Search Results (971)

The global buildings and construction sector remains a dominant contributor to anthropogenic climate change, and deep decarbonization has positioned digital twin technology as a transformative pathway for intelligent building energy management. Despite considerable research momentum, the field lacks a coherent synthesis mapping AI capabilities onto the full digital twin lifecycle—from sensor-driven calibration through real-world deployment to district-scale operation. This review addresses this gap through six objectives: analyzing AI-enhanced modeling approaches for building digital twins; examining data infrastructure and interoperability requirements; evaluating validation, calibration, and uncertainty quantification practices; synthesizing real-world implementation evidence across diverse building typologies; assessing integration with renewable energy systems and smart grids; and identifying challenges, research gaps, and a strategic deployment roadmap. Physics-based, data-driven, and hybrid modeling strategies occupy distinct and complementary roles. Physics-informed surrogate models preserve thermodynamic interpretability while reducing computational overhead; deep learning architectures—including recurrent networks and reinforcement learning agents—deliver adaptive control; and federated learning frameworks enable privacy-preserving optimization across distributed building portfolios. Rigorous multi-metric validation aligned with established calibration standards proves essential for trustworthy deployment, while Bayesian and ensemble-based uncertainty quantification methods emerge as indispensable components of operationally credible digital twins. Evidence from real-world deployments in residential, commercial, healthcare, and industrial facilities confirms that AI-powered digital twins consistently deliver substantial energy savings and measurable improvements in occupant comfort. Scaling to district and urban levels introduces challenges in data governance, computational architecture, and multi-stakeholder coordination, yet federated digital twin frameworks are beginning to demonstrate viable pathways. The paper concludes with a decade-long strategic roadmap spanning technological maturation, market development, regulatory alignment, and decarbonization impact—positioning AI-enhanced digital twins not as incremental optimization tools, but as the foundational infrastructure for the coordinated transformation of the global building stock. Full article

This paper proposes an advanced metering infrastructure (AMI)-informed hierarchical energy management framework for coordinated operation of electric vehicles (EVs), photovoltaic (PV) systems, and battery energy storage systems (BESS) in campus microgrids. The proposed two-layer architecture integrates a soft actor–critic (SAC) deep reinforcement learning (DRL) agent in the upper layer with a receding horizon model predictive control (MPC) optimizer in the lower layer. The key novelty is an AMI-to-control pipeline that transforms historical 15 min smart-meter measurements into operational flexibility features and embeds them into a hierarchical SAC–MPC architecture, where the DRL layer provides adaptive coordination and the MPC layer enforces grid, storage, and EV-service constraints. The proposed framework using the real-world Pecan Street data (15 min resolution) of 73 homes across Austin, Texas and California (2014–2019) achieves a 53.1% cost reduction and a 25.7% peak demand reduction when compared with uncontrolled charging, and the proposed framework outperforms MPC-only (50.9%), DRL-only (−5.2%), and rule-based (5.1%) baselines. The statistically significant contributions of network-aware constraints, demand-response activation, and predictive look-ahead horizon are statistically significant (n = 10 independent runs) contributions (p = 0.001). The state representation informed by AMI offers directional cost improvement (+8.4%, p = 0.055) with 11% faster convergence of training. The zero network constraint violation is observed in all evaluation scenarios and the average MPC solve time is around 150 ms, which is much less than the 15 min sampling period. Sensitivity analyses show that the hierarchical DRL–MPC architecture remains computationally feasible across EV penetration, seasonal, and forecast-uncertainty scenarios. However, BESS provided no net economic benefit under the evaluated energy-only TOU tariff, increasing weekly cost by $15.25 and peak grid demand by 14.2 kW. Break-even analysis indicates that demand charges of approximately $9.9/kW per month are required for BESS to become cost-effective in the proxy system, highlighting that storage value depends strongly on tariff design and peak-demand objective formulation. Full article

Rapid electric-vehicle adoption is reshaping urban energy and mobility systems, especially in multi-unit dwellings (MUDs), where concentrated charging in shared parking areas simultaneously stresses distribution transformers and amplifies the consequences of charger faults, battery thermal events, smoke spread, and emergency-access constraints. The central argument of this paper is that grid stress, resident-facing service quality, lifecycle cost, and fire-risk exposure in enclosed residential parking should be governed jointly rather than as four separate problems. To make that argument concrete, we develop an integrated framework that couples stochastic EV adoption, residential charging-behavior simulation, XGBoost demand forecasting, and linear-programming-based optimization for coordinated control, and we evaluate it through 1000 Monte Carlo trials on representative Turkish MUDs. Unmanaged charging triggers transformer overload at about 30% EV penetration, whereas coordinated control reduces peak demand by 44.7% (405 kW to 224 kW) and raises load factor from 0.40 to 0.68. Strict capacity protection exposes a sharp service–quality trade-off, with only 8.9% of users reaching 80% state of charge (SOC) by departure. Smart charging lowers upfront cost by about 55% ($200 vs. $439 per dwelling unit) and yields roughly $306 net present value per unit over ten years. Building on these results, we propose a five-pillar fire-risk-aware governance architecture—coordinated control, interoperability standards, time-of-use pricing, building–utility coordination, and monitoring—that turns coordinated charging into a preventive governance layer for reducing hazardous congestion in enclosed residential charging environments. Full article

This study addresses the power dispatching of a DC-coupled three-phase grid-connected photovoltaic (PV) and energy storage-integrated system by proposing a peak shaving and valley-filling control architecture based on time-of-use (TOU) pricing. This research involves achieving maximum power-point tracking (MPPT) for PVMAs using a boost converter combined with the perturb and observe (P&O) method. A lithium-iron phosphate battery pack is integrated into the DC link via a bidirectional buck-boost converter, where charging and discharging control is executed according to peak and off-peak periods to regulate and stabilize the DC link voltage. Furthermore, bidirectional power flow control for peak and off-peak electricity consumption is realized using hysteresis current control and sinusoidal pulse-width modulation (SPWM) technologies within a smart inverter. By integrating the aforementioned power control architecture, the grid system can store energy from the utility during off-peak hours and release the stored energy during peak hours to reduce the load demand on the utility side. Initially, a simulation environment was established using Matlab/Simulink (2024b version) software, followed by control verification of the proposed system on a physical platform. The simulation and experimental results confirm that the integrated control architecture can precisely control the system’s DC link voltage at 800 V and stabilize the grid-connected AC voltage at an effective value (RMS) of 380 V. Moreover, under conditions of peak/off-peak switching and load variations, the system effectively demonstrates its stability and efficacy in performing valley filling and peak shaving. The proposed strategy achieves a power factor above 0.99 and a total harmonic distortion (THD) below 5%, regulates the DC-link voltage at 800 V with a steady-state error within 1.75%, and prevents up to 66.4 kWh of over-contract energy consumption per day under a 35 kW contract capacity, thereby contributing to sustainable energy management and economic savings. Full article

This study develops a cooperative multi-agent deep reinforcement learning (MARL) framework for simulation-based cost-overrun mitigation in smart grid construction projects under dynamic engineering uncertainty. Modern smart grid construction involves digital substations, renewable-energy-connected facilities, flexible transmission assets, intelligent monitoring systems, and geographically distributed contractors; therefore, cost escalation is driven by sequential interactions among procurement, schedule execution, equipment deployment, supervision, weather, logistics, and price volatility. The proposed framework models procurement management, construction scheduling, equipment allocation, and supervision-control units as decentralized agents embedded in a calibrated construction simulation environment. The environment is parameterized from 42 smart grid construction projects in Henan Province, China and generates disturbance scenarios involving weather efficiency loss, transportation delay, market-price volatility, labor shortage, and supply-chain interruption. A hybrid DQN–PPO mechanism represents mixed decision structures: value-based DQN modules handle discrete managerial choices such as task acceleration, supplier switching, and procurement timing, whereas PPO modules adjust continuous resource-allocation and recovery-intensity decisions. A hierarchical reward function combines local departmental objectives with project-level penalties for cost overrun, schedule delay, idle resources, recovery expenditure, safety risk, and environmental impact. The experimental protocol uses 30 paired random seeds, nonparametric bootstrap confidence intervals, Holm-adjusted Wilcoxon signed-rank tests, and comparison with deterministic optimization, rolling-horizon MPC, stochastic/robust optimization, single-agent DRL, MAPPO, MADDPG/MATD3, QMIX, and HAPPO baselines. The proposed framework achieves a mean cost-overrun rate of 6.83% and a mean schedule deviation of 16.82 days, reducing cost overrun by 18.7% and schedule deviation by 21.4% relative to rule-based construction management under the reported disturbance settings. The calibrated simulation evidence establishes a statistically evaluated decision-support framework for coordinated construction cost control and provides an artifact-level reproducibility pathway through configuration files, random-seed lists, anonymized synthetic benchmarks, and aggregated logs. Full article

Smart-port decarbonisation requires operations-research decision support under day-ahead uncertainty. We present a rolling-horizon Model Predictive Control Energy Management System, formulated as a Mixed-Integer Linear Program with five forecast streams, and benchmark it against a deterministic rule-based controller on an identical configuration. A full-year proof-of-concept at the Port of Ancona (8760 hourly steps over the 2024 Italian Day-Ahead Market, 6.5 MWp PV, 1.0 MWh BESS) combines realised 2024 market, photovoltaic and auxiliary-demand series with a post-AFIR projected cold-ironing demand—the dominant load—and is therefore an operational proof-of-concept rather than a fully metered baseline. The principal MPC outcome is structural: anticipatory dispatch raises the mean BESS state of charge from 13.6% to 46.0% and cuts residence at the minimum SoC from 81% to 6% of hours. The forecasting layer attains sub-7% sMAPE on cold-ironing-loaded demand and 9–18% on the remaining streams (seasonal MASE₂₄ ≤ 0.74 on demand and price streams). At the relay-constrained 0.08 C pilot, the realised savings is 0.44% (€14,463 yr⁻¹; 95% moving-block bootstrap CI [€12,842, €15,742]); benchmarked against an enhanced rule-based controller that is itself permitted price-threshold grid charging, the residual value of predictive optimisation is €5652 yr⁻¹ (0.17%), with the remainder of the gap being the value of enabling grid charging. A C-rate sweep shows the savings doubling to 0.93% at 0.5 C, and a direct 20 MWh/±10 MW simulation yields a €0.57 M yr⁻¹ gross arbitrage savings whose net value, after a realistic battery-degradation penalty, is substantially smaller. Controller-level operational CO₂ rises marginally (+6.2 t, +0.13%), an effect distinct from—and dwarfed by—the system-level cold-ironing decarbonisation. The framework is reproducible in open-source Python (PuLP/HiGHS) from the actual data and is portable to other single-node smart city energy hubs. Full article

With the increasing complexity of distribution networks, higher demands have been placed on systems for efficient power conversion. The solid-state transformer (SST), which integrates power electronic converters with a high-frequency transformer (HFT), has become a major research focus in end-user power supply applications. This paper first compares the technical advantages of SSTs over conventional transformers and systematically explains their operating principles. It then reviews the development trajectory of SSTs in terms of topological evolution, prototype-based engineering validation, and the application of emerging materials. Next, it classifies and summarizes the current mainstream topologies and identifies core devices and key control technologies, including SiC devices and advanced soft magnetic materials. Finally, it introduces representative SST applications in data centers, smart grids, and charging stations and summarizes and discusses future research directions and challenges. This paper clarifies the technological evolution and existing bottlenecks of SSTs, provides a useful reference for the high-quality and highly flexible operation of distribution networks, and offers clear guidance and directions for the subsequent engineering deployment of SSTs. Full article

The increasing demand for clean, reliable, and sustainable energy has intensified the need for advanced control strategies in modern wind energy conversion systems. Although conventional backstepping control (BC) offers strong stability and robustness, its performance may deteriorate under parameter uncertainties and dynamic operating conditions, leading to power fluctuations and reduced energy quality. To overcome these challenges, this study proposes an intelligent fuzzy fractional-order BC (FFOBC) strategy for multi-machine wind energy systems. By integrating fuzzy logic with fractional-order calculus into the classical BC framework, the proposed approach enhances adaptability, dynamic response, and robustness against system disturbances and nonlinearities. The controller is implemented at the machine-side inverter and validated in MATLAB/Simulink under varying wind and load conditions. Comparative results demonstrate that the proposed FFOBC significantly outperforms conventional sliding mode control in terms of overshoot reduction, steady-state accuracy, response smoothness, and total harmonic distortion minimization. Furthermore, the proposed strategy improves energy conversion efficiency, reduces mechanical and electrical stress, and ensures stable power injection into the grid. These findings highlight the potential of the proposed intelligent control framework to support sustainable, resilient, and high-quality wind energy integration in future smart power systems. Full article

The growing demand for real-time data synchronisation has increased the importance of supervisory control systems in industrial automation, smart grids, healthcare monitoring, and environmental applications. Low-power wireless sensor networks (LPWSNs) have emerged as key enablers of scalable and energy-efficient monitoring. However, achieving reliable synchronisation remains challenging due to latency, energy constraints, scalability limitations, security vulnerabilities, and data integrity concerns. This review examines the role of time synchronisation in supervisory control systems and evaluates how LPWSNs support real-time monitoring and decision-making. Established synchronisation protocols, including Reference Broadcast Synchronisation (RBS), the Flooding Time Synchronisation Protocol (FTSP), and the Timing-Sync Protocol for Sensor Network (TPSN), are analysed in terms of accuracy, energy efficiency, and scalability. Key optimisation strategies, such as clock drift compensation, data aggregation and compression, and edge computing, are also discussed. Recent advances, including artificial intelligence and machine learning (AI/ML)-based predictive synchronisation, blockchain, software-defined networking (SDN), and 5G-enabled LPWSNs, are reviewed across industrial, energy, healthcare, and agricultural applications. The review critically evaluates their benefits and trade-offs and identifies remaining challenges related to cybersecurity, energy efficiency, and large-scale deployment. Finally, future research directions are outlined to support robust, scalable, and efficient real-time synchronisation in LPWSNs. Full article

Electricity systems are increasingly exposed to demand variability driven by extreme weather conditions, creating significant challenges for maintaining grid reliability and operational stability. Conventional forecasting approaches focus primarily on prediction accuracy and provide limited support for operational decision-making under dynamic conditions. This study proposes a hybrid deep learning framework for smart grid stress prediction and adaptive mitigation under extreme weather. The framework reformulates demand forecasting using residual learning. It further integrates grid stress modelling with control-oriented decision support. A sequence learning architecture with attention is employed to capture temporal demand dynamics, while a continuous Grid Stress Index (GSI) translates predictions into operational indicators of system stress. The model demonstrates stable performance on real-world UK electricity demand data, achieving a mean absolute error of 1827.51 MW and a root mean squared error of 2505.22 MW. Peak demand and ramp behaviour are captured with improved consistency, and grid stress is predicted with a mean absolute error of 0.1246. An adaptive mitigation module translates predicted stress into actionable control, resulting in approximately 5.37% peak demand reduction, with limited impact on ramp smoothing. The results demonstrate that integrating forecasting, stress modelling, and control delivers greater operational value than standalone predictive models. The proposed framework provides a scalable and practical approach for grid-aware decision support under increasing climate-driven demand uncertainty. 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

In modern power systems such as new energy generation and smart grids, inverters serve as core equipment for electrical energy conversion and transmission. Their operational reliability directly impacts system power supply quality and safety stability. Currently, research on inverter fault diagnosis technology primarily focuses on linear load conditions, with diagnostic method design and validation based on linear load characteristics. However, with the rapid advancement of power electronics technology, power electronic loads such as variable frequency drives, charging stations, and distributed power sources are increasingly prevalent in power systems. These loads exhibit nonlinear and time-varying characteristics under complex operating conditions, leading to a growing variety of inverter faults with significantly diversified and complex fault signatures. Traditional diagnostic methods fail to adapt to the unique characteristics of power electronic loads, making it difficult to accurately identify various faults. Consequently, they no longer meet the diagnostic demands of practical engineering scenarios. In addition, current diagnostic methods for open-circuit power transistors, intermittent faults, and sensor faults often employ different approaches, which consume significant controller resources and are prone to mutual interference, leading to false triggers. This paper takes a three-phase four-wire inverter as the research subject. Targeting the challenge of fault diagnosis under power electronic load conditions, it proposes a comprehensive diagnostic method capable of simultaneously diagnosing power switch open circuits, intermittent faults, and current sensor faults. First, the characteristics of various faults are analyzed. Subsequently, fault diagnosis variables are constructed using the actual arm voltage of the inverter and the ideal arm voltage. Logical rules for each type of fault are established, and diagnosis is performed through fuzzy logic inference. Finally, experiments validated the effectiveness of this fault diagnosis scheme, with open-circuit faults detected in less than 2 ms, intermittent faults in less than 0.5 ms, and sensor faults in less than 3 ms. 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

The increasing penetration of renewable energy sources introduces significant variability, low-inertia behaviour, and operational uncertainty into modern power systems, resulting in frequent frequency deviations and degraded dynamic stability. Conventional Load Frequency Control (LFC) approaches based on fixed-parameter PID controllers often exhibit limited disturbance rejection capability under nonlinear and stochastic operating conditions. This study proposes an enhanced LFC framework that integrates a PID controller optimised using the Flower Pollination Algorithm (FPA) with support from a Unified Power Flow Controller (UPFC) and a Redox Flow Battery (RFB) to improve frequency regulation, damping, and robustness in renewable-integrated low-inertia power systems. This study developed a MATLAB/Simulink single-area power system model comprising governor, turbine, and generator-load dynamics to evaluate controller performance under a 0.01 pu step disturbance, stochastic load variations, renewable energy fluctuations, and ±20% parameter uncertainty conditions. The FPA optimally tuned the PID controller gains using the Integral Time Absolute Error criterion to enhance transient response and disturbance rejection capability. Comparative analyses were conducted against conventional PID and fuzzy-based controllers using settling time, overshoot, RMS deviation, ITAE, and mean frequency deviation indices. Simulation results demonstrate that the proposed FPA–PID + UPFC framework significantly outperforms the conventional PID controller by achieving approximately 66.6% settling-time reduction, 72.1% RMS reduction, and 75.5% ITAE reduction. The proposed framework reduced settling time from 18.46 s to 6.16 s and substantially improved damping performance under stochastic disturbances. The coordinated integration of the UPFC and RFB further enhanced transient stability through dynamic power-flow regulation and rapid active-power compensation during disturbances. Sensitivity analysis under parameter uncertainty and stochastic operating conditions confirmed stable and reliable operation under stochastic disturbances and parameter uncertainty conditions. The proposed architecture, therefore, provides an effective, practically applicable solution for secondary frequency regulation in renewable-rich smart grids, low-inertia transmission systems, microgrids, and future distributed power networks. Full article