Search Results (644)

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

Renewable energy sources like wind turbines and solar panels are integrated into modern power grids as Distributed Energy Resources (DERs). These DERs can operate independently or as part of microgrids. Interconnecting multiple microgrids creates Networked Microgrids (NMGs) that increase reliability, resilience, and independent power generation. However, the trustworthiness of individual DERs remains a critical challenge in NMGs, particularly when integrating previously deployed or geographically distributed units managed by entities with varying expertise. Assessing DER trustworthiness ensuring reliability and security is essential to prevent system-wide instability. Thisresearch addresses this challenge by proposing a lightweight trust metric generation system capable of processing structured and semi-structured DER data to produce key trust indicators. The system employs a Small Language Model (SLM) with approximately 16 million parameters for textual data understanding and metric extraction, followed by a regression head to output bounded trust scores. Designed for deployment in computationally constrained environments, the SLM requires only 64.6 MB of disk space and 200–250 MB of memory that is significantly lesser than larger models such as DeepSeek R1, Gemma-2, and Phi-3, which demand 3–12 GB. Experimental results demonstrate that the SLM achieves high correlation and low mean error across all trust metrics while outperforming larger models in efficiency. When integrated into a full neural network-based trust framework, the generated metrics enable accurate prediction of DER trustworthiness. These findings highlight the potential of lightweight SLMs for reliable and resource-efficient trust assessment in NMGs, supporting resilient and sustainable energy systems in smart cities. Full article

As a core support for the integration of new energy and smart grids, Vehicle-to-Grid (V2G) networks face a core contradiction between user privacy protection and transaction security traceability—a dilemma that is further exacerbated by issues such as the quantum computing vulnerability of traditional cryptography, cumbersome key management in stateful ring signatures, and conflicts between revocation mechanisms and privacy protection. To address these problems, this paper proposes a post-quantum revocable linkable ring signature scheme based on SPHINCS+, with the following core innovations: First, the scheme seamlessly integrates the pure hash-based architecture of SPHINCS+ with a stateless design, incorporating WOTS+, FORS, and XMSS technologies, which inherently resists quantum attacks and eliminates the need to track signature states, thus completely resolving the state management dilemma of traditional stateful schemes; second, the scheme introduces an innovative “real signature + pseudo-signature polynomially indistinguishable” mechanism, and by calibrating the authentication path structure and hash distribution of pseudo-signatures (satisfying the Kolmogorov–Smirnov test with $D\le 0.05$), it ensures signer anonymity and mitigates the potential risk of distinguishable pseudo-signatures; third, the scheme designs a KEK (Key Encryption Key)-sharded collaborative revocation mechanism, encrypting and storing the $(I,pk,RID)$ mapping table in fragmented form, with KEK split into $KE{K}_1$ (held by the Trusted Authority, TA) and $KE{K}_2$ (held by the regulatory node), with collaborative decryption by both parties required to locate malicious users, thereby resolving the core conflict of privacy leakage in traditional revocation mechanisms; fourth, the scheme generates forward-secure linkable tags based on one-way private key updates and one-time random factors, ensuring that past transactions cannot be traced even if the current private key is compromised; and fifth, the scheme adopts hash commitments instead of complex cryptographic commitments, simplifying computations while efficiently binding transaction amounts to signers—an approach consistent with the pure hash-based design philosophy of SPHINCS+. Security analysis demonstrates that the scheme satisfies the following six core properties: post-quantum security, unforgeability, anonymity, linkability, unframeability, and forward secrecy, thereby providing technical support for secure and anonymous payments in V2G networks in the quantum era. Full article

The entire system collapses due to the issues of inadequate centralized storage capacity, poor scalability, low storage efficiency, and susceptibility to single point of failure brought on by huge power consumption data in the smart grid; thus, an alliance chain-driven multi-cloud storage and verifiable deletion method for smart grid data is proposed. By leveraging the synergy between alliance blockchain and multi-cloud architecture, the encrypted power data originating from edge nodes is dispersed across a decentralized multi-cloud infrastructure, which effectively mitigates the danger of data loss resulting from single-point failures or malicious intrusions. The removal of expired and user-defined data is guaranteed through a transaction deletion algorithm integrated into the indexed storage deletion chain and strengthens the flexibility and security of the storage architecture. Based on the Practical Byzantine Fault-Tolerant Consensus Protocol with Ultra-Low Storage Overhead (ULS-PBFT), by the hierarchical grouping of nodes, the system communication overhead and storage overhead are reduced. Security analysis proves that the scheme can resist tampering attacks, impersonation attacks, collusion attacks, double spend attacks, and replay attacks. Performance evaluation shows that the scheme improves compared to similar methods. Full article

The increasingly frequent and severe natural disasters have posed significant challenges to the resilience of power systems worldwide, creating an urgent need to investigate the security issues associated with these extreme events and to develop effective risk mitigation strategies. Meanwhile, as one of the leading topics in current research, artificial intelligence (AI) has demonstrated outstanding performance across various domains, such as AI-driven smart grids and smart cities. In particular, its efficiency in processing big data and solving complex computational problems has made AI a powerful tool for supporting decision-making in complex scenarios. This article presents a focused overview of power system resilience against natural disasters, highlighting recent advancements in AI-based approaches aimed at enhancing system security and response capabilities. It begins by introducing various types of natural disasters and their corresponding impacts on power systems. Then, a systematic overview of AI applications in power systems under disaster scenarios is provided, with a classification based on the task categories, i.e., predictive, descriptive and prescriptive tasks. Following this, this article analyzes current research trends and finds a growing shift from knowledge-based models towards data-driven models. Furthermore, this paper discusses the major challenges in this research field, including data processing, data management, and data analytics; the challenges introduced by large language models in power systems; and the limitations related to AI model interpretability and generalization capability. Finally, this article outlines several potential future research directions. Full article

The digital transformation of electric power systems into smart grids has significantly expanded the cybersecurity risk landscape of the energy sector. While advanced sensing, communication, automation, and data-driven control improve efficiency, flexibility, and renewable energy integration, they also introduce complex cyber–physical interdependencies and new vulnerabilities across interconnected technical and organisational domains. This study adopts a scoping review methodology in accordance with PRISMA-ScR to systematically analyse smart grid cybersecurity from an architecture-aware and resilience-oriented perspective. Peer-reviewed scientific literature and authoritative institutional sources are synthesised to examine modern smart grid architectures, key security challenges, major cyberthreats, and documented real-world cyber incidents affecting energy infrastructure up to 2025. The review systematically links architectural characteristics such as field devices, communication networks, software platforms, data pipelines, and externally operated services to specific threat mechanisms and observed attack patterns, illustrating how cyber risk propagates across interconnected grid components. The findings show that cybersecurity challenges in smart grids arise not only from technical vulnerabilities but also from architectural dependencies, software supply chains, operational constraints, and cross-sector coupling. Based on the analysis of historical incidents and emerging research, the study identifies key defensive strategies, including zero-trust architectures, advanced monitoring and anomaly detection, secure software lifecycle management, digital twins for cyber–physical testing, and cyber-resilient grid design. The review concludes that cybersecurity in smart grids should be treated as a systemic and persistent condition, requiring resilience-oriented approaches that prioritise detection, containment, recovery, and safe operation under adverse conditions. Full article

Threats that are caused by cyber-attacks on intelligent power networks promote the implementation of sophisticated intrusion detection devices, which can effectively detect advanced attacks. In this paper, a new model is introduced that combines the Modified Polar Fox Optimization Algorithm (MPFA) with an Inception-V4 deep neural network to enhance the effectiveness of the threat detection task. The MPFA optimizes inception-V4 hyperparameters and architecture to balance the exploration and exploitation processes of the courtship learning process and fitness-based scaling. The optimized model on the smart grid monitoring power is shown to perform well; it achieves over 99.5% accuracy, precision, recall, and F1-score on the detection of various attacks, including False Data Injection, Denial-of-Service, and Load Redistribution, and has a favorable computational overhead, thus it can be considered a formidable solution to protect critical smart grid infrastructure. The optimized model, evaluated on the Smart Grid Monitoring Power dataset, achieves state-of-the-art performance with an accuracy of 99.63%, a precision of 99.61%, a recall of 99.65%, and an F1-score of 99.63% for the detection of various cyber-physical attacks, including False Data Injection, Denial-of-Service, and Load Redistribution. It also maintains a favorable computational overhead, thus presenting a formidable solution for protecting critical smart grid infrastructure. Full article

With the acceleration of digital transformation, open-source software has become a fundamental component of modern smart grids and other critical infrastructures. However, the complex dependency structures of open-source ecosystems and the continuous emergence of vulnerabilities pose substantial challenges to software supply chain security. In power information networks and cyber–physical control systems, vulnerabilities in open-source components integrated into Supervisory Control and Data Acquisition (SCADA), Energy Management System (EMS), and Distribution Management System (DMS) platforms and distributed energy controllers may propagate along the supply chain, threatening system security and operational stability. In such application scenarios, large language models (LLMs) often suffer from limited semantic accuracy when handling domain-specific security terminology, as well as deployment inefficiencies that hinder their practical adoption in critical infrastructure environments. To address these issues, this paper proposes KD-SecBERT, a domain-specific semantic bidirectional encoder optimized through multi-level knowledge distillation for open-source software supply chain security in smart grid applications. The proposed framework constructs a hierarchical multi-teacher ensemble that integrates general language understanding, cybersecurity-domain knowledge, and code semantic analysis, together with a lightweight student architecture based on depthwise separable convolutions and multi-head self-attention. In addition, a dynamic, multi-dimensional distillation strategy is introduced to jointly perform layer-wise representation alignment, ensemble knowledge fusion, and task-oriented optimization under a progressive curriculum learning scheme. Extensive experiments conducted on a multi-source dataset comprising National Vulnerability Database (NVD) and Common Vulnerabilities and Exposures (CVE) entries, security-related GitHub code, and Open Web Application Security Project (OWASP) test cases show that KD-SecBERT achieves an accuracy of 91.3%, a recall of 90.6%, and an F1-score of 89.2% on vulnerability classification tasks, indicating strong robustness in recognizing both common and low-frequency security semantics. These results demonstrate that KD-SecBERT provides an effective and practical solution for semantic analysis and software supply chain risk assessment in smart grids and other critical-infrastructure environments. Full article

The large-scale integration of Distributed Energy Resources (DERs) in smart grids creates challenges related to real-time optimization, system scalability, and operational security. This paper presents GridOpt, a hybrid edge–cloud framework designed to address these challenges through distributed intelligence and coordinated control. In GridOpt, edge nodes handle latency-sensitive tasks, while cloud resources support the processing of large-scale grid data. Security is addressed through the integration of homomorphic encryption and blockchain-based consensus, together with an interoperability layer that enables coordination among heterogeneous grid components. Simulation results show that GridOpt achieves an average latency of 76 ms and an energy consumption of 25 Joules under high-throughput conditions. The framework further maintains scalability beyond 10 requests per second with a resource utilization of 54% in dense deployment scenarios. Comparative analysis indicates that GridOpt outperforms ECCGrid, JOintCS, and EdgeApp across key performance metrics. Full article

Applications of smart cities, smart buildings, smart agriculture systems, smart grids, and other smart systems benefit from Internet of Things (IoT) protocols, networks, and architecture. Wireless Sensor Networks (WSNs) in smart systems that employ IoT use wireless communication technologies between sensors in the Things layer and the Fog layer hub. Such wireless protocols and networks include WiFi, Bluetooth, and Zigbee, among others. However, the payload formats of these protocols are heterogeneous, and thus, they lack a unified frame format that ensures interoperability. In this paper, a lightweight, interoperable frame format for low-rate, small-size Wireless Sensor Networks (WSNs) in IoT-based systems is designed, implemented, and tested. The practicality of this system is underscored by the development of a gateway that transfers collected data from sensors that use the unified frame to online servers via message queuing and telemetry transport (MQTT) secured with transport layer security (TLS), ensuring interoperability using the JavaScript Object Notation (JSON) format. The proposed frame is tested using market-available technologies such as Bluetooth and Zigbee, and then applied to smart home applications. The smart home scenario is chosen because it encompasses various smart subsystems, such as healthcare monitoring systems, energy monitoring systems, and entertainment systems, among others. The proposed system offers several advantages, including a low-cost architecture, ease of setup, improved interoperability, high flexibility, and a lightweight frame that can be applied to other wireless-based smart systems and applications. Full article

Industrial Control Systems (ICS) are fundamental to the operation, monitoring, and automation of critical infrastructure in sectors such as energy, water utilities, manufacturing, transportation, and oil and gas. According to the Purdue Model, ICS encompasses tightly coupled OT and IT layers, becoming increasingly interconnected. Smart grids represent a critical class of ICS; thus, this survey examines encryption and relevant protocols in smart grid communications, with findings extendable to other ICS. Encryption techniques implemented at both the protocol and network layers are among the most effective cybersecurity strategies for protecting communications in increasingly interconnected ICS environments. This paper provides a comprehensive survey of encryption practices within the smart grid as the primary ICS application domain, focusing on protocol-level solutions (e.g., DNP3, IEC 60870-5-104, IEC 61850, ICCP/TASE.2, Modbus, OPC UA, and MQTT) and network-level mechanisms (e.g., VPNs, IPsec, and MACsec). We evaluate these technologies in terms of security, performance, and deployability in legacy and heterogeneous systems that include renewable energy resources. Key implementation challenges are explored, including real-time operational constraints, cryptographic key management, interoperability across platforms, and alignment with NERC CIP, IEC 62351, and IEC 62443. The survey highlights emerging trends such as lightweight Transport Layer Security (TLS) for constrained devices, post-quantum cryptography, and Zero Trust architectures. Our goal is to provide a practical resource for building resilient smart grid security frameworks, with takeaways that generalize to other ICS. Full article

This article explores various applications and advancements in the fields of energy management (EM), cybersecurity (CS), and automation across multiple sectors, including smart grids (SGs), the Internet of things (IoT), trading, e-commerce, and autonomous systems. A variety of innovative solutions and methodologies are discussed, such as enhanced impedance methods for simulation stability, decision support systems for resource allocation, and advanced algorithms for detecting cyber-physical threats. The integration of artificial intelligence (AI) and machine learning (ML) techniques is highlighted, particularly in addressing challenges such as fault tolerance, economic distribution in cyber-physical systems (CPSs), and protection coordination in complex environments. Additionally, the development of robust algorithms for real-time monitoring and control demonstrates significant potential for improving system efficiency and resilience against various types of attacks. Full article

This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments. Full article

The global transition to renewable energy sources is essential to carbon neutrality and ensuring energy security. First, the paper presents a comprehensive literature review of the main technological breakthroughs in bioenergy, hydro energy, solar energy, onshore and offshore wind energy, ocean energy, and geothermal energy, selecting the latest papers published. Next, key scientific challenges, environmental and economic constraints, and future research priorities for each of the six renewable energies were outlined. Then, to emphasize the important contribution of renewable energies to total energy production and the proportions of each type of renewable energy, the evolution of global electricity generation from all six renewable sources between 2000 and 2023 was analyzed. Thus, in 2023, the global electricity generation weight of each renewable energy in total renewable energy ranks hydro energy (47.83%) first, followed by onshore and offshore wind energy (25.8%), solar energy (18.19%), bioenergy (7.07%), geothermal energy (1.1%), and ocean energy (0.01%). After that, the bibliometric analysis, conducted between 1 January 2021 and 1 October 2025 on the Web of Science (WoS) database and using the PRISMA approach and VOSviewer version 1.6.20 software, enabled the identification of the most cited papers, publications and citation number by WoS categories, topics, correlation with Sustainable Development Goals, authors’ affiliation, publication title, and publisher. Furthermore, the paper presents a network visualization of the link between co-occurrences and all keywords, imposing minimum thresholds of 10, 20, and 30 occurrences per keyword, and computes the network density based on the number of edges and nodes. Finally, additional analysis included the most used keywords in different co-occurrences, a word cloud of occurrences by total link strength, regression of occurrences versus total link strength, and correlations between citations and documents and between citations and authors. Carbon neutrality and a resilient energy future can only be achieved by integrating renewable sources into hybrid systems and optimized smart grids. Each technological progress stage will bring new challenges that must be addressed cost-effectively. Full article

The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMSs), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMSs and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMSs is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions. Full article