论文标题
快速的DTW和模糊聚类用于电力系统计划中的场景生成问题
Fast DTW and Fuzzy Clustering for Scenario Generation in Power System Planning Problems
论文作者
论文摘要
如果一个人说明所有不确定的操作场景，那么电源系统计划问题就会在计算上棘手。因此，选择了代表可能/极端操作条件的方案子集，例如夏季沉重，冬季繁忙，夏季浅色等。但是，这种方法可能无法准确捕获可再生生成（RG）和系统负载之间的依赖关系。本文提出了使用快速动态的时间扭曲（FDTW）和模糊C-Means ++（FCM ++）聚类的使用来说明负载的关键统计属性，而RG则用于电源系统计划问题的场景生成。使用美国电力网络进行案例研究，并与现有场景生成技术进行比较，证明了拟议方法的好处。
Power system planning problems become computationally intractable if one accounts for all uncertain operating scenarios. Consequently, one selects a subset of scenarios that are representative of likely/extreme operating conditions, e.g. heavy summer, heavy winter, light summer, and so on. However, such an approach may not be able to accurately capture the dependencies that exist between renewable generation (RG) and system load in RG-rich power systems. This paper proposes the use of fast dynamic time warping (FDTW) and fuzzy c-means++ (FCM++) clustering to account for key statistical properties of load and RG for scenario generation for power system planning problems. Case studies using a U.S. power network, and comparison with existing scenario generation techniques demonstrate the benefits of the proposed approach.
