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A decision support system for the optimal exploitation of wind energy on regional scale

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 Jan 2012 Italy Publisher:Elsevier BVJournal:Renewable Energy, volume 37, pages 299-309 (issn: 0960-1481,

Authors: OUAMMI, AHMED; V. Ghigliotti; ROBBA, MICHELA; A. Mimet; SACILE, ROBERTO;

doi: 10.1016/j.renene.2011.06.027

handle: 11567/276485

A decision support system for the optimal exploitation of wind energy on regional scale

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Abstract

Abstract Wind is a promising sustainable energy resource that can help in reducing the dependence on fossil fuels. Models and tools can be effectively used to assess the resource availability, the possible exploitation, and the environmental impact. The aim of this work is to propose an Environmental Decision Support System (EDSS) for the sustainable design of wind power plants both in terms of the site selection over a regional territory and of the optimal technology to be installed. Specifically, the proposed EDSS is suited to territories with a complex orography (such as several regions of the Mediterranean coasts), and for the installation of plants in the class of power between 500 kW and 1000 kW. The different EDSS modules are applied to a specific case study, supporting the decision maker on the exploitation of wind power plants in the Savona District, Liguria Region, Italy.

Country

Italy

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Keywords

Decision Support System; wind energy.; Optimization

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	39
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%