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

Laís Domingues Leonel, Mateus Henrique Balan, Dorel Soares Ramos, Erik Eduardo Rego and Rodrigo Ferreira de Mello
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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

Energies, 2021, vol. 14, issue 19, 1-18

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.

Keywords: market intelligence; decision making; risk analysis; stochastic processes; uncertainty (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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