Effects of Snow on Photovoltaics - Literature Review

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This literature review supports work on the Effects of snow on photovoltaic performance

First results[edit | edit source]

Prediction of Energy Effects on Photovoltaic Systems due to Snowfall Events[edit | edit source]

The Effects of Snowfall on Solar Photovoltaic Performance[edit | edit source]

Source: Rob W. Andrews, Andrew Pollard, Joshua M. Pearce, “The Effects of Snowfall on Solar Photovoltaic Performance ”, Solar Energy 92, 8497 (2013). DOI: http://dx.doi.org/10.1016/j.solener.2013.02.014 open access

Other Literature[edit | edit source]

An Approach to the Impact of Snow on the Yield of Grid Connected PV Systems [1][edit | edit source]

Abstract: Although most of the yield of grid-connected systems will occur in summer, snowfall may decrease the yield considerably. The impacts of snow depend on many parameters such as the height of snow, its weight or the tilt angle of the modules. The analysis of the reference plant shows that the reduction of the annual yield in the Degrees of latitude of South Germany is perceptibly. This is an important information for instance for the construction of plants or the issue of yield prognosis.

Annual Energy Yield of 13 Photovoltaic Technologies in Germany and Cyprus [2][edit | edit source]

Abstract: In close cooperation with the University of Cyprus, the Universität Stuttgart operates two identical sets of 13 similar photovoltaic (PV) systems of different technologies at the locations Stuttgart (Germany) and Nicosia (Cyprus). This paper presents the PV and data acquisition systems, and analyze the annual energy yield over the first year of operation. The average annual yield of fixed mounted PV systems amounts to 1003 kWh/kWprated in Stuttgart, and to 1646 kWh/kWprated in Nicosia. In Cyprus, an additionally installed two-axis tracking system produces 28 % more electricity than the fixed one. The performance ratio of the systems is 80 %. The yield of a concentrating system is in the same range as the one of the fixed mounted systems.

Photovoltaics and Snow: An Update from two winters of measurement in the Sierra [3][edit | edit source]

Abstract: With snowy locations becoming common for large photovoltaic (PV) installations, analytical models are now needed to estimate the impact of snow on energy production. A generalized monthly snow loss model is introduced here. The model was calibrated using ongoing measurements that began in December 2009 for three panel orientations at a BEW test station in Truckee, CA. Supplemental data for a fourth orientation from a nearby municipal PV system were also used to calibrate the model. Overall, the energy prediction error for the four panel orientations has been just 2% RMS on an annual rolling-average basis. Short-term errors are higher, a consequence of the variable nature of snowfall timing, quantity, and quality, and its complex interaction with temperature, wind, humidity, and ground interference. Despite these limitations, good quality, unbiased monthly loss estimates can now be used as inputs to the simulation programs PV investors rely on for decision-making. Loss profiles for three sample cities with prominent PV markets are provided. The samples have not been validated, but are included to show what the model predicts for other climates.

Prediction of Energy Effects on Photovoltaic Systems due to Snowfall Events [4][edit | edit source]

Abstract: The accurate prediction of yields from photovoltaic systems (PV) is critical for their proper operation and financing, and in northern latitudes the effects of snowfall on yield can become significant. This work provides methods for identifying snowfall effects from commonly collected performance data, and recommends a model to allow for prediction of these effects based solely on meteorological time series. The model was validated with data from two large-scale (>8MW) operational PV plants. For the low tilt angles most affected by snowfall, this analysis was able to accurately predict both daily and mean values of snow effects. This methodology will enable system operators to utilize performance data to accurately identify and predict snowfall losses, and will assist system designers to optimize for the effects of snowfall on new system designs.

Snow accumulation on boards of different sizes and shapes [5][edit | edit source]

Abstract: Snow accumulation on trees plays a major role for the snow cover structure in snow hydrology, and in avalanche protection of boreal and subalpine forests. In order to better understand the processes associated with this, we investigated snow accumulation on boards of different sizes, shapes, and inclination. For this purpose we exposed different boards to natural and man-made snowfalls. After each snowfall, we measured the accumulated snow mass on the boards as well as different snow characteristics and meteorological conditions. The observed snow interception efficiency increased with board width for snowfalls at mean air temperatures below ÿ3 8C. This is explained by the decreasing rebound of the snow crystals near the edges. At temperatures above ÿ3 8C the snow interception efficiency was independent from board width because of strong wet-snow cohesion. For wet snowfalls bridging occurred even at distances of 10 cm between boards. The inclination and the shapes of the boards had a significant influence on the amount of accumulated snow. Based on the measured accumulation and meteorological data, we derived statistical models for the accumulation of snow on boards.

The Optimum Tilt Angles and Orientations of PV Claddings for Building-Integrated Photovoltaic (BIPV) Applications [6][edit | edit source]

Abstract: The tilt and azimuth angles of a photovoltaic (PV) array affect the amount of incident solar radiation exposed on the array. This paper develops a new mathematical model for calculating the optimum tilt angles and azimuth angles for building-integrated photovoltaic (BIPV) applications in Hong Kong on yearly, seasonal, and monthly bases. The influence of PV cladding orientation on the power output of PV modules is also investigated. The correlations between the optimum tilt angle and local weather conditions or local environmental conditions are investigated. The results give reasonable solutions for the optimum tilt angles for BIPV applications for both grid-connected and stand-alone systems.

Effects of snow cover on UV irradiance and surface albedo - A case study [7][edit | edit source]

Abstract: A heavy snowfall, followed by several days of cloudless skies before significant snow melt had occurred, enabled a quantitative study of the effects of snow on down welling UV spectral irradiances at the National Institute for Water and Atmospheric Research UV measurements site in Lauder, New Zealand. The largest UV enhancements(> 70%) were seen during partly cloudy conditions immediately after the snowfall. A radiative transfer model was used to quantify the enhancements due to the snow cover and the spectral albedo of the snow under clear-sky conditions. The first cloudless day on which the radiative transfer model could be used with confidence occurred 7 days after the snowfall. By this time, the maximum enhancements due to snow at solar zenith angle (SZA) 70 degrees were approximately 22% in the UV-A region. In the UV-B region,t he enhancements were approximately 28% and tended to increase slightly at larger SZA. The corresponding surface albedo was 0.62 +or- 0.08, and comparison with supplementary measurements indicated that the albedo decayed with time. Any spectral or SZA dependencies in the enhancements were below the measurement uncertainties in the UV region. Comparisons with supplementary data indicated that the albedo immediately after the snow was greater than 0.8.

Partial shadowing of photovoltaic arrays with different system configurations- literature review and field test results [8][edit | edit source]

Abstract: Partial shadowing has been identified as a main cause for reducing energy yield of grid-connected photovoltaic systems. The impact of the applied system configuration on the energy yield of partially shadowed arrays has been widely discussed. Nevertheless, there is still much confusion especially regarding the optimal grade of modularity for such systems. A 5-kWp photovoltaic system was installed at K.U. Leuven. The system consists of three independent subsystems: central inverter, string inverter, and a number of AC modules. Throughout the year, parts of the photovoltaic array are shadowed by vegetation and other surrounding obstacles. The dimensions of shadowing obstacles were recorded and the expectable shadowing losses were estimated by applying different approaches. Based on the results of almost 2 years of analytical monitoring, the photovoltaic system is assessed with regard to shadowing losses and their dependence on the chosen system configuration. The results indicate that with obstacles of irregular shape being close to the photovoltaic array, simulation estimates the shadowing losses rather imprecise. At array positions mainly suffering from a reduction of the visible horizon by obstacles far away from the photovoltaic array, a simulation returns good results. Significant differences regarding shadow tolerance of different inverter types or over proportional losses with long module strings could not be confirmed for the system under examination. The negative impact of partial shadowing on the array performance should not be underestimated, but it affects modular systems as well as central inverter systems.

Performance Parameters for Grid-Connected PV Systems [9][edit | edit source]

Abstract: The use of appropriate performance parameters facilitates the comparison of grid-connected photovoltaic (PV) systems that may differ with respect to design, technology, or geographic location. Four performance parameters that define the overall system performance with respect to the energy production, solar resource, and overall effect of system losses are the following: final PV system yield, reference yield, Performance ratio, and PVUSA rating. These performance parameters are discussed for their suitability in providing desired information for PV system design and performance evaluation and are demonstrated for a variety of, technologies, designs, and geographic locations. Also discussed are methodologies for determining system as power ratings in the design phase using multipliers developed from measured performance parameters.

Performance Ratio and Yield Analysis of Grid Connected Clustered PV Systems in Japan [10][edit | edit source]

Abstract: It is becoming more important to evaluate the installed PV system’s performance and loss factors to enhance the system’s efficiency and pull more electric power from the systems. This paper describes the evaluation method of PV systems and summarizes the results of annual performance and loss analysis. Grid voltage and snow coverage are two major serious loss factors for PV systems, optimized array configuration results more system yield on roof mounted residential PV systems.

Performance assessment of different Photovoltaics Systems under identical field conditions of high irradiation [11][edit | edit source]

Abstract: This paper presents the first year performance results and analysis of 14 grid connected photovoltaic (PV) systems under the weather conditions in Cyprus. The different PV technologies under test range from monocrystalline to multicrystalline and thin film technologies. As in all performance assessment practices of PV systems the results are provided according to their measured Performance Ratio (PR) and the Energy Yield (both dc and ac). Such evaluation of installed PV systems is very important in enhancing the efficiency in the quest for increased power production to be fed into the electricity grid. The use of these performance parameters further facilitates direct comparisons which are independent of geographic location of the installed systems. This is particularly important as in the scope of this project the same PV systems are installed in Germany and Egypt. Based on this background and by utilizing the acquired data from the sophisticated datalogging system installed, the performance ratio and energy yield of the installed systems has been evaluated and analyzed for the period of one year. The arrays under test have shown yearly energy yields (dc) between 1700 – 1800 kWh/kWp and energy yields (ac) between 1550 – 1650 kWh/kWp. Accordingly the PR (dc) was between 85% – 93% while the respective PR (ac) was between 77% - 85%.

Orientation and Tilt Dependence of a Fixed PV Array Energy Yield Based on Measurements of Solar Energy and Ground Albedo – a Case Study of Slovenia [12][edit | edit source]

Abstract: In the last decade solar photovoltaic (PV) systems have become available as an alternative electrical energy source not only in remote locations but even in densely populated areas as their price decreases and their performance increases. The chapter discusses fixed PV array potential in Slovenia with great geographical and topographical variety, which is a reason that the climate, and also PV potential, changes rapidly already on short distances. The study is based on the meteorological measurements of solar irradiance, air temperature and albedo from the MODIS satellite data. Simulations for four meteorological stations were employed to determine combinations of azimuth and tilt angle for fixed PV arrays that would enable their maximum efficiency. As expected, large tilt with southern orientation is optimal during winter and almost flat installations are optimal during summer. The optimal PV gains are compared also with the results obtained by using the rule of a thumb tilt angle showing some significant differences in some cases.

Performance Monitoring of a Building -Integrated Photovoltaic System in an Urban Area [13][edit | edit source]

Abstract: This paper focuses on the performance monitoring of a building-integrated PV system located in an urban area. The building has 3 rated power of 76kW array consisting of sub-arrays installed on the north, south and west walls, and north end south roofs. The field data acquisition has been continued since June 2001. In order to compare with the monitoring results, simulation for produced DC energy is carried out using a software package of PVFORM with standard meteorological data for Kobe. According to measured data monthly energy seem to depend on the length of daytime for each month. From comparison of measured with calculated energy outputs. it would seem that the neighboring buildings has an effect on the energy performance of an actual PV system in an urban area.

Advanced analysis of shading effect using minutely based measured data for PV systems [14][edit | edit source]

Abstract: Output energy loss due to shading effect is one of the concerns for roof mounted residential PV systems. Most of the PV arrays have series connected string configuration to achieve enough high voltage for the efficient DC/AC inversion. In this case, only a small area of the partial shading on the string will affect whole string if the bypass diodes are not installed within the string. As a result, both shading analysis and array configuration analysis are necessary to predict the amount of energy loss due to the shading. Shading may be caused by various reasons. For example, PV modules are not always installed on a simple gable roof but also installed on a complex hipped roof. Hipped roof sometimes makes a shadow on itself and this shadow will reduce the energy yield of the PV systems. Other causes of the shading are buildings, trees or poles and transformers of the power grid near the PV systems especially in urban area. The objective of this research is to develop a method to quantify the energy loss due to the shading effect using minutely based measured data without taking a picture of the light-blocking object or the location survey. This method can also determine the direction of light-blocking object by using 3D contour graph.

Effects of Shadowing on Photovoltaic Module Performance [15][edit | edit source]

Abstract: The reduction of output power in PV modules can be attributed to many factors, but may be the most important are maximum power point (MPP) mismatch and shadows. If a PV module is partially shaded, some of its cells can work in reverse bias, working as loads and not as power generators. When reverse bias exceeds the breakdown voltage of the shaded solar cell, the cell will be fully damaged; for example, cell cracking or hot spot formation appears and an open circuit exists at the serial branch where the cell is connected. The apparition of hot spots, resulting in irreversible damage of the PV module, is relatively easy if no protection is present on the series and parallel connection of the solar cells forming the PV module. Diagnosis of hot spots formation in PV power generation systems is a difficult task, but the importance of the consequent lowering of output due to this effect has motivated recently new approaches in hot spot investigations on PV modules. The aim of this work is to study the variation of PV module main characteristic parameters as a function of shading, with a special attention to the relationship between output power lowering due to shading and variation of the power losses associated with the series and shunt resistance of the PV module.

An Effect of Snow for Electric Energy Generation by 40KW PV System [16][edit | edit source]

Abstract: In the fiscal year 1999, a 40kW PV system was installed in Maizuru National College of Technology. In Mairuru-City located in the Sea of Japan side, there is much snowfall in the winter season. This snow obstructs the solar energy and decreases the irradiation on the PV system. Therefore, the total efficiency of electric power generation of the system is decreased. The research of equipment, which melts snow, is carried out in order to solve this problem, however, it has not yet come to the practical use. This study estimates the effect of the snow melting equipment by using the PV database which is previously consulted by us. Using a model for estimating the electric energy generated by 40kW PV system, it proposes the operation time and its position of the snow melting equipment. The position of the snow melting equipment is the lower edge portion of the bottom ones of solar cell modules. The operation time of the equipment is the time after the snow becomes pressured. These results improve the efficiency of the PV system in the snowfall region. Both the generated electric powers with and without snow melting device can evaluate by using our PV model and database.

PV system monitoring and performance of a grid connected PV power station located in Manchester-UK [17][edit | edit source]

Abstract: In the last two decades renewable resources have gained more attention due to continuing energy demand, along with the depletion in fossil fuel resources and their environmental effects to the planet. This paper presents a novel approach in monitoring PV power stations. The monitoring system enables system degradation early detection by calculating the residual difference between the model predicted and the actual measured power parameters. The model being derived using the MATLAB/SIMULINK software package and is designed with a dialog box to enable the user input of the PV system parameters. The performance of the developed monitoring system was examined and validated under different operating condition and faults e.g. dust, shadow and snow. Results were simulated and analyzed using the environmental parameters of irradiance and temperature. The irradiance and temperature data is gathered from a 28.8kW grid connected solar power system located on the tower block within the MMU campus in central Manchester. These realtime parameters are used as inputs of the developed PV model. Repeatability and reliability of the developed model performance were validated over a one and half year's period.

Performance analysis of a PV array installed on building walls in a snowy country [18][edit | edit source]

Abstract: This paper summarizes the performance and analysis of a 12 kWp PV array installed on building walls in a snowy country in Japan. The analysis is based on the monitoring results over 2 years since 1999. The highlight of this PV system is that the DC energy output increased by reflection of sunlight from fresh snow around the PV array. Analyzed results show daily integrated DC energy outputs on snowy days are much higher than those without snowfall. The analyzed results also show the reflection from snow affects not only the power output, but also a power rating of inverter for the building-wall PV system.

References[edit | edit source]

  1. G. Becker, B. Schiebelsberger, W. Weber, C. Vodermayer, M. Zehner, and G. Kummerle, “An Approach To The Impact Of Snow On The Yield Of Grid Connected PV Systems,” Bavarian Association for the Promotion of Solar Energy, Munich, 2006.
  2. B. Zinsser, G. Makrides, W. Schmitt, G. E. Georghiou, and J. H. Werner, “Annual energy yield of 13 photovoltaic technologies in Germany and in Cyprus,” in Proc 22nd European PV Solar Energy Conference, Milan, 2007.
  3. Y. Ueda, K. Kurokawa, T. Itou, K. Kitamura, Y. Miyamoto, M. Yokota, and H. Sugihara, “Performance Ratio and Yield Analysis of Grid Connected Clustered PV Systems in Japan,” in Conference Record of the 2006 IEEE 4th World Conference on Photovoltaic Energy Conversion, 2006, vol. 2, pp. 2296 –2299.
  4. R. W. Andrews and J. M. Pearce, “Prediction of energy effects on photovoltaic systems due to snowfall events,” in 2012 38th IEEE Photovoltaic Specialists Conference (PVSC), 2012, pp. 003386 –003391.
  5. R. Pfister and M. Schneebeli, “Snow accumulation on boards of different sizes and shapes,” Hydrological Processes, vol. 13, no. 14–15, pp. 2345–2355, 1999.
  6. H. Yang and L. Lu, “The optimum tilt angles and orientations of PV claddings for building-integrated photovoltaic (BIPV) applications,” TRANSACTIONS-AMERICAN SOCIETY OF MECHANICAL ENGINEERS JOURNAL OF SOLAR ENERGY ENGINEERING, vol. 129, no. 2, p. 253, 2007.
  7. McKenzie, R. L., K. J. Paulin, and S. Madronich. "Effects of snow cover on UV irradiance and surface albedo: A case study." Journal of Geophysical Research 103.D22 (1998): 28785-28.
  8. A. Woyte, J. Nijs, and R. Belmans, “Partial shadowing of photovoltaic arrays with different system configurations: literature review and field test results,” Solar Energy, vol. 74, no. 3, pp. 217–233, Mar. 2003.
  9. B. Marion, J. Adelstein, K. Boyle, H. Hayden, B. Hammond, T. Fletcher, B. Canada, D. Narang, A. Kimber, L. Mitchell, G. Rich, and T. Townsend, “Performance parameters for grid-connected PV systems,” pp. 1601–1606.
  10. Y. Ueda, K. Kurokawa, T. Itou, K. Kitamura, Y. Miyamoto, M. Yokota, and H. Sugihara, “Performance Ratio and Yield Analysis of Grid Connected Clustered PV Systems in Japan,” in Conference Record of the 2006 IEEE 4th World Conference on Photovoltaic Energy Conversion, 2006, vol. 2, pp. 2296 –2299
  11. G. Makrides, B. Zinsser, G. E. Georghiou, and J. Werner, “Performance assessment of different photovoltaic systems under identical field conditions of high irradiation,” in PV RES Conference, 2007, pp. 15–20.
  12. J. Rakovec, K. Zaksek, K. Brecl, D. Kastelec, and M. Topic, “Orientation and Tilt Dependence of a Fixed PV Array Energy Yield Based on Measurements of Solar Energy and Ground Albedo – a Case Study of Slovenia,” in Energy Management Systems, G. Kini, Ed. InTech, 2011.
  13. K. Yoshioka, T. Saitoh, and T. Yamamura, “Performance monitoring of a building-integrated photovoltaic system in an urban area,” in Proceedings of 3rd World Conference on Photovoltaic Energy Conversion, 2003, 2003, vol. 3, pp. 2362 –2365 Vol.3.
  14. Y. Ueda, T. Oozeki, K. Kurokawa, T. Itou, K. Kitamura, Y. Miyamoto, M. Yokota, and H. Sugihara, “Advanced analysis of shading effect using minutely based measured data for PV systems,” in 15th International Photovoltaic Science & Engineering Conference (PVSEC-15) Technical Digest, 2005, pp. 444–445.
  15. S. Silvestre and A. Chouder, “Effects of shadowing on photovoltaic module performance,” Progress in Photovoltaics: Research and Applications, vol. 16, no. 2, pp. 141–149, 2008.
  16. S. Nakagawa, T. Tokoro, T. Nakano, K. Hayama, H. Ohyama, and T. Yamaguchi, “An effect of snow for electric energy generation by 40 kW PV system,” in Proceedings of 3rd World Conference on Photovoltaic Energy Conversion, 2003, 2003, vol. 3, pp. 2447 –2450 Vol.3.
  17. E. M. Natsheh, E. J. Blackhurs, and A. Albarbar, “PV system monitoring and performance of a grid connected PV power station located in Manchester-UK,” in IET Conference on Renewable Power Generation (RPG 2011), 2011, pp. 1 –6.
  18. K. Yoshioka, J. Hasegawa, T. Saitoh, and S. Yatabe, “Performance analysis of a PV array installed on building walls in a snowy country,” in Conference Record of the Twenty-Ninth IEEE Photovoltaic Specialists Conference, 2002, 2002, pp. 1621 – 1624.