IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i12p2281-d239917.html
   My bibliography  Save this article

Wind Power Economic Feasibility under Uncertainty and the Application of ANN in Sensitivity Analysis

Author

Listed:
  • Paulo Rotela Junior

    (Department of Production Engineering, Federal University of Paraíba, João Pessoa 58051-970, Brazil)

  • Eugenio Fischetti

    (Department of Production Engineering, Federal University of Paraíba, João Pessoa 58051-970, Brazil)

  • Victor G. Araújo

    (Department of Production Engineering, Federal University of Paraíba, João Pessoa 58051-970, Brazil)

  • Rogério S. Peruchi

    (Department of Production Engineering, Federal University of Paraíba, João Pessoa 58051-970, Brazil)

  • Giancarlo Aquila

    (Institute of Industrial Engineering and Management, Federal University of Itajubá, Itajubá 37500-000, Brazil)

  • Luiz Célio S. Rocha

    (Department of Management, Federal Institute of Education, Science and Technology Northern of Minas Gerais, Almenara 39900-000, Brazil)

  • Liviam S. Lacerda

    (Department of Production Engineering, Federal University of Paraíba, João Pessoa 58051-970, Brazil)

Abstract

Wind power has grown popular in past recent years due to environmental issues and the search for alternative energy sources. Thus, the viability for wind power generation projects must be studied in order to attend to the environmental concerns and still be attractive and profitable. Therefore, this article aims to perform a sensitive analysis in order to identify the variables that influence most in the viability of a wind power investment for small size companies in the Brazilian northeast. For this, a stochastic analysis of viability through Monte Carlo Simulation (MCS) will be made and afterwards, Artificial Neural Networks (ANN) models will be applied for the most relevant variables identification. Through the sensitivity, it appears that the most relevant factors in the analysis are the speed of wind, energy tariff and the investment amount. Thus, the viability of the investment is straightly tied to the region where the wind turbine is installed, and the government incentives may allow decreasing in the investment amount for wind power. Based on this, incentives programs for the production of clean energy include cheaper purchase of wind turbines, lower taxing and financing rates, can make wind power more profitable and attractive.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2281-:d:239917
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/12/2281/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/12/2281/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rocha, Luiz Célio Souza & Aquila, Giancarlo & Rotela Junior, Paulo & Paiva, Anderson Paulo de & Pamplona, Edson de Oliveira & Balestrassi, Pedro Paulo, 2018. "A stochastic economic viability analysis of residential wind power generation in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 412-419.
    2. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    3. Tao, Jacqueline Yujia & Finenko, Anton, 2016. "Moving beyond LCOE: impact of various financing methods on PV profitability for SIDS," Energy Policy, Elsevier, vol. 98(C), pages 749-758.
    4. 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.
    5. Ringel, Marc, 2006. "Fostering the use of renewable energies in the European Union: the race between feed-in tariffs and green certificates," Renewable Energy, Elsevier, vol. 31(1), pages 1-17.
    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. Haufe, Marie-Christin & Ehrhart, Karl-Martin, 2018. "Auctions for renewable energy support – Suitability, design, and first lessons learned," Energy Policy, Elsevier, vol. 121(C), pages 217-224.
    8. 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.
    9. Anoune, Kamal & Bouya, Mohsine & Astito, Abdelali & Abdellah, Abdellatif Ben, 2018. "Sizing methods and optimization techniques for PV-wind based hybrid renewable energy system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 652-673.
    10. 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.
    11. 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.
    12. Mohammadi, Amin & Mehrpooya, Mehdi, 2018. "A comprehensive review on coupling different types of electrolyzer to renewable energy sources," Energy, Elsevier, vol. 158(C), pages 632-655.
    13. Reichelstein, Stefan & Yorston, Michael, 2013. "The prospects for cost competitive solar PV power," Energy Policy, Elsevier, vol. 55(C), pages 117-127.
    14. 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.
    15. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    16. Chakrabarty, Sayan & Boksh, F.I.M. Muktadir & Chakraborty, Arpita, 2013. "Economic viability of biogas and green self-employment opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 757-766.
    17. Bigdeli, Nooshin & Afshar, Karim & Gazafroudi, Amin Shokri & Ramandi, Mostafa Yousefi, 2013. "A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 20-29.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aquila, Giancarlo & Nakamura, Wilson Toshiro & Junior, Paulo Rotella & Souza Rocha, Luiz Celio & de Oliveira Pamplona, Edson, 2021. "Perspectives under uncertainties and risk in wind farms investments based on Omega-LCOE approach: An analysis in São Paulo state, Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    2. de Doile, Gabriel Nasser Doyle & Rotella Junior, Paulo & Rocha, Luiz Célio Souza & Janda, Karel & Aquila, Giancarlo & Peruchi, Rogério Santana & Balestrassi, Pedro Paulo, 2022. "Feasibility of hybrid wind and photovoltaic distributed generation and battery energy storage systems under techno-economic regulation," Renewable Energy, Elsevier, vol. 195(C), pages 1310-1323.
    3. Gustavo Leite Gonçalves & Raphael Abrahão & Paulo Rotella Junior & Luiz Célio Souza Rocha, 2022. "Economic Feasibility of Conventional and Building-Integrated Photovoltaics Implementation in Brazil," Energies, MDPI, vol. 15(18), pages 1-16, September.
    4. Gabriel Nasser Doyle de Doile & Paulo Rotella Junior & Priscila França Gonzaga Carneiro & Rogério Santana Peruchi & Luiz Célio Souza Rocha & Karel Janda & Giancarlo Aquila, 2021. "Economic Feasibility of Photovoltaic Micro-Installations Connected to the Brazilian Distribution Grid in Light of Proposed Changes to Regulations," Energies, MDPI, vol. 14(6), pages 1-14, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Gabriel Nasser Doyle de Doile & Paulo Rotella Junior & Priscila França Gonzaga Carneiro & Rogério Santana Peruchi & Luiz Célio Souza Rocha & Karel Janda & Giancarlo Aquila, 2021. "Economic Feasibility of Photovoltaic Micro-Installations Connected to the Brazilian Distribution Grid in Light of Proposed Changes to Regulations," Energies, MDPI, vol. 14(6), pages 1-14, March.
    3. de Oliveira, Lucas Guedes & Aquila, Giancarlo & Balestrassi, Pedro Paulo & de Paiva, Anderson Paulo & de Queiroz, Anderson Rodrigo & de Oliveira Pamplona, Edson & Camatta, Ulisses Pessin, 2020. "Evaluating economic feasibility and maximization of social welfare of photovoltaic projects developed for the Brazilian northeastern coast: An attribute agreement analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    4. Aquila, Giancarlo & Souza Rocha, Luiz Célio & Rotela Junior, Paulo & Saab Junior, Joseph Youssif & de Sá Brasil Lima, João & Balestrassi, Pedro Paulo, 2020. "Economic planning of wind farms from a NBI-RSM-DEA multiobjective programming," Renewable Energy, Elsevier, vol. 158(C), pages 628-641.
    5. Rocha, Luiz Célio Souza & Aquila, Giancarlo & Rotela Junior, Paulo & Paiva, Anderson Paulo de & Pamplona, Edson de Oliveira & Balestrassi, Pedro Paulo, 2018. "A stochastic economic viability analysis of residential wind power generation in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 412-419.
    6. Aquila, Giancarlo & Coelho, Eden de Oliveira Pinto & Bonatto, Benedito Donizeti & Pamplona, Edson de Oliveira & Nakamura, Wilson Toshiro, 2021. "Perspective of uncertainty and risk from the CVaR-LCOE approach: An analysis of the case of PV microgeneration in Minas Gerais, Brazil," Energy, Elsevier, vol. 226(C).
    7. de Doile, Gabriel Nasser Doyle & Rotella Junior, Paulo & Rocha, Luiz Célio Souza & Janda, Karel & Aquila, Giancarlo & Peruchi, Rogério Santana & Balestrassi, Pedro Paulo, 2022. "Feasibility of hybrid wind and photovoltaic distributed generation and battery energy storage systems under techno-economic regulation," Renewable Energy, Elsevier, vol. 195(C), pages 1310-1323.
    8. Aquila, Giancarlo & Nakamura, Wilson Toshiro & Junior, Paulo Rotella & Souza Rocha, Luiz Celio & de Oliveira Pamplona, Edson, 2021. "Perspectives under uncertainties and risk in wind farms investments based on Omega-LCOE approach: An analysis in São Paulo state, Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    9. Aquila, Giancarlo & Pamplona, Edson de Oliveira & Queiroz, Anderson Rodrigo de & Rotela Junior, Paulo & Fonseca, Marcelo Nunes, 2017. "An overview of incentive policies for the expansion of renewable energy generation in electricity power systems and the Brazilian experience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1090-1098.
    10. Stetter, Chris & Piel, Jan-Hendrik & Hamann, Julian F.H. & Breitner, Michael H., 2020. "Competitive and risk-adequate auction bids for onshore wind projects in Germany," Energy Economics, Elsevier, vol. 90(C).
    11. 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).
    12. Luiz Moreira Coelho Junior & Amadeu Junior da Silva Fonseca & Roberto Castro & João Carlos de Oliveira Mello & Victor Hugo Ribeiro dos Santos & Renato Barros Pinheiro & Wilton Lima Sousa & Edvaldo Per, 2022. "Empirical Evidence of the Cost of Capital under Risk Conditions for Thermoelectric Power Plants in Brazil," Energies, MDPI, vol. 15(12), pages 1-12, June.
    13. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    14. Xu Lei & Tang Shiyun & Deng Yanfei & Yuan Yuan, 2020. "Sustainable operation-oriented investment risk evaluation and optimization for renewable energy project: a case study of wind power in China," Annals of Operations Research, Springer, vol. 290(1), pages 223-241, July.
    15. Ioannou, Anastasia & Angus, Andrew & Brennan, Feargal, 2017. "Risk-based methods for sustainable energy system planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 602-615.
    16. Zaroni, Hebert & Maciel, Letícia B. & Carvalho, Diego B. & Pamplona, Edson de O., 2019. "Monte Carlo Simulation approach for economic risk analysis of an emergency energy generation system," Energy, Elsevier, vol. 172(C), pages 498-508.
    17. Vanderson Aparecido Delapedra-Silva & Paula Ferreira & Jorge Cunha & Herbert Kimura, 2021. "Economic Evaluation of Wind Power Projects in a Mix of Free and Regulated Market Environments in Brazil," Energies, MDPI, vol. 14(11), pages 1-18, June.
    18. Carvalho, Diego B. & Pinto, Bárbara L. & Guardia, Eduardo C. & Marangon Lima, José W., 2020. "Economic impact of anticipations or delays in the completion of power generation projects in the Brazilian energy market," Renewable Energy, Elsevier, vol. 147(P1), pages 1312-1320.
    19. Harish Kumar Ghritlahre & Purvi Chandrakar & Ashfaque Ahmad, 2021. "A Comprehensive Review on Performance Prediction of Solar Air Heaters Using Artificial Neural Network," Annals of Data Science, Springer, vol. 8(3), pages 405-449, September.
    20. Alexandra G. Papadopoulou & George Vasileiou & Alexandros Flamos, 2020. "A Comparison of Dispatchable RES Technoeconomics: Is There a Niche for Concentrated Solar Power?," Energies, MDPI, vol. 13(18), pages 1-22, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2281-:d:239917. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.