Igor Walter

Evolving fuzzy bidding strategies in competitive electricity markets

This paper suggests an evolutionary approach to generate bidding strategies for power auctions. Bidding strategies are represented by fuzzy rule-based systems due to its transparency and ability to naturally handle imprecision in input data, a key issue in bidding environments. Evolution of bidding strategies uncovers unknown and unexpected agent behaviors and allows a richer analysis of auction mechanisms and their role as a coordination protocol. Specific genetic operators have been developed in this paper. Simulation experiments show that the evolutionary, genetic-based design approach evolves strategies that enhance agents profitability when compared with the marginal cost-based approaches commonly adopted by agents in power markets.

Electricity market simulation: multiagent system approach

This paper suggests a multiagent system (MAS) approach for market simulation. This is achieved through analysis, modeling, implementation and simulation of artificial markets populated by software agents that represent economic self interested agents. Software agents are the constructs of a complex system, an artificial market that model a real existing market or an outline of a market design. The interest in simulating a market is multiple: exploiting existing market rules, searching for market design flaws and loopholes, and supporting decision making during a market mechanism design process. The main aim of the suggested approach is to analyze the behavior that emerges from the interaction of self interested agents acting in an artificial market. AEMAS (Artificial Economy MultiAgent System), a multiagent system architecture inspired by the Market Oriented Programming (MOP) approach is defined. In different economical sectors, e.g. energy markets, there is no consensus about which structures lead to social welfare maximization outcomes. An approach to find adequate architectures allows different market structure instances to be created and simulated, to ease the design and analysis of alternative structures. These alternatives can then be compared and potential design flaws eventually risen by simulation identified. Taking the electricity market as an example, two instances of the proposed architecture are presented, corresponding to the centralized dispatch arrangement common to non restructured markets, and the auction based pool, common to restructured markets.

Design of coordination strategies in multiagent systems via genetic fuzzy systems

This paper suggests an evolutionary approach to design coordination strategies for multiagent systems. Emphasis is given to auction protocols since they are of utmost importance in many real world applications such as power markets. Power markets are one of the most relevant instances of multiagent systems and finding a profitable bidding strategy is a key issue to preserve system functioning and improve social welfare. Bidding strategies are modeled as fuzzy rule-based systems due to their modeling power, transparency, and ability to naturally handle imprecision in input data, an essential ingredient to a multiagent system act efficiently in practice. Specific genetic operators are suggested in this paper. Evolution of bidding strategies uncovers unknown and unexpected agent behaviors and allows a richer analysis of auction mechanisms and their role as a coordination protocol. Simulation experiments with a typical power market using actual thermal plants data show that the evolutionary, genetic-based design approach evolves strategies that enhance agents profitability when compared with the marginal cost-based strategies commonly adopted

Coevolutionary fuzzy multiagent bidding strategies in competitive electricity markets

Following the development of online markets, trading practices as dynamic pricing, online auctions and exchanges have become relevant to a variety of markets. In this paper we suggest a machine learning approach to find a suitable bidding strategy for an auction participant using information commonly available in online auction settings. We take the electricity auction as the main application example, due to its importance as an experimental instance of the suggested approach. In previous works we evolved successful fuzzy bidding strategies. Here we introduce a coevolutionary algorithm to study how the evolving strategies react to each other in a more dynamic environment. By enabling a fuzzy system to learn trough an evolutionary algorithm one expects to find effective and transparent bidding strategies. By adopting a coevolutionary approach a more realistic representation of the agents participating in an auction based electricity market allows the evolutionary bidding strategies interact. The results show that the coevolutionary approach can improve agents profits at the cost of increasing system hourly price paid by demand.

Genetic fuzzy systems to evolve interaction strategies in multiagent systems

This article suggests an evolutionary approach to designing interaction strategies for multiagent systems, focusing on strategies modeled as fuzzy rule‐based systems. The aim is to learn models evolving database and rule bases to improve agent performance when playing in a competitive environment. In competitive situations, data for learning and tuning are rare, and rule bases must jointly evolve with the databases. We introduce an evolutionary algorithm whose operators use variable length chromosomes, a hierarchical relationship among individuals through fitness, and a scheme that successively explores and exploits the search space along generations. Evolution of interaction strategies uncovers unknown and unexpected agent behaviors and allows a richer analysis of negotiation mechanisms and their role as a coordination protocol. An application concerning an electricity market illustrates the effectiveness of the approach.

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.