not for commercial-scale installations. Thus, there is a need to study the effects of snow on

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1 1. Problem Statement There is a great deal of uncertainty regarding the effects of snow depth on energy production from large-scale photovoltaic (PV) solar installations. The solar energy industry claims that energy losses from snow are negligible [1]. However, controlled studies in heavy snow areas have revealed seasonal energy losses as large as 42% [2]. To date, most data on snow effects have been collected with small PV installations. The results of these studies are useful for homeowners, but not for commercial-scale installations. Thus, there is a need to study the effects of snow on energy loss with large-scale PV installations in climates typical of New York State. The solar array on campus has a 75-kilowatt capacity. The solar array is 14 feet wide and 1,25 feet long, and it contains 3,2 photovoltaic panels. The system was formally deployed on April 23, 212 [3]. The monitoring system at the university captures hourly data on the solar array, including energy production, solar irradiance, air temperature, and cell temperature. It was an ideal laboratory for a natural experiment on the effects of snow on energy production. Due to the large size of the installation, there is potential for significant energy loss during the winter months from snow. As any campus moves to more reliance on solar energy, energy loss from snow may affect the economics of energy generation in winter months. In addition, our project has applications throughout the state as the use of solar energy increases. 2. Project Summary and Background The study of snow effects on solar energy generation is complicated by the fact that snow is associated with cloudy weather. It was necessary to separate the effects of cloudy conditions and snow. We built a regression model to quantify the relationship between daily power production, solar irradiance, and possible sunshine (a commonly-reported meteorological metric) under R D S C 2 3

2 conditions with no snow. This model allowed us to predict expected daily power production during days where the solar panels were covered with snow. The difference between the expected and measured power production allowed us to quantify energy loss due to snow. We extrapolated our results using historical snowfall and possible sunshine data obtained from the National Weather Service to make predictions of energy loss from snow for our region of New York State under average snowfall conditions. This study represents a unique integration of four data sources: snow depth measurements on the solar panels, solar panel performance data, contemporary meteorological data, and historical meteorological data. The winter of provided five events where snow accumulated on the photovoltaic panels. Field measurements were made immediately after each significant snowfall event. An innovative model was developed by analogy with Beer s Law. The result of this project indicated that efficiency of photovoltaic panels is significantly negatively impacted during the winter season due to snow coverage. Production has a significant negative correlation with adverse winter weather. We conclude that large-scale PV systems in climates similar to ours should consider snow removal technologies to avoid this loss of power. 3. Relationship to Sustainability It is widely accepted that solar energy will become an increasingly important part of renewable energy production in the United States. Electricity production from solar energy is expected to grow almost eight-fold between 21 and 235 [4]. Every kilowatt-hour of electricity generated from solar power saves.718 tons of carbon dioxide and greenhouse gases, which equals the amount emitted from using.81 gallons of gasoline [3]. The amount of carbon dioxide saved from electricity generated from the solar array on our campus is equivalent to the annual R D S C 2 3

3 emissions from 4 American houses and 8 passenger cars. PV systems do not release emissions of sulfur dioxide or particulates during their operation and therefore can lead to a reduction in acid rain and air pollution. As a green energy generation technology, solar PV is applicable technology to other universities in New York State. This project quantified the efficiency of solar panels during the winter season and estimated potential economic loss. The new understanding of production during the winter provided by our work is expected to lead to a more realistic application of solar PV for electricity generation in New York State. Increased electricity produced from solar energy will result in less reliance on coal and other non-renewable fossil fuels. 4. Methods 4.1 Field Measurements All data were collected within Section B of the solar array (Figure 1). Prior to the first snowfall, the angle of inclination of the panels were measured to eliminate individual differences during the study. All panels were found to extend at 3.5 degrees from the horizontal. Snow depth was measured perpendicular to the surface of panels on randomly selected panels in Section B. Seventy-three snow depth measurements were taken during five snow events. Although there were numerous snow events on campus during the winter of , only five events provided sufficient snow for a measurable depth on the PV panels. These events occurred on 24 January, 2 February, 3 February, 9 February, and 3 March R D S C 2 3

4 Figure 1: Solar Array Layout 4.2 Other Data Data for irradiance at panel surface, cell and ambient temperature, and power production were obtained from a campus website [3]. Contemporary and historical snowfall and possible sunshine data were obtained from the National Weather Service [5]. 5. Results 5.1 Energy Production in the Absence of Snow Snow falls during cloudy days. Cloudiness affects solar irradiance and energy production. Therefore, it was necessary to separate the effects of cloudiness and snow on energy production. The relationship between energy production and solar irradiance is shown for 14 days during the study period in Figure R D S C 2 3

5 Energy Production (KWh) /27/213 2/5/213 2/18/213 2/22/213 2/25/ Solar Irradiance (W/m2) Figure 2: Relationship Between Energy Production and Solar Irradiance with No Snow It is clear from Figure 2 that energy production increases with solar irradiance, as expected. However, there is a great deal of scatter in the data. It is not possible to use the data in Figure 2 to determine the expected energy production from a solar panel under a given solar irradiance. We developed alternative measures of energy production and cloudiness to obtain a more usable relationship. We used peak hourly energy production and possible sunshine as measures of energy production and cloudiness, respectively. Data from nine randomly selected days (for which possible sunshine and sky cover data were consistent) are plotted in Figure 3 and given the following relationship: Peak hourly energy production (kwh) = 4.73( possible sunshine ) + 13, r 2 = R D S C 2 3

6 Peak Hourly Energy Prod. (kwh) "Possible Sunshine" (%) Figure 3: Relationship Between Peak Hourly Energy Production and Possible Sunshine for Nine Random Days Without Snow 5.2 Energy Production in the Presence of Snow Figure 4 shows the energy production as a function of solar irradiance in the presence of snow. Compared to Figure 2, note the extremely small energy production. There appears to be a threshold of about 12 W/m 2 below which there is almost no energy production in the presence of snow. By contrast, 12 W/m 2 of solar irradiance provided typically about 5 kwh of energy in the absence of snow (Figure 2). Energy Production (kwh) /2/213 2/3/213 2/9/213 3/3/ Solar Irradiance (W/m2) R D S C 2 3 Figure 4: Relationship Between Energy Production and Solar Irradiance with Snow

7 Summary data for the five snow events are listed in Table 1. Note that snow depth is an unreliable predictor of peak hourly energy production because the amount of sunlight varies between the days. Table 1: Summary Data from Snow Days Date Possible Sunshine (%) Snow Depth (cm) 1 Peak Hourly Energy Production (kwh) Predicted Peak Hourly Energy Production (kwh) 3 1/24/ (.7) /2/ (3.) /3/ (3.8) /9/ (2.1) /3/ (.7) Notes: 1. Mean snow depth, standard deviation in parentheses 2. Nine of 16 panels had no snow. 3. Predicted peak hourly energy production (kwh) = 4.73( possible sunshine ) Effect of Snow Depth The regression model allowed us to calculate predicted peak hourly energy production (see Table 1). It is attractive to assume that snow absorbs light. Light absorption by chemical species is described by Beer s Law: absorbance = (molar absorptivity)(path length)(concentration), where absorbance = log 1 (incident intensity/exit intensity). In the case of snow, the concentration of the absorbing species (snow) is constant. If the peak hourly energy production (PHEP) is proportional to light intensity, then the analogy to Beer s Law for snow-covered solar panels is: Apparent absorbance = log 1 (PHEP/pred. PHEP without snow) = (constant)(snow depth) A plot of apparent absorbance versus snow depth is shown in Figure 5. Note the linear behavior up to about 4 cm of snow. A regression line through the origin for the data up to 4 cm reveals that apparent absorbance =.4(snow depth in cm) [up to 4 cm, r 2 =.969]. In other words, the actual peak hourly energy production is expected to be only 1.4z of the expected peak hourly R D S C 2 3

8 energy production, where z is the snow depth in cm (valid up to 4 cm). Note that above 4 cm of snow, the expected peak hourly energy production is only about 1/1 of that without snow, so one can assume that almost no energy is produced at snow depths above 4 cm. 3 Apparent Absorbance Snow Depth (cm) Figure 5: Beer s Law Plot for Apparent Absorbance Versus Snow Depth 5.4 Recovery After Snowfall To determine the critical conditions for energy production recovery after a snow event, power production, cell temperature, and possible sunshine were plotted continuously against time one day before and at least two days after a snow event. An example for the 3/3/13 snow event is shown in Figure 6. Little or no energy was produced on the day of the snow fall. After this snow event, energy production was very low until the cell temperature rose to 52 F R D S C 2 3

9 Power (Kwh) Power Cell Temperature Percentage sunshine Time (hour) Cell Temperature (F) "Possible Sunshine" (%) Figure 6: Recovery of Energy Production After the 3/313 Snow Event (Event is indicated by the black box) 5.2 Economic Impact This work makes it possible to use historical or current data to predict the impact of snow on solar energy production. To demonstrate the impact for an average winter, we identified a winter ( ) where the average snowfall (97.6 in) was near the average snowfall over the last 2 years in our area (94.7 inches). Daily snowfall data from 1 November 1996 to 31 March 1997 were obtained [5]. By using the model discussed above, we calculated that only 67% of power would have been produced that winter compared to the energy production that would have been expected without snow. With the assumption of normal full production for the rest of the year, an average of 84% of normal production should be expected for the whole year (16% loss). According to U.S. Energy Information Administration, the average electricity usage per customer per year in New York State was 592 kwh for 211, and the average sale price of residential electricity in New York State is about 18.3 cents per kwh [6]. In the future, if all 1.1 million residents in our area were supplied by solar power, the economic loss from a 16% R D S C 2 3

10 reduction in output from snow would be (592 kwh per capita)(1.1 million people)($.183 per kwh)(.16) = $19.1 million per year. 6. Conclusions Our study showed that electric energy production from PV panels is significantly impacted by snow. After accounting for the effects of solar irradiance, we found that the actual peak hourly energy production is expected to be only 1.4z of the expected peak hourly energy production, where z is the snow depth in cm (valid up to 4 cm). Solar panels appear to recover when the cell temperature reaches about 52 F. Further work should be done with recovery by determining the exact time when snow falls off the panels. The economic impact of snow on PV panels is significant. For the most recent typical winter in our area, wintertime energy output would have been reduced by 33% and annual energy output reduced by 16%. This would have been a $19 million annual cost if all the people in our area were served by solar energy. Our study can be used to determine the cost-effectiveness of snow removal technologies for solar panels R D S C 2 3

11 References [1] Snow and Solar Energy in Canada: Let s Debunk some Myths! Canadian Solar Industries Association, N.p., n,d. Web. 4 April 213. < [2] Solar Winter Output Assessment: Measuring Snow-related Losses. T. Townsend and L. Powers, 15 June 212. Web. 4 April 213. < [3] [To maintain anonymity, parts of this reference that reveal the university name have been replaced by XXX] Solar Dashboard - XXX: An Initiative of the Office of Sustainability. Solar Dashboard - XXX: An Initiative of the Office of Sustainability. N.p., n.d. Web. 14 Nov < [4] Annual Energy Outlook 212 with Projections to 235. U.S. Energy Information Administration, June 212. [5] National Weather Service Climate. National Weather Service Climate. N.p., n.d. Web. 2 Mar [6] U.S. Energy Information Administration - EIA - Independent Statistics and Analysis. U.S. States. N.p., n.d. Web. 22 Mar R D S C 2 3