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.
Highlights
As explained in Section 5.1.2, Player 10 s strategies of the game study are emulated by crossing the utility function results shown in Figure 8 and the FEC monthly allocation (FECMA) factor ( f i,t ) for each criterion presented in Table 1 considering the variables’
This study presents a methodology of market intelligence and game theory tools considering the representativeness of stochastic behavior of some variables by applying a stochastic optimization model with an associated conditional value at risk (CVaR) metric
Using the FECMA decision methodology presented under the current Brazilian regulatory framework related to Energy Reallocation Mechanism (ERM), a behavioral change was noticed in the decision taken by the agents
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In the competitive electricity wholesale market, trading decisions are generally made under uncertain conditions depending on the variables associated with expected price, demand, weather forecast, and competitors’ strategies [1]. Knowledge about other players’ strategies becomes valuable information to formulate individual strategies in a competitive environment wherein each agent’s results are affected by its competitors’ decisions. According to Li et al [2], electricity price fluctuations are justified by the systems’
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