Search Results (55)

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

With rapid urbanization, rooftop photovoltaic (PV) systems play an important role in mitigating the energy crisis and reducing emissions, yet achieving scientific and cost-effective deployment at the urban block scale remains challenging. This study proposes a transferable framework that integrates spatial morphology quantification, clustering, and numerical simulation to evaluate PV potential in residential blocks of Jinan, China. Six key morphological indicators were extracted through principal component analysis (PCA), and blocks were classified into five typical types, followed by simulations under different PV material scenarios. The main findings are: (1) Block type differences: Cluster 1 achieved the highest annual generation, 61.76% above average, but with a 75.08% cost increase and a 3.54-year payback. Clusters 4 and 5 showed moderate generation and the shortest payback of 2.91–2.97 years, reflecting better energy–economic balance. (2) PV materials: monocrystalline silicon (m-Si) yielded the highest generation, suitable for maximizing output; polycrystalline silicon (p-Si) produced slightly less but reduced costs by 32.43% and shortened payback by 19.58%, favoring cost-sensitive scenarios. (3) Seasonal variation: PV output peaked in February–March and September–December, requiring priority in grid operation and maintenance. The proposed framework can serve as a useful reference for planners in developing PV deployment strategies, with good transferability and potential for wider application, thereby contributing to urban energy transition and low-carbon sustainable development. Full article

This study investigates the performance prediction of poly-crystalline photovoltaic (PV) systems in Jordan using experimental data, analytical models, and machine learning approaches. Two 5 kWp grid-connected PV systems at Applied Science Private University in Amman were analyzed: one south-oriented and another east–west (EW)-oriented. Both systems are fixed at an 11° tilt angle. Linear regression, Least Absolute Shrinkage and Selection Operator (LASSO), ElasticNet, and artificial neural networks (ANNs) were employed for performance prediction. Among these, linear regression outperformed the others due to its accuracy, interpretability, and computational efficiency, making it an effective baseline model. LASSO and ElasticNet were also explored for their regularization benefits in managing feature relevance and correlation. ANNs were utilized to capture complex nonlinear relationships, but their performance was limited, likely because of the small sample size and lack of temporal dynamics. Regularization and architecture choices are discussed in this paper. For the EW system, linear regression predicted an annual yield of 1510.45 kWh/kWp with a 2.1% error, compared to 1433.9 kWh/kWp analytically (3.12% error). The south-oriented system achieved 1658.15 kWh/kWp with a 1.5% error, outperforming its analytical estimate of 1772.9 kWh/kWp (7.89% error). Productivity gains for the south-facing system reached 23.64% (analytical), 10.43% (experimental), and 9.77% (predicted). These findings support the technical and economic assessment of poly-crystalline PV deployment in Jordan and regions with similar climatic conditions. 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

In the context of the global energy transition, enhancing the efficiency of polycrystalline silicon-based solar cells remains a critical research priority. This study investigates the integration of ZnO-based nanostructured layers. ZnO and Al-doped ZnO nanoparticles, synthesized via hydrothermal methods and concentrated solar power (CSP) vapor condensation, exhibiting diverse morphologies—nanorods, spheres, and whisker structures—were deposited onto commercial solar cells using the spin coating technique. Structural, morphological, and spectroscopic analyses confirmed the formation of crystalline layers with high active surface areas and controlled morphology. Photovoltaic performance was assessed using a dedicated hardware–software system under real sunlight conditions. The results demonstrate a significant increase in energy efficiency, reaching up to 10.97%, compared with 1.51% for polycrystalline silicon cells without any supplementary layers. This improvement is attributed to enhanced light absorption, reduced carrier recombination, and more efficient charge transport, driven by nanoscale design and doping. This study underscores the importance of sustainable synthesis and morphological control in the development of high-performance and cost-effective solar technologies. 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

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

In 2023, solar photovoltaic energy alone accounted for 75% of the global increase in renewable capacity. Moreover, this natural energy resource is the one that requires the least investment, which makes it accessible to developing countries. Increasing return on investment in these regions requires a particular evaluation of environmental parameters influencing PV systems performance. Higher temperatures decrease PV module efficiency and, as a result, their power output. Additionally, fluctuations in solar irradiance directly impact the energy generated by these systems. Consequently, it is essential for investors to improve accurate predictive models that assess the power generation capacity of photovoltaic systems under local environmental conditions. Therefore, accurate estimation of maximum power generation is then crucial for optimizing photovoltaic (PV) system performances and selecting suitable PV modules for specific climates. In this context, this study presents an experimental comparison of three maximum power prediction methods for four PV module types (amorphous silicon, monocrystalline silicon, micromorphous silicon, and polycrystalline silicon) under real outdoor conditions. Experimental data gathered over the course of a year are analyzed and processed for the four PV technologies. Three different methods taking into account environmental parameters are presented and analyzed. The first estimation method utilizes irradiance as the primary input parameter, while two additional methods incorporate ambient temperature and PV module temperature for enhanced accuracy. The performance of each method is evaluated using standard statistical metrics, including the root mean square error (RMSE) and coefficient of determination (R²). The results demonstrate the effectiveness of all three methods, with RMSE values ranging from 1.6 W to 3.8 W and R² values consistently above 0.95. The most appropriate method for estimating PV power output is determined by the specific type of photovoltaic module and the availability of meteorological parameters. This study provides valuable insights for selecting an appropriate maximum power prediction method and choosing the most suitable PV module for a given climate. 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 ecofriendly tin selenide (SnSe) is expected to find multiple applications in optoelectronic, photovoltaic, and thermoelectric systems. This work is focused on the thermoelectric properties of thin films. SnSe single crystals exhibit excellent thermoelectric properties, but it is not so in the case of polycrystalline bulk materials. The investigations were motivated by the fact that nanostructuring may lead to an improvement in thermoelectric efficiency, which is evaluated through a dimensionless figure of merit, ZT = S² σ T/λ, where S is the Seebeck coefficient (V/K), σ is the electrical conductivity (S/m), λ is the thermal conductivity (W/mK), and T is the absolute temperature (K). The main objective of this work was to obtain SnSe films via magnetron sputtering of a single target. Instead of common radiofrequency (RF) magnetron sputtering with a high voltage alternating current (AC) power source, a modified direct current (DC) power supply was employed. This technique in the classical version is not suitable for sputtering targets with relatively low thermal and electrical conductivity, such as SnSe. The proposed solution enabled stable sputtering of this target without detrimental cracking and arcing and resulted in high-quality polycrystalline SnSe films with unprecedented high values of ZT equal to 0.5 at a relatively low temperature of 530 K. All parameters included in ZT were measured in one setup, i.e., Linseis Thin Film Analyzer (TFA). The SnSe films were deposited at sputtering powers of 120, 140, and 170 W. They had the same orthorhombic structure, as determined by X-ray diffraction (XRD), but the thickness and microstructure examined by scanning electron microscopy (SEM) were dependent on the sputtering power. It was demonstrated that thermoelectric efficiency improved with increasing sputtering power and stable values were attained after two heating–cooling cycles. This research additionally provides further insights into the DC sputtering process and opens up new possibilities for magnetron sputtering technology. 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