With the increasing wind power integration, the security and economy of the power system operations are greatly influenced by the intermittency and fluctuation of wind power. Due to the flexible operational modes for charging/discharging, the hybrid energy storage system (HESS) is composed of battery energy storage system and super-capacitor can effectively mitigate the wind power uncertainty. This paper proposes a probabilistic forecasting-based HESS sizing and control scheme to cost-effectively smooth wind power fluctuations. First, probabilistic wind power forecasting is combined with multivariate Gaussian copula to generate temporally correlated wind power scenarios. Then, an adaptive variational mode decomposition (VMD) method is proposed to extract the frequency components of each wind scenario and determine the pre-scheduled power of wind-HESS system adaptively. Finally, a two-stage stochastic optimization model is constructed to determine the capacity and correct the pre-scheduled power of HESS with the objective of minimizing total cost. Case studies based on actual data from a Danish wind farm demonstrate that the proposed HESS sizing and control scheme can significantly reduce the installation cost and operation cost of HESS and prominently smooth wind power fluctuations.

中文翻译：

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基于概率预测的风电平滑混合储能的大小和控制

随着风电并网的不断深入，风电的间歇性和波动性极大地影响了电力系统运行的安全性和经济性。由于充放电运行模式灵活，由电池储能系统和超级电容器组成的混合储能系统（HESS）可以有效缓解风电的不确定性。本文提出了一种基于概率预测的 HESS 选型和控制方案，以经济高效地平滑风电波动。首先，概率风电预测与多元高斯 copula 相结合，以生成时间相关的风电场景。然后，提出了一种自适应变分模式分解（VMD）方法来提取每个风场景的频率分量并自适应地确定wind-HESS系统的预调度功率。最后，构建两阶段随机优化模型，以最小化总成本为目标，确定HESS的容量并修正预调度功率。基于丹麦风电场实际数据的案例研究表明，所提出的 HESS 选型和控制方案可以显着降低 HESS 的安装成本和运营成本，并显着平滑风电波动。