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Multi-objective demand side scheduling considering the operational safety of appliances

Author

Listed:
  • Du, Y.F.
  • Jiang, L.
  • Li, Y.Z.
  • Counsell, J.
  • Smith, J.S.

Abstract

The safe operation of appliances is of great concern to users. The safety risk increases when the appliances are in operation during periods when users are not at home or when they are asleep. In this paper, multi-objective demand side scheduling is investigated with consideration to the appliances’ operational safety together with the electricity cost and the operational delay. The formulation of appliances’ operational safety is proposed based on users’ at-home status and awake status. Then the relationships between the operational safety and the other two objectives are investigated through the approach of finding the Pareto-optimal front. Moreover, this approach is compared with the Weigh and Constraint approaches. As the Pareto-optimal front consists of a set of optimal solutions, this paper proposes a method to make the final scheduling decision based on the relationships among the multiple objectives. Simulation results demonstrate that the operational safety is improved with the sacrifice of the electricity cost and the operational delay, and that the approach of finding the Pareto-optimal front is effective in presenting comprehensive optimal solutions of the multi-objective demand side scheduling.

Suggested Citation

  • Du, Y.F. & Jiang, L. & Li, Y.Z. & Counsell, J. & Smith, J.S., 2016. "Multi-objective demand side scheduling considering the operational safety of appliances," Applied Energy, Elsevier, vol. 179(C), pages 864-874.
  • Handle: RePEc:eee:appene:v:179:y:2016:i:c:p:864-874
    DOI: 10.1016/j.apenergy.2016.07.016
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    References listed on IDEAS

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    1. Di Giorgio, Alessandro & Liberati, Francesco, 2014. "Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models," Applied Energy, Elsevier, vol. 128(C), pages 119-132.
    2. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    3. Kwon, Pil Seok & Østergaard, Poul, 2014. "Assessment and evaluation of flexible demand in a Danish future energy scenario," Applied Energy, Elsevier, vol. 134(C), pages 309-320.
    4. Behboodi, Sahand & Chassin, David P. & Crawford, Curran & Djilali, Ned, 2016. "Renewable resources portfolio optimization in the presence of demand response," Applied Energy, Elsevier, vol. 162(C), pages 139-148.
    5. Kobus, Charlotte B.A. & Klaassen, Elke A.M. & Mugge, Ruth & Schoormans, Jan P.L., 2015. "A real-life assessment on the effect of smart appliances for shifting households’ electricity demand," Applied Energy, Elsevier, vol. 147(C), pages 335-343.
    6. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    7. Kwag, Hyung-Geun & Kim, Jin-O, 2014. "Reliability modeling of demand response considering uncertainty of customer behavior," Applied Energy, Elsevier, vol. 122(C), pages 24-33.
    8. Ahn, Byeong Seok, 2011. "Compatible weighting method with rank order centroid: Maximum entropy ordered weighted averaging approach," European Journal of Operational Research, Elsevier, vol. 212(3), pages 552-559, August.
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    Cited by:

    1. Omaji Samuel & Sakeena Javaid & Nadeem Javaid & Syed Hassan Ahmed & Muhammad Khalil Afzal & Farruh Ishmanov, 2018. "An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes," Energies, MDPI, vol. 11(11), pages 1-27, November.
    2. Su, Huai & Zhang, Jinjun & Zio, Enrico & Yang, Nan & Li, Xueyi & Zhang, Zongjie, 2018. "An integrated systemic method for supply reliability assessment of natural gas pipeline networks," Applied Energy, Elsevier, vol. 209(C), pages 489-501.
    3. Su, Huai & Zio, Enrico & Zhang, Jinjun & Li, Xueyi, 2018. "A systematic framework of vulnerability analysis of a natural gas pipeline network," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 79-91.
    4. de Souza Dutra, Michael David & da Conceição Júnior, Gerson & de Paula Ferreira, William & Campos Chaves, Matheus Roberto, 2020. "A customized transition towards smart homes: A fast framework for economic analyses," Applied Energy, Elsevier, vol. 262(C).

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