IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v189y2022icp601-617.html
   My bibliography  Save this article

The impact of hourly pricing for renewable generation projects in Brazil

Author

Listed:
  • Marchetti, Isabella
  • Rego, Erik Eduardo

Abstract

The hourly price of the Brazilian electricity market came into operation in 2021, changing the previous pricing policy that worked in a weekly basis. The impact that this change has on wind and solar generators' business may be a cause for attention, since in an hourly price scenario with collateral and daily financial settlements, wind and solar power generators may be subject to a large financial exposure into the short-term market. Thus, the present article has as its main goal the evaluation of the impact caused on the intrinsic value of wind and solar power projects with the adoption of the new hourly pricing policy in the electricity sector compared to the old weekly pricing policy. To this end, a financial economic model was developed for generic and hypothetical wind and solar farms in the Brazilian electricity sector to find its fair value operating under two different pricing scenarios. The value of these farms was also sensitized through Monte Carlo simulations, after assigning probability distributions for certain model inputs. As a result of this work, wind power projects presented a negative impact on their values and solar power plants tend to present positive results, varying according to region. However, it is also discussed that this variation caused by hourly prices is a correction to the inefficient allocation of risks that previously existed.

Suggested Citation

  • Marchetti, Isabella & Rego, Erik Eduardo, 2022. "The impact of hourly pricing for renewable generation projects in Brazil," Renewable Energy, Elsevier, vol. 189(C), pages 601-617.
    • Handle: RePEc:eee:renene:v:189:y:2022:i:c:p:601-617
    DOI: 10.1016/j.renene.2022.03.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122003032
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.03.026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Paul Joskow & Jean Tirole, 2007. "Reliability and competitive electricity markets," RAND Journal of Economics, RAND Corporation, vol. 38(1), pages 60-84, March.
    2. Munhoz, Fernando Colli, 2021. "Two-settlement system for the Brazilian electricity market," Energy Policy, Elsevier, vol. 152(C).
    3. Ambec, Stefan & Crampes, Claude, 2021. "Real-time electricity pricing to balance green energy intermittency," Energy Economics, Elsevier, vol. 94(C).
    4. Raviv, Eran & Bouwman, Kees E. & van Dijk, Dick, 2015. "Forecasting day-ahead electricity prices: Utilizing hourly prices," Energy Economics, Elsevier, vol. 50(C), pages 227-239.
    5. Ketterer, Janina C., 2014. "The impact of wind power generation on the electricity price in Germany," Energy Economics, Elsevier, vol. 44(C), pages 270-280.
    6. Kristiansen, Tarjei, 2012. "Forecasting Nord Pool day-ahead prices with an autoregressive model," Energy Policy, Elsevier, vol. 49(C), pages 328-332.
    7. Pao, Hsiao-Tien & Fu, Hsin-Chia, 2013. "Renewable energy, non-renewable energy and economic growth in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 381-392.
    8. Severin Borenstein & Stephen Holland, 2005. "On the Efficiency of Competitive Electricity Markets with Time-Invariant Retail Prices," RAND Journal of Economics, The RAND Corporation, vol. 36(3), pages 469-493, Autumn.
    9. Sapio, Alessandro, 2019. "Greener, more integrated, and less volatile? A quantile regression analysis of Italian wholesale electricity prices," Energy Policy, Elsevier, vol. 126(C), pages 452-469.
    10. Huisman, Ronald & Huurman, Christian & Mahieu, Ronald, 2007. "Hourly electricity prices in day-ahead markets," Energy Economics, Elsevier, vol. 29(2), pages 240-248, March.
    11. Ferreira, Pedro Guilherme Costa & Oliveira, Fernando Luiz Cyrino & Souza, Reinaldo Castro, 2015. "The stochastic effects on the Brazilian Electrical Sector," Energy Economics, Elsevier, vol. 49(C), pages 328-335.
    12. Kyritsis, Evangelos & Andersson, Jonas & Serletis, Apostolos, 2017. "Electricity prices, large-scale renewable integration, and policy implications," Energy Policy, Elsevier, vol. 101(C), pages 550-560.
    13. Rintamäki, Tuomas & Siddiqui, Afzal S. & Salo, Ahti, 2017. "Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany," Energy Economics, Elsevier, vol. 62(C), pages 270-282.
    14. Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, vol. 77(1), pages 87-106, January.
    15. Ciarreta, Aitor & Pizarro-Irizar, Cristina & Zarraga, Ainhoa, 2020. "Renewable energy regulation and structural breaks: An empirical analysis of Spanish electricity price volatility," Energy Economics, Elsevier, vol. 88(C).
    16. da Silva, Neilton Fidelis & Rosa, Luiz Pinguelli & Araújo, Maria Regina, 2005. "The utilization of wind energy in the Brazilian electric sector's expansion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 9(3), pages 289-309, June.
    17. Corrêa da Silva, Rodrigo & de Marchi Neto, Ismael & Silva Seifert, Stephan, 2016. "Electricity supply security and the future role of renewable energy sources in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 328-341.
    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. Nametala, Ciniro Aparecido Leite & Faria, Wandry Rodrigues & Lage, Guilherme Guimarães & Pereira, Benvindo Rodrigues, 2023. "Analysis of hourly price granularity implementation in the Brazilian deregulated electricity contracting environment," Utilities Policy, Elsevier, vol. 81(C).

    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. Macedo, Daniela Pereira & Marques, António Cardoso & Damette, Olivier, 2021. "The Merit-Order Effect on the Swedish bidding zone with the highest electricity flow in the Elspot market," Energy Economics, Elsevier, vol. 102(C).
    2. Macedo, Daniela Pereira & Marques, António Cardoso & Damette, Olivier, 2022. "The role of electricity flows and renewable electricity production in the behaviour of electricity prices in Spain," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 885-900.
    3. Huisman, Ronald & Stet, Cristian, 2022. "The dependence of quantile power prices on supply from renewables," Energy Economics, Elsevier, vol. 105(C).
    4. Tselika, Kyriaki, 2022. "The impact of variable renewables on the distribution of hourly electricity prices and their variability: A panel approach," Energy Economics, Elsevier, vol. 113(C).
    5. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
    6. Bert Willems & Juulia Zhou, 2020. "The Clean Energy Package and Demand Response: Setting Correct Incentives," Energies, MDPI, vol. 13(21), pages 1-19, October.
    7. Ergemen, Yunus Emre & Haldrup, Niels & Rodríguez-Caballero, Carlos Vladimir, 2016. "Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads," Energy Economics, Elsevier, vol. 60(C), pages 79-96.
    8. Katarzyna Maciejowska & Rafał Weron, 2015. "Forecasting of daily electricity prices with factor models: utilizing intra-day and inter-zone relationships," Computational Statistics, Springer, vol. 30(3), pages 805-819, September.
    9. Maniatis, Georgios I. & Milonas, Nikolaos T., 2022. "The impact of wind and solar power generation on the level and volatility of wholesale electricity prices in Greece," Energy Policy, Elsevier, vol. 170(C).
    10. Peña, Juan Ignacio & Rodríguez, Rosa & Mayoral, Silvia, 2022. "Cannibalization, depredation, and market remuneration of power plants," Energy Policy, Elsevier, vol. 167(C).
    11. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.
    12. Sinha, Pankaj & Mathur, Kritika, 2016. "Empirical Analysis of Developments in the Day Ahead Electricity Markets in India," MPRA Paper 72969, University Library of Munich, Germany.
    13. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    14. Michail I. Seitaridis & Nikolaos S. Thomaidis & Pandelis N. Biskas, 2021. "Fundamental Responsiveness in European Electricity Prices," Energies, MDPI, vol. 14(22), pages 1-14, November.
    15. Ciarreta, Aitor & Pizarro-Irizar, Cristina & Zarraga, Ainhoa, 2020. "Renewable energy regulation and structural breaks: An empirical analysis of Spanish electricity price volatility," Energy Economics, Elsevier, vol. 88(C).
    16. Sirin, Selahattin Murat & Camadan, Ercument & Erten, Ibrahim Etem & Zhang, Alex Hongliang, 2023. "Market failure or politics? Understanding the motives behind regulatory actions to address surging electricity prices," Energy Policy, Elsevier, vol. 180(C).
    17. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    18. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    19. Raviv, Eran & Bouwman, Kees E. & van Dijk, Dick, 2015. "Forecasting day-ahead electricity prices: Utilizing hourly prices," Energy Economics, Elsevier, vol. 50(C), pages 227-239.
    20. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.

    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:eee:renene:v:189:y:2022:i:c:p:601-617. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.