DOI QR코드

DOI QR Code

Application of Opposition-based Differential Evolution Algorithm to Generation Expansion Planning Problem

  • Karthikeyan, K. (Dept. of Electrical and Electronic Engineering, Kalasalingam University) ;
  • Kannan, S. (Dept. of Electrical and Electronic Engineering, Kalasalingam University) ;
  • Baskar, S. (Dept. of Electrical and Electronic Engineering, Thiagarajar College of Engineering) ;
  • Thangaraj, C. (Anna University of Technology)
  • Received : 2012.10.15
  • Accepted : 2013.02.14
  • Published : 2013.07.01

Abstract

Generation Expansion Planning (GEP) is one of the most important decision-making activities in electric utilities. Least-cost GEP is to determine the minimum-cost capacity addition plan (i.e., the type and number of candidate plants) that meets forecasted demand within a pre specified reliability criterion over a planning horizon. In this paper, Differential Evolution (DE), and Opposition-based Differential Evolution (ODE) algorithms have been applied to the GEP problem. The original GEP problem has been modified by incorporating Virtual Mapping Procedure (VMP). The GEP problem of a synthetic test systems for 6-year, 14-year and 24-year planning horizons having five types of candidate units have been considered. The results have been compared with Dynamic Programming (DP) method. The ODE performs well and converges faster than DE.

Keywords

References

  1. Wang X, McDonald JR. Modern Power System Planning. London: McGraw Hill; 1994, pp. 208-229.
  2. Khokhar JS. Programming Models for the Electricity Industry. New Delhi, Delhi: Commonwealth Publishers;1997, pp. 21-84.
  3. Neelakanta PS, Arsali MH. "Integrated resource planning using segmentation method based dynamic programming,"IEEE Trans. Power Syst. 1999; 14(1): 375-385. https://doi.org/10.1109/59.744558
  4. Introduction to the WASP IV model, User's manual. International Atomic Energy Agency, Vienna, Austria: Nov 2001.
  5. H. M. Khodr, J. F. Gomez, L. Barnique, J. H. Vivas, P. Paiva, J. M. Yusta, and A. J. Urdaneta, "A Linear Programming Methodology for the Optimization of Electric Power-Generation Schemes", IEEE Transactions on Power systems, pp. 864-869, Vol.17, No.3, August 2002.
  6. J. A. Bloom, "Long-range generation planning using decomposition and probabilistic simulation", IEEE Trans. Power App. Syst., pp. 797-802, Vol.PAS-101, No. 4, 1982. https://doi.org/10.1109/TPAS.1982.317144
  7. Zhu J, Chow MY. A review of emerging techniques on generation expansion planning. IEEE Trans. Power Syst. 1997; 12(4): 1722-1728. https://doi.org/10.1109/59.627882
  8. Park JB, Park YM, Won JR, Kim DG. Generation expansion planning based on an advanced evolutionary programming. IEEE Trans. Power Syst. 1999; 14(1):299-305. https://doi.org/10.1109/59.744547
  9. Park JB, Park YM, Won JR, Lee KY. An improved genetic algorithm for generation expansion planning. IEEE Trans. Power Syst. 2000; 15(3): 916-922.
  10. Heloisa Teixeira Firmo, and Luiz Fernando Loureiro Legey, "Generation Expansion Planning: An Iterative Genetic Algorithm Approach", IEEE Transactions on Power systems, pp. 901-906, Vol. 17, No. 13, August 2002. https://doi.org/10.1109/TPWRS.2002.801036
  11. Argyris G. Kagiannas, "Power generation planning: a survey from monopoly to competition", Electrical Power and Energy Systems, Vol. 26, pp. 413-421, 2004. https://doi.org/10.1016/j.ijepes.2003.11.003
  12. Sung-Ling Chen, Tung-Sheng Zhan, and Ming-Tong Tsay, "Generation expansion planning of the utility with refined immune algorithm", Electric Power Systems Research, pp.251-258, Vol.76, 2006. https://doi.org/10.1016/j.epsr.2005.06.005
  13. Y. M. Park, J. B. Park, and J. R. Won, "A hybrid genetic algorithm/dynamic programming approach to optimal long-term generation expansion planning", Electrical Power and Energy Systems, pp. 295-303, Vol.20, No.4, 1998. https://doi.org/10.1016/S0142-0615(97)00070-7
  14. Kannan S, Mary Raja Slochanal S, Narayana Prasad Padhy. Application and comparison of meta-heuristic techniques to generation expansion planning problem. IEEE Trans. Power Syst. 2005; 20(1):466-475.
  15. Rainer Storn, and Kenneth Price, "Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces", Journal of Global Optimization, pp.341-359, Vol.11, 1997. https://doi.org/10.1023/A:1008202821328
  16. S. Kannan, S. Baskar, J.D. McCalley and P. Murugan, "Application of NSGA-II Algorithm to Generation Expansion Planning", IEEE Transactions on Power systems, Vol. 24, No. 1, pp. 454-460, 2009. https://doi.org/10.1109/TPWRS.2008.2004737
  17. Murugan, S. Kannan and S. Baskar, "NSGA-II algorithm for multi-objective generation expansion planning problem", Electric Power Systems Research, Vol. 79, No. 4, pp. 622-628, 2009. https://doi.org/10.1016/j.epsr.2008.09.011
  18. Hatice Tekiner, David W. Coit and Frank A. Felder, "Multi-period multi-objective electricity generation expansion planning problem with Monte-Carlo simulation", Electric Power Systems Research, Vol. 80, No. 12, pp. 1394-1405, 2010. https://doi.org/10.1016/j.epsr.2010.05.007
  19. S. Kamalinia and M. Shahidehpour, "Generation expansion planning in wind-thermal power systems", IET Generation, Transmission and Distribution, Vol. 4, No. 8, pp. 940-951, 2010. https://doi.org/10.1049/iet-gtd.2009.0695
  20. F. Careri, C. Genesi, P. Marannino, M. Montagna, S. Rossi and I. Siviero, "Generation Expansion Planning in the Age of Green Economy", IEEE Transactions on Power Systems, Vol. 26, No. 4, pp. 2214-2223, 2011. https://doi.org/10.1109/TPWRS.2011.2107753
  21. Yanyi He, Lizhi Wanga, Jianhui Wang, "Cap-andtrade vs. carbon taxes: A quantitative comparison from a generation expansion planning perspective", Computers & Industrial Engineering, Pages 708-716, Volume 63, Issue 3, November 2012. https://doi.org/10.1016/j.cie.2011.10.005
  22. Kannan S, Slochanal SR, Baskar S, Murugan P., "Application and comparison of Meta heuristic techniques to generation expansion planning in the partially deregulated environment", IET Generation Transmission & Distribution, Vol. 1, pp. 111-118, 2007. https://doi.org/10.1049/iet-gtd:20050271
  23. H.A. Shayanfar, H. Shayeghi, A. Pirayeshnegab, and A. Jalili, "Application of PSO technique for GEP in restructured power systems", Energy Conversion and Management, pp.2127-2135, 2009.
  24. H.A. Shayanfar, A. SaliminiaLahiji, J. Aghaei and A. Rabiee, "Generation Expansion Planning in pool market: A hybrid modified game theory and improved genetic algorithm", Energy Conversion and Management, pp. 1149-1156, 2009.
  25. Adelino J. C. Pereira & J. T. Saraiva., "A decision support system for generation expansion planning in competitive electricity markets", Electric Power Systems Research, pp.778-787, 2010.
  26. H.A. Shayanfar, A. Saliminia Lahiji, J. Aghaei and A. Rabiee, "Generation Expansion Planning in pool market: A hybrid modified game theory and Particle Swarm Optimization", Energy Conversion and Management, pp. 1512-1519, 2011.
  27. Storn, "Differential evolution research: Trends and open questions", in Advances in Differential Evolution, pp. 1-32, U. K. Chakraborty, Ed. Berlin, Germany: Springer, 2008.
  28. Swagatam Das, P. N. Suganthan, "Differential Evolution: A Survey of the State-of-the Art", IEEE Transactions on Evolutionary Computation, pp. 4-31, Vol. 15, No. 1, February 2011. https://doi.org/10.1109/TEVC.2010.2059031
  29. Shahryar Rahnamayan, "Opposition-based Differential Evolution", Thesis submitted to University of Waterloo, Canada, 2007.
  30. Shahryar Rahnamayan, Hamid R. Tizhoosh, and Magdy M. A. Salama, "Opposition-based Differential Evolution", IEEE Transactions on Evolutionary Computation, pp. 64-79, Vol. 12, No. 1, February 2008. https://doi.org/10.1109/TEVC.2007.894200

Cited by

  1. Generation expansion planning based on solar plants with storage vol.57, 2016, https://doi.org/10.1016/j.rser.2015.12.126
  2. Least cost generation expansion planning with solar power plant using Differential Evolution algorithm vol.85, 2016, https://doi.org/10.1016/j.renene.2015.07.026
  3. Opposition based learning: A literature review 2017, https://doi.org/10.1016/j.swevo.2017.09.010