Analysis of the outdoor performance and efficiency of two grid connected photovoltaic systems in northern Italy

Energy Conversion and Management 80 (2014) 436–445

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Analysis of the outdoor performance and efficiency of two grid connected photovoltaic systems in northern Italy Diego Micheli ⇑, Stefano Alessandrini, Robert Radu, Iosto Casula University of Trieste, Department of Engineering and Architecture, Italy

a r t i c l e

i n f o

Article history: Received 13 November 2013 Accepted 24 January 2014 Available online 19 February 2014 Keywords: Photovoltaic PV system outdoor monitoring PV temperature coefficient PV performance evaluation

a b s t r a c t This paper analyzes and compares the actual performance of two grid connected photovoltaic plants, which are of similar size but based on different modules technologies. The facilities are located on the roofs of two buildings of Area Science Park in the site of synchrotron ELETTRA in Basovizza (Trieste). The first system is equipped with modules of mono crystalline silicon wafer surrounded by ultra-thin amorphous silicon layers (hetero-junction with intrinsic thin layer), facing south and tilted at 30°, the second is equipped with mono crystalline silicon modules oriented 35° west and inclined at 10°. The set of data analyzed in this paper has been systematically acquired from October 15th 2011 to October 14th 2012 by means of a dedicated monitoring system. The aim of this study is to analyze the actual performances of the plants and their yearlong evolution, separating the effects of the variability of environmental conditions from those due to the variability of the performance of panels and electrical components. Particular attention is given to the influence that irradiance and temperature have on the efficiency of the system. An analysis methodology was applied to appropriately filter, classify and normalize the data in order to identify the modules temperature coefficients as a function of irradiance and to compare their actual efficiency with those declared by the manufacturer. Acquired data and mathematical correlations will allow the development of accurate simulation models, useful for assessing the actual profitability of similar installations. The latter can be obtained through the main performance indices of the systems that are calculated and reported in this paper for the period of observation. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction The increasing price of conventional fuels and the difficulty to sustain the world pollution level make renewable energy sources an absolute necessity. The economic incentives and the fast technological developments allow the use of grid connected photovoltaic plants in a simple, efficient and profitable way. The photovoltaic (PV) energy assumes, therefore, an increasing role within the spectra of the energy sources, especially for its simplicity of installation and integration in building architecture. The market offers many types of photovoltaic modules, which performances must be carefully analyzed in order to encourage a more efficient use of the sun energy and to limit the area designated to photovoltaic plants. Usually, the performances of photovoltaic modules refer to the Standard Test Conditions (STC), which are not always representative for the real module operation. In fact, it is well known that the performance of a photovoltaic module depends not only on cell ⇑ Corresponding author. E-mail address: [email protected] (D. Micheli). http://dx.doi.org/10.1016/j.enconman.2014.01.053 0196-8904/Ó 2014 Elsevier Ltd. All rights reserved.

technology, geographic position and installation configuration, but also on environmental parameters such as global irradiance and ambient temperature. The study of the influence of such parameters is of significant importance for carrying out a comparison between the on field and reference panel performances, for analyzing the long term performance degradation and for predicting the energetic performances of the PV systems. Performance analysis of PV plants in real operating conditions is then widely covered in the recent technical literature. The effects of various climatic conditions and different geographical locations are reported in [1–13]. The evaluations are done by calculating the values of the energy yield, yield factor, capacity factor, power efficiency and PV array efficiency, usually on annual basis. Results show that the choice of different panel technologies should be done taking into account the local environmental variables, that affect their efficiency and profitability in different ways. For example, marked differences in the behavior and output of different module types, and deterioration of the maximum power at standard test conditions are observed in [1]. The degradation trend for an amorphous module during the first

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months of operation, followed by a significant recovery in energy production, is highlighted in [6]. Knowledge of the actual spectrum distribution at each location under real working conditions is deemed necessary in [7], for optimizing PV panel design with amorphous and polycrystalline silicon cells. The detailed analysis of temperature and irradiance effects on performance by means of outdoor monitoring is reported in [7,14–20], sometimes complemented with indoor tests [1,15,14] or referred to a specific technology, as monocrystalline [9], multi crystalline [12] or amorphous silicon modules [21,22]. In [14] the performance comparison in outdoor and indoor conditions of five cell technologies show that, for all irradiance levels, the temperature coefficients depend on the determination method. This is mainly due to spectral mismatch, therefore the results of performance prediction models that are based on the calculated temperature coefficients are site specific, and possibly seasonally dependent. In [15], indoor tests show that temperature coefficients are practically constant over the normal range of operating conditions. The outdoor data presented in the same study emphasizes that, possibly due to the incidence angle and spectral effects, the temperature coefficient may assume a wide range of values, starting from the expected negative value at STC irradiance, to positive ones, for lower irradiances. In [16] authors analyze the techniques and errors in outdoor measurement of temperature coefficients of cells, modules and strings of panels. In [18] the temperature coefficients of twelve photovoltaic panels are obtained at various global radiations in two very different locations and with different irradiation sensors, but with air mass coefficients close to the STC value. The influence of ohmic and mismatch losses, together with the effects of errors in the tracking of the maximum power point tracking, are studied in [19]. The study also evaluates the uncertainties of the calculation models for the radiation on tilted surfaces and cell temperature. The influence of temperature on the operating losses of different types of panels is discussed in [20–22]. Outdoor results show that the output of polycrystalline modules is sensitive to temperature but not to spectrum distribution, while the output of amorphous silicon modules is higher under blue-rich spectrum and is subjected to the thermal annealing effect. In [23] the effect of wind on the module temperature is analyzed by comparing the experimental data with various literature correlations. Different installation solutions are also discussed in recent literature. In [24] fixed and tracking flat panels are compared to concentrating mirrors and tracking mechanism systems. Different tilt angle values can also be applied to the panels using manual systems, in summer or winter season [11,12] or approximately every 26 days [25]. In [9] a 960 kW system is divided in two subfields inclined at two different tilt angles, suitable for summer and winter solar insolation. The performance of two PV systems with different orientations are analyzed in [10], to found the theoretically optimal tilt angle, that resulted approximately equal to the local latitude angle. Many studies are dedicated to the comparison between nominal and actual performances and to the long term monitoring. In [26] a monitoring system is presented, that includes a unit for the on field measurement of the I–V panels curves. In [27], a methodology is presented to estimate the electrical production from outdoor testing data, based on an improved I–V curve model and a new maximum power output expression. The present work analyzes the performances of two photovoltaic plants placed into the same location, on the roof of two buildings in the area of the ELETTRA synchrotron, in Basovizza (Trieste). The first plant uses Sanyo HIT modules with solar cells made of a thin c-Si mono-crystalline silicon wafer surrounded by ultra-thin amorphous silicon layers [28]. According to the manufacturer data, HIT solar cells improve boundary characteristics and reduce power

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generation losses by forming impurity-free i-type amorphous silicon layers between the crystalline base and p and n-type amorphous silicon layers. The second plant uses Sunpower modules with mono-crystalline back-contact solar cells, a technology that allows optimizing the module efficiency by increasing the packing density of the cells and minimizing the electrical resistance of the interconnections [29,30]. The article gives a further contribution of data from the on field monitoring of PV plants. It has the double purpose of describing a methodology for verifying the nominal performance degradation on the long term, and of analyzing the actual energy yield of the plants by means of the global performance parameters. The work is part of a broader project regarding the monitoring and the integration of different systems of distributed generation. In the same district are available for this purpose a third photovoltaic plant with thin film panels, a cogenerator with reciprocating internal combustion engine operating at variable speed and another cogenerator with two micro gas turbines, both fueled by natural gas, and are in advanced stage of construction two solar cooling plants.

2. The photovoltaic plants The monitored photovoltaic systems are placed on the roof of two nearby buildings, called CTB and Q2, belonging to the Area Science Park Consortium in Trieste, Italy. The PV systems are different in both size and configuration and the installed modules are different by brand, technology, orientation and inclination. The installed modules technical specifications are shown in Table 1. They are equipped with monitoring systems that allow the storage of average data on a period of fifteen minutes. For both systems, the tilt angle was chosen as a compromise between the optimum value, the roof coverage and the avoidance of the self-shading. Considering the geographical position, according to the Photovoltaic Geographical Information System, the optimum value of the tilt angle, for a fixed panel, is 35°. For the Q2 system, in order to maximize the roof coverage, a tilt angle of only 10° was adopted. In the case of the CTB system the roof configuration allowed for the adoption of a tilt angle of 30°, much closer to the optimum one. For both PV systems, each inverter is monitored by means of a multifunction device (MFD) that acquires, using current and voltage dedicated measurement modules, the input DC electrical parameters for each string that is connected to the inverter and stores the resulting output AC electrical parameters. Both DC and AC data are acquired with a maximum error of 0.5%. In order to separate the actual performance of the PV modules from those of the inverter, in this work only string current and tension are taken into account. Furthermore, each MFD receives and stores data from the other sensors which measure the solar irradiance and both the panel and ambient temperatures. In particular, the accurate measurements of global irradiance and panel temperatures are key requirements for the evaluation of the PV plant efficiency. The irradiance measurements on modules planes are made with two pyranometers, one for each PV system. All pyranometers are secondary standard Kipp & Zonen CMP 11 type, with a maximum daily total uncertainty of 2%. Temperatures are measured with Pt100 sensors fixed on the back of some panels. The maximum error, including the sensor and the acquisition module contributions, is ±0.7 °C. In order to assign, at every time step, a single temperature value to each PV system, only the averages of all the contemporary measurements acquired by every plant sensors are taken into account. Regarding the weather data, a part from the global solar irradiance, each PV system is fitted with a Pt100 ambient temperature

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Table 1 Photovoltaic plants technical data.

Rated power (kWe) Module manufacturer Module type Module efficiency (%)a Module rated power (kWe)a Number of modules Module tilt (°) Module orientation (°) Number of inverters Inverter manufacturer Inverter number Inverter type Strings/inverter Modules/String a

Q2

CTB

15.90 Sunpower SPR300-WHT 18.4 300 53 10 215 3 SMA #1 6000A 3 7

17.94 Sanyo HIP 230 HDE1 16.6 230 78 30 180 2 Aurora #1 PVI-10 1 13

#2 5000A 3 6

#3 3800A 2 7

1 26

#2 PVI-10 1 13

1 26

Standard test conditions.

sensor, having the same accuracy of those mounted on the back of the panels. All the weather data, acquired by the two PV monitoring systems, can be checked by comparison with the corresponding data acquired by a full-equipped weather station, placed on the CTB roof. Figs. 1 and 2 show a schematic representation of the PV systems configuration.

3. Environmental climatic conditions As previously said, the most important factors to be considered for the correct evaluation of PV modules’ performance are the irradiance G and the modules’ operative temperature Tm. Fig. 3 shows the frequency distribution and the cumulative frequency of irradiance measured in the modules plane during the considered analysis period, that goes from October 15th 2011 to October 14th 2012. The irradiance data whose value is greater than zero is organized in classes with a width of 100 W/m2 each. The maximum recorded values are in the class 1001–1100 W/m2 but, for both systems, the majority of the samples fall on low-energy classes. In particular, about 50% of the total radiation received during the analysis period has a value lower than 300 W/m2. In addition, the study considered classes of module temperatures of 5 °C of width, between 15 °C and 45 °C. The amount of solar energy H that reaches the surface of the modules for each class of irradiance or temperature, normalized with respect to the total energy irradiated in all the classes during the analysis period are given by Eq. (1), in which Gk(i) is the value of the ith sample of G in the kth class of irradiance or temperature P and N = nk, with nk number of elements in the kth class, is the total number of samples G(i) recorded in the analysis period.

PN Gk ðiÞ H ¼ Pi¼1  100 N i¼1 Gð iÞ

Fig. 1. Picture and schematic representation of the CTB PV arrays and data acquisition systems: MFD – multifunction device, V – voltage acquisition module, A – current acquisition module, Tm – module temperature measure, G – irradiance measure, INV – inverter.

ð1Þ

Obviously, module surface area, A, and sampling period, ss, do not appear explicitly in Eq. (1) because they are constant. In Fig. 4 the values of H are shown as a function of the classes of irradiance on the modules planes, while in Fig. 5 they are given as a function of the classes of modules temperature. In both figures, the corresponding curve of the cumulative value of H is also depicted. As expected, the energy distributions of the two plants have almost the same trend. As presented in Fig. 4, the cumulative energy related to the CTB plant is slightly shifted to higher irradiance classes, due to the different modules orientation and tilt. For the same reason, in the analyzed period the Q2 pyranometer recorded fewer samples with irradiance values higher than 1000 W/m2 than that installed on the CTB. Generally, the cumulative energy has an almost linear growth up to 1000 W/m2. Half of the energy received by the Q2 system was in the irradiance classes lower than 600 W/m2, while the CTB system received the same energy fraction for irradiance values under 700 W/m2 (the difference is due again to the different orientation of the PV modules). Fig. 5 shows that about 65% of the energy was received by the two PV arrays at modules’ temperatures higher than the standard test conditions of 25 °C,

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Fig. 2. Picture and schematic representation of the Q2 PV arrays and data acquisition systems: MFD – multifunction device, V – voltage acquisition module, A – current acquisition module, Tm – module temperature measure, G – irradiance measure, INV – inverter.

Fig. 3. Irradiance frequency and cumulative frequency for CTB and Q2 PV systems. Data divided in irradiance classes of 100 W/m2.

Fig. 4. Distribution and cumulative energy values of the solar energy directed to the modules plane. Data divided in irradiance classes of 100 W/m2.

therefore with conversion efficiency lower than the corresponding nominal value of the panels.

4. Data processing methodology The direct electric power P generated by a PV module is given by the product of the active area of the module A, the solar irradi-

ance G and the conversion efficiency of the module g (Eq. (2)). This efficiency depends in turn on some parameters, the most important of which are the operative temperature Tm and the irradiance level.

P ¼ GAgðT m ; GÞ

ð2Þ

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– sorting of filtered data into irradiance classes [18,15]; – calculation of the power temperature coefficient for each irradiance class [18,14,15]; – normalization of the data to STC temperature, according to the temperature coefficient of each class; – further normalization of the data to the STC irradiance value.

Fig. 5. Distribution and cumulative energy values of the solar energy directed to the modules plane. Data divided in modules temperature classes of 5 °C.

In real operating conditions, many combinations of Tm and G are detected, and the occurrence of the standard test conditions is a very uncommon event. In order to monitor over time the actual performance of a PV system, or at least of a module, the generated output must be converted to the standard test conditions, therefore it is necessary to carefully study the influence of irradiance and temperature on the power generation. The power reduction with the increase of temperature is mainly due to the decrease of DC voltage, while the effect on DC current is one order of magnitude lower, and therefore almost negligible. In general, temperature influence should not be considered independent of the solar radiation, even though this effect could be limited. King et al. [16] observe that, for irradiance values between 100 and 1000 W/m2, the voltage temperature coefficient of a PV module changes less than 5%, while the less influential current coefficient must be scaled by the ratio between the actual irradiance level and the one used for its determination. Whitaker et al. [15] demonstrate that the power temperature coefficient can be considered constant in indoor tests, but it could become positive for low irradiance levels in outdoor operating conditions. Regarding the correlation between power and irradiance, the assumption (at constant reference temperature) of linearity [6] is acceptable if the loss due to the reduction of the module glass transmittance, which occurs with the increment of the radiation angle of incidence, is negligible [17]. With angles lower than 45°, the above mentioned requirement can be considered completely fulfilled [17]. The angle between the radiation and the normal to the module plane is also related to the air mass coefficient, AM, which defines the optical path length of solar radiation through the atmosphere, expressed as a ratio relative to the path length at the zenith. In [18] power data acquired in two plants with mono or poly-crystalline Si modules, installed in very different places, previously filtered for 1.4 < AM < 1.6 and organized into irradiance classes, exhibit linearity with global irradiation if the last one is measured by means of pyranometers, instead of c-Si sensors. On the contrary, linearity is not maintained at medium and low irradiance values in the case of thin film modules of various technologies. A complete procedure for testing the performances of a module over time and comparing them with their nominal values could be defined according to the following methodology: – filtering of the acquired data with respect to incidence angle [17] or to AM values [18] and to the linear trend of current vs. irradiance [6];

First, for both CTB and Q2 PV systems, only data with radiation incidence angle lower than 30° is taken into consideration. The choice of such a rather precautionary value for the incidence angle threshold was made since it depends on a considerable number of factors, which influence transmittance losses. In order to improve the accuracy in determining temperature coefficients, a second filter has been implemented. Assuming that large deviations from the linear trend of current vs. irradiance are to be attributed more to occasional events during the sampling period (like effects of wind, rain, sudden cloudiness or changes in atmospheric clarity) than to the module temperature effects, data with deviation from linearity above ±8% are not taken into account. Considering the fit line of DC current available data as linearity reference, string currents have been filtered separately and then they have been recombined to calculate the power of the PV plant. Fig. 6 shows the effect of the mentioned filters for a string of the CTB PV system. The second filter seems to be more selective than the first, showing the significant influence of hardly predictable atmospheric factors which alter the quality of the radiation that reaches the surface of the modules. After the data filtering process, the temperature coefficients can be calculated. All the data analysis presented below has been carried out considering the arrays for which a complete set of measurements was available. The considered PV systems’ sections are namely one array of 13 Sanyo 230 W modules of the CTB system and 6 arrays of Q2 system, which correspond to 39 Sunpower 300 W modules. Data has been sorted into irradiance classes starting from 50 W/m2 and then ranging with steps of 100 W/m2 from 100 to 1000 W/m2. For each class the measured DC power values, P, have been normalized to the class irradiance median, as suggested in [18,14] and according to the previous discussion, by means of the linear transformation in Eq. (3), where PN is the normalized power, G the measured and GM the median value of irradiance respectively.

Fig. 6. Effect of the angular and linear filter on a string current data.

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Fig. 7. Normalized DC power for the analyzed systems. The DC power is divided in irradiance groups with linear regression lines.

PN ¼

P  GM G

ð3Þ

The results of such a procedure are reported in Fig. 7, which refer to an analysis period that goes from October 15th 2011 to March 31st 2013. The figure shows also the linear regression lines used for the calculation of the temperature coefficients. As expected, at constant irradiance, DC power decreases when modules’ operating temperature increases. The peak power referred to the STC conditions, Pp, can be determined for each PV system through the intersection of the 25 °C vertical line with the regression line of the normalized power values belonging to the 1000 W/m2 irradiance class. The Pp values for the considered PV arrays are 2793 W for the CTB and 10484 W for the Q2, instead of 230  13 = 2990 W (6.6%) and 300  39 = 11700 W (10.3%) expected on the basis of their declared STC nominal power.The temperature coefficient of each irradiance class ck is given by the angular coefficient of the corresponding regression line divided by Pp and multiplied by 100. In this way it assumes the meaning of the percentage of the peak power at STC lost for an increase of panel temperature of one K. The values of temperature coefficients obtained for the 1000 W/ m2 irradiance class are 0.251% K1 for the CTB array and 0.308% K1 for the Q2 system, instead of 0.3% K1 (16.3%) and 0.38% K1 (18.9%) declared respectively by Sanyo and Sunpower. They are comparable between the two systems but rather different from the manufacturer data. In this regard, it must be said that the temperature coefficients declared on the datasheets are

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referred to AM 1.5. In the present analysis, it was not possible to fulfill this criterion, because it is a very rare circumstance at the considered latitude and it occurs only in the central hours of the day in June, July and August. In more detail, the range of AM related to the 7360 remaining data after filtering with respect to the incidence angle goes from 1.08 to 3.30. Considering only the samples characterized by a value of AM in the range between 1.4 and 1.6, the amount of data available for the further analysis would have decreased to 952 (87%). Fig. 8 shows the temperature coefficients obtained for all the irradiance classes. For lower irradiance values, the two PV systems are characterized by almost equal temperature related power losses. Significant differences occur at medium and high irradiances, with a higher temperature influence on the Q2 system performance. For both systems, the shape of the curves presents a local minimum at 500 W/m2 followed by a local maximum at 700–800 W/m2, that deserves to be further investigated. A first qualitative explanation is because temperature measurement can be affected by uneven temperature distribution in the not controlled portion of the string surface, introduced by wind, intermittent sunshine, module frames, junction boxes, mounting brackets, etc. [16]. Other measurement errors derive from the technique used for mounting of the temperature sensor [14], or they can be due to the effect of module’s heat capacitance during transient thermal conditions [16] and to temperature gradient between the external surface and the back side [19]. Moreover, the effects of temperature are mixed with those of the spectral mismatch between PV module and irradiance sensor [14] and between the standard solar spectral distribution of indoor STC tests [16] and the outdoor spectrum, that varies with AM and sky conditions. For these reasons, those reported in Fig. 9 are ‘‘effective’’ or ‘‘apparent’’ power temperature coefficients [16], which must be considered site dependent, specific properties of the two PV system, rather than technical specifications of the panels. The next step of the procedure is the normalization of the data to STC temperature, TSTC, according to the temperature coefficient of each class. The DC power resulting from this normalization, PNT, is obtained using Eq. (4), where Tm is the module operative temperature.

PNT ¼ PN þ ðT STC  T m Þ  ck  Pp =100

ð4Þ

The positive effect of the normalization on the achievement of the comparability of the actual performance to the nominal one can be well appreciated defining the normalized panel efficiencies gN and gNT given by Eqs. (5) and (6).

Fig. 8. Temperature coefficients of the two PV systems determined for each irradiance group.

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Fig. 9. CTB modules efficiency average values and standard deviations after the normalization procedures.

gN ¼

PN A  GM

gNT ¼

PNT A  GM

ð5Þ

ð6Þ

In Fig. 9, the mean values and the standard deviations of gN and gNT calculated for each irradiance class for the CTB system are shown. In Fig. 10 the same parameters with reference to the Q2 system are presented. Figs. 9 and 10 show that, due to the normalization with respect to STC temperature, the average values of gNT remain fairly constant for medium and high levels of irradiance, in particular for the Q2 system, especially when compared with the corresponding values of gN. In addition, in spite of the double normalization process and the initial filtering, data dispersion has not been completely removed, but it seems to reduce with the temperature normalization. It can also be noticed that the data dispersion is minimal at 1000 W/m2 and it is very low in particular for the Q2 system. The last step of the procedure for testing the performances of a module over time, and comparing them with their nominal values, is the further normalization of the DC power data to the STC irradiance value GSTC = 1000 W/m2. Data belonging to the lowest class of irradiance, 50 W/m2, are excluded from the normalization because of the low values of efficiency shown in Figs. 9 and 10, which are probably due to the very high AM values and spectral mismatch effects usually associated with the samples of this class. In such a way, the values of PNTG are obtained with the linear transformation of Eq. (7), and the corresponding values of efficiency gNTG with Eq. (8).

PNTG ¼

PNT  GSTC G

ð7Þ

gNTG ¼

PNTG A  GSTC

ð8Þ

Table 2 presents for each system the mean value of the measured DC efficiency, obtained without the data normalization processes, the mean value of gNTG and the efficiency declared by the manufacturers in STC conditions.

Fig. 10. Q2 modules efficiency average values and standard deviations after the normalization procedures.

In both systems, due to the actual weather conditions, the measured DC efficiency is lower than the corresponding value declared in STC conditions and, in spite of the rather significant difference between the reference values given by the two manufacturers, the real life mean DC efficiencies values of the PV systems are very close. A better agreement was found instead between the mean normalized efficiencies and the manufacturer data. The Q2 panels confirm their better performance when referred to STC conditions by means of the data processing procedure, while the CTB panels exhibit mean value of gNTG which, compared to the Q2 value, is closer to the value declared in the manufacturer data sheet. The differences between the mean normalized and the manufacturer efficiencies are in fact equal to 5% of the STC value for the CTB plant and to 10% for the Q2. In both cases the deviations are in agreement with literature data: for example, the on-field performance of a monocrystalline plant, under near STC operating conditions resulted to be 8.3% lower than the specifications, due to various power losses [19], the difference between rated and field power of PV plant of twelve different technologies and two different locations ranged between +5% and 20% [18]. In order to complete the analysis of the experimental data, the frequency distributions of gNTG for the two systems are presented in Fig. 11. The shape of the distributions of the two systems is quite similar, with a standard deviation of 0.0074 and 0.0071 respectively for the CTB and the Q2 modules. Considering also the above mentioned average data, it can be stated that the CTB system operated more frequently closer to the expected performance than the Q2 one.

5. Performance indexes calculation The evaluation of the effective energetic performances of the PV systems has been carried out with the calculation of the main Table 2 CTB and Q2 systems average efficiencies. PV system

CTB

Q2

Mean value of the measured DC efficiency (%) Mean value of the normalized measured DC efficiency gNTG (%) Manufacturer efficiency (STC conditions)

14.3 15.7 16.6

14.2 16.5 18.4

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performance indexes over the reference period of one year, from October 15th 2011 to October 14th 2012. The analysis regards the 13 modules of the CTB plant and the 39 modules of the Q2 plant for which a complete set of measurements were available, as just considered in the previous paragraph, and takes into account all the recorded data, without any previous filtering process. Also the performance indexes’ values have been obtained on the basis of the direct current side data as allowed by the standards [31], and consistent with the previous analysis. Fig. 12 shows, as an example, the distribution of a complete set of DC values for one string of the Q2 plant, as a function of the hour and of the irradiance level. It is interesting to observe that the current generated by PV modules, at the same irradiance level, is lower in the afternoon due to the higher module temperatures usually recorded in this part of the day. The considered performance indexes are the array yield, YA, and the reference yield, Yr, defined according to [31], and the DC performance ratio, PRDC. Their explanation is presented in the appendix. The calculated values, for both analyzed PV systems, are presented in Table 3. The CTB system DC performance ratio results higher, even if both PV systems are characterized by good PRDC values (over 80%), very similar to those found in sunnier areas of Italy [9,11]. In order to analize modules’ response to the variability of climatic conditions, in Fig. 13 the performance indexes calculated with Eqs. (9)–(11) are shown, but with the monitored energy output and the monitored incident energy calculated for each month of the analysis period. The recorded performances of CTB system are on the whole higher, but the influence of high module temperatures during the spring and summer months is more evident: PRDC decreases almost linearly from more than 95% in February to about 82% in August, when the two plants give similar results. In winter the recorded performances have similar trends, with a minimum in December, but the CTB plant is less penalized, probably thanks to the more favorable orientation of its modules. Figs. 14 and 15 offer a whole year image of the systems’ efficiencies and of their correlation with the solar energy measured in modules’ planes: for both the analyzed PV arrays, the monthly averaged values of the cumulative daily radiation and of the panel efficiency are presented. The vertical bars show the monthly standard deviation of the related parameter. As expected due to the vicinity of the arrays, the average values and the variability of the irradiated solar energy are very similar for

Fig. 12. Scatter diagram of one string current of the Q2 system as a function of time and irradiance values.

Table 3 CTB and Q2 systems performance indexes and energy productions values. PV system

CTB

Q2

Number of considered modules Rated power of the considered modules P0 (W) DC energy output E (kW h) Array yield YA (h) Reference yield Yr (h) DC performance ratio PRDC (%)

13 2990 4186 1400 1572 89.1

39 11,700 14,939 1277 1544 82.7

Fig. 13. Monthly performance indexes of the two PV systems calculated for the one year analysis period.

Fig. 11. Normalized efficiency frequency distribution of CTB andQ2 modules.

the two PV plants. The highest irradiance values but also the highest variabilities were recorded in spring and summer months, while in the late autumn or winter months the variability is much smaller. In both cases, the monthly average efficiency varies approximately between 11% and 16%, and only in a few spring months it is not so far from both the normalized value and the manufacturer declared value reported in Table 2. The CTB system

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Fig. 14. Monthly average of daily solar energy received by the CTB system and monthly average efficiency of its modules. Standard deviation bands are reported.

back contact patented technology (SPR300-WHT). The analysis of climatic data shows that both systems have worked for most of the time with medium – low values of irradiance and with operating temperatures of the modules above that defined by the standard conditions. An appropriate procedure for data filtering and processing has been developed to determine the outdoor DC power and efficiency of the two plants, normalized to STC conditions, and their ‘‘effective’’ or ‘‘apparent’’ power temperature coefficients at various irradiance values. Such temperature coefficients measured at 1000 W/m2 have been compared with the nominal ones, showing a difference of 16.3% and 18.9% respectively for CTB and Q2 modules. A better agreement has been found between the normalized outdoor efficiencies and the manufacturer ones, with a difference between them respectively of 5% and 10% for CTB and Q2 systems. The determination of the main performance indices, both globally and on a monthly basis, showed a higher level of performances of the CTB system with respect to the Q2 one. In particular, the yearlong calculated PRDC values are 89.1% for the CTB system and 82.7% for the Q2, which confirms a good quality of both the modules and their installation. The described data and procedures allow the comparison of the on field performance with the reference values, and the analysis on the long period to check performance degradation. The prediction of the energetic performances of the PV systems can then be carried out in the short and long term in a reasonably reliable way. Appendix A The array yield, YA, is defined in Eq. (A.1), and indicates the amount of time during which the array would be required to operate at the array rated output power P0 to provide the monitored DC energy output E.

YA ¼

E P0

ðA:1Þ

The reference yield, Yr, is calculated using Eq. (A.2), and represents the amount of time during which the solar radiation would need to be at reference irradiance level GSTC = 1000 W/m2 in order to contribute the same incident energy as was monitored, given by P ss  Ni¼1 GðiÞ. Fig. 15. Monthly average of daily solar energy received by the Q2 system and monthly average efficiency of its modules. Standard deviation bands are reported.

has reached its maximum monthly average efficiency in March, while the Q2 in May. Moreover, the variability of the PV efficiency is always very high, even in months with relatively low irradiance variability: as an example, in August the Q2 system has worked with efficiency between 11.5% and 18%. If data regarding only the warm months are taken into account, it can be noticed that going towards August the average efficiency of the Q2 panels decreases slightly, less than in the case of the CTB plant. This is due to the lower temperature coefficients of the Sunpower modules, so that the Q2 plant results to be more efficient under high irradiance conditions. 6. Conclusions The present study investigates the performance of two solar fields placed on the roofs of two buildings, named CTB and Q2, of the campus of Basovizza – Area Science Park during the period from October 15th 2011 to October 14th 2012. The CTB system is equipped with modules in mono-crystalline silicon covered with a thin layer of amorphous silicon (HIP-230 HDE1), while the panels of the field Q2 are in pure mono-crystalline silicon wafer with the

Yr ¼

ss 

PN

i¼1 GðiÞ

GSTC

ðA:2Þ

Both the monitored energy output and incident energy are yearlong calculated. Hereinafter, both YA and Yr values are given in hours. Finally, the DC performance ratio is defined as the percentage ratio between the array and the reference yields, as shown in Eq. (A.3).

PRDC ¼

YA  100 Yr

ðA:3Þ

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Glossary A: active module surface (m2) AC: Alternate Current (A) AM: air mass factor CTB: CTB (Centrale Tecnologica di Basovizza) building DC: Direct Current (A) E: output energy (kW h) G: irradiance measured in module plane (W/m2) GM: median value of each irradiance class (W/m2) Gk(i): value of the ith sample of G in the kth class of irradiance or temperature (W/m2) GSTC: standard test conditions irradiance equal to 1000 W/m2 Gtot: irradiance on the modules surface during the considered analysis period (W/m2) H: solar energy amount (for each irradiance class) (%) HIT: Heterojunction with Intrinsic Thin layer MFD: multifunction device N: total number of samples (–) nk: number of elements in the kth class of irradiance or temperature (–) P: power in real operative conditions (W) P0: rated output power (W) Pp: experimental on site peak power, referred to standard test conditions (W) PN: power normalized to the median value of each irradiance class (W) PNT: power normalized to the median value of each irradiance class and to STC temperature (W) PNTG: power normalized to the median value of each irradiance class, to STC temperature and to Gstc (W) PRDC: DC performance ratio (%) PV: Photovoltaic Q2: Q2 building STC: Standard Test Conditions, G = 1000 (W/m2), T = 25 °C, AM = 1.5 YA: array yield (h) Yr: reference yield (h) Tm: module operative temperature (°C) TSTC: standard test conditions temperature equal to 25 °C g: photovoltaic conversion efficiency (–) gN: photovoltaic conversion efficiency referred to PN (%) gNT: photovoltaic conversion efficiency referred to PNT (%) gNTG: photovoltaic conversion efficiency referred to PNTG [%] c: power temperature coefficient (% K1) ss: sampling period (s)