Zone Clustering LMP with Location information using an Improved Fuzzy C-Mean

Se-Hwan Jang, Jin-ho Kim, Sang-Hyuk Lee, Juneho Park
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引用次数: 6

Abstract

For the efficient zone clustering of Large-scale power system, this paper introduces an improved fuzzy C-means (FCM) approach. Due to physical characteristics of Power System, each node of the power system has their own locational marginal price (LMP) indicating the network-related characteristics of the system. In electricity market, it has been on the rise across much of a deregulation market to price market clearing prices considering LMP. It is inefficient to price on each node individually. Hence classification for the whole system into distinct several subsystems based on a dissimilarity measure is typically needed for the efficient operation of the whole system. Moreover, the location information on the system is taken into account in order to properly address the geometric mis-clustering problem such as grouping geometrically distant nodes with dissimilarity measures into a common cluster. Therefore, this paper proposes the improved FCM approach for clustering LMP with location information. We have conducted the clustering in real Korea power system to verify the usefulness of the proposed improved FCM approach.
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基于改进模糊c均值的区域聚类LMP
针对大型电力系统的高效区域聚类问题,提出了一种改进的模糊c均值(FCM)方法。由于电力系统的物理特性,电力系统的每个节点都有自己的位置边际价格(LMP),表示该系统与网络相关的特性。在电力市场中,考虑到LMP,在放松管制的市场中,它一直在上升。对每个节点单独定价是低效的。因此,为了使整个系统有效运行,通常需要根据不同的度量将整个系统划分为不同的几个子系统。此外,该方法还考虑了系统的位置信息,以适当地解决几何错误聚类问题,如将几何距离较远且度量不同的节点分组到一个共同的聚类中。因此,本文提出了基于位置信息的LMP聚类的改进FCM方法。我们在韩国的实际电力系统中进行了聚类,以验证所提出的改进FCM方法的有效性。
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