Smart Grid Energy Optimization and Scheduling Appliances Priority for Residential Buildings through Meta-Heuristic Hybrid Approaches

Ch Anwar ul Hassan, Jawaid Iqbal, Nasir Ayub, Saddam Hussain, Roobaea Alroobaea and Syed Sajid Ullah
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Ch Anwar ul Hassan: Department of Software Engineering, Capital University of Science and Technology, Islamabad 44000, Pakistan
Jawaid Iqbal: Department of Software Engineering, Capital University of Science and Technology, Islamabad 44000, Pakistan
Nasir Ayub: Department of Computer Science, Federal Urdu University of Arts, Sciences and Technology, Islamabad 44000, Pakistan
Saddam Hussain: School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
Roobaea Alroobaea: Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Syed Sajid Ullah: Department of Electrical and Computer Engineering, Villanova University, Villanova, PA 19085, USA

Energies, 2022, vol. 15, issue 5, 1-19

Abstract: Smart grid technology has given users the ability to regulate their home energy use more efficiently and effectively. Home Energy Management (HEM) is a difficult undertaking in this regard, as it necessitates the optimal scheduling of smart appliances to reduce energy usage. In this research, we introduce a metaheuristic-based HEM system which incorporates Earth Worm Algorithm (EWA) and Harmony Search Algorithms (HSA). In addition, a hybridization based on the EWA and HSA operators is used to optimize energy consumption in terms of electricity cost and Peak-to-Average Ratio (PAR) reduction. Hybridization has been demonstrated to be beneficial in achieving many objectives at the same time. Extensive simulations in MATLAB were used to test the performance of the proposed hybrid technique. The simulations were run for multiple homes with multiple appliances, which were categorized according to the usage and nature of the appliance, taking advantage of appliance scheduling in terms of the time-varying retail pricing enabled by the smart grid two-way communication infrastructure algorithms EWA and HSA, along with a Real-Time Price scheme. These techniques helped us to find the best usage pattern for energy consumption to reduce electricity costs. These metaheuristic techniques efficiently reduced and shifted the load from peak hours to off-peak hours and reduced electricity costs. In comparison to HSA, the simulation results suggest that EWA performed better in terms of cost reduction. In comparison to EWA and HSA, HSA was more efficient in terms of PAR. However, the proposed hybrid approach EHSA gave the maximum reduction in cost which was 2.668%, 2.247%, and 2.535% in the case of 10, 30, and 50 homes, respectively, as compared to EWA and HSA.

Keywords: metaheuristic algorithms; home energy; energy controller; smart grid; smart home (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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