Advertisement

Cognitive Computation

, Volume 7, Issue 5, pp 594–608 | Cite as

A Biologically Inspired Modified Flower Pollination Algorithm for Solving Economic Dispatch Problems in Modern Power Systems

  • Hari Mohan Dubey
  • Manjaree PanditEmail author
  • B. K. Panigrahi
Article

Abstract

Gradient-based traditional algorithms fail to locate optimal solutions for real-world problems with non-differentiable/discontinuous objective functions. But biologically inspired optimization algorithms, due to their unconventional random search capability, provide good solutions within finite time to multimodal and non-convex problems. The search capability of these methods largely depends on their exploration and exploitation potential. This paper presents a modified flower pollination algorithm (MFPA) in which (1) the local pollination of FPA is controlled by a scaling factor and (2) an intensive exploitation phase is added to tune the best solution. The effectiveness of MFPA is tested on some mathematical benchmarks and four large practical power system test cases.

Keywords

Modified flower pollination algorithm (MFPA) Intensive exploitation phase Biologically inspired (BI) techniques Lévy flight Ramp rate limits (RRL) Prohibited operating zones (POZ) Valve point loading (VPL) effects 

References

  1. 1.
    Wood AJ, Wollenberg BF. Power generation operation and control. 2nd ed. NewYork: Wiley; 1996.Google Scholar
  2. 2.
    Sinha N, Chakrabarti R, Chattopadhyay PK. Evolutionary programming techniques for economic load dispatch. IEEE Trans Evol Comput. 2003;7(1):83–94.CrossRefGoogle Scholar
  3. 3.
    Selvakumar A, Thanushkodi K. A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans Power Syst. 2007;22(1):42–51.CrossRefGoogle Scholar
  4. 4.
    Chaturvedi KT, Pandit M, Srivastava L. Self-organizing hierarchical particle swarm optimization for nonconvex economic dispatch. IEEE Trans Power Syst. 2008;23(3):1079–87.CrossRefGoogle Scholar
  5. 5.
    Vlachogiannis JG, Lee KY. Economic load dispatch a comparative study on heuristic techniques with an improved coordinated aggregation based PSO. IEEE Trans Power Syst. 2009;24(2):991–1001.CrossRefGoogle Scholar
  6. 6.
    Niknam T, Doagou Mojarrad H, Nayeripour M. A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch. Energy. 2010;35(4):1764–78.CrossRefGoogle Scholar
  7. 7.
    Noman N, Iba H. Differential evolution for economic load dispatch problems. Electr Power Syst Res. 2008;78(8):1322–31.CrossRefGoogle Scholar
  8. 8.
    Chiou JP. Variable scaling hybrid differential evolution for large-scale economic dispatch problems. Electr Power Syst Res. 2007;77(3–4):212–8.CrossRefGoogle Scholar
  9. 9.
    Panigrahi BK, Pandi VR. Bacterial foraging optimisation: Nelder Mead hybrid algorithm for economic load dispatch. IET Proc Gener Transm Distrib. 2008;2(4):556–65.CrossRefGoogle Scholar
  10. 10.
    Bhattacharya A, Chattopadhyay PK. Biogeography-based optimization for different economic load dispatch problems. IEEE Trans Power Syst. 2010;25(2):1064–77.CrossRefGoogle Scholar
  11. 11.
    Pandi VR, Panigrahi BK, Bansal RC, Das S, Mohapatra A. Economic load dispatch using hybrid swarm intelligence based harmony search algorithm. Electr Power Compon Syst. 2011;39(8):751–67.CrossRefGoogle Scholar
  12. 12.
    Dalvand M, Ivatloo B, Najafi A, Rabiee A. Continuous quick group search optimizer for solving non-convex economic dispatch problems. Electr Power Syst Res. 2012;93:93–105.CrossRefGoogle Scholar
  13. 13.
    Zare K, Haque M, Davoodi E. Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method. Electr Power Syst Res. 2012;84(1):83–9.CrossRefGoogle Scholar
  14. 14.
    Yang X, Hosseini S, Gandomi A. Firefly algorithm for solving non-convex economic dispatch with valve point loading effect. Appl Soft Comput. 2012;12(3):1180–6.CrossRefGoogle Scholar
  15. 15.
    Lohokare M, Panigrahi BK, Pattnaik S, Devi S, Mohapatra A. Neighborhood search-driven accelerated biogeography-based optimization for optimal load dispatch. IEEE Trans Syst Man Cybern Part C. 2012;42(5):641–52.CrossRefGoogle Scholar
  16. 16.
    Wang L, Li LP. An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems. Electr Power Energy Syst. 2013;44(1):832–43.CrossRefGoogle Scholar
  17. 17.
    Selvakumar A, Thanushkodi K. Optimization using civilized swarm: solution to economic dispatch with multiple minima. Electr Power Syst Res. 2009;79(1):8–16.CrossRefGoogle Scholar
  18. 18.
    Selvakumar A, Thanushkodi K. Anti-predatory particle swarm optimization: solution to nonconvex economic dispatch problems. Electr Power Syst Res. 2008;78(1):2–10.CrossRefGoogle Scholar
  19. 19.
    Park J, Jeong Y, Shin J, Lee K. An improved particle swarm optimization for nonconvex economic dispatch problems. IEEE Trans Power Syst. 2010;25(1):156–66.CrossRefGoogle Scholar
  20. 20.
    Hosseinnezhad V, Babaei E. Economic load dispatch using θ-PSO. Electr Power Energy Syst. 2013;49:160–9.CrossRefGoogle Scholar
  21. 21.
    Vishwakarma KK, Dubey HM. Simulated annealing based optimization for solving large scale economic load dispatch problems. International Journal Engineering Research and Technology (IJERT). 2012;1(3):1–8.Google Scholar
  22. 22.
    Pothiya S, Ngamroo I, Kongprawechnon W. Ant colony optimization for economic dispatch problem with non-smooth cost functions. Int J Electr Power Energy Syst. 2010;32(5):478–87.CrossRefGoogle Scholar
  23. 23.
    Coelho L, Souza R, Mariani V. Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems. Math Comput Simul. 2009;79(10):3136–47.CrossRefGoogle Scholar
  24. 24.
    Srinivasa Reddy A, Vaisakh K. Shuffled differential evolution for economic dispatch with valve point loading effects. Int J Electr Power Energy Syst. 2013;46:342–52.CrossRefGoogle Scholar
  25. 25.
    Hemamalini S, Simon SP. Artificial bee colony algorithm for economic load dispatch problem with non-smooth cost functions. Electr Power Compon Syst. 2010;38(7):786–803.CrossRefGoogle Scholar
  26. 26.
    Basu M, Chowdhury A. Cuckoo search algorithm for economic dispatch. Energy. 2013;60:99–108.CrossRefGoogle Scholar
  27. 27.
    Bhattacharjee K, Bhattacharya A, Halder nee Dey S. Oppositional real coded chemical reaction optimization for different economic dispatch problems. Int J Electr Power Energy Syst. 2014;55:378–91.CrossRefGoogle Scholar
  28. 28.
    Sailesh Babu G, Bhagwan Das D, Patvardhan C. Real-parameter quantum evolutionary algorithm for economic load dispatch. IET Proc Gener Transm Distrib. 2008;2(1):22–31.CrossRefGoogle Scholar
  29. 29.
    Sayah S, Hamouda A. A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems. Appl Soft Comput. 2013;13(4):1608–19.CrossRefGoogle Scholar
  30. 30.
    Pandit M, Srivastava L, Sharma M, Dubey HM, Panigrahi BK. Large scale multi-zone optimal power dispatch using hybrid hierarchical evolution technique. J Eng. IET digital library, doi:  10.1049/joe.2013.0262.
  31. 31.
    Bhattacharya A, Chattopadhyay PK. Hybrid differential evolution with biogeography-based optimization for solution of economic load dispatch. IEEE Trans Power Syst. 2010;25(4):1955–64.CrossRefGoogle Scholar
  32. 32.
    Dubey HM, Pandit M, Panigrahi BK, Udgir M. Economic load dispatch by hybrid swarm intelligence based gravitational search algorithm. Int J Intell Syst Appl. 2013;5(8):21–32.Google Scholar
  33. 33.
    Niu Q, Zhang H, Wang X, Li K, Irwin GW. A hybrid harmony search with arithmetic crossover operation for economic dispatch. Int J Electr Power Energy Syst. 2014;62:237–57.CrossRefGoogle Scholar
  34. 34.
    Coelho LDS, Bora TC, Mariani VC. Differential evolution based on truncated Lévy-type flights and population diversity measure to solve economic load dispatch problems. Int J Electr Power Energy Syst. 2014;57:178–88.CrossRefGoogle Scholar
  35. 35.
    Hosseinnezhad V, Rafiee M, Ahmadian M, Taghi Ameli MT. Species-based quantum particle swarm optimization for economic load dispatch. Int J Electr Power Energy Syst. 2014;63:311–22.CrossRefGoogle Scholar
  36. 36.
    Nazemi A, Nazemi M. A gradient-based neural network method for solving strictly convex quadratic programming problems. Cogn Comput. 2014;6(3):484–95.CrossRefGoogle Scholar
  37. 37.
    Tang Q, Shen Y, Hu C, Zeng J, Gong W. Swarm intelligence: based cooperation optimization of multi-modal functions. Cogn Comput. 2013;5(1):48–55.CrossRefGoogle Scholar
  38. 38.
    Boaro M, Fuselli D, De Angelis F, Liu D. Adaptive dynamic programming algorithm for renewable energy scheduling and battery management. Cogn Comp. 2013;5(2):264–77.CrossRefGoogle Scholar
  39. 39.
    Yang XS. Flower pollination algorithm for global optimization. In: Unconventional computation and natural computation 2012. Lecture notes in computer science. Vol. 7445, 2012. p 240–49.Google Scholar
  40. 40.
    Pavlyukevich I. Lévy flight, non local search and simulated annealing. J Comput Phys. 2007;226(2):1830–44.CrossRefGoogle Scholar
  41. 41.
    Yang XS, Karamanoglu M, He X. Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim. 2013;46(9):1222–37.CrossRefGoogle Scholar
  42. 42.
    Gandomi AH, Yang XS, Alavi AH. Cuckoo search algorithm: a meta-heuristic approach to solve structural optimization problems. Eng Comput. 2013;29(1):17–35.CrossRefGoogle Scholar
  43. 43.
    Orero S, Irving M. Large scale unit commitment using a hybrid genetic algorithm. Int J Electr Power Energy Syst. 1997;19(1):45–55.CrossRefGoogle Scholar
  44. 44.
    Rashedi E, Nezamabadi-pour H, Saryazdi S. GSA: a gravitational search algorithm. Inf Sci. 2009;179(13):2232–48.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Hari Mohan Dubey
    • 1
  • Manjaree Pandit
    • 1
    Email author
  • B. K. Panigrahi
    • 2
  1. 1.Department of Electrical EngineeringMadhav Institute of Technology and ScienceGwaliorIndia
  2. 2.Department of Electrical EngineeringIndian Institute of TechnologyHauz KhasIndia

Personalised recommendations