Journal of Electrical Engineering and Technology

  • 제4권3호
  • /
  • Pages.323-329
  • /
  • 2009
  • /
  • 1975-0102(pISSN)
  • /
  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지
DOI QR코드

DOI QR Code

Thermal Unit Commitment Using Binary Differential Evolution

  • 발행 : 2009.09.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This paper presents a new approach for thermal unit commitment (UC) using a differential evolution (DE) algorithm. DE is an effective, robust, and simple global optimization algorithm which only has a few control parameters and has been successfully applied to a wide range of optimization problems. However, the standard DE cannot be applied to binary optimization problems such as UC problems since it is restricted to continuous-valued spaces. This paper proposes binary differential evolution (BDE), which enables the DE to operate in binary spaces and applies the proposed BDE to UC problems. Furthermore, this paper includes heuristic-based constraint treatment techniques to deal with the minimum up/down time and spinning reserve constraints in UC problems. Since excessive spinning reserves can incur high operation costs, the unit de-commitment strategy is also introduced to improve the solution quality. To demonstrate the performance of the proposed BDE, it is applied to largescale power systems of up to 100-units with a 24-hour demand horizon.

키워드

참고문헌

  1. Wood, A. J., and Wollenberg, B. F., Power Generation, Operation, and Control. New York, John Wiley & Sons, Inc., 1984
  2. Burns, R. M., and Gibson, C. A., 'Optimization of priority lists for a unit commitment program', Proc. IEEE Power Engineering Society Summer Meeting, Paper A, 75 453-1, 1975
  3. Sheble, G. B., 'Solution of the unit commitment problem by the method of unit periods', IEEE Trans. on Power Systems, Vol. 5, No. 1, pp. 257-260, Feb. 1990 https://doi.org/10.1109/59.49114
  4. Snyder Jr., W. L., Powell Jr., H. D., and Rayburn, J. C., 'Dynamic programming approach to unit commitment' IEEE Trans. on Power Apparatus and Systems, Vol. PAS-2, pp. 339-350, May 1987
  5. Ouyang, Z., and Shahidehpour, S. M., 'An intelligent dynamic programming for unit commitment application', IEEE Trans. on Power Systems, Vol. 6, No. 3, pp. 1203-1209, Aug. 1991 https://doi.org/10.1109/59.119267
  6. Merlin, A., and Sandrin, P., 'A new method for unit commitment at Electricite de France', IEEE Trans. on Power Apparatus and Systems, Vol. PAS-102, pp. 1218-1255, May 1983 https://doi.org/10.1109/TPAS.1983.318063
  7. Zhuang, F., and Galiana, F. D., 'Toward a more rigorous and practical unit commitment by Lagrangian relaxation', IEEE Trans. on Power Systems, Vol. 3, No. 2, pp. 763-770, May 1988 https://doi.org/10.1109/59.192933
  8. Cohen, A. I., and Yoshimura, M., 'A branch-andbound algorithm for unit commitment', IEEE Trans. on Power Apparatus and Systems, Vol. PAS-102, pp. 444-451, Feb. 1983 https://doi.org/10.1109/TPAS.1983.317714
  9. Muckstadt, J. A., and Wilson, R. C., 'An application of mixed-integer programming duality to scheduling thermal generating systems', IEEE Trans. on Power Apparatus and Systems, pp. 1968-1978, 1968
  10. Kazarlis, S. A., Bakirtzis, A. G., and Petridis, V., 'A genetic algorithm solution to the unit commitment problem', IEEE Trans. on Power Systems, Vol. 11, No. 1, pp. 83-92, Feb. 1996 https://doi.org/10.1109/59.485989
  11. Swarup, K. S., and Yamashiro, S., 'Unit commitment solution methodology using genetic algorithm', IEEE Trans. on Power Systems, Vol. 17, pp. 87-91, Feb. 2002 https://doi.org/10.1109/59.982197
  12. Juste, K. A., Kita, H., Tanaka, E., and Hasegawa, J., 'An evolutionary programming solution to the unit commitment problem', IEEE Trans. on Power Systems, vol. 14, pp. 1452-1459, Nov. 1999 https://doi.org/10.1109/59.801925
  13. Chen, H, and Wang, X., 'Cooperative coevolutionary algorithm for unit commitment', IEEE Trans. on Power Systems, vol. 16, pp. 128-133, Feb. 2002
  14. Zhuang, F., and Galiana, F. D., 'Unit commitment by simulated annealing', IEEE Trans. on Power Systems, Vol. 5, No. 1, pp. 311-317, Feb. 1990 https://doi.org/10.1109/59.49122
  15. Simopoulos, D. N., Kavatza, S. D., and Vournas, C. D., 'Unit commitment by an enhanced simulated annealing algorithm', IEEE Trans. on Power Systems, Vol. 21, No. 1, pp. 68-76, Feb. 2006 https://doi.org/10.1109/TPWRS.2005.860922
  16. Zhao, B., Guo, C. X., Bai, B. R., and Cao, Y. J., 'An improved particle swarm optimization algorithm for unit commitment', Electrical Power & Energy Systems, Vol. 28, Issue 7, pp. 482-490, Sep. 2006 https://doi.org/10.1016/j.ijepes.2006.02.011
  17. Storn, R., and Price, K., 'Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces', Journal of Global Optimization, Vol. 11, pp. 341-359, 1997 https://doi.org/10.1023/A:1008202821328
  18. Chang, C. Wong, J., Chiou, J., and Su, C., 'Robust searching hybrid differential evolution method for optimal reactive power planning in large-scale distribution systems', Electric Power Systems Research, pp. 1-8, May 2006
  19. Arora, J.S., Introduction to Optimum Design, McGraw-Hill, Inc., 1989

피인용 문헌

  1. A Modified Differential Evolution Algorithm for the Solution of a Large-Scale Unit Commitment Problem vol.39, pp.12, 2014, https://doi.org/10.1007/s13369-014-1389-8
  2. Evaluation of Two Lagrangian Dual Optimization Algorithms for Large-Scale Unit Commitment Problems vol.7, pp.1, 2012, https://doi.org/10.5370/JEET.2012.7.1.17
  3. Solution to unit commitment in power system operation planning using binary coded modified moth flame optimization algorithm (BMMFOA): A flame selection based computational technique 2017, https://doi.org/10.1016/j.jocs.2017.04.011
  4. Hybrid HS–random search algorithm considering ensemble and pitch violation for unit commitment problem vol.28, pp.5, 2017, https://doi.org/10.1007/s00521-015-2114-6
  5. A novel hybrid DE–random search approach for unit commitment problem vol.28, pp.7, 2017, https://doi.org/10.1007/s00521-015-2124-4
  6. Binary Grey Wolf Optimizer for large scale unit commitment problem vol.38, 2018, https://doi.org/10.1016/j.swevo.2017.08.002
  7. A binary-real-coded differential evolution for unit commitment problem vol.42, pp.1, 2012, https://doi.org/10.1016/j.ijepes.2012.04.048
  8. A solution to energy and environmental problems of electric power system using hybrid harmony search-random search optimization algorithm vol.3, pp.1, 2016, https://doi.org/10.1080/23311916.2016.1175059
  9. Unit commitment problem with ramp rate constraint using a binary-real-coded genetic algorithm vol.13, pp.9, 2013, https://doi.org/10.1016/j.asoc.2013.05.002
  10. Binary-Real Coded Genetic Algorithm Based <i>k</i>-Means Clustering for Unit Commitment Problem vol.06, pp.11, 2015, https://doi.org/10.4236/am.2015.611165
  11. Implementation of hybrid harmony search/random search algorithm for single area unit commitment problem vol.77, 2016, https://doi.org/10.1016/j.ijepes.2015.11.045
  12. Binary whale optimization algorithm and its application to unit commitment problem pp.1433-3058, 2020, https://doi.org/10.1007/s00521-018-3796-3
  13. Modified Binary Differential Evolution Algorithm to Solve Unit Commitment Problem pp.1532-5016, 2018, https://doi.org/10.1080/15325008.2018.1510445