A thermal model of hybrid cooling systems for building integrated semitransparent photovoltaic thermal system

Effect of water flow on building integrated semitransparent photovoltaic thermal system with heat capacity

Exergy analysis of building integrated semitransparent photovoltaic thermal (BiSPVT) system

By using numerical simulation, the operating temperatures of a thin-film solar cell based on CuInSe2 have been determined and the solar radiation density values, at which stabilization of the temperature operating conditions of the thin-film solar cell is not required, have been optimized. The maximum possible efficiency value of ~14.8 % is achieved under actual operating conditions, and is maintained by the incoming thermal energy as both emitted in this cell and infrared radiation of the sun and the environment. A model of the proposed thin-film solar cell was implemented in the COMSOL Multiphysics program environment with the use of the Heat Transfer Module. The operating temperatures of the solar cell without thermal stabilization under conditions of the diurnal and seasonal variations of both the ambient temperature and the power density of the AM1.5 solar spectrum have been determined. The maximum value of this power density was varied from 1.0 to 500 kW/m2 when using concentrators. The obtained values of operating temperatures of the thin-film solar cell were used to determine its main parameters in the SCAPS-1D program. The graphs of the operating temperature, efficiency and fill factor of the thin-film solar cell versus the solar radiation density are provided. It is shown that in order to obtain the highest possible efficiency of a solar cell, it is necessary to use concentrated solar radiation with a power density, the maximum value of which should be 8 kW/m2 in July and 10 kW/m2 in January. In the case of lower and higher values of power density, an appropriate thermal stabilization of the cell under consideration is necessary. The dependencies of efficiency, fill factor and open-circuit voltage versus the stabilization temperature of the solar cell, temperature gradients at the interfaces of the thermoelectric layer were also calculated. It is shown that by choosing optimal values of the thermal stabilization, the efficiency of the proposed solar cell may be about 15 % or more.

A new model has been developed in this paper to evaluate the impact of different photovoltaic modules on room, floor, and solar cell temperature, along with daylight savings and solar cell electrical efficiency. Computations have been carried out on a building-integrated semitransparent photovoltaic thermal system integrated with the roof of a room for Varanasi climatic conditions in winter months. The electrical efficiency of the module and temperature coefficient impact the system's performance significantly. Mono-Si, poly-Si, amorphous Si, and Photovoltaic Thermal (PVT) systems are discussed. Compared with the previous model, the results of the study indicate that there is a 4.39% decrease in the overall exergy efficiency; hence, the PVT system is recommended for energy efficiency, and m-Si is suitable for electrical exergy.

Semi-transparent photovoltaic modules are integrated with the building’s rooftop with the provision of movable insulation during off-sunshine hours to reduce the heat losses from room to the ambient. A significant rise in room air temperature of about 6°C at desired hours for cold climatic conditions of Srinagar, India was noted in this study. Also, a metallic drum filled with water has been placed inside the room to reduce the room temperature fluctuations, thus reducing the thermal load leveling. The study is also focused to analyse the simultaneous impact of natural ventilation, direct gain and daylight savings through the south-facing window, thus leading to daylight savings and reducing the dependency on artificial modes of illumination. The effect of water mass and packing factor has also been studied on electrical energy and daylight savings. Analytical expressions for water, metallic drum/tank, room, floor and solar cell temperatures have been derived.

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.

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 (R 2) 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 R 2 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.

Performance of solar PV energy system is affected by many factors like solar radiation intensity, solar radiation geometry, temperature, wind speed and direction, relative humidity, dust, etc. Many researchers have been working on the effect of temperature on solar PV cell efficiency and have reported that increased cell temperature tends to reduce the efficiency of solar PV cell. Solar PV cell temperature also considerably deteriorates the mechanical properties of backsheet of solar PV panel. This paper reviews the various attempts to improve the performance of crystalline silicon solar photovoltaic (PV) system. Performance of a solar PV panel when exposed to hot and dry climate is different from performance of similar solar PV panel in cold climate. From solar beam radiation, light is a desired component whereas heat is not. Therefore many researchers have reported cooling techniques for Solar PV system. However, backsheet material of solar PV panel is one of the factors affecting the solar PV panel efficiency which is pulling the attention of many researchers for performance enhancement of Solar PV system. Solar PV cells using Perovskite material have emerged as one of next generation of photovoltaic technologies having capability of significant enhancement in solar cell efficiency.

Actual Calculation of Solar Cell Efficiencies

Energy-related activities are a major contributor of greenhouse gas (GHG) emissions. A growing body of knowledge clearly depicts the links between human activities and climate change. Over the last century the burning of fossil fuels such as coal and oil and other human activities has released carbon dioxide (CO2) emissions and other heat-trapping GHG emissions into the atmosphere and thus increased the concentration of atmospheric CO2 emissions. The main human activities that emit CO2 emissions are (1) the combustion of fossil fuels to generate electricity, accounting for about 37% of total U.S. CO2 emissions and 31% of total U.S. GHG emissions in 2013, (2) the combustion of fossil fuels such as gasoline and diesel to transport people and goods, accounting for about 31% of total U.S. CO2 emissions and 26% of total U.S. GHG emissions in 2013, and (3) industrial processes such as the production and consumption of minerals and chemicals, accounting for about 15% of total U.S. CO2 emissions and 12% of total ...

The temperature in solar cells is one of the main factors affecting their efficiency. Increasing the temperature in solar cells reduces efficiency. According to previously published and recently published studies by our team, with increasing temperature in 5-layer FTO/i-SnO2/CdS/CdTe/Cu2O solar cells, the efficiency has decreased by 8.86% per 100 K. In this research, phase change materials have been used to control the temperature in 5-layer solar cells. Our overall goal in this study is to control the temperature in FTO/i-SnO2/CdS/CdTe/Cu2O solar cells to increase their efficiency. The results obtained using simulations and numerical analysis and comparative analysis show that if one layer is used as a cooling arrangement in 5-layer FTO/i-SnO2/CdS/CdTe/Cu2O solar cells, it reduces the surface temperature of solar cells and increases efficiency.

he purpose of this study is to determine the effect of the addition of reflectors to increase energy production and simultaneously to determine the change in temperature of solar cell as a result of the addition of the quantity of incoming sunlight. Capacity of the solar cell module is approximately 50 Wp, where the test is done from 9 am to 3pm for three days.The reflector's slope angle used is 100 0 , 110 0 , and 120 0 . The results of this study indicate an increase in output power of the solar cell module an average of 2.59% (with 4.42% at maximum).The side effect is the increase in maximum temperature of the solar cells by 0.6 0 C(or 1.59%). Keywords: solar cell, reflector, slope angle, output power, temperature of solar cell

The temperature dependences of the efficiency η of high-efficiency solar cells based on silicon are calculated. It is shown that the temperature coefficient of decreasing η with increasing temperature decreases as the surface recombination rate decreases. The photoconversion efficiency of high-efficiency silicon-based solar cells operating under natural (field) conditions is simulated. Their operating temperature is determined self-consistently by simultaneously solving the photocurrent, photovoltage, and energy-balance equations. Radiative and convective cooling mechanisms are taken into account. It is shown that the operating temperature of solar cells is higher than the ambient temperature even at very high convection coefficients (~300 W/m2 K). Accordingly, the photoconversion efficiency in this case is lower than when the temperature of the solar cells is equal to the ambient temperature. The calculated dependences for the open-circuit voltage and the photoconversion efficiency of high-quality silicon solar cells under concentrated illumination are discussed taking into account the actual temperature of the solar cells.

In this communication, a building integrated semitransparent photovoltaic thermal (BiSPVT) system has been analysed, in which the effect of heat capacity has been studied by placing a water tank inside the room. The water tank helps in achieving better thermal comfort conditions. An analytical expression for water temperature, tank temperature, room air temperature, floor temperature, and the solar cell temperature of the BiSPVT system has been derived to evaluate the thermal load levelling (TLL), monthly thermal exergy, and yearly overall exergy, including natural day lighting. Numerical computations have been carried out for cold climatic conditions of Srinagar, India. Based on computations, the following conclusions have been drawn: The TLL decreases with an increase in the heat capacity (from 0 to 600 kg) by 20.39%. The exergy is minimum in December and maximum in June for all heat capacities of water mass. The thermal energy and electrical energy are the lowest in January. The electrical efficiency decreases by 8.3% with an increase in the water mass from 0 to 600 kg in January, and the average monthly thermal exergy and yearly overall exergy increase with an increase in the heat capacity of water mass.

Solar energy is considered as one of the most promising green energy due to clean, safe, long life, and renewable advantages. Solar cell is an electrical device that converts solar energy directly into electric power on the basis of photovoltaic effect. Fritts built the first solar cell in 1883. Although the initial efficiency was only 1%, it has been exciting to know that the power conversion efficiency of solar cells is endlessly improved since it was reported. Up to now, the efficiency of commercial silicon solar cell is between 10%–18%. The latest research shows that the best efficiency of perovskite solar cell has been improved to be over 20% within several years. As it is well known, the maximum power conversion efficiency of single-junction solar cells are only around 33% according to the Shockley- Queisser limit. With the development of new materials and high technologies, one issue is emerging: What is the ultimate efficiency of solar cells? The highest efficiency of solar cell is possible to break the Schockley-Queisser limit? How to further enhance the efficiency of solar cell? All the questions are hard to answer at present. Surrounding these concerns, this article presents a brief review about the efficiency limits of different solar cells. It might help to understand the ultimate efficiency of solar cells. The details address the basic principle, advantages and disadvantages of single-junction, multi-junction and other new concept solar cells regarding the power conversion efficiency. The review also includes the solar cell materials involving silicon, compound, perovskite, quantum dot, hybrid materials, etc. Finally, we look ahead into the prospect of new concept solar cells and the maximum power conversion efficiency.