为提高风电场预测功率精度,对于风电场和混合储能构成的风储混合系统,提出基于预测信息搭建的混合储能补偿方案。首先,依据风电预测误差进行概率统计分析,提出风电功率预测误差的分层补偿策略,制定针对补偿预测误差的允许误差域及置信区间补偿域。其次,利用储能对不同误差层的预测误差给出相应的储能补偿方案,基于小波包分解将超出区域外待补偿预测误差分解,通过不平衡功率DFT分频法确定临界点作为功率型储能和能量型储能的目标分配值,对于荷电状态越限问题通过自适应模糊控制进行二次修正。最后,以新疆某风电场为例进行仿真分析,结果表明所提策略可有效减少风电功率预测误差。
Abstract
In order to improve the wind farm prediction power accuracy, for wind farm and hybrid energy storage, a hybrid energy storage compensation scheme based on prediction information is proposed. Firstly, the probabilistic statistical analysis is carried out based on the prediction error, and the stratified compensation strategy of wind power prediction error is proposed, and the allowable error domain and confidence interval compensation domain are formulated for compensating the prediction error. Then, the corresponding energy storage compensation scheme is given for the prediction errors of different error layers by using energy storage, decomposing the prediction errors outside the area to be compensated based on wavelet packet decomposition, determining the critical point as the target allocation value of power-based energy storage and energy-based energy storage by the unbalanced power DFT frequency division method, and making secondary correction for the load state overrun problem by adaptive fuzzy control. Finally, a wind farm in Xinjiang is used as an example for simulation analysis, and the results show that the proposed strategy can effectively reduce the wind power prediction error.
关键词
风电功率 /
风电预测误差 /
储能 /
功率分配 /
置信区间补偿域 /
荷电状态
Key words
wind power /
wind power forecasting error /
energy storage /
power distribution /
confidence interval compensation domain /
state of charge
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基金
国家自然科学基金（62163034）; 新疆维吾尔自治区区域协同创新专项（科技援疆计划）（2018E02072）; 清华大学电力系统及大型发电设备安全控制与仿真国家重点实验室开放研究课题（SKLD2OM20）
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