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Analysis Application of Controllable Load Appliances Management in a Smart Home

Author

Listed:
  • Gerardo J. Osório

    (C-MAST, University of Beira Interior, Calcada Fonte Lameiro, 6201-001 Covilha, Portugal)

  • Miadreza Shafie-khah

    (School of Technology and Innovations, University of Vaasa, 65200 Vaasa, Finland)

  • Gonçalo C. R. Carvalho

    (Faculty of Engineering of University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal)

  • João P. S. Catalão

    (Faculty of Engineering of University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal
    INESC-TEC, R. Dr. Roberto Frias, 4200-465 Porto, Portugal)

Abstract

The residential sector is one of the sectors with the highest rates of electricity consumption worldwide. For years, many studies have been presented in order to minimize energy consumption at the residential level. The idea of such studies is that the residential customer (RC) is the interested party of their own consumption. Moreover, the algorithms that have been developed to predict and manage the energy consumption, also analyze the behavior of the loads, with the objective of minimizing the energy costs, with good safety, robustness, and comfort levels. In the context of the smart house (SH), one of the objectives of smart grids (SGs) is to enable the RC, with home energy management systems (HEM), to actively participate, allowing for higher reliability at different levels. In this work, a new model that simulates the behavior of an SH, considering heating, ventilation and air conditioning (HVAC) and sanitarian water heater (SWH) devices, is presented. For this purpose, the proposed model considers realistic physical parameters of the SH, together with customer comfort, in order to mitigate the RC disinterest. The proposed model considers the electric vehicle (EV), a battery-based energy storage system (ESS), a micro production unit, and different types of tariffs that the RC might choose, aiming to maximize the benefits, and temporarily shifting the proposed loads.

Suggested Citation

  • Gerardo J. Osório & Miadreza Shafie-khah & Gonçalo C. R. Carvalho & João P. S. Catalão, 2019. "Analysis Application of Controllable Load Appliances Management in a Smart Home," Energies, MDPI, vol. 12(19), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3710-:d:271636
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    References listed on IDEAS

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    1. Zhu, Jiawei & Lin, Yishuai & Lei, Weidong & Liu, Youquan & Tao, Mengling, 2019. "Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm," Energy, Elsevier, vol. 171(C), pages 944-955.
    2. Wilson, Charlie & Hargreaves, Tom & Hauxwell-Baldwin, Richard, 2017. "Benefits and risks of smart home technologies," Energy Policy, Elsevier, vol. 103(C), pages 72-83.
    3. Bhati, Abhishek & Hansen, Michael & Chan, Ching Man, 2017. "Energy conservation through smart homes in a smart city: A lesson for Singapore households," Energy Policy, Elsevier, vol. 104(C), pages 230-239.
    4. Newsham, Guy R. & Bowker, Brent G., 2010. "The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review," Energy Policy, Elsevier, vol. 38(7), pages 3289-3296, July.
    5. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
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    Cited by:

    1. Michael J. Ritchie & Jacobus A. A. Engelbrecht & M. J. (Thinus) Booysen, 2021. "Which Strategy Saves the Most Energy for Stratified Water Heaters?," Energies, MDPI, vol. 14(16), pages 1-12, August.
    2. Stefano Leonori & Luca Baldini & Antonello Rizzi & Fabio Massimo Frattale Mascioli, 2021. "A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells," Energies, MDPI, vol. 14(21), pages 1-29, November.
    3. Michael J. Ritchie & Jacobus A. A. Engelbrecht & Marthinus J. Booysen, 2022. "Centrally Adapted Optimal Control of Multiple Electric Water Heaters," Energies, MDPI, vol. 15(4), pages 1-24, February.
    4. Wadim Strielkowski & Dalia Streimikiene & Alena Fomina & Elena Semenova, 2019. "Internet of Energy (IoE) and High-Renewables Electricity System Market Design," Energies, MDPI, vol. 12(24), pages 1-17, December.

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