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Energies 2018, 11(10), 2861; https://doi.org/10.3390/en11102861

Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling

1
Department of Computer Science, PMAS Arid Agriculture University, Rawalpindi 46000, Pakistan
2
Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
3
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
4
Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantonment 47040, Pakistan
5
Department of Electronics and Communication Engineering, Kwangwoon University, Seoul 01897, Korea
*
Author to whom correspondence should be addressed.
Received: 21 August 2018 / Revised: 9 October 2018 / Accepted: 10 October 2018 / Published: 22 October 2018
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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%. View Full-Text
Keywords: demand side management; load scheduling; home energy management system; optimization techniques demand side management; load scheduling; home energy management system; optimization techniques
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Iqbal, Z.; Javaid, N.; Mohsin, S.M.; Akber, S.M.A.; Afzal, M.K.; Ishmanov, F. Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling. Energies 2018, 11, 2861.

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