电力系统规划问题情景生成的快速DTW和模糊聚类
Fast DTW and Fuzzy Clustering for Scenario Generation in Power System Planning Problems
摘要：
电力系统规划问题成为计算棘手的，如果一个帐户的所有不确定的操作方案。因此，人们选择一个子集的情况下，代表可能/极端的操作条件，例如，重夏，重冬，轻夏，等等。然而，这样的方法可能无法准确地捕捉之间存在的依赖关系，可再生能源发电（RG）和系统负载之间的富RG电力系统。本文提出了使用快速动态时间规整（FDTW）和模糊c-均值++（FCM++）聚类占关键统计特性的负荷和RG场景生成电力系统规划问题。使用美国电力网络的案例研究，并与现有的场景生成技术的比较证明了所提出的方法的好处。
英文摘要：
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.
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