Search

In the last decades, the public opinion has paid great attention to environmental pollution and global warming. Energy sector has a strong impact on environment. Thus, the use of renewable energies is becoming mandatory. The present paper deals with a study of ducted Savonius turbine performance as a function of geometric and operating parameters. Ducted turbines could be used as power take-off in multiple applications. In this paper, two main applications were studied. The first application is related to the possibility to install the turbine inside building with the purpose of architectonic integration of the system. The second one is related to wave energy harvesting. In both cases the same type of turbine was used to convert input energy (wind or wave energy) into electricity. Turbine performance were studied as a function of turbine overlap ratio, as well as air flux oscillating frequencies. On the basis of the obtained results, turbine performance increase in ducted configuration and a change in best overlap ratio was registered.

Nowadays the automotive market is oriented to the production of hybrid or electric propulsion vehicle equipped with Energy Management System that aims to minimize the consumption of fossil fuel. The EMS, generally, performs a local and not global optimization of energy management due to the impossibility of predicting the user’s energy demand and driving conditions. The aim of this research is to define a driving cycle (speed time) knowing only the starting and the arrival point defined by the driver, considering satellite data and previous experiences. To achieve this goal, the data relating to the energy expenditure of a car (e.g. speed, acceleration, road inclination) will be acquired, using on-board acquisition system, during road sections in the city of Messina. At the same time, the traffic level counterplot and others information provided, for these specific sections, from GPS acquisition software will be collected. On-board and GPS data will be compared and, after considering an adequate number of acquisitions, each value of the traffic level will be associated with a driving cycle obtained by processing the acquired data. After that, the numerical model of a car will be created which will be used to compare the energy demand of two driving cycles. The first one acquired on a section with a random starting and destination point inside the historic city centre of Messina. The second is the one assigned, for that same section, considering only the value of the traffic level counterplot.

The energy sustainability of producing biofuel from wet bioresidues needs proper energy integration to ensure sustainable exploitation. This study analyses the potentials of combined hydrogen, heat, power, and LOHC (Liquid Organic Hydrogen Carrier) production from the residues of citrus juice production, at a factory scale. In this work, the main constituents of LOHC are DME (Dimethyl ether) and methanol. The proposed system is based on air-steam gasification and direct CO2-to-DME process, integrated with hydrogen purification and a CHP unit. The DME reactor is operated at 30 bar in the temperature range 493-533 K. A thermodynamic model, which is validated experimentally, simulates the proposed polygeneration system. In addition to the potential amount of biofuel, hydrogen production, and net power production, the energy and exergy efficiencies are analyzed. Despite the variation of LOHC yield with the temperature, the results show that the whole system’s energy efficiency is not affected, while the small difference among the exergy efficiencies is negligible.

Wind power generation differs from other energy sources, such as thermal, solar or hydro, due to the inherent stochastic nature of wind. For this reason wind power forecasting, especially for wind farms, is a complex task that cannot be accurately solved with traditional statistical methods or needs large computational systems if physical models are used. Recently, the so-called learning approaches are considered a good compromise among the previous methods since they are able to integrate physical phenomena such as wake effects without presenting heavy computational loads. The present work deals with an innovative method to forecast wind power generation in a wind farm with a combination of GISbased methods, neural network approach and a wake physical model. This innovative method was tested with a wind farm located in Sicily (Italy), used as a case study. It consists of 30 identical wind turbines (850 kW each one), located at different heights, for an overall Power peak of 25 MW. The time series dataset consists of one year with a sampling time of 10 minutes considering wind speeds and wind directions. The output of this innovative model leaded to good results, especially for medium-term overall energy production forecast for the case study.