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A MPC-based load frequency control considering wind power intelligent forecasting

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Listed:
  • Wang, Pei
  • Guo, Jiang
  • Cheng, Fangjuan
  • Gu, Yifeng
  • Yuan, Fang
  • Zhang, Fangqing

Abstract

Currently, the significant randomness of wind power hampers the stability of the grid system. Furthermore, existing control strategies, which solely rely on current measured wind power output, are inadequate in addressing the rapid and continuous fluctuations of wind power. Based on this, a novel load frequency control (LFC) that combines wind power prediction and model predictive control (MPC) is proposed in this paper. The high-precision wind power forecasts are embedded into the MPC, enabling MPC to develop robust control strategies that flexibly respond to the random variability of wind power. For wind power prediction, an improved Reformer model with inversion and gated linear unit (GiReformer) is constructed, which achieves multi-step predictions of wind power at the microscale. In addition, Laguerre function is introduced in MPC to reduce the computational load, and the settings for frequency constraints, generate rate constraints (GRC), control input constraints and terminal constraints ensures the safe and stable operation of the power grid. According to simulations in a high-proportion hydropower system and a multi-energy and multi-regional interconnected power system, the proposed method alleviates system frequency fluctuations up to 71.88 % and 51.78 %, respectively, compared to the comparative methods. In addition, constraints are well handled by the proposed method.

Suggested Citation

  • Wang, Pei & Guo, Jiang & Cheng, Fangjuan & Gu, Yifeng & Yuan, Fang & Zhang, Fangqing, 2025. "A MPC-based load frequency control considering wind power intelligent forecasting," Renewable Energy, Elsevier, vol. 244(C).
    • Handle: RePEc:eee:renene:v:244:y:2025:i:c:s0960148125002988
    DOI: 10.1016/j.renene.2025.122636
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    References listed on IDEAS

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    1. Li, Baihua & Hu, Shaoyu & Zhong, Qishui & Shi, Kaibo & Zhong, Shouming, 2023. "Dynamic memory event-triggered proportional-integral-based H∞ load frequency control for multi-area wind power systems," Applied Mathematics and Computation, Elsevier, vol. 453(C).
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    Citations

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    Cited by:

    1. Zhenlan Dou & Chunyan Zhang & Xichao Zhou & Dan Gao & Xinghua Liu, 2025. "DDPG-ADRC-Based Load Frequency Control for Multi-Region Power Systems with Renewable Energy Sources and Energy Storage Equipment," Energies, MDPI, vol. 18(14), pages 1-21, July.
    2. Dong, Ruipeng & Wang, Yun & Huang, Yaohui & Zou, Runmin, 2025. "Feature-driven dynamic non-crossing quantile ensemble learning for reliable probabilistic wind power forecasting," Energy, Elsevier, vol. 335(C).
    3. Liu, Tianhao & Shan, Linke & Jiang, Meihui & Li, Fangning & Kong, Fannie & Du, Pengcheng & Zhu, Hongyu & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Huang, Chao & Zhang, Dongdong, 2025. "Multi-dimensional data processing and intelligent forecasting technologies for renewable energy generation," Applied Energy, Elsevier, vol. 398(C).
    4. Ming Lu & Miao Yu, 2025. "Prescribed Performance Load Frequency Control for Regional Interconnected Power System Under Energy Storage System Output Constraints," Energies, MDPI, vol. 18(13), pages 1-13, July.
    5. Shukla, Hiramani & Kumar, Shailendra, 2025. "Concurrent regulation of voltage and frequency of an isolated microgrid considering small hydro plant, DFIG and energy storage systems," Renewable Energy, Elsevier, vol. 248(C).
    6. Saleh A. Alnefaie & Abdulaziz Alkuhayli & Abdullah M. Al-Shaalan, 2025. "Terminal Voltage and Load Frequency Regulation in a Nonlinear Four-Area Multi-Source Interconnected Power System via Arithmetic Optimization Algorithm," Mathematics, MDPI, vol. 13(19), pages 1-49, September.

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