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Integrated Optimal Dispatch of a Rural Micro-Energy-Grid with Multi-Energy Stream Based on Model Predictive Control

Author

Listed:
  • Xin Zhang

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
    School of Information Engineering, Inner Mongolia University of Science & Technology, Baotou 014010, China)

  • Jianhua Yang

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Weizhou Wang

    (State Grid Gansu Electric Power Company, Lanzhou 730050, China)

  • Man Zhang

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Tianjun Jing

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

Abstract

Due to the randomness of the intermittent distributed energy output and load demand of a micro-energy-grid, micro-sources cannot fully follow the day-ahead micro-energy-grid optimal dispatching plan. Therefore, a micro-energy-grid is difficult to operate steadily and is challenging to include in the response dispatch of a distribution network. In view of the above problems, this paper proposes an integrated optimal dispatch method for a micro-energy-grid based on model predictive control. In the day-ahead optimal dispatch, an optimal dispatch model of a micro-energy-grid is built taking the daily minimum operating cost as the objective function, and the optimal output curve of each micro-source of the next day per hour is obtained. In the real-time dispatch, rolling optimization of the day-ahead optimal dispatching plan is implemented based on model predictive control theory. The real-time state of the system is sampled, and feedback correction of the system is implemented. The influence of uncertain factors in the system is eliminated to ensure steady operation of the system. Finally, the validity and feasibility of the integrated optimal dispatching method are verified by a case simulation analysis.

Suggested Citation

  • Xin Zhang & Jianhua Yang & Weizhou Wang & Man Zhang & Tianjun Jing, 2018. "Integrated Optimal Dispatch of a Rural Micro-Energy-Grid with Multi-Energy Stream Based on Model Predictive Control," Energies, MDPI, vol. 11(12), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3439-:d:189050
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    References listed on IDEAS

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    Cited by:

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    2. Yi, Zonggen & Luo, Yusheng & Westover, Tyler & Katikaneni, Sravya & Ponkiya, Binaka & Sah, Suba & Mahmud, Sadab & Raker, David & Javaid, Ahmad & Heben, Michael J. & Khanna, Raghav, 2022. "Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system," Applied Energy, Elsevier, vol. 328(C).
    3. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
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    5. Hang Liu & Yongcheng Wang & Shilin Nie & Yi Wang & Yu Chen, 2022. "Multistage Economic Scheduling Model of Micro-Energy Grids Considering Flexible Capacity Allocation," Sustainability, MDPI, vol. 14(15), pages 1-29, July.

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