Applied Computational Intelligence and Soft Computing
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The management of a very large number of distributed energy resources, energy loads and generators, to create aggregated quantity of power is a hot research topic. We consider a multi-agent system comprising multiple energy loads, each with a dedicated controller. This paper introduces our latest research in self-organization of coordinated behavior of multiple agents. Energy resource agents coordinate with each other to achieve a balance between the overall consumption by the multi-agent collective and the stress on the community. In order to reduce the overall communication load while permitting efficient coordinated responses, information exchange is through indirect communications between resource agents and a broker agent. It gives a decentralized coordination approach that does not rely on intensive computation by a central processor. The algorithm presented here can coordinate different types of loads by controlling their set-points. The coordination strategy is optimized by a genetic algorithm. A fast coordination convergence has been achieved.