This work is a detailed modeling and simulation of the PV cell and module. It is implemented under MATLAB/Simulink environment; the most used software by researchers and engineers. This model is first drafted in accordance with the fundamentals of semiconductors and the PV cell technology. In other words, the PV module parameters have been selected according to their variation with illumination and temperature. It means that for any type of PV module, one can use this model and determine all the necessary parameters under any new conditions of irradiance and temperature and then obtain the I(V) and P(V) characteristics. This model can be considered as a tool which can be used to study all types of PV modules available in markets, and especially their behavior under different weather data of standard test conditions (STC).
The PV module is the interface which converts light into electricity. Modeling this device, necessarily requires taking weather data (irradiance and temperature) as input variables. The output can be current, voltage, power or other. However, trace the characteristics I(V) or P(V) needs of these three variables. Any change in the entries immediately implies changes in outputs. That is why, it is important to use an accurate model for the PV module.
The wellknown fiveparameter model is selected for the present study, and solves using a novel combination technique which integrates an algebraic simultaneous calculation of the parameters at standard test conditions (STC) with an analytical determination of the parameters under real operating conditions. A monocrystalline solar module will be simulated using MATLAB/Simulink software at different ambient temperature and the output power of cell was recorded. Solar Radiation and its effect on power of module is also simulated. Simulation shows that the output power of solar cell get decreased with decrease in sun’s radiation and raising temperature also decreases the output. In addition, the simulation performance of the model will be compared with other models, and further validated by outdoor tests, which indicate that the proposed model fits well the entire set of experimental field test I–V curves of the PV module, especially at the characteristic points.
Content
Abstract
Chapter one
Introduction
1.1 Motivation
1.2 Electrical energy
1.3 Electrical energy consumption statistics
1.4 Energy generation
1.4.1 Solar energy
1.5 Contributions of the thesis
Chapter two
Literature review
2.1 Invention of solar cell
2.2 Semiconductors
2.3 PN Junction Diode
2.4 Modeling of the solar cell
2.2.1 Modeling photovoltaic systems
2.5 SIMULINK based modeling of circuits and systems
24 PV power output dependence on module operating temperature
Chapter three
Theory of photovoltaic solar energy
3.1 Solar energy conversion
3.1.1 Solar radiation
3.1.2 Photovoltaic (PV) cell operation
3.2 Modelling of PV devices
3.2.1 Ideal PV cell
3.2.2 Modelling of modules and arrays
3.3 Impact of environmental parameters on a PV Cell
3.4 Impact of other parameters on a PV cell
Chapter four
Experimental work and simulink implementation of solar module
4.1 Photovoltaic module and solar module analyzer
4.2 Solar power meter
4.3 PV Module temperature sensor
4.4 Experimental steps
4.5 Simulink
4.6 Block diagram of PV charging of a battery
4.7 Simulation of a solar cell in Simulink
4.8 Components of solar module Simulink
4.8.1 Solar Simulink
4.8.2 Irradiance
4.8.3 Current and voltage sensors
4.8.4 Solver configuration
4.8.9 Scope
4.9 MATLABSIMULINK model of PV solar module by Matlab
4.9.1 Block diagram of reference radiation and temperature
4.9.2 Subsystem of thermal voltage
4.9.3 Block diagram of module operating temperature
4.9.4 The PV module shunt resistance model (system 1)
4.9.5 The PV module photocurrent model (system 2)
4.9.6 The PV module open circuit model (system 3)
4.9.7 The PV module saturation current model
(system 4)
4.9.8 The PV module photovoltaic current model (system5)
4.9.9 The PV module photovoltaic ideality factor model (system 6)
4.9.10 The final Simulink model
Chapter five
Result and discussions
5.1 Overview
5.2 Experimental results
5.3 Extraction of module five internal parameters
5.4 Validation of fiveparameter model
5.5 Modeling of operating temperature in Matlab Simulink
5.5.1 Ambient temperature and wind speed effect on operating temperature of PV solar module
5.6 Comparison of maximum power validation with some previous studies
5.7 Summary
5.8 Effect of the weather condition on cell temperature
5.9 Effect of operation temperature and solar radiation on internal five parameter solar module.
Chapter six
Conclusion and recommendation for future work
6.1 Conclusions
6.2 Future research areas
References
Abstract
The PV module is the interface which converts light into electricity. Modeling this device, necessarily requires taking weather data (irradiance and temperature) as input variables. The output can be current, voltage, power or other. However, trace the characteristics I(V) or P(V) needs of these three variables. Any change in the entries immediately implies changes in outputs. That is why, it is important to use an accurate model for the PV module.
The wellknown fiveparameter model was selected for the present study, and solved using a novel combination technique which integrated an algebraic simultaneous calculation of the parameters at standard test conditions (STC) with an analytical determination of the parameters under real operating conditions. A monocrystalline solar module was simulated using MATLAB/Simulink software at different ambient temperature and the output power of cell was recorded. Solar Radiation and its effect on power of module is also simulated. Simulation shows that the output power of solar cell get decreased with decrease in sun�s radiation and raising temperature also decreases the output. In addition, the simulation performance of the model was compared with other models, and further validated by outdoor tests, which indicate that the proposed model fits well the entire set of experimental field test IV curves of the PV module, especially at the characteristic points. After validation, this model was employed to predict the PV system power output under real conditions. The results show that the predictions agree very well with the PV plant field collected data.
Chapter One
Introduction
1.1 Motivation
Energy is a burning issue in the present world, since all nonrenewable sources are going to become extinct due to our overuse. Hence we should depend on renewable energies among which solar energy is the most plentiful source. There is research indicating that if we could store solar energy for a day all over world it will be enough to give electricity for a year but it is very difficult to store the generated energy. In this project a Simulink model for a solar charger for a mobile phone is designed. The mobile phone has become a necessary component in our present life. If we can charge the mobile battery with solar energy then we can save substantial energy. In this research Simulink based modeling is explored for the design and simulation of a solar charger (Tarak, et. al., 2012).
An important component to be used in this model is the photovoltaic cell which converts solar energy into electrical energy at the atomic level. When light falls on these cells they absorb photons from the light and release electrons. If these electrons are collected they form an electric current flow which can be used as electricity. Hence they are also known as solar cells. These cells are made of semiconductor materials like silicon. A silicon wafer with positive charge on one side and negative on the other side can be said to act as a diode electrically. When photons strike the wafer, electrons are emitted and if we create a closed circuit between the positive and negative sides of the wafer (Saraju, et. al., 2015) then the electrons flow from negative to positive creating an electric current. This current can be filtered and amplified to produce enough power to charge a mobile device.
1.2 Electrical energy
Electrical energy is the most essential form of energy we use in present society.
Electric energy can be described as energy stored in charged particles moving in an electric field. It is easy to transmit and when electrons move in a conductor they produce electric current. Hence when electrons are forced to move in a conductor they produce electricity (Halliday, et. al., 1996). We use different forms of energy to do this. Generally electric energy is produced by converting other forms of energy (coal, nuclear, solar, wind, hydroelectric etc.).
1.3 Electrical energy consumption statistics
In the past ten years energy consumption has increased substantially. Table 1.1 shows a summary of the electricity consumption for the past ten years. From Figure 1.1 we observe that power consumption has increased by 30% in ten years (Vipin, et al., 2014). If this increase is continuous, it will be very difficult to generate the energy required to serve every one. Every second in our life is being attached to electrical energy; electricity is a basic need which is not always available for 40% of world population (Enerdata Information Services, 2015). Hence energy conservation is very important.
Table 1.1 World power consumption (Vipin, et al., 2014).
Abbildung in dieser Leseprobe nicht enthalten
1.4 Energy generation
Electrical energy is generated by forcing a charged particle moving in an electric field. Generally electrical energy is produced by rotating turbines using various methods. According to the above data it is clear that huge amounts of energy are consumed hence huge amounts of energy should be generated from available but limited resources. Since it has become a most essential commodity, with the increase in technology the demand for electric energy has increased rapidly. Hence generation of energy has become a very difficult task (JIANG, et al., 2009).
Abbildung in dieser Leseprobe nicht enthalten
Figure 1.1 World power consumption trends over the last 10 years (Vipin, et al.,2014).
1.4.1 Solar energy
Solar energy is the most abundant source of energy. It can be used in two ways to generate energy. It can be used to boil water and rotate turbines. Solar energy can also be used to generate electricity by using the photovoltaic effect. For this an electronic component called the photovoltaic cell (Zghal, et al. 2012) is needed which absorbs photons from the sunlight and breaks electron pairs which make charged particle move. They can be converted into DC electrical energy by using this effect. Photovoltaic cells are made from semiconductor materials. Solar energy is available everywhere and but photovoltaic cells are very inefficient. Constructing solar plants requires huge amount of investment hence solar energy power plants are limited. About 4% of the world's electricity is generated from solar energy. Solar Energy Solar energy is the most abundant source of energy; it is an inexhaustible energy source which is also environmentally friendly. Since 2010 solar energy use has been encouraged. After a few decades we will be out of all the nonrenewable sources which makes us to depend on solar energy (Haque, et al., 2013) more and more as time goes by. By 2050 solar energy will be the leading energy source. Hence solar power plants should be encouraged. The only obstacle for this is the efficiency of solar power plants. They need a very large initial investment, but it is not recurring.
1.5 Contributions of the thesis
The overall objective of this thesis is to design a Simscape based Simulink model of a silicon monocrystillaine solar module. In the part stage of the proposed work the Theory of photovoltaic solar energy studied. Then the behavior of a solar cell is simulated in Simulink. In the third part a MPPT is designed using Simulink R, followed by the design of a DCDC converter which completes the whole design. The novel contributions of this thesis include the idea to reduce the usage of non renewable energy sources by using solar energy.
Chapter Two
Literature Review
2.1 Invention of solar cell
Solar energy was first commercially used by Sir Frank Shuman in 1897 to generate energy by converting water into steam to run a steam engine by using dark colored pipes and mirrors. This is all mechanical energy generation. Before this happened, in 1839 the photovoltaic effect was demonstrated by Sir AlexandreEdmond Becquerel. The first solar cell was built by Aleksandr Stoletov in 1888 based on the photoelectric effect proposed by Heinrich Hertz (Peter Gevorkian et al., 2007). In 1905 Albert Einstein presented a paper on carrier excitation due to light for which he received the Nobel Prize in Physics in 1921. Later in 1946 Russell Ohl patented the junction semiconductor solar cell. In 1954 the first photovoltaic cell was demonstrated publicly at Bell Laboratories by Daryl Chapin, Calvin Souther Fuller and Gerald Pearson.
In the early 1960's solar cells where used in space applications only since they were costly. Later, due to the invention of integrated circuits, the cost reduced substantially (David Feldman et al., 2014).
2.2 Semiconductors
A semiconductor is a material that has special properties, with its electrical conductivity value between that of an insulator and that of a conductor. This is of huge importance in modern electronics. The IV A group elements silicon and germanium are the generally used materials to construct semiconductor devices, since they have four valence electrons in their outermost shell which gives them the ability to lose or gain an equal number of electrons at the same time. Mostly silicon is used in semiconductor devices, because it is the most abundant material
on earth (Jacob Millman et al., 1967). Silicon material can act as insulator, conductor and semiconductor by selective doping. Group III or V elements are doped into silicon to obtain a semiconductor. In this process impurities are added to silicon to change its conductivity and are called extrinsic semiconductors. There are two types of extrinsic semiconductors:
An ntype semiconductor is formed by doping an intrinsic semiconductor with group V elements which have five valence electrons, hence there will be an excess or free electron. On the other hand, the ptype semiconductor is formed by doping an intrinsic semiconductor with group III elements which have three valence electrons, hence it will be ready to accept an electron resulting in holes as the majority carrier (Godse et al., 2009). By thermal variations the electrons acts as minority carriers while a hole indicates the absence of an electron. In ptype semiconductors the Fermi level is below the Fermi level in intrinsic semiconductor. Hence the Fermi level is closer to the valence band compared to the conduction band. Generally Boron which is a group III element is doped into silicon to get ptype semiconductors.
2.3 PN Junction Diode
When ntype impurities which have donor impurities are added to silicon they form an ntype semiconductor with electrons as the majority carriers. When ptype impurities which have acceptor impurities are added to silicon they form a ptype semiconductor with holes (positive charge) as majority charge carriers. When these two types of semiconductor are joined, they form a PN junction diode (Saraju et al. 2015). The impurities in ntype which are electrons recombine with ptype impurities which are holes and form a depletion layer which stops further recombination.
The diode can be operated in two regions: forward bias or reverse bias as shown in Figure 2.1. In forward bias the ptype semiconductor is connected to the positive terminal and the ntype is connected to the negative terminal of the battery. At a certain voltage called threshold voltage, the diode acts as a conductor as seen from the IV characteristics in the graph. For reverse bias the ptype semiconductor is connected to the negative terminal and the ntype is connected to the positive terminal of the battery. The electrons from ntype and holes from ptype are pulled by the battery since they are connected to opposite charge which increases the depletion region (Godse et al., 2009) and the diode acts as an insulator up to a certain voltage. After that voltage, due to overheat it may cause thermal damage. The illustration of IV characteristics of a diode covering both its forward and reverse bias modes are depicted in Figure 2.2. The IV characteristic of a diode generated from a Simulink simulation is shown in Figure 2.3.
Abbildung in dieser Leseprobe nicht enthalten
Figure 2. Diode biasing (Godse et al., 2009).
Abbildung in dieser Leseprobe nicht enthalten
Figure 2.2 Illustrative I V characteristics of a diode for forward and reverse bias conditions (Godse et al., 2009).
Figure 2.3 IV characteristics of a diode for forward conditions generated using Simulink (Godse et al., 2009).
2.4 Modeling of the solar cell
In 1981 V.Cordes and K. P. Maass published a paper which discussed photovoltaic Power (Cordes et al., 1981) and proposed to use solar energy for telecommunication systems. In 1988 the conference on solar cells (Kerschen et al.,1988) [26] named by \Photovoltaic Specialists Conference, 1988" contained a large body of research on solar cells and the photovoltaic effect. In 2000 at the Eighth International IEEE conference on Power Electronics and Variable speed Drivers (Lloyd et al., 2000) , a Simulink model of a PV cell has been published which gives a detailed view of the cell. In 2002 a theoretical analysis of PV systems is provided in \IEEE Transactions on Energy Conversation 2002" which gives a number of related papers (Masoum et al., 2002).
2.4.1 Modeling photovoltaic systems
There are three main models usually used to study a PV cell/module; onediode model, twodiode model and empirical model (Metwally et al., 2005). Empirical PV cell model is widely used in modelling due to its simplicity and limited number of parameters, however it is not considered the most accurate. Whereas, the two diode model is very accurate, but not frequently used, for its complexity. It is mainly suitable for studies that require detailed cell information. Most researches use onediode model as it considers all the needed parameters to accurately model a PV that could used to identify the impacts of PV systems on the electric network. Four main parameters are usually what differ between models; shunt resistance (Rsh), series resistance (Rs), ideality factor (A) and the reversed saturated current (IOR).
Several researches used the empirical model to facilitate the modelling procedure. References (Metwally et al., 2005), (Hernanz et al., 2007), (Walker et al., 2001), (Gonzalez, 2006) and (Ritchie et al., 2007) all used the empirical model and applied it on Matlab, by eliminating the shunt resistance. The accuracy of each differed due to the variation in parameter calculations. (Metwally et al., 2005), (Hernanz et al., 2007), and (Walker et al., 2001) calculated the Rs by using dV/dI at Voc point on the IV curve, whereas for the ideality factor was kept as a random number between (12) that set to achieve the best curve match. In (Ritchie et al.,2007) two models were simulated; the first model is kept to adjust A for curve best fitting. It used temperature and solar radiation correction values for voltage and current, in a similar manner to (Atlas et al., 2008). The second model calculated A and Rs and used temperature dependence of voltage. By comparing the simulation results from both with experimental results, it was concluded that the second model was the most reliable one.
(Sera et al., 2007), (ElTayyan et al., 2001), (Houssamo et al., 2010), (OrtizRivera et al., 2005), (Wang et al., 2011), and (Autier et al., 2004) proposed a PV model using single diode based on datasheet values. In (Sera et al., 2007), and (ElTayyan et al., 2001) all parameters were modelled into a series of equations that were solved simultaneously. The obtained values have been used in the implemented model which exhibited a very good agreement with all the specifications given in the product datasheet. The algorithm was accurate but very complicated. Whereas in (Houssamo et al., 2010) , an accurate model of IOR was presented. However, Rs, Rp and A are assigned values for which the model gives least error. A novel method for the determination of Rs is presented in (Wang et al., 2011). The results was compared to actual values and showed to be relatively similar.
In (Saad Alam et al., 2010), (Rustemli et al., 2011), (Nema et al., 2011), (Ishaque et al., 2010) and (Liang et al., 2008) Matlab/Simulink was used to accommodate the implemented PV model. The author of (Liang et al., 2008) developed a dynamic PV model that simulated up to 21% more energy than static model when using actual weather data. Static PV models, are those of constant temperature and solar radiation inputs, whereas dynamic PV models are those with a varying temperature or solar radiation input. Whereas (Rustemli et al., 2011) implemented an accurate onediode model, but for a constant ideality factor and Rs. In addition, (Nema et al., 2011) carried out a study on PV cells, modules and array with experimental verification. A comprehensive behavioral study is performed under varying conditions of solar radiation, temperature, varying diode model parameters, series and shunt resistance.
On the other hand, (Ishaque et al., 2010) is based on twodiode model, although greater accuracy can be achieved using this model, yet it requires the computation of more parameters than the other two models. However, this paper proposed an improved twodiode model with reduced number of parameters. The Rs and Rp are estimated by an efficient iteration method.
There are other methods used to identify the photovoltaic model parameters, such as Robust Linear Regression Methods (Carmela et al., 2009) or using Fuzzy Regression Model (Fawzy et al. 1988). In (El Shahat et al., 2010) , the author used Artificial Neural Networks (ANN) technique to model an empirical PV model on the Matlab. However, the model was more accurate than previous proposed models for the extra considerations taken, such as; dividing the open circuit voltage on number of cells and calculating the ideality factor and Rs through mathematical equations. Yet, the Rs equations are considered complicated.
There are other environmental parameter that affect the power output of PV; shading and dust. The author of (Eissa et. al, 2004) studied the influence of long term dust accumulation on the surface of photovoltaic module. Besides, an investigation was carried out to determine the acceptable suitable interval between each two successive cleaning processes. In addition, a comparison was carried out between two cleaning methods; dry and wet. In (ElSayed et al., 2006) a simulation of uniform shading is presented. A comparison between results of a twodiode model simulation and experimental results was carried out and were of approximately the same values. (Patel et al., 2008), also modeled the performance of a PV array taking in considerations temperature, solar radiation, shading and the array's configuration. Results showed the impact of variation of parameters on IV and PV characteristic curves of the PV (Martin et al., 2010) studied how to optimise the energy yields from photovoltaic panels and proved that MPPT is of vital importance in ensuring optimal performance of any PV array.
2.5 SIMULINK based modeling of circuits and systems
Circuits and systems in general can be modeled at various levels of abstraction (Saraju et al., 2015). The abstractions allow a divideandconquer mechanism to handle large and complex circuit and system design and simulation. The modeling is also possible in a behavioral fashion as well as structural fashion. Behavioral simulation and modeling is faster, but does not capture any structural details and hence synthesizing circuits or systems out of behavioral models is a very difficult task. On the other hand, structural modeling captures the structure of the circuits and systems and hence can be taken to a level at which they can be built as an actual entity. However, it can be difficult to obtain at description of a circuit and system without following some sort of hierarchical mechanism.
SIMULINKR has been explored for nanoelectronic circuit modeling in (Joshi et al., 2015), (Shital et al., 2015). In this work, it is advocated that Simulink or Simscape based modeling can allow high speed simulation of circuits and systems. It can be performed using very minimal computational resources as compared to the case of a SPICE. SIMULINK models do not require any fab data; rather they rely on first principle models published in the physics/semiconductor literature. A specific case study of a grapheme based nanoelectronic system, a 45nm based LC VCO has been presented.
In 2001 Geoffrey Walker proposed a Maximum Power Point Tracking (MPPT) converter topology using MATLAB pv models (Geoffrey et al., 2001). Based on this research Francisco modeled a photovoltaic module using MATLABR (Francisco et al., 2005). Later an article which proposed a Simulink model of solar photovoltaic cells in the \International journal of Renewable Energy Research" gave a complete model of a solar cell using Simulink R. Based on that research an article was published in the \International Journal of Engineering Sciences and Research Technology" which gave a model of solar photovoltaic arrays for battery charging applications using Simulink (P.Sathya et al. 2013). This article is a good example model of a solar mobile charger using Simulink.
2.4 PV power output dependence on module operating temperature
The prediction of PV module performance in terms of electrical power output in the field, that is, the deviation from the standard test conditions reported by the manufacturer of the module, is modeled in a manner analogous to the above. For example ,the form of Equation 2.1, is
Abbildung in dieser Leseprobe nicht enthalten
(2.1)
in which tpv is the transmittance of the PV cells outside layers (Jie et al., 2007). Table 2.1 lists a number of correlations found in the literature for PV electrical power as a function of cell/module operating temperature and basic environmental variables. Many of them are linear and similar to Equations 2.1, while others are more complex, such as the following nonlinear multivariable regression equation (Rosell and Ibanez, 2006),
Abbildung in dieser Leseprobe nicht enthalten
(2.2)
resulting from an analysis which addresses the fact that the cells within a module are not identical. (Here, dj, j = 14 and m are model parameters.) Another unusual nonlinear correlation (Furushima et al., 2006) gives a correction coefficient for the output power  as defined by Equation 2.2 of a water cooled PV system, namely,
Abbildung in dieser Leseprobe nicht enthalten
(2.3)
in which Vc and Ic are the output voltage and current, respectively, while the parameter CTc takes values 1 or 3, for values of Tc below or above 50oC, respectively. Aside from correlations like those listed in Table 2.1, there are a few expressions which are trying to predict the power output of PV modules without direct reference to temperature, either the operating temperature or the ambient one. (Two such equations are given in the "notes" at the end of Table 2.1) It is expected that correlations of this kind over predict the performance of the modules. With regard to the wind's indirectly beneficial effect of lowering the operating temperature by forced convection and, thus, increasing the power output of the modules, it is considered in only two correlations among those in the listing of Table 2.1 which by no means is exhaustive. The relevant expression, that of the photovoltaics for utility scale application (PVUSA) model (Farmer, 1992), is of the form
Abbildung in dieser Leseprobe nicht enthalten
(2.4)
In this nonlinear equation, Vf is the freestream local wind speed, i.e., it is measured at a height of 10 m above ground, and the regression coefficients bj, j = 14 are determined using solar radiation flux values above 500 W/m2 (Meyer and van Dyk, 2000). In contrast, the wind speed is taken into account in many correlations for the efficiency (Table 2.2), either directly or indirectly, i.e., through the forced convection coefficient component of UL.
Table 2.2 PV anay efficiency as a function of temperature (Skoplaki et aJ., 2009).
Abbildung in dieser Leseprobe nicht enthalten
Chapter Three
Theory of Photovoltaic Solar Energy
The Sun is one of the most significant sources of renewable energy. In one hour the Earth receives enough energy from the Sun to meet its needs for nearly a year. A Photovoltaic (PV) cell is a semiconductor device that directly converts the energy of solar radiation into electric energy. In general, an element that converts sunlight into electricity is called a PV device. The fundamental PV device is the PV cell, while a set of connected cells form a panel or module. As an array either a module or a set of modules can be considered (Zeman, 2011).
The purpose of this chapter is to provide an introduction into the photovoltaic solar energy and to present a brief introduction to the behavior and functioning of the PV devices, without the intention of providing indepth analysis of the PV phenomenon and the semiconductor physics.
3.1 Solar energy conversion
Photovoltaic (PV) energy conversion is often described as the direct conversion of solar radiation into electricity, by means of the photovoltaic effect. Generally, the term photovoltaic effect refers to the generation of a potential difference at the junction of two different materials in response to visible or other radiation. Thus, the broad study area of solar energy conversion into electric energy is denoted as photovoltaics (Zeman, 2011).
3.1.1 Solar radiation
As explained above, the basic process of solar cell operation is the generation of the electron hole pairs as a result of the absorption of visible or other electromagnetic radiation by a semiconductor material (Zeman, 2011). The Sun is a light source with a radiation spectrum that can be compared to the spectrum of a black body at a temperature of nearly 6000K.
Abbildung in dieser Leseprobe nicht enthalten
Figure 3.1 Spectral distribution of the black body radiation and the Sun radiation in the extraterrestrial space (AM0) and on Earth's surface (AM1.5)( Möller, 1993).
A black body absorbs and emits electromagnetic radiation in all wavelengths and its theoretical distribution of wavelengths can be described by Planck's law. In Figure 3.1 the spectral distribution of the black body radiation compared to the extraterrestrial and terrestrial solar radiations is shown.
The spectrum of the sunlight on the surface of the Earth is influenced by different factors, like the variation of temperature on the solar disc and the influence of the atmosphere, making the study of the effect of the solar radiation on PV devices quite complicated.
[...]

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