Photovoltaic Cell Temperature Research Articles

Analysis and performance prediction of a building integrated photovoltaic thermal system with and without phase change material

Increasing energy demand in buildings in the last decade has led to more efficient usage of renewable energy. Building integrated photovoltaic/thermal systems is one of the effective ways of reducing energy consumption in buildings by providing heating, cooling, ventilation, hot water, and air. The enhancement of photovoltaic cell temperature reduces the electrical efficiency of photovoltaic panel systems. In this regard, the effects of cooling with air and phase change material layer are investigated in this study. The simulation of BIPV/T, PV, and heat pump was done in TRNSYS. As the main novelty of recent simulation, for the calculations of phase change material layer, MATLAB coupled with the TRNSYS, and Python software was used for the prediction process under the weather conditions of Sharurah, Saudi Arabia for 2022. Also, the performance of a building integrated photovoltaic/thermal system with a phase change material layer is predicted for 2024 and 2025 by the Random Forest machine learning method. The R squared, Root Mean Squared Error, and Mean Absolute Error are utilized for checking the accuracy of the model. Also, for more certainty, a validation was done on the simulated results of TRNSYS and the results showed high conformity of data. The simulated data for the first six months of 2022 showed that the maximum cooling effect happened in June with 25.47 °C and a 37.87 % reduction in photovoltaic cell temperature. Also, the best electrical efficiency improvement was 1.26 % while the thermal efficiency ranged from 29.65 % to 59.40 %, respectively. The predicted data was confirmed by R2, RMSE, and MAE equal to 96.64 %, 0.06 and 0.04. According to the simulation and predictions done, the annual electrical production of the building is achieved. The results indicate that, for the simulated system, about 29.7 %, 29.8 %, and 29.6 % of total energy utilization could be supplied by BIPV/T cooled with phase change material in 2022, 2024, and 2025, respectively.

Design and modeling of PV-integrated Double Skin Facades and application to retrofit buildings

Double Skin Façade (DSF) system comprises two glazing layers with a ventilated cavity. Integrating photovoltaic (PV) modules within the outer layer of DSFs offers an efficient method for electricity generation. Current tools for modeling and analyzing DSF systems are complex and resource-intensive, lacking the capability to evaluate the performance of innovative PV-DSF systems during the early design stage. This study develops a mathematical model to evaluate the electrical and thermal performance of PV-DSF systems, considering architectural design elements such as PV color and relative orientation. Based on an energy balance approach, the model is particularly suited for designing PV-DSF systems in heritage buildings, which often have color and relative orientation constraints. The model is applied to assess the performance of PV-DSF systems with conventional clear glass PV and colored front glass PV modules under the climatic conditions of Montreal, Canada. Results indicated that conventional clear glass PV module exhibit higher PV cell temperature than colored PV modules due to greater transmissivity, with peak temperature differences at noon of 5.5 °C, 6.2 °C, and 6.5 °C for orange, blue, and gray PV modules, respectively. On the contrary, the influence of PV's color front glass on room air temperature is non-significant. Furthermore, the optimal orientation for maximum energy yield is not always south-facing; it depends on the hourly distribution of the beam, diffuse solar irradiation, and ambient air temperature. For Montreal, west-facing DSFs produce more electrical and thermal energy on a summer design day because the hourly distribution of beam radiation is skewed towards afternoon hours.

Effects of using a finned cylindrical cooler in the nano-enhanced cooling channel on the exergetic performance of a PV/TEG-coupled system

ABSTRACT In this study, three different nano-enhanced channel models were integrated into the 3D photovoltaic (PV)/thermoelectric generator (TEG) system and the effect of the channel models on the PV/TEG system performance was examined. The flat rectangular channel, Model 1 while the cylindrical and fin-reinforced cylindrical object accommodated in the flat rectangular channel are defined as Model 2 and Model 3, respectively. A comparison of water and ternary nanofluid with various nanoparticle (solid particle volume fraction between 0.01 and 0.03) loading was performed in each channel model. In addition, impacts of cooling fluids at different inlet temperatures (Tg between 25°C and 12.5°C) were considered in the simulations. Among different cooling channels, lowest PV cell temperature and the highest PV output power were obtained in Model 3. The highest PV power (0.51 W) was obtained with Model 3 with ternary nanofluid (%3 volume fraction) at Tg = 12.5°C. This value is is 3.66% and 4.03% higher than Model 2 and Model 1, respectively. Under the same conditions, TEG output power reached its highest value with Model 2 and it was followed by Model 1 and Model 3, respectively. With the reinforcement of fins to the cylindrical object, an improvement in thermal performance was achieved, but a decrease in total output power was observed.

Improving the photovoltaic thermal system efficiency with nature-inspired dolphin turbulators from energy and exergy viewpoints

The main purpose of this study is to enhance the efficiency of the photovoltaic thermal (PVT) system using nature-inspired dolphin turbulators designed based on thermal images of the dolphin dorsal fin. The turbulators are placed in the cooling channel of the PVT. The effects of the number and length scale of the turbulators are investigated on electrical, thermal, and overall efficiencies from energy and exergy perspectives, as well as the photovoltaic cell temperature at different times of the day. The dolphin turbulator enhances heat transfer to the coolant by preventing the boundary layer growth, accelerating the fluid flow, directing and impacting the coolant to the absorber plate, and improving the mixing of coolant. Consequently, turbulators reduce the photovoltaic cell temperature, increase the coolant temperature, and improve efficiencies. The enhanced PVT improves the electrical, thermal, and overall efficiencies by about 2.59 %, 16.9 %, and 14.78 % from the energy viewpoint and 2.59 %, 12.87 %, and 3.77 % from the exergy viewpoint compared to the conventional PVT. In addition, this study investigates how operational conditions, such as the coolant Reynolds number, the coolant inlet temperature, and ambient wind speed, affect the efficiencies from energy and exergy viewpoints.

Enhancing PV solar panel efficiency through integration with a passive Multi-layered PCMs cooling system: A numerical study

Photovoltaic (PV) solar cells are at the forefront of sustainable electricity generation technologies, yet they exhibit relatively low efficiency. Typically, less than 20 % of the solar energy absorbed is converted into electrical energy, with the remainder converts into heat. This heat increases the PV panel's operating temperature, negatively impacting its efficiency and life expectancy. To mitigate this, a novel approach using multi-layered phase change materials (PCMs) has been examined in this study. The approach involves integrating organic PCMs (OPCMs) with metallic PCMs (MPCMs), enhancing PV panel cooling efficiency by attaching the multi-layer PCM to the panel's rear. This study, employing ANSYS FLUENT software's solidification and melting model, explores the impact of multi PCM layers thickness and total multi layers thickness on PV cell temperature and electrical efficiency. The analysis reveals that a multi-layered PCMs configuration with a thickness ratio of 15:85 between MPCM (CERROLOW-117® alloy) and OPCM (RT44) significantly decreases temperature rise in PV cells, resulting in a temperature drop of 59.6 °C. This improvement boosts PV panel performance by an average of 35.8 % compared to panels lacking cooling systems during peak sun hours under hot climate conditions characterized by ambient temperatures of 40 °C and high solar radiation exceeding 1000 W/m². This suggests that a carefully optimized PCM layering approach could markedly improve PV system efficiency, offering a practical solution to the overheating problem and extending the operational life of PV installations.

A Hybrid Experimental Modelling Approach to Solar Photovoltaic Cell Temperature Prediction

Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting especially in commercial power plants. The present study focused on the development of a machine learning based predictive model for solar photovoltaic cell temperature prediction in commercial solar photovoltaic power plants. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for those parameters difficult to measure experimentally and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed for each parameter used to mimic experimentally measured data. Statistical approaches were adopted to analyse the influence of both the dependent and independent variables on solar cell temperature. The analysis included multicollinearity and correlation analysis with the aid of heat maps meant to establish the relationships among the independent and dependent variables. Feature selection and dimensionality reduction was also performed to reduce the input variables and maintain relevant data in the modelling process. Parameters with a strong correlation to each other had some of them eliminated in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.83 °C while an R2 value of 0.93 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed a 3.4% difference in the annual energy generated between a simulation which considered solar cell temperature and that which ignored the solar cell temperature. The study also revealed that this difference is even larger in monetary terms when lifetime energy generation is considered. The present study demonstrated that Random Forests, a machine learning approach to predictive modelling, can handle complex models and can provide models with a higher accuracy compared to statistical modelling approaches. The study recommends the use of solar cell predictive models to improve the accuracy of energy prediction on solar photovoltaic power plants which in turn assists in energy planning and deployment.

Experimental investigation of photoelectrical performance and thermal characteristics of ventilated building-integrated photovoltaic system utilizing lightweight and flexible crystalline silicon module

The existing large-scale of industrial buildings with lightweight insulated roofing structures presents a challenge for traditional glass crystalline silicon photovoltaic (PV) systems due to insufficient load-bearing capacity. Meanwhile, traditional PV rooftop applications also face challenges due to limited rooftop resources. To address these issues, this study proposes a ventilated building-integrated lightweight photovoltaic (VL-BIPV) system. The VL-BIPV system incorporates lightweight and flexible crystalline silicon modules, which increase rooftop load by about 6 kg/m2. Additionally, the system features a ventilation channel design to enhance thermal management performance. To evaluate the PV performance and thermal characteristics of the proposed system, an experimental setup was implemented to compare the performances of the VL-BIPV system with a building-attached lightweight photovoltaic (L-BAPV) system that utilizes color steel sheet base plates. The results demonstrate the exceptional performance of VL-BIPV system, with a 100.56% higher ratio of the product of short-circuit current and open-circuit voltage compared to L-BAPV. Moreover, the VL-BIPV system achieved a 1.77% increase in PV efficiency and a 2.35% enhancement in daily electricity generation. In hot weather, the adoption of VL-BIPV reduced the extreme temperature of PV cells by 9.23 °C below 85 °C, surpassing the 6.29 °C reduction achieved by L-BAPV system. Furthermore, the ventilation channel exhibited a gradual temperature increase with slower changes in the flow direction, while the base plate temperature decreased by 13.41 °C, which indicates that VL-BIPV can effectively reduce solar heat gain in the building envelope and improve the indoor thermal environment. The research findings provide important guidance for promoting and applying VL-BIPV technology in large-scale industrial buildings with low rooftop load-bearing capacity, thereby promoting the production and utilization of clean electricity in industrial parks.

Experimental and simulation study on the thermoelectric performance of semi-transparent crystalline silicon photovoltaic curtain walls

This study aims to evaluate and optimize the thermoelectric performance of semi-transparent crystalline silicon photovoltaic (PV) curtain walls. An integrated thermoelectric performance coupling calculation model was developed, combining heat transfer and electricity generation calculations as a novel approach. Simulations and experiments were conducted to compare the performance of PV curtain walls with conventional curtain walls under various weather conditions, and were validated by experimental data. The results demonstrate that PV curtain walls enhance the thermal environment inside buildings and promote efficient power generation, with the arrangement of PV cells notably affecting performance. Under sunny conditions, the temperatures of the PV cell backplate and glass part backsheet in semi-transparent PV curtain walls exceeded those in ordinary glass curtain walls, yet the indoor air temperature was lower. Furthermore, when the working temperature of PV cells reaches to a certain level, it slightly deviates the electricity generation trend from the real-time solar radiation trend. Under cloudy conditions, the backsheet temperatures of semi-transparent PV curtain walls and standard glass curtain walls align with outdoor temperatures. Different PV module forms demonstrated that striped and square PV module forms offer better lighting effects than whole forms. The working temperatures among the three were close, with an annual average temperature difference of less than 1 °C and a peak temperature difference of approximately 3 °C, impacting the monthly total power generation by less than 1 %. This study presents a more precise and thorough approach for evaluating semi-transparent PV curtain walls' performance, providing insights for future sustainable architectural design.

Prediction of photovoltaic power using the Johansen VECM cointegration method in the Reduit region, Mauritius

ABSTRACT Accurate forecasting of the photovoltaic (PV) power output is crucial for the stability of power systems. In this paper, the Johansen vector error correction model (VECM) cointegration approach is used to predict the PV power output of a PV system in the tropical island of Mauritius. The predicted PV power output is compared to measured PV power output under real conditions. Only solar irradiation and PV cell temperature are considered as predictors and data is recorded from a 20 kW PV system installed in the Phoenix region in Mauritius. The performance of Johansen model is then compared to a conventional ANN forecasting model. Results showed that the Johansen VECM cointegration approach outperformed the Artificial Neural Network (ANN) model with RMSE and MAE values of 298.9W and 221.6W, respectively. The Johansen model proved to be a powerful tool, showing good potential for PV power forecasting.

Modeling, experimental investigation and real-time control of active water cooling system for photovoltaic module

ABSTRACT Photovoltaic (PV) cells are integral in harnessing solar energy, yet their performance is hindered by excessive heat generation, impacting efficiency and sustainability. Addressing the challenge of efficiency loss in photovoltaic (PV) cells due to overheating, this study focuses on optimizing active water cooling control for PV modules. The aim is to develop a dynamic, sustainable model and integrate a PID controller tuned by Sine Cosine Algorithm (SCA), targeting optimal operating temperatures. This study introduces a dynamic model and a closed-loop control system to manage PV cell temperature, investigating the correlation between water flow and temperature regulation. Experimental data is gathered using a pseudo-random binary sequence (PRBS) as an excitation signal, forming the foundation of an Auto Regressive eXogenous (ARX) model. The closed-loop system incorporates a PID controller and tuned using the Sine Cosine Algorithm (SCA) to optimize performance. The resulting model is rigorously validated through experimental investigation, demonstrating its precision in capturing the system’s dynamics. Moreover, the implementation of a controller-based cooling system substantiates the model’s practical efficacy. The research demonstrates significant improvements when implementing a controller-based water-cooling system for photovoltaic (PV) modules. Compared to the baseline scenario without cooling, the system achieves a 34.5% reduction in average PV temperature (from 59.2°C to 38.9°C) and a 9.46% increase in average power output (from 196.7W to 215.3W). Moreover, this system utilizes only 248.8 liters of water, marking a substantial 64% decrease in water consumption compared to traditional free-flow cooling methods, which use 790.9 liters. The research demonstrates that the controller-based cooling approach is a sustainable option, delivering power output comparable to the free-flow method, yet significantly lowering water consumption. This research signifies a turning point for sustainability, offering an efficient and water-conscious approach for enhancing PV system performance, a crucial step toward a greener and more environmentally responsible energy future.

Experimental investigation of electromagnetic interference impact on phase change material coated solar photovoltaic panel

ABSTRACT High Voltage Transmission Line Electromagnetic Field’s (HVTL-EMF) impact on solar PV performance is analyzed in the proposed work. Depending on HV-EMF, the electrical characteristics and conversion process of PhotoVoltaic (PV) panels are analyzed. Electromagnetic (EM) field from HV lines has a partially positive and somewhat negative impact on solar PV. When absorption probability is low, one photon and one phonon are required. Phonons are vibrational energy units. A solar PV cell made of Silicon (Si) semiconductor material requires phonons to have high conversion efficiency and electric field from HV transmission line induces phonon. In addition, electric field from HV line increases the solar cell temperature (10 to 15°C) to the ambient temperature. Because of high solar PV cell temperature and high EM field, solar PV power generation is affected by increasing panel temperature. The proposed system uses unnatural Phase Change Material (PCM-Glauber salt (Na2SO4.10 H2O)) coated to the backside of solar PV to normalize the temperature. The impact of electromagnetic fields produced by HV lines on solar cells coated with PCM is validated with experimental testing results. The experiment considers a HVTL of 230kV and solar PV rating of 20 W. The experimental results are measured at various distance ranging from 12 to 22 meters between solar PV and HV transmission lines. Conversion efficiency of solar PV with PCM coating under the impact of HVTL- EMF has increased by 3%.

Feasibility study on radioisotope-powered thermophotovoltaic/thermoelectric hybrid power generation system used in deep-sea: From design to experiment

This study aims to comprehensively examine the feasibility of a hybrid power generation system that integrates solar and thermoelectric technologies, with a focus on utilizing a radioisotope heat source (RHU) for deep-sea applications. The investigation encompasses the whole design process as well as rigorous testing procedures. The self-designed coupling device establishes a connection between the silicon photovoltaic (PV) cell and the bismuth telluride (Bi2Te3) thermoelectric modules (TEMs), therefore creating a photovoltaic-thermoelectric (PV-TE) hybrid system. The measuring platform incorporates the system to investigate the influence of varying light intensity (ranging from 400 to 1600 W/m2) on the electrical properties of the system, specifically focusing on three subgroups of PV cell temperature (20 °C, 25 °C, and 30 °C). The findings suggest that the enhanced performance of the photovoltaic (PV) cell and thermoelectric modules (TEMs) under identical PV cell temperatures may lead to an improvement in the electrical performance of the system due to variations in light intensity. Nevertheless, when the temperature of the photovoltaic (PV) cell increases, there is a gradual decline in the growth rate of the output power. The subsequent research provide evidence that there exist discrete mechanisms responsible for the reduction in intensity between thermoelectric modules (TEMs) and photovoltaic (PV) cells. The processes arise due to the impact of thermal effect on the photovoltaic materials and the increased temperature difference between hot and cold junctions of the thermoelectric modules (TEMs), respectively. Furthermore, a control group is incorporated by calculating the performance of the hybrid system under the same operating conditions as the single thermoelectric (S-TE) system. According to a comparative analysis comparing the Radioisotope Thermal-Photovoltaic-Thermoelectric (RTPV-TE) hybrid power production systems and the S-TE system, it has been shown that the former exhibits a potential increase in output power of up to 271% while operating under identical conditions. This work offers insights and methodologies for the design and measurement of RTPV-TE hybrid power generating systems in engineering application situations.

An Empirical Approach to Solar Photovoltaic Cell Temperature Prediction

Abstract Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting, especially in commercial power plants. The present study focused on the development of a hybrid machine learning based predictive model for solar photovoltaic cell temperature prediction in solar photovoltaic arrays. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for these parameters and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed to mimic experimentally measured data. Feature selection and dimensionality reduction were performed to reduce the input variables and maintain relevant data in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the Mean Absolute Percentage Error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.08 % while an R2 value of 0.99 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed an average of 25.52 % daily energy difference between a simulation which considered solar cell temperature and that which ignored solar cell temperature.

Optimal Control of Hybrid Photovoltaic/Thermal Water System in Solar Panels Using the Linear Parameter Varying Approach

During photovoltaic (PV) conversion in solar panels, a part of the solar radiation is not converted to electricity by the cells, producing heat that could increase their temperature. This increase in temperature deteriorates the performance of the PV panel. In this paper, a hybrid PV/thermal (PV/T) water system is proposed to mitigate this problem. This system combines a PV panel and a thermal collector. In this paper, we focused on the modeling and control of this hybrid system in the linear parameter varying (LPV) framework. An optimal linear quadratic regulator (LQR) is proposed to control the PV cell temperature around an optimal value that maximises electricity generation. Since the system model is nonlinear, an optimal LQR gain-scheduling state-feedback control approach based on an LPV representation of the nonlinear model is designed using the Linear Matrix Inequality (LMI) method. The goal is to obtain the maximum electrical power for each solar panel. Since a reduced number of sensors is available, an LPV Kalman filter is also proposed to estimate the system states required by the state-feedback controller. The obtained results in a laboratory setup in simulation are used to assess the proposed approach, showing promise in terms of control performance of the PV/T system.

Effects of graphene doping on shape stabilization, thermal energy storage and thermal conductivity properties of PolyHIPE/PEG composites

The development of clean and renewable energy sources has been necessitated by the ever-increasing energy consumption, increasing environmental degradation caused using fossil fuels and concerns over the rise in CO2 spreading. Functional phase change materials (PCMs)' energy storage capacity is appealing owing to their environmental friendliness, superior thermal energy storage (TES) capacity, and capability to improve energy efficiency. A series of hierarchical porous high internal phase emulsion polymer (PolyHIPE) materials with high PCM impregnation capacity were synthesized in this investigation. It has been achieved to encapsulate 80 % by mass of polyethylene glycol (PEG) into the PolyHIPE structure. However, the efficiency of energy storage and conversion systems is affected by a wide variety of factors; one of them is poor thermal conductivity. The development of carbon-based materials like graphene is particularly promising in the disciplines of energy storage and conversion. With this intention, the synthesis of composite PCMs in which the heat conductivity has been enhanced with graphene at three different ratios (wt% 3, 5, and 10) has been performed, conducting comprehensive investigations. The produced composite PCMs have an energy storage capability of over 120 J·g−1 and high thermal and structural stability. In the polyHIPE/graphene structures produced in the absence PCM, the thermal conductivity value exhibits a notable enhancement of up to 125 % due to the increased amount of graphene compared to the polyHIPE material. Following the impregnation of PolyHIPE@Graphene with PEG, it was observed that the thermal conductivity value improved by up to 51.2 % in comparison to PCM. The addition of graphene also gradually shortened the heat storage-release time. In short, herein a series of PCM composites with high energy storage capacity, capable of trapping different PCMs within their structure, has been proposed for TES applications. The manufactured composite PCMs have the potential to be used for a wide range of things, like maintaining the temperature of batteries and photovoltaic solar cells, packaging perishable foods, and providing thermal comfort in textiles and medicinal materials.

Experimental and numerical study of enhancing the electrical performance of photovoltaic panel using phase change material in transient conditions

ABSTRACTThis study investigates employing a phase change material (PCM) to enhance the electrical power production and efficiency of photovoltaic (PV) panels. The PCM was placed at the backside of the collector, and the actual PV cell temperature values were recorded and evaluated by comparison in both cases with and without PCM. During the daytime and according to experimental findings, the existence of PCM decreased the cell temperature by an additional 9.5°C compared to a standard PV panel. The maximum generated power values using the hybrid PV/PCM system were 38.76 W on August 5th and 47 W on October 23rd, respectively. Conversely, the conventional PV generated only 35.2 W and 37 W on the same days. The results also revealed that the best electrical efficiencies were reached using the PV/PCM system at values of 12.1% and 15%, compared to 9.2% and 11.9% for conventional PV on the same days. A 1.38% greater energy production efficiency was reached using the hybrid PV/PCM system. Finally, using a numerical approach, the differential equations governing the energy exchange between the different layers were solved for both systems, and an excellent agreement for both the numerical results and the experimental data was confirmed.

An integrated cooling mechanism for photovoltaic systems

Photovoltaic (PV) panels, despite their potential as a renewable energy source, suffer from performance degradation due to temperature rise. This research proposes an integrated passive cooling system that combines finned convective and radiative cooling with graphite-infused phase change material (PCM) in a cascade configuration. The cascade arrangement creates temperature stratification, delaying and restricting temperature increases. Graphite-infused PCM enhances thermal conductivity, facilitating efficient heat dissipation. Additionally, a finned heat sink augments convective heat transfer between PCM and the ambient environment, increasing the heat transfer area. A thin silica layer with pyramid lattices is incorporated to improve radiative heat transfer, particularly beneficial in hot climates with a significant temperature difference between the PV cell and the sky. A mathematical model is developed and validated against experimental data to assess the system’s performance. The model simulates PV cell temperature variations for six different scenarios to identify the most effective cooling mechanism. The study demonstrates a remarkable 27 °C reduction in peak PV cell temperature, translating to a substantial up to 100% increase in overall PV cell efficiency. This research contributes to the evolving landscape of PV cooling technologies and provides valuable insights for optimizing PV panel performance.

PV thermal regulation and water harvesting hybrid system using evaporative cooling of silica gel layer

A proposed system for a passive cooling technique of photovoltaic (PV) via desorption cooling and water harvesting using a silica gel layer is investigated numerically. The harvested water is discussed, and the performance of the desorption-cooled PV is compared with the naturally cooled PV cell. The silica gel layer provides the water needed for evaporative cooling, so the proposed system can be used where water does not exist. The system model is presented, solved using MATLAB, and validated. The impact of the ambient conditions (ambient temperature and relative humidity), solar intensity, and silica gel layer thickness on the PV performance and silica gel uptake are presented for constant conditions and transient input weather conditions. The findings show a considerable reduction in the cell temperature and an enhancement in the cell efficiency. Also, The harvested water quantities are obtained at different operating conditions and layer thicknesses. At constant input conditions, a maximum reduction in the average PV cell temperature of 16.18 °C and an efficiency enhancement of 8.76% are achieved using a silica gel layer of 1 cm thickness. In addition, the maximum harvested water is reported as 1.238 kg/m2 for 2 cm layer thickness. For transient input weather conditions, the same performance enhancement with a reduced daily average cell temperature of 7 °C and an efficiency enhancement of 4.8% is achieved at the different silica gel layer thicknesses during 13 hr of simulation. Also, the maximum harvested water is reported as 2.35 kg/m2 for 3 cm thickness. Increasing the silica gel layer thickness influences the cell performance adversely under constant operating conditions, while this effect vanishes for actual transient input weather conditions. In contrast, increasing the thickness layer can increase the harvested water, which depends on the incident flux.

Assessing the combined effects of local climate and mounting configuration on the electrical and thermal performance of photovoltaic systems. Application to the greater Sydney area

Extremely high urban temperatures adversely affect photovoltaic (PV) system performance. Accurate PV cell temperature assessment relies on local weather conditions, exacerbated by urban overheating, often overlooked by inadequate temperature models and non-local data. This study investigates the electrical and thermal PV performance, considering mounting configurations and local conditions. Data from ten weather stations in Greater Sydney (NSW) during 2016–2017, including a hot summer, are used. The Sandia model is used to predict cell temperatures and power output for four mounting configurations, from open rack to building-integrated (BIPV). A PV thermal model is implemented to analyse daytime convection, crucial for understanding PV impact on local climate. Results show peak cell temperatures of 60 °C (open rack) to over 90 °C (BIPV), causing up to 50% power loss and 11% reduction in monthly performance ratio. Local climate variations impact PV energy output up to 6%, with mounting configuration effects up to 11%. Daytime convective flux averages 150–180 W/m2, peaking at 700 W/m2. Convective release varies up to 22% based on local climate, generally higher for open rack than close roof mounts, with potential reversals under low wind speed conditions. These findings can improve the knowledge of PV performances in urban areas facing extreme temperatures.

Daily and annual performance analyses of the BIPV/T system in typical cities of Sudan

• A mathematical model of the BIPV/T system is established. • The energy-saving potential of the system for different regions of Sudan is analyzed. • Wadi Halfa has the highest total annual electrical power output. • The BIPV/T HVAC system satisfies a high percentage of the heating needs of Sudan. In this paper, a numerical study is carried out to investigate the performance of a coupled BIPV/T-AHU system in Sudan. A mathematical model was utilized, Matlab Simulink was used to do simulation. Results showed good agreement with experimental data from the literature. The problem studied in this paper is reducing the energy required for heating in winter (preheating BIPV/T) and avoiding the high photovoltaic cell temperature in summer. We compare the energy consumption with and without BIPV/T-AHU in different cities in Sudan. The results showed that utilizing the exhaust air to cool the photovoltaic cell could reduce the PV/T cell temperature in the range ( 9 − 12 ) ∘ C , which can increase the electrical power output in a range of ( 12 − 21.44 ) W / m 2 . In winter, utilization of the preheating BIPV/T system can decrease the heating load in Wadi Halfa in the range of ( 6 − 107.1 % ) . Damazein does not need a high heating power in the daytime for the air conditioning system depending on the local climate, so the heat energy produced by the system can be used for drying, desiccant cooling, or heating water, with increasing the electric power produced by cooling PV/T cells. The results indicated a great possibility to use the BIPV / T system under the studied conditions, in addition, this study provides important information for the application of the BIPV / T system in these areas.