双重不确定性预测下风电场超短期有功优化控制

OPTIMAL CONTROL OF WIND FARM POWER BASED ON DOUBLE UNCERTAINTY PREDICTION

针对风电场并网友好性提升问题,提出考虑风速预测不确定性和风电机组有功特性不确定性的风电场发电能力评估方案。对风速超短期预测误差和风电机组在各风速区间的出力特性进行双重不确定性分析并建立概率分布模型,进而利用贝叶斯网络构建风电机组超短期出力的双重不确定性概率预测模型。基于风电场各风电机机组超短期出力概率预测模型,以最大概率跟踪电网调度指令为目标设计场站功率分配策略。算例分析表明,所提考虑双重不确定性的概率预测模型对机风电组有功的概率分布描述更准确,该模型在场站控制中可有效提升电网功率指令的完成水平。

Aiming at the problem of improving the grid-connected friendliness of wind farms,a wind farm generation capacity evaluation scheme considering uncertain wind speed prediction and uncertain active power characteristics of wind turbines is proposed.By analyzing the wind speed ultra-short-term prediction error and the wind turbine output characteristics in each wind speed range and establishing two probability distribution models,a double uncertainty prediction model of the wind turbine output in the ultra-short term is constructed using Bayesian network.Based on the proposed prediction model for each wind turbine,the power distribution strategy of the wind farm is designed with the objective of tracking the dispatching command with maximum probability.The analysis shows that the proposed prediction model with double uncertainty is more accurate in describing the probability distribution of the wind turbines’active power,and the proposed model can effectively improve the completion level of the grid power command in the wind farm control.