Research on an optimization method for suppressing active power fluctuations in wind farms based on model predictive control

doi:10.3969/j.issn.2097-0706.2025.07.003

Abstract

Abstract:

Due to the spatial distribution characteristics of wind turbines and the randomness of wind speed， wind farm power output exhibits significant intermittency and fluctuation， which undermines the grid-friendly operation capability of wind farms. To address this issue， an optimization method for suppressing wind farm power fluctuations based on model predictive control （MPC） coupled with wake effect was proposed. A power output prediction model under varying wind speeds and directions was established using a coordinate transformation method， and wind speed forecasting was performed using a least squares support vector machine （LS-SVM）. Within the MPC framework， a multi-dimensional coupled optimization objective was formulated by integrating the wake effect， wind direction deviation， and turbine constraints. An optimization problem considering the variance of active power output was then solved. The case study showed that compared with the proportional distribution method， the proposed approach reduced the average relative deviation and root mean square deviation of active power output by 93% and 97%， respectively， verifying the effectiveness of the multi-dimensional coupling model in fluctuation suppression.

Key words: wind farm, wake control, fluctuation suppression, model prediction, active power, turbine constraints, multi-dimensional coupling

CLC Number:

TK08：TM614

QIN Xiaodong, SONG Ruijun, LYU Jie, ZHOU Wenqi, YAO Peng, WEI Shangshang. Research on an optimization method for suppressing active power fluctuations in wind farms based on model predictive control[J]. Integrated Intelligent Energy, 2025, 47(7): 23-31.

Figures/Tables 12

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Table 1

Comparison of wind farm fluctuation index under different optimization methods

优化方法	$ε M R E$	$ε M S E$ /MW
1	16.90	6.55
2	262.00	240.05

Table 1

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