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Scheduling policies for two-state smart-home appliances in dynamic electricity pricing environments

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  • Vardakas, John S.
  • Zorba, Nizar
  • Verikoukis, Christos V.

Abstract

In this paper we present and analyze online and offline scheduling models for the determination of the maximum power consumption in a smart grid environment. The proposed load models consider that each consumer's residence is equipped with a certain number of appliances of different power demands and different operational times, while the appliances' feature of alternating between ON and OFF states is also incorporated. Each load model is correlated with a scheduling policy that aims to the reduction of the power consumption through the compression of power demands or the postponement of power requests. Furthermore, we associate each load model with a proper dynamic pricing process in order to provide consumers with incentives to contribute to the overall power consumption reduction. The evaluation of the load models through simulation reveals the consistency and the accuracy of the proposed analysis.

Suggested Citation

  • Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2014. "Scheduling policies for two-state smart-home appliances in dynamic electricity pricing environments," Energy, Elsevier, vol. 69(C), pages 455-469.
    • Handle: RePEc:eee:energy:v:69:y:2014:i:c:p:455-469
    DOI: 10.1016/j.energy.2014.03.037
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    References listed on IDEAS

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    Cited by:

    1. Premarathne, Uthpala Subodhani, 2015. "Reliable context-aware multi-attribute continuous authentication framework for secure energy utilization management in smart homes," Energy, Elsevier, vol. 93(P1), pages 1210-1221.
    2. Tushar, Wayes & Lan, Lan & Withanage, Chathura & Sng, Hui En Karen & Yuen, Chau & Wood, Kristin L. & Saha, Tapan Kumar, 2020. "Exploiting design thinking to improve energy efficiency of buildings," Energy, Elsevier, vol. 197(C).
    3. Hong, Seung Ho & Yu, Mengmeng & Huang, Xuefei, 2015. "A real-time demand response algorithm for heterogeneous devices in buildings and homes," Energy, Elsevier, vol. 80(C), pages 123-132.
    4. Khemakhem, Siwar & Rekik, Mouna & Krichen, Lotfi, 2017. "A flexible control strategy of plug-in electric vehicles operating in seven modes for smoothing load power curves in smart grid," Energy, Elsevier, vol. 118(C), pages 197-208.
    5. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2015. "Performance evaluation of power demand scheduling scenarios in a smart grid environment," Applied Energy, Elsevier, vol. 142(C), pages 164-178.
    6. Li, Xin & Chen, Hsing Hung & Tao, Xiangnan, 2016. "Pricing and capacity allocation in renewable energy," Applied Energy, Elsevier, vol. 179(C), pages 1097-1105.
    7. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2016. "Power demand control scenarios for smart grid applications with finite number of appliances," Applied Energy, Elsevier, vol. 162(C), pages 83-98.
    8. Zhou, Kaile & Yang, Shanlin, 2015. "Demand side management in China: The context of China’s power industry reform," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 954-965.

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