The increasing penetration of distributed generation (DG) power plants into distribution networks (DNs) causes various issues concerning, e.g., stability, protection equipment, and voltage regulation. Thus, the necessity to develop proper control techniques to allow power delivery to customers in compliance with power quality and reliability standards (PQR) has become a relevant issue in recent years. This paper proposes an optimized distributed control approach based on DN sensitivity analysis and on decentralized reactive/active power regulation capable of maintaining voltage levels within regulatory limits and to offer ancillary services to the DN, such as voltage regulation. At the same time, it tries to minimize DN active power losses and the reactive power exchanged with the DN by the DG units. The validation of the proposed control technique has been conducted through a several number of simulations on a real MV Italian distribution system

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This paper presents a method for the medium-long-term wind speed prediction based on spatiotemporal evolution of weather fronts and Multi-Layer Perceptron Neural Network (MLP NN) data mining model. The proposed wind speed prediction model is achieved by using historical and current meteorological data, such as pressure, temperature and wind intensity, describing the evolution of the weather fronts in a wide area around the point of interest. This model, trained and tested using real weather data, predicts the 24-h ahead wind speed. The forecasting effectiveness is evaluated comparing the wind forecasted with real data registered in the test site