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Optimal Scheduling of Distributed Energy System for Home Energy Management System Based on Dynamic Coyote Search Algorithm

Author

Listed:
  • Chunbo Li

    (Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an 223003, China)

  • Yuwei Dong

    (Department of Mechanical and Electronic Engineering, Jiangsu Vocational and Technical College of Finance and Economics, Huai’an 223003, China)

  • Xuelong Fu

    (Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an 223003, China)

  • Yalan Zhang

    (Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an 223003, China)

  • Juan Du

    (Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an 223003, China)

Abstract

Renewable and distributed power generation have been acknowledged as options for the safe, secure, sustainable, and cost-effective production, delivery, and consumption of energy in future low-carbon cities. This research introduces the Dynamic Coyote Search Algorithm (DCSA)-based optimal scheduling of distributed energy systems for home energy management systems. According to the heat storage properties of the building, a smart building energy model is established and introduced into the optimal scheduling of the distributed energy system in order to optimize the adjustment of the room temperature within the user’s acceptable room temperature range. The DCSA algorithm used is to minimize the daily comprehensive operating cost, including environmental factors. According to the simulation results, the impact of smart energy storage on scheduling is analyzed, and the results show that the optimal scheduling of building smart energy storage participating in the system reduces the total cost by about 3.8%. In addition, the DCSA has a significantly faster convergence speed than the original coyote algorithm.

Suggested Citation

  • Chunbo Li & Yuwei Dong & Xuelong Fu & Yalan Zhang & Juan Du, 2022. "Optimal Scheduling of Distributed Energy System for Home Energy Management System Based on Dynamic Coyote Search Algorithm," Sustainability, MDPI, vol. 14(22), pages 1-12, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14732-:d:967002
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    References listed on IDEAS

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    1. Liu, Zhijian & Fan, Guangyao & Sun, Dekang & Wu, Di & Guo, Jiacheng & Zhang, Shicong & Yang, Xinyan & Lin, Xianping & Ai, Lei, 2022. "A novel distributed energy system combining hybrid energy storage and a multi-objective optimization method for nearly zero-energy communities and buildings," Energy, Elsevier, vol. 239(PE).
    2. Ahmed N. Abdalla & Yongfeng Ju & Muhammad Shahzad Nazir & Hai Tao, 2022. "A Robust Economic Framework for Integrated Energy Systems Based on Hybrid Shuffled Frog-Leaping and Local Search Algorithm," Sustainability, MDPI, vol. 14(17), pages 1-16, August.
    3. Qi, Ning & Cheng, Lin & Xu, Helin & Wu, Kuihua & Li, XuLiang & Wang, Yanshuo & Liu, Rui, 2020. "Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads," Applied Energy, Elsevier, vol. 279(C).
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    Cited by:

    1. Mohammed Hammam Mohammed Al-Madani & Yudi Fernando & Ming-Lang Tseng, 2022. "Assuring Energy Reporting Integrity: Government Policy’s Past, Present, and Future Roles," Sustainability, MDPI, vol. 14(22), pages 1-24, November.

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