The electric power grid constitutes a foundational pillar of modern critical infrastructure (CI), underpinning societal functionality and global economic stability. Yet, the increasing convergence of Information Technology (IT) and Operational Technology (OT), particularly through the integration of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS), has amplified the sector’s exposure to sophisticated cyber threats. This study conducts a comparative analysis of five major cyber incidents targeting electric power systems: the 2015 and 2016 Ukrainian power grid disruptions, the 2022 Industroyer2 event, the 2010 Stuxnet attack, and the 2012 Shamoon incident. Each case is examined with respect to its objectives, methodologies, operational impacts, and mitigation efforts. Building on these analyses, the research evaluates the extent to which such attacks could have been prevented or mitigated through the systematic adoption of leading cybersecurity maturity frameworks. The NIST Cybersecurity Framework (CSF) 2.0, the ENISA NIS2 Directive Risk Management Measures, the U.S. Department of Energy’s Cybersecurity Capability Maturity Model (C2M2), and the Cybersecurity Risk Foundation (CRF) Maturity Model alongside complementary technical standards such as NIST SP 800-82 and IEC 62443 have been thoroughly examined. The findings suggest that a proactive, layered defense architecture grounded in the principles of these frameworks could have significantly reduced both the likelihood and the operational impact of the reviewed incidents. Moreover, the paper identifies critical gaps in the existing maturity models, particularly in their ability to capture hybrid, cross-domain, and human-centric threat dynamics. The study concludes by proposing directions for evolving from compliance-driven to resilience-oriented cybersecurity ecosystems, offering actionable recommendations for policymakers and power system operators to strengthen the cyber-physical resilience of electric generation and distribution infrastructures worldwide.

ABSTRACT This article develops a novel computationally efficient methodology for joint probabilistic forecasting of multiple, numerical sequences. This approach to forecasting is relevant for many important applications such as transportation planning, electrical power grid operation, weather forecasting, etc. Three important characteristics of the proposed methodology are (a) extracting features (principal components) of fixed‐length output sequences from the training data, (b) multivariate forecasting in the principal component space, and c) accounting for the periodicity in the original space, and hence the principal component space, when choosing the forecasting model form. The structure of output sequences of a fixed length is exploited to devise a procedure for building the quantities of interest (QoI) matrix using the training data. The most informative features of this QoI matrix are extracted using principal component analysis (PCA). To account for the periodicity in the original time series as well as their principal component (PCs), separate probabilistic regression models are trained to simultaneously predict all the PCs for each time step of a periodic cycle (termed as the “periodic trick”). This modeling approach is illustrated by generating forecasts for the regional wind generation, load demand, and solar generation time series of the French electricity grid (RTE) and for the ambient temperature in two cities. It is shown, using various deterministic and probabilistic model validation metrics, that the proposed approach performs better than sequence‐to‐sequence (machine learning‐based) forecasting methods. A computationally efficient and accurate forecast can thus be obtained by exploiting the problem‐specific structure (fixed‐length input and output), leveraging feature extraction techniques, and employing a meaningful treatment of the periodicity in the data.

Abstract Integration of electricity based on intermittent renewable sources such as solar power to a grid can have adverse effect on electric power grid. In this work, we investigated the impact of integration of solar photovoltaic (SPV) on voltage stability. Six transmission buses (Kano, Kaduna, Gwagwalada, New Haven, Birnin Kebbi and Lokoja) with shortest distance to each of the 13 proposed locations have been identified and each of the Solar PV farms was integrated to the transmission bus closest to the proposed solar farm sites. The effects of SPV integration on Transmission lines loading have been performed and the Nigeria 56-bus transmission network was used for the investigation. Voltage stability analysis was carried out using the load margin obtained from the PV curve at each of the six identified buses and effect of SPV integration on the system voltage profile was identified. Sensitivity analysis was also performed in order to obtain the impact of increasing penetration on the voltage stability. The investigation was conducted using DigSilent Power Factory and MATLAB. The result shows that the safe region of integration for the six identified transmission buses is between 10 % (365.8 MW) at Gwagwalada bus and 19 % (695 MW) of base load power at Kaduna. 1 % of SPV was integrated simultaneously at Egbin, Ikeja West, Akangba, Sakete, Kano, Aja, Alagbon, and Osogbo with voltages lesser than 0.95 pu at the base case and the result reveals that all the buses in the system are within acceptable voltage level of 0.95–1.05 pu. Highest improvement in load margin is achieved when 1 % SPV is integrated at Kaduna bus among the six transmission buses considered. Different locations affect system load margins and voltage stability differently. 1 % integration of SPV at different buses significantly improve the load margin from 1,107.7 MW (Birnin Kebbi) to 1,448.9 MW (Kaduna).

Bilevel optimization for multi-user systems with mixed demand response programs for enhanced operational efficiency in electric power grids

The smart grid is an advanced evolution of the traditional electrical power grid, developed to meet the increasing energy demands and requirements of the 21st century by incorporating digital technologies and data management systems to improve efficiency and reliability. Unlike conventional grids, the smart grid relies on a network of interconnected digital devices, sensors, and computerized controls that enable real-time monitoring and management of electricity distribution across vast geographic areas. However, the growing dependence on digital technologies also brings heightened cyber security concerns, since their integration can expose the grid to an increased risk of malicious intrusions. This systematic literature review investigates the nature and scope of cyber-attacks and cyber defense strategies in smart grids, which are critical to modern energy infrastructure. Following established research guidelines, this review rigorously examines existing studies by focusing on peer-reviewed articles and conference papers to understand the range of cyber security threats and defense mechanisms that smart grids face. The review uses a structured methodology to identify, evaluate, and synthesize key findings, revealing trends and gaps in current knowledge about smart grid security. The outcomes of this analysis offer valuable clarity on the specific weaknesses and operational challenges that affect smart grid infrastructures, contributing to the ongoing efforts to enhance cyber security measures and guide future research in this vital field.

The Automatic Fault Detection and Isolation System for Electrical Grids increase the reliability and efficiency of modern power distribution networks. Real-time fault detection using sensors and microcontrollers in the proposed system helps to identify faults such as short circuits, over current, and voltage abnormalities. Then, it isolates the section containing the fault through circuit breakers or relays to ensure a continuous power supply to the rest of the network. The proposed system incorporates several hardware and software components: current sensors, voltage sensors, microcontrollers such as Arduino or ESP32, and the software algorithms that it runs to monitor grid parameters continuously. Real-time data acquisition will enable the system to interpret the fault conditions correctly and act accordingly without any human interference. A user interface is also provided to display the status of the system and fault alerts for quick response by grid operators. It minimises fault detection and response time, which in turn reduces equipment damage, power losses, and downtime. It ensures operational safety, thereby improving load management and supporting grid resilience. Besides, it contributes to efficient maintenance and long-term grid sustainability.

The ownership of electric vehicles (EVs) is rising rapidly. Meanwhile, the development of Internet of Vehicles (IoV) technology has established a platform for data interaction and collaborative control between EVs and the power grid. After comprehensively analyzing relevant domestic and international research results, this paper proposes an interaction and joint operation scheduling scheme between EVs and the power grid based on the IoV environment. This scheme takes user satisfaction, power grid operation stability, and system economy as multi-objective optimization directions, and establishes a mathematical model composed of constraints such as State of Charge (SOC) constraints, charging and discharging power constraints, and power grid balance constraints, which is solved by combining distributed optimization and heuristic algorithms. Experimental results show that it can effectively smooth the power grid load curve, improve the user demand response rate, and significantly reduce the system operation cost, thus proving that the IoV has practical value and development potential in Vehicle-to-Grid (V2G) interaction.

The paper presents methods for organizing decision-making and the functioning of deceptive multi-computer systems based on prior operational experience and multiple task execution options. A formal representation of system components and their interconnections is developed, distinguishing between the system center and the decision-making controller. The system center prepares possible task execution options, while the decision-making controller evaluates these options considering past performance and selects one. Analytical expressions are proposed to describe processes within multi-computer systems, enabling autonomous decision-making in task execution. A method is developed for organizing the decision-making controller’s operation to ensure the selection of a task option based on prior experience, component security levels, and system topology. This approach allows for the formation of polymorphic responses to external and internal actions in corporate networks. Additionally, a method for organizing system functioning enables systems to adapt their properties, structure, and interconnections in response to functional and cybersecurity conditions. This can be used especially in cybersecurity of critical infrastructure systems like electrical power grids, smart grid infrastructure, energy plants and industrial control systems. A prototype was developed and tested under two scenarios: choosing among five task options and having only one option. Results showed greater operational stability in the first case, confirming that incorporating prior experience enhances resilience and creates polymorphic responses that hinder attackers’ attempts to study and exploit corporate networks.

Abstract Wide-area monitoring, protection, and control (WAMPAC) systems are essential for ensuring stability in modern electric power grids with large shares of converter-based renewable generation. Laboratory environments allow risk-free validation before field deployment. Building on our Control Center Laboratory and Real-Time Simulation (CLARO) framework, we implement complete WAMPAC workflows that couple real-time grid simulation (Opal-RT), standardized communication protocols (IEEE C37.118, IEC 61850, IEC 60870-5-104, Modbus), and modern information-technology/operational-technology (IT/OT) architectures (Apache Kafka, Kubernetes, containerization). A real-time automation controller executes corrective actions using Generic Object Oriented Substation Event (GOOSE) messages based on a pre-computed catalog of measures. On a synthetic 118-bus benchmark, phasor-measurement-unit (PMU) driven end-to-end delays from disturbance to control action remain below 250 ms, while PMU-enhanced state estimation reduces bus-voltage magnitude error by over 40 % compared with Supervisory Control and Data Acquisition (SCADA) only. These results demonstrate the feasibility of realistic, lab-based WAMPAC validation and highlight how integrating modern IT/OT stacks strengthens grid resilience.

Abstract Geomagnetically induced currents (GICs) are naturally occurring electrical currents that flow through the Earth and long conductive infrastructure, such as power lines, during geomagnetic storms. GICs in the electric power grid can cause damage to electric power transformers, system failures, and wide‐scale blackouts. Here, the combined effects of the power network's topology and the Earth's underlying conductivity structure on GIC risk are examined using examples from the electric power grid in Alberta, Canada. Our results show that the electric field polarization generated by geomagnetic storms is strongly dependent on the subsurface conductivity structure. Moreover, due to Earth induction effects, the two components of the transverse electric fields can also be highly correlated in specific geological regions. Combined, this creates a preferred electric field directionality, presenting a GIC risk for power grids with specific directional topology. A direct comparison between the geoelectric field and the measured transformer neutral‐to‐ground (TNG) currents measured near Edmonton, Alberta, on 24‐04‐2023 is shown. At this location, the two horizontal components of the geoelectric field are strongly correlated, and and show strong temporal and waveform correspondence with the TNG current. Two truth tables illustrate the increased or decreased GIC risk in such cases demonstrating that the GIC in a segment of the electric power network may combine constructively or deconstructively depending on the power network configuration and the relative orientation of the geoelectric field polarization. This is further exemplified by a case study of two real‐world network configurations in central Alberta.

Development of a Fuzzy Impedance Distance Protection Scheme to Enhance Fault Detection and Mitigate Power Swings in Electrical Power Grid

From a sustainability perspective, the production of the city is a co-construction aimed at providing opportunities for residents to access different urban centres. In the absence of co-design of electricity network extension projects, Grand Lomé and Greater Accra face the challenge of social acceptance due to the diversity of perceptions. How do the residents of Accra and Lomé experience the diversity of perceptions of power grid extension projects? To answer this question, this paper uses a mixed and comparative approach to analyse the impact of this diversity of perception on access to electricity in Greater Accra and Grand Lomé, based on spatial equity. The results show that structural non-recognition and residents’ dissatisfaction lead to social contempt. When structural non-recognition and dissatisfaction accumulate, socio-spatial injustice occurs.

Physics-Based Cyberattacks Against Electric Power Grids and Alternating Current Equipment

Fault detection is critical to the resilience and operational integrity of electrical power grids, particularly smart grids. In addition to requiring a lot of labeled data, traditional fault detection approaches have limited flexibility in handling unknown fault scenarios. In addition, since traditional machine learning models rely on historical data, they struggle to adapt to new fault patterns in dynamic grid environments. Due to these limitations, fault detection systems have limited resilience and scalability, necessitating more advanced approaches. This paper presents a hybrid technique that integrates supervised and unsupervised machine learning with Generative AI to generate artificial data to aid in fault identification. A number of machine learning algorithms were compared with regard to how they detect symmetrical and asymmetrical faults in varying conditions, with a particular focus on fault conditions that have not happened before. A key feature of this study is the application of the autoencoder, a new machine learning model, to compare different ML models. The autoencoder, an unsupervised model, performed better than other models in the detection of faults outside the learning dataset, pointing to its potential to enhance smart grid resilience and stability. Also, the study compared a generative AI-generated dataset (D2) with a conventionally prepared dataset (D1). When the two datasets were utilized to train various machine learning models, the synthetic dataset (D2) outperformed D1 in accuracy and scalability for fault detection applications. The strength of generative AI in improving the quality of data for machine learning is thus indicated by this discovery.By emphasizing the necessity of using advanced machine learning techniques and high-quality synthetic datasets, this research aims to increase the resilience of smart grid networks through improved fault detection and identification.

This study presents a Particle Swarm Optimization (PSO)-based scheme for optimal targeted load shedding and contingency severity assessment in the electric power grid (EPG). The IEEE 14 EPG was used as a testbed. The study identified critical branches and quantitatively evaluated the operational performance of an EPG under base case, outage without and with targeted load shedding schemes, utilizing convergence characteristics, voltage magnitudes and angles, and branch load flows as diagnostic metrics. The base case demonstrated excellent numerical stability, with convergence achieved in fewer than 5 iterations, and all bus voltages maintained within the IEEE standard range. A critical outage scenario caused severe difficulty, as evidenced by prolonged convergence (exceeding 15 iterations), a drastic voltage at Bus 1 to 0.7214pu, and overloading of Line 2 to 2.0524pu, approximately 275% of its base case loading. These conditions signified an unstable operational state, posing severe risks to system security. Implementation of targeted load shedding significantly improved system conditions: convergence iterations reduced to approximately 6, Bus 1 voltage restored to 0.9682pu, and Line 2 loading decreased to 0.5683pu. Other buses consistently maintained voltages within acceptable margins, and branch flows on non-critical lines remained insignificant across all cases. Voltage angle profiles further corroborate the systemic stress during outage and the stabilization effect post-load shedding. The proposed technique quantitatively demonstrates that selective load shedding is an effective corrective control strategy, not only restoring voltage stability but also alleviating transmission line overloading, thus enhancing the EPG’s ability to maintain secure and reliable operation under severe contingency conditions.

The integration of renewable energy sources into the electrical power grid is a crucial aspect for achieving sustainable development and reducing environmental impacts. This paper explores the implementation of a three phase Neutral Point Clamped (NPC) DC AC converter with level shift carrier PWM modulation technique and predictive current control to facilitate the grid interconnection of renewable energy systems. Multilevel converters offer important advantages over traditional two level converters, such as reduced harmonic distortion and improved efficiency. Computational simulations using the PSIM software were conducted to validate the operation of the converter, both as active rectifier and as inverter. The results demonstrated the ability of the converter to adjust its current generation, even when considering sudden power variations in the load, as well as variations in the electrical power grid. Moreover, it effectively controls the DC link voltage, while maintaining a minimal ripple in the controlled AC current. The obtained results allow to verify the robustness and reliability of the converter for the integration of renewable energy (solar photovoltaic panels) into the power grid, contributing to the pursuit of sustainable energy solutions for a greener future.

In modern power engineering, the optimal operation aims to achieve the basic requirements of the electrical power grid, meet various technical and economic aspects, and preserve the environmental limits within their accepted bounds. In this line, the current paper finds the optimal operational scheduling of the power generation units that cover the load requirements, considering different frameworks of the optimal power flow (OPF) problem involving single- and multi-objective functions. Technical, economic, and emissions objective functions are considered. Artificial rabbits' optimization (ARO) is developed to find the optimal OPF framework solution. The effectiveness of the proposed algorithm is evaluated through a comprehensive comparison study with the existing works in the literature. With six IEEE standard power systems, 22 different cases are implemented to test the ARO performance as an alternative to solve the OPF problem. Two of these systems are considered small-size systems, 30-, and 57-test systems, while the other four are large-scale power systems (IEEE 300, 1354, 3012, and 9241 test systems) to expand the validation scope of this paper. This comparison validates the scalability and efficiency of the ARO algorithm. The impact of varied population size and maximum iteration number is tested for two test systems, the most benchmarking test systems. It was proven that the routine of ARO has robust and superior competitive performance compared with others at fine convergence rates. Significant improvements are acquired in the range of 47% in the technical and economic issues by accepting the environmental concerns.

Most past analyses on distributed energy sources have employed large-scale stochastic optimization while taking into account the physics of the network, its control, its dimension and sometimes its investment costs. One may call it the physical/control aspect of the network. What is missing is a higher level and a broader view of the distribution of the network resources - a business-like policy toward resource distribution that provides for clear criteria on the relationship between risk (uncertainty, or volatility) and gain-over-costs. The dynamics of the energy market, and specifically, the renewable sector carry volatility and risks with similarities to the financial market. Here, we leverage a well-established, return-risk approach, commonly used by equity portfolio managers and introduce it to energy resources: solar, wind, and biodiesel. We visualize the relationship between the resources' costs and their risks in terms of efficient frontiers. We apply this analysis to publically available data for various US regions: Central, Eastern and Western coasts. Since risk management is contingent on costs, this approach sheds useful light on assessing dynamic pricing in modern electrical power grids. By integrating geographical and temporal dimensions into our research, we aim at more nuanced and context-specific recommendations for energy resource allocation. As an example, the lowest risk of 0.124 (in terms of standard deviation) for an expected return of 1.93% in Newark, New Jersey, USA has energy portfolio distribution of: 50.54%, 18.62%, and 30.84% for solar, wind, and biodiesel, respectively. Decision-makers may benefit from this approach, making informed and transparent selections to curate their energy supply.

In the paper implementation in Ukraine of the 5Ds strategy is discussed. The Energy Community is one of the approaches to implementing the 5Ds strategy. Citizens want to use electricity produced from low-carbon energy sources. The paper focuses on the possibility of applying a method for calculating individual components of electricity flows in the branches of an electric power grid caused by generation and consumption in the nodes. In particular, it is about estimating the portion of electricity of a particular consumer that it receives from low-carbon energy sources (LCES). The method is based on methods and algorithms for calculating steady-state modes of power grids. The method is based on a mathematical model for determining the components of power flows in the branches of an electrical grid, which uses the coefficients of current distribution in the branches of the circuit from nodes with generation sources and nodal voltages. Since LCES, including renewable sources of energy (RSE) plants and nuclear power plants in electric power systems (EPS), use general power grids to transmit the electricity it generates, determining their share of power in the flows allows taking into account the impact of different sources of generation on the parameters of the EPS mode. In this paper, the steady-state mode of a 14-node circuit with different energy sources, including renewable energy sources, is calculated. In particular, by calculations using the method of guaranteed electricity origin, power flows in the branches of the electrical circuit and power losses in them were determined. The results of computer modelling in the Power Factory software complex are commensurate with the results of calculations, which confirms the adequacy of the method. Key words. origin of electricity, energy community, mathematical model, computer modelling, low-carbon energy sources, renewable sources of energy, matrix of LCES power distribution coefficients.

In this paper, an assessment of the impact of the electrification of the vehicle fleet in Bosnia and Herzegovina on the total electrical energy consumption is made, for different scenarios of increasing the number of electric vehicles. Based on a statistical analysis of the structure and number of vehicles in Bosnia and Herzegovina in the period from 2010 to 2024, an estimate of the total number of passenger cars, as well as the number of electric vehicles for the period up to 2050, is made. It is estimated that in 2050 the number of electric passenger cars will be around 300,000. For one representative electric passenger car, averaged annual electrical energy consumption is calculated. Based on the calculation and for the estimated number of electric vehicles in use, the total annual consumption of electrical energy for the segment of passenger cars is defined, for different scenarios of increasing the number of electric vehicles. Following the estimated increase in the number of passenger electric cars, an exponential increase in electrical energy consumption is estimated, reaching the annual amount of 635 GWh in 2050, which is 10 times higher than the total electrical energy consumption of the transport sector in 2024. In this way, for the period up to 2050, the additional amount of electrical energy that the electrical power grid should provide, due to the electrification of the vehicle fleet, is estimated.