Scalable Residential Demand Response Management

In this paper, a scalable framework based on a hierarchical architecture for residential demand response (DR) is introduced. The architecture, which overlays the physical hierarchy of the power system, allows to decompose the problem and solve it in a distributed manner. The computational time required to solve the DR optimization problem by this framework is shown to be only dependent on the number of levels in the hierarchical architecture. Hence, when the demand response computation is carried out entirely in parallel, adding more homes does not add to the optimization time, thus making the DR optimization scalable. Moreover, since the architecture overlays on the hierarchy of a physical power system, each node's physical constraints can also be integrated into the optimization problem. For DR management, consumer comfort as well as demand response target is considered. Generated schedules can be implemented as a direct load control by demand response aggregators and/or home energy management systems. Furthermore, new metrics are introduced to quantify the DR program's success, balancing between performance, number of participants in the DR program as well as stress on the consumer due to DR implementation. To demonstrate scalability of the proposed method a one-million home demand response program is successfully simulated and typical results are presented.