Outdoor environments expose materials to intense and prolonged ultraviolet (UV) radiation, fluctuating temperatures, moisture, and airborne contaminants. This paper reviews the preparation methods of UV-resistant and transparent coatings and their main strengths and limitations. The advantages and disadvantages of preparation methods are summarized. The final properties of coatings depend on several factors, such as the type of metal nanoparticles, and preparation methods. The potential applications of prepared coatings in various industries, such as solar panels, architectural glass, and automotive industry, are highlighted.

ABSTRACT This paper presents numerical and experimental evaluations of a compact linear Fresnel reflector concentrated solar photovoltaic (CSPV) device. The design method of the CSPV device is provided. To evaluate the optical performance of the CSPV device, solar concentration process of the CSPV device is simulated using ray tracing method. The results show that the CSPV device has relatively high solar concentration uniformity on PV panels. An experimental prototype is built to conduct the practical optical and PV performance evaluations of the CSPV device. The solar concentration performance evaluation results of the CSPV device reveal relatively good agreement between the simulation and experimental results. The sun tracking error effect analysis results show that the relative optical efficiency of the CSPV device can be kept at 88.9% when the sun tracking error increases to 0.2°, revealing relatively acceptable sun tracking error adaptability of the CSPV device. The PV performance test results reveal that the photo-electric conversion efficiencies of monocrystalline silicon cell (60.0 mm × 100.0 mm) and PV module (60.0 mm × 800.0 mm) installed in the experimental prototype are 16.9% and 15.9%, respectively.

ABSTRACT In the context of increasing global solar Photovoltaic (PV) technology adoption for electricity generation, the performance degradation of photovoltaic panels due to the accumulation of various soil and ash types is investigated by various studies and experimental works. Eight common particulate types – coal ash, brick powder, cement, wood ash, house dust, yellow sand, construction sand, and pit sand – were tested with a set laboratory condition for three irradiance levels (900, 680, and 360 W/m2). Experimental outcome reveals that coal ash had the most severe impact, reducing PV efficiency by 36.0%, 34.0%, and 33.2%, respectively, due to its fine particle size, high absorption coefficient (1.5–2.0 cm−1), and dense surface coverage, as confirmed by microscopic and X-ray imaging. In contrast, yellow and pit sand showed less than 15% performance loss. Optical parameters such as the reflection coefficient (~0.04–0.05) and refractive index (1.3–1.6) were analyzed to quantify light transmission losses. To evaluate the relative performance, Data Envelopment Analysis (DEA) was carried out, treating each test case as a decision-making unit (DMU), with irradiance and soiling weight as inputs and Pmax, Isc, Voc as electrical outputs of PV panels. The cross-efficiency score (CES) framework is used to eliminate bias in weight selection and validate rankings. Coal ash consistently ranked lowest in DEA results, reinforcing its status as the most detrimental contaminant. This multidisciplinary approach provides an evidence-based framework for optimizing site selection, scheduling maintenance, and policy formulation for solar PV deployment in dust-prone regions, especially those near thermal power plants and civil construction work zones, enabling strong support to the sustainable PV performance enhancement.

The output power of photovoltaic modules is significantly reduced by solar irradiance shading. To address this issue, innovative strategies for mitigating shading effects have been continuously explored. In this study, detailed research on the edge dust accumulation effect of modules has been conducted. It is found that under vertical installation, when the shading ratio reaches 50%, the output power of full-cell modules decreases by 42%, while that of half-cell modules drops by only 27%. Moreover, when the shading ratio reaches 100%, the output power of full-cell modules declines by nearly 99%. In contrast, half-cell modules are still able to maintain nearly 50% of their output power. These results demonstrate that half-cell modules exhibit significantly better resistance to shading compared to full-cell modules. On the other hand, under a horizontal layout, power degradation for both full-cell and half-cell modules is observed to be approximately 16% when the shading ratio is 25%, and around 36% when the coverage reaches 50%. Experimental results further revealed that shading under horizontal orientation leads to a multi-peak power output profile, which poses a risk of the PV inverter being trapped in local maxima. Overall, half-cell modules demonstrated better resistance to dust-induced shading under both layouts. Based on these findings, novel module design schemes are proposed to enhance resistance to dust accumulation effects. The proposed method can effectively reduce power losses caused by edge dust-induced shading and improve the annual power generation of PV modules, thereby offering technical support for effectively enhancing the operational stability of PV power generation systems.

This study aims to analyze the insulation resistance value of a 555 WP monocrystalline solar module under the influence of solar irradiation through outdoor testing and insulation assessment. The primary focus is to understand the impact of solar exposure on insulation durability, a crucial factor in the long-term performance and safety of solar modules. The testing method follows the SNI/IEC 61215 standard, involving initial and final measurements using a calibrated insulation tester at the Energy Conversion Laboratory, BRIN. The results indicate a 19.54% degradation in insulation resistance after 15 days of solar exposure. Despite this decline, the module still meets the IEC 61215 criteria for insulation resistance, maintaining a resistance value above 40 MΩ for a module with a surface area of 2.583 m². A comparison of initial and final data reveals a decrease in resistance from 3.470 GΩ in the initial test to 2.792 GΩ in the final test. This reduction underscores the importance of paying closer attention to maintenance and routine testing to ensure the module's long-term reliability. This study provides new empirical evidence on the dynamics of short-term insulation degradation under tropical solar conditions, a topic that has been rarely quantified in field-based PV reliability research. In addition, this study makes significant contributions to the development of industry standards that aim to enhance the reliability of solar modules and manage renewable energy systems.

SnO2 nanoparticles (NPs) solutions are considered a highly efficientinks for fabricating electron transport layers in state-of-the-art solution-processed perovskite solar cells (PSCs). However, SnO2 colloids exhibit thermodynamic instability in aqueous solution due to strong van der Waals attractions between nanoparticles, often leading to aggregation and precipitation. Here, a phosphate-buffered synthesis strategy is reported that effectively stabilizes SnO2 colloids. The phosphate buffer maintains a stable pH during synthesis, dynamically regulating the electrostatic repulsion between nanoparticles to suppress aggregation and promote homogeneous dispersion. This method enables precise control over surface hydroxyl groups and oxygen vacancies in the resulting SnO2 films, facilitating efficient electron transport and reducing interfacial recombination. As a result, PSCs achieve a high power convertion efficiency (PCE) of 26.40% while demonstrating exceptional operational stability. The encapsulated device maintains 99%, 84%, and 95% of their initial efficiency under ISOS-L-1, ISOS-L-2, and ISOS-O-1 protocols, respectively. Furthermore, a perovskite solar module (5cm × 5cm) with an active area of 12.6cm2 delivers an impressive PCE of 23.11%. These results highlight the scalability and practical viability of the strategy for developing large-area, high-performance photovoltaic modules.

Abstract An increasing number of countries are committing to carbon neutrality and plan a massive rollout of solar PV to meet this goal. Owing to its seasonal production patterns, solar PV electricity will become less valuable during summer and more valuable during winter, when electricity prices increase accordingly. Consequently, optimizing PV plants to balance seasonal production variability will become increasingly important. This can be achieved via deploying PV in locations with higher irradiation in winter and/or by changing the angle of PV panels. Current solar PV support policies overlook production seasonality, risking a lock-in of projects optimized for annual production, which is dominated by irradiation during the summer. Here, we analyze household-scale rooftop PV in Switzerland, applying a cash flow model to evaluate policies designed to boost winter solar production. Our findings show that seasonally differentiated feed-in tariffs increase the economic incentives for winter electricity generation in areas with the highest winter solar potential. By contrast, investment subsidies, which are resource agnostic, prove less effective in directing PV to high-resource locations. This analysis underscores the importance of regionally tailored policies to promote winter-adjusted PV plants, an approach transferable to regions with seasonal solar variation, and large geographic altitude differences.

This study explores the impact of synthetic data, both physically based and generatively created, on deep learning analytics for earth observation (EO), focusing on the detection of photovoltaic panels. A YOLOv8 object detection model was trained using a publicly available, multi-resolution very high resolution (VHR) EO dataset (0.8 m, 0.3 m, and 0.1 m), comprising 3716 images from various locations in Jiangsu Province, China. Three benchmarks were established using only real EO data. Subsequent experiments evaluated how the inclusion of synthetic data, in varying types and quantities, influenced the model’s ability to detect photovoltaic panels in VHR imagery. Physically based synthetic images were generated using the Unity engine, which allowed the generation of a wide range of realistic scenes by varying scene parameters automatically. This approach produced not only realistic RGB images but also semantic segmentation maps and pixel-accurate masks identifying photovoltaic panel locations. Generative synthetic data were created using diffusion-based models (DALL·E 3 and Stable Diffusion XL), guided by prompts to simulate satellite-like imagery containing solar panels. All synthetic images were manually reviewed, and corresponding annotations were ensured to be consistent with the real dataset. Integrating synthetic with real data generally improved model performance, with the best results achieved when both data types were combined. Performance gains were dependent on data distribution and volume, with the most significant improvements observed when synthetic data were used to meet the YOLOv8-recommended minimum of 1500 images per class. In this setting, combining real data with both physically based and generative synthetic data yielded improvements of 1.7% in precision, 3.9% in recall, 2.3% in mAP@50, and 3.3% in mAP@95 compared to training with real data alone. The study also emphasizes the importance of carefully managing the inclusion of synthetic data in training and validation phases to avoid overfitting to synthetic features, with the goal of enhancing generalization to real-world data. Additionally, a pre-training experiment using only synthetic data, followed by fine-tuning with real images, demonstrated improved early-stage training performance, particularly during the first five epochs, highlighting potential benefits in computationally constrained environments.

Accurate forecasting of solar power in utility-scale photovoltaic (USPV) systems is critical for grid stability but remains challenging due to meteorological variability and the large spatial scale of these systems. However, the choice of sliding window size in time-series forecasting remains underexplored. This study introduces a deep learning-based forecasting framework that systematically evaluates the impact of sliding window size on forecasting accuracy using multivariate time-series data. The data collected from a 25 MWac USPV system in Malaysia between August 2022 and April 2023, comprises 5-minute interval measurements of solar irradiance, module temperature and solar power output. Multiple deep learning (DL) models, namely LSTM, CNN and GRU across window sizes ranging from 12 to 288 steps and forecasting horizons of 1 to 12 hours were investigated. Results show that a 144-step window consistently improves accuracy over conventional one-step input methods, with LSTM outperforming other models by achieving up to 23.1% RMSE reduction, 30.7% MAE reduction and a 8.6% increase in R² at 60 minutes forecasting horizon. This work emphasizes the importance of window size selection in optimizing forecasting accuracy for USPV systems and supporting renewable energy grid integration. By improving forecasting capabilities, this research is expected to provide critical insights to enhance renewable energy integration into the grid system.

Background:This study explored practical strategies to reduce the environmental impact of aesthetic and surgical clinics. It provided an overview of key areas where aesthetic medicine contributes to waste, carbon emissions, and other environmental challenges. By examining life-cycle assessments and current practices, the study provided recommendations for clinics to adopt sustainable approaches while maintaining high standards of patient care.Methods:A structured review was conducted using PubMed to identify literature on sustainability and life-cycle assessment in aesthetic dermatology, plastic surgery, and cosmetics. The search covered publications from January 1, 2010, to December 31, 2023. Of 48 initially identified studies, 26 were included after manual screening. Data on environmental impact, waste, emissions, and cost savings were extracted.Results:Several clinic operations contribute significantly to environmental impact, including energy use, transportation, personal protective equipment, surgical supplies, and waste management. Retrofitting with light-emitting diode bulbs could save 2179 kg of CO2 annually, whereas solar panels may reduce emissions by 16,023 kg. Switching to reusable surgical instruments such as tungsten carbide tools could cut emissions by up to 97% and lower costs. Sustainable transportation options also offer notable environmental and financial benefits.Conclusions:Aesthetic and surgical medicine has a substantial environmental footprint. Implementing strategies such as energy-efficient upgrades, reusable materials, and eco-friendly transportation can reduce impact while preserving care quality. These changes support broader sustainability goals and improve clinic efficiency.

ABSTRACT Photovoltaic/thermal (PV/T) systems, which combine electricity and heat generation, are crucial for maximizing solar energy utilization; however, their performance is hindered by thermal‐induced efficiency losses in high‐irradiance environments. This study addresses the challenge of optimizing PV/T systems for arid, sunny climates, where elevated temperatures reduce electrical efficiency by up to 0.5% per °C. The aim is to develop a novel, energy‐efficient PV/T system with low‐flow‐rate air cooling to enhance thermal and electrical performance while minimizing operational complexity. Using computational fluid dynamics (CFD) simulations in ANSYS Fluent, we model a PV/T system integrated with a solar air collector operating at a low airflow rate of 0.02 kg/s, compared to a conventional PV panel, across solar radiation levels ranging from 300 to 1000 W/m². Parametric analysis of airflow rates (0.01–0.05 kg/s) and tube diameters (20–50 mm) was conducted to optimize performance. The proposed system achieves a thermal efficiency of 52.88% (125.4% higher than the reference case's 23.46%), an electrical efficiency of 14.04% (1.66% improvement), and a 2.67 K reduction in panel temperature. It reduces fan power by 50%–70% (5–10 W/m²) compared to high‐flow systems, increases exergy efficiency by 71%–186%, and enhances the sustainability index by 11.4%, leading to a 10%–15% reduction in CO 2 emissions. These findings demonstrate that the low‐flow‐rate PV/T design offers a scalable and sustainable solution for high‐irradiance regions, such as the Middle East and North Africa, enabling efficient energy harvesting with reduced environmental impact.

ABSTRACT The research considers an hourly residential load demand with a daily average of 988 kWh/day and investigates possible standalone systems, including solar panels (photovoltaic [PV]), wind turbines (WTs), diesel generator (DG), biogenerator (BG), and battery bank (Bat), to provide the load demand, for a case study located in Tabuk, Saudi Arabia, where the monthly solar radiation and wind speed are 5.74 kWh/m 2 /day and 5.33 m/s, respectively. In this study, enviroeconomic factors, including inflation and discount rates, capacity shortage and load demand, CO 2 and SO 2 penalties, diesel and biomass prices are considered, while they were not considered in the previous studies in Saudi Arabia. The results show that the net present cost and cost of energy of the optimized system are $1.03 M and 0.178 $/kWh, respectively. Additionally, the prices of diesel fuel and biomass have a significant impact on the CO 2 emissions of the system, even with a 10% increase in the renewable fraction. The results of sensitivity analyses show that increasing the CO 2 emission penalty from 20 to 80 $/ton leads to a decrease in CO 2 emissions by 50%. The effect of the initial cost of WT on the configuration of the optimal system is higher than that of PV, and increasing both prices significantly leads to an increase in CO 2 emissions.

The expansion of mobile networks generates high energy demand, especially in remote areas. In Benin, the use of diesel generators in these areas leads to pollution and energy losses on site in the event of fuel shortages. To solve these problems, the integration of renewable energy sources such as solar in mobile base stations is a promising solution. The objective of this study is to optimize the integration of intermittent renewable sources, powering the base transceiver stations (BTS). This leads to the reduction of CO2 gaze emissions and the reliability of power supply to mobile networks in remote areas. The work is based on optimizing a PV/Diesel/battery hybrid system in terms of energy production to permanently power the telecommunication systems. To achieve this goal, the NSGA-II genetic algorithm is employed, taking into account a non-linear load profile that perfectly reflects the energy demand of a BTS site. The results show that the optimal power system for BTS in Benin is composed of a 12 kW diesel generator and 50 solar panels with a peak power of 540 Wc and 30 modular 48V/150Aah batteries. This system would emit 3571.3282 kg/year CO2 with a Lost Power System Probability (LPSP) equal to 4%. The average monthly consumption of the existing site, which is the subject of this study and operates on a diesel generator, is 1500 liters per month, or 18,000 liters per month. This diesel consumption results in annual pollution of 53460 tons of CO2. With the integration of solar energy in this system, the theoretical results show a consumption of 1202 liters of diesel for a production of greenhouse gases of 3571 tonnes of CO2. We note a significant reduction of about 93% on the consumption of fuel oil and on the production of greenhouse gases.

ABSTRACT In recent years, photovoltaic (PV) encapsulant films marketed as polyolefins (POs), more specifically as PO elastomers (POEs) and thermoplastic POs (TPOs), have gained significant market share and are projected to become the dominant encapsulation films by 2030. Relative to other industries, there are significant misconceptions about the term PO in the PV industry. Both in the scientific literature as well as in sales and advertising, the terms PO, POE, and TPO are often misused to describe the same type of material with comparable properties, while in reality these may each consist of separate material classes. This paper provides a comprehensive literature and market review, to showcase a broad range of PO and other ethylene copolymer encapsulants from recent studies, and discusses the materials' properties to clarify what constitutes a “polyolefin.” In addition, to promote a clearer comparison of encapsulant properties, we propose a two‐dimensional taxonomy to categorize polymers used in module manufacturing, including POs. In terms of improving the reliability of solar PV modules, PO‐based encapsulants have several advantages (including lower water uptake and ion diffusion), but might come with disadvantages too, such as a more complex processing and a higher sensitivity to the storage conditions and shelf life. All this might prospectively impact adhesion properties of the encapsulant to other materials' interfaces (glass, cells etc.) and end‐product quality. Because the track record of field‐deployed PV modules containing PO encapsulants is also limited, we hope to contribute to better material understanding and precision in communication in PV to secure quality.

Abstract The Inflation Reduction Act (IRA) represented a milestone in U.S. industrial policy that transformed the clean energy manufacturing landscape in the United States. Two of the principal goals of the IRA were to onshore clean energy manufacturing and to make supply chains more resilient to geopolitical shocks. This article presents data for the solar and battery supply chains to assess what progress had been made toward these goals before the passage of the One Big Beautiful Bill Act (OBBBA) called many IRA provisions into question. The results revealed significant successes, but also persistent challenges in building these supply chains. While the U.S. was slated to produce 61% of expected solar module demand in 2025, it would have produced only 37% of the cells and 11% of the ingots and wafers needed for those modules. In batteries, the U.S. could have realistically produced 100% of expected cell demand in 2025, but announced projects accounted for only 47% of the cathode, 23% of the anode, and 24% of the precursors. U.S. supply would have slightly increased as a percent of expected demand in 2030, but the situation would have remained broadly the same. There are four implications of the analysis. First, it demonstrates the difficulties of onshoring, especially in upstream components. Second, it shows that expected U.S. production would not have squeezed out other countries but rather would have created global demand-pull. Third, it reveals that Biden's tariffs would have likely increased the cost of U.S.-made panels and cells. We propose an alternative policy instruments for achieving supply chain and manufacturing goals.

In this research describes the electrical vehicle (EV) charging station using PV panel with fault detection methods. The PV modules will failure for some time, because of some external factors and internal factors. In direct fault condition the monitor and analyze the external factors such as the life span, high intensity and breakage of the PV panels using Raspberry Pi (R-Pi) processor with internet of things (IoT) system. In power demand/day on the PV panel will be evaluated and analyzed through R-Pi processor and IoT. The efficiency and the range values of the PV panels will be monitored and analyzed through IoT. Proposed work explains, how the fault detection techniques have been improved and adopted in using R-Pi processor through IoT platform. The proposed dataset pre-processing system is incorporated with IoT module. The grid fault clearing time will be compared with the actual values through R-Pi processor. The PV panel faults are detected using thermal image processing, that image parameter values analysis through IoT based internal monitoring system.

Abstract Dust accumulation on photovoltaic (PV) panels significantly degrades energy conversion efficiency, posing a critical challenge to solar energy production. While deep learning (DL) methods surpass traditional techniques for dust detection, there is a persistent need for solutions that offer high accuracy in real-time, non-invasive applications. This study addresses this gap by proposing and evaluating a dust detection model based on the YOLOv5 architecture. A custom dataset of solar panel images, categorized by varying levels of soiling, was developed to train and test the model for practical deployment. Experimental results validate the model’s effectiveness, demonstrating high accuracy and robustness in identifying dust accumulation. The findings indicate that the YOLOv5-based approach is a practical and efficient tool for automated solar panel monitoring, enabling timely maintenance to optimize performance and address the limitations of existing detection systems.

Maximizing the productivity and efficiency of solar distillers using a novel design of rotating parallel multi-hollow copper drums covered with black burlap, integrated with reflectors and PV panels

Evaluation of a novel high-performance HP-HDH desalination system integrated with PV module and electrolyzer for fresh water and hydrogen productions

Power systems with standalone properties like remote unit telecommunication network requires high negative DC supply voltage. In such remote places, solar photovoltaic (PV) are used to generate power. Maximum power point tracking techniques (MPPT) gives unregulated voltage from solar panel. This unregulated voltage is converted into regulated voltage by providing proper pulse width modulation (PWM) signal to self-lift cuk converter (SLCC). In comparison with classic cuk converter, SLCC reduces load voltage and load current ripples. This paper focuses on state space controller design and implementation of SLCC used in MPPT based PV system. The switching pulse of SLCC can be generated by perturb and observe (P&O), incremental conductance (IC) and also using fuzzy control. The simulation of SLCC has been performed using MATLAB/Simulink and its specifications in time domain has been compared.