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

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.