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A Modified Coronavirus Herd Immunity Optimizer for the Power Scheduling Problem

Author

Listed:
  • Sharif Naser Makhadmeh

    (Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates)

  • Mohammed Azmi Al-Betar

    (Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates
    Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Irbid 21110, Jordan)

  • Mohammed A. Awadallah

    (Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza P860, Palestine
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates)

  • Ammar Kamal Abasi

    (School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia)

  • Zaid Abdi Alkareem Alyasseri

    (Information Technology Research and Development Center (ITRDC), University of Kufa, Kufa 54001, Iraq)

  • Iyad Abu Doush

    (Computing Department, College of Engineering and Applied Sciences, American University of Kuwait, Salmiya 20002, Kuwait
    Computer Science Department, Yarmouk University, Irbid 21163, Jordan)

  • Osama Ahmad Alomari

    (MLALP Research Group, University of Sharjah, Sharjah 346, United Arab Emirates)

  • Robertas Damaševičius

    (Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania)

  • Audrius Zajančkauskas

    (Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania)

  • Mazin Abed Mohammed

    (College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq)

Abstract

The Coronavirus herd immunity optimizer (CHIO) is a new human-based optimization algorithm that imitates the herd immunity strategy to eliminate of the COVID-19 disease. In this paper, the coronavirus herd immunity optimizer (CHIO) is modified to tackle a discrete power scheduling problem in a smart home (PSPSH). PSPSH is a combinatorial optimization problem with NP-hard features. It is a highly constrained discrete scheduling problem concerned with assigning the operation time for smart home appliances based on a dynamic pricing scheme(s) and several other constraints. The primary objective when solving PSPSH is to maintain the stability of the power system by reducing the ratio between average and highest power demand (peak-to-average ratio (PAR)) and reducing electricity bill (EB) with considering the comfort level of users (UC). This paper modifies and adapts the CHIO algorithm to deal with such discrete optimization problems, particularly PSPSH. The adaptation and modification include embedding PSPSH problem-specific operators to CHIO operations to meet the discrete search space requirements. PSPSH is modeled as a multi-objective problem considering all objectives, including PAR, EB, and UC. The proposed method is examined using a dataset that contains 36 home appliances and seven consumption scenarios. The main CHIO parameters are tuned to find their best values. These best values are used to evaluate the proposed method by comparing its results with comparative five metaheuristic algorithms. The proposed method shows encouraging results and almost obtains the best results in all consumption scenarios.

Suggested Citation

  • Sharif Naser Makhadmeh & Mohammed Azmi Al-Betar & Mohammed A. Awadallah & Ammar Kamal Abasi & Zaid Abdi Alkareem Alyasseri & Iyad Abu Doush & Osama Ahmad Alomari & Robertas Damaševičius & Audrius Zaja, 2022. "A Modified Coronavirus Herd Immunity Optimizer for the Power Scheduling Problem," Mathematics, MDPI, vol. 10(3), pages 1-29, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:315-:d:729506
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    References listed on IDEAS

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    1. Makhadmeh, Sharif Naser & Khader, Ahamad Tajudin & Al-Betar, Mohammed Azmi & Naim, Syibrah & Abasi, Ammar Kamal & Alyasseri, Zaid Abdi Alkareem, 2019. "Optimization methods for power scheduling problems in smart home: Survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    2. Khan, Ahsan Raza & Mahmood, Anzar & Safdar, Awais & Khan, Zafar A. & Khan, Naveed Ahmed, 2016. "Load forecasting, dynamic pricing and DSM in smart grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1311-1322.
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    Cited by:

    1. Mohammed Azmi Al-Betar & Ammar Kamal Abasi & Ghazi Al-Naymat & Kamran Arshad & Sharif Naser Makhadmeh, 2023. "Optimization of scientific publications clustering with ensemble approach for topic extraction," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2819-2877, May.
    2. Ziad M. Ali & Shady H. E. Abdel Aleem & Ahmed I. Omar & Bahaa Saad Mahmoud, 2022. "Economical-Environmental-Technical Operation of Power Networks with High Penetration of Renewable Energy Systems Using Multi-Objective Coronavirus Herd Immunity Algorithm," Mathematics, MDPI, vol. 10(7), pages 1-43, April.

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