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Optimizing energy consumption patterns of smart home using a developed elite evolutionary strategy artificial ecosystem optimization algorithm

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  • Youssef, Heba
  • Kamel, Salah
  • Hassan, Mohamed H.
  • Nasrat, Loai

Abstract

In recent times, the tremendous progress in electronic devices and making them available to all consumers has led to an increase in demand for energy, the formation of a peak, and an increase in the electricity bill of the consumer. Buildings are considered one of the main sectors of energy consumption. Improving the energy efficiency of these buildings will achieve economic and environmental goals. This paper proposes a modified algorithm for scheduling electrical appliances in the smart home to reduce the electricity bill, improve the network, maximize customer comfort and reduce peak formation. This paper uses load conversion to change the shape of the load, as it is one of the types of demand side management (DSM) strategies. A modified algorithm, called elite evolutionary strategy artificial ecosystem-based optimization (EESAEO), is used in scheduling household appliances. The obtained results using the EESAEO algorithm reduce the electricity bill, improve the network, maximize customer comfort and reduce peak formation. The original algorithm is used to compare the simulated results and to verify the strength of the proposed EESAEO algorithm. Different price strategies such as TOU, CPP, and RTP are used, to ensure the efficiency of the studied system. These successful results prove that the applied method can be used for scheduling household appliances.

Suggested Citation

  • Youssef, Heba & Kamel, Salah & Hassan, Mohamed H. & Nasrat, Loai, 2023. "Optimizing energy consumption patterns of smart home using a developed elite evolutionary strategy artificial ecosystem optimization algorithm," Energy, Elsevier, vol. 278(C).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:c:s0360544223011878
    DOI: 10.1016/j.energy.2023.127793
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    References listed on IDEAS

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    1. Tuomela, Sanna & de Castro Tomé, Mauricio & Iivari, Netta & Svento, Rauli, 2021. "Impacts of home energy management systems on electricity consumption," Applied Energy, Elsevier, vol. 299(C).
    2. Zhou, Bin & Li, Wentao & Chan, Ka Wing & Cao, Yijia & Kuang, Yonghong & Liu, Xi & Wang, Xiong, 2016. "Smart home energy management systems: Concept, configurations, and scheduling strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 30-40.
    3. Heutel, Garth, 2019. "Prospect theory and energy efficiency," Journal of Environmental Economics and Management, Elsevier, vol. 96(C), pages 236-254.
    4. Yang Jiao & Jing Wu & Qing-kun Tan & Zhong-fu Tan & Guan Wang, 2017. "An Optimization Model and Modified Harmony Search Algorithm for Microgrid Planning with ESS," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-11, August.
    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.
    6. Gowdy, John & Rosser, J. Barkley & Roy, Loraine, 2013. "The evolution of hyperbolic discounting: Implications for truly social valuation of the future," Journal of Economic Behavior & Organization, Elsevier, vol. 90(S), pages 94-104.
    7. 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.
    8. Zupančič, Jernej & Filipič, Bogdan & Gams, Matjaž, 2020. "Genetic-programming-based multi-objective optimization of strategies for home energy-management systems," Energy, Elsevier, vol. 203(C).
    9. Herter, Karen, 2007. "Residential implementation of critical-peak pricing of electricity," Energy Policy, Elsevier, vol. 35(4), pages 2121-2130, April.
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    Cited by:

    1. Durillon, Benoit & Bossu, Adrien, 2024. "Environmental assessment of smart energy management systems at distribution level — A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
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