application of blockchain and distributed ledger technologies for cybersecurity;
Protection schemes against coordinated cyber–physical attacks on microgrids or smart grids;
Secure communication protocols and authentication mechanisms for microgrids or smart grids;
Data analytics and information sharing for improved situational awareness in microgrids or smart grids;
Case studies and real-world implementation experiences of cyber-secure microgrids or smart grids;
Evaluation and testing methodologies for validating the effectiveness of cybersecurity solutions in microgrids or smart grids.

Dr. Sen Tan
Dr. José Matas
Dr. Jorge El Mariachet
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.

Published Papers (6 papers)

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Research

26 pages, 1618 KiB

Open AccessArticle

StegoDCF: A New Covert Channel for Smart Grids Utilizing the Channel Access Procedure in Wi-Fi Networks

by Marek Natkaniec and Jakub Dyrcz

Energies 2024, 17(9), 2021; https://doi.org/10.3390/en17092021 - 25 Apr 2024

Viewed by 137

Abstract

Wi-Fi networks within the smart grid play a vital role in enabling communication between smart meters and data collectors. They are also frequently used in automation and metering, distribution control and monitoring, and distribution protection. However, a significant challenge arises from the uncertainty surrounding the genuine identity of data recipients. In this paper, we propose an efficient and novel covert channel that leverages the IEEE 802.11 DCF to transmit data requiring a high level of security. It is also the world’s first covert channel supporting quality of service (QoS). Our protocol was implemented and tested in the ns-3 simulator, achieving very high-performance results. Its performance remains robust even under saturated network conditions with additional background traffic generated by other stations. This covert channel presents a novel approach to securely transmitting large amounts of QoS data within the smart grid. Full article

(This article belongs to the Special Issue Cyber Security in Microgrids and Smart Grids)

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34 pages, 24610 KiB

Open AccessArticle

Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

by Nakkeeran Murugesan, Anantha Narayanan Velu, Bagavathi Sivakumar Palaniappan, Balamurugan Sukumar and Md. Jahangir Hossain

Energies 2024, 17(8), 1965; https://doi.org/10.3390/en17081965 - 20 Apr 2024

Viewed by 353

Abstract

In the Industry 4.0 era of smart grids, the real-world problem of blackouts and cascading failures due to cyberattacks is a significant concern and highly challenging because the existing Intrusion Detection System (IDS) falls behind in handling missing rates, response times, and detection accuracy. Addressing this problem with an early attack detection mechanism with a reduced missing rate and decreased response time is critical. The development of an Intelligent IDS is vital to the mission-critical infrastructure of a smart grid to prevent physical sabotage and processing downtime. This paper aims to develop a robust Anomaly-based IDS using a statistical approach with a machine learning classifier to discriminate cyberattacks from natural faults and man-made events to avoid blackouts and cascading failures. The novel mechanism of a statistical approach with a machine learning (SAML) classifier based on Neighborhood Component Analysis, ExtraTrees, and AdaBoost for feature extraction, bagging, and boosting, respectively, is proposed with optimal hyperparameter tuning for the early discrimination of cyberattacks from natural faults and man-made events. The proposed model is tested using the publicly available Industrial Control Systems Cyber Attack Power System (Triple Class) dataset with a three-bus/two-line transmission system from Mississippi State University and Oak Ridge National Laboratory. Furthermore, the proposed model is evaluated for scalability and generalization using the publicly accessible IEEE 14-bus and 57-bus system datasets of False Data Injection (FDI) attacks. The test results achieved higher detection accuracy, lower missing rates, decreased false alarm rates, and reduced response time compared to the existing approaches. Full article

(This article belongs to the Special Issue Cyber Security in Microgrids and Smart Grids)

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24 pages, 3449 KiB

Open AccessArticle

Enhancing Smart Grid Resilience: An Educational Approach to Smart Grid Cybersecurity Skill Gap Mitigation

by Rūta Pirta-Dreimane, Andrejs Romanovs, Jana Bikovska, Jānis Pekša, Tero Vartiainen, Maria Valliou, Jirapa Kamsamrong and Bahaa Eltahawy

Energies 2024, 17(8), 1876; https://doi.org/10.3390/en17081876 - 15 Apr 2024

Viewed by 354

Abstract

Cybersecurity competencies are critical in the smart grid ecosystem, considering its growing complexity and expanding utilization. The smart grid environment integrates different sensors, control systems, and communication networks, thus augmenting the potential attack vectors for cyber criminals. Therefore, interdisciplinary competencies are required from smart grid cybersecurity specialists. In the meantime, there is a lack of competence models that define the required skills, considering smart grid job profiles and the technological landscape. This paper aims to investigate the skill gaps and trends in smart grid cybersecurity and propose an educational approach to mitigate these gaps. The educational approach aims to provide guidance for competence-driven cybersecurity education programs for the design, execution, and evaluation of smart grids. Full article

(This article belongs to the Special Issue Cyber Security in Microgrids and Smart Grids)

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18 pages, 1549 KiB

Open AccessArticle

Toward Wireless Smart Grid Communications: An Evaluation of Protocol Latencies in an Open-Source 5G Testbed

by Matthew Boeding, Paul Scalise, Michael Hempel, Hamid Sharif and Juan Lopez, Jr.

Energies 2024, 17(2), 373; https://doi.org/10.3390/en17020373 - 11 Jan 2024

Viewed by 1047

Abstract

Fifth-generation networks promise wide availability of wireless communication with inherent security features. The 5G standards also outline access for different applications requiring low latency, machine-to-machine communication, or mobile broadband. These networks can be advantageous to numerous applications that require widespread and diverse communications. One such application is found in smart grids. Smart grid networks, and Operational Technology (OT) networks in general, utilize a variety of communication protocols for low-latency control, data monitoring, and reporting at every level. Transitioning these network communications from wired Wide Area Networks (WANs) to wireless communication through 5G can provide additional benefits to their security and network configurability. However, introducing these wireless capabilities may also result in a degradation of network latency. In this paper, we propose utilizing 5G for smart grid communications, and we evaluate the latency impacts of encapsulating GOOSE, Modbus, and DNP3 for transmission over a 5G network. The OpenAirInterface open-source library is utilized to deploy an in-lab 5G Core Network and gNB for testing with off-the-shelf User Equipment (UE). This creates an effective 5G test platform for experimenting with different OT protocols such as GOOSE. The results are validated by measuring two different Intelligent Electronic Devices’ contact closure times for each network configuration. These tests are also conducted for varying packet sizes in order to isolate different sources of network latency. Our study outlines the latency impact of communication over 5G for time-critical and non-critical applications regarding their transition toward private 5G-based OT network implementations. The conducted experiments illustrate that in the case of GOOSE packets, simple encapsulation may exceed the protocol’s time-critical nature, and, therefore, additional measures must be taken to ensure a viable transition of GOOSE to 5G services. However, non-critical applications are shown to be viable for migration to 5G. Full article

(This article belongs to the Special Issue Cyber Security in Microgrids and Smart Grids)

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15 pages, 3125 KiB

Open AccessArticle

A Communication Encryption-Based Distributed Cooperative Control for Distributed Generators in Microgrids under FDI Attacks

by Han Fu, Wenpei Li, Long Qiu, Yongheng Ai and Zhixiong Liu

Energies 2023, 16(23), 7754; https://doi.org/10.3390/en16237754 - 24 Nov 2023

Viewed by 627

Abstract

To alleviate the hassle of false data injection (FDI) attacks on distributed generators (DGs) in microgrids, a communication encryption-based distributed cooperative control is proposed in this paper. Compared to the conventional distributed control strategies, the proposed control scheme is simpler with much less complex evaluation mechanism by upgrading the secondary control to a second-order control based on the finite-time control theory while combining an encryption strategy. The proposed algorithm provides constant injections to eliminate the impact of FDI attacks based on a robust communication system. The effectiveness and high efficiency of the proposed control scheme is validated in an IEEE 34 Node Test Feeder system with six DGs as a microgrid cyber-physical system (CPS) under different FDI attacks. Full article

(This article belongs to the Special Issue Cyber Security in Microgrids and Smart Grids)

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20 pages, 6391 KiB

Open AccessArticle

Deep Reinforcement Learning-Driven Mitigation of Adverse Effects of Cyber-Attacks on Electric Vehicle Charging Station

by Manoj Basnet and Mohd. Hasan Ali

Energies 2023, 16(21), 7296; https://doi.org/10.3390/en16217296 - 27 Oct 2023

Cited by 1 | Viewed by 1114

Abstract

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification; however, the EVCS has various vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. These standalone or networked EVCSs open up large attack surfaces for local or state-funded adversaries. The state-of-the-art approaches are not agile and intelligent enough to defend against and mitigate advanced persistent threats (APT). We propose data-driven model-free digital clones based on multiple independent agents deep reinforcement learning (IADRL) that uses the Twin Delayed Deep Deterministic Policy Gradient (TD3) to efficiently learn the control policy to mitigate the cyberattacks on the controllers of EVCS. Also, the proposed digital clones trained with TD3 are compared against the benchmark Deep Deterministic Policy Gradient (DDPG) agent. The attack model considers the APT designed to malfunction the duty cycles of the EVCS controllers with Type-I low-frequency attacks and Type-II constant attacks. The proposed model restores the EVCS operation under threat incidence in any/all controllers by correcting the control signals generated by the legacy controllers. Our experiments verify the superior control policies and actions of TD3-based clones compared to the DDPG-based clones. Also, the TD3-based controller clones solve the problem of incremental bias, suboptimal policy, and hyperparameter sensitivity of the benchmark DDPG-based digital clones, enforcing the efficient mitigation of the impact of cyberattacks on EVCS controllers. Full article

(This article belongs to the Special Issue Cyber Security in Microgrids and Smart Grids)

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