Risk-based methods for sustainable energy system planning: A review. (July 2017)

• Record Type: Journal Article
• Authors: Ioannou, Anastasia; Angus, Andrew; Brennan, Feargal
• Is Part Of: Renewable & sustainable energy reviews. Volume 74(2017)
• Page Start: 602
• Page End: 615
• Publication Date: 2017-07
• Subjects: Risk-based methods -- Sustainable power generation -- Risk -- Uncertainty -- Energy system planning and feasibility -- Mean variance portfolio -- Real options analysis -- Multi-criteria decision analysis -- Monte Carlo simulation -- Scenario analysis -- Stochastic optimisation; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2017.02.082
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: The value of investments in renewable energy (RE) technologies has increased rapidly over the last decade as a result of political pressures to reduce carbon dioxide emissions and the policy incentives to increase the share of RE in the energy mix. As the number of RE investments increases, so does the need to measure the associated risks throughout planning, constructing and operating these technologies. This paper provides a state-of-the-art literature review of the quantitative and semi-quantitative methods that have been used to model risks and uncertainties in sustainable energy system planning and feasibility studies, including the derivation of optimal energy technology portfolios. The review finds that in quantitative methods, risks are mainly measured by means of the variance or probability density distributions of technical and economical parameters; while semi-quantitative methods such as scenario analysis and multi-criteria decision analysis (MCDA) can also address non-statistical parameters such as socio-economic factors (e.g. macro-economic trends, lack of public acceptance). Finally, untapped issues recognised in recent research approaches are discussed along with suggestions for future research.