Photovoltaic (PV) systems in the USA are often perceived as useful only in warm and sunny climate regions. However, PV systems can be installed and operated in cold regions as well, including places that get snow. Sunlight is the source of electricity, and thermal heat is not required to generate electricity in PV systems. Given similar irradiation conditions, lower PV-cell temperatures can lead to increases in efficiency, which leads to an increase in power generation, thus allowing the user to benefit more from the PV technology. This study focuses on the relationship between the level of energy production and varying temperatures. Two models have been developed to show temperature effect on photovoltaic systems, using transient systems simulation (TRNSYS), a FORTRAN-based modular program to assess solar conversion and heat transfer. The first model (Model A) ignores temperature and the other (Model B) takes it into consideration. In Model A, the efficiency was assumed to be constant through the year. In Model B, the temperature and the resulting efficiency change of the PV cells are defined according to deviations from the nominal operating cell temperature (NOCT), using equations from the literature. These two models were executed for 236 cities across the USA by using second-generation Typical Meteorological Year (TMY) data. These two models calculate discrete outputs of power density, given irradiance and temperature conditions. Comparative analyses were made between these two models. The power output differences for 236 cities across the USA were used to generate contour maps indicating a continuous surface of differences between these two models. Comparing Model B relative to Model A power outputs increase during the months of November to February for the Northeast and the Midwest regions of the USA (16% -20 %), whereas they decrease slightly in May to August (-4%). On the other hand, power outputs decrease considerably from May to August for the South and Southwest of the USA (- 12% - 15%), whereas they increase slightly from December to February (5%). Geospatial trends show two different behaviours in winter time and in summer time due to ambient temperature.
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