基于双目标时变交集的电池储能改善风电场出力控制

为了抑制风电随机波动和提高风电超短期预测精度，提出了一种基于此双目标时变交集的电池储能控制方法。首先，分别制定了抑制风电波动、提高风电预测精度的单一控制域及此双目标的交集联合控制域，并在考虑两种单一控制目标的时间尺度不同后，制定了随时间变化的双目标交集联合控制域。在此基础之上，结合储能运行约束条件确定了最终的电池储能改善风电场出力控制策略。其次，首次建立了基于越限比和越限相对均值的波动与预测精度评估指标并形成了双目标综合评估体系。最后，应用某风电场实际出力数据，分别在采用神经网络时间序列和差分自回归滑动平均(ARIMA)两种预测模型的前提下，考虑电网对风电波动忍受度和超短期预测误差忍受度的三种大小关系，通过计算波动量总和与均方根误差验证了所建立评估指标的有效性，同时证实了应用该控制策略的电池储能系统能够同时达到抑制风电随机波动和提高风电超短期预测精度两个目标。

A control of BESS to improve the wind farm output based on time-varying intersection of dual targets

A battery energy storage system (BESS) control method based on time-varying intersection of the dual targets is proposed to suppress the random fluctuation of wind power and improve the accuracy of ultra short-term prediction of wind power. Firstly, a respective single control limit, intersection combined control limit and further time-varying intersection combined control limit once different time-scale of the dual control targets is considered are established. On this basis, combined with the operating constraints of the energy storage, the final control strategy of battery energy storage to improve the wind farm output is arrived at. Secondly, valuation and forecast accuracy assessment metrics based on off-limit probability and off-limit relative mean value is set up and comprehensive evaluation system of the dual targets is developed. Finally, by applying the real output data of a wind farm, on the premise of using neural network time series model or ARIMA model, three size relations of tolerance of wind fluctuation and tolerance of ultra short-term prediction error is considered to verify the effectiveness of the control strategy by calculating the total variation and RMSE and meanwhile both suppress random fluctuation and improve the ultra short-term prediction accuracy of wind power. This work is supported by National Natural Science Foundation of China (No. 51267020).