Abstract
Markets with signs of concentrations, interactions, barriers to entry/exit and coordination among participants are particularly prone to evidence tacit collusion. The liberalized power markets fulfill largely these conditions and they are therefore susceptible to suffer tacit collusion. An approach capable of analyzing such strategic behavior in power markets and quantifying it economically appears to be necessary. In this article, an agent-based model is proposed to analyze how market participants can learn tacitly collusive behavior. The competition among market participants is modeled as a repeated game with imperfect public information. Reinforcement learning is applied to model the flexible and adaptable behavior of the market participants. A test system with different levels of market concentration is used to quantify economically the relation between the market concentration and the exercise of tacit collusion. The effect of transmission constraints on the incentives to exercise tacit collusion is also analyzed.
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