Search Results (208)

This systematic narrative review examines recent advances in the application of swarm robotics and swarm intelligence to smart grid systems in the context of renewable energy sources. Smart grids represent a transformative paradigm in power systems, integrating advanced communication, monitoring, and control technologies to enhance the reliability, performance, and sustainability of power distribution. Swarm robotics, drawing inspiration from the collective behaviour of social insects, enables the coordination of numerous autonomous agents to carry out complex tasks in a decentralised, scalable, and fault-tolerant manner. Over the past decade, swarm intelligence approaches—including Particle Swarm Optimisation, Ant Colony Optimisation, and consensus-based distributed control—have emerged as promising methods for addressing smart grid challenges such as decentralised energy management, fault detection, infrastructure monitoring, and maintenance. This review was conducted through a two-stage systematic search of three databases (Scopus, IEEE Xplore, and ACM Digital Library; Web of Science was not accessible during the search period and was therefore excluded), which identified 54 primary studies meeting the full-text inclusion criteria, supplemented by 11 additional records located through hand-search, for a final corpus of 65 references. For each application domain (monitoring and inspection, energy distribution optimisation, fault detection and resilience, and cybersecurity and communication), we summarise the methodologies employed, the reported performance outcomes, and the evidence level of the available studies. We also analyse the scalability limits of current swarm approaches, communication constraints relevant to grid deployment, and the integration of swarm systems with existing SCADA and EMS infrastructure. The review identifies a significant gap between laboratory demonstrations and utility-scale deployment, and outlines priority directions for future research. Full article

The rapid growth of renewable energy sources, particularly solar and wind power, has increased the demand for efficient and reliable battery energy storage systems (BESSs) to address intermittency, enhance grid stability, and improve energy management. In recent years, artificial intelligence (AI) has emerged as a transformative technology for optimizing the operation, control, monitoring, and maintenance of battery storage systems. This review provides a comprehensive overview of AI-driven BESS technologies for renewable energy applications. The study examines recent advances in machine learning, deep learning, reinforcement learning, and hybrid intelligent algorithms applied to battery state estimation, energy management, fault diagnosis, predictive maintenance, thermal management, and lifetime prediction. Furthermore, the integration of AI-based BESSs with photovoltaic systems, wind farms, microgrids, and smart grids is critically analyzed. The review highlights the advantages of AI techniques in improving system efficiency, reliability, adaptability, and decision-making capabilities under uncertain operating conditions. Current challenges, including data quality, model interpretability, computational requirements, cybersecurity concerns, and real-time implementation issues, are also discussed. Finally, emerging research directions such as digital twins, explainable artificial intelligence, federated learning, and edge intelligence are explored to provide insights into the future development of intelligent battery storage systems. This review aims to serve as a valuable reference for researchers, engineers, and practitioners working at the intersection of artificial intelligence, battery technologies, and renewable energy systems. Full article

This paper presents a simulation-based cyber-quantum Internet of Things (IoT) security framework for modeling, securing, and detecting attacks in QKD-secured IoT communication environments. The proposed framework integrates heterogeneous IoT traffic generation, gateway-assisted routing, edge processing, QKD key-pool management, Quantum-Synchronized Ephemeral Encryption (Q-SEE), cross-layer adversarial attack injection, and AI-based multiclass detection. Unlike conventional IoT intrusion datasets that mainly capture packet- or flow-level abnormalities, the generated dataset represents the joint behavior of IoT sessions, network delay, queue pressure, QKD state, key consumption, encryption-mode transitions, ciphertext metadata, and cyber-quantum risk. A Python/SimPy/NetworkX simulation was developed using 80 IoT devices, 3 gateways, 2 edge servers, 4 cyber-quantum control-plane nodes, and 1 adversarial orchestrator. The final simulation produced 46,351 records with 76 features covering normal traffic, five traditional IoT attacks, and six novel cyber-quantum attacks, including QKD key-pool starvation, QBER camouflage, false QKD-health injection, encryption downgrade induction, queue–key coupling, and multi-vector cyber-quantum orchestration. Q-SEE adaptively selects among QKD-OTP, QKD-synchronized AES-256 ephemeral mode, PQC fallback, degraded mode, and blocked mode according to QBER, secret key rate, key availability, device criticality, downgrade pressure, and risk. A leakage-aware Quantum-Aware Kolmogorov–Arnold Network (QKAN) was then trained using deployable cyber-quantum evidence. The final nonrisk QKAN achieved 98.79% test accuracy, 98.61% macro-F1, 98.85% weighted-F1, and 99.78% macro-AUC, demonstrating effective detection of traditional and cyber-quantum IoT attacks. Full article

The growing deployment of distributed renewable generation, storage, electric vehicles, heat pumps, smart buildings, and controllable demand is expanding the flexibility available to local energy systems. Energy communities provide the governance context for collective participation and value creation, while community virtual power plants provide the operational mechanism for aggregating distributed resources and connecting them to local optimisation, flexibility markets, grid services, and resilience functions. This PRISMA-ScR-informed framework development study synthesises the literature from Scopus, Web of Science, and IEEE Xplore to examine how artificial intelligence supports community VPP operation, where agentic AI adds capabilities beyond established optimisation, reinforcement learning, and multi-agent systems, and which design requirements follow governed orchestration. The synthesis shows that current evidence is strongest for component-level forecasting, scheduling, bidding, adaptive control, distributed coordination, and digital-twin validation, whereas integrated agentic orchestration remains an emerging direction. Classical multi-agent systems already provide decentralised representation, communication, negotiation, and coordinated control; the additional role proposed for agentic AI is therefore narrower and concerns context-aware multi-step workflow orchestration, governed tool use, exception handling, grounded explanation, and bounded delegation across existing analytical and control services. The study introduces operational flexibility intelligence as the capability to transform potential distributed flexibility into deployable, authorised, market-, grid-, resilience-, and community-compatible action. It further develops a conceptual layered reference architecture in which agentic orchestration operates through governed tools and interfaces rather than bypassing validated resource controllers. Fairness, comfort, privacy, cybersecurity, resilience, auditability, and human-in-command authority are treated as cross-cutting operational constraints. The architecture defines a design and validation agenda rather than an empirically validated implementation. Full article

Artificial intelligence (AI) is transforming photovoltaic (PV) systems from passive generators into self-forecasting, self-diagnosing, and self-optimizing energy assets. This review synthesizes 170 Scopus-indexed documents published between 2020 and 2026 across six technical domains: solar resource and PV power forecasting; intelligent monitoring, fault diagnosis, and cybersecurity; AI-driven control and design optimization; smart grid integration and real-time energy management; AI-assisted PV materials, devices, and manufacturing; and cross-sectoral smart PV applications. Quantitative synthesis shows that hybrid deep learning forecasters reduce root mean square error by 31.9–43.9% relative to persistence and single-model baselines. Attention-based architectures achieve mean absolute percentage errors of up to 5%. Machine learning classifiers achieve fault detection accuracies of 92.3–99.4% with protection response times below 100 ms, enabling predictive maintenance at the fleet scale. Reinforcement learning energy management lowers electricity costs by 20–55%, reduces peak demand by 13–31.5%, and raises PV self-sufficiency from 71.5% to 89.7%. AI-optimized thermal and material interventions deliver efficiency gains of up to 22.2%, and indoor perovskite devices exceed 40% conversion efficiency. Reported performance metrics are derived from heterogeneous datasets, horizons, and baselines; they are therefore synthesized as indicative ranges rather than directly comparable benchmarks. Persistent barriers include data scarcity, model opacity, cybersecurity vulnerabilities, edge deployment constraints, and energy justice concerns. These barriers are mapped to research directions in explainable AI, federated and transfer learning, digital twins, and blockchain-enabled energy markets. A roadmap to 2040 consolidates the findings and charts the transition toward high-efficiency, resilient, and sustainable solar energy conversion. Full article

The rapid integration of solar photovoltaic (PV) generation, distributed energy resources, and advanced communication infrastructures is transforming conventional power systems into highly interconnected cyber–physical smart grids. Although this transition improves sustainability and operational flexibility, it also increases grid-management complexity and introduces cyber–physical vulnerabilities, including false data injection attacks, communication failures, equipment degradation, and renewable-induced operational instabilities. This paper presents the Digital-Twin-Enabled Resilience Framework (DTERF), a conceptual reference architecture for anomaly detection in high-PV cyber–physical smart grids. DTERF integrates heterogeneous cyber–physical data acquisition, Digital Twin-based contextual representation, machine-learning analytics, explainable decision support, adaptive operational response, continuous learning, and self-healing capabilities within a unified resilience cycle. The framework is grounded in a structured review and comparative assessment of contemporary machine-learning approaches and recent integrated smart-grid research. Its architecture is conceptually evaluated through requirements-to-architecture traceability, examining functional coverage and internal consistency across the complete operational cycle. The analysis shows that DTERF provides explicit architectural mechanisms addressing the principal requirements identified in the literature, including contextual anomaly analysis, interpretability, cybersecurity robustness, resilience support, and operational integration. Rather than proposing a new anomaly detection algorithm or claiming empirical performance superiority, DTERF provides a technology-agnostic architectural foundation for coordinating complementary capabilities required for resilient anomaly management. Future work should empirically validate the framework using Digital Twin simulation environments, representative high-PV distribution systems, cyber–physical anomaly scenarios, and real or utility-derived operational data. Full article

The rapid proliferation of Internet of Things (IoT) technologies and smart grids has substantially intensified the cybersecurity challenges associated with smart energy meters (SEMs). The data collected by the plugs are transmitted via a wireless communication protocol to a smart electricity meter that acts as a local gateway. This meter centralizes the information from the various sensors, may perform data pre-processing, aggregation, or validation operations, and then forwards the information to a central server. The main contributions of this system can be categorized into two key aspects. First, the implementation of a centralized wireless local energy consumption network using the Wi-Fi protocol to coordinate smart plugs over distances of up to 20 m. Second, the real-time acquisition of power characteristics and the remote control (ON/OFF switching) of household appliances for direct appliance-level submetering purposes. Data collected by the smart meter are transmitted to a processing unit through a Semtech SX1276 LoRa transceiver communication link. The central server constitutes the processing and storage layer of the system: it receives the collected data, archives it in a dedicated database, and makes it available through analysis, visualization, and decision-support tools. This architecture enables real-time monitoring of energy consumption, anomaly detection, optimization of electrical resource use, and the development of effective energy management strategies for smart electrical grids. Although current smart meter architectures incorporate multi-layer protection mechanisms at the hardware, communication, and data levels, additional security measures are required to counter advanced cyber threats aimed at data interception and manipulation. This paper improves the security framework of smart energy meters by integrating a chaos-based encryption layer to ensure secure data transmission. Chaotic systems exhibit intrinsic properties such as sensitivity to initial conditions, pseudo-randomness, and ergodicity, which render them particularly suitable for cryptographic applications. The proposed framework employs a Lorenz-based chaotic encryption module to secure SEM-utility data exchanges. Full article

The increasing penetration of distributed energy resources, variable renewable generation, battery energy storage systems, electric vehicles, and power-electronic interfaces is changing the way modern smart grids and microgrids are operated, protected, and controlled. This systematic literature review follows the PRISMA 2020 framework and examines 87 original research papers, complemented by a supplementary synthesis of 18 contextual studies that provide bibliometric, historical, and conceptual perspectives on the evolution of AI in smart grids. The primary studies are organized into six thematic clusters: energy management and forecasting; cybersecurity and intrusion detection; renewable energy integration and microgrid management; fault detection, diagnosis, and grid stability; explainable and trustworthy artificial intelligence; and emerging technologies, including digital twins, blockchain, the Internet of Things, edge computing, and federated learning. The review shows that deep learning, reinforcement learning, and ensemble machine learning are increasingly used for load forecasting, demand response, converter-interfaced renewable integration, intrusion detection, and operational optimization. However, the literature remains uneven. Fault detection, converter-aware protection, and real-time stability assessment receive considerably less attention than energy management and cybersecurity, despite their importance for inverter-based resources, grid-forming converters, electric-vehicle charging systems, and battery interfacing. Four critical gaps are identified: limited cross-grid generalizability, weak validation under realistic converter and protection constraints, insufficient adversarial robustness of AI-enabled defense systems, and limited explainability in real-time safety-critical applications. The paper provides a structured taxonomy, identifies deployment barriers, and proposes research directions for trustworthy AI in power-electronic-rich smart grids and microgrids. Full article

The rapid growth of Electric Vehicles (EVs) has accelerated the deployment of Electric Vehicle Charging Stations (EVCSs), making their integration into modern power systems increasingly important. While EVCSs support transportation electrification and global decarbonization goals, large-scale integration introduces technical challenges that affect power system operation, reliability, and planning. This review provides a comprehensive assessment of the impact of EVCS integration on power system performance by examining charging technologies, charging stations, charging modes, and the principal components of EVCSs. The review discusses the effects of EV charging on load demand, peak load, voltage profile, voltage stability, active and reactive power losses, transformer loading, and overall grid performance. It further evaluates mitigation strategies, including smart charging, coordinated charging, Demand Response (DR), Renewable Energy Sources (RESs), Battery Energy Storage Systems (BESSs), Vehicle-to-Grid (V2G) technology, and Artificial Intelligence (AI)-based energy management. The application of Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and advanced optimization algorithms for charging coordination and demand forecasting is also reviewed. Finally, the paper identifies current research challenges and future directions related to charging uncertainty, renewable energy integration, cybersecurity, interoperability, and infrastructure development. The findings demonstrate that intelligent charging strategies combined with renewable energy integration, energy storage, V2G, and AI significantly improve the reliability, efficiency, resilience, and sustainability of future EV-integrated power systems. Full article

Transport decarbonization requires infrastructure that can use time-resolved carbon information without overstating the representativeness of short proof-of-method runs. This study introduces Adaptive Energy Stations (AESs), multi-fuel transport-energy nodes that integrate marginal grid-emission signals, fuel life-cycle carbon intensities, wholesale electricity prices, and vehicle operating constraints into a station-level dispatch optimization. The implemented case is a one-week winter proof-of-method for CAISO/CAISO_NORTH using 168 hourly service events over 1–8 January 2026 Pacific time, archived WattTime marginal operating emissions, CAISO locational marginal prices, eGRID CAMX annual-average factors, and declared vehicle and fuel-pathway parameters. In the audited CAISO scenario, the attached dispatch outputs report a reduction from 181.76 to 123.38 g CO₂e/km relative to the specified static baseline, corresponding to a 32.12% reduction for the one-week winter service-event stream. The populated dispatch trace shows that the carbon-priority AES plug-in hybrid electric vehicle (PHEV) run selected cellulosic E85 for all 168 events and selected no electric events; this result is interpreted as an operational scenario result for the archived week, not as an annual fleet-average, smart-charging benefit, or deployment forecast. The revised analysis explicitly separates implemented CAISO evidence from ERCOT, MISO-MROW, and ISO–NE extension sensitivities, which remain hypothetical until equivalent marginal-emissions, price, and service-event data are supplied. Battery-production amortization is treated as a separate sensitivity because it can change battery electric vehicle (BEV)–cellulosic E85 equivalence conclusions: at 50–100 kg CO₂e/kWh over 240,000 km, a 75 kWh BEV pack contributes 15.6–31.3 g CO₂e/km and a 14 kWh PHEV pack contributes 2.9–5.8 g CO₂e/km. Practical-equivalence claims are therefore conditional on the declared boundary, equivalence margin, and production-emissions treatment. Full deployment requires validated marginal-emission access, transparent dispatch-audit outputs, supply-chain verification, user-behavior characterization, cost sensitivity analysis, and cybersecurity safeguards. Full article

Advanced Metering Infrastructure (AMI) is a key enabler of digital and intelligent power systems, particularly in microgrid environments. However, existing research often addresses AMI from fragmented perspectives, limiting a comprehensive understanding of its role within integrated and data-driven energy systems. This paper presents a structured analysis of AMI based on a bibliometric and thematic review of recent literature, identifying the main research trends, technological drivers, and emerging directions in the field. The results reveal a transition of AMI toward a data-centric platform that supports real-time monitoring, bidirectional energy management, and intelligent decision-making. Key domains include cybersecurity, data analytics, communication systems, and distributed energy integration, while emerging technologies such as artificial intelligence and the Internet of Energy play a critical role in future developments. Finally, the paper outlines key challenges and provides strategic recommendations to support the effective deployment of AMI in microgrids, contributing to the development of resilient and sustainable energy systems. Full article

The increasing integration of renewable energy resources, distributed energy systems, and intelligent sensing technologies has accelerated the transformation of conventional power grids into interconnected cyber–physical smart energy ecosystems. In this context, the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has emerged as a key enabler for intelligent monitoring, adaptive energy management, resilient grid operation, and sustainable energy coordination. Although numerous studies have investigated AI, IoT, blockchain, and cybersecurity technologies individually, many existing reviews focus on isolated domains without adequately addressing the interactions between intelligent operational control, communication infrastructures, decentralized coordination, sustainability, and cyber resilience. Accordingly, this paper presents a comprehensive system-level review of AI–IoT-enabled smart energy ecosystems, focusing on smart grids, microgrids, intelligent energy management, blockchain-enabled decentralized coordination, carbon emissions monitoring, and cyber-resilient energy infrastructures. Unlike existing surveys that primarily emphasize individual technologies or algorithmic performance, this work highlights the cross-layer integration and architectural interdependencies between AI-driven operational intelligence, IoT-enabled monitoring, secure communication frameworks, and sustainability-oriented energy management. The paper also discusses key challenges related to interoperability, scalability, cybersecurity, communication latency, and distributed coordination, in addition to future research directions toward resilient, autonomous, and sustainable intelligent energy ecosystems. Full article

This paper makes a contribution to smart-grid cybersecurity by proposing an application of our approach, previously published in this journal, to design—from smart-grid data—transparent, interpretable, and accurate systems for detection and classification of cyber-attacks, natural-event-related disturbances, and regular operation of the smart grid. The collection of 15 open-source simulated smart-grid data sets provided by Mississippi State University, USA, in collaboration with Oak Ridge National Laboratories, USA, is used in our experiments. To address this problem, we use our knowledge-based data-mining/machine-learning approach which ultimately generates a set of fuzzy rule-based classifiers—each with a different trade-off between its accuracy and the interpretability of its knowledge base (both are the subjects of maximization in a multi-objective optimization process using evolutionary algorithms). The paper also contributes an extensive cross-validation-based experiment showing that while our approach is not as accurate as alternative and inherently accuracy-oriented “black boxes,” it surpasses them in terms of transparency and interpretability of the generated classification decisions, while ensuring acceptable accuracy of these decisions. Therefore, it can act as a second cybersecurity layer that uses transparent rules to help SCADA operators instantly verify and explain alerts triggered by accurate, fast “black-box” detectors. This paper also provides a contribution to a more general and important area of the automatic and effective selection of input variables (features) that are essential in a given decision-making problem (such as smart-grid contingency classification considered in this work). Full article

The transition from centralized fossil fuel-based power systems toward decentralized smart grids with a high penetration of renewable energy sources (RES) introduces substantial challenges in monitoring, control, coordination, and management. These challenges are particularly evident at the active power grid periphery, defined in this work as the decentralized edge layer of modern power systems comprising low-voltage distribution networks, distributed energy resources (DERs), prosumers, energy storage systems, electric vehicles (EVs), and localized intelligent control entities operating near the consumer side of the grid. This review systematically examines the role of multi-agent systems (MASs) in addressing these emerging challenges. A total of 160 articles, drawn predominantly from top-tier Q1 journals and published up to March 2026, were systematically analyzed to evaluate recent methodological advances, identify persistent research gaps, and compare existing problem formulations and mathematical techniques. The review covers MAS-based applications including distributed energy management, voltage and frequency regulation, demand-side management, microgrid coordination, EV charging coordination, resilience enhancement, and cyber-physical supervisory control. The findings indicate that although MASs offer enhanced scalability, flexibility, resilience, and decentralized decision-making capabilities, existing approaches continue to face significant limitations associated with communication latency, cybersecurity vulnerabilities, interoperability constraints, heterogeneous agent dynamics, and limited real-time experimental validation. Furthermore, this review proposes six emerging research hypotheses targeting underexplored domains, presents a methodological decision flowchart for MAS implementation and selection, and discusses future research directions involving the integration of digital twins, blockchain technologies, edge intelligence, and advanced communication architectures with MAS frameworks. Full article

Power from Shore (PfS) is becoming more widespread for offshore petroleum installations, which have introduced new dependencies and the potential for a single point of failure. In addition, the cyber threat landscape is increasing, with state-sponsored actors demonstrating the capabilities and willingness to target Operational Technology (OT) systems. Threat actors have been seen using living off the land techniques, such as with the Industroyer malware, which utilized legitimate but malicious IEC 104 commands to open circuit breakers. To evaluate these vulnerabilities, in this study, a Design Science Research approach was applied to map a generalized substation and develop a Software-in-the-Loop simulator, which was used to test a specific attack vector against substation automation systems. The results confirm that an adversary with local network access can successfully inject valid IEC 61850 Manufacturing Message Specification (MMS) commands to trigger unauthorized circuit breaker operations. Furthermore, it is also shown that a simulated substation can be used as a tool when developing OT malware. Full article