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This paper presents an overview of a simulation platform for studying the behavior of energy retail markets where multiple energies enter in competition. This platform is based on autonomous agent techniques. The simulations include agents representing Residential, Commercial and Industrial Consumer Groups, Electricity, Gas, Heat Retail Suppliers and Energy Deliverers, Regulators, Market Operators, Economy and Information Environment. Each pursues its own interests and from their interaction a complex collective behavior emerges. Agents formulate their strategies namely by inner complex simulation process that try to guess other agent move s and define optimum decisions in energy purchasing, price fixing, market share wining, investing and capturing new consumers, among other. The process works on a FIPA complying platform being able to run in a parallel cluster machines.

This paper presents a comparison in performance of 3 variants of Genetic Algorithms (GA) vs. 2 variants of Evolutionary Particle Swarm Optimization (EPSO), made in the extremely complex context of a multi-energy market simulation where the behavior of energy retailers is observed. The simulations are on JADE, a FIPA compliant platform based on intelligent autonomous agents running in a cluster of PCs. Each agent formulates its strategy by an inner complex simulation process using a meta-heuristic that tries to define optimum decisions. The results suggest that an EPSO approach is more efficient than GA.

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