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Distributed Model Predictive Load Frequency Control for Virtual Power Plants with Novel Event-Based Low-Delay Technique Under Cloud-Edge-Terminal Framework

Author

Listed:
  • Kai Kang

    (PowerChina Hubei Engineering Co., Ltd., Wuhan 430040, China)

  • Nian Shi

    (PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China)

  • Si Cai

    (PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China)

  • Liang Zhang

    (PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China)

  • Xinan Shao

    (PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China)

  • Haohao Cao

    (PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China)

  • Mingjin Fei

    (PowerChina Hubei Electric Engineering Co., Ltd., Wuhan 430040, China)

  • Shisen Zhou

    (School of Automation, China University of Geosciences, Wuhan 430074, China)

  • Xiongbo Wan

    (School of Automation, China University of Geosciences, Wuhan 430074, China)

Abstract

In this paper, the distributed model predictive load frequency control problem for virtual power plants (VPPs) under the cloud-edge-terminal framework is addressed, where the data packets are transmitted under a novel dynamic event-triggered mechanism (DETM) with hybrid variables. The proposed DETM has the ability to flexibly manage packet releases and reduce network congestion, thus decreasing the communication delay of the VPP. A method of the DETM-based distributed model predictive control (DMPC) is proposed, which can shorten the data processing time and further decrease the communication delay. The DMPC problem is described as a “min-max” optimization problem (OP) with hard constraints on the system state. By utilizing a Lyapunov function with an internal dynamic variable, an auxiliary OP with matrix inequalities constraints is proposed to optimize the controller gain and the weighting matrix of the DETM. The effectiveness and superiority of the designed DETM and dynamic event-based DMPC algorithm are demonstrated through a case study on two-area VPPs.

Suggested Citation

  • Kai Kang & Nian Shi & Si Cai & Liang Zhang & Xinan Shao & Haohao Cao & Mingjin Fei & Shisen Zhou & Xiongbo Wan, 2025. "Distributed Model Predictive Load Frequency Control for Virtual Power Plants with Novel Event-Based Low-Delay Technique Under Cloud-Edge-Terminal Framework," Energies, MDPI, vol. 18(6), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1380-:d:1609812
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    References listed on IDEAS

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    1. Ce Wang & Xiangjie Liu & Kwang Y. Lee, 2023. "Two-Layer Robust Distributed Predictive Control for Load Frequency Control of a Power System under Wind Power Fluctuation," Energies, MDPI, vol. 16(12), pages 1-15, June.
    2. Lin, Wen-Ting & Chen, Guo & Zhou, Xiaojun, 2022. "Distributed carbon-aware energy trading of virtual power plant under denial of service attacks: A passivity-based neurodynamic approach," Energy, Elsevier, vol. 257(C).
    3. Yunrui Lan & Mahesh S. Illindala, 2024. "Robust Distributed Load Frequency Control for Multi-Area Power Systems with Photovoltaic and Battery Energy Storage System," Energies, MDPI, vol. 17(22), pages 1-18, November.
    4. Qingfeng Yang & Gang Chen & Mengmeng Guo & Tingting Chen & Lei Luo & Li Sun, 2024. "Model Predictive Hybrid PID Control and Energy-Saving Performance Analysis of Supercritical Unit," Energies, MDPI, vol. 17(24), pages 1-13, December.
    5. Zeyi Wang & Yao Wang & Li Xie & Dan Pang & Hao Shi & Hua Zheng, 2024. "Load Frequency Control of Multiarea Power Systems with Virtual Power Plants," Energies, MDPI, vol. 17(15), pages 1-10, July.
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