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• An intuitive methodology for assessment of shading losses in PV systems is proposed. • The methodology proposes tools for shadow prediction and power output estimation. • A study of different shadow pattern impact was performed. • The annual shading losses of a PV plant were evaluated. Frequently, PV systems are affected by dirt, dust or shadowing from surrounding elements, such as trees and buildings. The shadows can cause partial shading conditions, when the irradiance is not uniform on PV installation, which lead to losses of power. Thus, the shadow prediction is primordial for a better output power estimation and to minimize its losses. The aim of this article is to propose a methodology using intuitive and available tools for shading prediction and losses assessment on PV installations. A study about the shadow pattern and module orientation (portrait and landscape) influence and an analysis of the shading losses on a PV plant were performed in order to demonstrate the applicability of the methodology. The shading effect is drastic when the shading is parallel to the short edge, whereas the effect is lower when it is parallel to the long edge. On the other hand, the losses due to shading and the difference in the position of the modules (landscape or portrait) have a lower impact on the photovoltaic plant case. It was concluded that the methodology is feasible and applicable for shading impact evaluation, despite limitations for large systems due to the simulation time. [ABSTRACT FROM AUTHOR]

Several methods for analyzing photovoltaic (PV) systems under partial shading conditions (PSC) can be found in the literature. However, the simplest methods are not very accurate. This article presents (1) a simple and accurate method to model I–V curves for PSC and (2) an improvement of a simplified method, without calculating the I–V curves, to accurately estimate the energy generated by PV systems under PSC. The I–V curves of two PV modules under PSC were measured. The measured curves were compared with modeled curves using two methods from the literature and a new proposed method. Four simplified methods from the literature for estimating the energy generated by PV systems were analyzed. The accuracy of these methods was investigated by comparing the calculated values with measured data from a PV system. For modeling the I–V curves, the new method showed a mean absolute percentage error of 1.5% and proved to be accurate. For the evaluated PV system, using a monthly database and the proposed diffuse shading factor resulted in differences between the measured and modeled results of up to 10% per month and 5% for the entire measurement period. The two proposed methods are simple and accurate. [ABSTRACT FROM AUTHOR]

• Uniform EVF increase the differences between simulated and measured values. • The more detailed 3D model, better the energy forecasting. • Accuracy of the simulated results depends on the shading calculation model. • Shaded PV systems need to be simulated carefully. Software for simulation of photovoltaic (PV) systems is widely used for dimensioning and forecasting electrical production. A factor of losses in PV installations is the partial shading caused by surrounding elements, and these software allow the user to estimate this effect. However, the accuracy of these simulated results for shaded PV systems is not widely studied. The purpose of this article is to investigate the accuracy and quantify the differences between simulated and measured data of partially shaded PV systems, obtained with the widely used tools SAM and PVSyst. Measured data from a PV installation were compared to results from simulations performed using the different shading calculation options available in both tools. The simulated outputs were both underestimated and overestimated in the shading situations. This variation was related to the use of an hourly fraction of shading and, in the case of SAM, due to the limitations of the 3D tools available for representation. Another source of differences between simulated and measured values was the use of uniform shading factors for diffuse and albedo. In addition, the simplification of the 3D model had a significant impact on the predicted energy, mainly on cloudy days. Both software overestimated the electricity production for the entire measurement period, reaching differences between the predicted and the measured energy varying from 9% to 24%. Shaded PV systems must be carefully analyzed, and the simulated results may differ from the measured values, which may even influence the decision on the feasibility of an installation. [ABSTRACT FROM AUTHOR]