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Multi-Time Scale Cloud-Edge Collaborative Scheduling Strategy for Distribution Network Considering Spatiotemporal Characteristics of Demand Response

Author

Listed:
  • Wenbo Hao

    (State Grid Heilongjiang Electric Power Research Institute, Harbin 150030, China)

  • Maoda Xu

    (State Grid Heilongjiang Electric Power Company Limited, Harbin 150090, China)

  • Junming Lin

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Lida Fu

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Xiaonan Cao

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Qingquan Jia

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

Abstract

The increasing penetration rate of distributed resources in the distribution network has brought about significant volatility and uncertainty problems. Demand response (DR) can flexibly change the energy consumption method of the user to balance supply and demand. This paper first considers the spatial distribution characteristics of DR resources to schedule DR resources to construct a distributed resource cloud-edge collaborative scheduling framework. Based on this, the distribution network scheduling requirements are combined with the multi-time scale characteristics of DR. A three-stage cloud-edge collaborative optimization scheduling strategy for distributed resources in the distribution network is proposed, which allocates the multi-time scale scheduling tasks of DR resources to the cloud and edge. Secondly, taking the cloud and edge as the optimization platform, a three-stage optimization decision-making model of the distribution network is established. In the day-ahead stage, the global optimization decision is made by combining cloud-centralized optimization with edge-independent optimization. In the intraday stage, edge-rolling optimization is carried out. In the real-time stage, the edge-distributed calculation is based on the consensus algorithm. Finally, the effectiveness and economy of the proposed model and strategy are verified by an example analysis.

Suggested Citation

  • Wenbo Hao & Maoda Xu & Junming Lin & Lida Fu & Xiaonan Cao & Qingquan Jia, 2024. "Multi-Time Scale Cloud-Edge Collaborative Scheduling Strategy for Distribution Network Considering Spatiotemporal Characteristics of Demand Response," Energies, MDPI, vol. 17(8), pages 1-28, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1933-:d:1378200
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    References listed on IDEAS

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    1. Liu, Chunming & Wang, Chunling & Yin, Yujun & Yang, Peihong & Jiang, Hui, 2022. "Bi-level dispatch and control strategy based on model predictive control for community integrated energy system considering dynamic response performance," Applied Energy, Elsevier, vol. 310(C).
    2. Li, Zhengmao & Xu, Yan, 2019. "Temporally-coordinated optimal operation of a multi-energy microgrid under diverse uncertainties," Applied Energy, Elsevier, vol. 240(C), pages 719-729.
    3. Duan, Jiandong & Liu, Fan & Yang, Yao, 2022. "Optimal operation for integrated electricity and natural gas systems considering demand response uncertainties," Applied Energy, Elsevier, vol. 323(C).
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