Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling

With the emergence of the smart grid, both consumers and electricity providing companies can benefit from real-time interaction and pricing methods. In this work, a smart power system is considered, where consumers share a common energy source. Each consumer is equipped with a home energy management controller (HEMC) as scheduler and a smart meter. The HEMC keeps updating the utility with the load profile of the home. The smart meter is connected to a power grid having an advanced metering infrastructure which is responsible for two-way communication. Genetic teaching-learning based optimization, flower pollination teaching learning based optimization, flower pollination BAT and flower pollination genetic algorithm based energy consumption scheduling algorithms are proposed. These algorithms schedule the loads in order to shave the peak formation without compromising user comfort. The proposed algorithms achieve optimal energy consumption profile for the home appliances equipped with sensors to maximize the consumer benefits in a fair and efficient manner by exchanging control messages. Control messages contain energy consumption of consumer and real-time pricing information. Simulation results show that proposed algorithms reduce the peak-to-average ratio by 34.56% and help the users to reduce their energy expenses by 42.41% without compromising the comfort. The daily discomfort is reduced by 28.18%.