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Optimization methods for power scheduling problems in smart home: Survey

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  • Makhadmeh, Sharif Naser
  • Khader, Ahamad Tajudin
  • Al-Betar, Mohammed Azmi
  • Naim, Syibrah
  • Abasi, Ammar Kamal
  • Alyasseri, Zaid Abdi Alkareem

Abstract

Optimizing the power demand for smart home appliances in a smart grid is the primary challenge faced by power supplier companies, particularly during peak periods, due to its considerable effect on the stability of a power system. Therefore, power supplier companies have introduced dynamic pricing schemes that provide different prices for a time horizon in which electricity prices are higher during peak periods due to the high power demand and lower during off-peak periods. The problem of scheduling smart home appliances at appropriate periods in a predefined time horizon in accordance with a dynamic pricing scheme is called power scheduling problem in a smart home (PSPSH). The primary objectives in addressing PSPSH are to reduce the electricity bill of users and maintain the stability of a power system by reducing the ratio of the highest power demand to the average power demand, known as the peak-to-average ratio, and to improve user comfort level by reducing the waiting time for appliances. In this paper, we review the most pertinent studies on optimization methods that address PSPSH. The reviewed studies are classified into exact algorithms and metaheuristic algorithms. The latter is categorized into single-based, population-based, and hybrid metaheuristic algorithms. Accordingly, a critical analysis of state-of-the-art methods are provided and possible future directions are also discussed.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:rensus:v:115:y:2019:i:c:s1364032119305702
    DOI: 10.1016/j.rser.2019.109362
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    3. 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).
    4. Chreim, Bashar & Esseghir, Moez & Merghem-Boulahia, Leila, 2022. "LOSISH—LOad Scheduling In Smart Homes based on demand response: Application to smart grids," Applied Energy, Elsevier, vol. 323(C).
    5. 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).
    6. Tri-Hai Nguyen & Luong Vuong Nguyen & Jason J. Jung & Israel Edem Agbehadji & Samuel Ofori Frimpong & Richard C. Millham, 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    7. Rocha, Helder R.O. & Honorato, Icaro H. & Fiorotti, Rodrigo & Celeste, Wanderley C. & Silvestre, Leonardo J. & Silva, Jair A.L., 2021. "An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes," Applied Energy, Elsevier, vol. 282(PA).
    8. Khouloud Salameh & Mohammed Awad & Aisha Makarfi & Abdul-Halim Jallad & Richard Chbeir, 2021. "Demand Side Management for Smart Houses: A Survey," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    9. 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).
    10. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2020. "An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem," Energies, MDPI, vol. 13(16), pages 1-31, August.
    11. 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.
    12. Kim, Hakpyeong & Choi, Heeju & Kang, Hyuna & An, Jongbaek & Yeom, Seungkeun & Hong, Taehoon, 2021. "A systematic review of the smart energy conservation system: From smart homes to sustainable smart cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    13. 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).
    14. Sovacool, Benjamin K. & Furszyfer Del Rio, Dylan D., 2020. "Smart home technologies in Europe: A critical review of concepts, benefits, risks and policies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    15. Jin, Xiaolong & Wu, Qiuwei & Jia, Hongjie, 2020. "Local flexibility markets: Literature review on concepts, models and clearing methods," Applied Energy, Elsevier, vol. 261(C).
    16. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    17. Su, Yongxin & Zhou, Yao & Tan, Mao, 2020. "An interval optimization strategy of household multi-energy system considering tolerance degree and integrated demand response," Applied Energy, Elsevier, vol. 260(C).

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