IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i3p315-d729506.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/3/315/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/3/315/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Isaías Gomes & Karol Bot & Maria Graça Ruano & António Ruano, 2022. "Recent Techniques Used in Home Energy Management Systems: A Review," Energies, MDPI, vol. 15(8), pages 1-41, April.
    2. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    3. Wakiyama, Takako & Zusman, Eric, 2021. "The impact of electricity market reform and subnational climate policy on carbon dioxide emissions across the United States: A path analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    4. Da Liu & Kun Sun & Han Huang & Pingzhou Tang, 2018. "Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
    5. Fridgen, Gilbert & Kahlen, Micha & Ketter, Wolfgang & Rieger, Alexander & Thimmel, Markus, 2018. "One rate does not fit all: An empirical analysis of electricity tariffs for residential microgrids," Applied Energy, Elsevier, vol. 210(C), pages 800-814.
    6. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    7. Waseem, Muhammad & Lin, Zhenzhi & Liu, Shengyuan & Zhang, Zhi & Aziz, Tarique & Khan, Danish, 2021. "Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources," Applied Energy, Elsevier, vol. 290(C).
    8. Yuchun Li & Yinghua Han & Jinkuan Wang & Qiang Zhao, 2018. "A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort," Energies, MDPI, vol. 11(12), pages 1-20, December.
    9. Adnan Ahmad & Asif Khan & Nadeem Javaid & Hafiz Majid Hussain & Wadood Abdul & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources," Energies, MDPI, vol. 10(4), pages 1-35, April.
    10. Ghayour, Sepideh Saravani & Barforoushi, Taghi, 2022. "Optimal scheduling of electrical and thermal resources and appliances in a smart home under uncertainty," Energy, Elsevier, vol. 261(PA).
    11. Tu, Chunming & He, Xi & Shuai, Zhikang & Jiang, Fei, 2017. "Big data issues in smart grid – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1099-1107.
    12. Schellenberg, C. & Lohan, J. & Dimache, L., 2020. "Comparison of metaheuristic optimisation methods for grid-edge technology that leverages heat pumps and thermal energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    13. Andrew Blohm & Jaden Crawford & Steven A. Gabriel, 2021. "Demand Response as a Real-Time, Physical Hedge for Retail Electricity Providers: The Electric Reliability Council of Texas Market Case Study," Energies, MDPI, vol. 14(4), pages 1-16, February.
    14. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    15. Detroja, Ketan P., 2016. "Optimal autonomous microgrid operation: A holistic view," Applied Energy, Elsevier, vol. 173(C), pages 320-330.
    16. Calver, Philippa & Simcock, Neil, 2021. "Demand response and energy justice: A critical overview of ethical risks and opportunities within digital, decentralised, and decarbonised futures," Energy Policy, Elsevier, vol. 151(C).
    17. Stute, Judith & Klobasa, Marian, 2024. "How do dynamic electricity tariffs and different grid charge designs interact? - Implications for residential consumers and grid reinforcement requirements," Energy Policy, Elsevier, vol. 189(C).
    18. Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
    19. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    20. Jimyung Kang & Soonwoo Lee, 2018. "Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System," Energies, MDPI, vol. 11(11), pages 1-14, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:315-:d:729506. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.