Measuring and modeling the effect of snow on photovoltaic system performance

Powers, Loren; Newmiller, Jeff; Townsend, T.

doi:10.1109/pvsc.2010.5614572

Cited by 48 publications

(25 citation statements)

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“…For example, the rural CMX has the largest number of delayed and canceled flights in the U.S. due primarily to weather conditions [62]. In addition, the region it is located in is the upper peninsula of Michigan, which records some of the largest snow events in the U.S. [63], and snow has an impact for annual PV output [64][65][66][67][68]. Thus, in such cases the adverse (snow losses [63][64][65][66][67][68]) and positive effects of weather (i.e., surface albedo [69]) effects need to be taken into consideration in simulation and designs.…”

Section: Airport Type and Surface Selectionmentioning

confidence: 99%

General Design Procedures for Airport-Based Solar Photovoltaic Systems

et al. 2017

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A source of large surface areas for solar photovoltaic (PV) farms that has been largely overlooked in the 13,000 United States of America (U.S.) airports. This paper hopes to enable PV deployments in most airports by providing an approach to overcome the three primary challenges identified by the Federal Aviation Administration (FAA): (1) reflectivity and glare; (2) radar interference; and (3) physical penetration of airspace. First, these challenges and precautions that must be adhered to for safe PV projects deployment at airports are reviewed and summarized. Since one of the core concerns for PV and airport symbiosis is solar panel reflectivity, and because this data is largely estimated, a controlled experiment is conducted to determine worst-case values of front panel surface reflectivity and compare them to theoretical calculations. Then a general approach to implement solar PV systems in an airport is outlined and this approach is applied to a case study airport. The available land was found to be over 570 acres, which would generate more than 39,000% of the actual annual power demand of the existing airport. The results are discussed while considering the scaling potential of airport-based PV systems throughout the U.S.

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Section: Airport Type and Surface Selectionmentioning

confidence: 99%

General Design Procedures for Airport-Based Solar Photovoltaic Systems

et al. 2017

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“…The use of a comparative PV module or array pairs to determine the losses caused by snowfall is one of the preferred methods in the literature and this study. In the state of California (Truckee), USA, annual production loss for uncleared modules with tilt angles of 0 • , 24 • , and 39 • was estimated as 18%, 15%, and 12%, respectively [10]. Using the same method, the contribution of clearing snow cover on a PV system in the Czech Republic to the yearly yield was determined as 1.4% [11].…”

Section: Introductionmentioning

confidence: 99%

The effect of snowfall and icing on the sustainability of the power output of agrid-connected photovoltaic system in Konya, Turkey

Anadol¹,

Erhan²

2019

Turk J Elec Eng & Comp Sci

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When the module surface is covered with various factors such as snow and icing which prevent the solar irradiance from reaching the photovoltaic cells, the power production of the system and its performance decrease. The purpose of this study is to determine the energy production losses of a grid-connected photovoltaic plant due to snowfall and icing. The effect of snowfall and icing has been examined on a photovoltaic system consisting of a hybrid inverter with two separate maximum power point tracking inputs and 36 monocrystalline modules, which are mounted on the supporting system horizontally in the south direction and at a constant tilt angle of 30 • . The plant area is located in a priority and snowy region (Konya,Turkey) where large-scale photovoltaic system installations are carried out. In order to evaluate the effect of snowfall, the minute resolution data of the hybrid inverter which provides connection to the grid is used. The change over time of the power generated by the two arrays of the plant was examined comparatively. For comparison, one of the arrays was continuously cleared. The recorded data was used to determine the expected energy output of the array covered with snow. Besides, the solar irradiance and ambient temperature data obtained from the meteorological station were used to accurately identify and evaluate the effects of snowfall with digital images recorded in the site area.The results showed that surface clearing of modules had a significant positive effect on the power output of the system. In the array entirely covered with snow, the daily energy loss exceeds 93%. In months of heavy snowfall, the monthly energy loss is 18% depending on time of being covered with snow of the modules. When the production data of 2017 and 2018 is evaluated, it is seen that the total energy loss of the plant varies between 1% and 2%.

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“…More recently, researchers have developed add-on models to account for snow loss (Andrews and Pearce 2012;Marion et al 2013;Powers et al 2010). However, in many cases, the community has yet to incorporate these add-on models into freely available, online, up-to-date PV system prediction applications.…”

Section: Introductionmentioning

confidence: 99%

Performance modeling and valuation of snow-covered PV systems: examination of a simplified approach to decrease forecasting error

Bosman

Darling

2018

Environ Sci Pollut Res

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The advent of modern solar energy technologies can improve the costs of energy consumption on a global, national, and regional level, ultimately spanning stakeholders from governmental entities to utility companies, corporations, and residential homeowners. For those stakeholders experiencing the four seasons, accurately accounting for snow-related energy losses is important for effectively predicting photovoltaic performance energy generation and valuation. This paper provides an examination of a new, simplified approach to decrease snow-related forecasting error, in comparison to current solar energy performance models. A new method is proposed to allow model designers, and ultimately users, the opportunity to better understand the return on investment for solar energy systems located in snowy environments. The new method is validated using two different sets of solar energy systems located near Green Bay, WI, USA: a 3.0-kW micro inverter system and a 13.2-kW central inverter system. Both systems were unobstructed, facing south, and set at a tilt of 26.56°. Data were collected beginning in May 2014 (micro inverter system) and October 2014 (central inverter system), through January 2018. In comparison to reference industry standard solar energy prediction applications (PVWatts and PVsyst), the new method results in lower mean absolute percent errors per kilowatt hour of 0.039 and 0.055%, respectively, for the micro inverter system and central inverter system. The statistical analysis provides support for incorporating this new method into freely available, online, up-to-date prediction applications, such as PVWatts and PVsyst.

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Measuring and modeling the effect of snow on photovoltaic system performance

Cited by 48 publications

References 0 publications

General Design Procedures for Airport-Based Solar Photovoltaic Systems

General Design Procedures for Airport-Based Solar Photovoltaic Systems

The effect of snowfall and icing on the sustainability of the power output of agrid-connected photovoltaic system in Konya, Turkey

Performance modeling and valuation of snow-covered PV systems: examination of a simplified approach to decrease forecasting error

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