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PhotoVoltaic (PV) plants can provide important economic and environmental benefits to electric systems. On the other hand, the variability of the solar source leads to technical challenges in grid management as PV penetration rates increase continuously. For this reason, PV power forecasting represents a crucial tool for uncertainty management to ensure system stability. In this paper, a novel hybrid methodology for the PV forecasting is presented. The proposed approach can exploit clear-sky models or an ensemble of artificial neural networks, according to day-ahead weather forecast. In particular, the selection among these techniques is performed through a decision tree approach, which is designed to choose the best method among those aforementioned. The presented methodology has been validated on a real PV plant with very promising results.

This work aims to propose a pseudo-measurement modeling method for Distribution State Estimation (DSE) application embedded in a Distribution Management System (DMS). The entire system is already installed on the distribution MV network of Sanremo, in the North of Italy, within the Smartgen research project. The acquisition architecture consists of a SCADA system, which allows the data exchange from meters installed in the MV-LV substations. In order to satisfy the system observability conditions and to perform the State Estimation (SE) algorithm, real-time measures need to be integrate with the pseudo-measures of the non-monitored substations. The paper investigates a load modeling technique, based on Artificial Neural Network (ANN) and Fourier decomposition, that allow the generation of pseudomeasurements starting from the historical database of the monitored substations.

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A Smart Grid approach to electric distribution system management needs to front uncertainties in generation and demand thus making forecasting an up-to-date area of research in electric energy systems. This works aims to propose a dayahead load forecasting procedure for a medium voltage customer. The load forecasting is performed through the implementation of an artificial neural network (ANN). The proposed multi-layer perceptron ANN, based on backpropagation training algorithm, is able to take as inputs: loads, data concerning the type of day (e.g. weekday/holiday), time of the day and weather data (e.g. temperature, humidity). This procedure has been tested to predict the loads of a large university hospital facility located in Rome. Index Terms-Load forecasting, artificial neural network, distributed generation, smart gridsI.