Translation of the photovoltaic performance from Northern to Southern Europe

Transcription

1 TALLINN UNIVERSITY OF TECHNOLOGY FACULTY OF CHEMICAL AND MATERIALS TECHNOLOGY DEPARTMENT OF MATERIALS SCIENCE Translation of the photovoltaic performance from Northern to Southern Europe Master Thesis Rami Abuzuhri Supervisor: Andri Jagomägi, PhD Tallinn University of Technology Materials and Processes of Sustainable Energetics 2013

2 Declaration Hereby I declare that this master thesis, my original investigation and achievement, submitted for the master degree at Tallinn University of Technology has not been submitted for any degree or examination.... Rami Abuzuhri

3 TALLINNA TEHNIKAÜLIKOOL KEEMIA- JA MATERJALITEHNOLOOGIA TEADUSKONNA DEKANAAT MATERJALITEADUSE INSTITUUT Fotoelektriliste päikesepaneelide välitestide võrdlus Põhja- ja Lõuna-Euroopas Magistritöö Rami Abuzuhri Juhendaja: Andri Jagomägi, PhD Tallinna Tehnikaülikool Materjalid ja protsessid jätkusuutlikus energeetikas 2013 i

4 Foreword This master thesis was written during spring semester of 2013 under the teaching supervision of Dr. Andri Jagomägi, Tallinn University of Technology. The aim of the thesis is to is to examine the applicability of transferring the performance of different PV modules between different environments, Northern to Southern Europe, based on short time of outdoor measurements.. I would like to express my sincere appreciation and gratitude to my supervisor Dr. Andri Jagomägi for giving me the chance to work at the Tallinn University of Technology PV Outdoor Testing Laboratory, and for his precious tutelage and support. Very special thanks to the researchers from CIEMAT-Spain and INES-France institutes, who provided the raw measurements of PV modules which are the inevitable parts of this study. I would like to dedicate this work to all whom I hold so dear to my heart, and I am very grateful for all efforts done for me from my friends and our faculty members. Finally, words alone cannot express the thanks I owe to my family for their support and encouragement. Without them and a blessing from the one above, it would never have been possible. ii

5 Contents Foreword... ii Abbreviations... v 1. Introduction Background Structure of the thesis Motivation Research objectives Main characteristics of different photovoltaic (PV) technologies CdTe Technology Amorphous silicon technology Mono-crystalline silicon Technology Factors affecting photovoltaic cell efficiency Module Temperature Solar Irradiance Comparison of PV modules used in this study Approaches of rating PV modules Indoor rating Outdoor rating Description of irradiance measurement devices Pyranometer device Sources of uncertainty Solar cell sensor (reference cell) Description of power/energy prediction methods Theory Deviation of module temperature Used Methods Simple method Matrix method iii

6 ZENIT Method A power-rating model for c-si PV modules Parameters model Generic model MOTHERPV Methodology Main approach Experimental setup Description of the outdoor conditions at different sites Data handling Modules specifications Data Validation Stability of PV modules Fitting of model s equations Results and discussion Description of used statistical parameters Amorphous silicon (a-si) PV module Cadmium Telluride (CdTe) PV module Mono-crystalline (mc-si) PV module Development of models accuracy at low levels of irradiance Comparison of measurement procedures Conclusions Résumé Resümee References iv

7 Definitions and abbreviations 2j a-si A AM AOI APE ARC ARC Bandgap CdS CdTe c-si dev ev FF Double junction amorphous silicon Area of PV module Air mass Angle of incidence, The angle formed by a ray of light and the line perpendicular to the surface of PV at the point of impact Average photon energy Anti-reflective coatings Anti-reflective coating, a type of optical coating applied to the surface of PV modules to reduce reflection the energy difference between the top of the valence band and the bottom of the conduction band in semiconductors Cadmium sulfide Cadmium Telluride Crystalline silicon Deviation of the predicted energy from the measured Electron volt, a unit of energy equal to approximately joule Fill factor Gi Plane of array irradiance IEC International Electrotechnical Commission IM,SIT Maximum power point current derived at SIT IMPP Maximum power point current v

8 Inclination angle Solar module inclination angle, measured from the horizontal surface ISC Short circuit current I-V curve JRC kwp LID mc-si MPP ƞ NOCT NREL NREL A current voltage characteristic Joint Research Center Peak power, it indicates the rated output of PV system according to the standard test conditions (STC) Light-induced degradation Mono-crystalline silicon Maximum Power Point Efficiency The nominal operating cell temperature, defined as the module temperature at an ambient temperature of 20 C, plane of array irradiance of 800W/m 2, and wind speed of 1m/s The National Renewable Energy Laboratory National Renewable Energy Laboratory PM,SIT Maximum power point power derived at SIT PMPP Maximum power point power PR PV RE The performance ratio, it indicates the effective energy yield of a module in relation to the reference yield possible for this module A method of generating electrical power by converting solar radiation into direct current electricity using semiconductors that exhibit the photovoltaic effect. Relative efficiency, the ratio of the measured efficiency to efficiency measured at standard test conditions vi

9 RMAE RMBE SIT Solar Irradiance STC STDDEV Relative mean absolute error Relative mean bias error Standard irradiance and temperature conditions The power of solar radiation on surface, measured in watts per square meter (W/m 2 ) Standard test conditions, the conditions under which the electrical parameters of a PV module are measured W/m 2 solar irradiance, AM1.5 standard solar spectrum, and 25 C module temperature Standard deviation TAMB Ambient temperature TC Power temperature coefficient TCSIT Power temperature coefficient derived at SIT TMOD PV Module temperature VM,SIT Maximum power point voltage derived at SIT VMPP Maximum power point voltage VOC Open Circuit Voltage αimpp Current temperature coefficient βvmpp Voltage temperature coefficient vii

10 1. Introduction 1.1. Background The noteworthy expansion of the photovoltaic (PV) industry during the last two decades has led to a large progress in developing new PV technologies with better efficiency and more stable performance under outdoor conditions. This rapid evolution of the solar technology enhances the confidence and the credibility of its utilization, which in turn leads to increased number of installed small and large-scale PV systems all over the world. Due to the existence of high competition in producing new PV technologies, the PV modules must be characterized by a reliable testing system. On one hand, identifying the electrical parameters and efficiency of a PV module by accredited testing laboratory/institution enhances its credibility and its acceptance to the PV technology market. On the other hand, it is important to ensure the outdoor performance of the module by verifying the rated power given by the manufacturer, to investigate the changes in electrical behavior at different climatic conditions, and to quantify the amount of produced energy during the measurements period [1,2]. Different PV technologies have different electrical behavior and different output energy even at the same outdoor conditions; this difference is attributed to the variance in their electrical and optical properties, even for the same technology but different power. The output energy, the specific annual energy yield (kwh/kwp), of a PV module is the most important input to select the optimum PV technology for a specific site and to determine the availability and reliability of the small and large-scale PV systems in the first stage. Thus, the accuracy of energy prediction is getting high value by the end-users and investors due to the direct connection with the financial returns. A highly precise energy prediction of PV modules is fundamental to perform a valid comparison between PV systems with respect to their performance, stability and their final costs. This high accuracy becomes more critical in case of off-grid solar systems to insure the availability of the output power, especially in high latitude locations featured by cloudy weather and low elevation of the sun which means the need of a very good energy prediction at low levels of irradiance. 1

11 Chapter 1 Introduction 1.2. Structure of the thesis This work is divided into eight chapters. Chapter one is an introduction to the motivations and objectives of studying the accuracy of energy prediction for PV modules. It also states the problem of the study which is the lack of comprehensive comparison between new and previously-developed methods in terms of their quality of energy rating for different PV technologies, and in terms of their ability to transfer the performance of PV systems between different climatic conditions, from Northern to Southern Europe, based on short term measurement at complicated weather conditions. Chapter two deals with different type PV technologies, it describes the main characteristics of the used technologies in this study [cadmium telluride (CdTe), mono-crystalline silicon (mc-si), double junction amorphous silicon (2j a-si)]. It also reviews the effect of the environmental parameters, ambient temperature and level of plane of array irradiance, on the performance of PV modules. Chapter three refers the testing approaches of PV modules and shed the lights on the irradiance measurement s devices and the effect of their accuracy on the precision of energy prediction. Chapter four describes the energy rating methods and clarifies their procedures. Chapter five illustrates the followed approach in this work. Chapter six discusses the accomplished results of this work. Chapter seven demonstrates the comparison of measurement procedures at different institutes. Chapter eight is the conclusion of the study and recommendations for possible future work Motivation As mentioned earlier, the accuracy of energy prediction is very important to achieve optimum design of PV systems. Several energy rating methods have been developed to calculate the output energy of PV modules in variant climatic conditions; these methods provided a wellestimate of the outdoor performance of PV module at different sites [3]. To simplify the energy prediction and make it possible at all sites, most of the methods use the PV module parameters measured under the standard test conditions (STC), (1000 W/m 2 solar irradiance, AM1.5 standard solar spectrum, and 25 C PV module temperature), and utilize the module temperature and plane of array irradiance values. Module parameters at STC are not the ideal values under the outdoor conditions; the deviation of the electrical parameters of the module from the standard values at outdoor conditions depends on the optical and electrical characteristics of the module, even for the same technology but different product. Thus, the performance of a PV module under real conditions must be evaluated to obtain more precise 2

12 Chapter 1 Introduction energy rating. This fact has motivated PV researchers to investigate the possibility of transferring the performance of a PV module at specific site to other sites with different climatic conditions. G. Friesen et al. compared eight separate energy prediction methods in terms of their estimated DC energy for four different PV module technologies measured at four different sites, the results showed that the total output energy of all technologies had been predicted with uncertainty ±5% as an average for all methods on an annual basis. However, the discrepancies between the outdoor conditions of the selected sites were not large and CdTe technology wasn t included in the comparison campaign [4]. Additionally, T. Huld et al. have suggested new energy-rating model for crystalline silicon PV modules [5]; this model has been validated for mono-crystalline and poly-crystalline PV technologies in the central European climate, the model gave comparable results in predicting the output energy of c-si modules over one year with a deviation less than 2% from the actual output energy. However, this model hasn t been investigated for other technologies to widen the validation of its applicability in energy prediction of PV modules. All previously mentioned method s approaches do not take into account the secondary effects such as angle of incident and spectral distribution to keep their applicability for all sites, in which the ambient temperature and incident irradiance are available, which hypothetically increases the uncertainty of the final output-energy prediction. Even so, it has been shown that neglecting these secondary effects increases the deviation of the predicted energy to about 0.8% [3]. Likewise, G. Friesen et al. studied the improvement in energy prediction by implementing spectral and angle of incidence effects [6]. The results show that the models gave better accuracy for perfect days (clear sky days), while giving worse results for other days leading to unnoticeable improvement in their overall accuracy. In the latter study, The Tallinn University of Technology PV Outdoor Testing Laboratory (TTU) participated by transferring the measured data of a-si PV module with SUPSI - Institute of Applied Sustainability to the Built Environment (SUPSI-ISAAC), but the included results were concentrating on the overall improvement of the accuracy by implementing the spectral and angle of incidence effects at TTU site. The obtained results showed that the accuracy of the annual energy prediction had been improved by 1.7% due to the utilization of secondary effects. Studying the possibility and accuracy of transferring the module s behavior to different sites is critical especially in the case of significant difference in the climatic conditions between the 3

13 Chapter 1 Introduction testing institute/laboratory and the selected sites. Nonetheless, only limited studies have discussed this topic. On the other hand, it is important to compare the measurements infrastructure and cross-check the measurements results between different institutes. This comparison aims to check the compatibility of the irradiance and module temperature measurements between institutes and for further explanation to the uncertainty of energy prediction. To sum up, this study is triggered by a fundamental need to evaluate the quality of different energy prediction methods for different PV technologies. This evaluation gives a clear indication about the accuracy of energy prediction in such complicated weather conditions like Estonia and about the possibility of transferring the modules behavior to different climatic conditions Research objectives The general research objective of this work is to examine the applicability of transferring the performance of different PV modules between different environments, Northern to Southern Europe, based on short time of outdoor measurements. The first specific objective is to compare the accuracy of different five energy rating models (ZENIT, MATRIX, SIMPLE MODEL, MOTHERPV, and c-si Model) in predicting the output energy of different PV technologies at all levels of irradiance. The second objective is to compare the quality of simplified methods using less number of coefficients, with the quality of more complex methods requiring data fitting to derive their coefficients. To this end, SIMPLE MODEL and MOTHERPV were selected as representative of simplified methods; ZENIT, MATRIX, and c-si Model were selected as representative of more complex methods. Lastly, to investigate the possibility of improving the accuracy of power/energy prediction at low levels of irradiance as it is very important to be verified in such weather like Estonia due to the long period of these conditions. 4

14 2. Main characteristics of different photovoltaic (PV) technologies Long years of research and development in the field of solar energy have led to the availability of variant types of PV technologies in the market and others are under investigation and development. The main commercialized technologies are: Crystalline technology; Thin-film technology. Due to the high performance and electrical conversion efficiency, crystalline PV technologies are the most used in terms of small and large scale solar systems. In this chapter, the main features of the three technologies, cadmium telluride, amorphous silicon and mono-crystalline silicon, will be presented. The first two technologies are under the category of thin-film technology and the latter one is under the category of crystalline silicon technology. The main characteristics of these technologies are highlighted because this study focuses on their energy prediction. Sample pictures of different PV cell technologies are presented in Figure 2.1. a. b. c. Figure 2.1 a. Cadmium Telluride Panel (CdTe) [7]; b. amorphous silicon Panel (a-si) [8]; c. mono-crystalline silicon Panel (mc-si) [9] CdTe Technology Thin-film CdTe technology is currently considered the dominant thin-film technology in the global photovoltaic industry [10]. During the first quarter of 2013, First Solar Company achieved a new efficiency value of CdTe cells up to 18.1% certified by the U.S. Department of 5

15 Chapter 2 Main characteristics of different photovoltaic (PV) technologies Energy's National Renewable Energy Laboratory (NREL). This development has been transferred to the commercial CdTe modules to increase their efficiency up to 12.9% [11]. This technology combines durability, high efficiency and performance in hot weather conditions due to the low temperature coefficient with a low manufacturing cost. These features allow CdTe PV technology to provide the lowest energy payback time and the lowest carbon footprint of all PV technologies available [12]. CdTe-based PV solar cells are made of very thin stacked layers arranged in such a way to form efficient heterojunctions with CdS being the n-type partner, the main two features of this technology are its near ideal band gap (1.45 ev) for photovoltaic conversion efficiency, and its high optical absorption coefficient.[13]. Because CdTe PV modules are subjected to the light activation effect consisting of an increase in the maximum power that can be yielded after being exposed to sunlight for some hours, the stabilized power and the activation effect should be reached before taking the measurements. Research shows differences of up to 2 3% in maximum power have been observed between measurements obtained after a period of sunlight exposure [14]. As any kind of PV technologies, CdTe PV technology has a limitation in the performance during the lifetime of operation, the main reason restricting the performance and contributing in the degradation is the drop of fillfactor (FF) and open circuit voltage (VOC) parameters [15]. Moreover, there is industrial limitation for this technology because the two main raw materials are cadmium and tellurium. Cadmium is a by-product of zinc mining and tellurium is a byproduct of copper processing. The limitation occurs because the production quantity of tellurium is much lower than that of cadmium [16]. CdTe/CdS solar cells are well-matched for indoor applications due to their good performance at low level of irradiance, about 80% of the STC efficiency maintained at 2 W/m 2. This allows the modules to gain extra solar energy at low level of irradiance conditions - such as dawn, dusk, and cloudy skies. Therefore, CdTe modules will generally yield more electrical energy under the outdoor conditions comparing to other conventional solar modules with similar output power at low levels of irradiance. As the voltage value remains high at low light intensity, it is preferable for applications required to provide high level of voltage even under low irradiance conditions and for low cost high power generators [17]. 6

16 Chapter 2 Main characteristics of different photovoltaic (PV) technologies 2.2. Amorphous silicon technology Amorphous silicon technology is a non-crystalline form of silicon that has disordered structure, its random structure provides high band gap which is 1.7 ev. Recently, thin-film technologies contribute to the worldwide PV market by 14%. Amorphous silicon (a-si) thin-film PV technology represents about 2.3% of the total worldwide PV production. The relatively low market of this technology can be explained because of its relatively low energy conversion efficiency and expected high cost per-watt installed [18, 19]. The behavior of a-si differs to such extent from that of c-si solar cells in terms of the outdoor performance as under outdoor radiation cells are subjected to varying solar spectral contents and intensities in different seasons which vary considerably from summer to winter. This difference in the performance of a-si PV modules is observed as a result of the effect of both module temperature (TMOD) and spectral irradiance distribution [20]. Research shows that the performance ratio (PR) of a-si modules improves with increasing TMOD in summer and reduces due to low TMOD in winter. The performance ratio, which is defined as the ratio between the actual energy yield and the reference energy yield, of the a-si PV modules is higher in summer according to the annealing effect when TMOD is higher than in other seasons. Moreover, it has been shown that the autumn PR was higher than that of the spring in the region where average photon energy (APE) < 1.99 ev, neglecting of TMOD. This indicates that the PR of the a-si PV module is better while the module exposed to light containing a high proportion of long-wavelength components [20]. a-si thin-film technology is exposed to high degradation during the operating lifetime, the main factor contributes to this degradation is the initial light-induced degradation (LID) that occurs quickly during the first few hundreds sun hours leading to increase the number of defect centers in the material, resulting in increased recombination. This process can be responsible for up to a 30% decrease in the power output of a single junction PV module. Another factor contributing to efficiency degradation is cell mismatch; Cell mismatch may be produced by a number of factors such as partial shading of cells in the module, optical degradation, manufacturing defects, deterioration of anti-reflective coatings (ARC) and cracked cells [21]. 7

17 Chapter 2 Main characteristics of different photovoltaic (PV) technologies 2.3. Mono-crystalline silicon Technology The manufacturing of c-si modules typically involves growing ingots of silicon, slicing the ingots into wafers to make solar cells, electrically interconnecting the cells, and encapsulating the strings of cells to form a module. Compared to other types of photovoltaic technologies, crystalline silicon based solar cell has the highest efficiency and consisting from the silicon material which is considered the second easiest raw material that can be found on earth. Despite the later fact, silicon never exists as a pure element, but mostly as SiO2 based mineral; which makes the process of purifying the silicon very energy intensive. Mono-crystalline silicon PV technology has been widely used and promoted presently to have more than 40% market share due to its high efficiency compared to polycrystalline PV technology as the former technology has the highest efficiency exceeding 20%, but the efficiency value claimed by the manufacturers for commercialization lies between 15% - 17% [22]. Research shows that among different crystalline PV technologies, mono- and multicrystalline silicon, mono-crystalline PV technology gives the best performance at irradiance levels above 550 W/m 2 [23]. As mentioned before, the PV modules have different behavior in different climatic conditions. Mono-crystalline technology shows variant seasonal performance which is better in winter in comparison to summer as the optimum performance occurs when the outdoor temperature is low. This behavior is due to the high temperature coefficient of mono-crystalline technology compared to other technologies, which means higher losses of power during high temperature weather conditions [24]. Table 2.1 summarizes the recent achieved efficiency of different technologies based on laboratory and market-scale cells. It shows that the energy yield of the PV modules can be increased, which in turn enhance the availability and reliability of solar systems. This can be done by further development in the solar cells composition as the laboratory efficiency is higher compared to the commercialized one. 8

18 Chapter 2 Main characteristics of different photovoltaic (PV) technologies Table 2.1 Efficiency values of different PV technologies [11,22, 25] Efficiency CdTe a-si c-si Laboratory 18.1% 12.5% 25% commercial 12.9% 6-9% % 2.4. Factors affecting photovoltaic cell efficiency Module Temperature Module temperature is considered one of the primary factors that affect the performance of PV module, but the effect of the temperature varies from one PV technology to another. Crystalline silicon technology is more sensitive to the temperature influence compared to thin film solar technologies. A study showed that the efficiency decreased by 15% and 5% for mono-crystalline silicon solar cells and thin film solar cells respectively, as the temperature increased from 26 C to 56 C [22]. While the temperature increase, reverse saturation current (ISC) increases and the VOC decreases resulting in reduction of the efficiency of the solar cell. Due to the reduction of the bandgap with increasing temperature, the short circuit current increases which act to enhance the efficiency of the cell. The overall result of the tendency of VOC to decrease and ISC to increase with increasing temperature in the solar cells is a decrease in the output efficiency as the drop of voltage is very high compared to the increase of current [26]. Figure 2.2-a shows a slightly increase of current and a high decrease of voltage due to the increase of module temperature Solar Irradiance During the increase of in-plane solar irradiance intensity, the output power of PV modules increases due to the higher number of the photons reaching the surface of the module. During the increasing of the illumination level, higher number of electron-hole pairs can be formed to generate extra current leading to increased power. As shown in Figure 2.2-b, the I-V characteristics of a PV module differs according to level of solar irradiance. The power increases while the level of irradiance increases. The short circuit current is directly proportional to the incident in-plane irradiance while the open-circuit voltage is usually considered logarithmically dependent on the solar irradiance [27]. Thus, the increase 9

19 Chapter 2 Main characteristics of different photovoltaic (PV) technologies of irradiance levels increases directly the current values but the voltage increases only slightly leading to increase the power of PV module. Figure 2.2 I-V characteristics of a PV module based on module temperature and in-plane irradiance [27]. In study [28], it has been shown that at low levels of light intensity, the effect of shunt resistance becomes more important as the current decreases. As a result, the fraction of the total current passes the shunt resistance increases which leads to increase the fractional power loses. Therefore, one can conclude that under cloudy conditions, a solar cell with a high shunt resistance retains the larger fraction of its original power than a solar cell with a low shunt resistance Comparison of PV modules used in this study The behavior of the modules under varying outdoor conditions is investigated in terms of the dependency of maximum power point current (IMPP) on irradiance, and the dependency of both fillfactor and maximum power point voltage (VMPP) on the module temperature. The data is based on the outdoor measurements at The Tallinn University of Technology PV Outdoor Testing Laboratory. Table 2.2 lists the STC parameters of the PV modules that were used in this study. 10

20 Chapter 2 Main characteristics of different photovoltaic (PV) technologies Table 2.2 Modules parameters at standard test conditions STC, [TTU database]. Parameters mc-si 2j a-si CdTe ISC (A) 4.8 IMPP (A) 4.45 Voc (V) 44.4 VMPP (V) 36 PMPP (W) 160 FF (%) Temperature coefficient %/ C -0. 2%/ C %/ C Efficiency (%) mc-si PV module has the highest efficiency and highest temperature coefficient. In contrast, 2j a-si has the lowest efficiency and the lowest temperature coefficient. These differences in the physical parameters lead to differences of the behavior at outdoor conditions. As the modules have different values of STC parameters, the comparison of the performance under varying outdoor conditions is usually done by comparing normalized parameters (actual / rated at STC). 11

21 Chapter 2 Main characteristics of different photovoltaic (PV) technologies Figure 2.3 shows that the three modules have approximately the same linear dependency of current on the plane of array irradiance level, at fixed module temperature. Figure 2.3 The effect of in-plane irradiance on the maximum power point current for CdTe, 2j a-si, and mc-si PV modules at 25±1 C module temperature, TTU outdoor measurements. Figure 2.4 explains the difference of the VMPP between the three modules at different temperatures. It is clear that the mc-si and 2j a-si modules have strong voltage decrease while temperature increases, and CdTe has the lowest decrease of voltage with temperature. The case is different with fill factor, defined as the measure of the junction quality and series resistance; the nearer the FF to unity, the higher the quality of the cell [29]. The general formula to calculate FF is: FF = I MPP V MPP I SC V OC (2.1) where I MPP and V MPP are the maximum power point current and voltage respectively, I SC is the short circuit current, and V OC is the open circuit current. Figure 2.5 indicates that FF decreases with temperature in the case of CdTe and mc-si, and mc-si has the highest FF degradation with temperature. On the other hand, FF of the a-si cell increases with temperature. 12

22 Chapter 2 Main characteristics of different photovoltaic (PV) technologies Figure 2.4 The effect of temperature on the maximum power point voltage for CdTe, 2j a-si, and mc-si PV modules at 1000±10 W/m 2 in-plane irradiance level, TTU outdoor measurements. Figure 2.5 The effect of temperature on the fillfactor for CdTe, 2j a-si, and mc-si PV modules at 1000±10 W/m 2 in-plane irradiance level, TTU outdoor measurements. 13

23 3. Approaches of rating PV modules 3.1. Indoor rating Most of the PV modules available in the market have been rated by the manufacturers at standard test condition under a flash simulator. The rating of the modules is being done in accordance with IEC standards or for crystalline silicon, and for thin film respectively [29]. Unfortunately, the nameplate values of the parameters measured at STC conditions (claimed by the manufacturers) mostly deviate from the real values under outdoor conditions. Because of the over-estimation by the manufacturer, the system will put out less energy than the estimated, thus leading to a shortage of electricity supply which is not acceptable in the case of critical systems (e.g. off-grid systems, systems with high availability). On the other hand, the under-rating of the modules leads to the gain of energy, which means that the investment into the system could have been less. Accordingly, a well predicted energy is required and sometimes crucial for an optimized designing of a PV solar system Outdoor rating According to the latter discussion, the PV module s parameters are being derived at standard irradiance and temperature conditions (SIT) based on the outdoor measurements. Outdoor rating of PV module consists of installing the PV module according to a specific inclination angle and orientation in a specific location, and measuring the module parameters and the meteorological conditions for short or long-time measurements. The power/energy prediction models utilize the measured parameters under outdoor conditions to predict the power/energy of PV module. Despite the fact that the outdoor rating is more complex and needs more efforts in terms of maintaining high accuracy of meteorological parameters measurements specially irradiance values (will be discussed next section), it gives a detailed evaluation of the performance variation of a PV module associated with changing of environmental conditions. The outdoor-rating approach allows the researchers to investigate the effect of the plane of array irradiance levels, module temperature, and secondary effect (e.g. angle of incidence) on the performance of a wide range of PV technologies. Additionally, the PV module can be verified and compared to its nominal rated values which decrease the uncertainty level of its 14

24 Chapter 3 Approaches of rating PV modules energy prediction. The maximum power point power (PMPP) of a-si module that we used in this study had been derived at SIT conditions and compared to the labeled value measured at STC conditions. Figure 3.1 illustrates the performance ratio of this module based on the labeled PMPP and derived PMPP at 1000 W/m 2 in-plane irradiance, and TMOD 25 C. This model had been under-rated by the manufacturer as PR is higher based on labeled PMPP. It is clear that the labeled values of module s parameters do not provide realistic results as the derived parameters at SIT conditions do. Figure 3.1 PR of a-si PV based on labeled and derived maximum power after stabilization, at different bins of plane of array irradiance Description of irradiance measurement devices Global radiation is defined as the total solar radiation on a horizontal surface, this term including three types of radiation, beam radiation that reaches the surface directly from the sun on a straight line, diffused radiation which has been scattered by the molecules and particles in the atmosphere, as well as the reflected radiation that has been reflected off by the materials on the earth surface such as ground [30]. It is very important to obtain accurate measurements of irradiance for solar energy system design, deployment. In order to optimize any solar-based energy system, the energy available from the sun must be quantified accurately by the system designers. All the energy prediction methods of PV modules depend mainly on the measurements of the plane of the array irradiance. 15

25 Chapter 3 Approaches of rating PV modules The main tools used for irradiance measurements are pyranometer and solar reference cell devices. In following sections, the main characteristics of these devices will be discussed to show the effect of these devices accuracy on the precision of the output energy prediction of the PV modules Pyranometer device The pyranometer devices are divided into two types, Si-Pyranometer (photodiode) and thermopile pyranometer. Thermopile pyranometer consist of an absorbing detector covered by two glass filter domes. All the Solar radiation in the wavelength region from 285 nanometers (nm) to 2800 nm can pass through the glass domes and hits a black heat-absorbing sensor; the thermopile detectors, attached to the absorbing surface under the black sensor, become heated. A thermopile consists of two groups of junctions of dissimilar metals, the idea is that one group of these junctions is in thermal contact with absorber, called measuring junctions, and the second group of junctions is not in thermal contact with the absorbing surface, called reference junctions. Consequently, the temperature difference between the measuring and reference junctions produces a voltage that is proportional to the solar radiation [30]. The National Renewable Energy Laboratory (NREL) states that the response of a pyranometer changes with the change in solar zenith and azimuth angles. More precisely, the response varies with the cosine of angle of incident, so it will give full response when the solar radiation hits the sensor perpendicularly and weak response while the angle of incidence deviate far from zero degree. These response changes can be responsible for up to ±5% uncertainty in the calculated irradiance from the nominal values [31]. Figure 3.2 Construction of the Kipp&Zonen CM11 pyranometer [32]. 16

26 Chapter 3 Approaches of rating PV modules Sources of uncertainty As mentioned earlier, the measurements given by pyranometer devices are not perfectly match the real values of the irradiance. The imprecise measurements are attributed to sensitivity and response characteristics of a pyranometer. The main sources of the uncertainties are as follow [33]: Uncertainty of the wavelength: in the ideal case, the absorption coefficient of the radiation sensor surface and the transmission coefficient of the glass cover should be constant for all wavelengths of solar radiation, but in reality these coefficients change while the wavelength changes. As these characteristics vary somewhat from sensor to sensor, the measurements uncertainty differs from one to another. Uncertainty against temperature: since the heat conductivity inside the pyranometer relies on temperature, any change between ambient temperature and pyranometer temperature will increase the measurements uncertainty. Uncertainty against Elevation and Azimuth: the output of a pyranometer drops while the sun elevation angle decreases. This error can be attributed to the uneven absorption coefficient and may occur because of the irregular thickness or material of the glass cover. Normally, the sensitivity rapidly decreases at an elevation angle lower than 20 degrees Solar cell sensor (reference cell) The reference cell is commercially available as a simple photodiode sensor or as a single PV module cell calibrated at accredited laboratories. Usually, the used material is crystalline silicon; other semiconductors are used as well [34]. Ideally, these reference cells are sensitive to the spectrum range between 300nm nm, thus they cover just a part of the solar spectrum. This gives an advantage to the pyranometer devices as they are sensitive to wider range of spectrum, approximately 99% of the full solar spectrum [33]. Since the solar spectrum is changing with the variation of the elevation angles and the reference cells are calibrated for a specific spectrum, the sensor sensitivity will change in accordance with these changes. Due to the spectral variation, the measurement uncertainty may reach up to ±5% for low solar elevation angles comparing to pyranometer devices [34]. 17

27 Chapter 3 Approaches of rating PV modules On the other hand, the reference cell device is superior to the pyranometer in terms of tracking the rapid change of the irradiance values (mostly caused by fast clouds). As the pyranometer has much slower response time, the obtained measurements of pyranometer during the latter case are not valid to be used in evaluating the performance of PV modules. As a result, the outdoor measurement systems contain both devices. Once the difference between the measurements of pyranometer and reference cell is known, the pyranometer measurements can be calibrated in case of rapid changes in reference cell measurements. Figure 3.3 Silicon irradiance sensor [35]. 18

28 4. Description of power/energy prediction methods 4.1. Theory To be able to predict the output energy of a PV module, different information about this module is required that can be either obtained by indoor or outdoor measurements. This chapter presents the main used methods of PV energy prediction, and concentrates on the methods that have been used in this study to analyze the main boundaries of the used methods. During the last years of development in the field of the outdoor testing performance of PV modules, several energy rating methods have been developed to calculate the output energy of PV modules in variant climatic conditions, and they provide a truthful estimate of the outdoor performance of a PV module at different sites. Some of these methods use a complex modeling approach because it takes into the consideration all the parameters that affect the behavior of PV module, while other methods use a simpler modeling approach as they neglect secondary effects such as the variation of angle of incidence and spectral irradiance [3]. The idea behind this simplification of these methods is to minimize the number of input parameters specifically to the irradiance and the ambient temperature measurements. The reason of this limitation to these two weather parameters is that they are easily obtained for mostly all location, while it is not often the case for other meteorological parameters. Each method has its own calculation approach as these methods have different equations and different number of constants for calculating the energy output of a PV module. On the other hand, all the methods depend mainly on four parameters which are IMPP, VMPP, TMOD, and plane of array irradiance (Gi). The first two parameters can be derived from the outdoor measurements or used as claimed by the manufacturer. Figure 4.1 shows the modeling approach of power/energy predication methods. 19

29 Chapter 4 Description of power/energy prediction methods Model Inputs Input (1) Input (2) Weather parameters (G i,t MOD ) Module parameters (I MPP,V MPP, P MPP ) Modeling Fitting Producing Constants Power prediction Energy Prediction Figure 4.1 Energy rating methods approach. The rating methods can be easily compared as they are distinguished by their prediction of either the real power or real operating efficiency (η). These two terms can be transformed to each other by using the following equation P= η A Gi where P is the produced power, A is the module area, and Gi is the plane of array irradiance. The variation of the results is attributed to the difference in handling the input data, and to the difference of used equations for fitting. Table 4.1 presents the main equations of the methods developed for PV module energy prediction [4]. According to 22 nd European Photovoltaic Solar Energy Conference (2007), these methods have been categorized into two groups [4]. Group one (SSE, YIELD SIMULATOR, SOMES and MOTHERPV), which evaluates the module efficiency at (25 C module temperature and different irradiance levels) denoted as η(gi,25 C) and estimates energy of PV modules by transferring the measured efficiency to the available weather conditions. The second group (MATRIX METHOD, ESTI-ER, PV-SAT and ZENIT) treats the whole power surface P(Gi, TMOD) or η(gi, TMOD) as a single equation [4]. 20

30 Chapter 4 Description of power/energy prediction methods Table 4.1 List of energy rating methods and their main equations [4]. # Method Institution Power / efficiency equation Temperature coefficients 1 SSE CREST (UK) η(g,25 ) = C0 + C1G + C2lnG TC@1000W/m² 2 YIELD SIMULATOR ECN (NL) avg η(g,25 C) average TC (250,500,750,1000W/m²) 3 SOMES UU (NL) PMPP = θ(g) θ(1000). G 1000 TC=0.4%/ C (default value) 4 MOTHERPV INES (FR) avg η(g,25 C) TC(G) 5 PV-SAT 6 MATRIX 7 ESTI-ER 8 ZENIT H2M (DE) SUPSI (CH) JRC (IT) ISE (DE) ȠMPP (G,T) = a1 + a2 G + a3 ln(g*m 2 /W). (1+ α. (TMOD- 25)) IMPP = Im,stc. G/1000. [ 1+ αimpp. (TMOD -25)] VMPP = Vm,stc + C0. ln(g/1000) + C1. (ln(g/1000)) 2 + βvmpp. ( T + TMOD -25) P(G,T) = Im,stc. G/1000. [ 1+ αimpp. (TMOD-25)]. (Vm,stc + C0. ln(g/1000) + C1. (ln(g/1000)) 2 + βvmpp. (TMOD - 25)) P(G,T) = a. G 2 + b. log(g+1). G + c. [ (log(g+ e) 2 )/(G + 1) -1]. G + d. (TMOD -25) The variation of the output within Group 1 is due to the difference in how the efficiency curve at 25 C is extracted from the raw data and also due to variation of the temperature coefficient value used for temperature translation. CREST describes the efficiency curves by the equation η(gi,25 ) = C0+C1 Gi +C2ln Gi. The other three methods (ECN, UU and INES) use a statistical approach with no fitting, where the curves are averaged from the measured raw data [4]. On the other hand, In Group 2, MATRIX and ESTI-ER methods rely on the same set of equations, but with a small change in the ways applied to fit the equations. MATRIX method fits IMPP and VMPP separately and then estimates PMPP, while ESTI-ER method fits PMPP directly. 21

31 Chapter 4 Description of power/energy prediction methods ZENIT method fits the whole power surface P(Gi, TMOD), but with a different equation from the last two methods and with larger number of constants. The final method in the second group, H2M, applies the same equations as an SSE method does, but it handles the raw data in a different way [4]. According to these differences in the ways of handling the data and to the difference in the used equations. Energy prediction methods have difference in energy prediction for the same module under the same outdoor conditions. Study [4] on the intercomparison of different energy prediction methods within the European project Performance shows that the temperature coefficients of c-si module are in the range 0.4% to 0.52%/ C. Figure 4.2 shows the variation of the predicted efficiency (translated to 25 C) by different power/energy prediction methods for c-si module, which indicates that the methods must be examined to determine the most accurate method. Figure 4.2 Efficiency curves at 25 C of a c-si module extracted from the outdoor data [4] Deviation of module temperature The use of ambient temperature (TAMB) as an input for the energy prediction methods is one of the most important features of these methods. Using TAMB increases the applicability of the energy rating methods as this parameter is available almost for all sites. Since the equations of energy prediction methods deal with module temperature, a transformation from ambient to module temperature is required. As the difference between the 22

32 Chapter 4 Description of power/energy prediction methods ambient and module temperature is proportional to the irradiance, the module temperature can be estimated according to the following equation [36]: T MOD =[ NOCT 20 ] G 800 i + T AMB (4.1) where NOCT, the nominal operating cell temperature, is defined as the module temperature at an ambient temperature of 20 C, plane of array irradiance of 800W/m 2, and wind speed of 1m/s. G i is the measured plane of array irradiance, and T AMB is the measures ambient temperature [36]. Usually NOCT value is given by the manufacturer, and also can be calculated from the outdoor measurements by fitting the curve of plane of array irradiance versus module temperature where the ambient temperature range is 20 ± 1 C. During the real outdoor operation of PV modules, the module temperature will not respond to the fast variation of irradiance and wind values due to its thermal capacity. Therefore, an error in the module temperature prediction will be generated [36]. However, study [37] shows that the energy prediction is not largely sensitive to the absolute NOCT. It is indicated that there is a possibility of 1.5% change in the predicted energy for 5 C change in NOCT, and the typical uncertainty of NOCT is 1 C. So, an error in NOCT value will have relatively small impact on the energy prediction of PV modules Used Methods Simple method It is considered one of the simplest energy rating methods as it contains only one constant, which is the power temperature constant. This method, described in (Osterwald, 1986), relies on the principle of the direct dependency of power on the module temperature and incident irradiance on the plane of PV module [38]. The power of a PV module is calculated according to equation (4.2) as follows: P MPP =P m,stc. G i G. (1+ γ. (T MOD -25)) (4.2) where P MPP is module maximum power point; P m,stc is module maximum power at STC conditions, γ is module power temperature coefficient ( C -1 ), G * is the standard plane of array irradiance (1000 W/m 2 ), and T MOD is the calculated/measured module temperature. 23

33 Chapter 4 Description of power/energy prediction methods Matrix method The matrix method has been developed in the laboratory of Energy, Ecology, Economy LEEE- TISO (Switzerland); this method aims to give simplified procedures and steps for estimating the output energy production of a specific PV module and for comparing different PV technologies. This energy rating method is based on matrix calculations, the required input parameters are current matrix IMPP(Gi, TMOD), and voltage matrix VMPP(Gi, TMOD). These matrices indicate the dependency of current and voltage of the PV module on module temperature TMOD and plane of array irradiance Gi [37]. IMPP and VMPP are the average of measured maximum current and voltage respectively for each single cell in the grid (Gi, TMOD). The current and voltage data are fitted according to the empirical equations (4.3) and (4.4). By fitting the measured current and voltage, all the parameters (Im,stc, αimpp, Vm,stc, C0, and C1) are obtained and the power matrix PMPP(Gi, TMOD) can be calculated using equation (4.5) [37]. G i.. I MPP =I m,stc [1 + α. IMPP (T MOD -25)] (4.3) 1000 V MPP = V m,stc +C 0. ln [ G i 1000 ] + C 1. [ln [ G i 1000 ]] 2 + β VMPP. ( T + T MOD - 25) (4.4) P MPP = I MPP. V MPP (4.5) where I m,stc is the maximum power point current at STC, α IMPP is the temperature coefficient of I MPP at 1000W/m 2, V m,stc is the maximum power point voltage at STC, C 0 and C 1 are module specific parameters, T is temperature difference Tcell Tbom at 1000W/m 2, and β VMPP is the temperature coefficient of V MPP at 1000 W/m 2. The matrix of each parameter must cover all the possible climatic conditions, the irradiance varies from 100 to 1000 W/m 2 in steps of 100/m 2, the temperature range is 0-60 C in 5 C steps; the raw data averaged into irradiance bins of 10W/ m 2 and temperature bins of 1 C for each single cell of the grid. This is to ensure that the obtained measurements at all cells are well representative to the real measurements of a PV module, thus they can be used to extra/interpolate the measurements where the data are not available to completer the matrix 24

34 Chapter 4 Description of power/energy prediction methods [37]. If the measurements have been averaged in narrower range than 10 W/m 2, and 1 C, this may lead to obtain unrepresentative measurements for one or more cells in the grid due to the inefficient number of measurements in those cells. After obtaining the expected power values, the energy yield can be calculated easily. To guarantee the validity of any energy rating method, it must be compared with the real measurement to determine the percentage of the uncertainty in energy yield prediction. G. Friesen et. al. have used matrix method to estimate the energy yield of PV modules; they attributed the uncertainty in the final rated energy, defined as the difference between the real measured energy production and the calculated one, to the following reasons [39]: Smoothing and extrapolation of the raw measurements; The amount of available data in the selected mesh size; Ignoring the influence of angle of incidence (AOI) and air mass (AM) for simplification. The last factor, affecting the accuracy energy prediction, applies for all energy prediction methods presented in this study and its one of the main factors affecting their accuracy especially at low levels of irradiance ZENIT Method like the two previous energy rating methods, ZENIT method is based only on the module temperature and incident irradiance on the PV module plane as these meteorological parameters are available almost everywhere. ZENIT method fits the entire power surface by equation (4.6), which is different from the other methods because it doesn t take the values of Im,stc, and Vm,stc as a separate parameters. Instead, these parameters are tacitly included within its five constants [4]. P(G i, T MOD ) = a. 2 G i + b. log(g i +1). G i + c. [[ log(g i + e)2 ] 1]. G i + d. (T MOD -25) (4.6) ( G i + 1) A set of the unknown constants (a, b, c, d, e) can be obtained by fitting the raw data, then the inputs of the equation are G i and T MOD which can be obtained easily from suitable sources or from the outdoor real measurements. 25

35 Chapter 4 Description of power/energy prediction methods For all energy prediction methods, it is better to collect raw data that represent all environmental conditions (one year measurements). However, this study shows that raw data based on short period of operation gives optimistic results, which indicates that the accuracy of the outdoor measurements is more important than the period of these measurements for obtaining good accuracy of power/energy prediction A power-rating model for c-si PV modules Parameters model This model has been proposed to predict the energy of crystalline silicon PV modules [5]. The model expresses the output power as a function of module temperature and incident irradiance on the plane of PV module, including a set of parameters to be calculated by fitting the equation to the measured power from the outdoor measurements. This model is a similar version of previously used method called ESTR model developed at Joint Research Center (JRC), but with different mathematical equation containing the same parameters. It is considered as a simplified version of the equations given by King et al [40]. The power is calculated according to the following form: P(G i, TMOD)= G i * [P m,stc + k 1 ln[g i ] + k 2 ln[g i ] 2 + k 3 T MOD + k 4 ln[g i ] T MOD + k 5 ln[g i ] 2 T MOD + k 6 [T MOD ] 2 ] (4.7) where G i = G i /1000, T MOD = (T MOD 25), P m,stc and k 1 -k 6 are the empirical coefficients and must be determined by fitting the equation to the measured data Generic model In the later study [5], universal coefficients (k1-k6) have been proposed to be used for c-si without fitting. These coefficients were obtained from applying equation (4.7) to 8 monocrystalline and 10 poly-crystalline PV modules separately, their estimated maximum power point power at STC (Pm,stc) ranged from 38 to 225 W (estimated by fitting). The measured power values of each module were rescaled by dividing them by their Pm,stc. The rescaled measurements of the 18 th modules were combined as a new data set; the model was applied to the combined data set to obtain the combined fit coefficients. Table 4.2 shows the proposed coefficients for the relative efficiency of a c-si module. 26

36 Chapter 4 Description of power/energy prediction methods Table 4.2 The proposed coefficients for the relative efficiency of c-si module. Coefficient Value k k k k * 10-4 k * 10-4 k6 5.0 * 10-6 As clarified, these coefficients have been proposed to calculate the relative efficiency of a c-si module. Thus, these coefficients must be multiplied by the rated power (indoor/outdoor) in the case of power prediction MOTHERPV The MOTHERPV method calculates the non-linearity coefficient of the module s power under given climatic conditions by calculating the energy received and module temperature as a function of irradiance levels. The incoming energy is distributed into bins of irradiance with a given width, most likely if the irradiance expressed in Suns (S). The idea of this method is to obtain the relative 25 C as a function of irradiance bins and apply these values for different climatic conditions to evaluate the module performance. The predicted energy can be calculated according to the equation (4.8) after calculating the non-linearity coefficients for all bins. In this study, this method was used at the final stage for comparing the accuracy of energy prediction. Full description of this method has been discussed elsewhere [41]. E = P m,stc *h sun *R p (4.8) Where E is the total predicted energy during specific period, P m,stc is the maximum power point power at STC, h sun is hours of sun received during same period, R p is the average non-linearity coefficient. 27

37 5. Methodology This chapter discusses the followed approach to study the applicability of transferring the module s parameters, derived at The Tallinn University of Technology PV Outdoor Testing Laboratory (TTU), to different outdoor conditions. Section one illustrates the main approach of this study while the second one describes the experimental set up at TTU. Section three provides information regarding the environmental conditions during the measurements period at each site. Section four refers to the measurements collected from the partner institutes and data handling, it also provides information about the PV modules that had been distributed over different sites. Section five sheds light on the real performance of the used PV modules at all sites. Section six presents a comparison between modules parameter at SIT and STC conditions, and shows the extracted parameters of all used models Main approach This study has been focusing on the accuracy of the energy rating of single PV modules (2j a- Si, mc-si, CdTe) monitored and measured at various locations of different European PV test laboratories (TTU-Tallinn, INES-Cadarache, CIEMAT-Madrid). The models will be compared based on their overall precision of the estimated power and energy for the same module at different outdoor conditions, also another comparison according to their accuracy at low level of irradiance at various environments. The importance of getting the minimum uncertainty of power/energy prediction at low levels of irradiance becomes highly critical in case off-grid systems especially for systems designed for high availability value, since much high energy prediction leads to shortage of electricity supply at these levels of irradiance e.g. sunset, sunrise, cloudy weather. TTU site was used as base-site, in which all the module parameters were extracted and characterized from 3 weeks of the outdoor measurements. The extracted parameters were used to calculate the output power and the energy of the same modules at the other two sites. Figure 5.1 illustrates the steps employed in this study and describes the geographical location of each site. The modules were firstly installed at Tallinn and then were moved to Madrid, Spain and to Cadarache, France as a final site for the outdoor measurements. 28

38 Chapter 5 Methodology Base-Site (TTU) Technology: 2j a-si, mc-si, CdTe Location: Estonia Latitude: 59 23ꞌ 38ꞌꞌ Period: 27/08/09 16/09/09 (3Wks) Derived Module s Parameters Technology: 2j a-si, mc-si, CdTe Location: Spain Latitude: 40 19ꞌ 59 ꞌꞌ Period: 17/11 10/12/09 (3Wks) Technology: 2j a-si, mc-si, CdTe Location: France Latitude: 45 21ꞌ 17ꞌꞌ Period: 01/04 31/08/10 (5Months) Figure 5.1 Block diagram of the followed approach Experimental setup The PV outdoor testing field of TTU consists of different PV test modules installed in open rack configuration, south facing, and with fixed inclination angle of 48 as optimum inclination angle. The parameters that characterize the performance of the PV modules are: I-V curve which is scanned in one second and module temperature which is detected by temperature sensors, one per each module. The meteorological parameters are also monitored and recorded. Plane of array pyranometer and plane of array reference cell are used to measure the plane of array irradiance, while the horizontal plane irradiance is measured by a horizontal plane pyranometer. The diffuse solar irradiance is measured by diffuse light pyranometer and a weather station is used to measure ambient temperature, air pressure, air humidity, wind speed, wind direction, and precipitation amount. Madrid site has approximately the same experimental setup as the I-V curve is scanned in one second, while Cadarache has longer I-V scanning time. The test modules are installed based on the optimum inclination angle, 30 at Madrid, and optimum orientation at each site. Figure 5.2 shows the PV outdoor testing field of TTU. 29

39 Chapter 5 Methodology Plane of array pyranometer PV module Plane of array reference cell Weather Station 5.2-a Junction box Diffused light pyranometer Horizontal plane pyranometer Temperature sensor 5.2-b Figure 5.2 The PV outdoor measurements field of TTU in Tallinn, Estonia ( ). 30

40 Chapter 5 Methodology 5.3. Description of the outdoor conditions at different sites The measurements had been taken in different environments during different seasons; the difference of latitude between the base-site and other sites is 19, and 14 for Madrid, and Cadarache respectively. As shown in Figure 5.3, the measurements of CdTe module show that the average module temperature at Cadarache was higher than in Tallinn by 40%, while the average module temperature at Madrid was slightly lower than in Tallinn. On the other hand, the average plane of array irradiance level at Cadarache, Madrid, and base-site was 590, 470, and 250 W/m 2 respectively. The effect of the difference in latitude and the movement of sun can be observed in Figure 5.4. The distribution function of irradiance at three sites is completely different. Most of the solar energy, about 60%, was obtained at low irradiance levels below 0.5 Suns at the base-site, which was not the case in the other sites because around 80% of the collected energy at Madrid and Cadarache came from irradiance levels above than 0.5 Suns. At low levels of plane of array irradiance (Gi < 200 W/m 2), the obtained energy is about 9%, 2.9%, 30.5% of the total energy at Madrid, Cadarache, and the base-site respectively. So, one can conclude that the difference in angle of incidence and spectral distribution at these levels will not have large effect on final uncertainty of energy prediction at Cadarache and Madrid since the portion of the energy at these level is too low with respect to the total obtained energy at those sites. Figure 5.3 Average module temperatures as the function of irradiance level and the average plane of array irradiance of CdTe PV module at three sites. 31

41 Chapter 5 Methodology Figure 5.4 Normalized distribution function of the irradiation as a function of irradiance level Data handling Modules specifications Three different PV modules were measured and monitored in three different sites under different climatic conditions. The modules have different characteristics such as power and power temperature coefficient which widen the validity of transferring the models coefficients from the Northern Europe to South. The electrical characteristics, given by the manufacturer at STC conditions, of each module were presented previously in Table Data Validation During the measurements period, the cleanliness of modules and measurement sensors must be insured on regular basis. This is important as the modules and irradiance sensors can be periodically covered with dust, snow, ice, or dirt; this leads to decrease the quality of the measurements which in turn decreases the reliability of the statistical analysis conducted on these measurements. Accordingly, the performance of the modules and the sensors measurements must be verified in terms of the existence of snow, dust, ice, or dirt at all sites. Presently, the data validation approach is not unified in all research institutes leading to variation in the measured module performance up to 5%. Measurements obtained in cloudy weather, especially with fast moving clouds, must be checked in terms of irradiance stability by comparing the measurements of pyranometer and reference cell to insure the data validation [29]. 32

42 Chapter 5 Methodology The partner institutes provided the measurements without filtering. Also, the scanning time of I-V curve is different at Cadarache which had the longest scanning time. As a result the nonefiltered data affects the accuracy of the power/energy prediction. In this work the non-filtered raw data was used. Before fitting the model parameters based on the measurements at the base-site, the measurements were cleaned to increase the reliability of the derived constants of each model. For this purpose, the measurements of power at Tallinn were transferred to at the module temperature 25 C; the later calculated measurements were used with plane of array irradiance measurements as inputs for the 3Sigma data filtering procedures due to the linear relation between these two parameters, described in [29]. The filtering process consists of finding the best fit regression line through the data, and eliminating all outliers where the distance of the power measurements is bigger than 2.576* standard deviation (STDDEV). Figure 5.5 shows the effect of filtering the measured power at Tallinn site for mc-si module that used in this study. The existence of dirt and dust can be observed easily on the measured module and sensor devices. Obtaining low power, compared to the fit regression line, at high level of irradiance indicates that the module surface was not clear and covered by dust or dirt. On the other hand, obtaining high power at low levels of irradiance indicates that the irradiance sensors were covered by dust or dirt. The difference between the filtered and raw data shows that filtering the data is important to insure the consistency of the measurements. Figure 5.5 The effect of 3Sigma data filtering on the power measurements of mc-si at Tallinn. 33

43 Chapter 5 Methodology Additional filtering procedure had been done by eliminating the measurements where the difference between the plane of array pyranometer and reference cell measurements was bigger than 1%. The latter filtering procedure insures that the effect of fast clouds on the accuracy of pyranometer measurements had been minimized to obtain more reliable measurements at the base-site (Tallinn) Stability of PV modules To examine the transferability of the module parameters to other sites and to confirm that the accuracy of energy prediction is not affected by module degradation, the stability of the module must be checked during the measurements periods at all sites. To examine the stability of the modules, the efficiencies of all modules at all sites were calculated, transferred to 25 C, and then normalized according to their efficiencies at STC. Figure 5.6 shows the relative translated efficiency to 25 C (RE) of CdTe, a-si, and mc-si modules. It clarifies the variation of real efficiency for CdTe module as it was getting lower during the transferring from the base-site to the others indicating high predicted energy at these sites. a-si module was less stable than mc-si, but it didn t experience a noticeable degradation during the measurements campaign. mc-si module had the most stable RE during the measurements period at all sites. Figure 5.6 Relative translated efficiency to 25 C of all modules during the measurements period. 34

44 Chapter 5 Methodology 5.6. Fitting of model s equations As shown in Figure 5.6, the real efficiencies of the modules are different from the rated one especially for 2j a-si and CdTe PV modules. MATRIX, MOTHERPV, Generic, and SIMPLE methods use measured parameters at STC conditions, which in this case are different from the real value due to the over-rating of CdTe module and under-rating of 2j a-si module. Consequently, using these parameters to extract the other constants would have led to overestimate or under-estimate the real power by all methods depending on how well the module had been rated at STC conditions. In this study, all required module parameters were derived at standard irradiance and temperature conditions. Im,SIT, Vm,SIT, Pm,SIT, and TCSIT were used to extract the other parameters for each model; where Im,SIT is the derived maximum power point current at SIT, Vm,SIT is the derived maximum power point voltage at SIT, Pm,SIT is the derived maximum power point power at SIT, and TCSIT is the derived power temperature coefficient at SIT conditions. The derived parameters at Tallinn were used to predict the energy at Cadarache and Madrid according to the measured plane of array irradiance and the module temperature. The ambient temperature was not used in this study as the ambient temperature data at Madrid were not submitted according to each module measurements, so the comparison between three sites has been done based on the measured module temperature. Nevertheless, the energy prediction based on ambient temperature had been conducted for all methods at Tallinn and Cadarache. The results showed that using expected module temperature instead of the measured one increases the uncertainty of the overall energy prediction, obtained during five months at Cadarache, for maximum 1% in the worst case. As discussed earlier, using STC parameters gives higher uncertainty of power/energy prediction. A comparison has been done between the results of the overall energy prediction based on STC and SIT parameters. It has been concluded that the maximum increase of the uncertainty of energy prediction, in this study, because of utilizing IMPP, VMPP, PMPP at STC conditions is about 13%, which is the case of a-si module. 35

45 Chapter 5 Methodology Table 5.1 presents the values of the derived PMPP at SIT of all modules and their rated PMPP, the measured power values at the base-site (Tallinn) were used to fit the models coefficients for each module; the derived parameters then had been used to predict the power and energy at Cadarache and Madrid. Table 5.1 STC and SIT power of the modules at all sites. 2j a-si (W) CdTe (W) mc-si (W) Tallinn (SIT) Madrid (SIT) Cadarache (SIT) STC During the modeling process, it had been noticed that using the standard procedures, given in section to 4.2.3, occasionally leads to mathematical error from the physics point of view. When fitting all the parameters at the same time, the temperature coefficients must be checked carefully as it is possible to obtain positive voltage temperature coefficient (βvmpp), negative current temperature coefficient (αimpp) and positive power temperature coefficient, which is one of these models limitations. Thus, the physics behavior of current, voltage, and power must be verified during modeling process. This study proposes the following procedures for obtaining the best fitting results: Exclude the temperature coefficients in the first fitting trial. Fit the data to obtain temperature coefficients Repeat the previous two steps until getting the minimum fitting error. In the case of Simple Model, the PMPP and corresponding temperature coefficient parameters were derived at SIT conditions and used for other sites. The latter method is the most sensitive method to the over-rating or under-rating of the PV module during the indoor measurements at STC conditions. Figure 5.7 demonstrates the increase in the uncertainty of energy prediction due to utilizing STC parameters by Simple model. The highest increase of uncertainty is about 13% for a-si module. 36

46 Chapter 5 Methodology Figure 5.7 Deviation of the total predicted energy by Simple model from the measured energy of all modules at Cadarache. All the models parameters were obtained by fitting the equations, given in chapter 4, to the measurements of the PV modules at the base-site. The obtained parameters for each model, see Tables 5.2, 5.3, and 5.4, were used to predict the energy at Cadarache and Madrid sites. 37

47 Chapter 5 Methodology Table 5.2 The derived parameters of the six methods for 2j a-si PV module at the base-site. MATRIX ZINET SIMPLE MODEL MOTHERPV 6-Parameters IMPP 2.13 a E-06 PMPP PMPP PMPP VMPP b γ γ k αimpp c k βvmpp d k C e k C k k Table 5.3 The derived parameters of the six methods for CdTe PV module at the base-site. MATRIX ZINET SIMPLE MODEL MOTHERPV 6-Parameters IMPP 0.99 a E-05 PMPP 65.4 PMPP 65.4 PMPP 65.4 VMPP b γ γ k αimpp E-05 c k βvmpp d k C e k C k k

48 Chapter 5 Methodology Table 5.4 The derived parameters of the six methods for mc -Si at the base-site. MATRIX Generic Model ZINET SIMPLE MODEL MOTHERPV 6-Parameters IMPP 4.53 PMPP a E-05 PMPP PMPP PMPP VMPP k b γ γ k αimpp k c k βvmpp k d k C k e k C k k k k

49 Chapter 6 Results and discussion 6. Results and discussion In this study, all the used models (Simple model, 6-Parameters, Matrix, and ZENIT) were applied to six different data sets (a-si, CdTe, mc-si at Cadarache and Madrid) obtained from the whole measurements period at each site. These models were used to predict the power and energy for each data set. In addition, the Generic model was applied to three data sets (mc-si at Tallinn, Cadarache, and Madrid). MOTHERPV was used to predict the final total energy for the same six data sets described earlier; the total energy was based on the summation of the average output energy per one hour (kwh) Description of used statistical parameters The goodness of fitting is evaluated, in this work, by two statistical parameters, relative mean bias error (RMBE) and relative mean absolute error (RMAE). RMBE indicates the trend of the model prediction (over or under prediction) at all level of irradiance [5], which is given as RMBE = 1 N N i=1 (X t X r X r ) (6.1) Where N is the number of measurements, X t is the theoretical power predicted by the model, and X r is the real power measured at each site. RMAE is used to indicate how big the cloud of the predicted power is around the mean value of the real power, it was calculated as follows [42]: RMAE = 1 N N i=1 ( X t X r X r ) (6.2) The uncertainty of energy prediction is explained by the deviation of the predicted energy from the measured one and defined by the following equation [38]: dev = N i=1(e t ) N i=1(e r ) N (E r ) i=1 (6.3) where E t is the theoretical energy predicted by the model, and E r is the real energy measured at each site. 40

50 Chapter 6 Results and discussion 6.2. Amorphous silicon (a-si) PV module All the models gave high accurate power prediction at both sites with absolute value of RMBE within 4%, as shown in Figure Parameters model predicted the power with lowest RMBE (1%) at Cadarache, while Matrix methods predicted the power at Madrid with lowest RMBE value -1.3%. a-si module is subjected to the seasonal effects which usually increases the difficulty of predicting the energy of this technology, nonetheless the models gave good results as the average absolute RMBE values are 2%, 2.6%, 2.7%, and 2.7% for 6-Parameters, ZENIT, Simple model, and Matrix models respectively. Figure 6.1 RMBE of all models at Cadarache and Madrid for a-si module. Although 6-Parameters model has been proposed for c-si technologies, it gave a very good accuracy in predicting the power and energy of a-si technology. These results verify that 6- Parameters model is also applicable to be used for PV technologies other than crystalline silicon. Figure 6.2 clarifies that the most critical irradiance bins for all models are the zones with the irradiance less than 500 W/m 2. Simple model has different behavior in predicting the power as it over-predicted the power at low levels of irradiance while under-predicted the power at high levels of irradiance. This behavior can be explained by fact that it does not take into account the logarithmic dependency of voltage on irradiance, also it can be concluded that simple model has the worst reliability because a shortage of electricity will be experienced at wide range of low levels of irradiance due to the over-prediction of power/energy at those levels. 41

51 Chapter 6 Results and discussion A comparison between the RMAE of the four models is shown in Figure 6.3. The RMAE values are about 8% for all models at Cadarache, and about 9% at Madrid. Figure 6.2 RMBE of all models at different bins of irradiance for a-si module at Madrid. Figure 6.3 RMAE of all models at Cadarache and Madrid for a-si module. Regarding the energy prediction for a-si module as shown in Figure 6.4, the 6-Parameters had the highest accuracy in predicting the energy, with deviation up to -0.5%, at Madrid and Cadarache., Despite the simplicity of their procedures, MOTHERPV and simple model predicted the energy with average absolute deviation, the average of the absolute deviation at both sites, less than 1.5%. The Matrix method has the highest average absolute deviation up to 2%. The results of the overall deviation of energy prediction, for a-si module, show that the 42

52 Chapter 6 Results and discussion models succeeded to predict the energy with a high accuracy, about 97% at Cadarache and Madrid. Figure 6.4 Deviation of the total predicted energy of all models from the measured energy of a-si module at Cadarache and Madrid. Figure 6.5 explains the relatively high deviation of Matrix and ZENIT models compared to other models at Cadarache. The deviation of their energy prediction is around 3% at all irradiance bins higher than 200 W/m 2, which leads to final over-predicting about 3%. Figure 6.5 Deviation of the total predicted energy of all models from the measured energy at different bins of irradiance for a-si module at Cadarache. 43

53 Chapter 6 Results and discussion Regardless the overall under-prediction of energy by simple model, it gave very high overprediction at low level of irradiance, about 9%, and high under-prediction at high levels of irradiance compared to the other methods. This behavior of simple model leads to a comparable overall accuracy with respect to the other models Cadmium Telluride (CdTe) PV module CdTe module had experienced high variation in the outdoor performance during the measurements period. This variation obviously affected the results of power and energy prediction at these sites. The degradation of the efficiency of CdTe module was described earlier in Figure 5.6. This high fluctuation of its efficiency at Cadarache increases the difficulty of predicting the power due to the different measured power values for the same outdoor conditions. Figure 6.6 shows that the trend of all models is to over-predict the power at both sites with a higher RMBE at Cadarache as expected due to efficiency degradation. 6-Parameters and ZENIT model predicted the power the minimum RMBE values 2%, 6.3% at Madrid and Cadarache respectively. Matrix model predicted the power with the maximum RMBE, 13.8%, at Cadarache while Simple model predicted the power with average RMBE value up to 10%. Moreover, it is clear that the performance of the selected modules (to be predicted) at a given site must be compatible with performance of the modules used to derive the models coefficients at the basesite. The RMBE at different bins of plane of array irradiance at Madrid is shown in Figure 6.7. For all models, the RMBE is attributed mainly to the uncertainty at irradiance levels < 500 W/m 2 at both sites; these high values of RMBE were obtained as a result of the degradation of efficiency at these levels. On the other hand, the models predicted the power with RMBE less than 5% at high levels of irradiance. 44

54 Chapter 6 Results and discussion Figure 6.6 RMBE of all models at Cadarache and Madrid for CdTe module. Figure 6.7 RMBE of all models at different bins of irradiance for CdTe module at Madrid. The overall RMAE of each model at Madrid and Cadarache for CdTe module is shown in Figure 6.8. The 6-Parameters model predicted the power with lowest RMAE 11.5%, and 5% at Cadarache and Madrid respectively. In addition, the constant difference of RMAE values between Cadarache and Madrid for all models indicates the high spread of power measurements at this site compared to Madrid and the base-site. This high spread of the power measurements at Cadarache is attributed to the relatively longer I-V curve scanning time compared to other sites. 45

55 Chapter 6 Results and discussion Figure 6.8 RMAE of all models at Cadarache and Madrid for CdTe module. The results of overall energy prediction for CdTe module, presented in Figure 6.9, show that the 6-Parameters, ZENIT, and Matrix over-estimated the energy by about 5% at Cadarache as expected due to efficiency degradation of this module during the measurements campaign. In the case of simple model and MotherPV, the yielded energy was under-estimated at Cadarache despites its efficiency degradation which proves that these models give less reliable results than other models. Figure 6.9 Deviation of the total predicted energy of all models from the measured energy of CdTe module at Cadarache and Madrid. 46

56 Chapter 6 Results and discussion All the models except Simple model predicted the energy with a deviation less than 5% at high levels of irradiance, as described in Figure It is clear that the overall uncertainties were attributed to the high errors at low levels of irradiance as the deviation is more than 20% for Matrix and Simple models at those levels of irradiance. Figure 6.10 Deviation of the total predicted energy of all models from the measured energy at different bins of irradiance for CdTe module at Cadarache Mono-crystalline (mc-si) PV module Figure 6.11 shows that models predicted the power with average absolute RMBE less than 3% at Madrid, except simple model which predicted the power with the uncertainity of 4%. Despite the large distribution of measured power for the same level of irradiance at Cadarache, the 6- parameters model had the best performance at both sites. ZENIT, Simple model, and Matrix models predicted the power with RMBE 6.8%, 5.5%, and 5.2% respectively at Cadarache. This relatively high RMBE of Matrix methods at Cadarache is mostly attributed to the high uncertainty of predicting the power at low irradiance levels. Table 6.1 presents the available weather conditions used to derive coefficients of Matrix model at the base-site, while Table 6.2 presents the RMAE of Matrix model at Cadarache for mc-si module. It is clear that the cloud of the predicted power at Cadarache was higher at low irradiance levels, and the cloud was stable with the increase of module temperature. The 6-Parameters and Generic model gave the most realistic results for mc-si moduel as they over-estimated the power at Cadarache and under-estimated the power at Madrid, which is 47

57 Chapter 6 Results and discussion expected as the efficiency of this module was a bit higher at Madrid and lower at Cadarache than the efficiency at the base-site. Generic Model well-predicted the power at all sites with average absolute RMBE less than 3.5%. Figure 6.11 RMBE of all models at Cadarache and Madrid for mc-si module. Table 6.1 The available weather conditions used to derive Matrix coefficients of mc-si at Tallinn (marked by green color). Gi TMOD >1000 Table 6.2 RMAE of Matrix model for mc-si at different bins of module temperature and plane of array irradiance at Cadarache. 48

58 Chapter 6 Results and discussion Gi TMOD > The data set of mc-si module at Madrid was used to compare the behavior of power prediction between the Generic model, which has been produced by normalizing the power measurements of 18 c-si modules, and 6-Parameters model. The measurements at Madrid site were used for this purpose as the accuracy of the outdoor measurements at this site was more accurate than at Cadarache, and the I-V curve scanning time was shorter than at Madrid. The Generic model has the same behavoiur of 6-Parameters model, but it predicted the power with higher uncertainity at low levels of irradiance which is critical for off-grid solar systems, see Figure Moreover, in this study, the power of the used mc-si module is in the range of the labeled power used to derive the coefficients of Generic model. Thus, it is important to investigate the accuracy of this model for crystalline silicon module which has labeled power out of that range to determine the range of its availability. 49

59 Chapter 6 Results and discussion Figure 6.12 RMBE of Generic and 6-Parameters models at different bins of irradiance for mc- Si module at Madrid. Figure 6.13 illustrates the accuracy of energy prediction of each previously discussed model for mc-si at the three sites. All the models predicted the energy with absolute deviation less than 1.5% at each site, except ZENIT and Matrix models which over-predicted the energy at Cadarache by 7% and 3% respectively. The Generic model well-predicted the energy at all sites with high accuracy, about 98,5%. On the other hand, MOTHERPV gave spectacular results in predicting the energy of this module at both sites with absolute deviation less than 0.2%. Figure 6.13 Deviation of the total predicted energy of all models from the measured energy of mc-si module at Cadarache, Madrid, and Tallinn. 50

60 Chapter 6 Results and discussion 6.5. Development of models accuracy at low levels of irradiance As has been shown earlier in this chapter, the overall uncertainity of power/energy prediction mostly attributed to the uncertainity at low levels of irradiance. In this section, a new approach for fitting the models coefficients is being proposed to minimize the uncertainty of power/energy prediction at low levels of plane of array irradiance. Achieving high accuracy of power/energy prediction is necessary to obtain the highest availability of the solar systems especially at sites distinguished by long winter or cloudy weather like Northern Europe. To find the coefficients of a model for a specific PV module, the difference between the predicted and measured power is squared for each measurement (Ppredicted - Pmeasured) 2. These values are then summed as the least-squares fitting method minimizes the summation of the squared power differences to find the model coefficients. However, using the squared difference of power reduces the weight of the measurements at low level of irradiance during the fitting process due to the very small value of the squared difference at those levels of irradiance while it s very large at high level of irradiance. As a result to the previous discussion, the square of the predicted power deviation from measured one of each measurement ((Ppredicted Pmeasured) / Pmeasured) 2 was used instead of using the square of power difference. The new approach ensures the participation of the measurements at low levels of irradiance with a higher weight than in the standard approach. This leads to decrease the uncertainty of power/energy prediction at these levels of irradiance. For this objective, the 6-Parameters and Matrix models were examined for mc-si module, which had the highest performance stability among the others, at Cadarache and Madrid. Figure 6.14 clarifies the development of accuracy at low levels of irradiance as the uncertainty of energy prediction of mc-si decreased by about 2%, using Matrix model at Madrid. Likewise, the RMBE of 6-Parameters model was decreased by about1.5% for mc-si at Cadarache as shown in Figure

61 Chapter 6 Results and discussion Figure 6.14 Deviation of the predicted energy by Matrix model, based on standard and new fitting procedures, at different bins of irradiance for mc-si module at Madrid. Figure 6.15 RMBE of 6-Parameters model, based on standard and new fitting procedures, at different bins of irradiance for mc-si module at Cadarache. On the other hand, the new approach minimizes the weight of the measurements at high levels of irradiance due to small values of the power deviation at these levels. This leads to increase the uncertainty of power/energy prediction at these levels which in turn increases the uncertainty of the overall predicted energy. 52

62 Chapter 6 Results and discussion Figure 6.16 indicates that the overall uncertainty of the predicted energy by Matrix and 6- Parameters models increased by 1.7%, using the new approach. Thus, its applicability depends on the required availability of solar systems at low levels of irradiance. Figure 6.16 Deviation of the total predicted energy by Matrix model, based on standard and new fitting procedures, at different bins of irradiance for mc-si module at Madrid and Cadarache. 53

63 7. Comparison of measurement procedures Throughout this chapter, a comparison of the derived parameters (ISC, VOC, PMPP, and TMOD) has been done between different institutes at similar meteorological conditions. In addition to Madrid, Tallinn, and Cadarache, the modules were measured at other institutes that were not included in the comparison of power/energy prediction. Thus, Wroclaw-Poland site was included in this comparison of measurement procedures at different institutes. The aim of this comparison is to compare the measurements infrastructures of all institutes and to cross-check the measurement results. For this purpose, the mc-si module parameters were derived at different bins of in-plane irradiance and at fixed air temperature of 20. Figures 7.1, 7.2, and 7.3 demonstrate, respectively, the deviation of the derived PMPP, VOC, and ISC from their average values at same meteorological conditions. Figure 7.1 Deviation of derived PMPP at all institutes from the average value of derived PMPP for mc-si, at different bins of in-plane irradiance, [0-2] m/s wind speed, and 20 air temperature. The deviation of PMPP, VOC, and ISC is in the range of ±4%, which increases the uncertainty of power/energy prediction and limits the accuracy of the performance prediction. Accordingly, for better energy prediction and for more precise translation of the PV performance between sites, the measurement procedures in the future should be harmonized to higher level at all testing institutes. 54

64 Chapter 7 Comparison of measurement procedures Figure 7.3 shows that the ISC values at Cadarache are lower than the average value by 3.5%; this variation can be attributed to the different calibration of the irradiance sensors, or to some damage experienced by the module (e.g. cracks in solar cell) at a specific site. Because of the nature of round robin tests, the test modules are circulated one time between institutes, the reason of this variation cannot be determined clearly. Accordingly, the test modules in the future should be circulated back between institutes to investigate the reasons of the deviation of this parameter during round robin tests. Figure 7.2 Deviation of derived VOC at all institutes from the average value of derived VOC for mc-si, at different bins of in-plane irradiance, [0-2] m/s wind speed, and 20 air temperature. Figure 7.3 Deviation of derived ISC at all institutes from the average value of derived ISC for mc-si, at different bins of in-plane irradiance, [0-2] m/s wind speed, and 20 air temperature. 55

65 Chapter 7 Comparison of measurement procedures Figure 7.4 presents the derived module temperature at all institutes, at different bins of in-plane irradiance, and at fixed air temperature. The measurements of module temperature were lower than average in Tallinn and Wroclaw, but higher than the average in Madrid and Cadarache. As the module temperature is an input of the power/energy methods, this difference of the measured values of TMOD between the base-site and other sites affected the accuracy of translation the modules performance to these sites. Figure 7.4 Derived TMOD at all institutes for mc-si, at different bins of in-plane irradiance, [0-2] m/s wind speed, and 20 air temperature. The uncertainty of TMOD measurements is relatively high compared to that of air temperature. This high uncertainty is attributed to several factors: the position of the temperature sensor as the temperature is not homogeneous over the module area, the type of the temperature sensor, the heat conductivity between sensor and the module, and the isolation of the sensor from surrounding environment. Therefore, one can conclude that the air temperature measurements are more reliable than module temperature. As air temperature measurements are more reliable than module temperature measurements, air temperature is better to be utilized in power/energy prediction models to obtain higher accuracy of translation the performance of PV modules between sites. In that sense, the air temperature sensor must be placed in a way that it can measure the same air temperature experienced by PV modules. 56