LEARNING OLIGOPOLISTIC COMPETITION IN ELECTRICITY AUCTIONS
This paper addresses the problem of auction markets efficiency within the context of recently liberalized electricity markets. Two different auction mechanisms, i.e., the uniform and the discriminatory price setting rules, have been employed worldwide in designing electricity markets. In this paper, we study the relative efficiency of the two auction mechanisms in the framework of the learning-in-games approach. The behavior of electricity suppliers are modeled by means of an adaptive learning algorithm and the demand is assumed to be constant and inelastic, according to a common hypothesis in electricity market modeling. Computational experiments results are interpreted according game theoretical solutions, i.e., Nash equilibria and Pareto optima. Different economic scenarios corresponding to a duopoly and a tripoly competition with different level of demand are considered. Results show that in the proposed conditions, sellers learn to play competitive strategies, which correspond to Nash equilibria. Finally, this study establishes that, in the presented computational setting and economic scenarios, the discriminatory auction mechanism results more efficient than the uniform auction one.
electricity market adaptive learning agent-based computational economics game theory oligopolistic theory design economics