Smart Home Load Manager Algorithm under Demand Response

Automation and control of residential loads are an integral part of the smart grid initiatives under advance metering infrastructure. This paper presents a new approach combining clustering and scheduling algorithms, termed as Smart Home Load Manager (SHLM), for automatic scheduling of appliances considering Demand Response (DR). The clustering algorithm automatically calculates peak consumption hours of consumer, based on previous consumptions, using load index and cluster coefficient, whereas the scheduling algorithm schedules the interruptible loads based on appliance predefined priorities. The SHLM keeps operating load below power limit, especially during utility peak hours, considering various constraints like comfort, pricing signals and power outages to minimize Peak-to-Average Ratio (PAR). In addition, deployment of the proposed approach serves as a catalyst to promote DR potentials for the benefit of both end users and utilities. The effectiveness of the proposed approach and the practical application of DR signals are demonstrated on Virginia Tech Advanced Research Institute data.