The Transactions of the Korean Institute of Electrical Engineers A (대한전기학회논문지:전력기술부문A)

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지

On the convergence Rate Improvement of Mathematical Decomposition Technique on distributed Optimal Power Flow

수화적 분할 기법을 이요한 분산처리 최적조류계산의 수렴속도 향상에 관한 연구

  • 허돈 (서울대 공대 전기공학부) ;
  • 박종근 (서울대 공대 전기공학부) ;
  • 김발호 (홍익대 공대 전자전기공학부)
  • Published : 2001.03.01
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Abstract

We present an approach to parallelizing optimal power flow that is suitable for distributed implementation and is applicable to very large interconnected power systems. This approach can be used by utilities to optimize economy interchange without disclosing details of their operating costs to competitors. Recently, it is becoming necessary to incorporate contingency constraints into the formulation, and more rapid updates of telemetered data and faster solution time are becoming important to better track changes in the system. This concern led to a research to develop an efficient algorithm for a distributed optimal power flow based on the Auxiliary Problem Principle and to study the convergence rate improvement of the distributed algorithm. The objective of this paper is to find a set of control parameters with which the Auxiliary Problem Principle (Algorithm - APP) can be best implemented in solving optimal power flow problems. We employed several IEEE Reliability Test Systems, and Korea Power System to demonstrate the alternative parameter sets.

Keywords

References

  1. M. Huneault and F. D. Galliana, 'A Survey of the optimal power flow literature,' IEEE Transactions on Power Systems, vol. 6, No. 2, pp.762-770, May 1991 https://doi.org/10.1109/59.76723
  2. H. Singh, H.Shangyou, and A. Papalexopoulos, 'Transmission congestion management in Competitive Electric Markets,' IEEE Transactions on Power Systems, vol. 13, No. 2, pp.672-680, May 1998 https://doi.org/10.1109/59.667399
  3. Guy Cohen, 'Optimization by decomposition and coordination: A unified approach,' IEEE Transactions on Automatic Control, vol. 23, No. 2, pp.222-232, April 1978 https://doi.org/10.1109/TAC.1978.1101718
  4. Balho H. Kim and Ross Baldick, 'Coarse-grained distributed optimal power flow,'IIEEE Transactions on Power Systems, vol. 12, No. 2, pp.932-939, May 1997 https://doi.org/10.1109/59.589777
  5. B. Stott, O. Alsac, and A. J. Monticelli, 'Security analysis and optimization,' Proceedings of the IEEE, vol 75, No. 12, pp.1623-1644, December 1987
  6. J. Batut and A. Renaud, 'Daily generation scheduling optimization with transmission constraints: A new class of algorithms,' IEEE Transactions on Power Apparatus and Systems, vol. 7, No. 3, pp.982-989, August 1992 https://doi.org/10.1109/59.207311
  7. Guy Cohen, 'Auxiliary problem principle and decomposition of optimization problems,' Journal of Optimization Theory and Applications, vol. 32, No. 3, pp.277-305, November 1980 https://doi.org/10.1007/BF00934554
  8. Guy Cohen and Bernadette Miara, 'Optimization with an auxiliary constraint and decomposition,' SIAM Journal on Control and Optimization, vol. 28, No. 1, pp.137-157, January 1990 https://doi.org/10.1137/0328007
  9. Guy Cohen and Dao Li Zhu, 'Decomposition coordination methods in large scale optimization problems,' Advances in Large Scale Systems, vol. 1, pp.203-266, 1984
  10. Anthony Brooke, David Kendrick, and Alexander Meeraus, GAMS User's Guide, The Scientific Press, Redwood City, CA, 1990
  11. David Kendrick and Alexander Meeraus, GAMS: An Introduction, World Bank, 1985
  12. Allen J. Wood and Bruce F. Wollenberg, Power Generation, Operation and Control, Wiley, New York, 2nd edition, 1996
  13. Martin L. Baughman et al., 'Electric Utility Resource Planning and Production Costing Projects : Final Report,' Center for Energy Studies, The University of Texas at Austin, 1993