Load Scheduling in Home Energy Management System Using Meta-Heuristic Techniques and Critical Peak Pricing Tariff

Abstract

In this modern world, the demand of energy rises exponentially, that makes it a valuable resource. New techniques and methods are being developed to solve the problem of energy crisis in residential areas. The strategy to handle this problem is by integrating the demand side management (DSM) with smart grid (SG). DSM enables the consumer to schedule their load profile effectively in order to reduce electricity cost and power peak creation, referred as peak-to-average ratio (PAR). This paper evaluates the performance of home energy management system (HEMS) using meta-heuristic techniques; harmony search algorithm (HSA) and flower pollination algorithm (FPA). In this regard, a single home is considered with smart appliances classified as automatically operated appliances (AOAs) and manually operated appliances (MOAs). Moreover, critical peak pricing (CPP) is used as a price signal. In this paper, emphasis is placed on the cost minimization and load scheduling by shifting the load between off-peak and on-peak hours, while considering the user comfort. Simulation results shows that the performance of FPA is better in terms of cost and PAR reduction, whereas there exists a trade-offs between electricity cost and user comfort level.