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QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles

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  • Mingfu Li

    (Department of Electrical Engineering, School of Electrical and Computer Engineering, College of Engineering, Chang Gung University, Guishan District, Taoyuan 33302, Taiwan
    Neuroscience Research Center, Chang Gung Memorial Hospital, Linkou, Guishan District, Taoyuan City 33305, Taiwan)

  • Guan-Yi Li

    (Department of Electrical Engineering, School of Electrical and Computer Engineering, College of Engineering, Chang Gung University, Guishan District, Taoyuan 33302, Taiwan)

  • Hou-Ren Chen

    (Department of Electrical Engineering, School of Electrical and Computer Engineering, College of Engineering, Chang Gung University, Guishan District, Taoyuan 33302, Taiwan)

  • Cheng-Wei Jiang

    (Department of Electrical Engineering, School of Electrical and Computer Engineering, College of Engineering, Chang Gung University, Guishan District, Taoyuan 33302, Taiwan)

Abstract

To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed algorithm decreases the peak load and electricity bill by deferring starting times of delay-tolerant appliances from peak to off-peak hours, controlling the temperature setting of heating, ventilation, and air conditioning (HVAC), and properly scheduling the discharging and charging periods of an EV. In this paper, the user comfort is evaluated by means of QoE functions. To preserve the user’s QoE, the delay of the starting time of a home appliance and the temperature setting of HVAC are constrained by a QoE threshold. Additionally, to solve the trade-off problem between the peak load/electricity bill reduction and user’s QoE, a fuzzy logic controller for dynamically adjusting the QoE threshold to optimize the user’s QoE was also designed. Simulation results demonstrate that the proposed smart appliance control algorithm with a fuzzy-controlled QoE threshold significantly reduces the peak load and electricity bill while optimally preserving the user’s QoE. Compared with the baseline case, the proposed scheme reduces the electricity bill by 65% under the scenario with RES and EV. Additionally, compared with the method of optimal scheduling of appliances in the literature, the proposed scheme achieves much better peak load reduction performance and user’s QoE.

Suggested Citation

  • Mingfu Li & Guan-Yi Li & Hou-Ren Chen & Cheng-Wei Jiang, 2018. "QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles," Energies, MDPI, vol. 11(9), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2304-:d:167246
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    References listed on IDEAS

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    1. Mario Collotta & Giovanni Pau, 2015. "A Solution Based on Bluetooth Low Energy for Smart Home Energy Management," Energies, MDPI, vol. 8(10), pages 1-23, October.
    2. Sébastien Bissey & Sébastien Jacques & Jean-Charles Le Bunetel, 2017. "The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing," Energies, MDPI, vol. 10(11), pages 1-24, October.
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    Cited by:

    1. Ahmad H. Besheer & Momen S. Agamy & Hassan M. Emara & Ahmed Bahgat, 2018. "Unified Algorithm for Demand-Side Appliance Commitment," Energies, MDPI, vol. 11(12), pages 1-18, November.
    2. 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.
    3. Herie Park, 2020. "Human Comfort-Based-Home Energy Management for Demand Response Participation," Energies, MDPI, vol. 13(10), pages 1-15, May.
    4. Kim, Hakpyeong & Choi, Heeju & Kang, Hyuna & An, Jongbaek & Yeom, Seungkeun & Hong, Taehoon, 2021. "A systematic review of the smart energy conservation system: From smart homes to sustainable smart cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    5. Al Essa, Mohammed Jasim M., 2019. "Home energy management of thermostatically controlled loads and photovoltaic-battery systems," Energy, Elsevier, vol. 176(C), pages 742-752.
    6. Doğukan Aycı & Ferhat Öğüt & Ulaş Özen & Bora Batuhan İşgör & Sinan Küfeoğlu, 2021. "Energy Optimisation Models for Self-Sufficiency of a Typical Turkish Residential Electricity Customer of the Future," Energies, MDPI, vol. 14(19), pages 1-24, September.

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