基于长短期记忆神经网络超短期风功率预测的风储联合系统一次调频

Primary Frequency Regulation Strategy for a Wind-Storage-Joint System Based on LSTM Ultra-Short-Term Power Generation Prediction

  • 摘要: 针对高比例不确定性风力发电并网所引起的频率波动、以及电力系统惯性降低导致的电网调频能力减弱的问题,提出了基于长短期记忆网络风功率预测的风储联合系统一次调频策略。首先通过对历史数据的空间聚类和相关性分析进行风电场等值建模,并在此模型上基于长短期记忆网络进行风功率超短期预测;其次,根据风速区间选择风机变速或变桨调频的协调控制策略;最后,根据风功率预测偏差、电网频率偏差及其变化率调整功率型飞轮储能和能量型锂离子电池储能的辅助调频深度。在风储联合系统一次调频频率响应模型上进行仿真实验,结果显示风功率预测的准确率平均高达97.62%,所提风储联合一次调频策略相对于固定比例策略和单独风电调频策略,最大频率偏差分别减小了11.3%、28.6%,调节时间分别减小了27.1%和35.2%,有效地提升了风电调频性能,改善了电力系统频率特性。

     

    Abstract: Aiming to address the instability caused by the high proportion of uncertain wind turbines connected to the grid, as well as the weakened frequency modulation ability due to the inertia reduction of the power system, a wind power prediction and primary frequency modulation strategy is proposed for integrated wind turbines and energy storage systems based on long and short term memory networks. Firstly, based on the spatial clustering algorithm and correlation analysis method, the equivalent modeling for a wind farm is conduced by employing historical data. With this model, the ultra-short term power prediction of wind power output is carried out based on long and short term memory networks. Secondly, the coordination control strategy of variable speed or variable pitch frequency modulation is selected according to the wind speed range. Finally, the auxiliary frequency modulation depth of power type flywheel storage and energy type battery storage is adjusted according to the predicted output deviation, frequency deviation and change rate of the wind turbines. Simulation experiments are carried out on a primary frequency modulation response model for integrated wind turbines and energy storage systems, which is built on Matlab/Simulink. The results demonstrate that the average prediction accuracy of wind power LSTM model reaches 97.62%, while the maximum frequency deviation of the proposed strategy is reduced by 11.3% and 28.6% respectively, compared with the fixed proportion strategy and the single wind power frequency modulation. In addition, the stabilization time is reduced by 27.1% and 35.2% respectively, thereby significantly improving both the frequency modulation performance of wind power and frequency characteristics of the power system.

     

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