Improving the 11-Parameter Model for Photovoltaic Modules via Technology-Driven Refinements

An accurate prediction of the performance of photovoltaic (PV) power generation systems is becoming increasingly important as this technology continues to penetrate the market. The performance of a PV system depends on a number of factors, including the type of solar PV technology employed, solar irradiance, module temperature, wind speed, ambient temperature, and other outdoor environmental conditions. Manufacturers have traditionally provided PV module ratings at standard test conditions, but they are now offering extended ratings, including operation at low irradiance conditions and nominal operating cell temperature, allowing for the development of more refined models. Recently, an 11-parameter model capable of reproducing the performance of a PV module across a wide range of atmospheric conditions was proposed by some of the authors [1], [2]. While the proposed model outperforms all existing models, it was also shown in [2] that it exhibits slightly larger errors in the case of PV thin-film technologies. A related publication proposed improvements in PV thin-film technology performance prediction by modeling the shunt admittance in the electrical circuit as a power function of the voltage, characterized by a γ exponent [3]. Taking advantage of this improvement, we propose to incorporate such a modification into the 11-parameter model and, via a trial-and-error approach, determine its optimal value. Results indicate that, by incorporating this enhancement, the error for maximum power point prediction for a wide range of atmospheric conditions is reduced by up to 7% compared to the results presented in [2].