Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Hierarchical Cluster Coordination Control Strategy for Large-scale Wind Power Based on Model Predictive Control and Improved Multi-time-scale Active Power Dispatching

College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China

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This work was supported in part by the Joint Funds of the National Natural Science Foundation of China (No. U1966205) and Fundamental Research Funds for the Central Universities (No. B210202067).

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    The grid-connection of large-scale and high-penetration wind power poses challenges to the friendly dispatching control of the power system. To coordinate the complicated optimal dispatching and rapid real-time control, this paper proposes a hierarchical cluster coordination control (HCCC) strategy based on model predictive control (MPC) technique. Considering the time-varying characteristics of wind power generation, the proposed HCCC strategy constructs an improved multi-time-scale active power dispatching model, which consists of five parts: formulation of cluster dispatching plan, rolling modification of intra-cluster plan, optimization allocation of wind farm (WF), grouping coordinated control of wind turbine group (WTG), and real-time adjustment of single-machine power. The time resolutions are sequentially given as 1 hour, 30 min, 15 min, 5 min, and 1 min. In addition, a combined predictive model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), wavelet thresholding (WT), and least squares support vector machine (LSSVM) is established. The fast predictive feature of this model cooperates with the HCCC strategy that effectively improves the predictive control precision. Simulation results show that the proposed HCCC strategy enables rapid response to active power control (APC), and significantly improves dispatching control accuracy and wind power accommodation capabilities.

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  • Received:January 05,2022
  • Revised:April 29,2022
  • Adopted:
  • Online: May 23,2023
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