Regional Grouping of Transmission System Using the Sequential Clustering Technique

순차적 클러스터링기법을 이용한 송전 계통의 지역별 그룹핑

  • Published : 2009.05.01


This paper introduces a sequential clustering technique as a tool for an effective grouping of transmission systems. The interconnected network system retains information about the location of each line. With this information, this paper aims to carry out initial clustering through the transmission usage rate, compare the similarity measures of regional information with the similarity measures of location price, and introduce the techniques of the clustering method. This transmission usage rate uses power flow based on congestion costs and similarity measurements using the FCM(Fuzzy C-Mean) algorithm. This paper also aims to prove the propriety of the proposed clustering method by comparing it with existing clustering methods that use the similarity measurement system. The proposed algorithm is demonstrated through the IEEE 39-bus RTS and Korea power system.


Sequential Clustering;Transmission usage rate;Location marginal price;Regional information;FCM


  1. L. Chen, H. Suzuki, T. Wachi, and Y. Shimura, 'Components of Nodal Prices for Electric Power Systems', IEEE Trans. on Power Systems, Vol. 17, no. 1, Feb. 2002
  2. Jiulun Fan, Weizin Xie, 'Distance measure and induced fuzzy entropy', Fuzzy Sets and Systems, 104, 305-314, 1999
  3. S.H. Lee, J.H. Kim, S.H. Jang, J.B. Park, Y. H. Jeon, S. Y. Sohn, 'An Advanced Fuzzy C-Mean Algorithm for Regional Clusering of Interconnected Systems', LNAI, pp606-615, 2007
  4. The IEEE Reliability Test System-1996, A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommite, IEEE Transactions on Power Systems, Vol. 14, Issue3, 1999
  5. 한국 전력시스템
  6. Francois Leveque, 'Transport pricing of electricity networks'
  7. J.C. Bezdek, Fuzzy Mathematics in Pattern Classification, PhD Thesis, Applied Math. Center, cornell Univ., Ithaca, 1973
  8. W. Li and A. Bose, 'A coherency based rescheduling method for dynamic security', IEEE Trans. on Power Systems, Vol. 13, No.3, 810-815, 1998
  9. J.S.R. Jang, C.T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall, 1997
  10. T. Wu, Z. Alaywan, and A. Papalexopoulos, 'Locational Marginal Price Calculation Using the Distributed-Slack Power Flow Formulation', IEEE Trans. on Power Systems, Vol. 20, no. 2, May. 2005
  11. 연구보고서, '지역별 전력수급 계획 수립기준 정립에 관한 연구', 2008.3.
  12. Liu Xuecheng, 'Entropy, distance measure and similarity measure of fuzzy sets and their relations, Fuzzy Sets and Systems, 52, 305-318, 1992
  13. Y.S. Cho, G.S. Jang, 'Korean Power System Security Analysis Using Benchmark Systems', KIEE International Trans. on Power Engineering. Vol. 5-A, No. 3, 99.207-213, 2005
  14. Perez-Arriga, et al., 'Marginal pricing of transmission service: An analysis of cost recovery', IEEE Transactions on power system, Vol. 10, No.1, February 1995
  15. A.M. Gallai and R.J. Thomas, 'Coherency Identification for large electric power system', IEEE Trans. on Circuits and Systems, Vol. Cas-29, No. 11, 777-782, 1982