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Double-layer energy management system based on energy sharing cloud for virtual residential microgrid

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  • Li, Shenglin
  • Zhu, Jizhong
  • Chen, Ziyu
  • Luo, Tengyan

Abstract

The idea of energy sharing can contribute to achieving the goal of resource optimization by redistributing and sharing idle energy assets. How to design an appropriate energy management strategy in the energy sharing environment has been the focus of intensive research in energy sharing field. In this paper, a new effective double-layer energy management system (EMS) based on the energy sharing cloud (ESC) is developed for a virtual residential microgrid (VRMG). The proposed ESC can be regarded as an open energy sharing environment, where the cloud platform helps cloud users build their VRMGs by providing energy services including renewable energy sources (RESs) generation and energy storage. The mathematical model of the VRMG is formulated, and the energy service prices for RESs generation and energy storage are monthly set separately. Moreover, considering the changes in household load and RESs generation, an energy management strategy of double-layer EMS is designed. The upper-layer EMS helps VRMG obtain the monthly optimal capacity configuration of RESs and energy storage, and the lower-layer EMS realizes the daily electricity scheduling optimization for the VRMG whose objectives are to minimize the total operational cost and maximize the electrical comfort level. Simulation studies demonstrate that the proposed energy sharing mechanism can meet the changing energy needs of the cloud user, and numerical experiments also confirm the performance and effectiveness of the proposed double-layer EMS.

Suggested Citation

  • Li, Shenglin & Zhu, Jizhong & Chen, Ziyu & Luo, Tengyan, 2021. "Double-layer energy management system based on energy sharing cloud for virtual residential microgrid," Applied Energy, Elsevier, vol. 282(PA).
    • Handle: RePEc:eee:appene:v:282:y:2021:i:pa:s0306261920315154
    DOI: 10.1016/j.apenergy.2020.116089
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    Cited by:

    1. Song, Xiaoling & Zhang, Huqing & Fan, Lurong & Zhang, Zhe & Peña-Mora, Feniosky, 2023. "Planning shared energy storage systems for the spatio-temporal coordination of multi-site renewable energy sources on the power generation side," Energy, Elsevier, vol. 282(C).
    2. C. B. Sivaparthipan & Lydia J. Gnanasigamani & Ruchi Agrawal & Bakri Hossain Awaji & P. Sathyaprakash & Mustafa Musa Jaber & Awais Khan Jumani, 2023. "Internet of things enabled privacy-conserving health record virtual sharing using jungle computing," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-26, July.
    3. Li, Shenglin & Zhu, Jizhong & Dong, Hanjiang & Zhu, Haohao & Fan, Junwei, 2022. "A novel rolling optimization strategy considering grid-connected power fluctuations smoothing for renewable energy microgrids," Applied Energy, Elsevier, vol. 309(C).
    4. Ma, Mingtao & Huang, Huijun & Song, Xiaoling & Peña-Mora, Feniosky & Zhang, Zhe & Chen, Jie, 2022. "Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach," Applied Energy, Elsevier, vol. 307(C).
    5. Li, Xiaozhu & Chen, Laijun & Sun, Fan & Hao, Yibo & Du, Xili & Mei, Shenwei, 2023. "Share or not share, the analysis of energy storage interaction of multiple renewable energy stations based on the evolution game," Renewable Energy, Elsevier, vol. 208(C), pages 679-692.
    6. Han, Xiaojuan & Li, Jiarong & Zhang, Zhewen, 2023. "Dynamic game optimization control for shared energy storage in multiple application scenarios considering energy storage economy," Applied Energy, Elsevier, vol. 350(C).
    7. Chang, Weiguang & Dong, Wei & Yang, Qiang, 2023. "Day-ahead bidding strategy of cloud energy storage serving multiple heterogeneous microgrids in the electricity market," Applied Energy, Elsevier, vol. 336(C).
    8. Zhou, Yuan & Wang, Jiangjiang & Li, Yuxin & Wei, Changqi, 2023. "A collaborative management strategy for multi-objective optimization of sustainable distributed energy system considering cloud energy storage," Energy, Elsevier, vol. 280(C).

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