风电并网对于电力系统运行中的可用输电能力(available transfer capability,ATC)计算提出了新的要求。如何全面地考虑多种不确定性因素所具有的波动特性及其在地域上的相关性特点,进而准确地评估 ATC,并提高计算速度成为亟待解决的问题。为此,提出了一种快速计算含风电场电力系统概率ATC的新方法。该方法采用拉丁超立方采样提高了蒙特卡罗仿真的采样效率,利用 Cholesky 分解对输入随机变量进行相关性建模。对于所得到的样本场景,基于聚类思想和现代内点法提出了快速计算大量场景下ATC的最优潮流灵敏度方法。通过IEEE-RTS79测试系统验证了所提方法可在确保计算精度的同时提升计算效率,算例结果表明,风速强相关将加大ATC的波动性,需要在分析计算中予以考虑。
Wind power integration has made a new demand on the calculation of available transfer capability (ATC) in power system operation. How to accurately evaluate ATC considering the stochastic variation and spatial correlation of uncertainties as well as improve the calculation efficiency has become an urgent problem to be solved. A new fast calculation method for probabilistic ATC with the presence of wind farms was proposed. The method used Latin hypercube sampling to improve the sampling efficiency, combined with Cholesky decomposition to involve the correlation of input random variables into the ATC problem. Based on clustering algorithm and modern interior method, optimal power flow sensitivity was proposed to compute the ATC values for all sampled scenarios at a fast speed. The case studies in IEEE-RTS79 show that the proposed method can reduce the computation time and ensures its accuracy. The results also verify the strong correlation of wind speed can enhance the fluctuation of ATC, which needs to be considered in the calculation.