Search Results (1,826)

Although offshore photovoltaic (PV) systems have attracted increasing interest as a solution to land-use limitations, the influence of offshore-specific dynamic environmental conditions on PV performance remains insufficiently understood. Existing studies have primarily focused on static operating conditions or general energy yield comparisons between bifacial and monofacial PV technologies, while the combined effects of wave-induced motion, module tilt-angle, and sea-surface albedo on offshore PV performance have received limited attention. To address this gap, this study develops a parametric simulation framework to investigate the sensitivity of offshore bifacial photovoltaic (biPV) and monofacial photovoltaic (moPV) arrays to key offshore environmental and operational parameters. Given the scarcity of long-term operational data for offshore PV installations, a hypothetical offshore plant located in the Yellow Sea, China, is considered using real meteorological inputs. In this study, 16 kWp offshore biPV and moPV arrays are modeled and compared in terms of their performance through three case studies examining wave motions, tilt-angle variations, and surface albedo effects. Performance metrics such as maximum irradiance, total energy yield, energy yield losses, wave-induced power loss, and bifacial gain (BG) are analyzed and compared. The findings indicate that increasing wave motion diminishes the total energy yield due to higher tilt-angle fluctuations. Nevertheless, the biPV array regularly outperforms the moPV array because of the effect of the rear-side irradiance. The tilt angle analysis reveals a trade-off between energy yield and BG, with BG increasing from 0.05% to over 10% as the tilt angle increases from 10° to 45°. Higher surface albedo further enhances bifacial performance, increasing BG from 4.5% to 17.8% for albedo values of 0.05 and 0.25, respectively. Full article

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

Multimodal 3D object detection benefits from the complementary nature of camera images and LiDAR point clouds. However, existing voxel–pixel fusion methods typically rely on relatively coarse cross-modal interactions, which limit fine-grained structural modeling and degrade performance on small safety-critical objects. To address this issue, we propose ETA-SDQF, an edge-texture-aware semantic dual-query fusion framework designed to enhance 3D perception of vehicles, cyclists, and pedestrians. The proposed method first introduces an edge-texture-aware image backbone (ETAIB) based on the discrete wavelet transform (DWT), which improves the representation of multi-scale fine-grained image features. Then, we design a dual-query-guided attention fusion (DQGAF) module, which leverages deformable attention to adaptively aggregate voxel-aligned multi-scale image features under joint semantic and edge-texture guidance. Finally, we adopt a hybrid 3D feature learning strategy inspired by PV-RCNN, combining voxel-based feature learning with PointNet-style feature abstraction for processing fused features. This design improves the utilization of voxel features enriched with image semantics, thereby facilitating more reliable 3D object proposal generation. Experimental results on the KITTI dataset demonstrate that the proposed framework achieves better performance compared to existing baseline methods. It consistently improves pedestrian and cyclist detection, while maintaining competitive performance on car detection across different difficulty levels, showing potential benefits on challenging KITTI samples. Full article

The increasing penetration of distributed renewable energy sources and electric vehicles (EVs) introduces significant operational challenges for residential energy management systems (HEMS), including stochastic renewable generation, uncertain load demand, device coupling, and physical safety constraints. This paper proposes a digital-twin-aided safe multi-agent reinforcement learning framework for coordinated energy management in renewable-integrated residential systems. The proposed approach models the battery energy storage system and the EV as independent agents and employs a multi-agent soft actor–critic (MASAC) algorithm with a centralised critic to capture the interactions among distributed energy resources. To improve decision quality under uncertainty, a digital twin module is developed to maintain a virtual representation of the residential energy system, synchronise operational states, update degradation-sensitive parameters, and generate short-term predictive information on photovoltaic (PV) generation and household load. The updated digital twin states and forecasts are incorporated into the observations of the reinforcement learning agents. In addition, a safety projection layer is incorporated to improve operational feasibility during both training and deployment. The environment considers realistic residential characteristics, including time-of-use electricity prices, battery degradation, EV mobility patterns, and grid energy trading. Simulation results show that the proposed framework reduces daily energy costs compared with rule-based baselines while maintaining EV charging reliability and operational feasibility. These results highlight the potential of combining predictive information, safety-constrained action execution, and multi-agent reinforcement learning for intelligent residential energy management. Full article

This paper proposes a novel forecasting model termed U-Client, which integrates parallel cross-temporal and cross-variable attention branches with an adaptive gated fusion mechanism for ultra-short-term photovoltaic (PV) power forecasting. First, meteorological features are screened using the Pearson correlation coefficient to reduce input dimensionality. Second, parallel cross-temporal and cross-variable attention branches are designed to extract long-range temporal trends and nonlinear interaction features among meteorological variables, respectively. Third, a gating mechanism is introduced to adaptively fuse the two types of features based on input conditions. Finally, a linear module is combined to generate the final forecasting results. Experiments based on measured datasets from a photovoltaic station in Ningxia, China, demonstrate that the proposed U-Client model outperforms classical models such as Long Short-Term Memory (LSTM) and Informer across all evaluation metrics for 1–4 step forecasting tasks. Ablation studies and statistical significance tests further verify the effectiveness of each component. The proposed model provides reliable support for ultra-short-term power dispatching in new-type power systems. Full article

Against the background of climate change intensifying extreme hail events, the photovoltaic module industry faces a critical trade-off between improving hail resistance and maintaining environmental sustainability. This study establishes an emergy–life cycle coupling assessment framework to systematically evaluate the environmental sustainability of six typical hail resistance enhancement designs across four hail risk scenarios in China. Five hierarchical hypotheses are proposed and validated through quantitative analysis. The optimal design point occurs at 30 mm hail resistance using 3.2 mm tempered glass, achieving a minimum unit environmental impact per impact resistance UEIC of 9.63 × 10¹² sej/mm. The ranking divergence index SDR between coupled emergy–LCA and conventional LCA methods is 0.267, with ecosystem service dependence ESD reaching 0.241 for composite backsheet designs, revealing natural capital overlooked by traditional methods. A complete ranking reversal occurs at a threshold hail frequency of 1.3 events per year, above which the 3.2 mm glass design outperforms standard modules with life cycle emergy input LCEA of 3.20 × 10¹⁴ sej versus 3.41 × 10¹⁴ sej under high-risk scenarios. Material type dominates environmental impact over structural parameters by a factor of 2.32, with recycled aluminum frames reducing ELCI by 12.4%. The dual-optimum design is identified as the 3.2 mm tempered glass scheme, achieving a combined sustainability score CSS of 0.782 and emergy yield ratio EYR of 3.86, outperforming the industry average of 3.61. Multi-objective optimization using NSGA-II yields a Pareto front of 12 non-dominated solutions, with the 3.2 mm glass design maintaining Pareto optimal status in 72% of Monte Carlo iterations. This research provides a quantitative decision-making framework recommending standard modules for regions below one annual hail event, the 3.2 mm glass design for regions between one and four annual events, and steel frame combinations above four annual events, demonstrating that moderate enhancement achieves the optimal balance between hail protection and environmental sustainability. Full article

Urban heat stress is intensifying under climate change, particularly in outdoor public spaces where conventional mechanical cooling is impractical. This study develops a climate-driven, system-level numerical framework to evaluate the pre-deployment feasibility of modular, solar-powered spray cooling canopies across 110 cities in Türkiye. Hourly Typical Meteorological Year (TMYx) weather files, representing a single typical year constructed from 2009 to 2023 source data, are used to estimate photovoltaic (PV) energy yield, electrical load, feasible misting duration, water demand, and PV-to-load autonomy under summer daytime conditions. The misting operation is governed by a rule-based adaptive control strategy based on air temperature, relative humidity, and plane-of-array irradiance. To support transferable comparison, the cities are classified into six summer climate-archetype zones using k-means clustering of standardized climate variables, including temperature, humidity, irradiance, wind speed, and summer precipitation. Results show that evaporative cooling feasibility is governed primarily by humidity rather than temperature alone. Hot–Dry Inland cities exhibit the longest mean misting duration (501.90 h) and highest water demand (30,152 L per module), but the lowest PV-to-load autonomy ratio (1.55) because of high pump-driven electrical demand. In contrast, Humid Black Sea cities show minimal misting duration (11.43 h) and water use (465 L per module), but the highest autonomy ratio (39.68) due to very limited system activation. Thus, high autonomy does not necessarily indicate high cooling usefulness. The proposed framework provides a reproducible screening tool for identifying where PV-powered spray cooling canopies are climatically suitable, where water and PV sizing become limiting, and where alternative outdoor heat-mitigation strategies may be more appropriate. Full article

The increasing frequency of extreme weather events, particularly hailstorms, driven by climate change, poses growing threats to the resilience, environmental sustainability, and long-term performance of photovoltaic (PV) systems. This study evaluates the environmental impacts of a 12 kWp rooftop PV installation in Brescia, northern Italy, through a comparative Life Cycle Assessment (LCA) of three system configurations: a standard unprotected system (Scenario A), one equipped with a retractable polycarbonate hail-protection panel with automated weather-sensor activation (Scenario B), and one using thicker reinforced front-glass modules (Scenario C). The analysis follows a cradle-to-gate plus operational maintenance phase (30-year horizon, excluding end-of-life) system boundary and employs the ReCiPe 2016 Midpoint (H) methodology across 18 environmental impact categories. A novel integration of the Social Discount Rate (SDR) to the LCA framework—constituting a Discounted LCA (D-LCA)—incorporates both temporal discounting and risk dimensions into the environmental evaluation. A structured PESTEL-based risk taxonomy is applied to derive scenario-specific SDRs, with the Environmental risk category as the key differentiator between configurations. The static LCA identifies Scenario A as the lowest-impact option, while the D-LCA framework reverses this ranking: Scenario C achieves the highest Net Present Value of Emissions, followed by Scenario A. A negative NPV-E for Scenario B reflects the temporal cost of a large, front-loaded construction debt rather than absolute environmental harm. D-LCA framework should be interpreted as a complement to the full 18-category static LCIA profile, not a replacement. These results demonstrate that risk-informed D-LCA provides a more policy-relevant environmental sustainability assessment than static LCA for long-lived energy infrastructure subject to climate-driven operational risks. Full article

The Arabidopsis thaliana Heat Shock Protein-Related (AtHSPR) gene participates in plant growth and abiotic stress tolerance, while its role in biotic stress resistance remains unclear. Here, we report that the athspr mutant is sensitive to Pseudomonas syringae pv. tomato (Pst) DC3000, whereas over-expression of AtHSPR enhances the defense of Arabidopsis against the pathogen. AtHSPR expression was induced by treatment with Pst DC3000, flg22, or salicylic acid (SA). Transcriptome analysis showed that mutation of AtHSPR changed the expression patterns of genes associated with defense response, oxidation–reduction, and SA responses, as well as transcription factors. The biochemical evidence revealed that AtHSPR interacted with Thimet Oligopeptidase 1 (TOP1), which modulated the SA-mediated immune response. Co-expression of AtHSPR and TOP1 showed that the TOP1 protein, normally located in the chloroplasts, gathered around the nucleus in response to a pathogen. After pathogen treatment, dynamic tubular projections (stromules) were present, extending from the chloroplasts toward the nucleus, and TOP1 was observed in the nucleus, together with AtHSPR. The top1athspr double mutant had lower SA levels and was more sensitive to pathogens than the top1 and athspr single mutants. Taken together, our results demonstrated that the interaction between AtHSPR and TOP1 plays a positive role in SA-mediated plant resistance against Pst DC3000. Full article

Agrivoltaic systems, which enable simultaneous crop production and solar photovoltaic (PV) electricity generation on the same land, can support climate mitigation, food security, and rural development. Leguminous crops like beans are globally important, yet there is limited performance studies on diverse agrivoltaic trials. This limits appropriate policy guidance. To overcome these limitations, this study assessed organic green bush bean performance under thirteen PV configurations with varying transparency and spectral properties, comparing both agricultural outcomes against national yields and policy standards. The results in vegetative metrics indicated that blue-spectrum thin-film and intermediate-transparency c-Si modules supported growth near German productivity thresholds. Although no agrivoltaic system matched national average yields, combining crop and energy revenues revealed substantial benefits: the 44%—transparent c-Si configuration generated 340% more total revenue than traditional farming, and the blue 70%—transparent thin-film system achieved 94% of national yield but 164% of conventional farm revenue per acre. Electricity generation gains outweighed modest crop reductions, highlighting strong synergies between food and energy. The results of this study highlights the potential of agrivoltaic systems to enhance land-use efficiency, support renewable energy expansion, and improve rural economic resilience, while underscoring the need for multi-year trials and site-specific controls to validate long-term sustainability outcomes. Full article

Accurate plant-level photovoltaic (PV) power estimation is important for performance monitoring, model benchmarking, and grid-integration studies. In bifacial horizontal single-axis tracking (HSAT) systems, this task is complicated by the coupled effects of front-side irradiance, rear-side irradiance, tracker position, and module temperature. This study proposes an algorithmic framework for same-time-step AC power estimation in a bifacial HSAT PV plant using field measurements of irradiance, tracker angle, module temperature, and inverter active power. The framework is not intended as an operational forecasting model because future irradiance and weather conditions are not predicted; instead, it evaluates how compact physics-based structure, interpretable nonlinear learning, and ensemble learning estimate measured AC power under nominal operating conditions. An empirical rear-to-front irradiance relationship was derived using solar-elevation bins and incorporated into a compact physics-based benchmark. This benchmark was compared with an additive Explainable Boosting Machine (EBM) and a Random Forest (RF) on a common test subset of 3916 observations. The physics-based model achieved an RMSE of 19.6 kW, an R² of 0.72, and an NRMSE of 0.38. The EBM improved these values to 17.09 kW, 0.786, and 0.334, respectively, while the RF achieved 15.96 kW, 0.814, and 0.312. Chronological validation showed weaker and more variable performance than randomized validation, indicating that temporal generalization remains challenging. Overall, the results support the use of interpretable PV-domain-guided learning as a transparent intermediate approach between compact physics-based modeling and more flexible ensemble regression. Full article

The exponential increase in photovoltaic panel (PV) waste highlights the urgent need to develop efficient and sustainable recycling processes. It is estimated that by 2030, 8 million tons of PV modules will reach their end-of-life stage, posing a significant environmental challenge and requiring the development of green technologies for resource recovery. This study assessed the performance of imidazolium-based ionic liquids (ILs) as “designer solvents” for the selective leaching of copper and silver from disused PV panels. Specifically, four quaternary imidazolium salts were evaluated: [Bmim]HSO₄, [Emim]HSO₄, [Bmim]Cl, and [Emim]Cl. Leaching tests were conducted on silicon wafers containing 0.28% Ag and 0.19% Cu under varying temperatures (25, 50, and 80 °C), IL concentrations (20% and 60% v/v), and hydrogen peroxide (H₂O₂) dosages (0% and 3% v/v) as an oxidizing agent. The results identified [Bmim]HSO₄ as the most effective leaching agent. The system achieved a maximum copper extraction of 96.70% at 60% v/v concentration and 80 °C. For silver, the highest extraction of 45.13% was obtained using [Bmim]HSO₄ at 20% v/v and 80 °C. The addition of H₂O₂ was crucial, demonstrating a clear synergistic effect with the imidazolium-based ILs by promoting oxidative dissolution. These findings confirm that imidazolium-based ionic liquids represent a promising and environmentally friendly alternative for the recovery of high-value metals in the circular economy of photovoltaic recycling. Full article

Accurate short-term photovoltaic (PV) power forecasting is important for grid dispatch and PV integration. However, PV power under complex weather conditions has strong fluctuation, non-stationarity, and multi-frequency coupling. These features make accurate forecasting difficult. This paper proposes a short-term PV power forecasting model named WOA-MS-TFformer-BiTCN. The model first constructs similar-day samples through daily feature extraction, Gaussian mixture clustering, and physical consistency correction. Then, the whale optimization algorithm (WOA) optimizes the key parameters of variational mode decomposition (VMD) and the forecasting network. VMD decomposes the original power sequence into modes with different frequency features. The multi-scale frequency-domain perception (MS) module extracts multi-scale frequency-domain features from these modes. TFformer captures global temporal relationships, while BiTCN models local dynamic changes. Experiments are conducted using PV data from Gansu, China. The Alice Springs PV dataset is used for cross-regional validation. The results show that the proposed model achieves the lowest MAE, RMSE and the highest R² in all 16 season-weather cases, corresponding to four seasons and four weather types, for the 15 min-ahead task. Its average MAE, RMSE and the highest R² are 0.5439, 0.7910, and 0.99898, respectively. The model also performs best on rainy samples from the Alice Springs dataset. In addition, prediction intervals based on validation-set residual quantiles provide uncertainty information for point forecasts. The results show that the proposed method improves the accuracy and stability of short-term PV power forecasting under complex weather conditions. Full article

The rapid expansion of solar photovoltaic (PV) deployment has led to increasing concerns regarding end-of-life module management and the sustainability of material supply chains, where waste volumes are projected to reach 3.3–5.6 million tons by 2045. This study evaluates the environmental and economic impact of advanced photovoltaic recycling in Germany, focusing on high-value material recovery from crystalline silicon modules. A Full Recovery of End-of-Life Photovoltaics (FRELP) pathway is developed, integrating light-pulse delamination and molten salt etching, and a comparative life cycle assessment and economic assessment framework is applied. The results indicate that advanced recycling achieves high recovery rates for silicon, silver, aluminum, copper and low-iron glass, yielding around €1174.88 per ton of panels recycled. Economic analysis shows that manufacturing PV modules from recycled materials reduces costs by approximately 60–77% compared to virgin material production, mainly due to avoided energy-intensive upstream processes. From an environmental perspective, the recycling-based pathway yields net benefits across impact categories, as avoided impacts from primary material extraction outweigh additional burdens associated with recycling. Overall, PV recycling in Europe is shown to be environmentally and economically favorable; however, technological maturity and policy constraints remain key barriers to large-scale implementation and a holistic overall recycling process, indicating the need for targeted policy support. Full article

Early identification of thermal and electrical anomalies in grid-connected photovoltaic (PV) systems is becoming increasingly important to reduce energy losses, limit power quality (PQ) degradation, and avoid excessive operating stress on power electronic converters. Conventional electrical monitoring methods can provide overall performance information, but they are generally unable to detect and localize early-stage defects occurring at module or cell level. In this context, the present study proposes an integrated diagnostic framework that combines non-destructive infrared thermography (IRT) with advanced electrical signal processing techniques for PV condition monitoring. The proposed approach correlates thermographic information, capable of revealing defects such as hotspots, cell cracks, and bypass diode failures, with high-frequency electrical signal analysis based on frequency-domain and time–frequency methods, together with deep learning-driven thermographic segmentation. By associating thermal acquisitions with electrical PQ indicators, the framework enables the early detection of physical defects linked to inefficient Maximum Power Point Tracking (MPPT) operation and progressive degradation of PV system performance. The methodology was experimentally validated on a grid-connected photovoltaic installation under different fault conditions, including hotspots, bypass diode anomalies, and localized overheating effects, demonstrating the potential of the proposed approach for predictive maintenance and intelligent PV monitoring applications. The obtained results indicate that the proposed framework improves the reliability of photovoltaic fault detection by combining thermographic inspection with advanced electrical signal analysis and AI-based defect interpretation, thus supporting predictive maintenance strategies in smart PV infrastructures. The proposed approach demonstrates image segmentation capabilities, as evidenced by a precision (PA) of 96.88%, a mean IoU (mIoU) of 77.83% and a macro F1-score of 87.47%. The proposed framework maintained reduced computational requirements compatible with real-time monitoring applications. Full article