The installed capacity of photovoltaic (PV) systems has recently increased at a much faster rate than the development of grid codes to effectively and efficiently manage high penetrations of PV within the distribution system. In a number of countries, PV penetrations in some regions are now raising growing concerns regarding integration. Management strategies vary considerably by country-some still have an approach that PV systems should behave as passive as possible, whereas others demand an active participation in grid control. This variety of grid codes also causes challenges in learning from "best practice." This paper provides a review of current grid codes in some countries with high PV penetrations. In addition, the paper presents a number of country-specific case studies on different approaches for improved integration of PV systems in the distribution grid. In particular, we consider integration approaches using active and reactive power control that can reduce or defer expensive grid reinforcement while supporting higher PV penetrations

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The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I-V and P-V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported.

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