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

Author

Listed:
  • 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
    Scotland Academy, Wuxi Taihu University, Wuxi 214064, China)

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.

Suggested Citation

  • Zafar Mahmood & Benmao Cheng & Naveed Anwer Butt & Ghani Ur Rehman & Muhammad Zubair & Afzal Badshah & Muhammad Aslam, 2023. "Efficient Scheduling of Home Energy Management Controller (HEMC) Using Heuristic Optimization Techniques," Sustainability, MDPI, vol. 15(2), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1378-:d:1032150
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    References listed on IDEAS

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    1. Mohammad Ehsanifar & Fatemeh Dekamini & Cristi Spulbar & Ramona Birau & Moein Khazaei & Iuliana Carmen Bărbăcioru, 2023. "A Sustainable Pattern of Waste Management and Energy Efficiency in Smart Homes Using the Internet of Things (IoT)," Sustainability, MDPI, vol. 15(6), pages 1-18, March.

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