Measuring Soiling Losses at Utility-scale PV Power Plants Michael Gostein1, J. Riley Caron2, Bodo Littmann2 1Atonometrics, 8900 Shoal Creek Blvd., Suite 116, Austin, TX 78757, USA 2First Solar, 135 Main St., Suite 600, San Francisco, CA 94105, USA

Abstract — The effect of dust, dirt, and other contaminant In this work we aim to show how soiling measurement accumulation on PV modules, commonly referred to as soiling, is system data are indicative of the true effect of soiling on a an important environmental factor that causes reduced PV power utility-scale power plant and we outline how such data should plant energy generation. Accurate monitoring of soiling losses has be collected and analyzed. Using a large volume of soiling become increasingly important, especially for utility-scale PV power plants, and soiling measurement systems are now widely monitoring data from five First Solar power plants in the deployed at First Solar power plants. In this work we show how desert southwest of the United States, the Arabian Peninsula, soiling monitoring data are indicative of actual power plant and Western Australia, we study soiling levels and rates performance and we outline how such data should be collected alongside PV plant performance. The five power plants, which and analyzed. We study soiling levels and rates alongside PV we anonymously label A, B, C, D, and E, range in size from plant performance in the desert southwest of the United States, the Arabian Peninsula, and Western Australia. We demonstrate 10 to more than 200 MWac and also cover a wide range of that soiling loss measurements correlate with actual power plant soiling conditions. We show how instantaneous as-measured performance. In addition, we address measurement methodology soiling data may be integrated to provide daily, weekly, or questions, including measurement precision, site-wide spatial monthly loss estimates, and we demonstrate that these loss non-uniformity in soiling, and the amount of rainfall required for estimates correlate with actual measured power plant a full recovery in soiling losses. Index Terms — photovoltaic systems, performance analysis, performance. In addition, we explore key questions regarding solar power generation, solar energy. the optimization of soiling monitoring, including the number and within-plant spatial distribution of soiling monitors, I. INTRODUCTION optimal cleaning frequencies for the control modules, and the amount of rainfall necessary to completely clean dirty The effect of dust, dirt, pollen, and other contaminant modules. accumulation on PV modules, commonly referred to as soiling, is an important environmental factor that causes reduced PV power plant energy generation. Average annual II. SOILING MEASUREMENT METHODOLOGY energy losses due to soiling are typically in the range 1-6% A. Measurements [1],[2],[3] but strongly depend on conditions at the site, with higher annual, short-term, or monthly losses found in some As described above, soiling loss is measured by comparing cases [2],[4],[5]. Therefore, accurate monitoring of soiling the output of a dirty reference module to that from a clean losses has become increasingly important, especially for control module. The comparison may be made either on the utility-scale PV power plants, which might be subject to basis of the measured short-circuit currents, which serve as a performance guarantees that mandate a limit on loss proxy for the effective irradiance received by the soiled versus generation due to soiling. the clean module, or on the basis of the measured maximum Soiling measurement systems are now widely deployed at powers, which represent actual power production of soiled First Solar power plants. The systems use a simple and reliable versus clean modules. In both cases the measurements are method for measuring soiling losses that has been described in temperature-corrected and normalized. Previous work [8] previous works [3],[6],[7],[8]. The method uses the side-by- showed that for crystalline silicon modules the power-based side comparison of the measured output of two co-planar, method should be used, in order to capture the effects of normalized PV reference modules, the first of which is kept potential within-module non-uniform distribution of soiling, clean as a control and the second of which naturally while for First Solar CdTe modules short-circuit current based accumulates soiling at the same rate as the power-producing measurements are sufficient, because the module geometry modules of the plant. The control module is kept clean through minimizes the effect of shading non-uniformity on power manual washing or with an automated washing system. output. Commercial soiling monitoring equipment supporting This simple approach to measuring soiling loss is a good both methods is available. proxy for the soiling-induced power loss of a PV power plant B. Terminology [3]. In previous work we quantified the typical uncertainty of these measurements [7] and examined the effect of within- We begin by defining different metrics used in describing module soiling non-uniformities [8] on the measured power soiling losses. The soiling ratio SR is the instantaneously losses. measured ratio of dirty-to-clean (test-to-control) module

To appear in proceedings of IEEE Photovoltaic Specialists Conference, Denver, CO, June 2014. Copyright IEEE. outputs at any given point in time. In this work we focus on Station 00 CdTe power plants and accordingly we define the ratio in 0.98 Station 02 terms of effective irradiance received by each module: Station 13 Station 00 Gdirty 0.96 Station 02 SR  (1) Station 13 Gclean 0.94 where Gdirty and Gclean are determined from the temperature- corrected and normalized short-circuit current measurements of each module. Following the conventions outlined in the Soiling Ratio (SR) 0.92 International Guidelines of Uncertainty in Measurement (GUM), we found SR measurements can be performed with uncertainties on the order of ~±1% or better on an absolute

Day 1 Day 2 Day 3 Day 4 Day 5 basis, depending on calibration conditions, operating temperatures, angular alignments, and other factors [7]. Fig. 2. Soiling ratio measurements from three measurement We derive daily average soiling ratio SRD by filtering, stations at site A over a four day period. Small symbols show normalizing and averaging the SR data over the desired time instantaneous readings, taken once per minute, which vary period, as described below. In performance models, these throughout the day due as discussed in the text. Large symbols show daily soiling ratios can be applied directly as loss derate daily averaged values. factors for effective irradiance available to the PV plant, in a manner similar to shading factors, incidence angle factors, etc. typically shows a dependence on time of day. Fig. 1 illustrates We define the daily soiling loss SLD as 1 - SRD, and the factors contributing to this dependence. monthly soiling loss SLM as the monthly irradiation-weighted One factor is any residual differences in the tilt or azimuthal average of SLD. angles of the dirty and clean modules, which causes a We define a soiling accumulation rate Srate by measuring the difference in the in-plane irradiance received by each module slope of the time series of daily soil ratio SRD. Srate can be throughout the day and results in measurement artifacts not expressed as a daily or monthly value, i.e. 0.1% per day would representative of actual losses. To minimize these artifacts, tilt be equivalent to 3% per month in a 30-day month. and azimuthal differences should typically be kept to <0.5°. Another factor is the variation in actual soiling related losses C. Time-of-Day Dependence as a function of angle of incidence (AOI), which arises from It is important to note that instantaneously measured SR the details of light scattering from accumulated soil particles. Such variations were observed in a study by Zorrilla-Casanova 0.95 [5], which also included a theoretical model of the light b) Tilt alignment artifact scattering versus AOI. We depict this effect in Fig. 1 as profile

0.94 a) Azimuthal (c), using a normalized version of the model in [5] as alignment artifact representative. All three curves in Fig. 1 were calculated for 6% loss at 0.93 normal incidence (indicated by open circle symbol), for site A coordinates (see below) on January 1, 2014. 0.92 Fig. 2 shows an example of actual SR measurements, taken once per minute over a four day period at a First Solar utility- c) Soiling loss angular 0.91 dependence scale power plant. The data show a time-of-day dependence

similar to that depicted in Fig. 1(c) for AOI effects; we Measured SoilingRatio Measured confirmed that the data represent actual AOI dependent losses, 0.90 rather than artifacts from angular offsets between the dirty and 6 8 10 12 14 16 18 clean modules, by noting that the curved profiles disappear for Hour days when both modules in the pair were clean. Fig. 1. Examples of time-of-day dependence of instantaneous soiling ratio measurements. a) Measurement artifact due to 0.5° D. Filtering and Averaging azimuthal angle misalignment of the dirty and clean reference devices. For data analysis, instantaneously measured SR values are b) Measurement artifact due to 0.5° tilt angle misalignment. first filtered to remove points corresponding to low or quickly c) Representative variation in actual soiling loss due to angle of incidence, assuming loss model similar to that used in [5]. All three changing irradiance, during which measurements are likely to curves were calculated for 6% loss at normal incidence (indicated by be invalid. An irradiance-weighted average is then computed open circle symbol), for site A coordinates on January 1, 2014. from the remaining points over a daily period, in order to

1.02 1.00 A B A 1.00 1.00 0.98 0.96 >5 PPI PPI 0.94 3 0.95 0.95 0.92 1 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr 1.02 0.95 1.00 0.95 1.00 SR SR 1.00 0.98 B 0.96 C D >5 1.00 1.00 0.94 3 1

0.92 PPI PPI Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr 1.02 0.95 0.95 1.00 C 0.98 0.95 1.00 0.95 1.00 SR SR 0.96 >5 0.94 3 1 E 0.92 1.00 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr 1.02 1.00 D PPI 0.98 0.95 0.96 >5 0.94 3 0.95 1.00 SR 0.92 1 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr Fig. 4. Measured weekly average power plant performance (PPI 1.02 axes) versus measured weekly average soiling ratio (SR axes) for the 1.00 five PV power plants. 0.98 1000 W/m2, module temperature of 25 °C, and ASTM G173 0.96 E >5 solar spectrum (AM 1.5)). Calculating PPI involves 0.94 3 normalizing measured system power by irradiance and module 0.92 1 temperature using a linear regression [9]. We also spectrally Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr correct the results to account for differences in spectral Fig. 3. Time-series plots of daily soiling ratio (thick line) and sensitivity between reference irradiance measurement devices normalized weekly PPI (thin line) for the five PV power plants from and CdTe modules [10]. Over medium term time periods, the April 2013 through April 2014. Bars show rainfall in mm (right PPI metric is not affected by fluctuations in irradiance and air axes). temperature, allowing us to compare the bulk of any apparent remaining losses in system performance to soiling. On some obtain SLD. The irradiance-weighted averaging minimizes the contribution of artifacts arising from angular alignment offsets days the variability of weather conditions results in a low confidence in the normalized PPI result; we filter out these and also provides data most closely representative of lost 2 energy production over the respective period. The solid days based on the coefficient of determination (R ) of the symbols in Fig. 2 show examples of the reduction of linear regression between temperature-corrected power and instantaneous SR to daily averaged values. irradiance.

III. RESULTS III. POWER PLANT PERFORMANCE MEASUREMENT A. Measurements To correlate soiling measurement with plant performance, we use the Power Performance Index (PPI) metric. PPI values, The five power plants each included multiple soiling calculated daily, represent the power output of a system measurement stations distributed throughout the site, with normalized to standard test conditions (STC) (irradiance of more stations at larger sites. Each plant also included multiple

meteorological stations, from which irradiance and rainfall 1.02 data were collected. Data from all stations were averaged 1.00 Station 05 together, except where noted. Data shown were collected from 0.98 April 2013 through April 2014, with data for each plant 0.96 becoming available as the plant was commissioned. >5 0.94 3 B. Time Series Plots 0.92 1 Sep Oct Nov Dec Fig. 3 shows time series results of measurements at the five 1.02 PV power plants. The thick line shows the daily average 1.00 Station 03 soiling ratio SRD, while the thin line shows the weekly average PPI representing plant performance. The bars show daily 0.98 0.96 rainfall in millimeters, clipped to 5 mm in order to clearly >5 show small rainfall events. 0.94 3 The soiling ratio values decrease steadily on days between 0.92 1 rainfalls, reflecting the accumulation of soiling. Note that the Sep Oct Nov Dec soiling rate – the slope of the soiling ratio line – at each site is Fig. 6. Example of spatial variation in measured soiling ratio (left relatively constant throughout the time period, although the axes) at power plant D. Top: station 05. Bottom: station 03. Station minimum value of the soiling ratio varies according to rainfall 05 shows a recovery event in mid-October that is not apparent at frequency. Soiling rates range from ~0.5% per month (site E) Station 03. Bars show rainfall in mm (right axes). to ~5% per month (site A). PPI shows performance deviations that cannot be attributed to The downward stepped appearance of some of the soiling soiling. ratio curves (e.g. site D in September and October) reflects the Fig. 4 presents the same data in the form of correlation cleaning frequency of the clean control module in the soiling plots, where each point pairs the weekly average soiling ratio ratio measurement pair. At some sites the control module was with the weekly PPI. The plots again indicate the influence of cleaned only weekly, and in this case the measured soiling soiling on power plant performance. ratio shows a sharp drop following each weekly cleaning. D. Spatial Variability of Soiling C. Correlation with PV Plant Performance In order to understand how many soiling monitoring stations Fig. 3 shows weekly PPI values for each site overlaid on the might be needed for a given site, we examined the within-site measured daily soiling ratio. Comparisons of the time series variation of soiling at a number of the power plants. For the results indicate a good correlation between measured soiling five power plants, soiling generally showed similar trends loss and actual plant performance, although in some cases the throughout the site when comparing multiple soiling monitoring stations. However, there were some notable 1.00 exceptions. 0.99 Fig. 5 shows one such example of spatial variation in 0.98 measured soiling ratio. The top and bottom graphs show Station 02 0.97 >1 results from soiling measurement stations on east and west 0.5 sides, respectively, of power plant C. These two stations were 0.96 0 Jan Feb Mar Block 1 Block 2 Block 3

1.00 0.900 0.893 0.895 0.895 0.897 0.899 0.903 0.903 0.904 0.906 0.910 0.910 0.907 0.903

0.99 0.895 0.899 0.894 0.896 0.900 0.901 0.903 0.902 0.904 0.905 0.907 0.910 0.915 0.915 0.98 0.903 0.900 0.895 0.890 0.896 0.901 0.906 0.907 0.905 0.910 0.917 0.922 0.918 0.97 Station 09 >1 0.909 0.899 0.895 0.897 0.904 0.911 0.915 0.916 0.922 0.926 0.923 0.917 0.5 0.96 0 Block 5 0.912 0.913 0.918 0.916 0.919 0.914 0.914 0.913 0.909 Jan Feb Mar 0.902 0.907 0.926 0.921 0.918 0.950 Fig. 5. Example of spatial variation in measured soiling ratio. Top and bottom graphs show soiling ratio results (left axes) from two 0.918 0.930 0.934 0.931 0.927 0.920 measurement stations on east (station 02) and west (station 09) 0.929 0.930 0.926 0.924 0.925 0.926 sides of power plant C. Results from each station correlated with those from nearby stations, indicating an east-west variation in Fig. 7. Map of relative PPI at power plant D in late Oct., showing soiling during January and February. Bars show rainfall in mm spatial variation in plant performance by inverter and confirming the (right axes). trend shown by soiling measurement stations in Fig. 6.

separated by ~2 km. It is clear that the two stations measured may allow for a step signal to emerge, artificially lowering the different soiling patterns during the time period shown. Results average soiling level over a month. The daily SR will typically from each station correlated with those from nearby stations, hold at a given value until the next control cleaning where it indicating an east-west variation in soiling during January and will step down to the true soiling level on that day. Depending February. However, all stations showed a similar gradual on the difference between this step signal and the true soiling accumulation of soiling in March and April. level, it may be beneficial to clean the control modules two to Fig. 6 shows results from power plant D, in which there was three times per week. a notable difference in soiling recovery in a corner of the site – It is possible for the plant to experience a sudden soiling represented by station 05 – in mid-October. This recovery event such as a dust storm, followed by a rainfall. If these occurred despite there being no measureable rain. It is possible events fall between control cleaning days it is possible that that the recovery resulted from self-cleaning from condensed they will not be captured in the measured daily SR. If it is in dew, since the temperatures at the site were regularly very the operator’s interest to capture these standalone soiling and close to the dew point. Some areas of the site may have been cleaning events, they should clean the control modules at a just above and others just below the dew point. higher frequency, but for the purpose of producing monthly This hypothesis appears to be confirmed by examining the average soiling levels, these types of events, with recoveries, PPI by inverter at this site, as shown by the map in Fig. 7. The generally will have little influence on the monthly result. map shows average PPI values measured for each inverter on 3 days in late October (during the peak soiling) normalized by G. Performance Recovery Following Rain values for 3 days immediately following rainfall on Oct. 28 It is often necessary to predict soiling losses in a location These relative PPI values vary across the site. Higher- where soiling measurement has not yet been deployed. In this performing (presumably cleaner) sections are found near the case, historical rainfall can be used alongside predicted soiling location of soiling measurement station 05 which shows the accumulation rates to build an estimated monthly soiling partial recovery event in Fig. 6. The map may indicate a profile [3]. We examined the measured soiling data and difference of 3% in performance across the site. tabulated the rainfall levels that produced each significant Although the details of within-site spatial uniformity of plant performance recovery event at all five sites. Fig 8 shows soiling require more study, and are likely to be site-specific, the cumulative probability of rain events by rainfall quantity in these examples do illustrate the potential importance of using millimeters, comparing the probability for all rain events with multiple soiling measurement stations in order to fully capture that for events producing recoveries in the soiling loss. From the impact of soiling, particularly at large sites. the recovery curve, we estimate a rainfall threshold of ~3.5 mm required to produce soiling recovery with 50% E. Precision of Soiling Measurement probability, for this aggregation of sites. Specific rainfall In a previous work [7], the authors examined the uncertainty thresholds may vary by site due to the type of contaminants of soiling measurement based on a detailed uncertainty accumulated at each site as well as the overall soiling level that analysis. This yielded an uncertainty of ~±1%. Here we wished to compare the theoretical uncertainty analysis with experimental results. We used data from two soiling measurement stations at power plant D for this analysis. We selected days when the site All was known to be clean, due to rain events on the preceding day, and quantified the repeatability of the measured soiling Recoveries ratio. This yielded a value of ~0.6%. The repeatability value, as well as a qualitative examination of the measured data e.g. as shown in Fig. 3, including drops in the soiling ratio and rain-induced recoveries, suggests that soiling losses are detectable at a threshold better than ~1.0%. This is consistent with, and more favorable than, the uncertainty analysis performed previously. However, achieving this precision requires properly designed and calibrated equipment.

F. Soiling Pair Cleaning Frequency Fig. 8. Cumulative probability (left axis) of rainfall events by rainfall quantity in millimeters (bottom axis), shown for all rainfall events The data indicate that cleaning of the control module should and for rainfall events that produced recoveries in the soiling ratio. be performed at least once per week. In regions with From the recovery curve we estimate a rainfall threshold of ~3.5 mm particularly high soiling accumulation rates, weekly cleaning required to produce soiling recovery with 50% probability.

accumulates on the panels, and therefore additional work is future plant performance. Further work is anticipated to refine needed on this point. Furthermore, we did not observe daily the soiling measurement methodology, investigate within-site soiling levels above 8%, and high soiling levels may require soiling non-uniformity in more detail, and better determine higher rainfall thresholds for recovery. rain thresholds which may be regionally dependent. Since soiling losses are the third-most significant environmental factor affecting PV power plant performance, IV. DISCUSSION AND CONCLUSIONS following irradiance and temperature, they should be Quantifying soiling loss is of paramount importance when quantified with the same care taken to monitor other analyzing performance of a PV power plant, especially in performance factors. Furthermore, many performance regions with high soiling rates. Accurate measurements of guarantees are now requiring limits on soiling losses during soiling are therefore important for both performance the life of the power plant, resulting in site-specific cleaning prediction and monitoring. We recommend soiling monitoring regimens. This study has demonstrated the ability to accurately be deployed as standard instrumentation at PV power plants. measure and discriminate soiling losses at utility-scale PV When deployed in a pre-construction prospecting fashion, power plants using independent soiling measurement systems. soiling measurement systems can help to estimate the frequency of cleaning and thus the cost of cleaning that will be required to maintain a desired average annual soiling level in REFERENCES high soiling locations. For this reason First Solar has deployed soiling measurement systems in many regions. [1] A. Kimber, et al., “The Effect of Soiling on Large Grid- At operational power plants, ongoing measurement of Connected Photovoltaic Systems in California and the Southwest Region of the United States,” in Conference Record soiling loss is an important component of performance of the 2006 IEEE 4th World Conference on Photovoltaic Energy monitoring, especially at utility-scale facilities. While Conversion, Waikoloa, HI, 2006. deviations in plant performance metrics are often attributed to [2] S. Canada, “Quality Assurance: Impacts of Soiling on Utility- soiling, independent measurement of soiling loss provides Scale PV System Performance,” SolarPro Magazine, no. 6.3, verification and allows other potential sources of deviations to pp. 14–20, May-2013. [3] J. R. Caron and B. Littmann, “Direct Monitoring of Energy Lost be discriminated. Independent measurement of soiling also Due to Soiling on First Solar Modules in California,” IEEE permits more accurate determination of soiling losses than do Journal of Photovoltaics, vol. 3, no. 1, pp. 336–340, 2013. estimates based on plant performance metrics alone, since [4] T. U. Townsend and P. A. Hutchinson, “Soiling Analysis at soiling measurements have lower uncertainty and greater PVUSA,” in Proc. of ASES-2000, Madison, WI, 2000. resolution than typical plant performance metrics, which are [5] J. Zorrilla-Casanova, et al., “Analysis of Dust Losses in Photovoltaic Modules,” in Proc. of the World Renewable affected by many factors. In addition, independent soiling Energy Congress 2011, Linköping, Sweden, 2011. measurements are especially beneficial at power plants with [6] C. P. Ryan, F. Vignola, D. K. McDaniels, “Solar cell arrays: high DC-AC ratio, especially those employing trackers, since degradation due to dirt”, Proc. of the American Section of the for these plants frequent times of inverter clipping complicate International Solar Energy Society, pp. 234-237, Denver, the discrimination of individual loss sources from performance Colorado, June 1989. [7] M. Gostein, B. Littmann, J. R. Caron, L. Dunn, “PV Module data alone. Soiling Measurement Uncertainty Analysis,” 39th IEEE For the five First Solar utility-scale PV power plants Photovoltaic Specialists Conference, Tampa, FL, 2013. presented here, soiling rates varied from 0.5% per month to [8] M. Gostein, B. Littmann, J. R. Caron, L. Dunn, “Comparing PV 5% per month. Soiling measurements were found to correlate Power Plant Soiling Measurements Extracted from PV Module th with plant performance while providing better resolution and Irradiance and Power Measurements,” in 39 IEEE Photovoltaic Specialists Conference, Tampa, FL, 2013. independent verification of soiling losses. Each site employed [9] L. Nelson and C. Hansen, “Evaluation of photovoltaic system multiple soiling measurement stations, allowing instances of power rating methods for a cadmium telluride array”, 37th IEEE within-site soiling non-uniformity to be detected. The Photovoltaic Specialist Conference, 2011. aggregated data for the five sites permits an estimate of the [10] L. Nelson, M. Frichtl, A. Panchula, “Changes in cadmium rain threshold required for cleaning, which, together with telluride photovoltaic system performance due to spectrum”, 38th IEEE Photovoltaic Specialist Conference, 2012. regional soiling rates, is an important parameter for predicting