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Towards the Handling Demand Response Optimization Model for Home Appliances

Author

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  • Jaclason M. Veras

    (Graduate Program in Applied Informatics, University of Fortaleza (UNIFOR), Fortaleza—CE 60811-905, Brazil)

  • Igor Rafael S. Silva

    (Department of Computing, Federal University of Piauí (UFPI), Teresina—PI 64049-550, Brazil)

  • Plácido R. Pinheiro

    (Graduate Program in Applied Informatics, University of Fortaleza (UNIFOR), Fortaleza—CE 60811-905, Brazil)

  • Ricardo A. L. Rabêlo

    (Department of Computing, Federal University of Piauí (UFPI), Teresina—PI 64049-550, Brazil)

Abstract

The Demand Response (DR) is used by public electric utilities to encourage consumers to change their consumption profiles to improve the reliability and efficiency of the electric power system (EPS) and at the same time to minimize the electricity costs for the final consumers. Normally, DR optimization models only aim to reduce the energy consumption and reduce the final cost. However, this disregards the needs of the consumer. Therefore, proposals which appear excellent in theory are usually impracticable and non-commercial. This paper proposes a real-time Demand Response (DR) optimization model to minimize the electricity costs associated with consumption without compromising the satisfaction or comfort of residential consumers. The proposed DR here considered the different home appliance categories and level of consumer satisfaction for the new load scheduling of the appliances and is much more comprehensive than the other models analyzed. Moreover, it can be applied in any country, under any energy scenario. This model was developed as a nonlinear programming problem subject to a set of constraints. An energy consumption analysis of 10 families for 2015 from five geographic and climatic regions of Brazil was carried out. A computational validation of the model was performed using a genetic algorithm (GA) to determine the programming of residential devices for the time horizon. The computational simulations showed a decrease in the cost of the electricity. Moreover, the results showed that there was no impairment to consumer satisfaction and comfort due to the scheduling of appliances.

Suggested Citation

  • Jaclason M. Veras & Igor Rafael S. Silva & Plácido R. Pinheiro & Ricardo A. L. Rabêlo, 2018. "Towards the Handling Demand Response Optimization Model for Home Appliances," Sustainability, MDPI, vol. 10(3), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:616-:d:133761
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    References listed on IDEAS

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    1. Wang, Chengshan & Zhou, Yue & Wang, Jidong & Peng, Peiyuan, 2013. "A novel Traversal-and-Pruning algorithm for household load scheduling," Applied Energy, Elsevier, vol. 102(C), pages 1430-1438.
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    Cited by:

    1. Anna Visvizi & Miltiadis D. Lytras, 2018. "It’s Not a Fad: Smart Cities and Smart Villages Research in European and Global Contexts," Sustainability, MDPI, vol. 10(8), pages 1-10, August.
    2. Liu, Jin-peng & Zhang, Teng-xi & Zhu, Jiang & Ma, Tian-nan, 2018. "Allocation optimization of electric vehicle charging station (EVCS) considering with charging satisfaction and distributed renewables integration," Energy, Elsevier, vol. 164(C), pages 560-574.
    3. Siiri Söyrinki & Eva Heiskanen & Kaisa Matschoss, 2018. "Piloting Demand Response in Retailing: Lessons Learned in Real-Life Context," Sustainability, MDPI, vol. 10(10), pages 1-17, October.
    4. Angreine Kewo & Pinrolinvic D. K. Manembu & Per Sieverts Nielsen, 2023. "A Rigorous Standalone Literature Review of Residential Electricity Load Profiles," Energies, MDPI, vol. 16(10), pages 1-27, May.
    5. Nikolaos Kolokas & Dimosthenis Ioannidis & Dimitrios Tzovaras, 2021. "Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization," Energies, MDPI, vol. 14(11), pages 1-36, May.
    6. Evgenia Kapassa & Marinos Themistocleous, 2022. "Blockchain Technology Applied in IoV Demand Response Management: A Systematic Literature Review," Future Internet, MDPI, vol. 14(5), pages 1-19, April.

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