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Bi-level dispatch and control strategy based on model predictive control for community integrated energy system considering dynamic response performance

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  • Liu, Chunming
  • Wang, Chunling
  • Yin, Yujun
  • Yang, Peihong
  • Jiang, Hui

Abstract

Integrated energy systems have recently attracted interest for the purpose of energy development and utilization owing to the increase in environmental pollution and the shortage of fossil energy. However, the integration of a high proportion of renewable energies and controllable loads in the system increases the uncertainty of system operation significantly. In order to rapidly track the system fluctuations and accurately control the operating equipment, a bi-level and multi-timescale dispatch and control strategy based on model predictive control is proposed for a community integrated energy system (CIES) considering dynamic response performance. The upper-level optimizer completes the rolling forecast of renewable outputs and loads over a long-time horizon, and builds an economic rolling optimal scheduling model with consideration of feedback correction. The lower-level controller establishes a closed-loop dynamic performance optimization control model over a short time scale by real-time controlling the system’s upstream energy equipment. The simulation results on a modified CIES located in Beijing, China demonstrate that the proposed bi-level strategy increases the prediction accuracy while ensuring the economic efficiency of the system operation, and improves the dynamic tracking ability of the equipment and the overall energy supply dynamic response rate of the system, which provides a fundamental way for efficient application of CIES.

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  • 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).
    • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261922001106
    DOI: 10.1016/j.apenergy.2022.118641
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    8. 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.
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