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Despite various institutional efforts, about 22% of the total Nicaraguan population still do not have access to electricity. Due to the dispersed nature of many rural inhabitants, off-grid electrification systems that use renewable energy sources are a reliable and sustainable option to provide electricity to isolated communities. In this study, the design of an off-grid electrification project based on hybrid wind-photovoltaic systems in a rural community of Nicaragua is developed. Firstly the analysis of the location, energy and power demands of all users of the community is carried out. A detailed resource assessment is then developed by means of historical data, in-situ wind measurements and a specific micro-scale wind flow model. An optimization algorithm is utilized to support the design defining generation (number, type and location of generators, controllers, batteries and inverters) and distribution (electric networks) systems considering the detail of resource variations. The algorithm is modified in order to consider a long-term perspective and a sensitivity analysis is carried out considering different operation and maintenance costs' scenarios. The proposed design configuration combines solar home systems, solar based microgrids and wind based microgrids in order to connect concentrated groups of users taking advantage of best wind resource areas. (C) 2015 Elsevier Ltd. All rights reserved.Postprint (author's final draft

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A combination of physical and statistical treatments to post-process numerical weather predictions (NWP) outputs is needed for successful short-term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique. In this study, a MOS based on multiple linear regression is proposed: the model screens the most relevant NWP forecast variables and selects the best predictors to fit a regression equation that minimizes the forecast errors, utilizing wind farm power output measurements as input. The performance of the method is evaluated in two wind farms, located in different topographical areas and with different NWP grid spacing. Because of the high seasonal variability of NWP forecasts, it was considered appropriate to implement monthly stratified MOS. In both wind farms, the first predictors were always wind speeds (at different heights) or friction velocity. When friction velocity is the first predictor, the proposed MOS forecasts resulted to be highly dependent on the friction velocity-wind speed correlation. Negligible improvements were encountered when including more than two predictors in the regression equation. The proposed MOS performed well in both wind farms, and its forecasts compare positively with an actual operative model in use at Risø DTU and other MOS types, showing minimum BIAS and improving NWP power forecast of around 15% in terms of root mean square error. Further improvements could be obtained by the implementation of a more refined MOS stratification, e.g. fitting specific equations in different synoptic situations.