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.

The Coronavirus outbreak has imposed different levels of stay-at-home orders worldwide to help contain the disease spread, thus promoting severe impacts over the global economy. The World Bank expects 90% of the world economy to go into recession, being this the most severe global economic crisis since the second world war. Consequently, electricity consumption has fallen sharply in many countries, so as in Brazil, what provoked critical effects over many electricity sector stakeholders. In this context, this article discusses the main consequences of COVID-19s outbreak over the Brazilian electricity sector and some measures taken by the government and the regulator to address such issues. Further, this work proposes a long-term electricity demand forecast methodology, taking into consideration bottom-up technics for general demand growth and top-down technics for the expansion of distributed generation in Brazil. The model uses as input economic forecasts from the World Bank, the International Monetary Fund and Brazilian market consolidated expectations disclosed by the Brazilian Central Bank. The model proves to be useful to long-term demand forecast by producing results adherent to official forecasts published by Brazilian governmental institutions.