Effect of Dust and Weather Conditions on Photovoltaic Performance in Doha, Qatar

Transcription

1 Effect of Dust and Weather Conditions on Photovoltaic Performance in Doha, Qatar Bing Guo, W. Javed, B. W. Figgis and T. Mirza Abstract Photovoltaic (PV) power generation performance in desert environments is affected by surface soiling due to dust deposition. In this study, PV performance, ambient dust and weather conditions were measured continuously from June 1 through December 31, 2014 at a solar test facility in Doha, Qatar. Averaged over the seven months, the PV performance loss due to soiling was / per day for modules cleaned every sixth month, and / per day for modules cleaned every second month, in terms of a cleanness index based on the PV module s temperature-corrected performance factor. The daily change in a PV module s cleanness index was negatively correlated with the daily average ambient dust concentration, positively correlated with wind speed, and negatively correlated with the relative humidity. A multivariable regression model was developed to quantitatively relate the daily change in PV module cleanness index with the dust concentration, wind speed, and relative humidity. From the results of this study, it is clear that dust deposition on PV panels can cause significant loss in PV power generation in Qatar. Additional research is needed to refine the mathematical PV power generation performance and the ambient environmental variables, so as to enable accurate simulation of PV solar power plant performance based on environmental data. Index Terms Photovoltaic systems, power system reliability, dust deposition, wind speed, relative humidity, surface soiling. I. INTRODUCTION urface soiling contributes a significant portion to the S overall PV performance loss, in addition to other losses such as PV degradation loss, temperature loss, internal network loss, inverter loss, transformer loss and availability & grid connection loss [1]. An increasing number of studies have been carried out to investigate the effect of surface soiling on PV performance loss. Performance loss typically refers to the percentage loss of performance (e.g. electrical energy output) of a soiled PV device in comparison to a clean identical PV device placed in the identical position and environment. The performance loss may be averaged over a day, a month, or a year. Short circuit current is often used as a PV performance Submitted for review on February 5, This work was supported in part by the Qatar National Research Fund, Undergraduate Research Experience Program (UREP ). B. Guo is with Texas A&M University at Qatar, PO Box 23874, Doha, Qatar ( bing.guo@qatar.tamu.edu). W. Javed is with Texas A&M University at Qatar, PO Box 23874, Doha, Qatar ( wasim.javed@qatar.tamu.edu). B. W. Figgis is with Qatar Environment and Energy Research Institute, PO Box 5825, Doha, Qatar ( bfiggis@qf.org.qa). T. Mirza is with GreenGulf Inc. QSTP-B at Innovation Centre, QSTP, PO Box 2649, Doha, Qatar ( talha.mirza@green-gulf.com) index for gauging the effect of surface soiling on irradiation reduction, due to its dependence on irradiation and its insensitivity to temperature [2]. However, power output at maximum power point provides a more comprehensive measure of a PV module from a utility standpoint [3]. To accurately assess the effect of surface soiling one needs to continuously measure the PV performance and integrate over the day, due to the dependence of soiling loss on the solar incidence angle [4]. A number of PV soiling studies have been carried out around the world, most notably in the U.S. and in the Middle East and North Africa [4-9]. Multiple factors affect the performance loss caused by soiling. These include panel tilt angle, solar incidence angle, dust particle size and chemical composition, PV panel surface material, and weather patterns (wind, rainfall, humidity, airborne particle concentration, etc.). In the Gulf region, it has been believed that peak surface soiling occurs in the summer months [10]. However, studies have suggested that, even within the same region, PV surface soiling may vary significantly, which indicates site-specific studies are necessary to assess performance loss caused by surface soiling [5]. Field data are not only essential for modeling the economic impact of surface soiling, but also for properly scheduling cleaning [11]. Qatar has embarked on a significant endeavor to develop and deploy solar power in the country. A number of large solar power projects have been planned in Qatar. For example, Qatar General Electricity & Water Corporation (Kahramaa) will complete a 200-MW solar power plant by 2020 [12]. It should be noted that as of 2013 Qatar s total power generation capacity is 8,750 MW, exceeding its total demand by 2,700 MW [13]. In addition, Qatar s food security program has proposed to use solar power for sustainable water desalination [14]. Also, a number of smaller projects of photovoltaic panels on commercial building rooftops and car park shades have been constructed [15]. However, Qatar s climate and desert environment pose significant challenges to the successful deployment of solar power in the country. Dust deposited on the critical surfaces of a solar power system disrupts the transmission/reflection of light, and hence degrade the system s performance. With the airborne particulate matter concentration frequently exceeding 100 µg/m 3 in Qatar [16], surface soiling of solar power systems is expected to occur at a fast rate. To successfully develop and deploy solar power in Qatar, it is necessary to understand the impact of dust on solar power systems in Qatar, and to develop effective mitigation methods. Quantitative data on the impact of dust on PV power generation have not been /15/$ IEEE

2 2 available in Qatar until now. There is also a need to study the relationship between impact of dust and environmental conditions such as airborne particulate matter concentration, wind, temperature, and humidity [17]. The objective of the study was to obtain data of PV system performance degradation due to dust deposition, to obtain data of ambient dust and weather conditions, and to determine the correlation between the former and the latter. In the following sections the methods and results of this study are described, following which the conclusions are presented. II. METHODS Data collection of this study was carried out in the Solar Test Facility located at the Qatar Science & Technology Park (QSTP), Doha, Qatar. Data collection for this study occurred in June 01 through December 31, Fig. 1. A DustTrak airborne dust concentration monitor installed at the Solar Test Facility A. Measurement and Calculation of PV Module Performance Three PV arrays were used in this study, each comprising eight 220 Wp polysilicon PV modules, tilted at 22 and facing due South, in a single string connected to identical grid-tied inverters. The arrays DC electrical parameters and module back surface temperatures were measured at maximum power point condition once per minute. DC power, voltage and current were measured via transducers with +/- 0.5% accuracy. Module temperatures were measured via permanently attached thermocouple sensors, with unspecified accuracy. One array was cleaned every week ( high wash ), one every second month ( medium wash ), and one every sixth month ( low wash ). During the test period, the low wash event occurred on 25 th June 2014, and the medium wash events on 25 th June, 2 nd September, and 4 th November There were two significant rain events, which occurred on 24 th November and 1 st December A cleanness index was used in this study as a metric for the effect of soiling on PV performance ratio. It is defined as the ratio of a PV module s temperature-corrected performance ratio to that of a clean PV module. Its physical meaning is similar to the soiling ratio that has used by other researchers [3]. The temperature-corrected performance ratio of a PV module is determined as: P DC _i ( ) i 1+δ T PR T _ corr = cell _i T STC (1) # G P POA _i & STC % ( i $ G STC ' The summation is over ever 24-hour day, from the first minute after midnight to the last minute before midnight. P DC_i is the array s power at maximum power point in the i th minute of a day [kw]. P STC is the array s power rating at maximum power point, at standard test conditions (STC), from flash-test data [kw]. G POA_i is the measured plane of array (POA) irradiance in the i th minute of the day [kw/m 2 ]. G STC is the irradiance at the standard test conditions (1 kw m -2 ). T cell_i is the average array temperature in the i th minute of the day [ C]. T STC is the temperature at the standard test conditions (25 C). δ is the temperature coefficient for power of the arrays ( % C -1 ) The temperature-corrected performance ratio is similar in concept to the weather-corrected performance ratio defined in a NREL report [18]. The temperature-corrected performance ratio in this study uses the PV module s DC power output, and is corrected to the temperature at STC (25 C). In contrast, the weather-corrected performance ratio uses the PV module s AC power output, and is corrected to a locality-dependent temperature based on the project weather file [18]. The cleanness index of a PV module, in a 24-hour day, is then calculated as follows: PR CI = T _ corr (2) PR T _ corr _ clean PR T_corr is the temperature-corrected performance ratio of the PV module whose cleanness index is being evaluated. PR T_corr_clean is the temperature-corrected performance ratio of a clean PV module. Based on the average PR T_corr of a weekly-cleaned PV module during the test period, a constant value of 0.88 was assigned for PR T_corr_clean. The metric CI is a measure of a PV module s cleanness. Its value decreases as the PV module s soiling level increases. It takes into account the effect of soiling on module temperature. It was found through experiment that more heavily soiled modules tended to be several degrees cooler than clean modules, presumably because the deposited dust served as a thermal barrier from the sun s irradiation. A clean PV module should have a CI value of unity. Because a constant value of 0.88 was used for the clean module s temperature-corrected performance ratio, some daily CI values were slightly greater

3 than unity due to measurement uncertainty. However, this should have no effect on the objectives and conclusions of this study. The daily change of CI for each day was calculated using the following equation: CI n = CI n CI n 1 (3) CI n is the change in cleanness index of a PV module attributed to the n th day. CI n is the cleanness index of the PV module on the n th day. CI n-1 is the cleanness index of the PV module on the (n-1) th day. B. Measurement of Ambient Dust and Weather Conditions Ambient dust concentration (mg m -3 ) in term of PM 10 was continuously measured by using a TSI 8533EP DustTrak DRX Aerosol Monitor (TSI Inc., Shoreview, MN, USA) with a temporal resolution of 2 min, stationed at the Solar Test Facility over the entire study period. This instrument is a continuous 90 light-scattering laser photometer that produces size-segregated mass fraction concentrations corresponding to PM 1, PM 2.5, PM 4, PM 10 size fractions. It has a minimum detectable particle size of 0.1 µm and a sensitivity of mg m -3. It uses a constant sample flow rate of 3 l m -1, automatically controlled by an external pump. The instrument was set to auto-zero once every 15 minutes using an external zeroing module, in order to minimize the effect of zero drift. The instrument was placed inside an environmental enclosure, which was mounted on a tripod at a height of 1.5 m above ground. Ambient air temperature, relative humidity, wind speed and wind direction were recorded at one minute intervals continuously every day during the test period. Daily average of dust concentration, temperature, relative humidity and wind speed was computed using the usual arithmetic mean for all data points within a 24-hour day. Daily wind direction was computed by treating all angular measurements as point on the unit circle and computing the resultant vector of the unit vectors determined by data points [19]. C. Data Processing by Multi-Variable Regression In this study, a multi-variable linear regression model was used to examine the correlation between daily change of the cleanness index cleanness index and the daily ambient environmental conditions. Daily CI was used as the dependent variable. Three daily average ambient environmental parameters, namely dust concentration, wind speed, and relative humidity were used as the independent variables. The regression model predicts the dependent variable as a linear function of the independent variables: CI Pre = β 0 + β 1 PM 10 + β 2 WS + β 3 RH (4) CI Pre is the predicted value of CI on a 24-hour day, which is described in Eqn. (3). PM 10 is the 24-hour average concentration of particles smaller than 10 µm in aerodynamic diameter in ambient air, measured experimentally as described in previous sections. WS is the 24-hour average of wind speed based on experimental measurement, as described in previous sections. RH is the 24-hour average of relative humidity measured experimentally as described in previous sections. β 0, β 1, β 2, β 3 are coefficients to be determined using the experimental data, by minimizing the sum of squares of the error. III. RESULTS The cleanness index of the low wash and medium wash PV arrays decreased substantially over the course of this study. Ambient dust concentration had a significant effect on the daily change of cleanness index. Weather conditions also affected the daily change of the cleanness index. A. Cleanness Index and Daily Change Fig. 2 shows the cleanness index of the three test PV arrays in the months of June through December On average, the cleanness index decreased (standard deviation ) per day and (standard deviation ) per day over the study period (seven months) for the low wash and medium wash PV arrays, respectively. Two individual dust episodes on the days of 25 th August and 12 th December caused the cleanness index to decrease and 0.024, respectively (for both low wash and medium wash arrays). The PV arrays were also naturally cleaned by rain, on 24 th November and 1 st December 2014, which restored the test arrays cleanness index to around unity. Fig. 2. The cleanness index of the PV arrays with different cleaning frequencies B. Correlation with Dust and Meteorological Data Examination of the data revealed that, dust concentration (PM 10 ), wind speed (WS), and relative humidity (RH) had the most significant correlation with the daily CI. The correlation coefficient between any single environmental variable with CI was lower than 0.5, suggesting the complexity of the soiling process. The daily CI after a cleaning or rain event was not included in the correlation analysis. The mean and standard deviation values for PM10, WS, and RH are given in Table I, which will be referred to in the following sections. As shown in Fig. 3, daily CI (excluding cleaning and rainy days) and daily PM 10 both varied significantly from day to day

4 TABLE I STATISTICS OF AMBIENT CONDITION VARIABLES Variable Mean Standard Deviation PM10 (mg m-3) WS (m s-3) RH % % in the months studied. As can be seen from Fig. 3, CI is in fact positive on many days, suggesting that the level of soiling actually reduced on those days. By examining the weekly moving averages of daily CI and daily PM10, one may see that the trends of these two variables are generally opposite to each other. In other words, in periods when CI was increasing, PM10 would generally decrease. This correlation between CI and PM10 may also be observed in Fig. 4. However, it may also be observed that the relation between daily CI and daily PM10 was complex, suggesting there was interaction with other variables as well. Fig. 3. Daily CI of the medium wash PV array and daily dust concentration PM10 (lines are weekly moving average of the daily values) The correlation between wind speed and daily CI may be seen in Fig. 5. In general, the daily loss of PV performance loss due to soiling is greater at lower wind speeds (i.e., daily CI is more negative). On days with high wind speed, it is more likely to see positive daily CI (partial performance recovery of soiled PV modules), except during a dust storm. One explanation for this observation is that higher wind speeds cause higher re-suspension of deposited dust on PV panels [20]. Therefore it is possible for the dust deposition of a PV module to decrease on a high-wind day, and hence the PV performance would actually recover. The effect of wind speed on dust deposition on solar surfaces has been noted by other researchers, albeit under laboratory conditions that are very different from this study, using Belgian Brabantian loess dust at high concentrations ( g m-3) and a narrower range of wind speeds ( m s-1) [21, 22]. It should be pointed out that higher wind speeds may cause PV performance ratio to increase due to the stronger cooling of PV under high wind conditions [18, 23]. However, since the daily CI metric uses temperature-corrected performance ratio, the effect of wind speed on module temperature is taken into account. Therefore, the wind speed effect on daily CI observed in this study should be attributed to the role of wind speed in dust deposition and re-suspension of deposited dust. Fig. 4. Daily CI and daily average dust concentration Fig. 5. Daily CI and daily average wind speed Fig. 6. Daily CI and daily average relative humidity The relation between daily CI and daily average relative humidity is shown in Fig. 6, which suggests that relative humidity has some impact on the PV soiling. Overall, daily CI was more negative on days with higher relative humidity levels. This is intuitively consistent with the perception that higher relative humidity causes dust particles to more likely stick to the PV module, and less likely to be re-suspended by wind. In other words, with increasing higher relative

5 humidity, PV soiling is likely to be more severe, provided other parameters are kept the same. It should be noted that the opposing diurnal patterns wind speed and relative humidity might have enhanced the effect of wind speed on PV surface soiling. The daily peak of wind speed was found to occur at the same time as the daily minimum of the relative humidity (data no shown). This suggests that the dust resuspension effect of high winds is enhanced when the deposited dust particles contain the lowest moisture, which make them less sticky and more likely to be carried away by the wind. associated with the entire range of dust concentration, but wind from other directions is generally associated with medium-to-high dust concentrations. In other words, wind direction was not included in the multi-variable regression in this study. Due to the fact that wind direction is not independent of wind speed and dust concentration, it is not included in the multivariable regression. Fig. 9. Plot of daily average PM 10 and daily average wind direction Fig. 7. Daily CI and daily average wind direction (Note: the inside solid circular line represent a CI value of zero.) Fig. 8. Wind rose showing distribution of wind speed and direction The relation between wind direction and daily CI is complex, as can be seen in Fig. 7. The daily CI is mostly negative when the wind comes from the south; when the wind comes from the north, both negative and positive values are possible for CI. The prevailing wind is from the northwest, as shown in Fig. 8. The prevailing wind covers the entire spectrum of wind speed, but winds from other directions appear to be only available at relatively low wind speeds. In other words, wind direction and wind speed are not independent of each other. On the other hand, the PM 10 and wind direction plot (Fig. 9) shows that, the prevailing wind is C. Multivariable Regression Results The coefficients for Eqn. (4) derived from the multivariable regression analysis are given in Table II. Using Eqn. (4) and the set of coefficients in Table II, one can calculate the predicted CI under various ambient conditions. Using the mean values from Table I, we can calculate the predicted CI under Mean Ambient Conditions. We can see that the predicted CI under Mean Ambient Conditions is significantly larger in magnitude (more negative) than the mean measured CI. This discrepancy may be partly attributed to the fact that CI is apparently not a linear function of the ambient environmental variables, and hence the predicted CI under Mean Ambient Conditions should not be expected to equal the mean experimental CI. Nevertheless, as a semi-quantitative approximation, the linear regression model may be used to assess each environmental variable s contribution to the variation of the daily CI. Using the standard deviation values from Table I, one can calculate the predicted CI with each variable increased or decreased by one standard deviation, while keeping the other variables at their mean values. Such results are shown in the Varied by One Standard Deviation rows in Table III. We can see that a one standard deviation change in WS or RH causes significantly greater variation in the predicted CI, than a one standard deviation change in PM 10 would. This suggests that the variation of CI observed in this study may have been caused by wind speed or relative humidity variation more than by dust concentration variation. The multivariable regression model is only a preliminary approximation of the relationship between CI and the environmental variables. Dust deposition velocity, which relates dust deposition flux and ambient dust concentration,

6 Coefficient TABLE II MULTIVARIABLE REGRESSION RESULTS Value and Unit m 3 mg s m TABLE III PREDICTED CI UNDER VARIOUS AMBIENT CONDITIONS Independent Variables Predicted CI Mean Ambient Conditions PM Single Variable Varied by to WS to One Standard Deviation RH to has a non-linear dependence on wind speed and particle size [24], and likely has a non-linear relationship with relative humidity. Therefore, additional work is needed to have a better understanding of such relationships, so that one may be able to more accurately predict the effect of dust and weather conditions of PV performance loss over long periods of time. IV. CONCLUSIONS The results of this study show that surface soiling due to dust deposition causes significant loss in PV power output in Doha, Qatar. A cleanness index, defined as the ratio of temperature-corrected performance ratio of a soiled PV module to that of a clean module placed in identical position and environment, was introduced to quantify the soiling effect on PV power output. On average, the cleanness index of a PV module cleaned every second month may decrease by 0.45 percentage points per day, or percentage points per month, due to dust deposition alone. Dust concentration, wind speed, and relative humidity are the most important factors affecting surface soiling. The mathematical relationship between daily change of the cleanness index and the ambient environmental variables is yet to be determined through further research. REFERENCES [1] E. Kymakis, S. Kalykakis, and T. M. Papazoglou, "Performance analysis of a grid connected photovoltaic park on the island of Crete," Energy Conversion and Management, vol. 50, pp , Mar [2] H. Qasem, T. Betts, and H. 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