Comparison of Artificial Life Techniques for Market Simulation

Electricity industries worldwide are undergoing a period of profound upheaval. Conventional vertically integrated mechanism is replaced by a competitive market environment. A pure operating cost optimization is not enough to model the distributed, large-scale complex system. A market simulator will be a valuable training and evaluation tool to assist sellers, buyers & regulators to understand system’s dynamic performance and make better decisions avoiding bunch of risks. The objective of this research is to model market players by adaptive multi-agent system, compare the performances of different artificial life technique such as Genetic Algorithm (GA), Evolutionary Programming (EP) and Particle Swarm (PS) in simulating players’ behaviors, identify the best method to emulates real rational participants.