Search Results (16,389)

The high penetration of distributed photovoltaics (PV) into distribution networks alters the system’s short-circuit current characteristics, posing risks of maloperation and failure-to-operate to conventional inverse-time overcurrent protection. Based on an equivalent model of distributed PV during faults, this paper analyzes its impact on the protection characteristics of traditional distribution networks. With protection selectivity and the physical constraints of protection devices as conditions, an optimization model for inverse-time overcurrent protection is established, aiming to minimize the total operation time. To enhance the solution capability for this complex optimization problem, the standard Osprey Optimization Algorithm (OOA) is improved through the incorporation of three strategies: arccosine chaotic mapping for population initialization, a nonlinear convergence factor to balance global and local search, and a dynamic spiral search strategy combining mechanisms from the Whale and Marine Predators algorithms. Based on this improved algorithm, an optimized protection scheme for distribution networks with distributed PV is proposed. Simulations conducted in PSCAD/EMTDC (V4.6.2) and MATLAB (R2023b) verify that the proposed method effectively prevents protection maloperation and failure-to-operate under both fault current contribution and extraction scenarios of PV, while also reducing the overall relay operation time. Full article

Member 2 of the Xujiahe Formation in the Anyue area of the Sichuan Basin exhibits significant resource potential for tight sandstone gas. However, its characteristic of “extensive gas presence with localized enrichment” leads to substantial variations in single-well productivity, challenges in target zone optimization, and unclear enrichment mechanisms, which hinder efficient exploration and development. This study proposes a hierarchical classification scheme of “two-level, six-type” source–reservoir structures based on the developmental characteristics of fault–fracture systems and vertical source–reservoir configurations. The gas-bearing heterogeneity is quantitatively characterized using parameters such as effective gas layer thickness, charge intensity, and effective gas layer probability, thereby revealing the differential enrichment mechanisms of tight sandstone gas controlled by source–reservoir structures. Our key findings include the following: (1) Member 2 of the Xujiahe Formation develops six subtypes of source–reservoir structures grouped into two levels, with gas-bearing capacities ranked as follows: source–reservoir separation type > source–reservoir adjacent type I > source–reservoir adjacent type II. Among these, the source–reservoir separation type (Level I) and fault–fracture conduit type (Level II) represent the most favorable structures for gas enrichment. (2) Tight sandstone gas enrichment is governed by a tripartite synergistic mechanism: hydrocarbon supply from source rocks, vertical cross-layer migration dominated by fault–fracture systems, and reservoir storage capacity determined by fracture density and reservoir thickness. (3) Three enrichment models are established: (i) a strong enrichment model characterized by “multi-layer source rocks beneath the reservoir, cross-layer migration, and thick fractured reservoirs”; (ii) a moderate enrichment model defined by “single-layer source rocks, localized migration, and medium-thick fractured reservoirs”; and (iii) a weak enrichment model featuring “single-layer hydrocarbon supply, pore-throat migration, and thin tight reservoirs.” This research provides a theoretical basis for optimizing exploration targets in Member 2 of the Xujiahe Formation in the Anyue area and offers insights applicable to analogous continental tight gas reservoirs. Full article

With global air traffic surpassing pre-pandemic levels, improving the fuel economy of aircraft engines has become increasingly urgent, especially for small turbofan engines that suffer from high specific fuel consumption (SFC) at high power settings. This study aims to address the trade-off between compressor pressure ratio (CPR) and turbine entering temperature (TET), which strongly influence both engine performance and fuel utilization. A hierarchical optimization framework based on Stackelberg game theory is developed, and an Adaptive Chaotic Particle Swarm Optimization (ACPSO) algorithm is employed to solve the game-theoretic model. Surrogate models using artificial neural networks are integrated to capture nonlinear relationships among design parameters and performance metrics. The results show that the proposed Stackelberg-ACPSO method outperforms conventional multi-objective particle swarm optimization (MOPSO) in balancing thrust, SFC, and thermal efficiency. Specifically, it achieves a 0.1609% reduction in SFC and a 0.0904% increase in thrust, while also providing a 0.56% decrease in SFC with a 0.51% improvement in thermal efficiency under trade-off conditions. In addition, ambient temperature is found to significantly affect the interactions between objectives, further validating the robustness of the approach. Overall, this work demonstrates the effectiveness of applying Stackelberg game theory to small turbofan engine optimization and offers valuable insights into the coupling of fuel economy and performance for future engine design. Full article

Following the World Health Organization’s 2023 declaration, which ended the global health emergency, energy policy shifted from a short-term crisis response to a structural recovery focused on renewable energy. However, the current literature remains fragmented, often overlooking the realities of implementation in the Global South and failing to integrate diverse policy instruments. This study examines post-pandemic renewable recovery strategies to categorize instruments, evaluate effectiveness, and identify critical implementation gaps. An integrative review was conducted, combining bibliometric mapping of 113 documents (n = 113) and systematic thematic synthesis of 42 studies (n = 42), utilizing the SPIDER and PRISMA protocols. Policy instruments were classified into five groups: Recovery (REC), Fiscal/Financial (FISC), Regulatory (REG), Energy Efficiency (EE), and Social and Information (SOC), revealing a “Global North-South Asymmetry”, where advanced economies leverage fiscal–regulatory coupling while emerging markets face administrative bottlenecks. Findings identify coordination failures, such as missequencing, and propose a “Cascading Policy Logic” that prioritizes de-risking before mandatory standardization. This research bridges the evidence gap by validating the need for informal sector mechanisms and equity safeguards in developing nations. Ultimately, this review provides a strategic framework for policymakers to transition from a reactive stimulus to durable, socially legitimate decarbonization pathways beyond 2025. Full article

Accurately estimating the state of health (SOH) of lithium-ion batteries is vital for guaranteeing battery safety and prolonging their operational lifespan. However, current data-driven approaches often suffer from limited utilization of health indicators, weak noise suppression, and inefficient feature fusion across multiple branches. To overcome these limitations, this paper proposes DFFNet, a Dual-Branch Feature Fusion Network with Improved Dynamic Elastic Weight Consolidation (IDEWC) for battery SOH estimation. The proposed DFFNet captures both local degradation behaviors and global aging trends through two parallel branches, while a gated attention mechanism adaptively integrates multi-scale features. Moreover, IDEWC dynamically updates the Fisher Information Matrix to retain previously learned knowledge and adapt to new data, thereby mitigating catastrophic forgetting. Experimental validation on the NASA and CALCE datasets shows that DFFNet outperforms baseline methods in accuracy, demonstrating its robustness and generalizability for precise battery SOH estimation. Full article

In the process of continuous mining and continuous backfilling (CMCB) in close-distance coal seams, the supporting unit (CMCBSU), composed of coal pillar and filling body, is affected by mining-induced disturbances from adjacent coal seams. This study establishes a mechanical model for the CMCBSU, revealing that the coordination of the CMCBSU depends on the similarity degree of elastic modulus of the components. Subsequently, numerical simulations were conducted to analyze the stress conditions. The results showed that the σ₁ and σ₃ exhibited cyclic loading and unloading characteristics. Based on the stress paths, conventional triaxial compression tests were performed on coal (CTC-coal), filling body, and the CMCBSU, as well as triaxial cyclic loading and unloading tests on coal (TCLU-coal). The results indicated that coal exhibited significant brittleness, the filling body demonstrated strain-softening characteristics, and the CMCBSU showed strain-softening behavior. Hysteresis loops were observed in the elastic region of the TCLU-coal. The failure characteristics of the specimens indicated that the shear stress was the primary cause of specimen failure. After testing, the filling body exhibited radial fish-scale-like wrinkles on the specimen surface, the coal and the CMCBSU showed primary shear cracks. In the CMCBSU, the primary shear crack generated on the filling body side relates to that on the coal side. In contrast, secondary cracks on the filling body side rarely penetrate the coal side. Excluding the influence of internal weak planes on specimen failure, cyclic loading and unloading within the elastic region of the coal reduced its internal friction angle. Mechanical parameters indicate that the weaker load-bearing medium determined the load-bearing capacity of the CMCBSU, the medium with a higher elastic modulus primarily determined the CMCBSU’s resistance to elastic deformation, and the cyclic loading and unloading caused by CMCBSU in close-distance coal seams had minimal impact on the coal’s resistance to elastic deformation. Full article

Grid-forming power converters play a foundational role in modern microgrids and inverter-dominated distribution systems by establishing voltage and frequency references during islanded or low-inertia operation. However, when subjected to nonlinear or impulsive impact-type loads, these converters often suffer from severe harmonic distortion and transient current overshoot, leading to waveform degradation and protection-triggered failures. While virtual impedance control has been widely adopted to mitigate these issues, conventional implementations rely on fixed or rule-based tuning heuristics that lack adaptivity and robustness under dynamic, uncertain conditions. This paper proposes a novel reinforcement learning-based framework for real-time virtual impedance scheduling in grid-forming converters, enabling simultaneous optimization of harmonic suppression and impact load resilience. The core of the methodology is a Soft Actor-Critic (SAC) agent that continuously adjusts the converter’s virtual impedance tensor—comprising dynamically tunable resistive, inductive, and capacitive elements—based on real-time observations of voltage harmonics, current derivatives, and historical impedance states. A physics-informed simulation environment is constructed, including nonlinear load models with dominant low-order harmonics and stochastic impact events emulating asynchronous motor startups. The system dynamics are modeled through a high-order nonlinear framework with embedded constraints on impedance smoothness, stability margins, and THD compliance. Extensive training and evaluation demonstrate that the learned impedance policy effectively reduces output voltage total harmonic distortion from over 8% to below 3.5%, while simultaneously limiting current overshoot during impact events by more than 60% compared to baseline methods. The learned controller adapts continuously without requiring explicit load classification or mode switching, and achieves strong generalization across unseen operating conditions. Pareto analysis further reveals the multi-objective trade-offs learned by the agent between waveform quality and transient mitigation. Full article

This study examines the economic performance of residential photovoltaic systems combined with battery storage (PV-BESS) under Poland’s net-billing regime for a single-family household without subsidy support in 10-year operational horizon. These insights extend existing European evidence by demonstrating how net-billing fundamentally alters investment incentives. The analysis incorporates real production data from selected locations and realistic household consumption profiles. Results demonstrate that optimal system configuration (6 kWp PV with 15 kWh storage) achieves 64.3% reduction in grid electricity consumption and positive economic performance with NPV of EUR 599, IRR of 5.32%, B/C ratio of 1.124 and discounted payback period of 9.0 years. The optimized system can cover electricity demand in the summer half-year by over 90% and reduce local network stress by shifting surplus solar generation away from midday peaks. Residential PV-BESS systems can achieve economic efficiency in Polish conditions when properly optimized, though marginal profitability requires careful risk assessment regarding component costs, durability and electricity market conditions. For Polish energy policy, the findings indicate that net-billing creates strong incentives for regulatory instruments that promote higher self-consumption, which would enhance the economic role of residential storage. Full article

Given the pressing need to strengthen operational reliability in electrical distribution networks, this study proposes an optimization methodology based on the Teaching–Learning-Based Optimization (TLBO) algorithm for the strategic location of redundant lines. The model is validated on the “MV Distribution Network—Base Model” test system, considering the combination of the MTBF (Mean Time Between Failures) and MTTR (Mean Time To Repair) indicators as the objective function. After 500 independent runs, it is determined that the configuration with three redundant lines identified as LN_1011, LN_1058, and LN_0871 offers the most stable solution. Specifically, this topology increases the MTBF from 403.64 h to 409.42 h and reduces the MTTR from 2.351 h to 2.306 h. In addition, significant improvements are observed in the voltage profile and angle, along with a more balanced redistribution of active and reactive power, more efficient use of existing lines, and an overall reduction in energy losses. Full article

With the continuous increase in the proportion of renewable energy in the power grid, enhanced operational flexibility of the power system is required. As baseload generators, combined heat and power (CHP) units are prime candidates for flexibility retrofits that guarantee grid stability. Among the available options, molten-salt thermal energy storage (TES) offers an energetically efficient route to decouple heat and electricity production in CHP plants. In this study, a 660 MW ultra-supercritical coal-fired unit is taken as the object of investigation. Sixteen technical routes incorporating steam extraction and electric heating for thermal energy storage and discharging are systematically designed. Results demonstrate that all the combined schemes significantly improve the operational flexibility of the unit. Among them, the C1-S1 configuration exhibits the most outstanding overall economic performance, with a six-hour thermal storage capacity of 294.34 MWh. The system exergy destruction is measured at 6258 kW, while the round-trip efficiency and thermal efficiency are determined to be 81.11% and 45.48%, respectively. Full article

HVAC (Heating, Ventilation and Air Conditioning) system in buildings is a major component of energy consumption, and realizing high-precision energy consumption prediction is of great significance for intelligent building management. Aiming at the problems of insufficient modeling ability of nonlinear features and insufficient portrayal of long time-series dependencies in prediction methods, this paper proposes an HVAC energy consumption prediction model that combines time-sequence convolutional network (TCN), bi-directional gated recurrent unit (BiGRU), and Attention mechanism. The model takes advantage of TCN’s parallel computing and multi-scale feature extraction, BiGRU’s bidirectional temporal dependency modeling, and Attention’s weight assignment of key features to effectively improve the prediction accuracy. In this work, the HVAC load is represented by the building-level electricity meter readings of office buildings equipped with centralized, electrically driven heating, ventilation, and air-conditioning systems. Therefore, the proposed method is mainly applicable to building-level HVAC energy consumption prediction scenarios where aggregated hourly electricity or cooling energy measurements are available, rather than to the control of individual terminal units. The experimental results show that the model in this paper achieves better performance compared to the method on ASHRAE dataset, the proposed model outperforms the baseline by 2.3%, 22.2%, and 34.7% in terms of MAE, RMSE, and MAPE, respectively, on the one-year time-by-time data of the office building, and meanwhile it is significant 54.1% on the MSE metrics. Full article

A cascade deep learning approach is proposed for optimizing the design and control of a dual-frequency induction heating system used in semiconductor manufacturing. The system is composed of two independent power inductors, fed at different frequencies, to achieve a homogeneous temperature profile along a graphite susceptor surface, crucial for enhancing layer quality and integrity. The optimization process considers both electrical (current magnitudes and frequencies) and geometrical parameters of the coils, which influence the power penetration and subsequent temperature distribution within the graphite disk. A two-step procedure based on deep neural networks (DNNs) is employed. The first step, namely optimal design, identifies the optimal operating frequencies and geometrical parameters of the two coils. The second step, namely optimal control, determines the optimal current magnitudes. The DNNs are trained using a database generated through finite element (FE) analysis. This deep learning-based cascade approach reduces computational time and multiphysics simulations compared to classical methods by reducing the dimensionality of parameter mapping. Therefore, the proposed method proves to be effective in solving high-dimensional multiphysics inverse problems. From the application point of view, achieving thermal uniformity (±7% fluctuation at 1100 °C) improves layer quality, increases efficiency, and reduces operating costs of epitaxy reactors. Full article

In recent years, honeycomb sandwich structures have seen continuous development due to their excellent structural performance and design flexibility in heat dissipation. However, their complex heat transfer mechanisms and diverse modes of thermal exchange necessitate research on the air flow behavior and temperature distribution characteristics of micro-channels and lattice pores. This study investigates the internal flow field within a ventilated honeycomb sandwich structure through numerical simulation. The spatial flow characteristics and temperature distribution are analyzed, with a focus on the effects of turbulent kinetic energy, heat flux distribution on the heated surface, and varying pressure drop conditions on the thermal performance. The results indicate that the micro-channels inside the honeycomb core lead to a strong correlation between temperature distribution, flow velocity, and turbulence intensity. Regions with higher flow velocity and turbulent kinetic energy exhibit lower temperatures, confirming the critical role of flow motion in heat transfer. Heat flux analysis further verifies that heat is primarily removed by airflow, with superior heat exchange occurring inside the honeycomb cells compared to the solid regions. The intensive mixing induced by highly turbulent flow within the small cells enhances contact with the solid surface, thereby improving heat conduction from the solid to the flow. Moreover, as the inlet pressure increases, the overall temperature gradually decreases but exhibits a saturation trend. This indicates that beyond a certain pressure level, further increasing the inlet pressure yields diminishing returns in heat dissipation enhancement. Full article

Photovoltaic systems (PV) are increasingly recognized as fundamental to the worldwide adoption of renewable energy technologies. Nonetheless, the efficiency and longevity of solar panels can be compromised by various anomalies, ranging from physical defects to environmental impacts. Early and accurate detection of these anomalies is crucial for maintaining optimal performance and preventing significant energy losses. This study presents SolarAttnNet, a novel convolutional neural network (CNN) architecture with integrated channel and spatial attention mechanisms for solar panel anomaly detection. The proposed model addresses the critical need for automated detection systems, which are crucial for maintaining energy production efficiency and optimizing maintenance. This approach leverages attention mechanisms that emphasize the most relevant features within thermal and visual imagery, improving detection accuracy across multiple anomaly types. SolarAttnNet is evaluated on three distinct solar panel datasets, demonstrating its effectiveness through comprehensive ablation studies that isolate the contribution of each architectural component. Experimental results show that SolarAttnNet achieves superior performance compared to state-of-the-art methods, with accuracy improvements of 3.9% on the PV Systems-AD dataset (94.2% vs. 90.3%), 3.6% on the InfraredSolarModules dataset (92.1% vs. 88.5%), and 3.5% on the RoboflowAnomalies dataset (89.7% vs. 86.2%) compared to baseline ResNet-50. For challenging subtle anomalies like cell cracks and PID, the proposed model demonstrates even more significant improvements with F1-score gains of 4.8% and 5.4%, respectively. Ablation studies reveal that the channel attention mechanism contributes a 2.6% accuracy improvement while spatial attention adds 2.3% across datasets. This work contributes to advancing automated inspection technologies for renewable energy infrastructure, supporting more efficient maintenance protocols and ultimately enhancing solar energy production. Full article

The growing share of variable renewable energy sources in power systems is increasing the need for short-term operational flexibility—particularly from large industrial electricity consumers. This study proposes a practical, two-stage optimization framework to unlock this flexibility in cement manufacturing and support participation in electricity balancing markets. In Stage 1, a mixed-integer linear programming model minimizes electricity procurement costs by optimally scheduling the raw milling subsystem, subject to technical and operational constraints. In Stage 2, a flexibility assessment model identifies and evaluates profitable deviations from this baseline, targeting participation in Spain’s manual Frequency Restoration Reserve market. The methodology is validated through a real-world case study at a Spanish cement plant, incorporating photovoltaic (PV) generation and battery energy storage systems (BESS). The results show that flexibility services can yield monthly revenues of up to €800, with limited disruption to production processes. Additionally, combined PV + BESS configurations achieve electricity cost reductions and investment paybacks as short as six years. The proposed framework offers a replicable pathway for integrating demand-side flexibility into energy-intensive industries—enhancing grid resilience, economic performance, and decarbonization efforts. Full article