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A new home energy management algorithm with voltage control in a smart home environment

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  • Elma, Onur
  • Selamogullari, Ugur Savas

Abstract

Energy management in electrical systems is one of the important issues for energy efficiency and future grid systems. Energy management is defined as a HEM (home energy management) on the residential consumer side. The HEM system plays a key role in residential demand response applications. In this study, a new HEM algorithm is proposed for smart home environments to reduce peak demand and increase the energy efficiency. The proposed algorithm includes VC (voltage control) methodology to reduce the power consumption of residential appliances so that the shifting of appliances is minimized. The results of the survey are used to produce representative load profiles for a weekday and for a weekend. Then, case studies are completed to test the proposed HEM algorithm in reducing the peak demand in the house. The main aim of the proposed HEM algorithm is to minimize the number of turned-off appliances to decrease demand so that the customer comfort is maximized. The smart home laboratory at Yildiz Technical University, Istanbul, Turkey is used in case studies. Experimental results show that the proposed HEM algorithm reduces the peak demand by 17.5% with the voltage control and by 38% with both the voltage control and the appliance shifting.

Suggested Citation

  • Elma, Onur & Selamogullari, Ugur Savas, 2015. "A new home energy management algorithm with voltage control in a smart home environment," Energy, Elsevier, vol. 91(C), pages 720-731.
    • Handle: RePEc:eee:energy:v:91:y:2015:i:c:p:720-731
    DOI: 10.1016/j.energy.2015.08.094
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    References listed on IDEAS

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    1. Nien-Che Yang & Yan-Lin Zeng & Tsai-Hsiang Chen, 2021. "Assessment of Voltage Imbalance Improvement and Power Loss Reduction in Residential Distribution Systems in Taiwan," Mathematics, MDPI, vol. 9(24), pages 1-17, December.
    2. Ouedraogo, Kiswendsida Elias & Ekim, Pınar Oğuz & Demirok, Erhan, 2023. "Feasibility of low-cost energy management system using embedded optimization for PV and battery storage assisted residential buildings," Energy, Elsevier, vol. 271(C).
    3. Choi, Dae-Hyun & Xie, Le, 2016. "A framework for sensitivity analysis of data errors on home energy management system," Energy, Elsevier, vol. 117(P1), pages 166-175.
    4. Khemakhem, Siwar & Rekik, Mouna & Krichen, Lotfi, 2019. "Double layer home energy supervision strategies based on demand response and plug-in electric vehicle control for flattening power load curves in a smart grid," Energy, Elsevier, vol. 167(C), pages 312-324.
    5. Elma, Onur & Taşcıkaraoğlu, Akın & Tahir İnce, A. & Selamoğulları, Uğur S., 2017. "Implementation of a dynamic energy management system using real time pricing and local renewable energy generation forecasts," Energy, Elsevier, vol. 134(C), pages 206-220.

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