Wind farm investments portfolio formation using GIS-based suitability analysis and simulation procedures. (15th December 2019)

• Record Type: Journal Article
• Authors: Vavatsikos, Athanasios P.; Arvanitidou, Aikaterini; Petsas, Dimitrios
• Is Part Of: Journal of environmental management. Volume 252(2019)
• Publication Date: 2019-12-15
• Subjects: Spatial decision support systems -- Multicriteria decision analysis -- Wind energy -- Robustness analysis -- Monte Carlo simulation; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2019.109670
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: The use of renewable energy sources instead of conventional energy sources is at the core of policy actions to reduce dependency on fossil fuels worldwide. As a result, especially during the last decade, the cost of renewable energy has significantly decreased, enriching renewable energy cost-competitiveness. Due to the spatial nature of renewable energy sector-related decisions, the synergy of geographical information systems (GIS) and Multiple Criteria Decision Analysis (MCDA) models can enrich the quality of the related decisions given their ability to effectively support land management considerations. Moreover, their implementation significantly enriches the performance of the traditional capital projects evaluation methods (CPEM) by providing physical data to the sizing process in a quick and accurate manner. Thus, decision-making frameworks that combine GIS-based suitability analysis with traditional financial evaluation techniques can significantly enrich the planning phase to achieve efficient installations in terms of required area reduction, power generation maximization and local characteristics examination. With respect to the realization of wind energy exploitation projects, the paper at hand proposes a framework capable of expanding the use of the traditional GIS-based derived suitability index to establishing portfolios. Moreover, the proposed framework is enriched by robust analysis using Monte Carlo Simulation (MCS), which provides significant insights regarding the stability of the derived portfolios and the projects that they comprise. The proposed framework is illustrated through a case study in the Thrace region in northeastern Greece. Highlights: Wind farms portfolio formation combining spatial and nonspatial attributes. Simulation provides computational efficiency in modeling uncertainty and imprecise knowledge. Portfolio Selection Index provides a measure of the derived portfolios stability. Portfolio Alternative Selection Index measures the eligibility of the examined wind farms.