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Evaluation of Investments in Wind Energy Projects, under Uncertainty. State of the Art Review.
- Source :
- Applied Sciences (2076-3417); Nov2021, Vol. 11 Issue 21, p10213, 28p
- Publication Year :
- 2021
-
Abstract
- The use of renewable energy sources, especially wind energy, has been widely developed, mostly during the last decade. The main objective of the present study is to conduct a literature review focused on the evaluation under uncertainty of wind energy investment using the real options approach to find out whether public opposition (NIMBY projects) has been contemplated, and if so, what have been the flexible strategies applied for its intervention. Overall, 97 publications were analyzed, identifying 20 different models or approaches, which were grouped into eight categories: 1. Real options, 2. Optimization, 3. Stochastics, 4. Financial evaluation, 5. Probabilistic, 6. Estimation, 7. Numerical prediction, and 8. Others. The real options approach, present in 32% of the studies, was the most popular. Twenty-eight types of uncertainties were identified, which were grouped, for better analysis, into nine categories. In total, 62.5% of the studies included the price of electricity as a source of uncertainty; 18.8%, the velocity of wind; and 15.6%, the feed-in rates-subsidy. Both random and non-random techniques were applied to assess the real options and to model the uncertainties. When evaluating real options, the Monte Carlo simulation technique was the most preferred, with 16 (51.6%) applications, followed by non-randomized techniques, decision tree, and dynamic programming, with eight (25.8%) applications each. There is a marked tendency to use stochastic processes to model uncertainty, particularly geometric Brownian motion, which was used in 61.3% (19) of the studies in the sample. When searching for "real options AND (nimby OR public opposition)", no study was found, which shows the possibility of developing research on this aspect to determine its impact on investments in wind energy projects. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 11
- Issue :
- 21
- Database :
- Complementary Index
- Journal :
- Applied Sciences (2076-3417)
- Publication Type :
- Academic Journal
- Accession number :
- 153603664
- Full Text :
- https://doi.org/10.3390/app112110213