Clustering distributed energy resources for large-scale demand management

Managing demand for electrical energy allows generation facilities to be run more efficiently. Current systems allow for management between large industrial consumers. There is, however, an increasing trend to decentralize energy resource management and push it to the level of individual households, or even appliances. In this work we investigate the suitability of using adaptive clustering to improve the scalability of decentralized energy resource management systems by appropriately partitioning resources. We review the area of distributed energy resource management and propose a simple yet realistic model to study the problem. Simulations using this model show that straightforward clustering and distributed planning methods allow systems to scale, but may be limited to only a few hundred-thousand appliances. Results indicate that there is an opportunity to apply adaptive clustering techniques in order to discover more advanced grouping criteria that would enable groups to change as appliances' behavior changes. The simulations further suggest that even an extremely limited exchange of information between clusters can greatly improve management solutions.