Search Results (1,538)

Renewable integration is increasingly important for sustainable off-grid computing. The inherent variability of solar output frequently produces unusable midday surpluses. Leveraging recent Artificial Intelligence (AI) advances and established literature, we evaluate an AI-driven demand-response framework for scaling Large Language Models (LLMs) training servers using real-time solar energy data, Solcast forecasts, and battery storage records collected from Battery Management Systems (BMS), Maximum Power Point Tracking (MPPT) units, and smart inverters. An n8n AI Agent using the Ollama chat model gpt-oss:20b assesses surplus solar energy, activating selected servers to utilize otherwise wasted capacity. Workloads consistently align with solar availability, demonstrating 99% operational reliability, sub-second responsiveness, and accurate surplus-energy detection. This research demonstrates how Artificial Intelligence can repurpose surplus solar output into usable computational capacity, thereby contributing to a broader transition toward renewable-powered infrastructures. Full article

The exponential growth of machine learning (ML) applications in smart grid (SG) research over the past decade has generated a vast and fragmented body of literature that lacks systematic synthesis. This study addresses that gap by presenting a comprehensive bibliometric and science mapping analysis of the ML–smart grid (MLSG) research landscape to date, drawing on 4156 peer-reviewed publications indexed in the Elsevier Scopus database from 2009 to 2025. The principal contributions of this study are fourfold. First, it provides a rigorous quantitative mapping of MLSG publication growth from one document in 2009 to 1163 publications in 2025, representing a growth rate of 116,200%, thereby establishing a definitive baseline for tracking future scholarly development in the field. Second, it identifies the key actors driving MLSG research, including the most prolific authors (Nadeem Javaid, Alsabaan M.), leading institutions (King Saud University, Tennessee Technological University), and dominant nations (India, China, United States), which offers researchers and funding bodies actionable intelligence on collaboration opportunities and research leadership. Third, through keyword co-occurrence and cluster analysis, the study maps the three dominant thematic hotspots structuring current MLSG research—Smart Grid Security, Power Load Forecasting, and Advanced Energy Management—providing a structured intellectual framework that can guide future research prioritization. Fourth, the study delivers a critical thematic literature review of these three hotspots, synthesizing the most impactful ML methodologies and applications reported across 4156 publications, including deep learning-based intrusion detection, ensemble forecasting models, and reinforcement learning-driven energy management. Collectively, these contributions offer a robust evidence base for researchers, policymakers, and industry practitioners seeking to navigate, benchmark, and advance the field of ML-enabled smart grid systems. Full article

Ports combine clusters of operational buildings, shared energy infrastructure, and structurally critical assets requiring coordinated management to ensure safety and efficiency. Nevertheless, existing Digital Twin (DT) frameworks for building energy management rarely integrate Structural Health Monitoring (SHM) with energy performance assessment, while port-specific implementations remain scarce. This paper presents a pre-operational energy-aware DT architecture for port building clusters, structured in a unified five-layer framework integrating three capabilities: (i) EGMS/InSAR-based SHM screening with planned in situ sensing and computer-vision inspection workflows; (ii) smart metering and measurement and verification (M&V) protocols aligned with ISO 50001/50015 and IPMVP standards; and (iii) weighted multi-criteria prioritization considering structural condition, energy saving potential, service continuity, and cost. The framework is applied to the Port of Formia (Italy), a brownfield district comprising nine buildings (3371 m²), 16 high-mast lighting towers, shore power infrastructure, and 90 kWp of planned photovoltaics. In the absence of operational metering, energy and carbon values are reported as bounded ex-ante scenario estimates, not as verified performance outcomes. The analysis estimates photovoltaic generation of 116–137 MWh/year and lighting retrofit savings of 31.5–36.8 MWh/year; the related carbon values are treated as gross grid-displacement upper bounds pending measured self-consumption and export data. A four-phase validation roadmap with quantitative acceptance criteria supports the transition from feasibility assessment to verified performance. Full article

The rapid digitalization of energy systems and the increasing integration of distributed energy resources, renewable energy technologies, and prosumer-oriented infrastructures have accelerated the development of IoT-enabled Smart Grids as a foundation for intelligent and adaptive energy management. Modern Smart Grids increasingly depend on the coordinated interaction of IoT architectures, artificial intelligence, distributed analytics, and decentralized control mechanisms to ensure reliability, scalability, and real-time operational flexibility. Despite extensive research activity, existing studies remain predominantly technology-centric, focusing on isolated architectural layers or individual intelligent methods without providing a unified system-level perspective on their coordinated operation and interoperability. This article presents a system-level integrative review and challenge-oriented comparative synthesis of intelligent operational and architectural foundations of IoT-enabled Smart Grids. The study analyzes data-driven, model-driven, knowledge-driven, agent-based, and hybrid-oriented intelligent paradigms within multi-layer IoT energy infrastructures. In addition, the research establishes a cross-layer mapping between Smart Grid operational challenges, enabling technologies, and corresponding analytical approaches while identifying interoperability constraints, scalability limitations, and coordination challenges associated with decentralized energy ecosystems. The conducted synthesis demonstrates that hybrid-oriented intelligent approaches represent the most promising direction for future Smart Grid evolution due to their ability to integrate AI, ML, digital twins, semantic reasoning, and decentralized multi-agent coordination within unified IoT architectures. The conducted comparative synthesis identifies the ongoing transition from isolated intelligent solutions toward integrated hybrid cyber–physical energy ecosystems and highlights key characteristics of future adaptive, interoperable, scalable, and explainable Smart Grid architectures. Full article

Demand-side management (DSM) is a security-critical function in residential smart grids. The same communication and sensing infrastructure that enables fine-grained load flexibility also exposes schedulers to corrupted measurements, price manipulation, and delayed control signals. Conventional DSM formulations generally treat cyber and communication impairments as external disturbances, which are addressed only after the schedule has already been calculated. This study proposes and evaluates Cyber-Resilient and QoS-Aware Demand-Side Management (CQ-DSM) as a hierarchical optimization framework that embeds cyber-risk likelihood and communication quality-of-service (QoS) directly into the scheduling objective. Local home energy management systems (HEMSs) solve mixed-integer linear programs at the appliance level, and central aggregators broadcast compact coordination signals based on real-time prices, measured QoS, and a sliding-window GRU-feature MLP risk estimator. The key intuition is to convert uncertainty about trust and actuation reliability into scheduling prices: high cyber risk discourages exposed loads during vulnerable periods, whereas poor QoS increases the value of locally preserving thermal flexibility. Under the simulation conditions (NYISO August pricing, P = 50 prosumers, Seed 42), CQ-DSM reduces overall system costs by 5.75% and imbalance procurement costs relative to an attack-unaware baseline under normal operation, limits the FDI-induced cost increase to 0.46% versus 0.83% (44% reduction in cost overrun), and reduces thermal-violation penalties by 81% under degraded QoS. The ablation results are consistent with cyber-risk pricing and QoS-aware fallback being complementary rather than redundant under the scenarios tested. Full article

Optoelectronic devices are emerging as a cornerstone of advanced renewable energy technologies, offering innovative routes for energy harvesting, conversion, and management with high efficiency and versatility. This review summarizes recent advances in the semiconductor materials engineering field, device configurations, and light–matter interaction mechanisms that underpin advanced optoelectronic systems for solar energy harvesting, solar-driven chemical conversion, and smart grid integration, among others. Emphasis is placed on the breakthroughs achieved in the perovskite and hybrid photovoltaics, photoelectrochemical energy conversion, and nanostructured optoelectronic platforms that enable much-increased light absorption, reduced recombination losses, and scalable large-scale fabrications. Moreover, the challenges closely linked with long-term stability, environmental durability and benevolence, and worldwide deployment are critically addressed, together with the emerging opportunities in AI design, tandem device technological solutions, integrated energy systems, and machine learning approaches for optimizing device performance, thermal management, and energy storage capabilities. Finally, the present review concludes by outlining the future research directions that could accelerate the transition toward high-performance, cost-effective, and sustainable optoelectronic solutions responsive to global renewable energy requirements. Full article

Electric Vehicle Charging Stations (EVCSs) have become increasingly important due to the growing penetration of electric vehicles (EVs) and renewable-based power generation. However, challenges such as fluctuating renewable energy availability, increasing charging demand, power losses, operational cost, and charging delays continue to affect overall grid performance and stability. To address these issues, this study proposes a hybrid Goat Optimization Algorithm–Hierarchical Multi-Granularity Interaction Graph Convolutional Network (GOA–HMGIGCN) framework for intelligent smart grid energy management and EV charging coordination. The proposed framework combines the Goat Optimization Algorithm (GOA) for optimal EVCS placement and charging scheduling with the Hierarchical Multi-Granularity Interaction Graph Convolutional Network (HMGIGCN) for forecasting renewable generation, charging demand, and load variations. The framework was implemented and evaluated in MATLAB/Simulink R2024a using the IEEE 14-bus smart grid test system under varying operating conditions. Simulation results demonstrated that the proposed framework achieved superior performance compared with the Coot Optimization Algorithm–Fractional Backpropagation Physics-Informed Neural Network (COA-FBPINN), Dingo Optimization Algorithm–Convolutional Hypergraph Graph Neural Network (DOA-CHGNN), Self-Feedback Feedforward Artificial Neural Network (SFFANN), Deep Neural Network (DNN), and Golden Jackal Optimization–Attention-Based Probabilistic Convolutional Neural Network (GJO-APCNN) techniques by attaining the lowest operational cost of USD 1561, the highest efficiency of 99.2%, the minimum power loss of 10.6 kW, and the shortest charging time of 32 min. In addition, the proposed framework and overall grid reliability, confirming its effectiveness for intelligent renewable-integrated smart grid applications. Full article

The worldwide shift to electric mobility has intensified in recent years owing to heightened apprehensions over greenhouse gas emissions, energy security, and the necessity for sustainable transportation systems. Electric vehicles (EVs) are acknowledged as a viable alternative for diminishing reliance on fossil fuels and enhancing energy efficiency in the transportation sector. While affluent nations have achieved considerable advancements in electric vehicle adoption and charging infrastructure, numerous developing countries still encounter significant technical and infrastructural obstacles that hinder extensive EV integration. In South Africa, these difficulties are exacerbated by ongoing electrical supply limitations, deteriorating transmission and distribution facilities, and recurrent load shedding, which heighten worries about the dependability and stability of the national power grid. The rising adoption of electric vehicles adds extra electrical demands to power systems, especially at the distribution network level, where most of the charging takes place. Disorganized EV charging can substantially modify current load patterns, leading to heightened peak demand, voltage variations, transformer overload, and network congestion. The technical consequences are especially significant in South Africa, where the power grid functions with constricted generation capacity and minimal reserve margins. Various mitigating measures have been suggested to tackle these difficulties, including intelligent charging, demand-side management, time-of-use pricing, and vehicle-to-grid technologies. This paper establishes a basic theoretical framework through an extensive literature review to investigate the technological problems related to electric vehicle adoption in South Africa, while assessing the environmental and economic ramifications for sustainable urban transportation systems. Full article

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

The increasing penetration of renewable energy sources and converter-interfaced loads has intensified the need for fast and reliable grid-support services. Although electric vehicle (EV) battery chargers have emerged as promising resources for Vehicle-to-Grid (V2G) applications, existing solutions typically focus on individual services such as virtual inertia or frequency regulation, while limited attention has been given to the coordinated provision of multiple ancillary services within a unified framework. Furthermore, the use of batteries alone for fast frequency support may accelerate battery degradation due to frequent high-power transients. To address these challenges, this paper proposes a hybrid energy storage-based EV battery charger architecture and a coordinated multi-timescale control strategy capable of simultaneously providing virtual inertia support, long-term frequency regulation, reactive power compensation, and harmonic mitigation. The proposed approach utilizes a DC-link capacitor to deliver fast inertial response while the battery supplies sustained frequency support, thereby reducing battery stress and improving energy management efficiency. An enhanced frequency estimation method based on a phase-locked loop combined with a low-pass filter is also introduced to improve dynamic performance. Simulation results demonstrate the effectiveness of the proposed strategy under various grid disturbances. The system achieves an equivalent virtual inertia constant of approximately 1.85 s and delivers up to 786 W of transient inertial support within 80 ms during frequency events. The enhanced frequency estimation method significantly reduces transient overshoot, while harmonic compensation limits the grid current and voltage total harmonic distortion to 1.50% and 3.23%, respectively. In addition, the controller provides up to 400 VAR of reactive power support during voltage disturbances while maintaining stable battery operation. These results demonstrate that the proposed EV battery charger can function as a multifunctional grid-support resource, enhancing frequency stability, voltage regulation, power quality, and overall V2G capability in future smart grids. Full article

Accurate forecasting of hourly electricity consumption is critical for smart grid management, energy market operations, national policy planning, and—particularly for import-dependent economies such as Türkiye—energy security. This study presents, to the best of the authors’ knowledge, the first systematic benchmark of four state-of-the-art time series architectures—TimesNet, PatchTST, iTransformer, and Temporal Fusion Transformer (TFT)—conducted specifically on a national-scale Turkish multivariate energy dataset from the Energy Exchange Istanbul (EPİAŞ), covering 72,322 hourly observations across 15 generation, consumption, and market-clearing price variables from January 2018 to April 2026. While benchmark studies of Transformer-based architectures exist on general time-series datasets, no prior work has applied this specific combination of architectures to the EPİAŞ dataset under unified experimental conditions with an explicit energy-security interpretation. All models were trained under standardized preprocessing (StandardScaler), a 24 h lookback window, and systematic hyperparameter optimization. Experimental results demonstrate that iTransformer achieves the best predictive performance (MAE = 521.34 MWh, RMSE = 748.12 MWh, R² = 0.9881, MAPE = 1.34%), followed by TFT (R² = 0.9863) and PatchTST (R² = 0.9844). TimesNet, while the most computationally efficient, achieves an R² of 0.9791. Beyond predictive benchmarking, this study situates the findings within Türkiye’s energy security agenda: the dataset captures fossil fuel dependency, the growing share of domestic renewables, and market-clearing price dynamics shaped by geopolitical shocks, including the Russo–Ukrainian war and evolving EU–Türkiye energy relations. Comprehensive analysis of model architectures, attention mechanisms, temporal feature importance, and computational efficiency is provided. These findings establish a rigorous baseline for deploying modern sequence models in large-scale, real-time national energy forecasting systems that serve both market-efficiency and strategic-energy-autonomy objectives. The results specifically highlight how high-fidelity forecasting can serve as a risk-mitigation tool against geopolitical supply disruptions by quantifying the impact of domestic renewable integration. Full article

While rapid digital transformation has significantly optimized sectors such as finance and e-commerce, maintenance management in industrial environments has historically received lower levels of technological and capital investment. This lag creates critical gaps in operational efficiency and asset longevity, particularly within renewable energy infrastructures where sustainability and resilience are paramount. Addressing this technological disparity is essential for minimizing ecological footprints and maximizing the viability of net-zero systems. This paper introduces an advanced multi-platform digital solution designed to optimize the operation and maintenance of renewable energy systems and smart infrastructures. The platform addresses traditional management gaps by implementing standardized protocols that integrate real-time remote monitoring, sensor networks, and cloud-based data acquisition. By centralizing historical and real-time data from solar, wind, and hybrid grids, it facilitates advanced analytics, such as predictive modeling of component degradation. Real-world validation across photovoltaic plants and wind farms demonstrates significant impacts: a 30% reduction in unplanned outages and a 20% to 25% decrease in operational and maintenance costs. The results confirm that digitalizing maintenance processes is a strategic pillar for the energy transition, aligning industrial performance with global low-carbon pathways. Full article

Smart grid communication services often have heterogeneous and time-varying bandwidth demands, which makes fixed resource allocation and flat pricing less suitable for on-demand service provisioning. This paper studies a demand-aware dynamic pricing problem in a resource-allocable communication network enabled by software-defined networking, where service requests observe network resource status and purchase bandwidth according to the announced price. Unlike pricing models that mainly describe the price–quantity tradeoff, the proposed model incorporates the minimum acceptable bandwidth demand of each request as a service-side acceptance threshold. A Stackelberg game is formulated in which the communication resource manager acts as the leader and service requests act as followers. We define the utility and revenue functions, analyze the existence of equilibrium, and derive the follower-side best response through backward induction. The resulting response is piecewise, which makes exact equilibrium characterization difficult in multi-user settings. Therefore, a distributed iterative algorithm and a backward-induction-based genetic algorithm are developed for different information settings. Simulation results show how service-side parameters affect pricing behavior and evaluate the proposed methods under heterogeneous-user, multi-user, and capacity-limited scenarios. Full article

Accurate short-term prediction of network service traffic is essential for communication resource allocation and proactive fault warning in smart grids. However, smart grid service traffic is characterized by nonlinear fluctuations, strong spatio-temporal coupling, and considerable uncertainty, making it difficult for existing methods to capture long-range dependencies, adapt to dynamic topological relationships, and reflect prediction risks. To address these issues, this work develops a deep learning framework that integrates a spatio-temporal Transformer with a diffusion mechanism. The spatio-temporal Transformer extracts temporal evolution patterns and spatial logical correlations from historical traffic matrices, while the diffusion module improves robustness to abrupt traffic variations through latent uncertainty modeling. Furthermore, attention-guided recurrent units are used to generate stable multi-step forecasting sequences. Experiments on a real-world network dataset show that, compared with mainstream benchmark models, the proposed framework reduces Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Root Relative Squared Error (RRSE) by 46.62%, 47.05%, and 44.18%, respectively. These results indicate that the framework improves prediction accuracy and stability while alleviating error accumulation in long-horizon forecasting, thereby providing reliable technical support for smart grid network management. Full article

The global transition toward renewable energy has intensified interest in dispatchable low-carbon sources that can support reliability-critical infrastructure in smart grid systems. Data centers represent one of the fastest-growing electricity loads globally, yet their compatibility with biomass-based energy systems as a dispatchable renewable source within smart grid architectures remains poorly understood. This study presented a comprehensive review of biomass power generation, data center energy management, and smart grid integration, drawing on a corpus of 347 peer-reviewed sources. A staged analytical design separated demand characterization from supply evaluation, ensuring that data center energy requirements emerged independently of supply-side assumptions. Using Latent Dirichlet Allocation topic modeling validated with BERTopic and VOSviewer network analysis, the study identified four distinct thematic clusters and found no single topic spanning data center reliability requirements, biomass supply dynamics, and smart grid integration simultaneously, a pattern that points to an underexplored cross-domain space in the literature. A demand–supply–grid alignment framework was introduced to illustrate compatibility conditions across temporal resolution, reliability requirements, and grid management dimensions. The alignment framework and illustrative simulation developed here are offered as analytical starting points to guide future engineering and empirical investigation rather than as demonstrations of operational readiness. An illustrative application demonstrated that biomass feedstock logistics constraints create persistent availability gaps at data center operational timescales, suggesting that supply chain resilience and grid-mediated buffering are likely necessary conditions for viable integration, a proposition that warrants empirical validation through full-scale engineering studies. The findings indicate that integration constraints reflect temporal and operational misalignment rather than technological infeasibility, providing a new analytical perspective for evaluating renewable energy integration in reliability-critical digital infrastructure. Full article