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Optimal Congestion Management Based on Sensitivity in Power System with Wind Farms

민감도를 이용하여 풍력단지가 연계된 송전계통의 최적혼잡처리

  • Choi, Soo-Hyun (Dept. of Electrical Engineering, Hankyong National University) ;
  • Kim, Kyu-Ho (Dept. of Electrical Engineering, Hankyong National University, IT Fusion Research Institute)
  • Received : 2016.08.30
  • Accepted : 2016.11.25
  • Published : 2016.12.01

Abstract

This paper studies generator rescheduling technique for congestion management in power system with wind farms. The proposed technique is formulated to minimize the rescheduling cost of conventional and wind generators to alleviate congestion subject to operational line overloading. The generator rescheduling method has been used with incorporation of wind farms in the power system. The locations of wind farms are selected based upon power transfer distribution factor (PTDF). Because all generators in the system do not need to participate in congestion management, the rescheduling has been done by generator selection based on the proposed generator sensitivity factor (GSF). The selected generators have been rescheduled using linear programming(LP) optimization techniques to alleviate transmission congestion. The effectiveness of the proposed methodology has been analyzed on IEEE 14-bus systems.

Keywords

References

  1. Murphy, Colleen, and Andrew Keane. "Optimisation of wind farm reactive power for congestion management." PowerTech (POWERTECH), 2013 IEEE Grenoble. IEEE, 2013.
  2. A. Kumar, S. C. Srivastava, and S. N. Singh, "Congestion management in competitive power market: A bibliographical survey," Elect. Power Syst. Res, vol. 76, pp. 153-164, 2005. https://doi.org/10.1016/j.epsr.2005.05.001
  3. Hazra and Sinha, "Congestion Management Using Multiobjective Particle Swarm Optimization." IEEE transactions on power systems. 22(4), 1726-34, 2007. https://doi.org/10.1109/TPWRS.2007.907532
  4. Dutta and Singh, "Optimal Rescheduling of Generators for Congestion Management Based on Particle Swarm Optimization." IEEE transactions on power systems. 23(4), 1560-69, 2008. https://doi.org/10.1109/TPWRS.2008.922647
  5. Venkaiah, Ch., and Vinodkumar, D.M, "Fuzzy adaptive bacterial foraging congestion management using sensitivity based optimal active power rescheduling of generators." Applied Soft Computing, 11, 4921-4930, 2011. https://doi.org/10.1016/j.asoc.2011.06.007
  6. Singh, K., Padhy, N.P., and Sharma, J, "Congestion management considering hydro-thermal combined operation in a pool based electricity market." Electrical Power and Energy Systems, 33, 1513-1519, 2011. https://doi.org/10.1016/j.ijepes.2011.06.037
  7. A. Kumar, S. C. Srivastava, and S. N. Singh, "A zonal congestion management approach using real and reactive power rescheduling,," IEEE Trans. Power Syst., vol. 19, no. 1, pp. 554-562, Feb. 2004.
  8. Kyu-Ho Kim, et al. "An efficient operation of a micro grid using heuristic optimization techniques: Harmony search algorithm, PSO, and GA." IEEE PES General Meeting, 2012.
  9. Kyung-bin Song, Kyu-Hyung Lim, Young-Sik Baek, "A Case Study of the Congestion Management for the Power System of the Korea Electric Power Cooperation", KIEE, vol. 50A, no. 12, 2001
  10. Allen J. Wood, Bruce F. Wollenberg, "Power generation, operation, and control" Wiley-Interscience, 2002