IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1173-d1042860.html

Considering Forward Electricity Prices for a Hydro Power Plant Risk Analysis in the Brazilian Electricity Market

Author

Listed:
  • Arthur Lauro

    (Electrical Energy Department, Federal University of Juiz de Fora, UFJF, Juiz de Fora 36036-330, Brazil)

  • Daniel Kitamura

    (Electrical Energy Department, Federal University of Juiz de Fora, UFJF, Juiz de Fora 36036-330, Brazil)

  • Waleska Lima

    (Electrical Energy Department, Federal University of Juiz de Fora, UFJF, Juiz de Fora 36036-330, Brazil)

  • Bruno Dias

    (Electrical Energy Department, Federal University of Juiz de Fora, UFJF, Juiz de Fora 36036-330, Brazil)

  • Tiago Soares

    (Center for Power and Energy Systems, Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal)

Abstract

The Brazilian Power System is mainly composed of renewable generation from hydroelectric and wind. Hence, spot and forward electricity prices tend to represent the inherently stochastic nature of these resources, while risk management is a measure taken by agents, especially hydro power plants (HPPs) to hedge against deep financial losses. A HPP goal is to maximize its profit considering uncertainties in forward electricity prices, spot prices, and generation scaling factor (GSF) for years ahead. Therefore, the objective of this work is to simulate the real decision-making process of a HPP, where they need to have a perspective of the forward market and future spot price assessment to negotiate forward electricity contracts. To do so, the present work models the uncertainty in electricity forward prices via two-stage stochastic programming, assessing the benefits of the stochastic solution in comparison to the deterministic one. In addition, different risk aversion levels are assessed using conditional value at risk (CVaR). An important conclusion is that the results show that the greater the HPP risk aversion is, the greater the energy selling via electricity forward contracts. Moreover, the proposed model has benefits in comparison to a deterministic approach.

Suggested Citation

  • Arthur Lauro & Daniel Kitamura & Waleska Lima & Bruno Dias & Tiago Soares, 2023. "Considering Forward Electricity Prices for a Hydro Power Plant Risk Analysis in the Brazilian Electricity Market," Energies, MDPI, vol. 16(3), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1173-:d:1042860
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1173/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/3/1173/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Machado, Bruno Goulart F. & Bhagwat, Pradyumna C., 2020. "The impact of the generation mix on the current regulatory framework for hydropower remuneration in Brazil," Energy Policy, Elsevier, vol. 137(C).
    2. Maier, Sebastian & Street, Alexandre & McKinnon, Ken, 2016. "Risk-averse portfolio selection of renewable electricity generator investments in Brazil: An optimised multi-market commercialisation strategy," Energy, Elsevier, vol. 115(P1), pages 1331-1343.
    3. Boroumand, Raphaël Homayoun & Goutte, Stéphane & Porcher, Simon & Porcher, Thomas, 2015. "Hedging strategies in energy markets: The case of electricity retailers," Energy Economics, Elsevier, vol. 51(C), pages 503-509.
    4. Rego, Erik Eduardo & Parente, Virginia, 2013. "Brazilian experience in electricity auctions: Comparing outcomes from new and old energy auctions as well as the application of the hybrid Anglo-Dutch design," Energy Policy, Elsevier, vol. 55(C), pages 511-520.
    5. 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.
    6. Lak, Omidreza & Rastegar, Mohammad & Mohammadi, Mohammad & Shafiee, Soroush & Zareipour, Hamidreza, 2021. "Risk-constrained stochastic market operation strategies for wind power producers and energy storage systems," Energy, Elsevier, vol. 215(PB).
    7. Laís Domingues Leonel & Mateus Henrique Balan & Dorel Soares Ramos & Erik Eduardo Rego & Rodrigo Ferreira de Mello, 2021. "Financial Risk Control of Hydro Generation Systems through Market Intelligence and Stochastic Optimization," Energies, MDPI, vol. 14(19), pages 1-18, October.
    8. Gomes, I.L.R. & Melicio, R. & Mendes, V.M.F. & Pousinho, H.M.I., 2019. "Decision making for sustainable aggregation of clean energy in day-ahead market: Uncertainty and risk," Renewable Energy, Elsevier, vol. 133(C), pages 692-702.
    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. Danial Esmaeili Aliabadi & Tiago Pinto, 2025. "Modeling Electricity Markets and Energy Systems: Challenges and Opportunities," Energies, MDPI, vol. 18(2), pages 1-4, January.
    2. Yan Zhang & Xue Hu & Xiangzhen Wang & Xiaoqian Zhou & Yuyang Liu & Bohan Zhang & Yapeng Li, 2025. "Stochastic Bidding for Hydro–Wind–Solar Systems in Cross-Provincial Forward–Spot Markets: A Dimensionality-Reduced and Transmission-Aware Framework," Energies, MDPI, vol. 18(16), pages 1-22, August.
    3. Silva, Walquiria N. & Rego, Erik Eduardo & Vieira, Giovani G.T.T. & Lourenço, Luís F.N. & Salles, Mauricio B.C., 2025. "An overview of Brazil's electricity market: Planning, dispatch models, pricing, and modernization," Utilities Policy, Elsevier, vol. 96(C).
    4. Ecer, Fatih & Saha, Abhijit & Dinçer, Hasan & Yüksel, Serhat, 2025. "Evaluating energy management investments for sustainable transition of smart cities through a new intuitionistic fuzzy consensus-based CoCoSo methodology," Renewable Energy, Elsevier, vol. 244(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. Zhu, Dafeng & Yang, Bo & Ma, Chengbin & Wang, Zhaojian & Zhu, Shanying & Ma, Kai & Guan, Xinping, 2022. "Stochastic gradient-based fast distributed multi-energy management for an industrial park with temporally-coupled constraints," Applied Energy, Elsevier, vol. 317(C).
    2. Souhir, Ben Amor & Heni, Boubaker & Lotfi, Belkacem, 2019. "Price risk and hedging strategies in Nord Pool electricity market evidence with sector indexes," Energy Economics, Elsevier, vol. 80(C), pages 635-655.
    3. Chai, Shanglei & Zhou, P., 2018. "The Minimum-CVaR strategy with semi-parametric estimation in carbon market hedging problems," Energy Economics, Elsevier, vol. 76(C), pages 64-75.
    4. Shin, Hunyoung & Baldick, Ross, 2018. "Mitigating market risk for wind power providers via financial risk exchange," Energy Economics, Elsevier, vol. 71(C), pages 344-358.
    5. Lak, Omidreza & Rastegar, Mohammad & Mohammadi, Mohammad & Shafiee, Soroush & Zareipour, Hamidreza, 2021. "Risk-constrained stochastic market operation strategies for wind power producers and energy storage systems," Energy, Elsevier, vol. 215(PB).
    6. Jean-Patrick Mascom`ere & J'er'emie Messud & Yagnik Chatterjee & Isabel Barros Garcia, 2025. "Unified Approach to Portfolio Optimization using the `Gain Probability Density Function' and Applications," Papers 2512.11649, arXiv.org.
    7. Sofiane Aboura, 2014. "When the U.S. Stock Market Becomes Extreme?," Risks, MDPI, vol. 2(2), pages 1-15, May.
    8. Gordon J. Alexander & Alexandre M. Baptista, 2004. "A Comparison of VaR and CVaR Constraints on Portfolio Selection with the Mean-Variance Model," Management Science, INFORMS, vol. 50(9), pages 1261-1273, September.
    9. Domagoj Demeterfi & Kathrin Glau & Linus Wunderlich, 2025. "Function approximations for counterparty credit exposure calculations," Papers 2507.09004, arXiv.org.
    10. Christina Büsing & Sigrid Knust & Xuan Thanh Le, 2018. "Trade-off between robustness and cost for a storage loading problem: rule-based scenario generation," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 339-365, December.
    11. Winter, Peter, 2007. "Managerial Risk Accounting and Control – A German perspective," MPRA Paper 8185, University Library of Munich, Germany.
    12. Cui, Xueting & Zhu, Shushang & Sun, Xiaoling & Li, Duan, 2013. "Nonlinear portfolio selection using approximate parametric Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2124-2139.
    13. Jiang Cheng & Hung-Gay Fung & Tzu-Ting Lin & Min-Ming Wen, 2024. "CEO optimism and the use of credit default swaps: evidence from the US life insurance industry," Review of Quantitative Finance and Accounting, Springer, vol. 63(1), pages 169-194, July.
    14. Walter Farkas & Pablo Koch-Medina & Cosimo Munari, 2014. "Beyond cash-additive risk measures: when changing the numéraire fails," Finance and Stochastics, Springer, vol. 18(1), pages 145-173, January.
    15. Li, Xiao-Ming & Rose, Lawrence C., 2009. "The tail risk of emerging stock markets," Emerging Markets Review, Elsevier, vol. 10(4), pages 242-256, December.
    16. Castaño-Martínez, A. & Pigueiras, G. & Ramos, C.D. & Sordo, M.A., 2025. "Ordering higher risks in Yaari's dual theory," Insurance: Mathematics and Economics, Elsevier, vol. 125(C).
    17. Choo, Weihao & de Jong, Piet, 2015. "The tradeoff insurance premium as a two-sided generalisation of the distortion premium," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 238-246.
    18. Louis Anthony (Tony)Cox, 2008. "What's Wrong with Risk Matrices?," Risk Analysis, John Wiley & Sons, vol. 28(2), pages 497-512, April.
    19. Jay Cao & Jacky Chen & John Hull & Zissis Poulos, 2021. "Deep Hedging of Derivatives Using Reinforcement Learning," Papers 2103.16409, arXiv.org.
    20. Ji, Ronglin & Shi, Xuejun & Wang, Shijie & Zhou, Jinming, 2019. "Dynamic risk measures for processes via backward stochastic differential equations," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 43-50.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:16:y:2023:i:3:p:1173-:d:1042860. 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 The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (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.