Probability Characterization of Solar Farm Power Output and Impact on System Reliability

The deployment of renewable-resources, such as solar, is expected to rise significantly within the coming few years. An issue with such sources is their variability. Hence, modeling and studying their probabilistic behavior become a crucial matter. This paper presents three different methods for computing the generated power probability distribution function (pdf) of a photovoltaic (solar) generation farm (SF) containing large number of solar cell generators (SCGs). The methods are: (1) analytical method, (2) non-sequential Monte Carlo simulation, and (3) sequential Monte Carlo simulation. All three methods are utilized to calculate the pdf of a sample SF with 10 SCGs. Historical solar radiation data is utilized to perform the study. Further, force outage rates (FOR) of components are incorporated in the process of computing the SF generated power pdf. The results from each of the three methods are compared. While the analytical method is considered the benchmark method, all methods yield comparable results. The usefulness of the computed pdf is demonstrated by integrating them into a probabilistic production costing (PPC) model for assessing the reliability of a system comprising one or more SFs.