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The importance of renewable energy exploitation reduces the energy dependence on fossil fuels. Despite technological progress, in several remote areas and small islands the energy production is nowadays dominated by the utilization of fossil fuels. With new, increasingly stringent laws on polluting emissions and the need to lower production costs, it is necessary to exploit as many renewable sources as possible. In order to implement these considerations, it was decided to study renewable energy production. The study was carried out by estimating the energy production on a monthly and annual basis considering a mix of three plants, namely marine, solar, and wind. Simulations on wave production were carried out on a new device developed by the research team at the University of Palermo. In order to be able to perform these simulations, input climate data are required. These data are normally available in literature or obtainable by using specific GIS tools. As criterium, the Levelized Cost of Energy, normally applied to a single technology, is extended to the entire energy mix. Minimizing this parameter, the best solution is individuated, and capable of supplying 50% of the summer electrical load with renewable energy sources. The results carried out from a case study based in aeolian islands show that the solar production reaches 10.2%, the wind production reaches 45.47% and sea wave production reaches 3.04%. In this way, the diesel production decreases to 41.29%. This method can be easily applied for several small islands, estimating for several sites the ability to reduce the energy production from fossil fuels. [ABSTRACT FROM AUTHOR]

Abstract: In this study, Data Envelopment Analysis approach was used to analyze the energy efficiency of farmers, to find efficient and inefficient ones and to identify the wasteful uses of energy in kiwifruit production. Moreover, the effect of optimization of energy on improvement of input costs and energy indices was investigated. The results revealed that based on variable returns to scale model, 62.79% of growers were efficient, though, based on constant returns to scale model it was just 23.26%. The technical, pure technical and scale efficiencies of farmers were calculated as 0.942, 0.993 and 0.948, respectively. Total optimum energy requirement was found to be 26,604.11 MJ ha−1; showing that 12.17% of input energy could be saved if the farmers follow the results recommended by this study. The contribution of chemical fertilizers input from total saving energy was about 78% which was the highest share. Optimization of energy use improved the energy use efficiency, specific energy and net energy by 13.86%, 12.17% and 22.56%, respectively. The results of economical analysis showed that optimization of energy decreases the total costs of production by 4.91%; and the benefit–cost ratio and productivity increased by 5.15% and 5.19%, respectively. [Copyright &y& Elsevier]