In PV applications, under mismatching conditions, it is necessary to adopt a maximum power point tracking (MPPT) technique which is able to regulate not only the voltages of the PV modules of the array but also the DC input voltage of the inverter. Such a technique can be considered a hybrid MPPT (HMPPT) technique since it is neither only distributed on the PV modules of the PV array or only centralized at the input of the inverter. In this paper a new HMPPT technique is presented and discussed. Its main advantages are the high MPPT efficiency and the high speed of tracking which are obtained by means of a fast estimate of the optimal values of PV modules voltages and of the input inverter voltage. The new HMPPT technique is compared with simple HMPPT techniques based on the scan of the power versus voltage inverter input characteristic. The theoretical analysis and the results of numerical simulations are widely discussed. Moreover, a laboratory test system, equipped with PV emulators, has been realized and used in order to experimentally validate the proposed technique.

This paper presents an electrical battery model that can be used for dynamic simulations of both stationary and distributed energy storage systems, such as storage plants or electric vehicles. Although the model has been validated for lead-acid and lithium batteries, it can be easily adapted to other battery chemistries as well. The parameters can be extracted from simple measurement sets. The model provides several operational variables, such as state of charge, terminal voltage, open circuit voltage, as well as five internal parameters, including the internal resistance. In this paper, the model structure and parameters extraction are explained in detail. The batteries under test are a 12 V Lead-Acid and a 3.75 V Lithium. Parameters are extracted from experimental measurements and the model is validated by superimposing measured and simulated waveforms

Abstract. Identifying a customer profile of interest is a challenging task for sellers. Preferences and profile features can range during the time in accordance with current trends. In this paper we investigate the application of different model-based Collaborative Filtering (CF) techniques and in particular propose a trusted approach to user-based clustering CF. We propose a Trust-aware Clustering Collaborative Filtering and we compare several approaches by means of Epinions, which contains explicit trust statements, and MovieLens dataset, where we have implicitly defined a trust information. Experimental results show an increased value of coverage of the recommendations provided by our approach without affecting recommendation quality. To conclude, we introduce a tool, based on recommender systems, able to assist merchants in delivering special offers or in discovering potential interests of their customers. This tool allows each merchant to identify the products to suggest to the target customer in order to best fit his profile of interests.