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A comprehensive review of cyber-attacks and defense mechanisms for improving security in smart grid energy systems: Past, present and future
Abstract
Due to the advancement in communication networks, metering and smart control systems, as well as the prevalent use of Internet-based structures, new forms of power systems have seen moderate changes with respect to several aspects of contradictory Cyber–Physical Power Systems (CPPSs). These structures usually have connections between power sections and cyber parts. CPPSs confront newly emerging issues including stability, resiliency, reliability, vulnerability and also security. Studying, analyzing and providing solutions to mitigate or solve these problems highly depend on accurate modeling methods and examining the interaction mechanisms associated with the cyber-security of Smart Grids (SGs). This paper aims to systematically summarize different methods and techniques and to review corresponding solution approaches in cyber-security in energy systems. In the first step, we discuss the interactive features of cyber-security; then, their modeling and mechanisms are reviewed and summarized in detail. Furthermore, the characteristics and applicability of different cyber-attack models are technically discussed and analyzed. The cutting-edge cyber security approaches such as blockchain and quantum computing in SGs and power systems are stated, and recent research directions are highlighted. The decisive problem-solving approaches and defense mechanisms are presented. Finally, some points regarding the role of cyber-security in the future of SGs are presented.
... Contemporary power plants, industrial systems, smart grids, and even more so PVs can be treated as cyber-physical systems (CPSs) [5,6]. CPSs are controlled remotely by programmable logic controllers (PLCs) and monitored by supervisory control and data acquisition (SCADA) systems [7][8][9][10]. These are heavily reliant on their secure communication. ...
... In recent years there has been an increased IoT presence in such systems, which is regarded as the weak link [4,14,15] that can be outlined whenever a cyber-attack has occurred. Most cyber-attacks [6,16] that can impact the whole grid, a single power plant, an industrial system, etc., are first and foremost human-directed social engineering [17][18][19] (phishing, spoofing, eavesdropping, identity theft, ransomware, spam, etc.) based on emails and websites and then further communications and equipment-targeted attacks such as a watering hole attack [20], man-in-the-middle (MITM) attack [4,8,9,11,17,21,22], denial of service (DoS) [4,9,11,13,18,23,24], distributed denial of service (DDoS) [9,13,17,18,[25][26][27], data integrity attack (DIA) [4,9], false data injection attack (FDIA) [9,22,[28][29][30][31][32], cyber-physical attack [28], replay attack [4,5,9,17,33], time-delay attack [9], data manipulation [28], stealthy attack [2,34,35], etc. Of course, purely physical attacks purposed to damage or destroy the respective equipment itself are also present [2]. ...
... In recent years there has been an increased IoT presence in such systems, which is regarded as the weak link [4,14,15] that can be outlined whenever a cyber-attack has occurred. Most cyber-attacks [6,16] that can impact the whole grid, a single power plant, an industrial system, etc., are first and foremost human-directed social engineering [17][18][19] (phishing, spoofing, eavesdropping, identity theft, ransomware, spam, etc.) based on emails and websites and then further communications and equipment-targeted attacks such as a watering hole attack [20], man-in-the-middle (MITM) attack [4,8,9,11,17,21,22], denial of service (DoS) [4,9,11,13,18,23,24], distributed denial of service (DDoS) [9,13,17,18,[25][26][27], data integrity attack (DIA) [4,9], false data injection attack (FDIA) [9,22,[28][29][30][31][32], cyber-physical attack [28], replay attack [4,5,9,17,33], time-delay attack [9], data manipulation [28], stealthy attack [2,34,35], etc. Of course, purely physical attacks purposed to damage or destroy the respective equipment itself are also present [2]. ...
Photovoltaics with energy storage are the current trend in solar energy. Hybrid inverters are the backbone of low-power installations of this type. If a single installation is compromised, there are no significant security concerns. However, multiple devices can be targeted simultaneously. Taking into account their increasing share in the energy mix, distributed cyber-attacks against these devices can threaten grid stability. The Bulgarian electric power system has been analyzed in order to determine its development which is in line with EU-wide trends. It can be concluded that hybrid inverters are expected to grow rapidly in number and in installed power. The vulnerability of hybrid inverters to cyber-attacks has been analyzed, and the possible consequences for the energy system have been identified. The technology allows it to be used as a hybrid means of influence, and this aspect is poorly addressed in existing cybersecurity regulations. A risk assessment has been made, based on which measures to improve security have been proposed.
... Recent research has suggested several strategies to defend against APTs, particularly within Cyber-Physical Power Systems. Blockchain technology is recommended for secure, decentralized control, while quantum computing offers advanced encryption methods (Ghiasi et al., 2023). Blockchain enhances security in decentralized networks by preventing tampering, making it more difficult for APTs to modify or erase digital traces. ...
... The adoption of cutting-edge cybersecurity approaches is emphasized to enhance the resilience and reliability of smart grids and power systems against evolving threats. These strategies are designed to address critical challenges in stability, security and vulnerability in Cyber-Physical Power Systems which help to ensure robust protection against APTs and other sophisticated cyber-attacks (Ghiasi et al., 2023). ...
Purpose-The purpose of this study is to investigate how to enhance cybersecurity strategies by integrating semantic network analysis with the MITRE ATT&CK framework. This study's primary goal was to deepen understanding of advanced persistent threats (APTs) dynamics by examining how various attack techniques are connected and their roles within cyberattacks. Design/methodology/approach-This study used a detailed analysis of data from the MITRE ATT&CK framework, applying semantic network analysis to examine how different attack techniques are interconnected across various environments, including enterprise systems, industrial control systems and mobile networks. The methodology focused on identifying central techniques and the relational dynamics among tactics that contribute to the efficacy of APTs. Findings-The findings of this study reveal that certain techniques, such as "Valid Accounts" and "Credential Dumping," consistently play central roles in multiple attack pathways, making them critical targets for cybersecurity defenses. The analysis also uncovers intricate patterns of interconnections among various tactics and techniques, demonstrating how attackers exploit these relationships in a sequential manner. This knowledge is crucial for developing targeted defense strategies that effectively mitigate the most significant threats and address the interconnected nature of APT attacks. Research limitations/implications-This study primarily relies on the MITRE ATT&CK framework, which may not encompass all emerging techniques, potentially limiting the generalizability of the findings. Additionally, the focus on specific domains (enterprise, industrial control systems and mobile) might overlook other critical areas like cloud services or Internet of Things devices. Interpretation of semantic network analysis results involve some subjectivity, which could introduce bias. Practical implications-By identifying key techniques with high centrality, this study provides actionable insights for cybersecurity professionals. The results can guide the development of more focused defense strategies, particularly by prioritizing techniques that are central to multiple attack pathways. This can lead to more efficient resource allocation for monitoring and protecting critical points in a network. Social implications-Improving cybersecurity defenses against APTs can protect critical infrastructure, enterprises and individuals from significant threats, including data breaches, intellectual property theft and sabotage. Enhanced understanding and mitigation of APTs can contribute to national security, economic stability and the safeguarding of personal privacy in an increasingly interconnected world. Originality/value-The integration of semantic network analysis with the MITRE ATT&CK framework represents a novel approach to cybersecurity analysis. This methodology provides a comprehensive means to identify and prioritize key areas of vulnerability, thereby enhancing defensive measures against sophisticated cyber threats. This research offers detailed analysis and practical insights that can guide cybersecurity professionals in strengthening defenses against the evolving landscape of APTs. Introduction Advanced persistent threats (APTs) are a significant global problem because of their sophisticated nature and the substantial resources often backing them (Huang and Zhu, 2020). These threats are often orchestrated by state-sponsored groups or highly organized crime syndicates with strategic objectives that include espionage, intellectual property theft or critical infrastructure disruptions (Geers, 2009). The persistent and stealthy nature of APTs allows attackers to remain undetected within systems for extended periods of time during which they can cause extensive damage, steal sensitive information or sabotage operations (Tankard, 2011). The global increase in cyber espionage, attacks on critical infrastructure and economic damage caused by these threats stresses their importance (Wilson, 2014). Understanding and mitigating these threats require innovative approaches that go beyond traditional linear analysis of cyberattack patterns. The goal of this research is to provide a deeper understanding of APTs through the integration of semantic network analysis (SNA) with the MITRE ATT&CK framework. While the MITRE framework is widely recognized for its detailed documentation of how adversaries operate, integrating it with SNA provides a more nuanced understanding of the interrelationships between various techniques. SNAs ability to uncover hidden patterns and connections within complex data sets enables the identification of critical nodes and connections in the network of tactics, techniques and procedures (TTPs), offering insights into the most effective points for cybersecurity intervention. The novelty of this research lies in its methodological approach. By leveraging SNA on the MITRE ATT&CK framework, this study aims to reveal the underlying structure of APT techniques across three different domains, enterprise environments, industrial control systems (ICSs) and mobile platforms. This approach allows for a more sophisticated analysis of how these techniques interconnect and evolve, which has not been broadly explored in the existing literature. The theoretical foundation of this research is enhanced through the integration of SNA and the MITRE ATT&CK framework, each rooted in well-established scholarly principles. SNA, underpinned by graph theory and network analysis, offers a sophisticated lens for examining the complexities within cyber threat data, enabling the identification of pivotal relationships and nodes within the landscape of cybersecurity threats (Wasserman and Faust, 1994). Concurrently, the MITRE ATT&CK framework, which catalogs comprehensive adversarial tactics and techniques based on empirical data, provides a structured taxonomy that enriches the analytical depth of the study. This integrative approach not only bridges rigorous theoretical constructs with empirical cybersecurity challenges but also applies SNA innovatively to decipher the complex network of cyber adversaries, thereby significantly advancing the scholarly dialogue and practical strategies within the field of cybersecurity. To guide this investigation, this study addressed the following research questions:
... According to the International Energy Agency (IEA), global investment in clean energy technologies reached $1.7 trillion in 2023, with over 45% allocated to digital technologies such as smart grids and IoT devices [2,3]. However, cyberattacks on critical infrastructure have surged by 38% in the same year, highlighting a growing risk to these systems' stability and reliability. ...
... However, they are vulnerable to cyberattacks, which could undermine their effectiveness. Studies like [3] and [37] identified significant cybersecurity risks in these interconnected systems. ...
The adoption of green technologies such as smart grids and IoT devices enhances environmental sustainability by minimizing emissions and promoting efficient energy use. However, their reliance on digital infrastructure introduces significant cybersecurity vulnerabilities, posing risks to critical systems. This study focuses specifically on the intersection of cybersecurity with smart grid and IoT security, analyzing recent studies to identify key challenges such as cyber threats to smart grids and IoT vulnerabilities. It proposes a novel framework, the Cyber-Resilient Green Technology (CRGT) Framework, which integrates multi-layered defenses, blockchain security audits, and policy recommendations to safeguard green technologies. Through systematic analysis, the study quantifies the impact of cyberattacks on energy systems and outlines specific measures to enhance resilience.
... Given the critical role of DC microgrids in modern power infrastructure, securing these systems is essential. This has led to the development of advanced cyber-attack detection mechanisms and enhanced security frameworks tailored for smart DC-MGs [1]. To effectively combat these threats, a range of solutions has emerged, including blockchain technology, artificial intelligence (AI)-based detection models, and machine learning algorithms. ...
... EMD decompose each type of signals into intrinsic state function, which are continuous and explained that for every defined discrete signal s(t), µ 1 describes the mean value of the more and fewer envelope curves of the local values. The first arche member is calculated by using Equation (1). ...
The DC-Microgrids (DC-MGs) are increasingly prone to various cyber-attacks due to the advancement of intelligent controlling, monitoring, operation methods. A typical DC-MGs integrates components like batteries, super capacitors, electronic devices, Photovoltaic (PV) systems, and loads. Given these vulnerabilities, cyber-attack detection, and the security of data exchanged in smart DC-MGs, similar to Cyber-Physical Systems (CPS), have become critical areas to focus. This paper proposes a novel approach to detect false data injection attack (FDIAs) in DC-MGs using Wavelet transform and Support Vector Machines (SVMs) with Blockchain technology. The analysis shows that the output voltage dropped from 350 V to 300 V during the False Data Injection Attack (FDIA) at 0.4 s and returned to 350 V by 0.7 s. Significant oscillations observed between 0.4 and 0.7 s and detection model achieved 400 true negatives, 191 true positives, 10 false negatives, and no false positives, demonstrating high accuracy in identifying FDIA instances.
... These threats could compromise the link between the sensors and the cloud, as well as the connection between the cloud and the battery units [13]. Several literature reviews [5,16,17] in the domain of cyber-physical systems have revealed common attacks such as time delay switch (TDS) attacks, false data injection (FDI) attacks, denial of service (DoS), replay attacks, and load altering (LA) attacks. The comprehensive survey paper on FDI attacks [17] shows that although these attacks are more challenging for an adversary to successfully launch in power systems, they are still possible. ...
... If they detect a significant difference, the system knows it is under attack or operating under abnormal conditions [19,21]. Attacks such as LA attacks can be mitigated by a sliding mode control algorithm (SMCA) together with a BESS [1] and false data injection (FDI) attacks can be mitigated by making use of signal processing and blockchain technology [4,16,22]. Gumrukcu They propose a solution where distributed screening is used to identify attacks and a fault detection metric is used to distinguish between attacks and sensor faults. ...
Battery energy storage systems are an important part of modern power systems as a solution to maintain grid balance. However, such systems are often remotely managed using cloud-based control systems. This exposes them to cyberattacks that could result in catastrophic consequences for the electrical grid and the connected infrastructure. This paper takes a step towards advancing understanding of these systems and investigates the effects of cyberattacks targeting them. We propose a reference model for an electrical grid cloud-controlled load-balancing system connected to remote battery energy storage systems. The reference model is evaluated from a cybersecurity perspective by implementing and simulating various cyberattacks. The results reveal the system’s attack surface and demonstrate the impact of cyberattacks that can critically threaten the security and stability of the electrical grid.
... Smart grids combine information and communication technologies (ICT) to provide an efficient, reliable, and sustainable electric energy service, aiming for greater systemic or multisectoral decarbonization (Ghiasi et al., 2023). This increased interconnection and dependence on digital systems in smart grids expand the attack surface for cyber threats. ...
... The distributed and interconnected characteristics of smart grids present new cybersecurity threats, with interoperability between devices and legacy systems and infrastructure being significant challenges (Arpilleda, 2023). To safeguard smart grids, a holistic strategy beyond traditional security procedures is needed, including complex and multi-layered defense systems, continuous surveillance, and interaction between relevant parties (Ghiasi et al., 2023). ...
Smart grids are modernizing the future of providing energy for everyone, allowing us to increase the efficiency of power generation, transmission, or distribution using information and communication technologies. However, the network structure of smart grids makes them vulnerable to varying levels of cyber threats. This paper provides a broad overview of cyber threats against smart grids, considering attack surfaces, communication network layers, and the core security triad of confidentiality, integrity, and availability. This survey also outlines emerging threats and covers current protection, prevention, detection, mitigation, and recovery measures, focusing on emerging technologies such as artificial intelligence and large language models (LLMs) in smart grid security. We analyze and show how previous work has tackled and approached similar themes in this area. Amongst our contributions are categorizing the critical parts of smart grids that are most vulnerable to attack, several threat taxonomies, and a review of the increasing importance of LLMs for enhancing grid security. This evaluation underscores the need for effective and robust security technologies to avoid the compromises that result from more sophisticated cyber attacks.
... IOP Publishing doi: 10.1088/1742-6596/2979/1/012021 2 project auxiliary review system, multi-dimensional, multi-label tagging grid equipment, the establishment of the terminal equipment feature tag collection [4] . Then using machine learning algorithms, the collection terminal security detection and defense, timely detection and early warning of potential security risks, to ensure the fairness and accuracy of the evaluation results. ...
The core of the conventional auxiliary evaluation system for power transmission and substation projects is to build a library of engineering key characteristic index system, which can make the evaluation results more accurate, but a large number of resource design increases the burden of the system, and the response performance is weak. Therefore, a knowledge graph-based grid T&D project evaluation system is designed. For hardware, a wireless data communication interface card and an intelligent scorer are designed. In terms of software, the knowledge map of power transmission and substation engineering auxiliary evaluation is embedded, and the semantic information in the knowledge map is transformed into vector representation to capture the semantics between entities and relationships. Configure the grid transmission and transformation project auxiliary review template, create templates to configure the functions of management class, control class, business realization class, database operation realization class, entity class, and so on, to avoid the problem of system operation error. Using system testing, the system is verified to have better operational performance and can be applied in real life.
... The open-source software (OSS) landscape has seen exponential growth over the past decade, becoming a backbone for a wide range of industries, including aerospace [1], energy systems [2], [3], finance [4], [5], healthcare [6], and government projects. As the adoption of OSS starts to dominate across different sectors, its inherent benefits-such as transparency, collaboration, and rapid innovation-are accompanied by important challenges. ...
Open-source software (OSS) has become increasingly more popular across different domains. However, this rapid development and widespread adoption come with a security cost. The growing complexity and openness of OSS ecosystems have led to increased exposure to vulnerabilities and attack surfaces. This paper investigates the trends and patterns of reported vulnerabilities within OSS platforms, focusing on the implications of these findings for security practices. To understand the dynamics of OSS vulnerabilities, we analyze a comprehensive dataset comprising 31,267 unique vulnerability reports from GitHub's advisory database and Snyk.io, belonging to 14,675 packages across 10 programming languages. Our analysis reveals a significant surge in reported vulnerabilities, increasing at an annual rate of 98%, far outpacing the 25% average annual growth in the number of open-source software (OSS) packages. Additionally, we observe an 85% increase in the average lifespan of vulnerabilities across ecosystems during the studied period, indicating a potential decline in security. We identify the most prevalent Common Weakness Enumerations (CWEs) across programming languages and find that, on average, just seven CWEs are responsible for over 50% of all reported vulnerabilities. We further examine these commonly observed CWEs and highlight ecosystem-specific trends. Notably, we find that vulnerabilities associated with intentionally malicious packages comprise 49% of reports in the NPM ecosystem and 14% in PyPI, an alarming indication of targeted attacks within package repositories. We conclude with an in-depth discussion of the characteristics and attack vectors associated with these malicious packages.
... Cyber security is important because cyber-attacks and cybercrime have the power to disrupt damage or destroy businesses, communities and lives. Successful cyber-attacks lead to identity theft, personal and corporate extortion, loss of sensitive information and business-critical data, temporary business outages, lost business and lost customers and, in some cases, business closures [1]. ...
The research deals with a group of problems and breakthroughs in the previous period and the largest attacks in history that caused failures in technical systems around the world. The research discussed cyber-attacks and disruptions and how to deal with them, better understand the difference, and enhance cyber security. Through the research, many examples of malfunctions, hacks, and cyber-attacks on modern global systems are discussed. The research aims to identify the basic causes, weaknesses, and methods that contribute to finding solutions security and the challenges of espionage and electronic penetration of countries through cyberspace ،it aims to clarify the various cyber challenges and risks that threaten the security of countries.The research cyber security crimes and methods and techniques for protection and security in cyberspace. Cyber-attacks addressed a set of measures and techniques and identified important points in technical protection methodsThrough research, it is explained how to identify security vulnerabilities, cybercrimes, and the necessary measures for protection.
... Therefore, effectively identifying, classifying, and managing risks in new-type power systems has become a pivotal issue for ensuring the safe and reliable operation of the power system. Simultaneously, the proliferation of risk categories necessitates that new-type power systems adopt more targeted defense strategies [2]. ...
In the evolving new-type power systems, numerous and intricate risks make targeted risk classification and optimal defense resource allocation very difficult. This paper addresses the pressing issue by introducing a comprehensive risk assessment and defense model based on deep learning techniques. Initially, the methodology involves the extraction of potential risk vulnerabilities and their corresponding descriptive information within the new-type power systems. Subsequently, the Word2Vec and TextCNN models are harnessed to categorize these vulnerabilities based on their descriptive attributes. Furthermore, the analytic hierarchy process (AHP) is employed to assign appropriate defense weights to each classification, and a cost-effective defense strategy is formulated through the integration of linear programming. This approach ensures the optimization of resource utilization while aiming to achieve the overarching goal of resource conservation. Experimental evaluations have demonstrated the proficiency of the proposed method in accurately classifying risks within new-type power systems, while minimizing defense costs, thereby paving the way for the implementation of targeted defense strategies. The approach accurately classifies risks, minimizes defense costs, and offers practical guidance for system security, enhancing the safety and stability of new-type power systems.
... The talk covers essential protection mechanisms and problem-solving strategies. Finally, some thoughts on SGs' cyber-security in the future are expressed [21]. The new power system will face significant risk and security concerns because of the extensive integration of cyber and physical systems. ...
Significant advancements in the electrical grid include enhanced regulation, communication, metering, and customer interaction, driven by information communication technologies (ICTs) and cyber-physical systems (CPS). The adaptation of synchro phasor devices like phasor measurement units (PMUs) enables real-time monitoring and control, aiding in power system security assessment. PMUs record voltage and current phasors with GPS time stamps, transmitting data to phasor data concentrators (PDCs) for decision-making. However, ensuring the stability and security of this method against cybersecurity threats is crucial due to its reliance on Internet Protocol (IP) networks. Dynamic security assessment utilizes PMU data, reported up to 30–60 times per second, to evaluate power system safety. To address security issues, a Python-based fault detection system employing a stack ensemble learning algorithm is developed. This approach consistently outperforms traditional methods, producing satisfactory results with superior AUC-ROC curves, validated through correctness checks and graphical analysis. The dataset includes both natural and man-made security threats, facilitating comprehensive assessment and mitigation strategies. The ensemble learning algorithm performed better than the individual algorithms by obtaining 95% in the AUC-ROC curve.
... The author's survey illustrates how attackers exploit vulnerabilities in the grid, e.g., manipulation of sensor measurements, to initiate cascading failures and highlights the need to develop scalable detection methods to ensure system reliability. Similarly, Ghiasi et al. provide a comprehensive survey of cyberattacks in smart grids, including adversarial attacks, and recommend intelligent methods like deep learning to detect and mitigate threats [11]. Their work verifies the effectiveness of unsupervised learning to detect stealthy FDIAs, demonstrating its potential for field implementation in power systems. ...
Advanced as they are, DL models in cyber-physical systems remain vulnerable to attacks like the Fast Gradient Sign Method, DeepFool, and Jacobian-Based Saliency Map Attacks, rendering system trustworthiness impeccable in applications with high stakes like power systems. In power grids, DL models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are commonly utilized for tasks like state estimation, load forecasting, and fault detection, depending on their ability to learn complex, non-linear patterns in high-dimensional data such as voltage, current, and frequency measurements. Nevertheless, these models are susceptible to adversarial attacks, which could lead to inaccurate predictions and system failure. In this paper, the impact of these attacks on DL models is analyzed by employing the use of defensive countermeasures such as Adversarial Training, Gaussian Augmentation, and Feature Squeezing, to investigate vulnerabilities in industrial control systems with potentially disastrous real-world impacts. Emphasizing the inherent requirement of robust defense, this initiative lays the groundwork for follow-on initiatives to incorporate security and resilience into ML and DL algorithms and ensure mission-critical AI system dependability.
... These challenges are compounded by the evolving nature of cyber threats, which have grown more sophisticated and pervasive, targeting everything from critical infrastructure to personal data. As organizations increasingly rely on digital systems for their operations, the role of secure systems administration has expanded from merely maintaining system uptime and performance to ensuring robust defense mechanisms against cyberattacks (Ghiasi et al., 2023). respond to incidents. ...
This review paper explores the transformative impact of machine learning (ML) on secure systems administration, highlighting current trends, challenges, and future opportunities. As cybersecurity threats become more sophisticated, ML has become a crucial tool for enhancing threat detection, automating responses, and predicting potential risks. This paper discusses the application of ML in areas such as anomaly detection, user behavior analytics, and predictive analytics while also examining the challenges related to data quality, adversarial attacks, model interpretability, and integration with existing security systems. The review underscores the practical implications for cybersecurity professionals, emphasizing the need for continuous learning and adaptation as ML technologies evolve. Additionally, the paper calls for further research to address existing challenges and explore future directions, including advances in adversarial machine learning and the development of AI-driven cybersecurity operations centers. The findings of this review provide valuable insights for practitioners and researchers, offering a roadmap for the continued integration of ML in secure systems administration.
... However, cyber threats can happen deliberately or unknowingly. Deliberate attacks aim to gain unauthorized access, cause harm, and disrupt system principles [19,20]. Attacks caused by end users who have less knowledge of cyber security issues are unconscious attacks. ...
Ensuring the safe, reliable, and energy-efficient provision of electricity is a complex task for smart grid (SG) management applications. Internet of Things (IoT) and edge computing-based SG applications have been proposed for time-responsive monitoring and controlling tasks related to power systems. Recent studies have provided valuable insights into the potential of machine learning algorithms in SGs, covering areas such as generation, distribution, microgrids, consumer energy market, and cyber security. Integrated IoT devices directly exchange data with the SG cloud, which increases the vulnerability and security threats to the energy system. The review aims to provide a comprehensive analysis of privacy-preserving machine learning (PPML) applications in IoT-Integrated SGs, focusing on non-intrusive load monitoring, fault detection, demand forecasting, generation forecasting, energy-management systems, anomaly detection, and energy trading. The study also highlights the importance of data privacy and security when integrating these applications to enable intelligent decision-making in smart grid domains. Furthermore, the review addresses performance issues (e.g., accuracy, latency, and resource constraints) associated with PPML techniques, which may impact the security and overall performance of IoT-integrated SGs. The insights of this study will provide essential guidelines for in-depth research in the field of IoT-integrated smart grid privacy and security in the future.
... The reason for writing this paper is due to the growing concerns about the threats posed by IoT and the need for robust attack detection mechanisms [14][15] [16]. As the IoT evolves, the vulnerabilities and potential for large-scale botnet intrusions become increasingly apparent [17]. ...
The proliferation of IoT devices has heightened their susceptibility to cyberattacks, particularly botnets. Conventional security methods frequently prove inadequate because of the restricted processing capabilities of IoT devices. This paper suggests utilizing machine learning methods to enhance the detection of attacks in Internet of Things (IoT) environments. The paper presents a novel approach to detect different botnet assaults on IoT devices by utilizing ML methods such as XGBoost, Random Forest, LightGBM, and Decision Tree. These algorithms were examined using the N-BaIoT dataset to classify multi-class botnet attacks and were specifically designed to accommodate the limitations of IoT devices. The technique comprises the steps of data preparation, preprocessing, classifier training, and decision-making. The algorithms achieved high detection accuracy rates: XGBoost (99.18%), Random Forest (99.20%), LGBM (99.85%), and Decision Tree (99.17%). The LGBM model demonstrated exceptional performance. The incorporation of the attack evaluation model greatly enhanced the identification of botnets in IoT networks. The paper displays the efficacy of machine learning techniques in identifying botnet assaults in IoT networks. The models generated exhibit exceptional accuracy and can be seamlessly integrated into existing cybersecurity systems.
... To apply the optimization problem to the distribution network, it is necessary that an intelligent platform is established on this network. In other words, the network must be equipped with intelligent devices and intelligent algorithms [69][70][71][72][73] . ...
In this article, the energy management of the intelligent distribution system with charging stations for battery-based electric vehicles (EVs) and plug-in hybrid EVs, hydrogen station for fuel cell-based EVs, and renewable integrated energy systems (IESs) with hydrogen storage devices in accordance with the estimation of economic, operational, security and environmental goals of distribution system operator is presented. Hydrogen storage is used to store electric energy and feed hydrogen consumers. The methodology adopted here is expressed as a multi-objective formulation to be solved. Objective functions are minimizing the cost of buying energy by distribution system from the upstream network, minimizing distribution system energy losses, minimizing environmental emissions, and maximizing voltage security in the distribution system. In this issue, AC power flow model, operation and voltage security boundaries in the network, performance model of charging station for EVs, hydrogen station model for fuel cell vehicles, and renewable IES operation model with hydrogen storage is the boundaries specific to the problem. The problem in the single-objective model uses the Pareto optimization that relies on the sum of weighted functions method. Next, the fuzzy decision-making technique extracts an optimal compromised solution between the operational, economic, security and environmental objectives of the network operator. In the present scheme, load, energy prices, renewable phenomena, electric vehicles have uncertainty. In this article, stochastic optimization based on Unscented Transform is incorporated to provide a suitable modeling of the uncertain parameters appearing in the problem. Modelling of the performance of EVs charging station and hydrogen fulling station, using hydrogen storage as electricity energy storage and feeding hydrogen loads, energy management of renewable bio-waste and tidal units in IES, considering the different objectives of network operator, and using Unscented Transform approach to model of uncertainty parameters are the innovations of this article. Findings show that the method improves the technical, environmental and economic conditions of the grid, and the integrated system with its optimal performance is able to enhance the economic, environmental, security and operation status of the distribution system up to roughly 45.8%, 38%, 32–45% and 10.6%, respectively.
... As one of the subsequent consequences, green emissions (CO2, NOx, and SOx) resulting from the burning of fossil fuels can be reduced. International Maritime Organization (IMO) aims to decline emissions by 50 % by 2050 to meet the environmental objectives of the Paris Agreement [4,5]. The IMO, a specialized agency of the United Nations (UN) tasked with promoting collaboration to establish feasible benchmarks for maritime security and overseeing the shipping industry, has exhibited delays and a degree of inactivity in addressing regulatory measures pertaining to various cybersecurity standards. ...
Integration of communication networks and Shipboard Microgrids (SHMGs) brings significant benefits in the advanced control, monitoring, and remote diagnostics, particularly facilitating the data exchange between various generation components, such as sustainable energy resources, energy storage systems, and connected loads. However, the utilization of communication technologies brings serious cyber-security challenges that highly threaten maritime power systems from a security and stability perspective. This paper aims to present a comprehensive review of cybersecurity issues in marine power systems. Vulnerable points of the system to cyber-attacks, such as phasor measurement units and area control error channels are elaborated. Prevalent attacks in the load frequency control, such as denial of service and false data injection, covert attacks, and reply attacks are discussed. Recent detection/mitigation mechanisms in the cybersecurity field to tackle various cyber-attacks have been introduced. In the detection part, various observers, such as the Kalman filter, Luenberger observer, and machine learning algorithms are studied. In the mitigation mechanism, various methodologies, such as active disturbance rejection control and model predictive control are presented. This survey reviews the recent cyber-security developments and challenges in SHMG, and it is helpful for contemporary researchers in the field of cybersecurity in maritime power systems.
... SVM is a supervised classification engine that has the potential to provide high quality predictions in a large number of studies. The basic models of SVM consist of linear or nonlinear kernel functions (such as quadratic, cubic and Gaussian) (Ghiasi et al., 2023). The basic principle of SVM modeling is that a linear regression function can be computed in a high-dimensional feature space where the input data are fitted using a nonlinear function. ...
Diatom indices are used to assess the quality of aquatic plants in sustainable river ecosystems. The traditional assessment of diatom indices involves complicated and lengthy process steps. Today, artificial intelligence-based modelling plays a key role in overcoming this complexity. The aim of this work is to model selected diatom indices Biological Diatom Index (BDI), Trophic Diatom Index (TDI) and General Diatom Index (GDI) based on the physicochemical structure of river ecosystems using artificial intelligence and machine learning methods. The application part of the study used surface water variables from rivers monitored by 5 different stations for 24 months as a data set. Traditional analyses were compared with artificial intelligence and machine learning methods using the MATLAB programme. Different algorithms were considered, including Neural Network/Multilayer Perceptron (MLP), Support Vector Machine (SVM), Linear Regression (LR), Gaussian Process Regression (GPR), Decision Tree and Levenberg-Marquardt (LM) approach. To evaluate the quality of the models, the coefficient of determination (R²), root mean square error squared (RMSE) and mean absolute percentage error (MAPE) were compared. The R² values of the Levenberg-Marquardt model, which gave the best prediction results for BDI, TDI and GDI, were found to be Validation; 0.7691, Training; 0.9620 Testing; 0.8613, Validation 0.9273, Training; 0.9303, Testing; 0.9199, Validation; 0.9273, Training; 0.9303, Testing; 0.9199, respectively. Levenberg Marquardt efficiently predicted Diatom index results accurately with high precision. Our results show that artificial intelligence and machine learning methods are highly efficient tools for the prediction of diatom indices. A time-efficient and labour-saving application in sustainable ecosystem management was successfully demonstrated.
... To apply the optimization problem to the power system, an intelligent platform is needed. This platform is based on smart algorithms and telecommunication devices [58][59][60][61][62] . ...
This study presents a planning approach that considers the simultaneous expansion of generating and transmission systems, taking into account the location and sizing of generation units, AC transmission lines, and high-voltage direct-current (HVDC) systems. The HVDC system utilizes AC and DC substations equipped with AC/DC and DC/AC power electronic converters, respectively, to effectively regulate and control the reactive power of the transmission network. The problem aims to minimize the combined annual cost of constructing the specified parts and operating the generation units. This is subject to constraints such as the size and investment budget limits, an AC optimum power flow model, and the operational limits of both renewable and non-renewable generation units. The scheme incorporates a non-linear model. The Red Panda Optimization (RPO) is utilized to solve the provided model in order to attain a dependable and optimal solution. This research focuses on several advances, including the planning of the HVDC power system, the regulation of reactive power in HVDC substations, and the resolution of related issues using the RPO algorithm. The numerical findings collected from several case studies demonstrate the effectiveness of the suggested approach in enhancing the economic and technical aspects of the transmission network. Efficiently coordinating the generation units, AC transmission lines, and HVDC system leads to a significant enhancement in the economic performance of the network, resulting in a 10–40% improvement compared to the network power flow studies.
... As a result, cybersecurity has emerged as a focal topic to ensure the safe and reliable functioning of smart grids. It has also demonstrated significant success in several contexts, largely due to deep learning's inclusion abilities to extract key features from accessed data (Ghiasi et al., 2023). ...
... Although JD.com first offered smartphone leasing services in 2017, its investment in publicity and technology for the leasing business has been relatively small. For example, other leasing agencies have already implemented blockchain technology to address trust and security concerns, but JD.com has not yet followed their lead [13,14]. Consequently, JD.com's smartphone leasing market remains lackluster [15]. ...
Leasing is an important sustainable PSS model of recycling smartphones, and they have emerged as a crucial component of retailers’ business evolution in recent times. Using hybrid selling–leasing transformations, retailers not only provide selling services but also leasing services, which not only increases revenue sources but also triggers internal competition. Due to this, retailers are reluctant to promote smartphone leasing. How can we enhance retailers’ motivation to promote smartphone leasing? This paper aims to answer this question by exploring the potential of a manufacturer’s buyback program and analyzing three price decision models: pure selling (S), hybrid selling–leasing without a buyback program (SL), and hybrid selling–leasing with a buyback program (HSL). The results show that (1) when consumers’ acceptance of leasing is moderate, retailers can benefit from hybrid selling–leasing transformation. (2) If the manufacturer chooses to buy back used leasing smartphones from the retailer, it is advisable to set a high buyback price that is at least equal to their residual value. (3) The buyback program can increase consumers’ leasing demand and manufacturer’s profits, as well as decrease the environmental impact of the supply chain system. More importantly, it has the potential to drive retailers to conduct hybrid selling–leasing transformation and can establish a positive correlation between retailers’ profits and consumers’ acceptance of leasing. This means that buyback programs can promote smartphone leasing and can be beneficial for smartphone recycling and urban sustainable development.
... These virtual replicas of physical systems enable seamless integration of real-world data with machine learning algorithms, facilitating performance optimization, forecasting, and proactive maintenance. Their applications in renewable energy, particularly in wind turbines and power plants, exemplify their utility in optimizing energy generation, streamlining maintenance schedules, and predicting system failures before they occur, thereby enhancing efficiency and minimizing downtime [92,184]. The integration of digital twins into energy infrastructure significantly improves the management of distributed energy resources (DERs), supporting the transition to resilient and sustainable energy systems [185]. ...
The evolution of the electrical grid from its early centralized structure to today’s advanced “smart grid” reflects significant technological progress. Early grids, designed for simple power delivery from large plants to consumers, faced challenges in
efficiency, reliability, and scalability. Over time, the grid has transformed into a decentralized network driven by innovative technologies, particularly artificial intelligence (AI). AI has become instrumental in enhancing efficiency, security, and resilience by enabling real-time data analysis, predictive maintenance, demand response optimization, and automated fault detection, thereby improving overall operational efficiency. This paper examines the evolution of the electrical grid, tracing its transition from early limitations to the methodologies adopted in present smart grids for addressing those challenges. Current smart grids leverage AI to optimize energy management, predict faults, and seamlessly integrate electric vehicles (EVs), reducing transmission losses and improving performance. However, these advancements are not without limitations. Present grids remain vulnerable to cyberattacks, necessitating the adoption of more robust methodologies and advanced technologies for future grids. Looking forward, emerging technologies such as Digital Twin (DT) models, the Internet of Energy (IoE), and decentralized grid management are set to redefine grid architectures. These advanced technologies enable real-time simulations, adaptive control, and enhanced human–machine collaboration, supporting dynamic energy distribution and proactive risk management. Integrating AI with advanced energy storage, renewable resources, and adaptive access control mechanisms will ensure future grids are resilient, sustainable, and responsive to growing energy demands. This study emphasizes AI’s transformative role in addressing the challenges of the early grid, enhancing the
capabilities of the present smart grid, and shaping a secure, efficient, and adaptive next-generation grid aligned with future needs.
Keywords Electrical grid, Smart grid, Energy transition, Renewable energy, Decentralized energy, Grid modernization, Artificial intelligence, IoT, IoE, Energy storage
... Information security (IS) of public, corporate, financial and other systems becomes the key task of digital transformation. Number and preparation level of cyberattacks increase yearly, and what is more, the share of targeted attacks on production sector grows (Ghiasi, 2023, Duo 2022. At the same time, the market of IS specialists is experiencing an acute shortage of competent personnel, and the need for them will only increase (Defitsit kadrov v IB-otrasli RF dostigayet poryadka 100 tys. ...
This paper explores the possibility of using digital twins of enterprise information systems, based on the Cyber polygon, to train Information Security students. The term “cyber polygon” is defined as a digital twin of the enterprise IT structure, allowing the implementation of various attack scenarios. A cyber polygon allows students to develop skills in eliminating vulnerabilities without harming real systems. The authors conducted a study with Information Security students to assess how cyber-range-based training affects their learning. Statistical analysis showed that second- and third-year students practiced scenarios as both the monitoring and response teams. Repeated practice significantly reduced the time to close vulnerabilities, but the same scenarios should not be overused to prevent memorization. Moreover, third-year students showed better results than second-years due to greater experience with the cyber range.
... The cyber security landscape can be likened to an ongoing arms race occurring in the digital stratum of energy systems, pitting system operators against potential adversaries. This perpetual race is inherently asymmetric, as defenders must shield the system from any possible attack, while attackers can persistently attempt disruptions without facing immediate consequences [85]. Given the interconnection between power system operators and the natural gas network, the injection of erroneous information is a significant concern. ...
Energy systems and their related technologies are susceptible to natural extreme events, categorized as high-impact low-probability (HILP) events, posing a significant threat to their reliable functioning. Gas-to-power (G2P) and power-to-gas (P2G) technologies establish a bidirectional interface between these energy systems, leading to the creation of integrated power and natural gas systems (IPGS). Due to their extensive geographical coverage, IPGS are particularly vulnerable to severe damage from natural calamities. Given the growing interest and research focus on IPGS resilience, this comprehensive review meticulously navigates the intricate terrain of resilience differentiation, offering a detailed roadmap fortified by insights from significant instances of grid failures and weather-driven contingencies. The review examines preemptive cyber security fortifications and strategic planning imperatives, scrutinizing each aspect with conviction and clarity. Temporally stratified into long-term and short-term horizons, it not only delineates prevailing approaches and methodologies but also identifies emerging trends poised to shape the future landscape of resilience enhancement. This paper provides an exhaustive review of existing research on the resilience of IPGS, introducing a visual framework for comparing different studies and facilitating easy understanding through multiple figures. Since uncertainties play a crucial role in decision-making within this field, this paper broadly explores methods for addressing them as presented in previous studies. Furthermore, the literature is meticulously classified to offer a clear and organized overview, highlighting the impact of HILP events, such as natural disasters and cyber-attacks, on IPGS resilience. This review underscores the critical need to fortify IPGS, emphasizing its importance as a crucial component of integrated energy systems in preparing for the continuous impact of natural disasters in future research.
Adopting Advanced Machine Learning for Smart Grids (ML-SG) is a promising strategy that revolutionizes the energy industry to optimize energy usage, improve grid management, and foster sustainability. It also increases the efficiency, reliability, and sustainability of contemporary power systems. \Furthermore, incorporating machine learning into smart grids has important practical ramifications and can help address some of the most pressing issues facing contemporary energy systems. By precisely forecasting consumption trends and facilitating dynamic pricing models that take into account current grid circumstances, Machine Learning (ML) can improve demand response tactics. Additionally, it is essential for preserving grid stability since it can promptly identify irregularities and react to system oscillations, preventing blackouts and equipment failures. Furthermore, through supply and demand balance, energy dispatch optimization, and solar and wind power forecasts, ML makes it easier to seamlessly integrate renewable energy sources. These characteristics facilitate the shift to a more robust, adaptable, and ecologically friendly energy infrastructure in addition to increasing operating efficiency. In this paper, we investigate the development of ML solutions that benefit from the enormous amounts of data generated by IoT devices in the smart grid. Furthermore, this study examines the benefits and drawbacks of the adoption of ML-SG and offers an outline of their use while highlighting the implications of integrating ML into smart grids. In addition, it explores and analyzes how ML algorithms can be used for load forecasting and enabling accurate and real-time decision making in smart grids. The objective of this work is to analyze smart grid operations at different levels, such as predicting energy demand, identifying abnormalities, and reducing cybersecurity threats by using sophisticated ML-based algorithms, especially discussing attacks and countermeasures against these ML models. This work concludes with suggestions and recommendations that highlight the importance of improving the security and accuracy of ML-SG, while shedding some light on future directions. In the future, this work aims to contribute to the development of efficient ML solutions for energy infrastructure to become more effective and sustainable, by discussing data science and ML issues related to smart grids.
As cyber threats grow, leveraging tools like ChatGPT offers a strategic advantage in ethical hacking and cybersecurity. This chapter examines ChatGPT's potential in enhancing ethical hacking skills through scenario-based learning, topic exploration, and critical evaluation of its responses. A questionnaire with ten questions on tools, techniques, certifications, and hacker psychology was answered by 20 ethical hacking experts. While feedback on operating systems and cybersecurity principles was positive, some questioned the practicality of moderate recommendations. Statistical analysis showed a Cronbach's alpha of 0.878, indicating high internal consistency, with overall positive responses. The chapter underscores ChatGPT's value in tracking trends, tools, and methodologies while acknowledging its practical limitations.
The growing penetration of IoT devices in power grids despite its benefits, raises cybersecurity concerns. In particular, load-altering attacks (LAAs) targeting high-wattage IoT-controllable load devices pose serious risks to grid stability and disrupt electricity markets. This paper provides a comprehensive review of LAAs, highlighting the threat model, analyzing their impact on transmission and distribution networks, and the electricity market dynamics. We also review the detection and localization schemes for LAAs that employ either model-based or data-driven approaches, with some hybrid methods combining the strengths of both. Additionally, mitigation techniques are examined, focusing on both preventive measures, designed to thwart attack execution, and reactive methods, which aim to optimize responses to ongoing attacks. We look into the application of each study and highlight potential streams for future research.
The integration of Artificial Intelligence (AI) and Blockchain technologies in the
energy sector has the potential to revolutionize the management and
optimization of smart grids, especially in developing countries like Pakistan. The
country’s energy sector faces persistent challenges such as inefficiency, unreliable
supply and limited integration of renewable energy sources. This paper explores
how AI and Blockchain can address these issues by enhancing grid efficiency,
improving security, and enabling decentralized, transparent energy trading
systems. AI can contribute to predictive maintenance, load forecasting, and
optimization of renewable energy integration, while Blockchain offers secure,
transparent, and immutable records for energy transactions, facilitating peer-to
peer energy trading and smart contract automation. By combining these
technologies, Pakistan’s energy sector can unlock significant improvements in
operational efficiency, cost reduction, and sustainability. The paper also
highlights the barriers to adoption, including technological infrastructure
challenges, regulatory hurdles, and the need for skilled workforce development.
Through an examination of global case studies and potential solutions tailored to Pakistan’s context, this paper outlines a pathway for the successful deployment
of AI and Blockchain in smart grids, positioning Pakistan to lead in energy
innovation and contribute to global sustainability goals.
The expanding cyber threat landscape has compelled organizations to adopt AI-driven security systems for robust defense against sophisticated attacks. This chapter explores artificial intelligence in cybersecurity, emphasizing its role in intelligent threat detection, analysis, and response. AI models, including supervised and unsupervised learning, deep learning, and reinforcement learning, have redefined cybersecurity by enabling behavior-based anomaly detection and automated threat mitigation. Key discussions highlight autonomous systems making real-time decisions, leveraging adaptive control loops, and employing self-healing mechanisms for resilience. This chapter also examines challenges in operational scalability, ethical implications of automation, and the necessity of human oversight in decision-making. The findings underscore the need for synergy between automation and human expertise to foster an intelligent, adaptive cyber defense ecosystem.
The tight integration of cyber and physical networks in integrated energy cyber–physical systems (IECPS) improves system awareness and coordinated control but also heightens susceptibility to targeted attacks. A robust IECPS defense system is crucial for increasing the system’s resilience against deliberate attacks. Reducing the associated risks is essential to ensure the safe and stable operation of IECPS. In order to enhance the defense capability of IECPS against deliberate attacks, this paper discusses cyberattacks, physical attacks, and coordinated cyber physical attacks (CCPAs) in detail. The attack principles and attack models of each type of attack are described, and then the intentional attack threats faced by IECPS are analyzed. Based on this, the paper reviews the current research landscape regarding countermeasures against deliberate attacks, categorizing the findings into three key areas: preemptive prevention, process response, and post–event recovery and summarizing. The theoretical foundations, system planning, optimal scheduling, and cyber security technologies required for existing defense research are further elaborated. The unresolved issues within these key technologies are analyzed and summarized, followed by the presentation of the problems and challenges faced in defending against deliberate IECPS attacks.
To achieve low‐carbon sustainable energy development, new technologies such as Internet of Energy (IoE), intelligent systems and Internet of Things (IoT) as well as distributed energy generations via smart grids (SG) are gaining attention. The interoperability between intelligent energy systems, realised through the web, enables automatic consumption optimisation and increases network efficiency and intelligent management. IoE is an intriguing topic in close connection with the IoT, communication systems, SG and electrical mobility that contributes to energy efficiency to achieve zero‐carbon technologies and green environments. Furthermore, nowadays, the widespread growth and utilisation of processors for mining digital currency in homes and small warehouses are some other factors to be considered in terms of electric energy consumption and greenhouse gas emission. However, research on the use of the Internet for evaluating the misallocation of energy and the effect it can have on CO2 emissions is often neglected. In this study, the authors present a detailed overview regarding the evolution of SG in conjunction with the employment of IoE systems as well as the essential components of IoE for decarbonisation. Also, mathematical models with simulation are provided to evaluate the role of IoE in reducing CO2 emission.
Computational complexities in modern power systems are reportedly increasing daily, and it is anticipated that traditional computers might be inadequate to provide the computation prerequisite in future complex power grids. In that given context, quantum computing (QC) can be considered a next-generation alternative solution to deal with upcoming computational challenges in smart grids. The QC is a relatively new yet promising technology that leverages the unique phenomena of quantum mechanics in processing information and computations. This emerging paradigm shows a significant potential to overcome the barrier of computational limitations with better and faster solutions in optimization, simulations, and machine learning problems. In recent years, substantial progress in developing advanced quantum hardware, software, and algorithms have made QC more feasible to apply in various research areas, including smart grids. It is evident that considerable research has already been carried out, and such efforts are remarkably continuing. As QC is a highly evolving field of study, a brief review of the existing literature will be vital to realize the state-of-art on QC for smart grid applications. Therefore, this article summarizes the research outcomes of the most recent papers, highlights their suggestions for utilizing QC techniques for various smart grid applications, and further identifies the potential smart grid applications. Several real-world QC case studies in various research fields besides power and energy systems are demonstrated. Moreover, a brief overview of available quantum hardware specifications, software tools, and algorithms is described with a comparative analysis.
As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grid. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transformer, as a detector deployed in edge nodes, delves deep into the connection between individual electrical quantities by using its multi-head self-attention mechanism. By using federated learning framework, our approach utilizes the data from all nodes to collaboratively train a detection model while preserving data privacy by keeping the data locally during training. To improve the security of federated learning, a secure federated learning scheme is designed by combing Paillier cryptosystem with federated learning. Through extensive experiments on the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of the proposed method are verifed.
Purpose of Review
This paper provides a SWOT analysis of the Interdependent and Complex Electric Power and Transportation Systems (INTERCEPTS). The SWOT analysis is conducted to highlight the strengths, weaknesses, opportunities, and threats for the safe, secure, and successful implementation and operations of the INTERCEPTS.
Recent Findings
The INTERCEPTS stakeholders need to take advantage of the existing strengths such as the state-of-the-art technology for energy storage and V2G and public awareness on climate change to take advantage of the opportunities such as modern business models for market participants and plan accordingly to eliminate the weaknesses and threats for safe and secure operations of the INTERCEPTS.
Summary
EVs have shown great potential to reduce the green gas emission and fossil fuel usage. The bidirectional flow of energy provided by the Vehicle to Grid (V2G) technology strengthens the renewable energy sources adaption and creates numerous benefits such as grid stability, peak load management, and cost-saving for the stakeholders and market participants. However, the integration of large-scale EVs to the power grid increases the load substantially and may make the power grid exposes to some threats such as overloaded lines or even cyberattacks. The SWOT analysis provides insights for the decision makers of the INTERCEPTS and market participants and puts more emphasis on thoughtful planning and preparedness before full integration of the electric power and transportation systems.
Modern power systems equipped with advanced communication infrastructure are cyber-physical in nature. The traditional approach of leveraging physical measurements for detecting cyber-induced physical contingencies are insufficient to reflect the accurate cyber-physical states. Moreover, deploying conventional rule-based and anomaly-based intrusion detection systems for cyberattack detection results in higher false positives. Hence, independent usage of detection tools of cyberattacks in cyber and physical sides has a limited capability. In this work, a mechanism to fuse real-time data from cyber and physical domains, to improve situational awareness of the whole system is developed. It is demonstrated how improved situational awareness can help reduce false positives in intrusion detection. This cyber and physical data fusion results in cyber-physical state space explosion which is addressed using different feature transformation and selection techniques. Our fusion engine is further integrated into a cyber-physical power system testbed as an application that collects cyber and power system telemetry from multiple sensors emulating real-world data sources found in a utility. These are synthesized into features for algorithms to detect cyber intrusions. Results are presented using the proposed data fusion application to infer False Data and Command Injection (FDI and FCI)-based Man-in-The-Middle attacks. Post collection, the data fusion application uses time-synchronized merge and extracts features. This is followed by pre-processing such as imputation, categorical encoding, and feature reduction, before training supervised, semi-supervised, and unsupervised learning models to evaluate the performance of the intrusion detection system. A major finding is the improvement of detection accuracy by fusion of features from cyber, security, and physical domains. Additionally, it is observed that the semi-supervised co-training technique to perform at par with supervised learning methods with the proposed feature vector. The approach and toolset as well as the dataset that are generated can be utilized to prevent threats such as false data or command injection attacks from being carried out by identifying cyber intrusions accurately.
Nowadays, the role of cyber-physical systems (CPSs) is of paramount importance in power system security since they are more vulnerable to different cyber-attacks. Detection of cyber-attacks on a direct current microgrid (DC-MG) has become a pivotal issue due to the increasing use of them in various electrical engineering applications, from renewable power generations to the distribution of electricity and power system of public transportation and subway electric network. In this study, a novel strategy was provided to diagnose possible false data injection attacks (FDIA) in DC-MGs to enhance the cyber-security of electrical systems. Accordingly, to diagnose cyber-attacks in DC-MG and to identify the FDIA to distributed energy resource (DER) unit, a new procedure of wavelet transform (WT) and singular value decomposition (SVD) based on deep machine learning was proposed. Additionally, this paper presents a developed selective ensemble deep learning (DL) approach using the gray wolf optimization (GWO) algorithm to identify the FDIA in DC-MG. In the first stage, in the paper, to gather sufficient data within the ordinary performance required for the training of the DL network, a DC-MG was operated and controlled with no FDIAs. In the information generation procedure, load changing was considered to have diagnosing datasets for cyber-attack and load variation schemes. The obtained simulation results were compared with the new Shallow model and Hilbert Huang Transform methods, and the results confirmed that the presented approach could more precisely and robustly identify multiple forms of FDIAs with more than 95% precision.
Abstract Man‐in‐The‐Middle (MiTM) attacks present numerous threats to a smart grid. In a MiTM attack, an intruder embeds itself within a conversation between two devices to either eavesdrop or impersonate one of the devices, making it appear to be a normal exchange of information. Thus, the intruder can perform false data injection (FDI) and false command injection (FCI) attacks that can compromise power system operations, such as state estimation, economic dispatch, and automatic generation control (AGC). Very few researchers have focused on MiTM methods that are difficult to detect within a smart grid. To address this, we are designing and implementing multi‐stage MiTM intrusions in an emulation‐based cyber‐physical power system testbed against a large‐scale synthetic grid model to demonstrate how such attacks can cause physical contingencies such as misguided operation and false measurements. MiTM intrusions create FCI, FDI, and replay attacks in this synthetic power grid. This work enables stakeholders to defend against these stealthy attacks, and we present detection mechanisms that are developed using multiple alerts from intrusion detection systems and network monitoring tools. Our contribution will enable other smart grid security researchers and industry to develop further detection mechanisms for inconspicuous MiTM attacks.
Cyber-physical threats as false data injection attacks (FDIAs) in islanded smart microgrids (ISMGs) are typical accretion attacks, which need urgent consideration. In this regard, this paper proposes a novel cyber-attack detection model to detect FDIAs based on singular value decomposition (SVD) and fast Fourier transform (FFT). Since new research are mostly focusing on FDIAs detection in DC systems, paying attention to AC systems attack detection is also necessary; hence, AC state estimation (SE) have been used in SI analysis and in considering renewable energy sources effect. Whenever malicious data are added into the system state vectors, vectors’ temporal and spatial datum relations might drift from usual operating conditions. In this approach, switching surface based on sliding mode controllers is dialyzed to regulate detailed FFT’s coefficients to calculate singular values. Indexes are determined according to the composition of FFT and SVD in voltage/current switching surface to distinguish the potential cyber-attack. This protection layout is presented for cyber-attack detection and is studied in various types of FDIA forms like amplitude and vector derivation of signals, which exchanged between agents such as smart sensor, control units, smart loads, etc. The prominent advantage of the proposed detection layout is to reduce the time (less than 10 milliseconds from the attack outset) in several kinds of case studies. The proposed method can detect more than 96% accuracy from 2967 sample tests. The performances of the method are carried out on AC-ISMG in MATLAB/Simulink environment.
Automation in power distribution systems and supervisory control and data acquisition (SCADA), which perform network switching automatically and remotely, depending on the type of configuration, allows distribution companies to flexibly control distribution power grids. Cross-section switches also has a significant role in the automation in distribution systems; in that the operational optimization of these switches is able to enhance the supply power quality and reliability indicators. This strategy can be a prosperous solution to increase the reliability, efficiency and overall service quality in energy network and services to customers. In this regard, in this work, a genetic algorithm (GA) approach is proposed to sketch the optimal location and control of automatic and manual cross-section switches and protection relay systems in distribution power systems. Hence, an optimization formulation is proposed to improve the value-based reliability of the suggested layout considering the cost of customer downtime and the costs related to segmentation of switches and relay protection devices. Also, a distributed generation (DG) system in distribution networks is considered based on the islanded state of generation units. The effectiveness of the optimal suggested procedure is evaluated and represented via performing a real and practical test system in the distribution network of Ahvaz city in Iran. The results show that using proposed method and by optimally allocating switches maneuver, energy losses without switches are reduced from 310.17 (MWh) to 254.2 (MWh), and also by using DG, losses are reduced from 554.01 to 533.61 which confirms the ability and higher accuracy of the proposed method to improve reliability indices.
The supremacy and fast development of quantum techniques are stimulating the arrival of an ultra‐secure and super‐fast quantum internet, which accordingly will shape future electric grids. This paper develops a novel scheme for resilient electric grids by using a quantum direct communication (QDC) network. The novelty of this work includes: (a) a novel QDC‐based electric grid architecture is devised to provide ultra‐secure communication; (b) we investigate QDC protocols for grid communication and analyze the impacts of attacks and noises; and (c) we demonstrate how to establish a QDC‐enabled electric grid testbed to evaluate the system's performance. Architecture of quantum direct communication network empowered electric grids is developed to provide great resilience and ultra‐security. Protocols for communication are investigated to prove the performance under attacks and noises with the built testbed.
Today, in various leading power utilities in developing countries, achieving optimal operational energy management and planning, taking into account the costs reduction of generation, transmission and distribution of electricity, and also reducing the emission of an environmental pollutant becomes more and more important. Optimal use of renewable energy sources (RESs) is an effective way to achieve these goals. In this regard, in this research article, an improved multi-objective differential evolutionary (IMODE) optimization algorithm is suggested and performed to dispatch electricity generations in a smart microgrid (MG) system, taking into account economy and emission as competitive issues. In this paper, a nonlinear equation of multi-objective optimization issue with various equality and inequality limitations is formulated in order to lower the total operational costs of the MG considering environmental pollution effects simultaneously. In order to address the issue of optimal operation of the MG in single-objective and multi-objective forms, an intelligent method according to the improved differential evolutionary (IDE) optimization is utilized and performed and the proposed algorithm is implemented on different problems. First, it is assumed that there is no limit to the exchange of power overhead, and secondly, the limitation of power exchange with the upstream grid is considered. In multi-objective mode, these two modes are also considered. In order to show the impact of renewable energy on the cost, in the third part of the simulations, the operation is solved with maximum participation of renewable energy sources. In the final section, the sensitivity analysis on the number of populations in this problem is performed. The obtained results of the simulation are compared to differential evolutionary (DE) and particle swarm optimization (PSO) techniques. The effectiveness of the suggested multi-operational energy management method is confirmed by applying a study case system.
Unexpected natural disasters or physical attacks have various consequences, including extensive and prolonged blackouts on power systems. Energy systems should be designed to be resistant to unwanted events, and its performance is not easily affected by such conditions. Power system should also have sufficient flexibility so that it can adapt to severe disturbances without losing its full performance; it should restore itself immediately after resolving disturbance. This critical feature of the behavior of infrastructure systems in power grids is called resilience. In this paper, the concepts related to resilience in the power system against severe disturbance are explained, and the components of resilience and evaluation process are introduced. An optimal design of resilient substations in Noorabad City distribution grid against physical attack is presented. This research proposes an optimal solution for simultaneously allocating the feeder routing issue and substation facilities and finding the models of installed conductors and economic hardening of power lines due to intentional attacks on vital urban operational infrastructure. The values of distribution networks are calculated using the grey wolf optimization algorithm to solve the problem of designing optimal distribution network and optimal resilient distribution network schemes. Obtained results confirm the effectiveness of the proposed approach.
Due to the simultaneous development of DC-microgrids (DC-MGs) and the use of intelligent control, monitoring and operation methods, as well as their structure, these networks can be threatened by various cyber-attacks. Overall, a typical smart DC-MG includes battery, supercapacitors and power electronic devices, fuel cell, solar Photovoltaic (PV) systems, and loads such as smart homes, plug-in hybrid electrical vehicle (PHEV), smart sensors and network communication like fiber cable or wireless to send and receive data. Given these issues, cyber-attack detection and securing data exchanged in smart DC-MGs like CPS has been considered by experts as a significant subject in recent years. In this study, in order to detect false data injection attacks (FDIAs) in a MG system, Hilbert-Huang transform methodology along with blockchain-based ledger technology is used for enhancing the security in the smart DC-MGs with analyzing the voltage and current signals in smart sensors and controllers by extracting the signal details. Results of simulation on the different cases are considered with the objective of verifying the efficacy of the proposed model. The results offer that the suggested model can provide a more precise and robust detection mechanism against FDIA and improve the security of data exchanging in a smart DC-MG.
In this study, a privacy-based demand response (DR) trading scheme among end-users and DR aggregators (DRAs) is proposed within the retail market framework and by distribution platform optimiser. This scheme aims to obtain the optimum DR volume to be exchanged while considering both DRAs' and customers' preferences. A bi-level programming model is formulated in a day-ahead market within retail markets. In the upper-level problem, the total operation cost of the distribution system is minimised. The production volatility of renewable energy resources is also taken into account in this level through stochastic two-stage programming and Monte-Carlo simulation method. In the lower-level problem, the electricity bill for customers is minimised for customers. The income from DR selling is maximised based on DR prices through secure communication of household energy management systems and DRA. To solve this convex and continuous bi-level problem, it is converted to an equivalent single-level problem by adding primal and dual constraints of lower level as well as its strong duality condition to the upper-level problem. The results demonstrate the effectiveness of different DR prices and different number of DRAs on hourly DR volume, hourly DR cost and power exchange between the studied network and the upstream network.
With the aging of electricity transmission and distribution infrastructures and increasing intensity of extreme weather events, the aggravated vulnerability of electric distribution systems to extreme weather events has motivated the study of resilient distribution systems. This study presents a review of the state-of-the-art research on distribution grid resilience. First, the definition and quantifying metrics of resilience in the electrical distribution system are summarised. Second, the long-term and short-term measures to enhance the distribution system resilience are discussed. In particular, the recent studies on distributed generation and microgrid-assisted resilience enhancements are reviewed. Finally, recommendations for future research are presented.
Due to the widespread use of electric motors in various industries, it is very important to have optimally designed motors in that they have high efficiency and lower negative effects on the quality of the power grid. Therefore, in this paper, the effects of winding type (wide and concentrated) on ripple torque in internal permanent magnet motor (IPMM) are investigated. In order to reduce the ripple torque and to increase the average torque, by making optimal holes in the rotor surface and using the sensitivity analysis method, the structure of the IPMM is improved. In this method, the number, dimensions and location of holes are optimized using the sensitivity analysis approach, which reduces the ripple torque of the motor. Using a concentrated winding instead of a wide winding, the toothed ripple torque is reduced by approximately 75% while maintaining the average torque value. Also, by making holes in the rotor surface and optimizing them using the finite element technique and sensitivity analysis, it is demonstrated that the amount of ripple torque by 20%. In the proposed approach, it is proved that in the concentrated winding, in addition to reducing the spatial harmonics, the average amount of torque can also be improved. Obtained results of the simulation confirm the effectiveness of the proposed method. INDEX TERMS Permanent magnet synchronous motor, sensitivity analysis, ripple toothed torque, harmonic effects.
Since Smart Islands (SIs) with advanced cyber infrastructure are extremely vulnerable to cyber-attacks, there is an increasing attention on their cyber security. The False Data Injection attacks by manipulating measurements may result in wrong state estimation (SE) solutions or interfere with the central control system performance. There is a possibility that conventional attack detection methods do not detect many cyber-attacks thus system operation be interfered. Research works are more focused on detection of cyber-attacks that target DC-SE, however due to more widely utilizes of AC Smart-Islands, investigation on cyber-attack detection in AC systems is more crucial. A new mechanism to detect injection of any false data in AC-SE is proposed in this paper. Malicious data injection in the state vectors may result in deviation of their temporal and spatial data correlations from their ordinary operation. The suggested detection method is based on analyzing temporally consecutive system states via wavelet singular entropy. In this method, to adjust singular value matrices and wavelet transforms’ detailed coefficients, switching surface based on sliding mode controller are decomposed then applying stochastic process, expected entropy values are calculated. Indices are characterized based on the wavelet singular entropy (WSA) in switching level of current and voltage for cyber-attack detection. The suggested protection method is applied to detect cyberattacks and various types of false data injection such as amplitude and vector deviation of signals are investigated. The significant characteristics of this detection method are its ability in fast detection (10 ms from the attack initiation).
Computing plays a significant role in power system analytics. As mathematical challenges increase and data become the epicenter of modern decision making, substantial progress needs to be made to draw on emerging analytics and computing technologies. Quantum computing is a groundbreaking technology in information processing that can support the global efforts in addressing power system challenges and in further envisioning the grid of the future. However, despite extensive research activities in quantum computing applications in various sectors, its application to power systems has remained mostly unexamined. It is necessary to have an across-the-board view of the quantum computing technology applications in power systems, and in particular, in building the grid of the future. This paper discusses the essential elements of quantum computing and presents a review of issues concerning this technology. The paper further provides an in-depth discussion of the potential of quantum computing in improving analytical and computing capabilities in solving multiple power system problems. INDEX TERMS Quantum computing, superposition and entanglement, grid of the future.
This study is concerned with resilient load frequency control (LFC) scheme design of multi‐area power systems with communication delay and aperiodic denial‐of‐service (DoS) attacks. First, to identify DoS attacks, a detection mechanism in the actuator side is proposed which is a time counter measuring the input delay. The identified DoS attacks are constrained by its upper bound of frequency and duration. When DoS attacks are identified, actuator switches to zero‐input strategy from hold‐input strategy. According to the switching control inputs, switched time delay system model is established to describe the attack influence on LFC system. Further, a criterion of preserving weighted H∞ performance is derived by combining piecewise Lyapunov–Krasovskii functional method with switched system method. Based on the criterion, the resilient control gain is designed by solving a set of LMIs. Finally, numerical simulations are given to verify the validness of the attack detection based resilient LFC scheme.
Smart grid depends on an advanced communication network to collect information from the power grid, and to disseminate control commands to the control devices. To safeguard the power grid, it is crucial to ensure information confidentiality in the communication networks. Quantum key distribution (QKD) protocols help in generating, and distributing secret keys between communication parties, and such secret keys are required in symmetric cryptography. The combination of QKD protocols, and symmetric cryptography are known to be unconditionally secure, which means information confidentiality can be guaranteed even against an eavesdropper, who has unlimited resources. This article provides a concise review of existing works on QKD protocols, and their applications in smart grid communications. Deploying QKD protocols in smart grid is challenging because distance between the control center, and control devices can be larger than the limits of existing protocols. Also, QKD protocols require an expensive quantum channel between each pair of sender, and receiver nodes, and there is large number of control devices with diverse capabilities in smart grid. We have classified existing works based on the challenges they have dealt with. Compared to the rich literature on QKD protocols in general, there are significantly fewer works in the specific context of smart grid. This can be an indication for opportunity to make a significant contribution. We have also identified a few research challenges that can be potential future works.
In Smart Island (SI) systems, operators of power distribution system usually utilize actual-time measurement information as the Advanced Metering Infrastructure (AMI) to have an accurate, efficient, advanced control and monitor of whole their system. SI system can be vulnerable to complicated information integrity attacks such as False Data Injection Attack (FDIA) on some equipment including sensors and controllers, which can generate misleading operational decision in the system. Hence, lack of detailed research in the evaluation of power system that links the FDIAs with system stability is felt, and it will be important for both assessment of the effect of cyber-attack and taking preventive protection measures. In this regards, time–frequency-based differential approach is proposed for SI cyber-attack detection according to non-stationary signal assessment. In this paper, non-stationary signal processing approach of Hilbert–Huang Transform (HHT) is performed for the FDIA detection in several case studies. Since various critical case studies with a small FDIA in data where accurate and efficient detection can be a challenge, the simulation results confirm the efficiency of HHT approach and the proposed detection frame is compared with shallow model. In this research, the configuration of the SI test case is developed in the MATLAB software with several Distributed Generations (DGs). As a result, it is found that the HHT approach is completely efficient and reliable for FDIA detection target in AC-SI. The simulation results verify that the proposed model is able to achieve accuracy rate of 93.17% and can detect FDIAs less than 50 ms from cyber-attack starting in different kind of scenarios.
Power systems are complex systems that have great importance to socio-economic development due to the fact that the entire world relies on the electric network power supply for day-to-day life. Therefore, for the stable operation of power systems, several protection and control techniques are necessary. The power system controllers should have the ability to maintain power system stability. Three important quantities that should be effectively controlled to maintain the stability of power systems are frequency, rotor angle, and voltage. The voltage control in power systems maintains the voltage and reactive power within the required limits and the power factor control enhances the efficiency of power distribution systems by improving load power factors. Among various controls, the frequency control is the most time-consuming control mechanism of power systems due to the involvement of mechanical parts. As the control algorithms of frequency stabilization deliver control signals in the timescale of seconds, load frequency control (LFC) systems cannot handle complicated data validation algorithms, making them more vulnerable to disturbances and cyber-attacks. In addition, the LFC system has extended digital layers with open communication networks and is designed to operate with less human intervention. Moreover, the frequency fluctuation due to load change or cyber-attack in one area affects all other interconnected areas, and thus threatens the stability of the entire network. Due to these circumstances, research activities are still carried out in the field of frequency control and cyber-security. In this paper, a comprehensive review of the cyber-security of the LFC mechanism in the power system is presented. The highlights of the paper include the identification of attack points of different configurations of the LFC system, discussion of the attack strategies, formulation of various attack models, and a brief review of the existing detection and defense mechanisms against cyber-attacks on LFC.
This paper explores the problem of protection against false data injection attacks on the power system state estimation. Although many research works have been reported previously to solve the same problem, yet most of them are only for perfect false data injection attacks. To address the problem reasonably, all related factors influencing the success probability and corresponding attack impact of imperfect false data injection attacks should also be considered. Based on such considerations, a TOTAL (TOpology, parameTer, Accuracy, Level) protection strategy considering all corresponding factors is proposed. The TOTAL protection strategy minimizes the attack impact of typical imperfect false data injection attacks (single measurement attacks) while defending against typical perfect false data injection attacks (single state variable attacks). Depending on whether the protection scheme contains Phasor Measurement Units (PMUs), we formulate the meter selection as a linear binary programming or integer programming problem, which can be solved by suitable solvers. The proposed strategy is compared with existing methods in the literature and evaluated using standard IEEE test cases.
Recently, the penetration rate of plug-in hybrid electric vehicles (PHEVs) and renewable/distributed energy resources (RERs/DERs) has increased in microgrids (MGs). However, due to the high uncertainties on both the demand (i.e., PHEV) and the supply (i.e., RER) sides, it is an indisputable fact that these components should be coordinated intelligently with the rest of the grid. In this paper, a smart charging scheme is proposed for PHEVs that can minimize the energy drawn from the main grid, and this minimizes the dependence of MGs on the main grid by maximizing the use of RERs/DERs. Two scenarios are conducted on the modified version of the IEEE 33-bus test system to verify the effectiveness of the proposed power management strategy
for different penetration levels of PHEVs, and the results of the proposed scheme are compared with the results of other, previously reported strategies. The obtained results from off-line digital time-domain simulations and software verification indicate that the energy exchanged between the MG and the main grid to charge PHEVs is decreased since the RER/DER generation is increased. Therefore, the obtained results reveal the superiority of the proposed power management strategy over other, previously reported strategies.
The dependence on advanced information and communication technology increases the vulnerability in smart grids under malicious cyber-attacks. Moreover, recent research on unobservable false data injection attacks (FDIAs) reveals the high risk of secure system operation, since these attacks can bypass current bad data detection mechanisms. To mitigate this risk, this paper proposes a data-driven learning-based algorithm for detecting unobservable FDIAs in distribution systems. We use autoencoders for efficient dimension reduction and feature extraction of measurement datasets. Further, we integrate the autoencoders into an advanced generative adversarial network (GAN) framework, which successfully detects anomalies under FDIAs by capturing the unconformity between abnormal and secure measurements. Also, considering that the datasets collected from practical power systems are partially labeled due to expensive labeling costs and missing labels, the proposed method only requires a few labeled measurement data in addition to unlabeled data for training. Numerical simulations in three-phase unbalanced IEEE 13-bus and 123-bus distribution systems validate the detection accuracy and efficiency of this method.
Because of the existence of communication networks and control applications, DC microgrids can be attacked by cyber-attackers. False data injection attack (FDIA) is one type of cyber-attacks where attackers try to inject false data to the target DC microgrid to destruct the control system. This work discusses the effect of FDIAs in DC microgrids that are structured by parallel DC/DC converters and they are controlled by droop based control strategies to maintain the desired DC voltage level. Also, an effective and proper strategy based on an artificial neural network-based reference tracking application is introduced to remove the FDIAs in the DC microgrid.
This paper proposes a localized protection scheme for DC microgrids with radial configuration under the impact of CPLs to determine the location of faults accurately. The proposed fault location scheme is primarily designed for fault location of CPLs in DC Microgrids. First, a local protection relay for CPL is designed based on the transient behavior of the current and voltage in the main distribution line. Then, the estimation of the fault resistance is formulated based on the power-sharing in the system to improve the accuracy of the protection system. To realize a robust protection scheme considering the variation of fault resistance, a fault resistance estimation procedure is employed to design a system that locates both low- and HIFs. Finally, the effectiveness of the proposed strategy is evaluated based on offline digital time-domain simulations in Digsilent PowerFactory software environment and experimentally verified by implementing on a laboratory scale hardware setup. The obtained simulation and experimental test results, and comparison with other methods prove that the proposed method scheme is immune against these disturbances and can efficiently and reliably estimate the location and resistance of faults with high accuracy and acceptable error margin.
This paper presents an enhanced control strategy for renewable energy resources connected to the grid through voltage-sourced converters (VSCs) in microgrids (MGs). The proposed scheme contains a voltage control loop with the minimum inverter switching, a power-sharing controller with the minimum inverter switching, a negative sequence current controller, and a loop to identify the control system operation mode. All the controllers are designed using the multipurpose finite control set-model predictive control (MFCS-MPC) strategy. Since these controllers use the dynamic current and VSC voltage, they can be applied in grid-connected and island operation modes (GCOM and IOM) and transferred between them. The method uses voltage-frequency control instead of power control for VSCs. One inverter controls voltage, and the other controls current. The conventional finite control set-model predictive control (FCS-MPC) is enhanced to reduce the computation power by eightfold. This improvement is significant because the maximum switching frequency is limited in practical implementations. Also, the superiority of the proposed multipurpose control scheme is proved theoretically. Simulation is implemented using MATLAB software and compared with methods in the literature. The simulation demonstrates that the presented control strategy is efficient, authentic, and compatible. The proposed method is also tested and validated in hardware experiments. Index Terms-Current controller, finite control set-model predictive control, grid-connected operation mode, islanded operation mod, voltage controller.
Power system substations have intelligent electronic devices (IEDs) that collect data and control other devices. As the bridge between the physical and cyber parts of the power system, IEDs capture some key system behaviors. Since adversaries can modify the system’s behavior, physical and cyber data can be used to infer characteristics about the adversary. In this article, we present alert and control logic for hardware-based power system defense using the physical data and communication status in substation IEDs for
cyber threat detection
,
cyber-physical contingency detection and response
, and
physical contingency identification and response
. The proposed alert and control logic routines are implemented in an industrial real-time automation controller using IEC 61131-3 in the resilient energy systems lab testbed. The goal is to help operators identify adversaries and protect the power grid in a cyber-physical environment. The effectiveness and accuracy of logic schemes are validated under different adversarial scenarios. Comparing the proposed schemes with an intrusion detection system, Snort, our results also suggest the benefits of using cyber and physical data to identify threats. The results also suggest the use of such hardware-based schemes with software algorithms in a next-generation cyber-physical energy management system, which can implement automatic control actions to protect power grids and its physical equipment against cyber threats.
Wireless sensor network (WSN) with several sensors is used to group measurements of certain physical quantities or environmental conditions, including sound, temperature, pressure, vibration, motion, or pollution, at various locations and ranges. In WSNs, several protocols address the issue of routing. Sensor nodes in WSN usually have limited energy resources and storage capacities. Therefore, the issue of energy usage in sensors and protocols is very important. This paper analyzes and compares the quality of service (QoS) performances of three important routing protocols of the mobile ad-hoc network (MANET) including Ad-hoc on-demand distance vector (AODV), dynamic source routing (DSR), and destination sequenced distance vector (DSDV), in a smart building case study. The QoS evaluation metrics include residual energy of nodes, instant throughput (IT), average throughput (AT), packet delivery ratio (PDR), packet loss ratio (PLR), and route discovery latency (RDL) based on IEEE 802.15.4 MAC protocol standard. The simulation is carried out using NS2, and the WSN has 16 fixed and 4 mobility nodes with different speeds and paths. The simulation results illustrate that average throughput in AODV is 1.985118 (kbps), however, the figures for DSR and DSDV are 1.977780 and 1.720700 (kbps), respectively. PDR, also, in DSR stands at 1.0, but the figures for AODV and DSDV are lower with the range of 0.999572 and 0.997930, respectively. Overall, The DSR protocol provides a better performance compared to AODV and DSDV routing protocols in terms of PLR and PDR. Also, AODV has better efficiency in RDL and AT compared to other assumed protocols.
We model the risk posed by a malicious cyber-attacker seeking to induce grid insecurity by means of a load redistribution attack, while explicitly acknowledging that such an actor would plausibly base its decision strategy on imperfect information. More specifically, we introduce a novel formulation for the cyber-attacker’s decision-making problem and analyze the distribution of decisions taken with randomly inaccurate data on the grid branch admittances or capacities, and the distribution of their respective impact. Our findings indicate that inaccurate admittance values most often lead to suboptimal cyber-attacks that still compromise the grid security, while inaccurate capacity values result in notably less effective attacks. We also find common attacked cyber-assets and common affected physical-assets between all (random) imperfect cyber-attacks, which could be exploited in a preventive and/or corrective sense for effective cyber–physical risk management.
Monitoring the smart grid involves analyzing continuous data-stream from various measurement devices deployed throughout the system, which are topologically distributed and structurally interrelated. In this paper, a graph signal processing (GSP) framework is used to represent and analyze the inter-related smart grid measurement data for security and reliability analyses. The effects of various cyber and physical stresses in the system are evaluated in different GSP domains including vertex domain, graph-frequency domain, and the joint vertex-frequency domain. Two novel techniques based on vertex-frequency energy distribution, and the local smoothness of graph signals are proposed and their performance have been evaluated for detecting and locating various cyber and physical stresses. Based on the presented analyses, the proposed techniques show promising performance for detecting sophisticated stresses with no sharp changes at the onset, for detecting abrupt load changes, and also for locating stresses.
Distributed secondary control stands out for its flexibility and expandability in microgrids (MGs) control, in where communication network plays a fundamental and critical role. The communication topology and link-weights have significant impact on the attack-resilience and dynamic performance of MGs, which should be appropriately designed. However, the joint optimization problem of communication topology and link-weights in the existing literature of consensus-based secondary voltage control of MGs is usually neglected. To bridge this gap, we propose a novel two-stage optimization approach for the communication network design, which jointly optimizes the topological structure to enhance the structural survivability and link weights to improve the dynamic performance (including the speed of convergence and robustness to time-delay). The first stage problem is formulated into a mixed-integer semi-define programming (MISDP) model based on convex relaxation technique, which is then converted equivalently into an integer quadratic programming (IQP) problem and then a MISDP feasibility problem to facilitate the solution. The second stage problem is formulated into a bi-objective SDP model to compromise between the convergence performance and robustness to time-delay. Simulations based on a microgrid with 10 distributed generation (DG) units under different scenarios are implemented to verify the effectiveness of the proposed method. Index Terms-Communication network design, convex relaxation, structural survivability, speed of convergence, robustness to time-delay, the multiple deliberate cyber-attack, microgrid, consensus-based secondary voltage control.
The deregulated electricity market operation in smart grid has resulted in a conducive scenario for both the
utility and consumer. However, the deep integration of cyber components in the physical infrastructure has
increased the vulnerability to attacks by intruders. As compared to an isolated cyber or physical attack, the
coordinated attack involving both the physical and cyber layer has a wider impact on the smart grid operation.
This paper proposes an AC state estimation (AC-SE) based coordinated cyber-physical attack for disrupting the
market operation by manipulating the nodal price. Knowledge of the system configuration and real-time sensor
information eases the task of launching an attack. However, the availability of such knowledge is somewhat
unrealistic due to accessibility and budget constraints. In this regard, the proposed attack design considers the
realistic scenario of incomplete information regarding network topology and sensor data. The first stage of the
attack involves identifying the most vulnerable branch that causes maximum deviation in the nodal price allocation
between pre-attack and post-attack scenarios. In the second stage, a cyber-attack is synthesized to hide the
impact of a physical attack. The effectiveness of the scheme has been validated on benchmark IEEE 14, 39, and
118 bus power systems.
The deregulated electricity market operation in smart grid has resulted in a conducive scenario for both the utility and consumer. However, the deep integration of cyber components in the physical infrastructure has increased the vulnerability to attacks by intruders. As compared to an isolated cyber or physical attack, the coordinated attack involving both the physical and cyber layer has a wider impact on the smart grid operation. This paper proposes an AC state estimation (AC-SE) based coordinated cyber-physical attack for disrupting the market operation by manipulating the nodal price. Knowledge of the system configuration and real-time sensor information eases the task of launching an attack. However, the availability of such knowledge is somewhat unrealistic due to accessibility and budget constraints. In this regard, the proposed attack design considers the realistic scenario of incomplete information regarding network topology and sensor data. The first stage of the attack involves identifying the most vulnerable branch that causes maximum deviation in the nodal price allocation between pre-attack and post-attack scenarios. In the second stage, a cyber-attack is synthesized to hide the impact of a physical attack. The effectiveness of the scheme has been validated on benchmark IEEE 14, 39, and 118 bus power systems.
In this article, a new procedure is proposed on the basis of Hilbert-Huang Transform and deep learning for cyber-attacks detection in direct current (DC) micro-grids (MGs) as well as detection of the attacks in distributed generation (DG) units and its sensors. An advanced elective group deep learning method with Krill Herd Optimization (KHO) algorithm is proposed. At first, Hilbert-Huang Transform is used with the aim of extracting the signals feature and next these features are applied as the multiple deep input basis models are made with the aim of capturing automatically sentient traits from raw fluctuation signals. At third, to make sure the variety of the basis patterns, linear decoder, denoising autoencoder and sparse autoencoder are applied to make various deep autoencoders, respectively. Further, Bootstrap is applied with the aim of designing separate educational data subsets for any base model. Fourth, for implementing selective ensemble learning, a combination strategy of enhanced weighted voting (EWV) with class-particular thresholds is studied. Eventually, KHO algorithm is applied with the aim of adaptive selecting the optimal class-specific thresholds. In the offered tactic, firstly, a DC micro-grid is functioned and controlled with the lack of any false data injection attacks (FDIAs) to collect adequate information within the usual operation needed for the educating of deep learning networks. It is noteworthy that, in the procedure of datum production, load variable is also determined with the aim of having distinctive datasets for cyber-attack scenarios and load variables. Also, to provide more realistic method, the smart plug-in electric vehicle is also considered in the model. Outcomes of Simulation in various scenarios are applied with the aim of verifying the benefit of the offered procedure. The outcomes propose that the offered procedure is able to more accurate and robust know various type of false data injection attack over than 93.76% accuracy detection of true rate.
A group of distributed generators (DGs) systems including wind, solar, diesel, energy storage (ES), etc., that are under a central management and control is often considered as virtual power plant (VPP) concept. One of the components of a VPP is ES, whose presence and participation in the electricity market can create business opportunities. In this paper, a new mathematical-based strategy for identifying different types of trading situations considering VPPs effects is proposed in the electricity market to obtain maximum benefit. Also VPP trading between energy and ancillary services is considered and analysed. The presented model considers all limitations of the VPP including network constrains and the structure of VPPs. The optimal management of distributed energy units determines the state of charge (SoC) or discharge of ES resources and the amount of intermittent load for the day ahead electricity market. By implementing the proposed model on the microgrid (MG), two different modes of trading for VPPs are examined and the changes of efficiency related to energy storages are analysed. In order to solve the issue of optimal operation strategy, an intelligent approach based on differential evolution (DE) algorithm is used. The obtained simulation results of both modes are compared with those VPP without energy storage. The results show notable profits in both modes.
Industrial Internet of Things (IIoT) has been defined as an architecture that uses the Internet of Things (IoT) and cloud computing to facilitate distributed control of modern industrial systems like AC smart microgrids (MGs). This paper proposes a novel secure energy policy and load sharing approach for renewable MGs for independent utilization of off-grid MGs with power electronic jointing (PEJ) on the basis of master-slave (M-S) which is formed in the IIoT environment. Assume that computations for system dispatch are performed by an upper layer however a lower layer calculates proper control proceedings for the PEJ. A decentralized multi-agent system (MAS) realizes the upper layer of intelligent control on the basis of communication. The layer has 2 control mechanisms: economic dispatch and MAS power balance control. Numerous operating, controlling, and planning to be in the energy industry pay special attention to Blockchain technology. In addition to allowing a common and distributed database, Blockchain technology (B.CT) enables safe, automated, transparent, and economic operations in power distribution systems. If a hacker manipulates and alters the data exchanged between agents, it will result in disrupting system performance in terms of economy and stability MG voltage profile, load distribution, optimized parameters including cost, environmental pollution, and unit output. Therefore, it is necessary to maintain the cyber security of AC smart MG and increase the security of data measured in the sensors and the transaction data between agents. In this paper, B.CT is presented to secure the exchanged data against malicious cyber-attacks in an AC smart MG whose control layers are M-S organized. The simulated system consists of the MG with several distributed generation units that examine cyber-attack points and then compare the results in normal mode and cyber-attack mode and B.CT is presented to increase the cyber security of AC smart MG.
Communication has always played a vital role in microgrids to maintain reliable operations and achieve great benefits and will be even more critical with the increasing deployment of renewable energies, information technologies, and real-time automation and control systems. The existing classical cryptographic methods for securing microgrid communication, however, rely on mathematical assumptions, which are vulnerable to attacks from quantum computers. This article reviews the current status of developing quantum-secure microgrids, namely microgrids that are secure against attacks from quantum computers. We introduce some potential issues associated with applying existing quantum cryptography methods in the context of microgrids and provide future perspectives to make quantum security more practical in microgrids.
Today, due to the several benefits of using the subway as a clean transportation system and also its expansion in many cities around the world, electrification to urban and suburban railway systems is experiencing a very important development procedure. Optimal power supply and management of energy that is economically viable is one of the important issues in the design of such systems. In this paper, in order to provide an optimal energy management and to determine the location of rectifier substations (RSs), and also to calculate power supply capacity (PSC) of the traction units, an optimal design and simulation of a DC railway traction power supply system (RTPSS) in urban area is proposed. In this regard, first, the structure of power system of RSs used in urban area is presented and analyzed in detail. Then, considering the importance of the standard criteria in designing the dynamics of a city’s RTPSS, an equivalent circuit for the desired network is provided. After defining the governing equations of the network and using the dogleg optimization method, to ensure convergence, the speed of solving equations is improved. In this study, in order to verify the performance of the presented method, the cost-based convergence characteristic curve for Dogleg optimization method is compared to the particle swarm optimization (PSO) approach. In order to confirm the robustness, applicability, and superiority of the proposed approach for optimal design and energy management in a city railway power system, the presented method is applied to a real study case. The obtained results through the simulation approve the effectiveness of using the Dogleg optimization method in power consumption by approximately 255 kWh in reducing energy compared to the practical energy consumption for one train during the trip in normal condition.
A new local current-based fast high impedance fault (HIF) detection scheme for DC microgrid clusters using mathematical morphology (MM) is proposed in this paper. The proposed strategy consists of two MM based parts. The first part is MM erosion filtering to extract the current signals and its components for extraction of the differential feature vector, and the second part is MM regional maxima, for defining a determinative value to detect faults in a line segment by the lowest possible time. Also, this scheme uses local measured values to eliminate the need for communication channels, which provide a low cost, reliable, and fast fault detection method for DC microgrid clusters. Moreover, to provide an accurate HIF detection method, the accurate HIF model in DC systems is presented and used in the proposed method. To demonstrate the efficiency, authenticity, and compatibility of the proposed method, digital time-domain simulations are carried out in MATLAB/Simulink environment under different scenarios such as overload, noise, low and HIFs to distinguish between overloads and HIFs, and the results are compared with several reported algorithms. The obtained simulation results are verified by experimental tests, which validate the accuracy and speed of the proposed strategy under different conditions.
The blockchain technology has been applied to nuclear power plants in which the cyber performance using the security analysis information is highly enhanced. Following this study, other kinds of information in the nuclear stuff could be applied using the blockchain technology. The simulations are performed as the conventional with system dynamics (SD) and blockchain modified modeling. The blockchain quantifications are done for conventional algorithm and SD as the highest value is 2.0. In addition, the encryption analysis is done, which is obtained by the hash code with the modulus algorithm that the remainder is calculated. For example, in the case of Long-term Conduction, the highest value is 0.6. After taking the information of hash code, the decoding could be accomplished. The networking bodies have same information and can take the secured information.
Smart Island (SI) refers to the condition where distributed generation (DG) units e.g. wind turbines (WT), fuel cell (FC), and photovoltaic cells (PV) have to control current, voltage, and frequency of the grid by themselves without any support from the main grid. Therefore, design and implementation of robust controller to overcome disturbances and load variations is very crucial. This paper presents a new approach using General Type-II Fuzzy controller to control smart island in combination with a novel modified optimization algorithm to increase the load sharing throughout the DGs operating in an islanding mode. This case study assumes that there are two DGs in the smart island where each DG has its own responsibilities. In particular, one of the DGs regulates the frequency and voltage based on a reference feedback, while the other DG operates in load current control mode to share load between each DG properly. The simulation results indicate that the suggested controller has a quick response to load alternations with a low steady state error and low THD. Moreover, the proposed controller is independent from system states and other units. The experimental analysis indicate that the suggested control system is effectively capable to coordinate the operations of the DG units in smart island to validate the stable operation of the overall smart island.
Both frequency and intensity of natural disasters have intensified in recent years. It is, therefore, essential to design effective strategies to minimize their catastrophic consequences. Optimizing recovery tasks, including distribution system reconfiguration (DSR) and repair sequence optimization (RSO), are the key to enhance the agility of disaster recovery. This article aims to develop a resilience-oriented DSR and RSO optimization model and a mechanism to quantify the recovery agility. In doing so, a new metric is developed to quantify the recovery agility and to identify the optimal resilience enhancement strategies. The metric is defined as “the number of recovered customers divided by the average outage time of the interrupted customers.” A Monte-Carlo-based methodology to quantify the recovery agility of different DSR plans is developed. It will be shown that if the total number of interrupted customers over the recovery horizon is minimized, the metric will be maximized. Accordingly, theDSRandRSOoptimization models are modified to maximize the introduced metric. The proposed optimization model is formulated as a mixed-integer linear programming model that can be solved via commercial off-the-shelf solvers. Finally, the proposed methodology is applied to several case studies to examine its effectiveness. It will be also shown how the proposed methodology can be utilized for distributed generator (DG) and tie-line placement problems in planning for enhanced structural resilience.
This paper presents a distributed multi-layer cloud-fog computing architecture for the optimal energy management in the smart grids considering the high penetration of plug-in hybrid electric vehicles. The distributed framework is constructed on the basis of alternating direction method of multipliers (ADMM) to let neighboring agents to get into a consensus with each other. Due to the high attendance of various renewable energy sources looking like wind turbines and photolytic panels, it is tried to model the uncertainties based on Monte Carlo simulations. Three various charging layouts containing the smart charging, controlled charging and uncontrolled charging are assumed and compared in the smart grid test system. A powerful optimization method based on Grey Wolf Optimizer is proposed to solve the augmented Lagrangian function in each agent. The simulation results show clearly the appropriate efficiency of this layout.
In this paper, we consider the security problem of dynamic state estimations in cyber–physical systems (CPSs) when the sensors are compromised by false data injection (FDI) attacks with complete stealthiness. The FDI attacks with complete stealthiness can completely remove its influences on monitored residuals, which have better stealthy performance against residual-based detectors than existing FDI attacks. Based on self-generated FDI attacks that are independent of real-time data of CPSs, we propose the necessary and sufficient condition of attack parameters such that FDI attacks can achieve complete stealthiness. Furthermore, we introduce the energy stealthiness of FDI attacks, which is a special case of complete stealthiness and makes the accumulated attack energy on residuals is bounded. Then, the existence and design conditions of FDI attacks with energy stealthiness are given. Finally, the superiority of the FDI attacks with complete stealthiness is demonstrated by the IEEE 6 bus power system.
This paper introduces quantum computing as a necessary and viable tool in addressing the needs of a modernized power grid. The application of quantum computing in enhancing physical security of the grid — an increasingly difficult problem to solve— is investigated. A comparative study based on mathematically proven computing performance measures shows the merits of the proposed method and further unveils the potential benefits of quantum computing in improving grid performance.