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Abstract Mediterranean islands have the advantage of favourable climatic conditions to use different marine renewable energy sources. Remote sensing can provide data to determine wind energy production potential and observational activity to identify, assess and detect suitable points in large marine areas. In this paper, a new combined model has been developed to integrate wind speed assessment, mapping and forecasting using Sentinel 1 satellite data through images processing and Adaptive Neuro-Fuzzy Inference System and the Bat algorithm. Synthetic Aperture Radar (SAR) satellite images from the Sentinel 1 satellite have been used in order to detect offshore and nearshore wind potential. Particularly, Sentinel 1 images have been analysed by means of the SNAP software. Then, to extract data about wind speed and direction, a GIS software for mapping the wind climate has been used. This new methodology has been applied to the North-Central coasts of Sardinia Island and then focused on six main small islands of La Maddalena archipelago. Furthermore, ten Hot Spots (HSs) have been identified as interesting because of their high-energy potential and the possibility to be considered as sites for future implementation of Wind Turbine Generators (WTGs). Finally, the ten identified HS have been used as input data to train and test the proposed forecast model.

Recent European Community directives introduce Renewable Energy Communities (REC) and Jointly Acting Renewable Self-Consumers (JARSC). Both entities are constituted by communities of residential and/or non-residential prosumers, located in proximity of renewable generators and Electrical Storage Systems (ESS) owned and managed by the REC/JARSCs. These aggregations of prosumers are aimed at providing environ-mental and economic benefits by maximizing their global self-consumption. In this frame, it is relevant to introduce a control strategy which considers the whole system represented by the REC/JARSCs and performs optimal management of energy production, storage and consumption. The present paper proposes a Model Predictive Control (MPC) based control design, targeted at the minimization of electricity cost and equivalent CO2 emissions, considering the whole ensemble of loads included in the REC/JARSCs over a 24-h prediction horizon. To exploit the MPC ability of including forecasts in the optimization problem, predictors including Artificial Neural Networks (ANN) are developed for solar irradiance, air temperature, electricity price and carbon intensity. The proposed control performance is evaluated considering a case study located in Milan, Italy, and its advantages with respect to traditional control algorithms are highlighted by comprehensive numerical simula-tions. Lastly, an economic evaluation of the considered system is presented.

Abstract The widespread use of the renewable energy sources in the future's energy production is necessary, in order to avoid the predicted environmental, economic and social effects which can be derived from the overuse of fossil fuels. Denmark has announced to be fossil fuel independent with a renewable energy based heat and power source by 2050. Theoretically, the centralized biogas combined heat and power plants can play a determinant role in the subsequent Danish energy supply scheme, due to its feature to satisfy base load demand. The productivity and efficiency analysis of the currently operating Danish centralizes biogas CHP power plants is crucial in order to study whether there is a most efficient power plant which can be introduced as a “best practice” innovative technology for the future's Danish biogas power plants. In this paper we use the intertemporal Malmquist total factor productivity DEA method to analyze the change in the efficiency and productivity of the Danish centralized biogas power plants in the period 1992–2005.

Abstract Offshore Wind (OW) speed assessment is a key aspect for the development of new wind farms at sea. Satellites can be used to globally obtain ocean and sea distribution, compensating limited in-situ measurements. In this study, a new methodology to estimate the wind’s speed potential is here proposed. Preliminary, Sentinel-1 (S-1) images have been analyzed by means of the Sentinel Application Platform (SNAP) software, extrapolating wind speed data for each cell pixel size of a testing area. Then GIS (Geographic Information System) software has been used to map wind data and find the best pixel location comparing these data with in-situ data. Furthermore, wind speed has been analyzed using the ERA-Interim reanalysis dataset for areas within 11 km and 40 km from the Lillgrund OW farm in the Baltic Sea to better understand wind regimes. Finally, wind speed parameters obtained by S-1 in Sea Surface Water (SSW) with the 10 m standard high have been compared with wind speed recorded by Supervisory Control and Data Acquisition (SCADA) systems of two turbine using wind profile formula. Obtained results show the comparison accuracy of wind speed assessment for each center of the pixels by S-1 satellite images and in-situ (SCADA) measurements. Data actually depends on the distance between the selected center pixel and the location of the turbines. The obtained wind speed differences (0.26 m/s - RMSE = 1.38 and 0.92 m/s - RMSE = 1.82) pinpointed the direct effect of the distance between the selected pixel center and the in-situ measurements location in the S-1 imagery for wind speed potential assessment. Obtained results proved an improvement of the OW assessment accuracy using multiple satellite observations, demonstrating that SAR wind maps can support OW speed sites assessment by introducing observations in different phases of an OW farm project.

The Global Geothermal Energy Database of the International Geothermal Association (IGA) is an internet-based platform providing access to the world geothermal production data. This platform is unique for the geothermal sector, and provides an excellent tool for showing geothermal use in the world and promoting the development of geothermal energy. The platform is also an important example of organization and access to widely distributed data, since it allows analyzing, synthetizing and quickly interpreting stored data. The global and country-specific information regard both electricity generation and direct use applications. The platform, which has been built using an open-source Business Intelligence application and can be accessed through the IGA website, allows to access, navigate and organize information in various ways. Data lists related to geothermal fields and plants, direct uses, geothermal turbine manufactures and geothermal companies can be accessed, aggregated and filtered, producing reports, charts and maps. Aggregation and filtering options synthetize and organize data for direct use, power plants and installed capacity by region, category and operative status. The mapping tool, providing geothermal fields location, power plants and direct uses geographical references, allows also map browsing, and to zoom, to measure distance, to pan and to query for further information. The chart analysis produces pie charts and bar diagrams of data, dynamically sorted and aggregated.

Abstract Access to modern energy services is still low in developing countries and this lack of access affects in particular the Least Developed Countries (LDCs) and sub-Saharan Africa. The large majority of population in the Logone Valley at the border between Chad and Cameroon still relies on wood fuel burnt in smoky and inefficient fireplaces for cooking. The promotion of wood saving stoves locally produced and appropriate for the traditional cooking practices has been implemented by an international cooperation project. Two stove models were compared to the traditional 3-stone fire and a gas stove by Water Boiling Tests and Controlled Cooking Tests. The results showed significant fuel savings thanks to the use of the improved stoves. Data collected during the tests, crossed with information about the local cooking habits, allowed to estimate the impact, in term of money savings, on each household adopting an improved stove. The Centrafricain improved stove resulted being the most performing model occurring in a 25% reduction of the expenditure per family for cooking purposes in a short-medium term. This study witnesses that the use of improved wood stove is likely to be a sustainable way to achieve an appropriate minimum level of energy access for cooking purposes for the poor people in the LDCs, in particular in rural areas.

An assessment is made as to whether renewable energy use for electricity generation in the EU was beneficial throughout the cycle of high and low oil prices. Costs and benefits are calculated with the EU statistics for the period of low oil prices 1998–2002 and high oil prices 2003–2009. The share of renewable energy in electricity production was 21% of all energy resources in 2008, growing on average 5% a year during 2003–2008 compared to nil growth of the fossil fuels mix. Correlations show significant impacts of growing renewable energy use on changes in consumers' electricity prices during the high and rapidly increasing fossil fuel prices in the period 2005–2008. The growing use has contributed to price decrease in most countries that use more renewable energy and price increase in many countries that use little renewable energy. Costs and benefits are assessed through comparison between the observed consumers' electricity prices and simulated prices had they followed the costs of fossil fuel mix. A net benefit of 47 billion euro throughout the oil price cycle is attributable to the growing use of renewable energy, which is on average 8 billion euro a year. This net benefit is larger than the total public support for renewable energy. The net benefit would be larger had the EU anticipated high oil prices through more public support during low oil prices, as this would create productive capacity, but countries' interests increasingly differed. An anti-cyclic EU policy is recommended

Abstract The paper describes the design process of a photovoltaic (PV)-wind power system to be installed in the very challenging ambient conditions of the French-Italian Antarctic Base. Concordia Base has been built with the collaboration of Italian consortium PRNA, French Polar Institute IPEV and European Space Agency ESA. It is one of the three bases not located on the coast and is open all the year. The electrical load of the base, presently supplied by three diesel generators, has been previously characterised measuring the relevant quantities during a period of one year. During the same year an experimental campaign has been conducted to collect the necessary solar irradiance and wind data of the site. Models of the PV panels and of the wind turbine, previously set up and validated, have been used to simulate the plant behaviour and to estimate the possible contribution of renewable energies to the Concordia Antarctic Base supply in the different seasons. Finally, some economical aspects are discussed and the payback period is calculated.