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The applicability of computer simulation using Monte Carlo techniques in windfarm profitability analysis

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  • Montes, German Martinez
  • Martin, Enrique Prados
  • Bayo, Javier Alegre
  • Garcia, Javier Ordoñez

Abstract

Obtaining external financing for windfarms may be hindered over the next few years by the high degree of uncertainty that is inherent in these types of project. Therefore, promoters must carefully plan and analyse their projects and attempt to optimise the profitability/risk factor of each investment. The objective of this paper is to demonstrate that Monte Carlo sampling represents an excellent approach to economic risk management in windfarm projects, as it provides a practical method of calculating the distribution of the NPV via the various random input variables. To this end, we defined a windfarm model, which has also allowed us to identify the basic parameters of current projects of this nature in Spain. We analysed the profitability of these projects using Monte Carlo techniques.

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  • Montes, German Martinez & Martin, Enrique Prados & Bayo, Javier Alegre & Garcia, Javier Ordoñez, 2011. "The applicability of computer simulation using Monte Carlo techniques in windfarm profitability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4746-4755.
    • Handle: RePEc:eee:rensus:v:15:y:2011:i:9:p:4746-4755
    DOI: 10.1016/j.rser.2011.07.078
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    Cited by:

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    3. Farrell, Niall & Donoghue, Cathal O’ & Morrissey, Karyn, 2015. "Quantifying the uncertainty of wave energy conversion device cost for policy appraisal: An Irish case study," Energy Policy, Elsevier, vol. 78(C), pages 62-77.
    4. Aquila, Giancarlo & Rotela Junior, Paulo & de Oliveira Pamplona, Edson & de Queiroz, Anderson Rodrigo, 2017. "Wind power feasibility analysis under uncertainty in the Brazilian electricity market," Energy Economics, Elsevier, vol. 65(C), pages 127-136.
    5. Kumar, Indraneel & Tyner, Wallace E. & Labi, Samuel & Sinha, Kumares C., 2021. "“The Answer My Friend is Blowin’ in the Wind”: A stochastic assessment of wind farms financial feasibility and economic efficiency," Energy Policy, Elsevier, vol. 159(C).
    6. Caralis, George & Diakoulaki, Danae & Yang, Peijin & Gao, Zhiqiu & Zervos, Arthouros & Rados, Kostas, 2014. "Profitability of wind energy investments in China using a Monte Carlo approach for the treatment of uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 224-236.
    7. Paulo Rotela Junior & Eugenio Fischetti & Victor G. Araújo & Rogério S. Peruchi & Giancarlo Aquila & Luiz Célio S. Rocha & Liviam S. Lacerda, 2019. "Wind Power Economic Feasibility under Uncertainty and the Application of ANN in Sensitivity Analysis," Energies, MDPI, vol. 12(12), pages 1-10, June.
    8. Kucukali, Serhat, 2016. "Risk scorecard concept in wind energy projects: An integrated approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 975-987.

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