Predicting the behavior of a grid-connected photovoltaic system from measurements of solar radiation and ambient temperature - Hernandez, J.; Gordillo, G.; Vallejo, W. (Applied energy, 2013)
► A model to predict in a reliable way the behavior of a GCPV system is presented. ► Radiation and temperature behavior were shaped with probability density functions. ► This probability density functions were made from real measurements. ► This model was verified for comparing their behavior with real measurements. ► It can be used in any electrical systems language which have programming routines. This paper presents a methodology to predict in a statistically reliable way the behavior of a grid-connected photovoltaic system. The methodology developed can be implemented either in common programming software or through an off-the-shelf simulation of electrical systems. Initially, the atmospheric parameters that influence the behavior of PV generators (radiation and temperature) are characterized in a probabilistic manner. In parallel, a model compound by various PV generator components is defined: the modules (and their electrical and physical characteristics), their connection to form the generator, and the inverter type. This model was verified for comparing their behavior with output measured on a real installed system of 3.6kWp. The solar resource characterized and the photovoltaic system model are integrated in a non-deterministic approach using the stochastic Monte Carlo method, developed in the programming language DPL of the electrical-systems simulation software DIGSILENT®. It is done to estimate the steady-state electrical parameters describing the influence of the grid-connected photovoltaic system. Specifically, we estimated the nominal peak power of the PV generator to minimize network losses, subject to constraints on nodes voltages and conductor currents.