Multiagent coevolutionary genetic fuzzy system to develop bidding strategies in electricity markets: computational economics to assess mechanism design

Abstract

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.

Born to trade: A genetically evolved keyword bidder for sponsored search

“…Similarly as in [14], we discovered that co-evolving a population by running internal competitions with no external agents involved can have some negative side effects. Occasionally multiple members of the population appeared to develop cooperative strategies, such as all bidding low.…”

“…[11], [13], [14]), to the best of our knowledge, we are the first to propose the use of evolutionary computation techniques to keyword bidding for sponsored search. The rapid growth in the online advertising industry and its relationship with search engines is attracting a growing research interest in the topic of online keyword based ad auctions, particularly towards per-click pricing rather than per-impression strategies [5].…”

Evolutionary neural networks for strategic bidding in electricity markets

“…A block-based bidding model was considered, with total system demand as input to the fuzzy rule-based system, and bid the price and quantity as output. In a follow-up paper (Walter and Gomide, 2009), the authors extended the approach to multi-agent scenario by co-evolving fuzzy bidding strategies. The rival suppliers were assumed to bid their marginal cost functions in both the cases.…”

Connecting Community-Grids by supporting job negotiation with coevolutionary Fuzzy-Systems

“…Also Casillas et al (2002) use cooperative CAs to evolve membership functions and Fuzzy-Rule sets in a more conceptual way for improving Fuzzy-Rule-based systems while Akbarzadeh et al (2003) use genetic algorithms for evolving membership functions and genetic programming to optimize Fuzzy-Rules in a cooperative coevolutionary approach. Finally, we mention recent publications by Nejad et al (2008) as well as Walter and Gomide (2009) who also optimize Fuzzy-Logic interference systems with cooperative CAs. While the former apply their optimized Fuzzy-Systems to real-time signal pre-processing, the latter address bidding strategies for the electricity market.…”