Temperature Dependent Power Modeling of Photovoltaics

Open Access
Bayrakci Boz, Mesude
Graduate Program:
Energy and Mineral Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
July 01, 2011
Committee Members:
  • Jeffrey Brownson, Thesis Advisor
  • GIS
  • Temperature
  • Photovoltaic
  • Avoided Cost
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 (TMY2) data. These two models calculate discrete outputs of power density $(W/m^2)$, 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. The simulated power output results were validated with measured data. Model B was found to have a lower mean error than Model A in single day comparison with PV data from New Jersey. Model A overpredicts the real power output. Additionally, the avoided cost calculations for 50 cities and payback times for 12 cities have been calculated. Temperature has minor effects on avoided costs and payback times. On the other hand, payback times are much more sensitive to Renewable Energy Credits than annual shifts in ambient temperature.