Search Results (11,752)

Ion-exchange-strengthened 0.7 mm aluminosilicate glass offers a promising route to lightweight, mechanically robust front covers for building-integrated photovoltaic (BIPV) modules, but systematic characterization at sub-millimeter thicknesses remains limited. This study investigated 100 × 60 × 0.7 mm glass samples subjected to Na⁺/K⁺ ion exchange (6 h, 430 °C, KNO₃) and characterized mechanical and optical properties relevant to BIPV applications. Depth of layer (DOL) was cross-validated using three independent methods, mass gain diffusion modeling (31–37 μm), elasto-optic measurements (FSM-6000: 38–42 μm), and EDS Na/K depth profiling (35–40 μm), confirming consistent strengthened layer depth of 35–40 μm. Surface compressive stress measured 733 MPa (Series 2) and 773 MPa (Series 3), significantly exceeding conventional PV cover glass (490–515 MPa, 1 mm thickness). Vickers hardness increased by 17.7% (490 → 596 HV, p < 0.0001), demonstrating enhanced damage tolerance. Spectrophotometric analysis (200–2400 nm) showed transmittance >91% (380–2000 nm) and >92% (600–2000 nm) for both as-received and strengthened glass, confirming no optical degradation (p = 0.29–0.41). The 78–83% mass reduction relative to standard 3.2–4 mm glass, combined with superior CS/DOL and preserved optical performance, establishes ion-exchanged 0.7 mm aluminosilicate glass as a strong material-level candidate for next-generation lightweight BIPV modules. Future work requires module-scale mechanical validation (bending, impact testing per EN/IEC standards) and techno-economic assessment to verify system-level benefits. Full article

As a critical component of sustainable energy systems, the efficient maintenance of photovoltaic (PV) panels is essential. While deep learning is an important approach for PV panel defect detection, the high complexity of existing models and their substantial computational demand make deployment on edge platforms difficult. This paper studies an acceleration method for photovoltaic panel defect detection on the Zynq-7020 heterogeneous platform. We design DARE-YOLO, a lightweight network for photovoltaic panel defect detection, together with a Zynq-based accelerator. In DARE-YOLO, we introduce RepConv and a lightweight single-path backbone to reduce the memory bandwidth overhead caused by multi-branch structures. We further design a Dilated Context Block (DCB) and a Dual-scale Decoupled Head (DDH), which effectively improve the detection accuracy of DARE-YOLO. On the Zynq platform, we develop the accelerator through a mixed fixed-point quantization strategy, a custom convolution IP core, and pipeline unrolling. These optimizations reduce data access latency, improve computational parallelism, and increase computational throughput. Experimental results show that DARE-YOLO achieves 93.84% mAP@0.5 with only 6.4 M parameters. The accelerator has a total on-board power consumption of only 1.95 W, while delivering a throughput of 37.5 GOPS, an energy efficiency of 19.23 GOPS/W. The image inference latency is 661.3 ms. This low-power, high-efficiency co-design paradigm ensures the long-term reliability of renewable energy facilities. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

Aiming to mitigate renewable energy curtailment and curb the carbon emissions of traditional thermal power units (TPUs), this paper proposes a stochastic optimal scheduling of a multi-energy complementary base considering multi-resource reserve and TPU doped with ammonia-concentrated solar power coordination. Firstly, the proton exchange membrane (PEM) electrolyzer (EL) and coal-to-hydrogen (C2H) technology are combined to produce hydrogen, and a mixed-hydrogen-source ammonia production model is constructed. The low-carbon characteristics of ammonia gas are used for thermal power mixed ammonia combustion. Secondly, to alleviate the operational burden on TPUs, a collaborative operating framework integrating a concentrating solar power (CSP) plant, an electric heater (EH), and an ammonia-coal co-fired power unit (ACCPU) is introduced. Furthermore, its low-carbon mechanisms during both peak and off-peak load intervals are thoroughly investigated. Thirdly, the ‘electricity–hydrogen–ammonia’ conversion link inside the deep excavation base and the reserve potential of the CSP plant are constructed, and a variety of flexible resource collaborative reserve models are constructed. Building upon this foundation, to account for the diverse uncertainties associated with load demand, wind, and PV generation, a fuzzy chance-constrained programming method is formulated. Seeking to enhance economic efficiency, the framework focuses on lowering the aggregate operational expenditures. Ultimately, the example results demonstrate that the presented approach effectively expands the accommodation capacity for renewable energy, lowers the base’s carbon emission, and alleviates the operational strain on TPUs. Full article

Rooftop photovoltaic systems are a key component of sustainable urban energy strategies; however, their performance is strongly influenced by environmental variability across spatial scales. This study develops and validates a mathematical model to quantify the influence of Global Horizontal Irradiation (GHI), air temperature, wind speed, and dust on rooftop solar energy efficiency at country, regional, and city levels. The model is applied to environmental and energy data from 96 countries and 17 regions and further validated using four large-scale rooftop PV projects in Bahrain. The results show strong agreement between predicted and actual solar energy production, with coefficients of determination of R² = 0.77 at the country level, R² = 0.84 at the regional level, and R² = 0.998 at the city level, while mean absolute percentage errors generally remain below 10%. Regression and sensitivity analyses showed that at least one environmental factor exerts a statistically significant influence on rooftop solar energy yield, supporting the alternative research hypothesis. GHI is identified as the most influential driver at the national scale, whereas temperature and dust effects become more pronounced at finer spatial resolutions. Deployment gap analysis further reveals substantial untapped rooftop solar potential, highlighting the importance of non-environmental constraints in shaping real-world solar adoption. Full article

Desalination by reverse osmosis (RO) of brackish water and seawater is a cost-competitive solution for supplying irrigation water in off-grid and water-stressed regions. This paper presents an experimental evaluation, thermodynamic analysis, and cost assessment of a solar photovoltaic brackish-water reverse osmosis (PV-BWRO) desalination system. Five feed salinity levels ranging from 993.6 to 3191.8 mg/L were tested. The results show that water production decreased with increasing feed salinity, from 3.29 m³/day at 24.6 mg/L to 1.48 m³/day at 152.9 mg/L. The calculated specific energy consumption values ranged from 1.80 to 3.61 kWh/m³ for solar irradiances of 1005.99 and 1018.47 W/m², respectively. An exergy analysis revealed that the solar panels and pump operated at efficiencies of 11.7% and 38.9%, while exergy destruction was mainly concentrated in the pretreatment stage (28.72%), membrane modules (42.5%), and reject stream (28.5%). Although the overall system efficiency remained low (maximum of 1.39%), the results highlight substantial potential for improvement through enhanced maintenance, optimized pretreatment, and exergy recovery strategies. The unit water production cost ranged from USD 0.49 at 993.6 mg/L to USD 1.87 at 3191.8 mg/L, assuming a target permeate total dissolved solids concentration of 500 mg/L. Full article

The increasing penetration of single-phase photovoltaic (PV) generation and electric vehicle (EV) charging has aggravated phase current asymmetry in low-voltage distribution networks. In contrast to voltage-oriented control strategies, this work focuses directly on mitigating current imbalance at the point of common coupling (PCC). A coordinated control framework based on multi-agent deep deterministic policy gradient (MADDPG) is developed to regulate distributed battery energy storage systems (BESS). The control objective is formulated in terms of the Current Unbalance Factor (IUF), derived from symmetrical component theory. A linearized DistFlow model is embedded in the learning environment to preserve physical consistency while maintaining computational tractability. Device-level constraints, including state-of-charge limits and ramp-rate bounds, are enforced through action projection, whereas network security limits are incorporated via reward penalties. Case studies on a modified residential feeder indicate that coordinated BESS control reduces the peak IUF from 2.75% to 2.50% under the studied operating condition. The maximum dominant-phase current decreases from 125 A to 110 A. The performance is close to that of centralized convex optimization while enabling decentralized real-time execution after offline training. These results suggest that multi-agent reinforcement learning can serve as a feasible alternative for phase imbalance mitigation in distribution networks with high renewable penetration. Full article

As global energy demands rise and concerns about environmental sustainability intensify, renewable energy sources like solar photovoltaic (PV) systems have gained significant attention. An integrated approach is proposed, leveraging spatial analysis using Helioscope, a 3D solar design tool, incorporated with Geographic Information System (GIS) data. This study conducted a spatial analysis of Cape Peninsula University of Technology (CPUT) Bellville campus’s potential for renewable energy, and the results are promising. The research indicated that the campus has enough rooftop space to optimally place solar panels with a capacity of 7.8 megawatts, which is more than the campus’s total energy needs of 6.3 megawatts. This study identified 13,249 modules that can be optimally placed to achieve this. Full article

The market for coloured photovoltaic modules offers a key opportunity for building-integrated photovoltaics (BIPV), as it enables more aesthetic and seamless integration into architecture. This study investigates how three common BIPV colours—anthracite, green, and terracotta—affect the performance of a BIPV ventilated façade. It presents a year-long field comparison, including thermal modelling and residual spectral loss estimation, of three screen-printed coloured BIPV strings installed on an east-facing ventilated façade, at the CIEMAT research centre in Madrid, Spain. Although anthracite modules exhibit the highest efficiency under standard test conditions (STC), green modules achieve the best performance ratio (PR) due to their lower spectral and thermal impacts. Results indicate that system design factors—such as façade orientation, module positioning and rear ventilation—significantly influence thermal and electrical performance. In particular, changes in solar spectral irradiance can have a strong impact on the performance of coloured modules, mainly due to their distinct spectral reflectance characteristics. This effect is especially relevant for reddish modules mounted on east- and west-facing façades, which, on clear days, receive sunlight with spectra shifted toward the near-infrared (NIR) region compared with midday conditions, which are closer to the standard AM1.5G solar spectrum. Prior optical characterisation, particularly spectral reflectance measurements, is therefore essential to accurately assess and predict the performance of coloured modules under real operating conditions. Full article

With the rapid development of the photovoltaic industry, the issue of high-value conversion and utilization of end-of-life photovoltaic modules emerges. This study proposes using them in silicon-air batteries and designs a one-step pretreatment process to obtain two types of anode materials: AB@Si and TC@Si. Additionally, to enhance the electrochemical performance of retired crystalline silicon from PV modules as anodes for silicon-air batteries and improve their mass conversion efficiency, this study introduces Triton X-100 into the KOH electrolyte to inhibit chemical corrosion of the anodes and investigates the mechanism of action of Triton X-100. The results indicate that the surfaces of AB@Si and TC@Si exhibit a pyramidal structure, demonstrating excellent passivation resistance when used in silicon-air batteries, with maximum mass conversion efficiencies of 3.5% and 1.83%, respectively. Under the influence of Triton X-100, the maximum mass conversion efficiencies reach 6.39% and 3.09%, respectively. Polarization curves and mass loss under non-current conditions indicate that Triton X-100 primarily affects the chemical corrosion process of the silicon anode, while its impact on electrochemical corrosion is negligible. Results from contact angle measurements and adsorption energy calculations indicate that Triton X-100 adsorbs onto the silicon surface via benzene ring groups or OH groups, reducing hydrophilicity and delaying the self-corrosion process of silicon, thereby improving the battery′s discharge lifespan and mass conversion efficiency. Full article

Background/Objectives: This paper presents a bi-objective mathematical programming model for sizing hybrid renewable energy systems (HRESs) in isolated mini-grids and compares the optimized solutions with the first-year operation of a real system deployed in La Miel, Panama. Methods: The model minimizes the levelized cost of energy (LCOE) and the expected energy not served (EENS), using an $\epsilon$-constraint approach over a one-year time series (8760 h) of measured demand. For La Miel, the annual demand is 132,578 kWh with a peak load of 28.4 kW. Four configurations are evaluated: (A) diesel-only, (B) photovoltaic (PV)+diesel, (C) PV+batteries, and (D) PV+diesel+batteries. The results are compared with the installed plant (E) including 107 kWp PV, a 40 kVA diesel generator, and lead-acid battery banks (4560 Ah nominal capacity). Results: The optimized hybrid configuration (D) achieves near-zero EENS with an LCOE of 41.4–41.8 cts-USD/kWh, compared to 56.6 cts-USD/kWh for diesel-only. The real system achieves EENS = 0% with LCOE = 48.3 cts-USD/kWh and an annual renewable penetration of 53.2% (up to 68.4% in March 2020), while the optimized case reaches 79.6% on average (up to 95.3% in March). Conclusions: The distinctive contribution of the study is the direct ex ante versus ex post comparison between optimized planning outcomes and the documented first-year operation of the installed system. Operational constraints observed on site (e.g., minimum battery SoC of 60% to comply with voltage quality limits) and demand growth explain part of the LCOE gap between optimized and real performance. Full article

Hydro-wind-solar hybrid power systems have become a mainstream configuration for new-type power systems. However, the high proportion of power-electronics-interfaced generation alters system inertia and damping characteristics, leading to complex high-dimensional frequency dynamics and severe stability challenges. This paper investigates the frequency response mechanism and Hopf bifurcation characteristics of the aggregated frequency response model for hydro-wind-solar hybrid power systems. First, primary frequency response models for hydropower, wind power, and photovoltaic (PV) generation are established under a small-signal analysis framework. On this basis, a tenth-order nonlinear dynamic model of the integrated system is constructed by considering hydraulic nonlinearities, virtual inertia control of wind power, and reserve-based frequency regulation of PV systems. Then, Hopf bifurcation theory is applied to analyze stability and oscillatory instability mechanisms of the high-dimensional system. The bifurcation conditions are derived via high-dimensional Jacobian matrix analysis and Routh-Hurwitz criterion, with supplementary normal form calculation and first Lyapunov coefficient derivation to identify the supercritical/subcritical nature of the bifurcation. Finally, numerical simulations under both small and large disturbances validate the theoretical analysis. The results demonstrate that variations in key control parameters may induce Hopf bifurcation, leading the high-dimensional system from a stable equilibrium to sustained low-frequency oscillations. The findings provide insights and practical guidance for stable operation and parameter tuning of hydro-wind-solar hybrid power systems. Full article

A novel transition-metal-free alkyne–thiol click polymerization with 100% atom economy is reported. Using ^tBuOLi as a catalyst at 80 °C, the polymerization efficiently yields poly(vinyl sulfide)s (PVSs) with molecular weights up to 11,800 g/mol and yields up to 91%. These sulfur-rich polymers exhibit high thermal stability (T_d up to 293 °C) and high refractive indices (1.8375–1.6383) across the visible range. By integrating abundant sulfur coordination sites with aggregation-induced emission (AIE) properties, the PVS aggregates serve as high-performance fluorescent chemosensors. The sensor enables exclusive, sensitive trace detection of Pd²⁺ and Au³⁺ with remarkable anti-interference capability and pH robustness (pH 1–7). Notably, an ultrafast response (1–2 min) for Pd²⁺ is achieved, with limits of detection (LOD) reaching 7.11 × 10⁻⁷ M for Pd²⁺ and 1.06 × 10⁻⁶ M for Au³⁺, and corresponding limits of quantification (LOQ) reaching 2.37 × 10⁻⁶ M and 3.53 × 10⁻⁶ M, respectively. This methodology offers a sustainable route to heteroatom-rich macromolecules for next-generation optical engineering and environmental monitoring. Full article

To address the challenges of strong volatility, pronounced non-stationarity, and the inability of single models to simultaneously capture local dynamics and global dependencies in photovoltaic (PV) power series under complex weather conditions, this study proposes a short-term PV power forecasting framework that integrates weather-based clustering, signal decomposition, parameter optimization, and hybrid neural networks. First, a density-based clustering algorithm, namely Density-Based Spatial Clustering of Applications with Noise (DBSCAN), is employed to partition historical samples into distinct weather regimes, thereby mitigating the impact of heterogeneous meteorological conditions on model stability. Second, to handle the strong non-stationarity of PV power series, Variational Mode Decomposition (VMD) is introduced to decompose the original signal into multiple intrinsic components. The Crested Porcupine Optimizer (CPO) is further utilized to adaptively optimize key VMD parameters, including the number of modes and the penalty factor, thereby improving decomposition quality. Finally, a hybrid LSTM–Transformer forecasting model is constructed to jointly capture local temporal dynamics and long-range dependencies. The Newton–Raphson-Based Optimizer (NRBO) is employed to optimize critical hyperparameters, including the learning rate, regularization coefficient, and the number of hidden units, thereby enhancing model performance. The proposed method is validated using real-world data from a PV power station in Alice Springs, Australia. Experimental results demonstrate that, compared with the LSTM–Transformer baseline, the proposed model achieves reductions in RMSE of 0.086, 0.082, and 0.097 kW, and reductions in MAE of 0.062, 0.082, and 0.081 kW under clear-sky, cloudy, and rainy/snowy conditions, respectively. The corresponding R² values reach 0.993, 0.968, and 0.958. These results indicate that the proposed framework exhibits strong predictive performance across different weather scenarios and provides a reliable reference for short-term PV power forecasting and grid dispatching decisions. Full article

This study investigates the integration of categorical inverter alarm data into data-driven frameworks for photovoltaic (PV) system monitoring. While most existing approaches rely exclusively on continuous SCADA measurements, the potential of categorical operational data remains largely unexplored. In this work, categorical alarm signals are incorporated into power forecasting to enable anomaly detection. The proposed approach is evaluated on a large-scale real-world dataset comprising multiple PV plants and more than 100 inverters, representing over 1000 inverter-years of operation. The four most popular time series forecasting models, including Multi-Layer Perceptron, Long Short-Term Memory, Extreme Gradient Boosting, and Mamba, are used to estimate power output from continuous inputs, while categorical variables are integrated using one-hot encoding and entity embeddings. Anomaly detection is performed by analyzing residuals between predicted and measured power output. The results show that categorical alarm data contain relevant operational information and can be effectively incorporated into forecasting-based monitoring frameworks. However, their impact on predictive performance varies depending on the encoding strategy and model choice, highlighting important trade-offs between model complexity and feature representation. By providing a systematic evaluation of categorical data integration across a large, diverse dataset, this work addresses a gap in the literature and establishes a benchmark for future research on hybrid continuous–categorical approaches for PV inverter monitoring. Full article