Modeled and Measured Operating Temperatures of Floating PV Modules: A Comparison

Abstract

The power output of a photovoltaic system is dependent on the operating temperature of the solar cells. For floating PV (FPV), increased wind speeds can result in increased yield due to lowered operating temperatures, which has long been stated as a key advantage for FPV. So far, this effect has not been included in commercial software packages for yield estimation. Typically, only standard settings are provided, taking into account the mounting type (PVsyst) or mounting and module type (Sandia). This may result in an underestimation of the yield, and consequently, the estimated Levelized Cost of Electricity (LCOE) of the FPV project. In this study, a linkage between recorded module temperatures from FPV systems located in The Netherlands and Sri Lanka and the prevalent models employed within PVsyst and Sandia software for estimating module temperatures are established. Our findings reveal that the models within PVsyst and Sandia tend to overestimate module temperatures by 2.4% and 3%, respectively, for each 1 m/s increment in wind speed. We present two methods for determining the single heat loss coefficient, or U-value, tailored to specific sites accounting for local wind conditions. The first method computes the U-value based on the average monthly wind speed, whereas the second employs the irradiance-weighted average monthly wind speed. The latter method can be advantageous for locations characterized by significant fluctuations in wind speeds between night and day. Through a statistical residual analysis comparing measured and modeled module temperatures, we demonstrate that our proposed methods offer a more accurate representation of module temperature compared to the PVsyst and Sandia models when default settings are used. When we subsequently compute the specific yield using both measured and modeled temperatures, we observe that the approach using irradiance-weighted average wind speed shows a higher yield of up to 2% compared to the traditional methods.

Keywords

• floating PV
• wind speed
• convective cooling
• temperature modeling