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Financial Risk Control of Hydro Generation Systems through Market Intelligence and Stochastic Optimization

Author

Listed:
  • Laís Domingues Leonel

    (Department of Energy Engineering and Electrical Automation, University of São Paulo, São Paulo 05508-900, SP, Brazil)

  • Mateus Henrique Balan

    (Department of Energy Engineering and Electrical Automation, University of São Paulo, São Paulo 05508-900, SP, Brazil)

  • Dorel Soares Ramos

    (Department of Energy Engineering and Electrical Automation, University of São Paulo, São Paulo 05508-900, SP, Brazil)

  • Erik Eduardo Rego

    (Department of Production Engineering, University of São Paulo, São Paulo 05508-010, SP, Brazil)

  • Rodrigo Ferreira de Mello

    (Group CTG Brazil, São Paulo 04551-060, SP, Brazil)

Abstract

In the competitive electricity wholesale market, decisions regarding hydro generators are generally made under uncertain conditions, such as pool price, hydrological affluence, and other players’ strategies. From this perspective, this work presents a computational model formulation with associated market intelligence and game theory tools to support a decision-making process in a competitive environment. The idea behind using a market intelligence tool is to apply a stochastic optimization model with an associated conditional value at risk metric defining a utility function, which calculates the weight that the agents attribute to each stochastic variable associated with the problem to be faced. Subsequently, this utility function is used to emulate the other agents’ strategies based on their previous decisions. The final step finds the Nash equilibrium solution between a player and their competitors. The methodology is applied to the monthly allocation of firm energy by hydro generators under the current Brazilian regulatory framework. The results show a change in the generators’ behavior over the years, from risk-neutral agents seeking to maximize their return with 88% of decisions based on spot price forecasts in 2015, to risk-averse agents with 100% of decisions following a factor that is directly impacted by the hydrological affluence forecasts in 2018.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6368-:d:650191
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    References listed on IDEAS

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    1. Shapiro, Alexander & Tekaya, Wajdi & da Costa, Joari Paulo & Soares, Murilo Pereira, 2013. "Risk neutral and risk averse Stochastic Dual Dynamic Programming method," European Journal of Operational Research, Elsevier, vol. 224(2), pages 375-391.
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    3. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, March.
    4. Vinel, Alexander & Mortaz, Ebrahim, 2019. "Optimal pooling of renewable energy sources with a risk-averse approach: Implications for US energy portfolio," Energy Policy, Elsevier, vol. 132(C), pages 928-939.
    5. Fernandes, Gláucia & Gomes, Leonardo Lima & Brandão, Luiz Eduardo Teixeira, 2018. "A risk-hedging tool for hydro power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 370-378.
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    Cited by:

    1. Fellipe Fernandes Goulart dos Santos & Marcus Vinícius de Castro Lobato & Douglas Alexandre Gomes Vieira & Adriano Chaves Lisboa & Rodney Rezende Saldanha, 2022. "A Nash Equilibrium Approach to the Brazilian Seasonalization of Energy Certificates," Energies, MDPI, vol. 15(6), pages 1-13, March.
    2. 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.
    3. Laís Domingues Leonel & Mateus Henrique Balan & Luiz Armando Steinle Camargo & Dorel Soares Ramos & Roberto Castro & Felipe Serachiani Clemente, 2024. "Stochastic Decision-Making Optimization Model for Large Electricity Self-Producers Using Natural Gas in Industrial Processes: An Approach Considering a Regret Cost Function," Energies, MDPI, vol. 17(21), pages 1-19, October.

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