基于概率预测的混合储能平抑风电波动随机优化调控方法

Probabilistic Forecasting Based Stochastic Optimal Dispatch and Control Method of Hybrid Energy Storage for Smoothing Wind Power Fluctuations

在风电场中安装不同类型的储能设备可以有效平抑风电波动,减少风电不确定性给电力系统安全运行带来的影响。对于由风电场和混合储能系统构成的风储混合系统,提出了基于概率预测的混合储能平抑风电波动随机优化调控方法。首先,通过风电概率预测结合多元Copula函数生成具有时间相关性的风电场景集;随后,提出自适应变分模态分解方法计算并网风电功率和混合储能系统充/放电功率的目标预调度值;接着,基于所获得的预调度目标功率,考虑储能充/放电循环次数约束和存储能量机会约束,构建并求解了混合储能平抑风电波动随机优化调控模型;最后,通过算例分析说明了所提混合储能平抑风电波动随机优化调控方法的有效性和经济性。

Installing different types of energy storage devices in wind farms can effectively smooth wind power fluctuations and reduce the impact of wind power uncertainty on the secure operation of power systems. For the hybrid wind-storage system composed of wind farms and hybrid energy storage systems(HESSs), this paper proposes a probabilistic forecasting based stochastic optimal dispatch and control method of HESS for smoothing wind power fluctuations. Firstly, the wind power probabilistic forecasting is combined with multivariate Copula function to generate the temporally correlated wind power scenario set. Then, an adaptive variational mode decomposition method is proposed to calculate the pre-dispatched target of grid-connected wind power and charging/discharging power of HESS. After that, based on the pre-dispatched power, a stochastic optimal dispatch and control model of HESS for smoothing wind power fluctuations is constructed and solved, which considers the lifecycle constraint of energy storage charging/discharging and the chance constraint of stored energy. Finally, the effectiveness and economy of the proposed stochastic optimal dispatch and control method of HESS for smoothing wind power fluctuations are illustrated through case analysis.