An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms

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• 作者：Danial Esmaeili Aliabadi ^{danialesm@sabanciuniv.edu} ; Murat Kaya ^{mkaya@sabanciuniv.edu} ; Gü ; venç ; Şahin ; ^{guvencs@sabanciuniv.edu}
• 关键词：Agent-based simulation ; Reinforcement learning ; Uniform pricing ; Pay-as-bid pricing ; DC-OPF ; Game-theory
• 刊名：Energy Policy
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：100
• 期：Complete
• 页码：191-205
• 全文大小：2255 K
• 卷排序：100

文摘

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.