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The paper deals with the energy transition, focusing on the decisions to be taken about the change in the energy conversion capacity of specified portion of the universe, such as industrial or domestic districts, regions, countries, etc. Conversion capacity is intended here as the set of all energy conversion and storage units characterized by type, size and number, and interacting with the natural gas and electricity grids. The goal is to find guidelines for new installations, obtained by simulations of a model able to describe properly the behavior of each energy conversion technology and the interaction with grids. The model is based on a traditional MILNP approach, but contains unique features in the literature, which allow to obtain results of general validity for applications to very different geographical areas, or to countries including very different regions that transmit energy with each other or export/import it from abroad. The main novelty of this work is to identify the best planning for the decarbonisation of energy demand sectors, according to the criterion of minimum cost, both at global and at regional level. The results applied to the case of Italy allow identifying clearly the energy demand sectors to which new installations should be applied first to minimize the cost for the total energy system, and the units that must be foreseen to satisfy these energy demands, i.e. a precise strategy for the energy transition towards a 100% renewable system. • A multi-regional model is built to find the most convenient energy transition path. • General guidelines for Western countries are derived from the Italian case study. • Replacement of boilers with heat pumps allows increasing RES share from 20 to 30%. • For RES share >60%, PtG is the low-carbon strategy for high temperature heat demand. • Transportation is the last energy sector that should be decarbonized. [ABSTRACT FROM AUTHOR]

Abstract: Wind energy evaluation is an important goal in the conversion of energy systems to more environmentally friendly solutions. In this paper, we present a novel approach to wind speed spatial estimation on the isle of Sicily (Italy): an incremental self-organizing neural network (Generalized Mapping Regressor – GMR) is coupled with exploratory data analysis techniques in order to obtain a map of the spatial distribution of the average wind speed over the entire region. First, the topographic surface of the island was modelled using two different neural techniques and by exploiting the information extracted from a digital elevation model of the region. Then, GMR was used for automatic modelling of the terrain roughness. Afterwards, a statistical analysis of the wind data allowed for the estimation of the parameters of the Weibull wind probability distribution function. In the last sections of the paper, the expected values of the Weibull distributions were regionalized using the GMR neural network. [Copyright &y& Elsevier]

Abstract: The interest in solid oxide fuel cell systems comes from their capability of converting the chemical energy of traditional fuels into electricity, with high efficiency and low pollutant emissions. In this paper, a study of the design space of solid oxide fuel cell and gas turbine hybrids fed by methanol and kerosene is presented for stationary power generation in isolated areas (or transportation). A 500kW class hybrid system was analysed using WTEMP original software developed by the Thermochemical Power Group of the University of Genoa. The choice of fuel-processing strategy and the influence of the main design parameters on the thermoeconomic characteristics of hybrid systems were investigated. The low capital and fuel cost of methanol systems make them the most attractive solutions among those investigated here. [Copyright &y& Elsevier]

• A novel hybrid model used to forecast energy production in onshore wind farms. • Evaluation of wakes phenomena of wind turbines at different conditions. • Definition of an availability model of the wind turbine generator. • Comparison between corrective and preventive maintenance strategy for a real case study. • Conception of a novel key indicator for the preventive maintenance. The contribution of wind power systems to the reduction of the impact of fossil fuels sources has increased more and more during the last decades leading to a greater attention to the estimation of the performances of renewable power plants. However, forecast methods of productivity of onshore/offshore wind farms still suffer, in terms of accuracy, the innate variability of the energy resources and the effect of components failures. This paper proposes a novel "hybrid" approach for the estimation of the energy conversion of onshore wind farms. The model combines the Jensen wake mathematical theory with a stochastic dependability model, a Fault Tree, to better forecast the energy production. A new key index was conceived to optimize the preventive maintenance of wind turbines. This model was tested on a real case study, a wind farm (25.5 MW p) located in the south of Italy. Results were promising because the model achieved a twofold objective to improve the accuracy of the energy conversion forecast and to provide a support decision system for the activities of maintenance planning. [ABSTRACT FROM AUTHOR]