Soiling is the accumulation of dust on solar panels that causes a decrease in the solar 7 photovoltaic (PV) system's efficiency. The changes in conversion efficiency of 186 residential 8 and commercial PV sites were quantified during dry periods over the course of 2010 with respect 9 to rain events observed at nearby weather stations and using satellite solar resource data. Soiling 10 losses averaged 0.051% per day overall and 26% of the sites had losses greater than 0.1% per 11 day. Sites with small tilt angles (<5 o ) had larger soiling losses while differences by location were 12 not statistically significant. 13 Introduction With the rapid increase in the use of photovoltaic (PV) power in California, which has 15 47% of the installed PV capacity in the US, the optimal management and analysis of expected 16 performance of PV sites becomes increasingly important. Soiling can have a large effect on 17 efficiency during long droughts [1] , which mainly occur during the summer season coincident 18 with the largest solar resource. Dust from air pollution particles, sea salt, pollen, agricultural 19 activity, construction and other anthropogenic and natural sources accumulates on the panels 20 until it is removed either by rain or washing. Research on soiling has primarily been conducted in the middle-east [2] due to the large 22 aerosol loading in the air and the greater abundance of or plans for concentrating solar power 23 plants that are much more affected by soiling. For a concentrating solar power desalination plant 24 in Abu Dhabi, UAE soiling was found to be strongest during sandstorms in the summer season 25 [3]. The transmittance of glass panels after 30 days of exposure in India decreased from 90% to 26 30% for horizontal and from 90% to 88% for vertical panels [4] . Another more recent study examined the effects of soiling for 250 sites monitored by 28 PowerLight (now SunPower) [1] . Since several of these sites are in areas with frequent rain their 29 study focused on sites in the southwestern United States where long droughts are more common. They also excluded sites with an R 2 value between soiling energy losses and time of less than 0.7 31 which left a total of 46 sites. Between rain events, soiling losses were found to aggregate linearly 32 with time with an average daily soiling loss of 0.2%. While this paper provides a methodological 33 foundation for analyzing soiling losses, the site selection criteria may have led to an overestimate 34 of soiling losses. 35 2 The goals of this study are to quantify performance decrease due to soiling and to provide 36 guidance on the necessity of cleaning solar panels in California. In Section 2 the PV power 37 dataset and quality control are described and three different methods for identifying soiling 38 losses on PV panels are introduced. Soiling results are presented, stratified by location and panel 39 tilt, and discussed in Section 3. The conclusions are given in Section 4. 40 2. Data 41 2.1. California Solar Initiative Sites 42 Under the Performance Based Incentive (PBI) program of the California Solar Initiative 43 (CSI) rebate payouts are based on AC energy output metering in 15 minute intervals [5]. The AC 44 power produced from 194 San Diego Gas and Electric (SDG&E), 385 Southern California 45 Edison (SCE), and 403 Pacific Gas and Electric (PGE) sites were obtained for the year of 2010. 46 These data were then quality controlled one-by-one to eliminate sites that had more than 70 % 47 63 0.5 % K -1 , respectively. Inverter efficiency was modeled using a 3 rd order polynomial 64 versus power factor as in [9]. To be able to intercompare soiling effects between sites, was 65 then normalized by its average for the year to obtain a relative performance . 66 2.3 Rain Data 103 104 105 106 107 108 109 110 111 112 113 6 excursions are typically a result of an error in the solar resource model. The daily soiling losses 134 for the drought period are calculated as divided by the days since the previous rainfall. The calculations for method 1 are demonstrated in Fig. 2b , where 0.28 and the 136 soiling losses for the preceding 165 day drought period were -0.0017/day. The second method -similar to the first method -uses weekly averaging, but the 138 efficiencies averaged over the week after the previous rain event and the week before the next 139 rain event are compared. Days that had efficiency above 1.5 or below 0.5 were also excluded 140 from the average. Again the daily soiling losses for the drought period are calculated as 141 divided by the days since the previous rainfall. This method can be observed in Fig. 2c , where 142 0.41 and the soiling losses for that drought period were found to be -0.0025/day. The final method calculates soiling losses by applying a linear regression fit to the entire 144 data during the drought period. The slope of the best fit line is then assumed to be the daily 145 soiling for that drought. For quality control, droughts when more than 20% of the efficiency data 146 were above 1.5 or below 0.5 were excluded. Also efficiency data greater than 1.5 or less than 0.5 147 were not used in the fit. This method can be observed in Fig. 2d , where the soiling losses for that 148 drought period were found to be -0.0029/day. Each of these methods has particular benefits and assumptions. The method of choice in 150 this analysis is method 3, which was previously shown to quantify soiling [1]. Method 3 only 151 includes the assumption that the efficiency changes are caused by soiling and not by other factors 152 such as panel degradation and seasonal errors in the SAW resource model. In general, these other 153 factors are small or should average out over many sites and rain events. Method 3 also uses the 154 largest amount of data points. Method 2 uses similar assumptions but is limited because it uses a 155 smaller amount of data. Finally method 1 assumes that the panel is equally clean at the start of 156 the drought period and after the next rain event such that differences in efficiency are only 157 related to soiling during that drought period. For long droughts, method 1 has the advantage that 158 the data used for soiling calculations fall within a continuous 2 week period such that panel 159 degradation and seasonal errors in the SAW resource model become minimal. Overall little 160 difference in the overall soiling losses was observed for the different methods (see Fig. 4 later) 161 indicating that the results are robust. 162 3. Results and Discussion 163
These soiling losses averaged 0.051%/day during dry periods for a sample of 1286 residential and commercial PV sites 5 in California REF .
Soiling losses for solar photovoltaic systems in California
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