IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i8p1933-d1378200.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/8/1933/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/8/1933/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tan, Bifei & Lin, Zhenjia & Zheng, Xiaodong & Xiao, Fu & Wu, Qiuwei & Yan, Jinyue, 2023. "Distributionally robust energy management for multi-microgrids with grid-interactive EVs considering the multi-period coupling effect of user behaviors," Applied Energy, Elsevier, vol. 350(C).
    2. Ahsan, Syed M. & Khan, Hassan A. & Hassan, Naveed-ul & Arif, Syed M. & Lie, Tek-Tjing, 2020. "Optimized power dispatch for solar photovoltaic-storage system with multiple buildings in bilateral contracts," Applied Energy, Elsevier, vol. 273(C).
    3. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(C).
    4. Wang, Yunqi & Qiu, Jing & Tao, Yuechuan & Zhang, Xian & Wang, Guibin, 2020. "Low-carbon oriented optimal energy dispatch in coupled natural gas and electricity systems," Applied Energy, Elsevier, vol. 280(C).
    5. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).
    6. Yang, Hongming & Liang, Rui & Yuan, Yuan & Chen, Bowen & Xiang, Sheng & Liu, Junpeng & Zhao, Huan & Ackom, Emmanuel, 2022. "Distributionally robust optimal dispatch in the power system with high penetration of wind power based on net load fluctuation data," Applied Energy, Elsevier, vol. 313(C).
    7. Yao, Wenliang & Wang, Chengfu & Yang, Ming & Wang, Kang & Dong, Xiaoming & Zhang, Zhenwei, 2023. "A tri-layer decision-making framework for IES considering the interaction of integrated demand response and multi-energy market clearing," Applied Energy, Elsevier, vol. 342(C).
    8. Zheng, Shunlin & Qi, Qi & Sun, Yi & Ai, Xin, 2023. "Integrated demand response considering substitute effect and time-varying response characteristics under incomplete information," Applied Energy, Elsevier, vol. 333(C).
    9. Zhai, Junyi & Wang, Sheng & Guo, Lei & Jiang, Yuning & Kang, Zhongjian & Jones, Colin N., 2022. "Data-driven distributionally robust joint chance-constrained energy management for multi-energy microgrid," Applied Energy, Elsevier, vol. 326(C).
    10. Hua, Weiqi & Jiang, Jing & Sun, Hongjian & Tonello, Andrea M. & Qadrdan, Meysam & Wu, Jianzhong, 2022. "Data-driven prosumer-centric energy scheduling using convolutional neural networks," Applied Energy, Elsevier, vol. 308(C).
    11. Zhou, Dezhi & Wu, Chuantao & Sui, Quan & Lin, Xiangning & Li, Zhengtian, 2022. "A novel all-electric-ship-integrated energy cooperation coalition for multi-island microgrids," Applied Energy, Elsevier, vol. 320(C).
    12. Kong, Xiangyu & Liu, Dehong & Wang, Chengshan & Sun, Fangyuan & Li, Shupeng, 2020. "Optimal operation strategy for interconnected microgrids in market environment considering uncertainty," Applied Energy, Elsevier, vol. 275(C).
    13. Zheng, Lingwei & Zhou, Xingqiu & Qiu, Qi & Yang, Lan, 2020. "Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output," Energy, Elsevier, vol. 209(C).
    14. Pan, Chongchao & Jin, Tai & Li, Na & Wang, Guanxiong & Hou, Xiaowang & Gu, Yueqing, 2023. "Multi-objective and two-stage optimization study of integrated energy systems considering P2G and integrated demand responses," Energy, Elsevier, vol. 270(C).
    15. Huang, Hongxu & Liang, Rui & Lv, Chaoxian & Lu, Mengtian & Gong, Dunwei & Yin, Shulin, 2021. "Two-stage robust stochastic scheduling for energy recovery in coal mine integrated energy system," Applied Energy, Elsevier, vol. 290(C).
    16. Lian, Yicheng & Li, Yuanzheng & Zhao, Yong & Yu, Chaofan & Zhao, Tianyang & Wu, Lei, 2023. "Robust multi-objective optimization for islanded data center microgrid operations," Applied Energy, Elsevier, vol. 330(PB).
    17. Moser, A. & Muschick, D. & Gölles, M. & Nageler, P. & Schranzhofer, H. & Mach, T. & Ribas Tugores, C. & Leusbrock, I. & Stark, S. & Lackner, F. & Hofer, A., 2020. "A MILP-based modular energy management system for urban multi-energy systems: Performance and sensitivity analysis," Applied Energy, Elsevier, vol. 261(C).
    18. Çiçek, Alper & Şengör, İbrahim & Erenoğlu, Ayşe Kübra & Erdinç, Ozan, 2020. "Decision making mechanism for a smart neighborhood fed by multi-energy systems considering demand response," Energy, Elsevier, vol. 208(C).
    19. Zhang, Gang & Wen, Jiaxing & Xie, Tuo & Zhang, Kaoshe & Jia, Rong, 2023. "Bi-layer economic scheduling for integrated energy system based on source-load coordinated carbon reduction," Energy, Elsevier, vol. 280(C).
    20. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2022. "Economic model predictive control of integrated energy systems: A multi-time-scale framework," Applied Energy, Elsevier, vol. 328(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1933-:d:1378200. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.