Search Results (38)

Agrivoltaics integrates photovoltaic (PV) power generation with agricultural practices, enabling dual land-use and mitigating land-use competition between agriculture and energy production. China has 3.43 million hectares of tea fields, offering significant potential for PV-integrated tea plantations (PVtea) to address land scarcity in clean energy development. This study aimed to investigate the impact of PV modules above tea bushes in PVtea on the yield and quality of tea, as well as tea plant resistance to environmental stresses. The PV system uses a single-axis tracking system with a horizontal north–south axis and ±45° tilt. It includes 70 UL-270P-60 polycrystalline solar panels (270 Wp each), arranged in 5 columns of 14 panels, spaced 4500 mm apart, covering 280 m². The panels are mounted 2400 mm above the ground, with a total capacity of 18.90 kWp (656 kWp/ha). Tea yield, quality-related components, leaf photosystem II (PSII) activity, and plant resistance to environmental stresses were investigated in comparison to an adjacent open-field tea plantation (control). The mean photosynthetic active radiation (PAR) reaching the plucking table of PVtea was 52.9% of the control, with 32.0% of the control on a sunny day and 49.0% on a cloudy day, accompanied by an increase in ambient relative humidity. These changes alleviated the midday depression of leaf PSII activity caused by high light, resulting in a 9.3–15.3% increase in leaf yield. Moreover, PVtea summer tea exhibited higher levels of amino acids and total catechins, resulting in tea quality improvement. Additionally, PVtea enhanced the resistance of tea plants to frost damage in spring and heat stress in summer. PVtea integrates photovoltaic power generation with tea cultivation practices, which not only facilitates clean energy production—an average annual generation of 697,878.5 kWh per hectare—but also increases tea productivity by 9.3–15.3% and the land-use equivalence ratio (LER) by 70%. Full article

This paper presents a performance evaluation of a 140 kW solar array installed on the rooftop of the Mountain Line Transit Authority (MLTA) building in Morgantown, West Virginia (WV), USA, covering the period from 2013 to 2024. The grid-connected photovoltaic (PV) system consists of 572 polycrystalline PV modules, each rated at 245 watts. The study examines key performance parameters, including annual electricity production, average daily and annual capacity utilization hours (CUH), current array efficiency, and performance degradation. Monthly ambient temperature and global tilted irradiance (GTI) data were obtained from the NASA POWER website. During the assessment, observations were made regarding the tilt angles of the panels and corrosion of metal parts. From 2013 to 2024, the total electricity production was 1588 MWh, with an average annual output of 132 MWh. Over this 12-year period, the CO₂ emissions reduction attributed to the solar array is estimated at 1,413,497 kg, or approximately 117,791 kg/year, compared to emissions from coal-fired power plants in WV. The average daily CUH was found to be 2.93 h, while the current PV array efficiency in April 2024 was 10.70%, with a maximum efficiency of 14.30% observed at 2:00 PM. Additionally, an analysis of annual average performance degradation indicated a 2.28% decline from 2013 to 2016, followed by a much lower degradation of 0.17% from 2017 to 2023, as electricity production data were unavailable for most summer months of 2024. Full article

The study investigates a hybrid energy system integrating photovoltaic (PV) panels, micro-CHP units, battery storage, and thermal storage to meet the winter energy demands of a residential building in Bacău, Romania. Using real-world experimental data from amorphous, polycrystalline, and monocrystalline PV panels, C++ Model 1 simulates building energy needs and PV system performance under varying irradiance levels. The results show that PV systems alone cannot meet the total winter demand, with polycrystalline slightly outperforming monocrystalline, yet still falling short. A second computational model (C++ Model 2) simulates hybrid energy flow, demonstrating how the CHP unit and storage systems can ensure off-grid autonomy. The model dynamically manages energy between components based on daily irradiance scenarios. The findings reveal critical thresholds for PV surplus, optimal CHP sizing, and realistic battery and thermal storage needs. This paper provides a practical framework for designing efficient, data-driven hybrid solar–CHP systems for cold climates. The novelty lies in the integration of real-world PV efficiency data with a dynamic irradiance-driven simulation framework, enabling precise hybrid system sizing for winter-dominant regions. Full article

This study investigates the potential of low-cost, naturally available porous materials (PoMs), gravel, marble, flint, and sandstone, as thermal management for photovoltaic (PV) panels. Experiments were conducted in a controlled environment at a solar energy laboratory, where variables such as solar irradiance, ambient temperature, air velocity, and water flow were carefully regulated. A solar simulator delivering a constant irradiance of 1250 W/m² was used to replicate solar conditions throughout each 3 h trial. The test setup involved polycrystalline PV panels (30 W rated) fitted with cooling channels filled with PoMs of varying porosities (0.35–0.48), evaluated across water flow rates ranging from 1 to 4 L/min. Experimental results showed that PoM cooling significantly outperformed both water-only and passive cooling. Among all the materials tested, sandstone with a porosity of 0.35 and a flow rate of 2.0 L/min demonstrated the highest cooling performance, reducing the panel surface temperature by 58.08% (from 87.7 °C to 36.77 °C), enhancing electrical efficiency by 57.87% (from 4.13% to 6.52%), and increasing power output by 57.81% (from 12.42 W to 19.6 W) compared to the uncooled panel. The enhanced heat transfer (HT) was attributed to improved conductive and convective interactions facilitated by lower porosity and optimal fluid velocity. Furthermore, the cooling system improved I–V characteristics by stabilizing short-circuit current and enhancing open-circuit voltage. Comparative analysis revealed material-dependent efficacy—sandstone > flint > marble > gravel—attributed to thermal conductivity gradients (sandstone: 5 W/m·K vs. gravel: 1.19 W/m·K). The configuration with 0.35 porosity and a 2.0 L/min flow rate proved to be the most effective, offering an optimal balance between thermal performance and resource usage, with an 8–10% efficiency gain over standard water cooling. This study highlights 2.0 L/min as the ideal flow rate, as higher rates lead to increased water usage without significant cooling improvements. Additionally, lower porosity (0.35) enhances convective heat transfer, contributing to improved thermal performance while maintaining energy efficiency. Full article

Nonlinear characteristics of solar photovoltaic (PV) and nonuniform surrounding conditions, including partial shading conditions (PSCs), are the major factors responsible for lower conversion efficiency in solar panels. One major condition is the cause of the multiple peaks and oscillation around the peak point leading to power losses. Therefore, this study proposes a novel hybrid framework based on an artificial neural network (ANN) and fractional order PID (FOPID) controller, where new algorithms are employed to train the ANN model and to tune the FOPID controller. The primary aim is to maintain the computed power close to its true peak power while mitigating persistent oscillations in the face of continuously varying surrounding conditions. Firstly, a modified shuffled frog leap algorithm (MSFLA) was employed to train the feed-forward ANN model using real-world solar PV data with the aim of generating a reference solar PV peak voltage. Subsequently, the parameters of the FOPID controller were tuned through the application of the Sanitized Teacher–Learning-Based Optimization (s-TLBO) algorithm, with a specific focus on achieving maximum power point tracking (MPPT). The robustness of the proposed hybrid framework was assessed using two different types (monocrystalline and polycrystalline) of solar panels exposed to varying levels of irradiance. Additionally, the framework’s performance was rigorously tested under cloudy conditions and in the presence of various partial shading scenarios. Furthermore, the adaptability of the proposed framework to different solar panel array configurations was evaluated. This work’s findings reveal that the proposed hybrid framework consistently achieves maximum power point with minimal oscillation, surpassing the performance of recently published works across various critical performance metrics, including the $MP{P}_{efficiency}$, relative error (RE), mean squared error (MSE), and tracking speed. Full article

The aim of this study was the hydrothermal leaching of silver from waste monocrystalline silicon (m-Si) and polycrystalline silicon (p-Si) photovoltaic panel (PV) cells using organic acids, namely oxalic acid (OA) and citric acid (CA). Before leaching, two different pretreatment procedures were applied. First, the fluoropolymer backsheet was manually removed from the panel pieces and, then, the samples were subjected to high-temperature heating for the thermal degradation of the ethylene vinyl acetate (EVA) polymer. When removal by hand was not feasible, the second pretreatment procedure was followed by toluene immersion to remove the EVA and backsheet and separate the cells, glass, and films. After pretreatment, 4 M HCl leaching was applied to remove the aluminum layer from the cells. The remaining cells were subjected to hydrothermal leaching with organic acids to extract the silver. Several hydrothermal parameters were investigated, such as acid concentration (1-1.5-2 M), processing time (60-105-150 min), and temperature (150-180-210 °C), while the liquid-to-solid (L/S) ratio was fixed at 30 mL: 1 g, based on preliminary tests. Response surface methodology (RSM) was applied to optimize the hydrothermal leaching parameters. The optimized parameters were 210 °C, 95 min, 2 M CA or 210 °C, 60 min, 1 M OA. OA was more effective in Ag leaching than CA. The results were compared to HNO₃ leaching. The green leaching of silver from end-of-life PV panels with organic acids is an environmentally beneficial route. Full article

The advantages of green roofs and solar panels are numerous, but in dry periods, green roofs can place urban water resources under pressure, and the efficiency of solar panels can be affected negatively by high temperatures. In this context, our analysis investigated the advantages of bio-solar green roofs and evaluated the impact of green roofs on solar panel electricity production and solar panels on green roof water consumption. The assessment was conducted through simulation in a selected case study located in Cosenza, a city with a Mediterranean climate, with solar panels covering 10% to 60% of the green roof. Analyses were performed on the power outputs of four kinds of photovoltaic panels: polycrystalline, monocrystalline, bifacial, and Passivated Emitter and Rear Contact (PERC). The energy production and shade frequencies were simulated using PVGIS 5.3 and PVSOL 2024 R3. The impact of photovoltaic (PV) shade on the water consumption of green roofs was evaluated by image processing of a developed code in MATLAB R2024b. Moreover, water–energy interconnections in bio-solar green roof systems were assessed using the developed dynamic model in Vensim PLE 10.2.1. The results revealed that the water consumption by the green roof was reduced by 30.8% with a bio-solar coverage area of 60%. However, the electricity production by the PV panel was enhanced by about 4% with bio-solar green roofs and was at its maximum at a coverage rate of 50%. This investigation demonstrates the benefits of bio-solar green roofs, which can generate more electricity and require less irrigation. Full article

This article describes the CURiE (Composites and photovoltaics Undergoing Radiation Exposure) stratospheric experiment, which was designed and built in 2024 for the BEXUS 35 stratospheric flight campaign in Sweden. One of the main objectives of the experiment was to investigate the electric currents generated in polycrystalline photovoltaic panels, shielded from visible light, and exposed in stratospheric conditions to cosmic radiation. The experiment’s registered data correlate with the X-ray fluxes registered by the GOES satellites, which are presented with the inclusion of the atmosphere’s attenuation. A single voltage-generating event may have been linked to the impact of a high-energy proton. The article forms a basis for the next research with the exposed photovoltaics and the next generation of experiments involving novel radiation-proof panels. Full article

The present study employs machine learning regression analyses to investigate the efficiency of photovoltaic (PV) panels utilizing solar energy under the influence of environmental factors. The experimental study was conducted on two 100-watt monocrystalline and two polycrystalline PV panels, which were divided into clean and dirty groups. The following variables were monitored and recorded for a period of six months: radiation, panel temperature, air temperature, wind speed, humidity, pressure, and ultraviolet (UV) radiation. Additionally, current, voltage, and power were recorded. These measurements were taken during the production of energy by PV panels. Monocrystalline PV panels exhibited an 8.6% increase in energy efficiency, while polycrystalline PV panels demonstrated a 6.2% increase, following the collection and cleaning of data in accordance with the IEC 61724 standard. Six distinct machine learning regression analyses were conducted on the dataset. The results were compared using the Root Mean Square Error (RMSE) and the coefficient of determination (R²). The Random Forest model demonstrated the greatest predictive success, while the Support Vector Regression (SVR) model exhibited the lowest performance. Full article

Due to the supply problems of fossil-based energy sources, the tendency towards alternative energy sources is relatively high. For this reason, the use of solar energy systems is increasing today. This study combines experimental data and machine learning algorithms to evaluate the energy performance of four different photovoltaic (PV) panel designs (monocrystalline, polycrystalline, flexible, and transparent) under harsh environmental conditions on Horseshoe Island (Antarctica). In this research, the effects of environmental factors, such as solar radiation, temperature, humidity, and wind speed, on the panels were analyzed. Electrical power output of the PV panels are analyzed using six machine learning models. Random forest (RF) and CatBoost (CB) models showed the highest accuracy and reliability among these models. According to the experimental results, Monocrystalline PV provided the highest electrical power (20.5 Watts on average), and Flexible PV provided the highest energy efficiency (19.67%). However, Flexible PV was observed to have higher surface temperatures compared to the other panel types. Furthermore, using Monocrystalline PV resulted in an average reduction of 4.1 tons of CO₂ emissions per year, demonstrating the positive environmental impact of renewable energy systems. Thanks to this study, renewable energy research for temporary stations in Antarctica will focus on explainable and interpretable artificial intelligence models that will provide an understanding of the factors affecting the energy performance of PV panels. The research results will be an important guide for optimizing energy consumption, management, and demand forecasting in temporary research stations in Antarctica. Full article

The negative effect of the operating temperature on the functioning of photovoltaic panels has become a significant issue in the actual energetic context and has been studied intensively during the last decade. The very high operating temperatures of the photovoltaic panels, even for lower levels of solar radiation, determine a drop in the open-circuit voltage, with consequences over the electrical power generated and PV-conversion efficiency. The temperature effect over the efficiency of monocrystalline and polycrystalline photovoltaic panels by using a double-climatic chamber and a solar simulation device was studied experimentally for two photovoltaic panels, one monocrystalline and another polycrystalline, with the same nominal power of 30 Wp. The double-climatic chamber used is composed of two separate rooms, a cold and a hot one, while the PV panel is placed as a barrier between them. The study is focused on establishing the effect of raising the temperature of PV panels over electrical parameters: voltage, current, and power produced and for efficiency and fill factor to promote sustainable energy consumption. The findings highlight the positive impact of cooling on enhancing system efficiency, with the primary focus on quantifying its overall performance. The operating temperature is controlled by the flow of air on the backside of the PV panel inside the cold room. The level of radiation studied corresponds to a vertical integration of PV panels in building façades. The coefficient of the mean variation of the efficiency with the photovoltaic panels’ temperature was −0.52%/°C; for voltage, −0.48%/°C, and for current, +0.10%/°C. Full article

Solar photovoltaic (PV) panels that use polycrystalline silicon cells are a promising technique for producing renewable energy, although research on the cells’ efficiency and thermal control is still ongoing. This experimental research aims to investigate a novel way to improve power output and thermal performance by combining solar PV panels with burned fly-ash tiles. Made from burning industrial waste, torched fly ash has special qualities that make it useful for architectural applications. These qualities include better thermal insulation, strengthened structural integrity, and high energy efficiency. Our test setup shows that when solar PV panels are combined with torched fly-ash tiles, power generation rises by 7% and surface temperature decreases by 3% when compared to standard panels. The enhanced PV efficiency is ascribed to the outstanding thermal insulation properties of fly ash tiles and their capacity to control panel temperature. To ensure longevity and safety in building applications, the tiles employed in this study had a water absorption rate of 5.37%, flexural strength of 2.95 N/mm², and slip resistance at 38 km/h. Furthermore, we find improved structural resilience and lower cooling costs when up to 30% of the sand in floor tiles is replaced with torched fly ash, which makes this method especially appropriate for sustainable buildings. Key performance indicators that show how effective these tiles are in maximizing energy use in buildings include thermal emissivity (0.874), solar reflectance (0.8), and solar absorption (0.256). While supporting more ecofriendly building techniques, this study highlights the advantages of utilizing burned fly ash in solar PV systems: enhanced power generation and thermal comfort. The main results open a greater potential for fly ash use in different building materials. The use of torched fly ash in building materials enhances thermal insulation and structural integrity while lowering cooling costs, making it an ideal choice for eco-friendly construction and highlighting the potential for further research into environmentally responsible, energy-efficient solutions. Full article

Among the various renewable energy-based technologies, photovoltaic panels are characterized by a high rate of development and application worldwide. Many efforts have been made to study innovative materials to improve the performance of photovoltaic cells. However, the most commonly used crystalline panels also have significant potential to enhance their energy yield by providing cooling and cleaning solutions. This paper discusses the possibility of introducing a dedicated direct-water cooling and cleaning system. As assumed, detailed schedules of the operation of the developed direct water cooling and cleaning system should be fitted to actual weather conditions. In this context, different cooling strategies were proposed and tested, including different intervals of opening and closing water flow. All tests were conducted using a dedicated experimental rig. 70 Wp monocrystalline panels were tested under laboratory conditions and 160 Wp polycrystalline panels were tested under real conditions. The results showed that introducing a scenario with a 1-min cooling and a 5-min break allowed for proving the panel’s surface temperature lower than 40 °C. In comparison, the temperature of the uncooled panel under the same operating conditions was close to 60 °C. Consequently, an increase in power generation was observed. The maximum power increase was observed in July and amounted to 15.3%. On the other hand, considering selected weeks in May, July, and September, the average increase in power generation was 3.63%, 7.48%, and 2.51%, respectively. It was concluded that the division of photovoltaic installation allows reasonable operating conditions for photovoltaic panels with a lower amount of energy consumed to power water pumps. Full article

The research work explores the impact of temperature on Silicon photovoltaic (PV) panels, considering Nigeria as a case study. It is found that high solar radiation in Nigeria increases the surface temperature of PV panels above 25 °C of the optimal operating temperature. The redundant energy gain from solar irradiance creates heat at the rear of solar panels and reduces their efficiency. Cooling mechanisms are therefore needed to increase efficiency. In this study, we demonstrated a unique hybrid system design employing a heat exchanger at the back of the panel, with water circulated through the back of the PV panel to cool the system. The system was simulated using TRNSYS at three locations in Nigeria—Maiduguri, Makurdi, and Port Harcourt. The results of the peak annual electrical power output in Maiduguri give a power yield of 1907 kWh/kWp, which is the highest, due to a high solar radiation average of 727 W/m² across the year. For Makurdi, the peak annual electrical power output is 1542 kWh/kWp, while for Port Harcourt the peak power output is 1355 kWh/kWp. It was observed that the surface temperature of Polycrystalline Si-PV was decreased from 49.25 °C to 38.38 °C. The electrical power was increased from 1526.83 W to 1566.82 W in a day, and efficiency increased from 13.99% to 15.01%. Full article

Solar photovoltaic (PV) systems are becoming increasingly popular because they offer a sustainable and cost-effective solution for generating electricity. PV panels are the most critical components of PV systems as they convert solar energy into electric energy. Therefore, analyzing their reliability, risk, safety, and degradation is crucial to ensuring continuous electricity generation based on its intended capacity. This paper develops a failure mode and effects analysis (FMEA) methodology to assess the reliability of and risk associated with polycrystalline PV panels. Generalized severity, occurrence, and detection rating criteria are developed that can be used to analyze various solar PV systems as they are or with few modifications. The analysis is based on various data sources, including field failures, literature reviews, testing, and expert evaluations. Generalized severity, occurrence, and detection rating tables are developed and applied to solar panels to estimate the risk priority number (RPN) and the overall risk value. The results show that the encapsulant, junction box, and failures due to external events are the most critical components from both the RPN and risk perspectives. Delamination and soiling are the panels’ most critical FMs, with RPN values of 224 and 140, respectively, contributing 16.2% to the total RPN. Further, moderately critical FMs are also identified which contribute 56.3% to the RPN. The encapsulant is the most critical component, with RPN and risk values of 940 (40.30%) and 145 (23.40%), respectively. This work crucially contributes to sustainable energy practices by enhancing the reliability of solar PV systems, thus reducing potential operational inefficiencies. Additionally, recommendations are provided to enhance system reliability and minimize the likelihood and severity of consequences. Full article