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Optimal scheduling of household appliances for smart home energy management considering demand response

Author

Listed:
  • Xinhui Lu

    (Hefei University of Technology
    Ministry of Education)

  • Kaile Zhou

    (Hefei University of Technology
    Ministry of Education)

  • Felix T. S. Chan

    (The Hong Kong Polytechnic University)

  • Shanlin Yang

    (Hefei University of Technology
    Ministry of Education)

Abstract

As an important part of demand-side management, residential demand response (DR) can not only reduce consumer’s electricity costs, but also improve the stability of power system operation. In this regard, this paper proposes an optimal scheduling model of household appliances for smart home energy management considering DR. The model includes electricity cost, incentive and inconvenience of consumers under time-of-use (TOU) electricity price. Further, this paper discusses the influence of inconvenience weighting factor on total costs. At the same time, the influence of incentive on optimization results is also analyzed. The simulation results show the effectiveness of the proposed model, which can reduce 34.71% of consumer’s total costs. It also illustrates that the total costs will be raised with the increase in inconvenience weighting factor. Thus, consumers will choose whether to participate in DR programs according to their preferences. Moreover, the result demonstrates that incentives are conducive to shifting load and reducing the consumer’s total energy costs. The presented study provides new insight for the applications of residential DR.

Suggested Citation

  • Xinhui Lu & Kaile Zhou & Felix T. S. Chan & Shanlin Yang, 2017. "Optimal scheduling of household appliances for smart home energy management considering demand response," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1639-1653, September.
    • Handle: RePEc:spr:nathaz:v:88:y:2017:i:3:d:10.1007_s11069-017-2937-9
    DOI: 10.1007/s11069-017-2937-9
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    References listed on IDEAS

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    Cited by:

    1. Dhiaa Halboot Muhsen & Haider Tarish Haider & Yaarob Al-Nidawi & Ghadeer Ghazi Shayea, 2023. "Operational Scheduling of Household Appliances by Using Triple-Objective Optimization Algorithm Integrated with Multi-Criteria Decision Making," Sustainability, MDPI, vol. 15(24), pages 1-24, December.
    2. Haider, Haider Tarish & Muhsen, Dhiaa Halboot & Al-Nidawi, Yaarob Mahjoob & Khatib, Tamer & See, Ong Hang, 2022. "A novel approach for multi-objective cost-peak optimization for demand response of a residential area in smart grids," Energy, Elsevier, vol. 254(PB).
    3. Chen Wang & Kaile Zhou & Lanlan Li & Shanlin Yang, 2018. "Multi-agent simulation-based residential electricity pricing schemes design and user selection decision-making," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(3), pages 1309-1327, February.
    4. Muhammad Saidu Aliero & Muhammad Asif & Imran Ghani & Muhammad Fermi Pasha & Seung Ryul Jeong, 2022. "Systematic Review Analysis on Smart Building: Challenges and Opportunities," Sustainability, MDPI, vol. 14(5), pages 1-28, March.
    5. Zhou, Kaile & Cheng, Lexin & Lu, Xinhui & Wen, Lulu, 2020. "Scheduling model of electric vehicles charging considering inconvenience and dynamic electricity prices," Applied Energy, Elsevier, vol. 276(C).
    6. Alam, Muhammad Raisul & St-Hilaire, Marc & Kunz, Thomas, 2019. "Peer-to-peer energy trading among smart homes," Applied Energy, Elsevier, vol. 238(C), pages 1434-1443.
    7. Essiet, Ima O. & Sun, Yanxia & Wang, Zenghui, 2019. "Optimized energy consumption model for smart home using improved differential evolution algorithm," Energy, Elsevier, vol. 172(C), pages 354-365.

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