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Multiobjective Decision-Making Model for Power Scheduling Problem in Smart Homes

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  • Chen-Yu Chang

    (Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Pei-Fang Tsai

    (Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan)

Abstract

The aim of this study was to solve power scheduling issues in smart homes to enable demand response in smart grids. The objective of demand response is to match demand with supply by reflecting supply expectations through consumer price signals, and especially to avoid peak demand during times of high prices and when supply is limited. Three objectives were considered: first, economic rationing by minimizing the total costs for consumers with the given hourly prices; second, to achieve better efficiency in terms of supply and greater stability in a power system by reducing peaks in usage or load, which is defined by minimizing the percentage of power rate; third, related to consumer comfort levels, by reducing variance in the schedule of appliances to actual usage periods requested. This multiobjective power scheduling problem for smart homes (PHPSH) was explored using a nondominated sorting genetic algorithm, called NSGA-II. The results showed that the Pareto-optimal solutions from NSGA-II are compatible with the weighted-sum-based model from the literature, and viable alternatives are available for end users with different weighted objectives.

Suggested Citation

  • Chen-Yu Chang & Pei-Fang Tsai, 2022. "Multiobjective Decision-Making Model for Power Scheduling Problem in Smart Homes," Sustainability, MDPI, vol. 14(19), pages 1-13, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11867-:d:920524
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    References listed on IDEAS

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    1. Hussein Jumma Jabir & Jiashen Teh & Dahaman Ishak & Hamza Abunima, 2018. "Impacts of Demand-Side Management on Electrical Power Systems: A Review," Energies, MDPI, vol. 11(5), pages 1-19, April.
    2. Tuballa, Maria Lorena & Abundo, Michael Lochinvar, 2016. "A review of the development of Smart Grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 710-725.
    3. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    4. Christian Lücken & Benjamín Barán & Carlos Brizuela, 2014. "A survey on multi-objective evolutionary algorithms for many-objective problems," Computational Optimization and Applications, Springer, vol. 58(3), pages 707-756, July.
    5. Tehreem Nasir & Syed Sabir Hussain Bukhari & Safdar Raza & Hafiz Mudassir Munir & Muhammad Abrar & Hafiz Abd ul Muqeet & Kamran Liaquat Bhatti & Jong-Suk Ro & Rooha Masroor, 2021. "Recent Challenges and Methodologies in Smart Grid Demand Side Management: State-of-the-Art Literature Review," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-16, August.
    6. Kabalci, Yasin, 2016. "A survey on smart metering and smart grid communication," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 302-318.
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