Efficient Scheduling of Home Energy Management Controller (HEMC) Using Heuristic Optimization Techniques

Zafar Mahmood, Benmao Cheng (), Naveed Anwer Butt, Ghani Ur Rehman, Muhammad Zubair, Afzal Badshah and Muhammad Aslam
Additional contact information
Zafar Mahmood: Department of Computer Science, University of Gujrat, Gujrat 50700, Pakistan
Benmao Cheng: Jiangsu Key Lab of IoT Application Technology, Wuxi Taihu University, Wuxi 214064, China
Naveed Anwer Butt: Department of Computer Science, University of Gujrat, Gujrat 50700, Pakistan
Ghani Ur Rehman: Department of Computer Science and Bioinformatics, Khushal Khan Khattak University, Karak 27000, Pakistan
Muhammad Zubair: Department of Computer Science and Bioinformatics, Khushal Khan Khattak University, Karak 27000, Pakistan
Afzal Badshah: Department of Computer Science & Software Engineering, International Islamic University, Islamabad 44000, Pakistan
Muhammad Aslam: School of Computing Engineering and Physical Sciences, University of West of Scotland, Paisley G72 0LH, UK

Sustainability, 2023, vol. 15, issue 2, 1-22

Abstract: The main problem for both the utility companies and the end-used is to efficiently schedule the home appliances using energy management to optimize energy consumption. The microgrid, macro grid, and Smart Grid (SG) are state-of-the-art technology that is user and environment-friendly, reliable, flexible, and controllable. Both utility companies and end-users are interested in effectively utilizing different heuristic optimization techniques to address demand-supply management efficiently based on consumption patterns. Similarly, the end-user has a greater concern with the electricity bills, how to minimize electricity bills, and how to reduce the Peak to Average Ratio (PAR). The Home Energy Management Controller (HEMC) is integrated into the smart grid, by providing many benefits to the end-user as well to the utility. In this research paper, we design an efficient HEMC system by using different heuristic optimization techniques such as Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO), to address the problem stated above. We consider a typical home, to have a large number of appliances and an on-site renewable energy generation and storage system. As a key contribution, here we focus on incentive-based programs such as Demand Response (DR) and Time of Use (ToU) pricing schemes which restrict the end-user energy consumption during peak demands. From the results figures, it is clear that our HEMC not only schedules all the appliances but also generates optimal patterns for energy consumption based on the ToU pricing scheme. As a secondary contribution, deploying an efficient ToU scheme benefits the end-user by paying minimum electricity bills, while considering user comfort, at the same time benefiting utilities by reducing the peak demand. From the graphs, it is clear that HEMC using GA shows better results than WDO and BPSO, in energy consumption and electricity cost, while BPSO is more prominent than WDO and GA by calculating PAR.

Keywords: optimization techniques; demand-supply system; energy consumption patterns; genetic algorithm; particle swarm optimization; wind driven optimization; home energy management controller (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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