Demand Management of Distributed Energy Loads Based on Genetic Algorithm Optimization

Abstract

Management of a very large number of distributed energy resources, energy loads, and generators, is a hot research topic. Such energy demand management techniques enable appliances to control and defer their electricity consumption when price soars and can be used to cope with the unpredictability of the energy market or provide response when supply is strained by demand. We consider a multiagent system comprising multiple energy loads, each with a dedicated controller. This paper introduces our latest research in selforganization of coordinated behavior of multiple agents. Energy resource agents (RAs) coordinate with each other to achieve a balance between the overall consumption by the multiagent 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 RAs and a broker agent (BA). This 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 setpoints. The coordination strategy is optimized by a genetic algorithm (GA) and a fast coordination convergence has been achieved.