The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Optimization of Long-term Generator Maintenance Scheduling considering Network Congestion and Equivalent Operating Hours

송전제약과 등가운전시간을 고려한 장기 예방정비계획 최적화에 관한 연구

  • Shin, Hansol (Dept. of Electrical and Computer Engineering, Pusan National University) ;
  • Kim, Hyoungtae (Dept. of Electrical and Computer Engineering, Pusan National University) ;
  • Lee, Sungwoo (Dept. of Electrical and Computer Engineering, Pusan National University) ;
  • Kim, Wook (Dept. of Electrical and Computer Engineering, Pusan National University)
  • Received : 2016.11.15
  • Accepted : 2017.01.20
  • Published : 2017.02.01
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Abstract

Most of the existing researches on systemwide optimization of generator maintenance scheduling do not consider the equivalent operating hours(EOHs) mainly due to the difficulties of calculating the EOHs of the CCGTs in the large scale system. In order to estimate the EOHs not only the operating hours but also the number of start-up/shutdown during the planning period should be estimated, which requires the mathematical model to incorporate the economic dispatch model and unit commitment model. The model is inherently modelled as a large scale mixed-integer nonlinear programming problem and the computation time increases exponentially and intractable as the system size grows. To make the problem tractable, this paper proposes an EOH calculation based on demand grouping by K-means clustering algorithm. Network congestion is also considered in order to improve the accuracy of EOH calculation. This proposed method is applied to the actual Korean electricity market and compared to other existing methods.

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References

  1. Korea Power Exchange, Cost-Based Pool Market Rules, Dec., 2016.
  2. Korea Electric Power CO., A Study on the Optimization of Generator Maintenance Scheduling during the High Reserve Rate Periods, Sep., 2015.
  3. Shahidehpour, M., & Marwali, M, Maintenance scheduling in restructured power systems, Springer Science & Business Media, pp. 53-54, 2012.
  4. Park, Y. S., Kim, J. H., & Park, J. H., "Generating Unit Maintenance Scheduling Considering Regional Reserve Constraints and Transfer Capability Using Hybrid PSO Algorithm", The Transaction of The Korean Institute of Electrical Engineers, Vol. 56, No. 11, pp. 1892-1902, Nov., 2007.
  5. Bisanovic, S., Hajro, M., & Dlakic, M, "A profit-based maintenance scheduling of thermal power units in electricity market", International Journal of Electrical and Electronics Engineering, Vol. 5, No. 3, pp. 156-164, 2011.
  6. Kim, J. H., Park, J. B., Park, J. K., & Chun, Y. H. "Generating unit maintenance scheduling under competitive market environments" International Journal of Electrical Power & Energy Systems, Vol. 27, No. 3, pp. 189-194, Mar., 2005. https://doi.org/10.1016/j.ijepes.2004.07.011
  7. Han, S. M., Shin, Y. G., & Kim, B. H., "An Algorithm for Generator Maintenance Scheduling Considering Transmission System", The Transaction of The Korean Institute of Electrical Engineers A, Vol. 54A, No. 7, pp. 352-357, Jul., 2005.
  8. Kim, M. S., "Application of TEOH for the determination of maintenance period in gas turbine engineers", Thesis for the degree of master of science, Yonsei University Graduate School of Engineering, Dec., 1999.
  9. Lobato, E., Sanchez-Martin, P., & Saiz-Marin, E., "Long Term Maintenance Optimization of CCGT Plants", In 2012 Asia-Pacific Power and Energy Engineering Conference, pp. 1-4, Mar., 2012.
  10. Han, S., Kim, H., Lee, S., & Kim, W., "Optimization of Generator Maintenance Scheduling with Consideration on the Equivalent Operation Hours", Journal of Electrical Engineering and Technology, Vol.11, No. 2, pp. 338-346, Mar., 2016. https://doi.org/10.5370/JEET.2016.11.2.338
  11. MacQueen, J., "Some methods for classification and analysis of multivariate observations", In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 1, No. 14, pp. 281-297, Jun., 1967.
  12. Krishnan, V., Ho, J., Hobbs, B. F., Liu, A. L., McCalley, J. D., Shahidehpour, M., & Zheng, Q. P., "Co-optimization of electricity transmission and generation resources for planning and policy analysis: review of concepts and modeling approaches". Energy Systems, Vol. 7, No.2, pp. 297-332, May, 2016. https://doi.org/10.1007/s12667-015-0158-4
  13. Jang, J. S. R., Sun, C. T., & Mizutani, E., Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence, Prentice Hall, Upper Saddle River, pp. 423-425, 1997.
  14. R. E. Rosenthal, GAMS - A User's Guide, GAMS Development Corporation, 2014.