Search Results (5,309)

The Internet of Things (IoT) has transformed multiple industries, providing significant potential for automation, efficiency, and enhanced decision-making. The incorporation of IoT and data analytics in smart grid represents a groundbreaking opportunity for the energy sector, delivering substantial advantages in efficiency, sustainability, and customer empowerment. This integration enables smart grids to autonomously monitor energy flows and adjust to fluctuations in energy demand and supply in a flexible and real-time fashion. Statistical analytics, as a fundamental component of data analytics, provides the necessary tools and techniques to uncover patterns, trends, and insights within datasets. Nevertheless, it is crucial to address privacy and security issues to fully maximize the potential of data analytics in smart grids. This paper makes several significant contributions to the literature on secure, privacy-aware aggregation schemes in smart grids. First, we introduce a Fog-enabled Secure Data Analytics Operations (FESDAO) scheme which offers a distributed architecture incorporating robust security features such as secure aggregation, authentication, fault tolerance and resilience against insider threats. The scheme achieves privacy during data aggregation through a modified Boneh-Goh-Nissim cryptographic scheme along with other mechanisms. Second, FESDAO also supports statistical analytics on metering data at the cloud control center and fog node levels. FESDAO ensures reliable aggregation and accurate data analytical results, even in scenarios where smart meters fail to report data, thereby preserving both analytical operation computation accuracy and latency. We further provide comprehensive security analyses to demonstrate that the proposed approach effectively supports data privacy, source authentication, fault tolerance, and resilience against false data injection and replay attacks. Lastly, we offer thorough performance evaluations to illustrate the efficiency of the suggested scheme in comparison to current state-of-the-art schemes, considering encryption, computation, aggregation, decryption, and communication costs. Moreover, a detailed security analysis has been conducted to verify the scheme’s resistance against insider collusion attacks, replay attack, and false data injection (FDI) attack. Full article

Nanoparticle superlattices—periodic assemblies of uniformly spaced nanocrystals—bridge the nanoscale precision of individual particles with emergent collective properties akin to those of bulk materials. Recent advances demonstrate that multivalent ions and charged polymers can guide the co-assembly of nanoparticles, imparting electrostatic gating and enabling semiconductor-like behavior. However, the specific roles of anion geometry, valency, and charge density in mediating ion transport remain unclear. Here, we employ coarse-grained molecular dynamics simulations to investigate how applied electric fields (0–0.40 V/nm) modulate ionic conductivity and spatial distribution in trimethylammonium-functionalized gold nanoparticle superlattices assembled with four phosphate anions of distinct geometries and charges. Our results reveal that linear anions outperform ring-shaped analogues in conductivity due to higher charge densities and weaker interfacial binding. Notably, charge density exerts a greater influence on ion mobility than size alone. Under strong fields, anions accumulate at nanoparticle interfaces, where interfacial adsorption and steric constraints suppress transport. In contrast, local migration is governed by geometrical confinement and field strength. Analyses of transition probability and residence time further indicate that the rigidity and delocalized charge of cyclic anions act as mobility barriers. These findings provide mechanistic insights into the structure–function relationship governing ion transport in superlattices, offering guidance for designing next-generation ion conductors, electrochemical sensors, and energy storage materials through anion engineering. Full article

Accurate demand forecasting contributes to improved energy efficiency and the development of short-term strategies. Predictive management of energy storage using redox flow batteries is presented as a robust solution for optimizing the operation of microgrids from the demand side. This study proposes an intelligent architecture that integrates demand forecasting models based on artificial neural networks and active management strategies based on the instantaneous production of renewable sources within the microgrid. The solution is supported by a real-time monitoring platform capable of analyzing data streams using continuous evaluation algorithms, enabling dynamic operational adjustments and active methods for predicting the storage system’s state of charge. The model’s effectiveness is validated using performance indicators such as RMSE, MAPE, and MSE, applied to experimental data obtained in a specialized microgrid laboratory. The results also demonstrate substantial improvements in energy planning and system operational efficiency, positioning this proposal as a viable strategy for distributed and sustainable environments in modern electricity systems. Full article

As power systems decentralize, Virtual Power Plants (VPPs) offer a promising approach to coordinate distributed energy resources (DERs) and enhance grid flexibility. However, real-world validation of VPP performance in Indonesia remains limited, especially regarding internationally aligned test standards. This study presents the design and experimental validation of a cluster-based VPP framework integrated with a centralized VPP Management System (VMS). Each cluster integrates solar photovoltaic (PV) system, battery energy storage system (BESS), and controllable load. A Local Control Unit (LCU) manages cluster operations, while the VMS coordinates power export–import dispatch, cluster-level aggregation, and grid compliance. The framework proposes a scalable VPP architecture and presents the first comprehensive experimental verification of key VPP performance indicators, including response time, adjustment rate, and accuracy, in the Indonesian context. Testing was conducted in alignment with the IEC TS 63189-1:2023 international standard. Results suggest real time responsiveness and indicate that, even at smaller scales, VPPs may contribute effectively to voltage control while exhibiting minimal influence on system frequency in interconnected grids. These findings confirm the capability of the proposed VPP framework to provide reliable real time control, ancillary services, and aggregated energy management. Its cluster-based architecture supports scalability for broader deployment in complex grid environments. Full article

Quasi-two-dimensional (Q2D) perovskite films have garnered significant attention as novel gain media for lasers due to their tunable bandgap, narrow linewidth, and solution processability. Q2D perovskites endowed with intrinsic quantum well structures demonstrate remarkable potential as gain media for cost-effective miniaturized lasers, owing to their superior ambient stability and enhanced photon confinement capabilities. However, the mixed-phase distribution within Q2D films constitutes a critical determinant of their optical properties, exhibiting pronounced sensitivity to specific fabrication protocols and processing parameters, including annealing temperature, duration, antisolvent volume, injection timing, and dosing rate. These factors frequently lead to broad phase distribution in Q2D perovskite films, thereby inducing incomplete exciton energy transfer and multiple emission peaks, while simultaneously making the fabrication processes intricate and reducing reproducibility. Here, we report a novel annealing-free and antisolvent-free method for the preparation of Q2D perovskite films fabricated in ambient atmosphere. By constructing a tailored mixed-solvent vapor atmosphere and systematically investigating its regulatory effects on the nucleation and growth processes of film via in situ photoluminescence spectra, we successfully achieved the fabrication of Q2D perovskite films with large n narrow phase distribution characteristics. Due to the reduced content of small n domains, the incomplete energy transfer from small n to large n phases and the carriers’ accumulation in small n can be greatly suppressed, thereby suppressing the trap-assistant nonradiative recombination and Auger recombination. Ultimately, the Q2D perovskite film showed a single emission peak at 519 nm with the narrow full width at half maximum (FWHM) of 21.5 nm and high photoluminescence quantum yield (PLQY) of 83%. And based on the optimized Q2D film, we achieved an amplified spontaneous emission (ASE) with a low threshold of 29 μJ·cm⁻², which was approximately 60% lower than the 69 μJ·cm⁻² of the control film. Full article

The increasing demand for reliable, sensitive, and cost-effective gas sensors drives ongoing research in this field. Ideal gas sensors must demonstrate high sensitivity and selectivity, stability, rapid response and recovery times, energy efficiency, and affordability. One-dimensional (1D) metal oxide semiconductors (MOSs) are prominent candidates due to their excellent sensing properties and straightforward fabrication processes. The sensing efficacy of 1D MOSs is heavily dependent on their surface area and porosity, which influence gas interaction and detection efficiency. Polymeric templates serve as effective tools for enhancing these properties by enabling the creation of uniform, porous nanostructures with high surface area, thereby improving gas adsorption, sensitivity, and dynamic response characteristics. This review systematically examines the role of polymeric templates in the construction of 1D MOSs for gas sensing applications. It discusses critical factors influencing polymer template selection and how this choice affects key microstructural parameters, such as grain size, pore distribution, and defect density, essential to sensor performance. The recent literature highlights the mechanisms through which polymer templates facilitate the fine-tuning of nanostructures. Future research directions include exploring novel polymer architectures, developing scalable synthesis methods, and integrating these sensors with emerging technologies. Full article

Non-perturbative approaches to linear and nonlinear responses (NLR) of atoms, molecules, and molecular aggregates are reviewed in relation to low and high harmonic generations (HG) by laser fields. These response properties are effective for the generation of entangled light pairs for quantum information processing by spontaneous parametric downconversion (SPDC) and stimulated four-wave mixing (SFWM). Quasi-energy derivative (QED) methods, such as QED Møller–Plesset (MP) perturbation, are reviewed as time-dependent variational methods (TDVP), providing analytical expressions of time-dependent linear and nonlinear responses of open-shell atoms, molecules, and molecular aggregates. Numerical Liouville methods for the low HG (LHG) and high HG (HHG) regimes are reviewed to elucidate the NLR of molecules in both LHG and HHG regimes. Three-step models for the generation of HHG in the latter regime are reviewed in relation to developments of attosecond science and spectroscopy. Orbital tomography is also reviewed in relation to the theoretical and experimental studies of the amplitudes and phases of wave functions of open-shell atoms and molecules, such as molecular oxygen, providing the Dyson orbital explanation. Interactions between quantum lights and molecules are theoretically examined in relation to derivations of several distribution functions for quantum information processing, quantum dynamics of molecular aggregates, and future developments of quantum molecular devices such as measurement-based quantum computation (MBQP). Quantum dynamics for energy transfer in dendrimer and related light-harvesting antenna systems are reviewed to examine the classical and quantum dynamics behaviors of photosynthesis. It is shown that quantum coherence plays an important role in the well-organized arrays of chromophores. Finally, applications of quantum optics to molecular quantum information and quantum biology are examined in relation to emerging interdisciplinary frontiers. Full article

Fish species’ biological vocalizations serve as essential acoustic signatures for passive acoustic monitoring (PAM) and ecological assessments. However, limited availability of high-quality acoustic recordings, particularly for region-specific species like the brown croaker (Miichthys miiuy), hampers data-driven bioacoustic methodology development. In this study, we present a framework for reconstructing brown croaker vocalizations by integrating fk14 wavelet synthesis, PSO-based parameter optimization (with an objective combining correlation and normalized MSE), and deep learning-based validation. Sensitivity analysis using a normalized Bartlett processor identified delay and scale (length) as the most critical parameters, defining valid ranges that maintained waveform similarity above 98%. The reconstructed signals matched measured calls in both time and frequency domains, replicating single-pulse morphology, inter-pulse interval (IPI) distributions, and energy spectral density. Validation with a ResNet-18-based Siamese network produced near-unity cosine similarity (~0.9996) between measured and reconstructed signals. Statistical analyses (95% confidence intervals; residual errors) confirmed faithful preservation of SPL values and minor, biologically plausible IPI variations. Under noisy conditions, similarity decreased as SNR dropped, indicating that environmental noise affects reconstruction fidelity. These results demonstrate that the proposed framework can reliably generate acoustically realistic and morphologically consistent fish vocalizations, even under data-limited scenarios. The methodology holds promise for dataset augmentation, PAM applications, and species-specific call simulation. Future work will extend this framework by using reconstructed signals to train generative models (e.g., GANs, WaveNet), enabling scalable synthesis and supporting real-time adaptive modeling in field monitoring. Full article

Gluons within the proton may accumulate near a critical momentum due to nonlinear QCD effects, leading to a gluon condensation. Surprisingly, the pion distribution predicted by this gluon distribution could answer two puzzles in astronomy and high-energy physics. During ultra-high-energy cosmic ray collisions, gluon condensation may abruptly produce a large number of low-momentum pions, whose electromagnetic decays have the typical broken power law. On the other hand, the Large Hadron Collider (LHC) shows weak but recognizable signs of gluon condensation, which had been mistaken for BEC pions. Symmetry is one of the fundamental laws in natural phenomena. Conservation of energy stems from time symmetry, which is one of the most central principles in nature. In this study, we reveal that the connection between the above two apparently unrelated phenomena can be fundamentally explained from the fundamental principle of conservation of energy, highlighting the deep connection and unifying role symmetry plays in physical processes. Full article

Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks. Full article

The digital transformation of low-voltage distribution networks demands a renewed perspective on both regulatory frameworks and metering technologies. This article explores the intersection between incentive structures and metering technologies, focusing on how smart metering can act as a strategic enabler for flexibility in electricity distribution. Starting with the Spanish regulatory evolution and European benchmarking, the shift from asset-based regulation and how it can be complemented with performance-oriented incentives to support advanced metering functionalities is analyzed. On the technical side, the capabilities of smart meters and the performance of communication protocols (such as PRIME, G3-PLC, and 6LoWPAN) highlighting their suitability for real-time observability and control are examined. The findings identify a way to enhance regulatory frameworks for fully harnessing the operational potential of smart metering systems. This article calls for a hybrid, context-aware approach that integrates regulatory evolution with metering structures innovation to unlock the full value of smart metering in the energy transition. Full article

Conventional statistical models consider all wind speed ranges as equally important, causing significant prediction errors, particularly in wind speed intervals that contribute the most to wind turbine power generation. To overcome this limitation, this study proposes a novel parameter estimation method—Weighted Maximum Likelihood Estimation (WMLE)—to improve the accuracy of annual energy production (AEP) predictions for wind turbine systems. The proposed WMLE incorporates wind-speed-specific weights based on power generation contribution, along with a weighting amplification factor (β), to construct a power-oriented wind distribution model. WMLE performance was validated by comparing four offshore wind farm candidate sites in Korea—each exhibiting distinct wind characteristics. Goodness-of-fit evaluations against conventional wind statistical models demonstrated the improved distribution fitting performance of WMLE. Furthermore, WMLE consistently achieved relative AEP errors within ±2% compared to those of time-series-based methods. A sensitivity analysis identified the optimal β value, which narrowed the distribution fit around high-energy-contributing wind speeds, thereby enhancing the reliability of AEP predictions. In conclusion, WMLE provides a practical and robust statistical framework that bridges the gap between statistical distribution fitting and time-series-based methods for AEP. Moreover, the improved accuracy of AEP predictions enhances the reliability of wind farm feasibility assessments, reduces investment risk, and strengthens financial bankability. Full article

Amid the global energy transition, rapidly expanding wind and solar installations challenge power grids with variability and uncertainty. We propose an adaptive framework for renewable energy accommodation assessment under high-dimensional uncertainties, integrating three innovations: (1) Response Surface Methodology (RSM) is adopted for the first time to construct a closed-form polynomial of renewable energy accommodation in terms of resource hours, load, installed capacity, and transmission limits, enabling millisecond-level evaluation; (2) LASSO-regularized RSM suppresses high-dimensional overfitting by automatically selecting key interaction terms while preserving interpretability; (3) a Bayesian kernel density extension yields full posterior distributions and confidence intervals for renewable energy accommodation in small-sample scenarios, quantifying risk. A case study on a renewable-rich grid in Northwest China validates the framework: two-factor response surface models achieve R² > 90% with < 0.5% mean absolute error across ten random historical cases; LASSO regression keeps errors below 1.5% in multidimensional space; Bayesian density intervals encompass all observed values. The framework flexibly switches between deterministic, sparse, or probabilistic modes according to data availability, offering efficient and reliable decision support for generation-transmission planning and market clearing under multidimensional uncertainty. Full article

Electrical energy supply services are characterised by unpredictable risks that affect both distribution network operators (DSOs) and electricity consumers. This paper presents an innovative cooperative game theory (GT) framework to enhance electric energy supply reliability and demand-side flexibility by aligning the interest of DSOs and consumers. The research investigates the performance of the proposed GT model under different distribution network (DN) topologies and fault intensities, explicitly considering outage durations and restoration times. A cooperation mechanism based on penalty compensation is introduced to simulate realistic interactions between DSOs and consumers. Simulation results confirm that adaptive cooperation under this framework yields significant reliability improvements of up to 70% in some DN configurations. The GT-based approach supports informed investment decisions, improved stakeholder satisfaction, and reduced risk of service disruptions. Findings suggest that integrated GT planning mechanisms can lead to more resilient and consumer-centred electricity distribution systems. Full article

Accurate short-term forecasting of urban water demand is a persistent challenge for utilities seeking to optimize operations, reduce energy costs, and enhance resilience in smart distribution systems. This study presents a multi-scale Artificial Neural Network (ANN) modeling approach that integrates temporal lag optimization to predict daily and hourly water consumption across heterogeneous user profiles. Using high-resolution smart metering data from the SunRise Smart City Project in Lille, France, four demand nodes were analyzed: a District Metered Area (DMA), a student residence, a university restaurant, and an engineering school. Results demonstrate that incorporating lagged consumption variables substantially improves prediction accuracy, with daily R² values increasing from 0.490 to 0.827 at the DMA and from 0.420 to 0.806 at the student residence. At the hourly scale, the 1-h lag model consistently outperformed other configurations, achieving R² up to 0.944 at the DMA, thus capturing both peak and off-peak consumption dynamics. The findings confirm that short-term autocorrelation is a dominant driver of demand variability, and that ANN-based forecasting enhanced by temporal lag features provides a robust, computationally efficient tool for real-time water network management. Beyond improving forecasting performance, the proposed methodology supports operational applications such as leakage detection, anomaly identification, and demand-responsive planning, contributing to more sustainable and resilient urban water systems. Full article