A Novel Dynamic Appliance Clustering Scheme in a Community Home Energy Management System for Improved Stability and Resiliency of Microgrids

Abstract

Power scheduling of domestic appliances is a vital preference for bridging the gap between demand and generation of electricity in a microgrid. For a stable microgrid, an acceptable mechanism must reduce the peak to average ratio (PAR) of power demand with supplementary benefits for consumers as reduced electricity charges. Recent studies have focused on PAR and cost reduction for a small consumer population. Furthermore, researchers have mainly considered homogeneous consumer loads. This study focuses on residential power scheduling for electricity cost reduction for consumers and load profile PAR curtailment for a relatively large consumer population with non-homogeneous loads. A sample population of 1000 consumers from various classes of society is considered. The proposed dynamic clustered community home energy management system (DCCHEMS) allows the clustering of appliances based on time overlap criteria. Comparatively flatter power demand is attained by utilizing the clustered appliances in conjunction with particle swarm optimization under the influence of user-defined constraints. Modified inclined block rates with real-time electricity pricing strategies are deployed to minimize the electricity costs. DCCHEMS achieved higher efficiency rates in contrast to the traditional non-clustering and static clustering optimization schemes. An improvement of 21% in peak to average ratio, 4% in cost reduction, and 19% in variance to mean ratio is obtained.

Keywords

• Controllable appliances
• demand response
• dynamic clustering
• home energy management system
• microgrid
• optimization