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Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems

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  • Jeddi, Babak
  • Mishra, Yateendra
  • Ledwich, Gerard

Abstract

Recently, home energy management systems (HEMS) are gaining more popularity enabling customers to minimize their electricity bill under time-varying electricity prices. Although they offer a promising solution for better energy management in smart grids, the uncoordinated and autonomous operation of HEMS may lead to some operational problems at the grid level. This paper aims to develop a coordinated framework for the operation of multiple HEMS in a residential neighborhood based on the optimal and secure operation of the grid. In the proposed framework customers cooperate to optimize energy consumption at the neighborhood level and prevent any grid operational constraints violation. A new price-based global and individualized incentives are proposed for customers to respond and adjust loads. The individual customers are rewarded for their cooperation and the network operator benefits by eliminating rebounding network peaks. The alternating direction method of multipliers (ADMM) technique is used to implement coordinated load scheduling in a distributed manner reducing the computational burden and ensure customer privacy. Simulation results demonstrate the efficacy of the proposed method in maintaining nominal network conditions while ensuring benefits for individual customers as well as grid operators.

Suggested Citation

  • Jeddi, Babak & Mishra, Yateendra & Ledwich, Gerard, 2021. "Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems," Applied Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:appene:v:300:y:2021:i:c:s0306261921007601
    DOI: 10.1016/j.apenergy.2021.117353
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    References listed on IDEAS

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    Cited by:

    1. Fatma Gülşen Erdinç & Alper Çiçek & Ozan Erdinç, 2022. "Resiliency-Sensitive Decision Making Mechanism for a Residential Community Enhanced with Bi-Directional Operation of Fuel Cell Electric Vehicles," Energies, MDPI, vol. 15(22), pages 1-17, November.
    2. Luan, Wenpeng & Wei, Zun & Liu, Bo & Yu, Yixin, 2022. "Non-intrusive power waveform modeling and identification of air conditioning load," Applied Energy, Elsevier, vol. 324(C).
    3. Kanakaraj Parangusam & Ramesh Lekshmana & Tomas Gono & Radomir Gono, 2023. "Evolution of a Summer Peak Intelligent Controller (SPIC) for Residential Distribution Networks," Energies, MDPI, vol. 16(18), pages 1-18, September.
    4. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    5. Liu, Zhijian & Li, Ying & Fan, Guangyao & Wu, Di & Guo, Jiacheng & Jin, Guangya & Zhang, Shicong & Yang, Xinyan, 2022. "Co-optimization of a novel distributed energy system integrated with hybrid energy storage in different nearly zero energy community scenarios," Energy, Elsevier, vol. 247(C).
    6. Mota, Bruno & Faria, Pedro & Vale, Zita, 2024. "Energy cost optimization through load shifting in a photovoltaic energy-sharing household community," Renewable Energy, Elsevier, vol. 221(C).
    7. Erdinç, Fatma Gülşen, 2023. "Rolling horizon optimization based real-time energy management of a residential neighborhood considering PV and ESS usage fairness," Applied Energy, Elsevier, vol. 344(C).

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