IDEAS home Printed from https://ideas.repec.org/a/eee/juipol/v81y2023ics0957178723000255.html
   My bibliography  Save this article

Analysis of hourly price granularity implementation in the Brazilian deregulated electricity contracting environment

Author

Listed:
  • Nametala, Ciniro Aparecido Leite
  • Faria, Wandry Rodrigues
  • Lage, Guilherme Guimarães
  • Pereira, Benvindo Rodrigues

Abstract

Brazil’s electricity market is the largest in Latin America and the ninth largest in the world. It has been implemented as a mixed market in which regulated and deregulated contracting environments coexist. The volume of transactions in the deregulated market has experienced steep growth over the last few years and is expected to surpass the regulated market. Different programs to diversify the country’s energy matrix have been devised, especially by integrating intermittent renewable sources to address the deregulated market expansion. Consequently, such an energy policy path has prompted the need to increase the granularity of the Brazilian deregulated market’s spot price, namely the Difference Settlement Price (DSP). The DSP had been weekly defined accounting for three loading levels and four submarkets, and, as of 2021, it has been hourly defined accounting for four submarkets; the weekly DSP is inefficient in actually signaling prices based on ex-ante marginal cost of operation of the interconnected Brazilian power system. Besides such granularity alteration, Brazil has also undergone a severe hydrological crisis in 2021 that led to significantly lower water inflows into major hydrographic watersheds and, as a result, most hydroelectric power plant reservoirs hit a 91-year low. The described scenario is relevant in utility policies and energy economics since it depicts a significant paradigm shift experience in such a large electricity market. This study presents the first hourly DSP behavior analysis since its implementation in the Brazilian electricity market and explores its statistical characteristics and relationships with exogenous variables throughout 2021. Additionally, we discuss the hourly DSP’s volatility observed in the year 2021 and how it has resulted in price spikes. At last, we compare the behavior of the Brazilian hourly DSP with the energy prices of five other countries’ electricity markets. Despite being a significant market improvement, the DSP granularity increase per se could not accurately represent the actual marginal cost of operation over the year 2021 since, besides instabilities observed in the hourly DSP, market intervention mechanisms had to be applied by Brazilian regulatory agencies to minimize the hydrological crisis’ impacts.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:juipol:v:81:y:2023:i:c:s0957178723000255
    DOI: 10.1016/j.jup.2023.101513
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0957178723000255
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jup.2023.101513?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. Rocha Filho, Tareísio M. & Rocha, Paulo M.M., 2020. "Evidence of inefficiency of the Brazilian stock market: The IBOVESPA future contracts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    2. Boungou, Whelsy & Yatié, Alhonita, 2022. "The impact of the Ukraine–Russia war on world stock market returns," Economics Letters, Elsevier, vol. 215(C).
    3. Soto, Esteban A. & Bosman, Lisa B. & Wollega, Ebisa & Leon-Salas, Walter D., 2021. "Peer-to-peer energy trading: A review of the literature," Applied Energy, Elsevier, vol. 283(C).
    4. Joisa Dutra & Flavio Menezes, 2022. "Energy Transition in the Brazilian Electric Power System," Competition and Regulation in Network Industries, , vol. 23(2), pages 119-134, June.
    5. Grossi, Luigi & Nan, Fany, 2019. "Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 305-318.
    6. Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, Elsevier, vol. 293(C).
    7. Alvaro Cartea & Marcelo Figueroa & Helyette Geman, 2009. "Modelling Electricity Prices with Forward Looking Capacity Constraints," Applied Mathematical Finance, Taylor & Francis Journals, vol. 16(2), pages 103-122.
    8. Ioannidis, Filippos & Kosmidou, Kyriaki & Savva, Christos & Theodossiou, Panayiotis, 2021. "Electricity pricing using a periodic GARCH model with conditional skewness and kurtosis components," Energy Economics, Elsevier, vol. 95(C).
    9. Imlak Shaikh, 2022. "Impact of COVID-19 pandemic on the energy markets," Economic Change and Restructuring, Springer, vol. 55(1), pages 433-484, February.
    10. Moreira, Fabrícia de Souza & Lopes, Mariana Padilha Campos & de Freitas, Marcos Aurélio Vasconcelos & Antunes, Adelaide Maria de Souza, 2021. "Future scenarios for the development of the desalination industry in contexts of water scarcity: A Brazilian case study," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    11. Hahsler, Michael & Hornik, Kurt & Buchta, Christian, 2008. "Getting Things in Order: An Introduction to the R Package seriation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i03).
    12. Mayer, Klaus & Trück, Stefan, 2018. "Electricity markets around the world," Journal of Commodity Markets, Elsevier, vol. 9(C), pages 77-100.
    13. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521608275.
    14. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    15. Tao Hong & Pierre Pinson & Yi Wang & Rafal Weron & Dazhi Yang & Hamidreza Zareipour, 2020. "Energy forecasting: A review and outlook," WORking papers in Management Science (WORMS) WORMS/20/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    16. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    17. Tafakori, Laleh & Pourkhanali, Armin & Fard, Farzad Alavi, 2018. "Forecasting spikes in electricity return innovations," Energy, Elsevier, vol. 150(C), pages 508-526.
    18. 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.
    19. Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
    20. 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.
    21. Hong, Tao & Wang, Pu & White, Laura, 2015. "Weather station selection for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 31(2), pages 286-295.
    22. Hunt., Julian David & Stilpen, Daniel & de Freitas, Marcos Aurélio Vasconcelos, 2018. "A review of the causes, impacts and solutions for electricity supply crises in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 208-222.
    23. Friendly M., 2002. "Corrgrams: Exploratory Displays for Correlation Matrices," The American Statistician, American Statistical Association, vol. 56, pages 316-324, November.
    24. Narajewski, Michał & Ziel, Florian, 2020. "Ensemble forecasting for intraday electricity prices: Simulating trajectories," Applied Energy, Elsevier, vol. 279(C).
    25. García, Sebastián & Parejo, Antonio & Personal, Enrique & Ignacio Guerrero, Juan & Biscarri, Félix & León, Carlos, 2021. "A retrospective analysis of the impact of the COVID-19 restrictions on energy consumption at a disaggregated level," Applied Energy, Elsevier, vol. 287(C).
    26. Ramos, Dorel Soares & Del Carpio Huayllas, Tesoro Elena & Morozowski Filho, Marciano & Tolmasquim, Mauricio Tiomno, 2020. "New commercial arrangements and business models in electricity distribution systems: The case of Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    27. Abbasi, Kashif Raza & Abbas, Jaffar & Tufail, Muhammad, 2021. "Revisiting electricity consumption, price, and real GDP: A modified sectoral level analysis from Pakistan," Energy Policy, Elsevier, vol. 149(C).
    28. Filippos Ioannidis & Kyriaki Kosmidou & Kostas Andriosopoulos & Antigoni Everkiadi, 2021. "Assessment of the Target Model Implementation in the Wholesale Electricity Market of Greece," Energies, MDPI, vol. 14(19), pages 1-22, October.
    29. Ferrer, Román & Shahzad, Syed Jawad Hussain & López, Raquel & Jareño, Francisco, 2018. "Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices," Energy Economics, Elsevier, vol. 76(C), pages 1-20.
    30. Nunes, Luis Eduardo & Lima, Marcus Vinicius Andrade de & Davison, Matthew & Leite, André Luis da Silva, 2021. "Switch and defer option in renewable energy projects: Evidences from Brazil," Energy, Elsevier, vol. 231(C).
    31. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    32. Hochberg, Michael & Poudineh, Rahmatallah, 2021. "The Brazilian electricity market architecture: An analysis of instruments and misalignments," Utilities Policy, Elsevier, vol. 72(C).
    33. 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.
    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. Faria, Wandry Rodrigues & Muñoz-Delgado, Gregorio & Contreras, Javier & Pereira Junior, Benvindo Rodrigues, 2024. "A trilevel programming model for the coordination of wholesale and local distribution markets considering GENCOs and proactive customers," Applied Energy, Elsevier, vol. 357(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. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    2. Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, Elsevier, vol. 293(C).
    3. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
    4. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    5. Serafin, Tomasz & Marcjasz, Grzegorz & Weron, Rafał, 2022. "Trading on short-term path forecasts of intraday electricity prices," Energy Economics, Elsevier, vol. 112(C).
    6. Bartosz Uniejewski, 2023. "Electricity price forecasting with Smoothing Quantile Regression Averaging: Quantifying economic benefits of probabilistic forecasts," Papers 2302.00411, arXiv.org, revised Jan 2024.
    7. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    8. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2020. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 466-479.
    9. Cramer, Eike & Witthaut, Dirk & Mitsos, Alexander & Dahmen, Manuel, 2023. "Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows," Applied Energy, Elsevier, vol. 346(C).
    10. Bartosz Uniejewski, 2024. "Regularization for electricity price forecasting," Papers 2404.03968, arXiv.org.
    11. Silvia Golia & Luigi Grossi & Matteo Pelagatti, 2022. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
    12. Michał Narajewski, 2022. "Probabilistic Forecasting of German Electricity Imbalance Prices," Energies, MDPI, vol. 15(14), pages 1-17, July.
    13. Ciarreta, Aitor & Martinez, Blanca & Nasirov, Shahriyar, 2023. "Forecasting electricity prices using bid data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1253-1271.
    14. Micha{l} Narajewski & Florian Ziel, 2021. "Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs," Papers 2104.14204, arXiv.org, revised Feb 2022.
    15. Ehsani, Behdad & Pineau, Pierre-Olivier & Charlin, Laurent, 2024. "Price forecasting in the Ontario electricity market via TriConvGRU hybrid model: Univariate vs. multivariate frameworks," Applied Energy, Elsevier, vol. 359(C).
    16. Olivares, Kin G. & Challu, Cristian & Marcjasz, Grzegorz & Weron, Rafał & Dubrawski, Artur, 2023. "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," International Journal of Forecasting, Elsevier, vol. 39(2), pages 884-900.
    17. Lehna, Malte & Scheller, Fabian & Herwartz, Helmut, 2022. "Forecasting day-ahead electricity prices: A comparison of time series and neural network models taking external regressors into account," Energy Economics, Elsevier, vol. 106(C).
    18. Arkadiusz Jędrzejewski & Grzegorz Marcjasz & Rafał Weron, 2021. "Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO," Energies, MDPI, vol. 14(11), pages 1-17, June.
    19. Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
    20. Narajewski, Michał & Ziel, Florian, 2022. "Optimal bidding in hourly and quarter-hourly electricity price auctions: Trading large volumes of power with market impact and transaction costs," Energy Economics, Elsevier, vol. 110(C).

    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:juipol:v:81:y:2023:i:c:s0957178723000255. 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: https://www.sciencedirect.com/journal/utilities-policy .

    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.