Evaluation of Investments in Wind Energy Projects, under Uncertainty. State of the Art Review
Abstract
:1. Introduction
2. Strategies to Beat Public Opposition
3. Methodology
4. Results Analysis
4.1. Studies with a Real Options Approach, Based on the Ends and Purposes of Its Application
4.1.1. Analysis by Group According to Their Application Purposes
Evaluation of Wind Energy Generation Project or Investment
Evaluation of Energy Auctions, Portfolios, and Energy Market Investments
Evaluation of Renewable Energy Technologies
Evaluation of the Impact of Regulatory Policies on Wind Energy Projects
Evaluation of Wind Energy Systems
Evaluation of Wind Resources
Evaluation of Design, Size, and Location of Wind Farm Projects
4.2. Types of Uncertainties Explored in Publications with a Real Options Approach
- Power generation contains the uncertainties that affect the process of generating wind energy, such as electricity charge, wind energy level, energy production, wind turbine hours of use, investment planning, and future deployment of wind energy. Energy production prevails, with three applications, as shown in Figure 4.
- 2.
- Environment refers to the uncertainties present in environmental conditions necessary for the development of projects such as climate change and solar irradiation. Solar irradiation stands out, with two applications, as shown in Figure 5.
- 3.
- Energy price considers the uncertainties generated by price fluctuations in the market, in response to the behavior of energy demand and supply, including among others the price of electricity, coal price, and the price of corn. The price of electricity is the source of uncertainty most studied by researchers, being considered in 20 (65.5%) publications, as shown in Figure 6.
- 4.
- Costs studies were integrated that analyze the uncertainties caused by the incidence of the behavior of costs in the profitability of the investment, whether they are capital costs or investment costs. Both costs were studied in four publications, as shown in Figure 7.
- 5.
- Regulatory policies include the uncertainties caused by changes implemented in the policies for the generation of wind energy, among which are production certificates, changes in asset tariff, credit policy, government policies, and feed-in tariffs-subsidies. The rates of food and subsidies have been the most studied by researchers, being considered in five publications, as reflected in Figure 8.
- 6.
- Market relates sources of uncertainties other than prices and costs which affect their market behavior, such as market conditions, price indices, and electricity demand. Market conditions prevail, with three applications, as shown in Figure 9.
- 7.
- Wind conditions addresses uncertainties caused by variability of the wind characteristics in the generation of wind energy, such as the availability of wind, its intensity, its speed, and its intermittency. After the price of electricity, wind conditions are the source of uncertainty that generates the greatest interest in the studies analyzed, especially wind speed, as it has a great impact on the generation of wind energy. Wind speed was studied in six publications, as shown in Figure 10.
- 8.
- Technological progress focuses on the analysis of the uncertainties caused by the generation and use of knowledge (R&D) that affect the productivity, such as alternative technology, technology levels, and learning speed. All sources of uncertainties have only been studied in one investigation, as shown in Figure 11.
4.3. Evaluation of Real Options and Modeling of Uncertainties
- (1)
- Decision trees: initially introduced by Cox, Ross, and Rubinstein (1979) as a binomial model to value American-type options. It was later adapted to simulate the evolution of uncertainty in discrete scenarios of multiple interrelated options [132]. It allows modeling the evolution of the price of the underlying asset under uncertainty in discrete scenarios, assuming that the underlying asset follows a multiplicative binomial process [133].
- (2)
- Dynamic programming: an optimization method by way of dividing the problem into superimposed subproblems and optimal substructures, especially when the subproblems are not independent. It is based on the principle of optimum as enunciated by Bellman in 1957: “In an optimal decision sequence, every subsequence must also be optimal”, and it allows combining different types of real options with various possible scenarios [134].
- (3)
- Partial Differential Equations: a set of equations, initially used for the valuation of financial options [135,136], later adapted to evaluate specific real options under fixed assumptions [132]. The results of the Black-Scholes model can be obtained from a binomial model for n periods, where n tends to infinity [137].
- (4)
- Option-Games model: a valuation tool that combines the real option approach with game theory, with the aim of quantifying the values of flexibility, allowing for better investment decisions to be taken [138].
- (5)
- Sensitivity analysis: measures the impact that variations in one of the independent variables have on the model [31].
- (1)
- Monte Carlo simulation: a numerical method used to evaluate options when there are no closed formulas such as Black-Scholes [137]. Its purpose is to easily value real options for complex projects, since it does not require the formulation of cash flow through differential equations or trees [139]. It creates a distribution of project values from all given sources of uncertainty [140]. An advantage of this method is that it offers the distribution of the volatility factor, which is key in evaluating the sensitivity of the value of the real options of a project [141].
- (2)
- Least squares Monte Carlo: combines the Monte Carlo simulation with least squares regression, which helps reduce the number of scenarios while still producing an accurate assessment [142]. At any time, the holder of an American option can optimally compare the reward of exercising that option immediately, with the expected reward of not exercising it yet [143].
- (3)
- Optimization algorithms: includes traditional algorithms, such as gradient-based methods and quadratic programming, evolutionary algorithms, heuristic or metaheuristic algorithms, and various hybrid techniques. Optimization problems tend to be non-linear with complex objectives [144].
- (1)
- Geometric Brownian Motion, used in 61.3% (19) of the studies, is a stochastic process in continuous time, generated from a transformation of a standard Wiener process, with the particularity of not allowing asset prices to take negative values [145]. Geometric Brownian Motion appears to be better suited to long-term energy-related investments [146].
- (2)
- Reversal of the mean indicates that values, such as prices, may move away from the mean or intrinsic value, but, over time, they will eventually return to those mean values [147].
- (3)
- The Weibull distribution is the probability function that best describes the wind speed path, thanks to the orthogonal composition of two correlated Gaussian functions. The Weibull density function consists of two parameters: one refers to the maximum speed and the other indicates the degree of dispersion of the samples (Muñoz et al., 2009).
- (4)
- The Box-Jenkins and Ornstein-Uhlenbeck model is the basis of all modern time series analysis theory, and consists of the analysis of probabilistic or stochastic properties of economic time series where the endogenous variable (Yt) is explained by past values or lags of itself and by stochastic error terms [148].
- (5)
- The ARIMA distribution is an econometric methodology based on dynamic models using time series data, made up of three components: the autoregressive (AR), the integration (I), and the moving average (MA), represented by the parameters p, d, and q, respectively. The model includes the values of the series, prediction of errors, and a random term [149].
- (6)
- The normal distribution is a mathematical model that allows determining probabilities of occurrence for different values of the variable. The graph of the normal distribution has the shape of a bell, and for this reason it is also known as the Gaussian bell, whose central elements are the mean and the variance [150].
- (7)
- Markov chains are discrete stochastic processes used to study the evolution of certain systems in repeated trials, in which the probability of an event occurring depends only on the immediately preceding event. Transition probabilities are used to describe the way the system passes to the next state.
4.4. Public Opposition under Real Options
5. Discussion
6. Conclusions
- Within a framework of Real Options, investigate the decision of communities to invest in wind energy [72].
- Delve into how the application of the real options approach has the potential to increase the expected value of the investment in wind energy, by addressing the impact of uncertainty in the evaluation of the wind resource and contemplating the flexibility within the design and in the project planning [113].
- Deepen the impact of the uncertainty caused in the future development of regulatory policies for the generation of renewable energy [114].
- Investigate the impact of additional, sequential, or staged investment options at the optimal investment time and size.
- Develop studies that consider the real options approach to evaluate the impact of public opposition on investment in renewable energy projects.
- Study on how to create institutional capital for wind energy and other renewable resources. This implies that more participatory planning practices and inclusive politics of the communities are needed [153].
- Research aimed at establishing the determinants for indigenous and Afro-descendant communities within their uses and custom to accept the location of renewable energy projects [155].
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Type of Barrier | Description |
---|---|
Technological |
|
Economic and market-based |
|
Regulatory, political, and social |
|
Environmental |
|
Authors | Year | Models or Approaches in the Literature | |||||||
---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | Others | ||
Kinias, Tsakalos, & Konstantopoulos [31] | 2020 | ✓ | |||||||
Detemple & Kitapbayev [32] | 2020 | ✓ | |||||||
Krömer [33] | 2020 | ✓ | |||||||
Nasrolahpour, Zareipour, & Rosehart [34] | 2020 | ✓ | |||||||
Oh & Son [35] | 2020 | ✓ | |||||||
Ioannou, Angus, & Brennan [36] | 2020 | ✓ | |||||||
Mehrjerdi & Hemmati [37] | 2020 | ✓ | |||||||
Liu, He, Liang, Yang, & Xia [38] | 2020 | ✓ | |||||||
Chowdhury, Pilo, & Pisano [39] | 2020 | ✓ | |||||||
Tan et al. [40] | 2020 | ✓ | |||||||
Zhan et al. [41] | 2020 | ✓ | |||||||
Verleysen, Coppitters, Parente, Paepe, & Contino [42] | 2020 | ✓ | |||||||
G. Yang, Jiang, & You [43] | 2020 | ✓ | |||||||
Al, Sirjani, & Daneshvar [44] | 2020 | ✓ | |||||||
Hübler et al. [45] | 2020 | ✓ | |||||||
Ge et al. [46] | 2020 | ✓ | |||||||
H. Yang et al. [47] | 2020 | ✓ | |||||||
Kong, Li, Liang, Xia, & Xie [48] | 2020 | ✓ | |||||||
Keck & Sondell [49] | 2020 | ✓ | |||||||
Stetter, Piel, Hamann, & Breitner [50] | 2020 | ✓ | |||||||
Abdalla, Smieee, Adma, & Ahmed [51] | 2020 | 17 | |||||||
Henckes, Frank, Küchler, Peter, & Wagner [52] | 2020 | 18 | |||||||
Niromandfam, Movahedi, & Zarezadeh [53] | 2020 | 19 | |||||||
Zhou, Wu, Dong, Tao, & Xu [54] | 2020 | 20 | |||||||
Zhao, Yao, Sun, & Pan [55] | 2019 | ✓ | |||||||
Maeda & Watts [56] | 2019 | ✓ | |||||||
Vavatsikos, Arvanitidou, & Petsas [57] | 2019 | ✓ | |||||||
Askari, Zainal, Ab, Tahmasebi, & Bolandifar [58] | 2019 | ✓ | |||||||
M. A. Abdulgalil, Khalid, & Alismail [59] | 2019 | ✓ | |||||||
Junior et al. [60] | 2019 | ✓ | |||||||
M. Abdulgalil, Khalid, & Alismail [61] | 2019 | ✓ | |||||||
Yunhao Li, Wang, Gu, Liu, & LI [62] | 2019 | ✓ | |||||||
Pizarro-alonso, Ravn, & Münster [63] | 2019 | ✓ | |||||||
Yan, Zhang, Liu, Han, & Li [64] | 2019 | ✓ | |||||||
Tagliapietra, Zachmann, & Fredriksson [65] | 2019 | ✓ | |||||||
Thang & Trung [66] | 2019 | ✓ | |||||||
Quan & Kim [67] | 2019 | ✓ | |||||||
Borràs, Spelling, Weijde, & Pavageau [68] | 2019 | ✓ | |||||||
Fuchs, Marquardt, Kasten, & Skau [69] | 2019 | 9 | |||||||
Zhang et al. [70] | 2019 | 8 | |||||||
Ioannou, Angus, & Brennan [71] | 2019 | 16 | |||||||
Ribeiro, Finotti, Perobelli, & Baumgratz [72] | 2018 | ✓ | |||||||
Finjord, Hagspiel, Lavrutich, & Tangen [73] | 2018 | ✓ | |||||||
Dalby, Gillerhaugen, Hagspiel, Leth-olsen, & Thijssen [74] | 2018 | ✓ | |||||||
Yanbin Li, Wu, & Li [75] | 2018 | ✓ | |||||||
Gazheli & Bergh [76] | 2018 | ✓ | |||||||
Romanuke [77] | 2018 | ✓ | |||||||
Y. Yu, Wen, Zhao, Xu, & Li [78] | 2018 | ✓ | |||||||
Aaboud et al. [79] | 2018 | ✓ | |||||||
Valinejad et al. [80] | 2018 | ✓ | |||||||
Jiang et al. [81] | 2018 | ✓ | |||||||
Deshmukh, Mileva, & Wu [82] | 2018 | ✓ | |||||||
Z. Li et al. [83] | 2018 | ✓ | |||||||
Ioannou, Angus, & Brennan [84] | 2018 | ✓ | |||||||
Esmaieli & Ahmadian [85] | 2018 | 15 | |||||||
Kristiansen, Svendsen, Korpas, & Fleten [86] | 2017 | ✓ | |||||||
Jannati, Yazdaninejadi, & Talavat [87] | 2017 | ✓ | |||||||
Chen & Macdonald [88] | 2017 | ✓ | |||||||
Hamoudi & Maule [89] | 2017 | ✓ | |||||||
Aquila, Rotela, de Oliveira, & de Queiroz [90] | 2017 | 14 | |||||||
Eryilmaz & Homans [91] | 2016 | ✓ | |||||||
Kitzing, Juul, Drud, & Krogh [92] | 2016 | ✓ | |||||||
Pazouki & Haghifam [93] | 2016 | ✓ | |||||||
Lamadrid, Maneevitjit, & Mount [94] | 2016 | ✓ | |||||||
Caralis et al. [95] | 2016 | ✓ | |||||||
Werner & Scholtens [96] | 2016 | ✓ | |||||||
Sjoerd, Broek, Özdemir, Koutstaal, & Faaij [97] | 2016 | 12 | |||||||
Xiao, Wang, Wang, & Wu [98] | 2016 | 13 | |||||||
Díaz, Gómez-aleixandre, & Coto [99] | 2015 | ✓ | |||||||
Díaz, Moreno, Coto, & Gómez-aleixandre [100] | 2015 | ✓ | |||||||
Fang, Li, Wei, & Azim [101] | 2015 | ✓ | |||||||
Seljom & Tomasgard [102] | 2015 | ✓ | |||||||
Hong, Lai, Chang, Lee, & Liu [103] | 2015 | ✓ | |||||||
Siddons, Allan, & Mcintyre [104] | 2015 | ✓ | |||||||
Rodríguez, del Río, Jaramillo, & Martínez [105] | 2015 | 11 | |||||||
Abadie & Chamorro [106] | 2014 | ✓ | |||||||
Krogh, Meade, & Fleten [107] | 2014 | ✓ | |||||||
Correa, Gomes, & Teixeira [108] | 2014 | ✓ | |||||||
Weibel & Madlener [109] | 2014 | ✓ | |||||||
Serrano, Burgos, & Riquelme [110] | 2014 | ✓ | |||||||
Monjas-barroso & Balibrea-Iniesta [111] | 2013 | ✓ | |||||||
Jin, Botterud, & Ryan [112] | 2013 | ✓ | |||||||
Kaiser & Snyder [113] | 2013 | ✓ | |||||||
Reuter, Szolgayová, Fuss, & Obersteiner [114] | 2012 | ✓ | |||||||
Heinrich, Fuss, Szolgayová, & Obersteiner [115] | 2012 | ✓ | |||||||
Martinez-cesena, Member, Mutale, & Member [116] | 2012 | ✓ | |||||||
Ochoa, Betancur, David, Múnera, & Mauricio [117] | 2012 | ✓ | |||||||
Al-yahyai, Charabi, Al-badi, & Gastli [118] | 2012 | ✓ | |||||||
Lee [119] | 2011 | ✓ | |||||||
Dicorato, Forte, Pisani, & Trovato [120] | 2011 | ✓ | |||||||
Barradale [121] | 2010 | 10 | |||||||
Méndez, Goyanes, & Lamothe [122] | 2009 | ✓ | |||||||
Muñoz, Contreras, Caamaño, Correia, & Carlo [123] | 2009 | ✓ | |||||||
Dykes & Neufville [124] | 2008 | ✓ | |||||||
Magnus, Fleten, Maribu, & Wangensteen [125] | 2006 | ✓ | |||||||
W. Yu, Sheblé, Lopes, & Matos [126] | 2006 | ✓ | |||||||
| OTHERS:
|
|
Purpose | Authors (Years) |
---|---|
Evaluation of wind energy generation project or investment | [31,56,72,75,77,99,100,106,117,119,123,124,125]. |
Evaluation of energy auctions, portfolios, and energy market investments | [31,57,58,86,106,108,122,126]. |
Evaluation of renewable energy technologies | [32,58,76]. |
Evaluation of the impact of regulatory policies on wind energy projects | [32,55,73,74,91,92,107,111,126]. |
Evaluation of wind energy systems | [86,109,114]. |
Evaluation of wind resources | [116]. |
Evaluation of design, size, and location of wind farm projects | [109,116]. |
1. Power Generation | 5. Regulatory Policies |
1.1. Electricity charge | 5.1. Production certificates |
1.2. Wind energy level | 5.2. Changes in asset rates |
1.3. Energy production | 5.3. Credits policy |
1.4. Wind turbine hours of use | 5.4. Government policies |
1.5. Investment planning | 5.5. Feed-in rates-subsidies |
1.6. Future deployment of wind energy | |
6. Market | |
2. Environmental | 6.1. Market conditions |
2.1. Climate change | 6.2. Price index |
2.2. Solar irradiation | 6.3. Demand for electricity |
3. Prices | 7. Wind Conditions |
3.1. Electricity price | 7.1. Wind disponibility |
3.2. Carbon price | 7.2. Wind intensity |
3.3. Corn price | 7.3. Wind speed |
7.4. Wind intermittency | |
4. Costs | |
4.1. Capital costs | 8. Technological Progress |
4.2. Investment costs | 8.1. Alternative technology |
8.2. Technology levels | |
8.3. Learning speed |
Authors-Years | 1. Power Generation | 2. Environmental | 3. Prices | 4. Costs | 5. Regulatory Policies | 6. Market | 7. Wind Conditions | 8. Technological Progress | Total | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1.1 | 1.2 | 1.3 | 1.4 | 1.5 | 1.6 | 2.1 | 2.2 | 3.1 | 3.2 | 3.3 | 4.1 | 4.2 | 5.1 | 5.2 | 5.3 | 5.4 | 5.5 | 6.1 | 6.2 | 6.3 | 7.1 | 7.2 | 7.3 | 7.4 | 8.1 | 8.2 | 8.3 | ||
[125] | ✓ | ✓ | ✓ | 3 | |||||||||||||||||||||||||
[124] | ✓ | ✓ | 2 | ||||||||||||||||||||||||||
[122] | ✓ | ✓ | ✓ | 3 | |||||||||||||||||||||||||
[106] | ✓ | ✓ | ✓ | 3 | |||||||||||||||||||||||||
[72] | ✓ | ✓ | 2 | ||||||||||||||||||||||||||
[119] | ✓ | ✓ | 2 | ||||||||||||||||||||||||||
[31] | ✓ | 1 | |||||||||||||||||||||||||||
[126] | ✓ | ✓ | ✓ | 3 | |||||||||||||||||||||||||
[115] | ✓ | ✓ | 2 | ||||||||||||||||||||||||||
[107] | ✓ | ✓ | ✓ | ✓ | 4 | ||||||||||||||||||||||||
[108] | ✓ | 1 | |||||||||||||||||||||||||||
[91] | ✓ | 1 | |||||||||||||||||||||||||||
[73] | ✓ | ✓ | ✓ | 3 | |||||||||||||||||||||||||
[74] | ✓ | 1 | |||||||||||||||||||||||||||
[55] | ✓ | ✓ | ✓ | 3 | |||||||||||||||||||||||||
[32] | ✓ | ✓ | ✓ | 3 | |||||||||||||||||||||||||
111] | ✓ | ✓ | ✓ | ✓ | 4 | ||||||||||||||||||||||||
[92] | ✓ | ✓ | 2 | ||||||||||||||||||||||||||
[56] | ✓ | ✓ | 2 | ||||||||||||||||||||||||||
[99] | ✓ | ✓ | ✓ | 3 | |||||||||||||||||||||||||
[123] | ✓ | ✓ | 2 | ||||||||||||||||||||||||||
[116] | ✓ | 1 | |||||||||||||||||||||||||||
[117] | ✓ | ✓ | ✓ | ✓ | ✓ | 5 | |||||||||||||||||||||||
[109] | ✓ | ✓ | ✓ | ✓ | 4 | ||||||||||||||||||||||||
[100] | ✓ | ✓ | ✓ | 3 | |||||||||||||||||||||||||
[75] | ✓ | ✓ | ✓ | ✓ | 4 | ||||||||||||||||||||||||
[76] | ✓ | ✓ | 2 | ||||||||||||||||||||||||||
[57] | ✓ | 1 | |||||||||||||||||||||||||||
[58] | ✓ | ✓ | 2 | ||||||||||||||||||||||||||
[86] | ✓ | 1 | |||||||||||||||||||||||||||
[77] | ✓ | 1 | |||||||||||||||||||||||||||
Total | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 2 | 20 | 3 | 1 | 4 | 4 | 2 | 1 | 1 | 2 | 5 | 3 | 1 | 1 | 2 | 2 | 6 | 2 | 1 | 1 | 1 | 74 |
Author-Year | Technique of Assessment | Modeling Technique | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Not Random | Random | Random | Not Random | ||||||||||||||
1 | 2 | 3 | 4 | 5 | 1 | 2 | 3 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 1 | 2 | |
[125] | ✓ | ✓ | |||||||||||||||
[124] | ✓ | ✓ | ✓ | ✓ | |||||||||||||
[106] | ✓ | ✓ | ✓ | ✓ | |||||||||||||
[72] | ✓ | ||||||||||||||||
[58] | ✓ | ✓ | ✓ | ||||||||||||||
[31] | ✓ | ✓ | ✓ | ✓ | |||||||||||||
[123] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||
[116] | ✓ | ✓ | ✓ | ✓ | |||||||||||||
[117] | ✓ | ✓ | ✓ | ✓ | |||||||||||||
[111] | ✓ | ✓ | ✓ | ✓ | |||||||||||||
[126] | ✓ | ✓ | ✓ | ||||||||||||||
[115] | ✓ | ✓ | |||||||||||||||
[32] | ✓ | ✓ | |||||||||||||||
[56] | ✓ | ✓ | |||||||||||||||
[109] | ✓ | ✓ | |||||||||||||||
[75] | ✓ | ✓ | |||||||||||||||
[57] | ✓ | ✓ | |||||||||||||||
[122] | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||||
[119] | ✓ | ✓ | ✓ | ||||||||||||||
[107] | ✓ | ✓ | ✓ | ||||||||||||||
[108] | ✓ | ✓ | |||||||||||||||
[91] | ✓ | ||||||||||||||||
[73] | ✓ | ✓ | |||||||||||||||
[74] | ✓ | ✓ | ✓ | ✓ | |||||||||||||
[78] | ✓ | ✓ | |||||||||||||||
[92] | ✓ | ✓ | |||||||||||||||
[99] | ✓ | ✓ | |||||||||||||||
[100] | ✓ | ✓ | ✓ | ||||||||||||||
[76] | ✓ | ✓ | ✓ | ||||||||||||||
[86] | ✓ | ✓ | ✓ | ✓ | |||||||||||||
[77] | ✓ | ✓ | |||||||||||||||
8 | 8 | 5 | 2 | 2 | 16 | 5 | 1 | 19 | 6 | 3 | 2 | 1 | 2 | 4 | 2 | 3 | |
Valuation of options • Non-random
| Modeling of uncertainties • Random
|
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Murgas, B.; Henao, A.; Guzman, L. Evaluation of Investments in Wind Energy Projects, under Uncertainty. State of the Art Review. Appl. Sci. 2021, 11, 10213. https://doi.org/10.3390/app112110213
Murgas B, Henao A, Guzman L. Evaluation of Investments in Wind Energy Projects, under Uncertainty. State of the Art Review. Applied Sciences. 2021; 11(21):10213. https://doi.org/10.3390/app112110213
Chicago/Turabian StyleMurgas, Benjamin, Alvin Henao, and Luceny Guzman. 2021. "Evaluation of Investments in Wind Energy Projects, under Uncertainty. State of the Art Review" Applied Sciences 11, no. 21: 10213. https://doi.org/10.3390/app112110213
APA StyleMurgas, B., Henao, A., & Guzman, L. (2021). Evaluation of Investments in Wind Energy Projects, under Uncertainty. State of the Art Review. Applied Sciences, 11(21), 10213. https://doi.org/10.3390/app112110213