Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Bacterial foraging optimisation: Nelder–Mead hybrid algorithm for economic load dispatch

Bacterial foraging optimisation: Nelder–Mead hybrid algorithm for economic load dispatch

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A novel stochastic optimisation approach to solve constrained economic load dispatch problem using hybrid bacterial foraging (BF) technique is presented. In order to explore the search space for finding the local minima of the current location, the simplex algorithm called Nelder–Mead is used along with BF algorithm. The proposed methodology easily takes care of solving non-convex economic dispatch problems along with different constraints such as transmission losses, dynamic operation constraints (ramp rate limits) and prohibited zones. Simulations were performed over various standard test systems with different number of generating units and comparisons are performed with other existing relevant approaches. The findings affirmed the robustness and proficiency of proposed methodology over other existing techniques.

References

    1. 1)
      • L.D.S. Coelho , V.C. Mariani . Erratum correlation to-combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve point effect. IEEE Trans. Power Syst. , 3
    2. 2)
      • Eberhart, R.C., Shi, Y.: `Comparison between genetic algorithms and particle swarm optimization', Proc. IEEE Int. Conf. Evol. Comput., May 1998, p. 611–616.
    3. 3)
      • A. Bakirtzis , V. Petridis , S. Kazarlis . Genetic algorithm solution to the economic dispatch problem. Proc. Inst. Electr. Eng. Gener. Transm. Distrib. , 4 , 377 - 382
    4. 4)
    5. 5)
      • Sheble, G.B., Brittig, K.: `Refined genetic algorithm–economic dispatch example', IEEE Paper 94 WM 199-0 PWRS, Presented at the IEEE/PES 1994, Winter Meeting.
    6. 6)
      • Ma, H., El-Keib, A.A., Smith, R.E.: `A genetic algorithm-based approach to economic dispatch of power systems', IEEE Conf., 1994.
    7. 7)
      • A.J. Wood , B.F. Wollenberg . (1996) Power generation, operation and control.
    8. 8)
      • K.P. Wong , Y.W. Wong . Genetic and genetic/simulated – annealing approaches to economic dispatch. Proc. Inst. Electr. Eng. Gener. Transm. Distrib. , 5 , 507 - 513
    9. 9)
      • D.B. Fogel . (2000) Evolutionary computation: toward a new philosophy of machine intelligence.
    10. 10)
    11. 11)
      • K.Y. Lee , Y.M. Park , J.L. Ortiz . Fuel cost minimization for both real and reactive power dispatches. IEE Proc. C, Gener. Transm. Distrib. , 3 , 85 - 93
    12. 12)
      • C.E. Lin , G.L. Viviani . Hierarchical economic dispatch for piecewise quadratic cost functions. IEEE Trans. Power Appar. Syst. , 1170 - 1175
    13. 13)
    14. 14)
    15. 15)
      • H.H. Happ . Optimal power dispatch – a comprehensive survey. IEEE Trans Power Appar. Syst. , 841 - 854
    16. 16)
      • J.A. Nelder , R. Mead . A simplex method for function minimization. Comput. J. , 308 - 313
    17. 17)
      • Barton, R.R., Ivey, J.S.: `Modifications of the Nelder–Mead simplex method for stochastic simulation response optimization', Winter Simulation Conf. Proc, 8–11 December 1991, p. 945–953.
    18. 18)
      • Yalcionoz, T., Altun, H., Uzam, M.: `Economic dispatch solution using a genetic algorithm based on arithmetic crossover', Proc. IEEE Proto Power Tech. Conf., September 2001, Proto, Portugal.
    19. 19)
      • J.G. Damousis , A.G. Unkirlzis , P.S. Dokopoulos . Network-constrained economic dispatch using real-coded genetic algorithm. IEEE Trans. Power Syst. , 4 , 198 - 205
    20. 20)
    21. 21)
    22. 22)
      • J.H. Mathews , K.D. Fink . (1999) Numerical methods using Matlab.
    23. 23)
    24. 24)
      • R. Rardin . (1998) Optimization in operations research.
    25. 25)
      • Available at: http://www.cse.uiuc.edu/eot/modules/optimization/NelderMead/--Demo module in Java.
    26. 26)
    27. 27)
      • A. Pereira-Neto , C. Unsihuay , O.R. Saavedra . Efficient evolutionary strategy optimisation procedure to solve the nonconvex economic dispatch problem with generator constraints. Proc. Inst. Electr. Eng., Gen., Transm., Distrib. , 5 , 653 - 660
    28. 28)
      • K.Y. Lee , A. Sode-Yome , J.H. Park . Adaptive hopfield neural network for economic load dispatch. IEEE Trans. Power Syst. , 2 , 519 - 526
    29. 29)
    30. 30)
      • Fung, C.C., Chow, S.Y., Wong, K.P.: `Solving the economic dispatch problem with an integrated parallel genetic algorithm', Proc. PowerCon Int. Conf., 2000, 3, p. 1257–1262.
    31. 31)
    32. 32)
      • K.M. Passino . (2005) Biomimicry for optimization, control and automation.
    33. 33)
      • Gazi, V., Passino, K.M.: `Stability analysis of swarms in an environment with an attractant/repellent profile', Proc. American Control Conf., May 2002, Anchorage, Alaska, p. 1819–1824.
    34. 34)
      • C. Jiejin , M. Xiaoqian , L. Lixiang , P. Haipeng . Chaotic particle swarm optimization for economic dispatch considering the generator constraints. Energy convers. Manage. , 645 - 653
    35. 35)
    36. 36)
    37. 37)
      • Tomick, J.J., Arnold, S.F., Barton, R.R.: `Sample size selection for improved Nelder–Mead performance', Winter Simulation Conf. Proc, 3–6 December 1995, p. 341–345.
    38. 38)
      • B.H. Choudhary , S. Rahman . A review of recent advances in economic dispatch. IEEE Trans. Power Syst. , 4 , 1248 - 1259
    39. 39)
    40. 40)
    41. 41)
      • Gazi, V., Passino, K.M.: `Stability analysis of swarms', Proc. American Control Conf., May 2002, Anchorage, Alaska, p. 1813–1818.
    42. 42)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd_20070422
Loading

Related content

content/journals/10.1049/iet-gtd_20070422
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address