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Wind power feasibility analysis under uncertainty in the Brazilian electricity market


  • Aquila, Giancarlo
  • Rotela Junior, Paulo
  • de Oliveira Pamplona, Edson
  • de Queiroz, Anderson Rodrigo


Investors must be able to plan and analyze their investments in order to optimize decisions and turn them into profits associated with a particular project. Since electricity producers in the Brazilian electric power system are exposed to a short-term market, the goal of this paper is to propose a framework for investment analysis capable of encompassing different uncertainties and possibilities for wind power generators in a regulated market, characterized by auctions. In order to reach the proposed objective we employ a simulation technique which allows modeling cash flows considering uncertainties in variables related to project financial premises, electricity generation and producer exposure to the short-term market. For such goal, this study presents a new approach for investment analysis that allows the identification of the main uncertainty parameters and risks associated to this class of projects in the Brazilian electricity market. We also employ the Value at Risk technique to perform a risk management analysis in such context.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:eneeco:v:65:y:2017:i:c:p:127-136
    DOI: 10.1016/j.eneco.2017.04.027

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    1. Solangi, K.H. & Islam, M.R. & Saidur, R. & Rahim, N.A. & Fayaz, H., 2011. "A review on global solar energy policy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 2149-2163, May.
    2. Grieser, Benno & Sunak, Yasin & Madlener, Reinhard, 2015. "Economics of small wind turbines in urban settings: An empirical investigation for Germany," Renewable Energy, Elsevier, vol. 78(C), pages 334-350.
    3. Peña, Ivonne & Lima Azevedo, Inês & Ferreira, Luís António Fialho Marcelino, 2014. "Economic analysis of the profitability of existing wind parks in Portugal," Energy Economics, Elsevier, vol. 45(C), pages 353-363.
    4. Yamai, Yasuhiro & Yoshiba, Toshinao, 2005. "Value-at-risk versus expected shortfall: A practical perspective," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 997-1015, April.
    5. Arnold, Uwe & Yildiz, Özgür, 2015. "Economic risk analysis of decentralized renewable energy infrastructures – A Monte Carlo Simulation approach," Renewable Energy, Elsevier, vol. 77(C), pages 227-239.
    6. Silva, Neilton Fidelis da & Rosa, Luiz Pinguelli & Freitas, Marcos Aurélio Vasconcelos & Pereira, Marcio Giannini, 2013. "Wind energy in Brazil: From the power sector's expansion crisis model to the favorable environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 686-697.
    7. Wachsmann, U & Tolmasquim, M.T, 2003. "Wind power in Brazil—transition using German experience," Renewable Energy, Elsevier, vol. 28(7), pages 1029-1038.
    8. Signorini, Guilherme & Ross, R. Brent & Peterson, H. Christopher, 2015. "Governance strategies and transaction costs in a renovated electricity market," Energy Economics, Elsevier, vol. 52(PA), pages 151-159.
    9. Georgios Tziralis & Konstantinos Kirytopoulos & Athanasios Rentizelas & Ilias Tatsiopoulos, 2009. "Holistic investment assessment: optimization, risk appraisal and decision making," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 30(6), pages 393-403.
    10. Martins, Douglas Eduardo Costa & Seiffert, Mari Elizabete Bernardini & Dziedzic, Maurício, 2013. "The importance of clean development mechanism for small hydro power plants," Renewable Energy, Elsevier, vol. 60(C), pages 643-647.
    11. Hung, Jui-Cheng & Lee, Ming-Chih & Liu, Hung-Chun, 2008. "Estimation of value-at-risk for energy commodities via fat-tailed GARCH models," Energy Economics, Elsevier, vol. 30(3), pages 1173-1191, May.
    12. Blanco, María Isabel, 2009. "The economics of wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1372-1382, August.
    13. Holdermann, Claudius & Kissel, Johannes & Beigel, Jürgen, 2014. "Distributed photovoltaic generation in Brazil: An economic viability analysis of small-scale photovoltaic systems in the residential and commercial sectors," Energy Policy, Elsevier, vol. 67(C), pages 612-617.
    14. Mastropietro, Paolo & Batlle, Carlos & Barroso, Luiz A. & Rodilla, Pablo, 2014. "Electricity auctions in South America: Towards convergence of system adequacy and RES-E support," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 375-385.
    15. 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.
    16. Islam, M.R. & Mekhilef, S. & Saidur, R., 2013. "Progress and recent trends of wind energy technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 456-468.
    17. Martins, Fernando Ramos & Pereira, Enio Bueno, 2011. "Enhancing information for solar and wind energy technology deployment in Brazil," Energy Policy, Elsevier, vol. 39(7), pages 4378-4390, July.
    18. Dutra, Ricardo Marques & Szklo, Alexandre Salem, 2008. "Incentive policies for promoting wind power production in Brazil: Scenarios for the Alternative Energy Sources Incentive Program (PROINFA) under the New Brazilian electric power sector regulation," Renewable Energy, Elsevier, vol. 33(1), pages 65-76.
    19. Li, Cun-bin & Lu, Gong-shu & Wu, Si, 2013. "The investment risk analysis of wind power project in China," Renewable Energy, Elsevier, vol. 50(C), pages 481-487.
    20. Schmidt, J. & Lehecka, G. & Gass, V. & Schmid, E., 2013. "Where the wind blows: Assessing the effect of fixed and premium based feed-in tariffs on the spatial diversification of wind turbines," Energy Economics, Elsevier, vol. 40(C), pages 269-276.
    21. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    22. Walters, Ryan & Walsh, Philip R., 2011. "Examining the financial performance of micro-generation wind projects and the subsidy effect of feed-in tariffs for urban locations in the United Kingdom," Energy Policy, Elsevier, vol. 39(9), pages 5167-5181, September.
    23. Colmenar-Santos, Antonio & Campíñez-Romero, Severo & Pérez-Molina, Clara & Mur-Pérez, Francisco, 2015. "Repowering: An actual possibility for wind energy in Spain in a new scenario without feed-in-tariffs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 319-337.
    24. Pereira, Marcio Giannini & Camacho, Cristiane Farias & Freitas, Marcos Aurélio Vasconcelos & Silva, Neilton Fidelis da, 2012. "The renewable energy market in Brazil: Current status and potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3786-3802.
    25. Safari, Bonfils & Gasore, Jimmy, 2010. "A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda," Renewable Energy, Elsevier, vol. 35(12), pages 2874-2880.
    26. Castro-Santos, Laura & Filgueira-Vizoso, Almudena & Carral-Couce, Luis & Formoso, José Ángel Fraguela, 2016. "Economic feasibility of floating offshore wind farms," Energy, Elsevier, vol. 112(C), pages 868-882.
    27. Dalbem, Marta Corrêa & Brandão, Luiz Eduardo Teixeira & Gomes, Leonardo Lima, 2014. "Can the regulated market help foster a free market for wind energy in Brazil?," Energy Policy, Elsevier, vol. 66(C), pages 303-311.
    28. Hemmati, Reza & Saboori, Hedayat & Saboori, Saeid, 2016. "Stochastic risk-averse coordinated scheduling of grid integrated energy storage units in transmission constrained wind-thermal systems within a conditional value-at-risk framework," Energy, Elsevier, vol. 113(C), pages 762-775.
    29. Juárez, Alberto Aquino & Araújo, Alex Maurício & Rohatgi, Janardan Singh & de Oliveira Filho, Oyama Douglas Queiroz, 2014. "Development of the wind power in Brazil: Political, social and technical issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 828-834.
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    More about this item


    Wind power; Stochastic power generation; Electricity markets; NPV; Renewable energy;

    JEL classification:

    • G3 - Financial Economics - - Corporate Finance and Governance
    • G31 - Financial Economics - - Corporate Finance and Governance - - - Capital Budgeting; Fixed Investment and Inventory Studies
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • M1 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration


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