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Distributed collaborative optimal economic dispatch of integrated energy system based on edge computing

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  • Yang, Jun
  • Sun, Fengyuan
  • Wang, Haitao

Abstract

A distributed collaborative optimal dispatching strategy for the integrated energy system (IES), based on edge computing and consistency algorithm, is proposed in this paper. To solve the optimal dispatching problem of the IES coupled with electricity, heat, and gas, a cloud-edge-device architecture is constructed. A distributed group consensus algorithm (DGCA) is designed with two consistency protocols. The first protocol defines three groups of consistency variables, represented by the incremental cost of electricity, heat, and gas, respectively. The output estimation deviation of each component is used as a feedback. The second protocol considers the output estimation deviation of the three components as the consistency variables. The coupling variables of the two consistency protocols tend to be consistent, which can obtain the optimal output of each unit and achieve the optimal operation of the system. Furthermore, the proposed strategy accounts for transmission loss during transmission. Finally, through the hardware in the loop simulation based on the edge intelligent terminal (EIT), the effectiveness and feasibility of the strategy are verified, considering different cases.

Suggested Citation

  • Yang, Jun & Sun, Fengyuan & Wang, Haitao, 2023. "Distributed collaborative optimal economic dispatch of integrated energy system based on edge computing," Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223025884
    DOI: 10.1016/j.energy.2023.129194
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    References listed on IDEAS

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    1. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    2. Qian, Tong & Tang, Wenhu & Wu, Qinghua, 2020. "A fully decentralized dual consensus method for carbon trading power dispatch with wind power," Energy, Elsevier, vol. 203(C).
    3. Yin, Linfei & Sun, Zhixiang, 2021. "Multi-layer distributed multi-objective consensus algorithm for multi-objective economic dispatch of large-scale multi-area interconnected power systems," Applied Energy, Elsevier, vol. 300(C).
    4. Wu, Kunming & Li, Qiang & Chen, Ziyu & Lin, Jiayang & Yi, Yongli & Chen, Minyou, 2021. "Distributed optimization method with weighted gradients for economic dispatch problem of multi-microgrid systems," Energy, Elsevier, vol. 222(C).
    5. Foti, Magda & Mavromatis, Costas & Vavalis, Manolis, 2021. "Decentralized blockchain-based consensus for Optimal Power Flow solutions," Applied Energy, Elsevier, vol. 283(C).
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