Multiagent coevolutionary genetic fuzzy system to develop bidding strategies in electricity markets: computational economics to assess mechanism design
This paper suggests a genetic fuzzy system approach to develop bidding strategies for agents in online auction environments. Assessing efficient bidding strategies is a key to evaluate auction models and verify if the underlying mechanism design achieves its intended goals. Due to its relevance in current energy markets worldwide, we use day-ahead electricity auctions as an experimental and application instance of the approach developed in this paper. Successful fuzzy bidding strategies have been developed by genetic fuzzy systems using coevolutionary algorithms. In this paper we address a coevolutionary fuzzy system algorithm and present recent results concerning bidding strategies behavior. Coevolutionary approaches developed by coevolutionary agents interact through their fuzzy bidding strategies in a multiagent environment and allow realistic and transparent representations of agents behavior in auction-based markets. They also improve market representation and evaluation mechanisms. In particular, we study how the coevolutionary fuzzy bidding strategies perform against each other during hourly electric energy auctions. Experimental results show that coevolutionary agents may enhance their profits at the cost of increasing system hourly price paid by demand.
genetic fuzzy systems electricity markets computational economics auctions multiagent systems