An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms. (January 2017)

• Record Type: Journal Article
• Authors: Aliabadi, Danial Esmaeili; Kaya, Murat; Şahin, Güvenç
• Is Part Of: Energy policy. Volume 100(2017)
• Page Start: 191
• Page End: 205
• Publication Date: 2017-01
• Subjects: Agent-based simulation -- Reinforcement learning -- Uniform pricing -- Pay-as-bid pricing -- DC-OPF -- Game-theory; Energy policy -- Periodicals; Politique énergétique -- Périodiques; Electronic journals; 333.79
• DOI: 10.1016/j.enpol.2016.09.063
• Languages: English
• ISSNs: 0301-4215

Abstract

Abstract: Deregulated electricity markets are expected to provide affordable electricity for consumers through promoting competition. Yet, the results do not always fulfill the expectations. The regulator's market-clearing mechanism is a strategic choice that may affect the level of competition in the market. We conceive of the market-clearing mechanism as composed of two components: pricing rules and rationing policies. We investigate the strategic behavior of power generation companies under different market-clearing mechanisms using an agent-based simulation model which integrates a game-theoretical understanding of the auction mechanism in the electricity market and generation companies' learning mechanism. Results of our simulation experiments are presented using various case studies representing different market settings. The market in simulations is observed to converge to a Nash equilibrium of the stage game or to a similar state under most parameter combinations. Compared to pay-as-bid pricing, bid prices are closer to marginal costs on average under uniform pricing while GenCos' total profit is also higher. The random rationing policy of the ISO turns out to be more successful in achieving lower bid prices and lower GenCo profits. In minimizing GenCos' total profit, a combination of pay-as-bid pricing rule and random rationing policy is observed to be the most promising. Abstract : Highlights: An agent-based simulation of generation company behavior in electricity markets is developed. Learning dynamics of companies is modeled with an extended Q-learning algorithm. Different market clearing mechanisms of the regulator are compared. Convergence to Nash equilibria is analyzed under different cases. The level of competition in the market is studied.