A RBFN hierarchical clustering based network partitioning method for zonal pricing

• Main Authors: Hongming Yang, Renjun Zhou, Jianhua Liu
• Format: Conference Proceeding
• Language: English
• Subjects: Cities and towns; Clustering algorithms; Educational institutions; Equations; Hierarchical clustering; Humans; IEEE catalog; Joining processes; Kernel; power market; price zone; Pricing; radial basis function network; Radial basis function networks
• DOI: 10.1109/ICEEE.2005.1529627

In order to overcome the difficulty in using nodal pricing, the notion of zone is widely adopted in actual pricing scheme. The key for establishing a simple and efficient zonal pricing scheme is to accurately partition transmission network in the presence of congestion. Unfortunately, in actual power market operation, the operators usually establish zones based on their experiences, considering the locations of congested lines, without mathematical analysis. In order to achieve accurate price zone partition without any priori partition knowledge, this paper firstly extracts the sensitivities of nodal power injections to power flows on all congested lines as cluster features of nodal price. Secondly, a scale hierarchical clustering method based the radial basis function network (RBFN) for price zone partition is proposed. Finally, test results on IEEE 118-node system show the validity and feasibility of the proposed method.