Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling

Abstract

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%.

Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation

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Towards Sustainable Energy-Efficient Communities Based on a Scheduling Algorithm

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A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

“…On the other hand, to protect the environment and climate change from heavy CO 2 emissions due to massive utilization of conventional energy resources, it is required to either optimize the energy consumption trends or to integrate VER or the combination of both [4]. Because, studies show that it is more beneficial to develop hybrid energy management solutions [5]. In doing so, the load management problem can also be solved at the micro-grid level by investigating the energy consumption of individual residential consumers rather than aggregated demand.…”