Optimum selection of photovoltaic modules using probabilistic approach based on capacity factor estimation

ABSTRACT In this paper, the optimum selection of photovoltaic module using probabilistic techniques based on capacity factor estimation is proposed. The approach entails modelling the solar irradiance characteristics of the site by fitting probability distributions to the irradiance data for different hours of a typical day in each month of the year. The parameters of the probability distribution that best fits the solar irradiance data for a particular hour are thereafter used to estimate the capacity factor of different designs of PV modules. The module with the highest average capacity factor across all the months is identified as the best suited module for the given site. The proposed technique is investigated using 5 years data (2008–2012) of solar irradiance and temperature. The choice of the year of observation of the data was due to accuracy and completeness of the data for the period. The data sheets of 10 commercially available PV modules were also obtained from different manufacturers. The proposed method is simple, easy to use, and can be applied to any solar regimes around the world.