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Distributed Low-Carbon Energy Management of Urban Campus for Renewable Energy Consumption

Author

Listed:
  • Kan Yu

    (Architectural Design and Research Institute of Zhejiang University Co., Ltd., Hangzhou 310028, China
    Center for Balance Architecture, Zhejiang University, Hangzhou 310028, China)

  • Qiang Wei

    (Architectural Design and Research Institute of Zhejiang University Co., Ltd., Hangzhou 310028, China
    Center for Balance Architecture, Zhejiang University, Hangzhou 310028, China)

  • Chuanzi Xu

    (Hangzhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310016, China)

  • Xinyu Xiang

    (Hangzhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310016, China)

  • Heyang Yu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

In order to solve the mismatch between renewable energy and load in urban building microgrids, that is, the problem of renewable energy consumption in building microgrid clusters, while preserving the privacy of each user, this paper proposes a distributed low-carbon energy management method for urban building microgrid clusters. First, a low-carbon energy management method for the urban building microgrid is proposed in order to coordinate the power sharing of various subjects to minimize the total economic cost, unleash the consumption potential of low-carbon building clusters for renewable energy, and reduce carbon emissions on the spatial and time scale. Second, an ADMM-based distributed optimal energy management method is proposed to meet user energy needs while preserving local privacy; this includes energy storage systems, renewable energy generation, and the loads within each urban building microgrid. Finally, simulation experiments are conducted based on actual data from a certain area in Hangzhou, China, and the results verify the effectiveness of the model.

Suggested Citation

  • Kan Yu & Qiang Wei & Chuanzi Xu & Xinyu Xiang & Heyang Yu, 2024. "Distributed Low-Carbon Energy Management of Urban Campus for Renewable Energy Consumption," Energies, MDPI, vol. 17(23), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6182-:d:1539023
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    References listed on IDEAS

    as
    1. Dong, Haiyan & Fu, Yanbo & Jia, Qingquan & Zhang, Tie & Meng, Dequn, 2023. "Low carbon optimization of integrated energy microgrid based on life cycle analysis method and multi time scale energy storage," Renewable Energy, Elsevier, vol. 206(C), pages 60-71.
    2. Yu, Heyang & Zhang, Jingchen & Ma, Junchao & Chen, Changyu & Geng, Guangchao & Jiang, Quanyuan, 2023. "Privacy-preserving demand response of aggregated residential load," Applied Energy, Elsevier, vol. 339(C).
    3. Hua, Weiqi & Jiang, Jing & Sun, Hongjian & Wu, Jianzhong, 2020. "A blockchain based peer-to-peer trading framework integrating energy and carbon markets," Applied Energy, Elsevier, vol. 279(C).
    4. Feng, Peiling & He, Xing, 2021. "Mixed neurodynamic optimization for the operation of multiple energy systems considering economic and environmental aspects," Energy, Elsevier, vol. 232(C).
    Full references (including those not matched with items on IDEAS)

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