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Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm

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  • Zhu, Jiawei
  • Lin, Yishuai
  • Lei, Weidong
  • Liu, Youquan
  • Tao, Mengling

Abstract

It will easily make peak loads happen with the increasing usage of residential high-power appliances, which may damage the power grid, cause unforeseen disasters, and reduce the global profit. Towards the optimization of energy consumption, this paper aims to provide an attempt to schedule the operations of household appliances considering their characteristics as well as customer convenience. Bottom-up engineering models that can obtain better understanding of residential electricity demand patterns are developed. Since the formulations are nonlinear complex combinatorial problems, the scheduling of household appliances within multiple smart homes is a challenging optimization problem. In order to solve this challenging optimization problem efficiently, an improved cooperative heuristic approach is proposed to achieve a near optimal solution with better performance. Experimental results confirm the effectiveness of the proposed algorithm. Moreover, a case study is conducted to show that by employing this proposed approach, user comfort is guaranteed, electricity cost is reduced and total loads on the main grid are flattened so that the global energy efficiency is improved.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:171:y:2019:i:c:p:944-955
    DOI: 10.1016/j.energy.2019.01.025
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    5. Luz, G. Pontes & Brito, M.C. & Sousa, J.M.C. & Vieira, S.M., 2021. "Coordinating shiftable loads for collective photovoltaic self-consumption: A multi-agent approach," Energy, Elsevier, vol. 229(C).
    6. Wakui, Tetsuya & Sawada, Kento & Yokoyama, Ryohei & Aki, Hirohisa, 2019. "Predictive management for energy supply networks using photovoltaics, heat pumps, and battery by two-stage stochastic programming and rule-based control," Energy, Elsevier, vol. 179(C), pages 1302-1319.
    7. Melendez, Kevin A. & Subramanian, Vignesh & Das, Tapas K. & Kwon, Changhyun, 2019. "Empowering end-use consumers of electricity to aggregate for demand-side participation," Applied Energy, Elsevier, vol. 248(C), pages 372-382.
    8. 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.
    9. Christoforos Menos-Aikateriniadis & Ilias Lamprinos & Pavlos S. Georgilakis, 2022. "Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision," Energies, MDPI, vol. 15(6), pages 1-26, March.
    10. Yeongenn Kwon & Taeyoung Kim & Keon Baek & Jinho Kim, 2020. "Multi-Objective Optimization of Home Appliances and Electric Vehicle Considering Customer’s Benefits and Offsite Shared Photovoltaic Curtailment," Energies, MDPI, vol. 13(11), pages 1-16, June.
    11. Wei, Congying & Wu, Qiuwei & Xu, Jian & Sun, Yuanzhang & Jin, Xiaolong & Liao, Siyang & Yuan, Zhiyong & Yu, Li, 2020. "Distributed scheduling of smart buildings to smooth power fluctuations considering load rebound," Applied Energy, Elsevier, vol. 276(C).
    12. Tüysüz, Metin & Okumuş, Halil Ibrahim & Aymaz, Şeyma & Çavdar, Bora, 2024. "Real-time application of a demand-side management strategy using optimization algorithms," Applied Energy, Elsevier, vol. 368(C).
    13. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(C).
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    15. Liu, Youquan & Li, Huazhen & Zhu, Jiawei & Lin, Yishuai & Lei, Weidong, 2023. "Multi-objective optimal scheduling of household appliances for demand side management using a hybrid heuristic algorithm," Energy, Elsevier, vol. 262(PA).

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