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Modernizing power systems into smart grids has introduced numerous benefits, including enhanced efficiency, reliability, and integration of renewable energy sources. However, this advancement has also increased vulnerability to cyber threats, particularly False Data Injection Attacks (FDIAs). Traditional Intrusion Detection Systems (IDS) often fall short in identifying sophisticated FDIAs due to their reliance on predefined rules and signatures. This paper addresses this gap by proposing a novel IDS that utilizes hybrid feature selection and deep learning classifiers to detect FDIAs in smart grids. The main objective is to enhance the accuracy and robustness of IDS in smart grids. The proposed methodology combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for hybrid feature selection, ensuring the selection of the most relevant features for detecting FDIAs. Additionally, the IDS employs a hybrid deep learning classifier that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture the smart grid data’s spatial and temporal features. The dataset used for evaluation, the Industrial Control System (ICS) Cyber Attack Dataset (Power System Dataset) consists of various FDIA scenarios simulated in a smart grid environment. Experimental results demonstrate that the proposed IDS framework significantly outperforms traditional methods. The hybrid feature selection effectively reduces the dimensionality of the dataset, improving computational efficiency and detection performance. The hybrid deep learning classifier performs better in key metrics, including accuracy, recall, precision, and F-measure. Precisely, the proposed approach attains higher accuracy by accurately identifying true positives and minimizing false negatives, ensuring the reliable operation of smart grids. Recall is enhanced by capturing critical features relevant to all attack types, while precision is improved by reducing false positives, leading to fewer unnecessary interventions. The F-measure balances recall and precision, indicating a robust and reliable detection system. This study presents a practical dual-hybrid IDS framework for detecting FDIAs in smart grids, addressing the limitations of existing IDS techniques. Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.

Smart grid solutions enable utilities and customers to better monitor and control energy use via information and communications technology. Information technology is intended to improve the future electric grid’s reliability, efficiency, and sustainability by implementing advanced monitoring and control systems. However, leveraging modern communications systems also makes the grid vulnerable to cyberattacks. Here we report the first use of quantum key distribution (QKD) keys in the authentication of smart grid communications. In particular, we make such demonstration on a deployed electric utility fiber network. The developed method was prototyped in a software package to manage and utilize cryptographic keys to authenticate machine-to-machine communications used for supervisory control and data acquisition (SCADA). This demonstration showcases the feasibility of using QKD to improve the security of critical infrastructure, including future distributed energy resources (DERs), such as energy storage.

The current energy transition combined with the modernization of power systems has provided meaningful transformations in the transmission, distribution, operation, planning, monitoring, and control of power systems. These advancements are heavily dependent on the employment of new computing and communications technologies, which, combined with traditional physical systems, lead to the emergence of cyber–physical systems (CPSs). In this sense, besides the traditional challenges of keeping a reliable, affordable, and safe power grid, one must now deal with the new vulnerabilities to cyberattacks that emerge with the advancement of CPSs. Aware of this perspective and the severity of the ongoing challenges faced by the industry due to cyberattacks, this paper aims to provide a comprehensive survey of the literature on cybersecurity in cyber–physical power systems. For this, clear definitions, historical timelines, and classifications of the main types of cyberattacks, including the concepts, architectures, and basic components that make up, as well as the vulnerabilities in managing, controlling, and protecting, a CPS are presented. Furthermore, this paper presents defense strategies and future trends for cybersecurity. To conduct this study, a careful search was made in relevant academic and industrial databases, leading to a detailed reporting of key works focused on mitigating cyberattacks and ensuring the cybersecurity of modern CPSs. Finally, the paper presents some standards and regulations that technical and international institutions on cybersecurity in smart grids have created.

Ensuring the economic security of the industry sector and its element, energy security, is critical for the economies of countries, especially in the current environment of escalating military conflicts. The purpose of this study was to investigate the most likely risks to the security of the industrial sector, taking the example of the oil industry. The study employed the formal legal method, the method of qualitative textual analysis, the descriptive method, the method of statistical analysis, and the survey method. The study determined the place of energy security, specifically the security of the oil industry, in the system of economic security and emphasised its exceptional significance, especially in times of war. The principal global risks to the security of the oil industry were identified, including increased economic dependence for import-dependent countries and for countries dependent on oil exports; escalation of conflicts due to disagreements over resource control; and terrorist and cyberattacks. It was found that the main threats to Ukraine are generated by Russia’s invasion of its territory, which entails such critical risks as the physical destruction of oil industry facilities and cybersecurity breaches. The study confirmed that the war in Ukraine could have a substantial impact on the energy security of the European Union. This impact may result in the postponement of the association’s environmental goals due to the need to urgently ensure its own energy security. The study identified ways to improve security in the Ukrainian oil industry in times of war, including ensuring physical security and cybersecurity, developing crisis response plans, and improving the energy efficiency of the national industry. The findings of this study may be useful in developing measures to optimise energy policy

The heterogeneous and interoperable nature of the cyber-physical system (CPS) has enabled the smart grid (SG) to operate near the stability limits with an inconsiderable accuracy margin. This has imposed the need for more intelligent, predictive, fast, and accurate algorithms that are able to operate the grid autonomously to avoid cascading failures and/or blackouts. In this paper, a new comprehensive identification system is proposed that employs various machine learning architectures for classifying stability records in smart grid networks. Specifically, seven machine learning architectures are investigated, including optimizable support vector machine (SVM), decision trees classifier (DTC), logistic regression classifier (LRC), naïve Bayes classifier (NBC), linear discriminant classifier (LDC), k-nearest neighbor (kNN), and ensemble boosted classifier (EBC). The developed models are evaluated and contrasted in terms of various performance evaluation metrics such as accuracy, precision, recall, harmonic mean, prediction overhead, and others. Moreover, the system performance was evaluated on a recent and significant dataset for smart grid network stability (SGN_Stab2018), scoring a high identification accuracy (99.90%) with low identification overhead (4.17 μSec) for the optimizable SVM architecture. We also provide an in-depth description of our implementation in conjunction with an extensive experimental evaluation as well as a comparison with state-of-the-art models. The comparison outcomes obtained indicate that the optimized model provides a compact and efficient model that can successfully and accurately predict the voltage stability margin (VSM) considering different operating conditions, employing the fewest possible input features. Eventually, the results revealed the competency and superiority of the proposed optimized model over the other available models. The technique also speeds up the training process by reducing the number of simulations on a detailed power system model around operating points where correct predictions are made.

The stability of modern economies relies on the uninterrupted supply of electricity, heat, and transport fuels, making the energy sector highly exposed to various risks and disruptions, including floods, which are among the major natural hazards affecting energy infrastructure in Poland. Despite risks, a scalable and integrated modelling framework for operational flood risk management in energy infrastructure is still lacking. Such a framework should account for increasing climate-related hazard dynamics, integrate robust fragility and damage models with comprehensive flood risk assessments at both asset and system levels, and explicitly consider interdependencies among energy system components and associated critical infrastructure. This integration is essential for analyzing cascading failures and their consequences, while complying with the EU CER Directive requirements for resilience and continuity of critical infrastructure services. An original three-stage spatial vulnerability analysis method was developed, involving GIS data preparation, classification of asset importance, and flood scenario modelling, demonstrated on selected rivers in the Odra River basin. The Expected Damage Factor (EDF) metric was applied to combine flood probability with infrastructure significance. The analysis enabled spatial identification of the most vulnerable components of the energy system and illustrated the dynamics of threats in time and space. The EDF coefficient allowed for quantitative vulnerability assessment, supporting more precise adaptive planning. The approach innovatively combines infrastructure criticality assessment with probabilistic flood scenarios and explicitly incorporates systemic interdependencies in accordance with the CER Directive, enhancing operational flood risk management capabilities. The method provides a practical tool for critical infrastructure protection, operational planning, and the development of adaptive strategies, thereby increasing the flood resilience of the energy system and supporting stakeholders responsible for risk management.

Energy security and the transition to renewable energy are strategic issues in global energy policy, especially for developing countries that still face the challenge of dependence on fossil energy. Indonesia, as one of the countries with high primary energy consumption, has set a target of 23% renewable energy mix by 2025. However, the achievement is still far from the set target, indicating structural barriers in the energy transition. One of the key determinants in the energy transition is the Energy Security Index and primary energy consumption, which may have implications for the share of renewable energy in the national energy mix. Although various studies have explored the relationship between energy security and sustainable energy mix, empirical studies that comprehensively analyse the impact of Energy Security Index and primary energy consumption on the share of renewable energy in Indonesia are still limited. Therefore, this study aims to examine the relationship between the Energy Security Index, primary energy consumption, and the share of renewable energy and evaluate the extent to which the two independent variables affect the energy transition in Indonesia. This study uses a quantitative approach with the robust least squares method to produce more accurate parameter estimates. The results show that the energy security index has a positive and significant effect on the share of renewable energy, with a coefficient of 1.570 (0.0000 < 0.05), indicating that increased energy security contributes to the acceleration of the renewable energy transition. In contrast, primary energy consumption shows a negative impact on the share of renewable energy, with a coefficient of −3.802 (0.0433 < 0.05), indicating that dependence on fossil energy hinders the increase in the share of clean energy. In addition, the F-test shows that the Energy Security Index and primary energy consumption simultaneously have a significant influence on the share of renewable energy (0.0000 < 0.05), with a coefficient of determination R2 of 89.36%, indicating that the model used is able to explain most of the variability in the share of renewable energy. This finding confirms that improving energy security through energy source diversification and energy efficiency are key factors in accelerating the energy transition. The government needs to reduce dependence on fossil fuels and accelerate incentives for renewable energy so that the renewable energy mix target can be achieved.

Around the globe, electric power networks are transforming into complex cyber–physical energy systems (CPES) due to the accelerating integration of both information and communication technologies (ICT) and distributed energy resources. While this integration improves power grid operations, the growing number of Internet-of-Things (IoT) controllers and high-wattage appliances being connected to the electric grid is creating new attack vectors, largely inherited from the IoT ecosystem, that could lead to disruptions and potentially energy market manipulation via coordinated load-altering attacks (LAAs). In this article, we explore the feasibility and effects of a realistic LAA targeted at IoT high-wattage loads connected at the distribution system level, designed to manipulate local energy markets and perform energy storage (ES) arbitrage. Realistic integrated transmission and distribution (T&D) systems are used to demonstrate the effects that LAAs have on locational marginal prices at the transmission level and in distribution systems adjacent to the targeted network.

Investment opportunities are analyzed from the perspective of the variables that influence risk. The present study analyzes some energy characteristics using data from the Eurostat Data Browser. First, we identified a gap in energy research. Second, we proposed a multicriteria analysis using the analytic hierarchy process (AHP). An algorithm was developed to simulate how experts think to determine pairwise comparisons. A procedure identified the levels of importance of each criterion and alternative based on extracted data from the Eurostat website. The method was used to rate countries according to data regarding their energy policy results. The present study shows that applying the AHP method is possible without expert support and using data regarding the theme studied. The results show that Malta and Estonia are the most suitable countries to receive investments since they are presently at the top of the energy security ranking. The selected set of criteria seems to properly correspond with the assessment of the sector security as far as risk investment is concerned. The results of the current study may represent a base to support investment decision-making in the energy sector of EU countries.

Escalating events such as extreme weather conditions, geopolitical incidents, acts of war, cyberattacks, and the intermittence of renewable energy resources pose substantial challenges to the functionality of global electric grids. Consequently, research on enhancing the resilience of electric grids has become increasingly crucial. Concurrently, the decentralization of electric grids, driven by a heightened integration of distributed energy resources (DERs) and the imperative for decarbonization, has brought about significant transformations in grid topologies. These changes can profoundly impact flexibility, operability, and reliability. However, there is a lack of research on the impact of DERs on the electric grid’s resilience, as well as a simple model to simulate the impact of any disturbance on the grid. Hence, to analyze the electric grid’s resilience, this study employs an extrapolation of Leontief’s input–output (IO) model, originally designed to study ripple effects in economic sectors. Nodes are treated as industries, and power transmission between nodes is considered as the relationship between industries. Our research compares operability changes in centralized, partially decentralized, and fully decentralized grids under identical fault conditions. Using grid inoperability as a key performance indicator (KPI), this study tests the three grid configurations under two fault scenarios. The results confirm the efficacy of decentralization in enhancing the resilience and security of electric grids.