Anna Rita Di Fazio

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A new short-term probabilistic forecasting method is proposed to predict the probability density function of the hourly active power generated by a photovoltaic system. Firstly, the probability density function of the hourly clearness index is forecasted making use of a Bayesian auto regressive time series model; the model takes into account the dependence(More)
Demand response (DR) can be very useful for an industrial facility, since it allows noticeable reductions in the electricity bill due to the significant value of energy demand. Although most industrial processes have stringent constraints in terms of hourly active power, DR only becomes attractive when performed with the contemporaneous use of battery(More)
— The evolution of existing distribution systems toward smart grids requires the exploitation of all the capabilities of the new Distributed Generation (DG). To this aim, the present paper addresses the problem of improving the voltage profile regulation in distribution networks with DG. According to a decentralized approach, the set-point of a reactive(More)
Smart grid behaviour is characterized by significant uncertainties due to the time-varying nature of powers generated by random energy sources and of load demands. These uncertainties introduce several technical problems in smart grid planning and operation and new issues have to be addressed. In this context, an important role is played by probabilistic(More)
In the voltage regulation problem of MV distribution systems with Distributed Generation (DG), the regulator of the On Load Tap Changer (OLTC) equipped with Line Drop Compensator (LDC) interferes with the DG voltage regulator. This interaction can significantly changes the voltage profile along the feeders. With the aim to propose a solution to this(More)
A key issue in Low Voltage (LV) distribution systems is to identify strategies for the optimal management and control in the presence of Distributed Energy Resources (DERs). To reduce the number of variables to be monitored and controlled, virtual levels of aggregation, called Virtual Microgrids (VMs), are introduced and identified by using new models of(More)
Radiation forecast accounting for daily and instantaneous variability was pursued by means of a new bi-parametric statistical model that builds on a model previously proposed by the same authors. The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical expression but is not bound by a specific clear sky model; it(More)