Search Results (6,827)

The trace water content in industrial oil critically affects the operational stability and service life of industrial equipment and serves as a key indicator for evaluating oil quality. Therefore, the rapid, sensitive, and visual detection of trace water in oil is of great engineering significance for equipment condition monitoring and early fault warning. Existing detection methods predominantly rely on precision instruments; although they enable quantitative analysis, their operational procedures are complicated and time-consuming, which are unsuitable for on-site real-time monitoring. Consequently, there is an urgent need for a novel trace water detection sensor that offers high sensitivity, visualization, and adaptability to oil-phase environments. Herein, a coplanar electrode alternating current electroluminescent (ACEL) sensor is developed for the visual detection of trace water in oil. The ACEL sensor features a multilayer structure comprising a substrate layer, a coplanar electrode layer, and a humidity-sensitive luminescent layer. The humidity-sensitive luminescent layer consists of humidity-sensitive hydrogel and ZnS: Cu electroluminescent powder, forming a loose and porous film that enables high-sensitivity humidity sensing and simultaneously electroluminescent visual signal output. The sensing mechanism study reveals that variations in trace water content modulate the dielectric properties of the humidity-sensitive layer, which further affect the electroluminescent intensity of the ACEL sensor. In addition, the ACEL sensor enables the rapid, naked-eye recognition of humidity changes under trace water conditions without the need for precision instruments, achieving a rapid response time of 3 s and a detection limit as low as 60 ppm, all making it applicable for different types of industrial oils. Thus, this ACEL sensor features a novel detection mechanism, excellent universality, fast response, and ease of operation, offering a new visual sensing strategy for trace water detection in industrial oil and holding broad prospects for practical applications. Full article

Salt cavern Compressed Air Energy Storage (CAES) is one of the critical technologies for energy storage and an important infrastructure supporting the construction of new power systems and facilitating the achievement of the dual carbon goals. The salt rock resources in China are primarily composed of continental strata salt rocks, characterized by high heterogeneity, well-developed thin-layer interbedding, dissolution resistance among different lithologies, and significant creep variations. These features, to some extent, limit the improvement of wellbore construction accuracy, the reliability of abandoned well sealing, the safety of natural gas storage operations, and enhancements in gas injection–brine displacement efficiency. This study takes the continental bedded salt rock in the Dawenkou Basin as the research object and adopts a method combining theoretical analysis and field engineering verification to improve the systematic construction technology system, covering the whole process of drilling engineering, abandoned well plugging, the design of an injection and brine extraction device, and gas injection and brine drainage. The research results optimize four key technologies, including precise wellbore trajectory control, dual-section milling, and multi-stage redundant plugging of abandoned wells and long-term anti-corrosion completion with laser cladding, and dual-mode adaptive gas injection and brine drainage, and improve the technical system from wellbore construction to salt cavity formation. This study can provide valuable theoretical references and engineering demonstration guidance for underground space development projects in similar salt basins in China. Full article

Long-term decarbonization of urban residential buildings in southern China depends on the joint evolution of building stock, end-use efficiency, and electricity carbon intensity. This study develops a dynamic stock-energy-carbon framework for urban residential buildings in China’s hot-summer warm-winter region from 2010 to 2060, using Guangdong, Guangxi, Fujian, and Hainan as case provinces. The model links demographic and housing-space change with stock survival, retrofit of the base-year stock, cohort-specific performance levels for post-2022 new construction, and time-varying provincial grid emission factors. EnergyPlus simulations of seven high-rise residential archetypes show that nearly zero-energy performance reduces province-level EUI by 19.2–26.5% relative to the baseline, with cooling-load reductions forming the dominant part of the improvement in the warmer provinces. Across coupled demand-side scenarios, stricter new-build performance standards reduce 2026–2060 cumulative operational energy by 5.3–10.1% relative to the conservative demand-side setting, while increasing retrofit intensity provides a smaller but consistent additional reduction. Carbon outcomes are more sensitive to electricity-sector assumptions: under the main demand-side setting, moving from the conservative to the accelerated grid pathway advances the operational-carbon peak by 8–15 years across the four provinces and lowers 2060 residual emissions by about 71%. A comparison with available observed provincial household-electricity statistics is added as a plausibility check; it confirms the relevant order of magnitude but also indicates that absolute demand estimates should be interpreted cautiously because of boundary and EUI-representation differences. These results suggest that demand-side efficiency policies must be coordinated with rapid provincial power-sector decarbonization if the residential sector in Hot-Summer Warm-Winter Region is to reach earlier carbon peaks and lower residual operational emissions. Full article

The rapid evolution of next-generation electronics urgently demands high-performance functional materials. Two-dimensional (2D) semiconductors, characterized by tunable bandgaps, magnetic properties, and excellent optical and electronic properties, hold significant potential for applications in nanoelectronic devices, magnetic storage, and optoelectronics. However, the high computational cost of traditional Density Functional Theory (DFT) severely restricts large-scale high-throughput screening. Meanwhile, problems such as insufficient datasets and non-uniform data quality remain prevalent. Against this background, machine learning (ML), which captures intricate nonlinear correlations and accelerates the discovery of novel materials, has emerged as an efficient technical approach. This review systematically summarizes recent advances in ML-driven property prediction for 2D semiconductors. It first elaborates the fundamental properties and classifications of 2D semiconductors, and then compares traditional computational simulations with ML algorithms, clarifying the distinct advantages of data-driven approaches. Subsequently, this work focuses on the latest progress in predicting critical properties, including bandgap, magnetism, and other physical characteristics. For bandgap prediction, classical algorithms such as random forests are compared with deep learning models represented by graph neural networks. The results demonstrate that deep learning performs much better in low-data regimes and complex material systems. For magnetic property prediction, the impact of feature engineering strategies on model accuracy and efficiency is systematically analyzed. In addition, the research progress of other physical property prediction tasks is briefly summarized. Finally, future research directions for machine learning, including standardized materials databases, physics-informed machine learning, multimodal modeling, and the integration of machine learning with experimental and theoretical methods, are outlined to address challenges in data quality, model interpretability, and cross-system generalization ability. This work aims to provide a systematic theoretical foundation and methodological guidance for research on two-dimensional semiconductor materials assisted by machine learning. Full article

The increasing deployment of distributed energy storage systems (ESSs) presents new opportunities to enhance power system flexibility and enable innovative market participation models. However, many small-scale energy storage system assets remain underutilized due to fragmented ownership, uncertainty in market prices and revenue opportunities, as well as regulatory and operational constraints, and heterogeneous decision making behaviors. To address these challenges, this paper proposes an enhanced energy storage as a service (ESaaS) framework that enables distributed ESS owners to lease idle storage capacity to a centralized platform for coordinated participation in multiple grid support services. The proposed platform aggregates the distributed ESS capacity and allocates it across several value streams. Unlike conventional approaches that assume fully rational agents, this work incorporates behavioral decision making dynamics using prospect theory (PT), which captures loss aversion, asymmetric risk perception, and the subjective valuation of uncertain outcomes. The interaction between the ESaaS operator and ESS owners is formulated as a bi-level optimization problem, where the upper level determines leasing prices and operational strategies across multiple services while the lower-level models ESS owner participation decisions. Prospect theory is integrated at both decision layers to capture the behavioral preferences of the ESaaS operator and ESS owners under uncertainty. The resulting mixed-integer bi-level model is solved using a modified reformulation-and-decomposition approach that incorporates a nested column-and-constraint generation (NC&CG) method to ensure computational tractability. Numerical studies demonstrate that behavioral decision modeling significantly influences pricing strategies and the overall profitability of both the ESaaS platform and the participating energy storage system owners. Full article

Large Language Models (LLMs) offer opportunities for processing heterogeneous information streams relevant to energy-market decision-making, but their practical role in forecasting-oriented analytical workflows remains uncertain. This paper presents an exploratory feasibility study of LLM use across four energy-market tasks: geopolitical event monitoring for Dutch Title Transfer Facility (TTF) market context using Global Database of Events, Language, and Tone (GDELT)-based data, structured summarisation of commodity-intelligence articles, prompt-engineered solar-power and grid-load forecasting for Austria, and a short-horizon exploratory TTF price-estimation case. The study is positioned as a pilot investigation and hybrid workflow blueprint rather than as a statistically conclusive forecasting benchmark. A four-layer reference architecture was devised, including structured market data, semi-structured news intelligence, web-scraping concepts, and implemented Twitter/X and GDELT monitoring layers. The empirical cases indicate that LLMs are most useful for text-heavy reasoning, event-context integration, source triage, and structured interpretation. In the 20-article summarisation corpus, Gemini 1.5 Pro achieved higher commodity-direction accuracy than GPT-4, while GPT-4 showed stronger output-format stability. In selected solar case checks, OpenAI models produced plausible generation curves close to the Fraunhofer ISE Energy Charts reference, while Energy Charts remained more accurate for aggregate load estimation in the available benchmark comparison. The two-day TTF experiment illustrated that LLMs can incorporate qualitative geopolitical context into short-horizon reasoning, but it did not establish reliable price-forecasting capability. The Twitter/X monitoring layer is retained as a documented negative pathway, showing the limitations of informal social-media scraping for reproducible market intelligence. Full article

This study develops a bottom-up cost optimization model (DC-DECOM) to evaluate decarbonization pathways for Japan’s data center industry, targeting carbon neutrality of the information and communications technology (ICT) sector by 2040. The model represents Power Usage Effectiveness (PUE) as a dynamic function of ambient temperature and cooling technology, and integrates technology selection, regional energy supply, and carbon pricing within a single cost-minimization framework. Three scenarios are compared: a reference case (REF), a centralized carbon-neutral scenario (C-CN) that restricts new capacity to metropolitan areas, and a regional decentralization scenario (R-CN) that allows for nationwide siting. Input parameters are calibrated against data from the International Energy Agency (IEA), the Uptime Institute, Japan’s Ministry of Internal Affairs and Communications (MIC) White Papers, and the Japan Science and Technology Agency (JST). The R-CN scenario achieves the 2040 net-zero target at 18–23% lower total system cost than C-CN. The cost gap decomposes into four channels (cooling-energy reduction ∼35%, lower regional renewable procurement cost ∼30%, lower carbon cost ∼25%, and lower siting-related cost ∼10%). Sensitivity analysis identifies the carbon-price trajectory and the hardware-efficiency improvement rate as the most influential parameters; the R-CN advantage remains positive across all $\pm 1\sigma$ parameter variations and across two combined-scenario stress tests. Full article

The development of a new-type power system requires addressing the long-timescale imbalance between electricity supply and demand caused by the high penetration of wind and solar energy, which places higher demands on the secure and stable operation of power systems. Conventional single-type energy storage cannot simultaneously satisfy short-term power regulation and medium- to long-term energy balancing requirements. Therefore, coordinated optimal allocation of multi-type energy storage is necessary. This study investigates the optimal capacity allocation of short- and long-duration energy storage in high-renewable-penetration power grids to improve renewable energy accommodation, enhance system flexibility, and optimize life-cycle cost. A mathematical model of a Multi-Type Energy Storage Coupled System (MTESCS) considering both power and energy balance is first established, together with a life-cycle economic model. Then, a source-load time-series reduction method based on Ward’s method is adopted to preserve the original temporal trends while reducing optimization complexity, and an optimal capacity allocation model is developed with the objective of minimizing system life-cycle cost. Finally, different storage configuration scenarios are constructed for comparative analyses under various renewable energy penetration levels. Results show that the proposed MTESCS can effectively improve renewable energy accommodation and economic performance, providing useful support for system design and engineering applications. Full article

Road freight contributes over half of China’s transport carbon emissions, making its decarbonization critical for carbon neutrality. This study combines total cost of ownership (TCO) and life cycle assessment (LCA) to analyze the economic efficiency and carbon emission effects of diesel, electric, and hydrogen fuel cell trucks. Combined with the LSTM neural network and vehicle ownership model, this study predicts the fleet emission reduction potential from 2020 to 2050. The results show that all new energy trucks can achieve TCO parity with diesel trucks before 2050, and electrification shows better economic competitiveness than hydrogen fuel cell technology across all vehicle types in the Chinese context. Fuel cell trucks powered via solar-powered water electrolysis exhibit the lowest carbon intensity, and grid decarbonization can significantly improve the emission reduction effects of electric and fuel cell trucks. Freight fleet carbon emissions are expected to peak around 2030. In an ideal scenario, emission reductions of 19.5%, 41.9%, and 82.9% can be achieved by 2030, 2040, and 2050, respectively. Heavy-duty trucks are the main emission contributors (47–58%) and the main target of emission reduction strategies. Short-term reduction depends on fuel economy, while long-term reduction prioritizes new energy substitution. Policy recommendations include promoting alternative fuel trucks, upgrading emission standards, and adopting differential taxation. Full article

The rapid growth of renewable energy is driving a profound transformation of electricity grids toward architectures characterized by high shares of inverter-based generation, increased decentralization, and extensive digitalization. While wind and solar technologies have matured at the component level, their large-scale integration introduces technical, operational, and institutional challenges that extend beyond conventional power-system design paradigms. This review provides an integrated synthesis of the technologies, control strategies, and management processes that enable renewable energy integration into emerging electricity grids. Key challenges are analyzed across multiple timescales: fast frequency and voltage dynamics in low-inertia systems (milliseconds to seconds), forecasting, optimization, and automated control (real-time to near-real-time), and long-term planning of transmission, storage, and flexibility resources (years to decades). The synthesis covers grid-forming and grid-following inverter control, with quantitative comparison across short-circuit-ratio regimes; HVDC and HVAC transmission technologies; energy storage systems, including emerging electrochemical and mechanical solutions; smart-grid digitalization through EMS, SCADA, and digital twins; artificial intelligence and machine-learning deployments at major transmission system operators; sector coupling involving hydrogen and carbon capture; and cybersecurity considerations. Real-world case studies are used to illustrate practical lessons, with explicit attention to the brownfield–greenfield distinction between modernization of legacy systems and the design of new networks in developing regions. The review concludes by identifying key research and development priorities for achieving reliable, resilient, and economically efficient high-renewable energy systems. Full article

Modern power systems are undergoing a structural transformation driven by the rapid integration of renewable energy sources, distributed energy resources, electrification, and increasing operational uncertainty. These developments expose the limitations of traditional centralized energy management and rule-based automation in highly distributed, data-intensive, and dynamically coupled energy infrastructures. In response, recent advances in artificial intelligence offer new opportunities for improving prediction, coordination, and adaptive control. This paper develops a reference architecture for Autonomous Energy Systems based on the integration of generative AI, agentic AI, digital twins, and distributed cyber–physical energy infrastructures. Rather than treating forecasting, control, simulation, and market coordination as separate research tracks, the paper organizes them within a common architectural perspective. Generative AI is positioned as a source of scenario intelligence, synthetic data generation, and uncertainty-aware forecasting, while agentic AI is framed as a bounded decision layer for perception, reasoning, planning, and coordinated action under operational constraints. The paper further clarifies the distinction between agentic AI, conventional multi-agent systems, and multi-agent reinforcement learning in energy applications. Representative application domains are discussed, including self-healing power grids, autonomous energy markets, and digital twin training environments. Major open challenges are identified in relation to scalability, physical consistency, safety verification, sim-to-real transfer, cybersecurity, interoperability with legacy infrastructures, and governance. The paper concludes by outlining a research agenda for the staged and safe development of increasingly autonomous energy systems. Full article

Abnormal circulating current induced by sheath grounding faults in cross-bonded high-voltage cables is a major cause of single-phase grounding faults. For the early detection and localization of sheath grounding faults, this paper constructs an equivalent circuit model for three-phase nine-section cross-bonded cables. Circuit model parameters are estimated via online monitoring data. The relational equation between sheath electrical quantities, fault impedance, and distance is derived for typical sheath grounding faults. Using the Adam algorithm, the solution of fault impedance and location is converted into the minimization of an optimization objective function. Simulation results show that under the influences of phase current imbalance, measurement error, and fault impedance fluctuation, the Adam algorithm exhibits superior optimization accuracy and computational efficiency in comparison with the ED and GA algorithms. Experimental results show that for low-resistance sheath grounding, the proposed method has a fault impedance calculation error ≤ 0.59% and a fault positioning error ≤ 1.89%. For metallic sheath grounding with zero resistance, the positioning error is ≤1.37%. Field test results demonstrate that the proposed method performs similarly to the time-domain reflectometry method, with a positioning deviation ≤ 0.15 m, and can meet online monitoring requirements. Full article

Utility disruptions may stem from insufficient power generation, inferior infrastructure, or secondary weather perils (e.g., tornadoes, floods, snowstorms) that take energy infrastructure offline. The latter present a unique risk that not all existing power options can mitigate. Regardless of their origin, power disruptions have the potential to cripple food supply chains and undermine food system sustainability. To prepare for managing future disruptions, food and beverage manufacturers may couple electrical microgrid and thermal district heating infrastructure with small modular reactors (SMRs) or smaller microreactor systems to form low-carbon power islands. Although SMR technology is a somewhat new source of energy and has not yet achieved commercial viability, it provides the potential to make food and beverage manufacturing more resilient and sustainable when it becomes broadly available. To assess the potential cost–benefit of activating such technology as a sustainability-oriented resilience investment, we conducted a technoeconomic downtime threshold analysis. The case assumes that the technology is the full-time power source and the SMR yields stronger returns as facility downtime or downtime costs rise. The analysis found the breakeven point to range from 12.3 h down to 613.2 h down annually for a 5 MW system, depending on facility scale and assumed downtime costs. At a representative downtime opportunity cost of $10,000/h, SMR adoption requires approximately 61.3 h (5 MW) of annual outages to break even, highlighting scale effects on feasibility. Incorporating a 20% thermal energy credit reduces required outage thresholds by roughly 20%, lowering the breakeven level to 49.1 h. These results highlight the potential role of SMR-enabled power islanding in supporting sustainable food manufacturing through improved energy resilience, low-carbon power, and thermal energy recovery. Full article

Thunderstorms are the most frequent type of severe convective weather, which pose great threats to buildings, power transmission, communication facilities, and air transportation. Their analysis and forecasting have long been challenges in meteorological operations. Currently, deep learning-based lightning forecasting has a short valid period, mostly relying on satellite imagery, radar echoes, and lightning location data, focusing on very-short-range forecasting. The longest valid period does not exceed 6 h, and the forecasting accuracy is not high. Based on the physical quantities of the ECMWF numerical prediction model and the actual observations of single-point thunderstorms, this paper constructs a single-point thunderstorm forecasting model with a long validity period (>6 h). The study integrates multi-dimensional parameters such as thermal, dynamic, water vapor, and stratification instability, introduces the second-order moist potential vorticity S as a comprehensive predictor, systematically compares the forecasting performance of eight models, such as 1D PreRNN and ConvLSTM, and verifies the actual operational capability of the model through independent cases. The results show that the 1D PreRNN model has the best overall performance in all periods, which can effectively capture the temporal evolution characteristics of meteorological physical quantities and still has stable generalization performance under unbalanced samples. The model performs well in the 1st, 2nd, and 4th periods, and especially still has significant operational reference value in the 4th period with the longest forecasting validity period; only the 3rd period is weakly affected by the small number of samples. The effect of second-order moist potential vorticity has significant time-dependent differences. Its overall improvement effect is limited in short-term forecasting, but it can provide key disturbance signals in the 4th period with the longest forecasting validity period, and the model forecasting performance drops significantly after removal. The original binary cross-entropy loss is most suitable for the unbalanced sample scenario in this study, and weighted losses are prone to overcorrection. The method in this paper can achieve stable and reliable single-point thunderstorm forecasting for more than 6 h, and can provide long-term fixed-point meteorological support for key scenarios such as aerospace and new energy stations. Full article

The safe operation of power battery systems relies on the dynamic symmetric equilibrium of electrochemical distribution and thermal management states, whereas fault occurrence is often accompanied by symmetry breaking. To achieve accurate fault diagnosis and symmetry restoration, this study proposes a symmetrical closed-loop framework (DBP-KG-FBN) that integrates digital battery passport (DBP) text mining, knowledge graph (KG), and fuzzy Bayesian network (FBN). Power battery fault diagnosis is critical to new energy vehicle (NEV) safety; however, conventional methods face two key limitations: (1) they inadequately exploit multi-source heterogeneous textual data in DBPs; and (2) they fail to handle uncertainty in fault propagation. The methodology proceeds as follows. First, a BERT-BiLSTM-CRF model extracts fault-related entities and relations from unstructured DBP text, which are structured into a Neo4j-based knowledge graph. Second, via rule-based topological mapping, the KG topology is transformed into a Bayesian network through structurally symmetric transformation between the semantic and probabilistic layers, with cyclic dependencies resolved by introducing latent variables. Third, network parameters are determined by integrating fuzzy set theory with game theory-based weighting to quantify uncertainty and subjectivity in expert evaluations, thereby achieving symmetric utilization of subjective and objective information. This enables bidirectional symmetric reasoning for forward fault prediction and backward fault traceability. Experimental results demonstrate that while maintaining symmetric stability of the diagnostic knowledge topology, the proposed DBP-KG-FBN method achieves a diagnostic accuracy of 0.92 (Top-3). This symmetrical closed-loop framework significantly outperforms fault tree analysis (FTA) and event tree analysis (ETA) in diagnostic accuracy and reasoning efficiency. It transforms unstructured DBP data into computable knowledge for intelligent battery diagnosis. Future work will expand the corpus via transfer learning and optimize adaptive weighting algorithms for expert evaluations. Full article