Agent-based Optimization to Estimate Nash Equilibrium in Power Markets

Abstract

In most deregulated power markets firms bid daily into a day-ahead power market. The auction mechanism supply and demand determine the equilibrium at each hour. In this environment firms aim to maximize their revenues by carefully determining their bids. This requires the development of effective computational methods that help them estimate their competitors' behaviors under incomplete information. In this article an agent-based method that uses particle swarm optimization is described to simulate the behavior of market participants. Particle swarm optimization is used in the bidding process and an agent-based model is applied to find a Nash equilibrium. Different stopping conditions are used to determine the equilibrium. Experimental results are presented for two power systems.