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Dynamic adaptive bacterial foraging algorithm for optimum economic dispatch with valve-point effects and wind power

Dynamic adaptive bacterial foraging algorithm for optimum economic dispatch with valve-point effects and wind power

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This study presents a dynamically adapted bacterial foraging algorithm (BFA) to solve the economic dispatch (ED) problem considering valve-point effects and power losses. In addition, wind power is included in the problem formulation. Renewable sources and wind energy in particular have recently been getting more interest because of various environmental and economical considerations. The original BFA is a recently developed evolutionary optimisation technique inspired by the foraging behaviour of the Escherichia coli bacteria. The basic BFA has been successfully implemented to solve small optimisation problems; however, it shows poor convergence characteristics for larger constrained problems. To deal with the complexity and high-dimensioned search space of the ED problem, essential modifications are introduced to enhance the performance of the algorithm. The basic chemotactic step is adjusted to have a dynamic non-linear behaviour in order to improve balancing the global and local search. The stopping criterion of the original BFA is also modified to be adaptive depending on the solution improvement instead of the preset maximum number of iterations. The proposed algorithm is validated using several test systems. The results are compared with those obtained by other algorithms previously applied to solve the problem considering valve-point effects and power losses in addition to wind power.

Inspec keywords: iterative methods; wind power; optimisation; power generation dispatch

Other keywords: valve-point effects; renewable sources; wind power; BFA; dynamic nonlinear behaviour; ED problem; Escherichia coli bacteria; optimum economic dispatch; chemotactic step; wind energy; iterative methods; adaptive bacterial foraging algorithm; power losses; evolutionary optimisation technique; convergence characteristics

Subjects: Power system management, operation and economics; Interpolation and function approximation (numerical analysis); Optimisation techniques; Energy resources

References

    1. 1)
      • M.E. El-Hawary , G.S. Christensen . (1979) Optimal economic operation of electric power systems.
    2. 2)
    3. 3)
    4. 4)
      • Chen, H., Zhu, Y., Hu, K.: `Self-adaptation in bacterial foraging optimization algorithm', Third Int. Conf. on Intelligent System and Knowledge Engineering (ISKE 2008), 2008, 1, p. 1026–1031.
    5. 5)
    6. 6)
      • J.A. Momoh , R. Adapa , M.E. El-Hawary . A review of selected optimal power flow literature to 1993. I. Nonlinear and quadratic programming approaches. IEEE Trans. Power Syst. , 1 , 96 - 104
    7. 7)
    8. 8)
      • Z. Michalewicz , M. Schoenauer . Evolutionary algorithms for constrained parameter optimization problems. Evol. Comput. , 1 , 1 - 32
    9. 9)
    10. 10)
      • Chu, Y., Mi, H., Liao, H., Ji, Z., Wu, Q.H.: `A fast bacterial swarming algorithm for high-dimensional function optimization', IEEE Congress Evolutionary Computation (IEEE World Congress on Computational Intelligence) (CEC 2008), 2008, p. 3135–3140.
    11. 11)
    12. 12)
      • A.R. Bergen , V. Vittal . (2000) Power systems analysis.
    13. 13)
    14. 14)
      • Kennedy, J., Eberhart, R.C.: `A discrete binary version of the particle swarm algorithm', 1997 IEEE Int. Conf. on Systems, Man and Cybernetics, 1997, 5, p. 4104–4108.
    15. 15)
      • T.K. Das , G.K. Venayagamoorthy , U.O. Aliyu . Bio-inspired algorithms for the design of multiple optimal power system stabilizers: SPPSO and BFA. IEEE Trans. Ind. Appl. , 5 , 1445 - 1457
    16. 16)
      • Shi, Y., Eberhart, R.C.: `Fuzzy adaptive particle swarm optimization', Proc. 2001 Congress Evolutionary Computation, 2001, 1, p. 101–106.
    17. 17)
      • J.C. Smith . Wind power: present realities and future possibilities. Proc. IEEE , 2 , 195 - 197
    18. 18)
      • AlHajri, M.F., El-Hawary, M.E.: `Pattern search optimization applied to convex and non-convex economic dispatch', IEEE Int. Conf. on Systems, Man and Cybernetics (ISIC 2007), 2007, p. 2674–2678.
    19. 19)
      • Tang, W.J., Wu, Q.H., Saunders, J.R.: `Bacterial foraging algorithm for dynamic environments', IEEE Congress Evolutionary Computation (CEC 2006), 2006, p. 1324–1330.
    20. 20)
    21. 21)
      • Joines, J.A., Houck, C.R.: `On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's', Proc. First IEEE Conf. on Evolutionary Computation, 1994 (IEEE World Congress on Computational Intelligence), 1994, 2, p. 579–584.
    22. 22)
      • J.A. Momoh , M.E. El-Hawary , R. Adapa . A review of selected optimal power flow literature to 1993. II. Newton, linear programming and interior point methods. IEEE Trans. Power Syst. , 1 , 105 - 111
    23. 23)
    24. 24)
      • AlHajri, M.F., AlRashidi, M.R., El-Hawary, M.E.: `Hybrid particle swarm optimization approach for optimal distribution generation sizing and allocation in distribution systems', Canadian Conf. on Electr. Comp. Eng. (CCECE 2007), 2007, p. 1290–1293.
    25. 25)
    26. 26)
      • J.C. Smith , R. Thresher , R. Zavadil . A mighty wind. IEEE Power Energy Mag. , 2 , 41 - 51
    27. 27)
      • P. Attaviriyanupap , H. Kita , E. Tanaka , J. Hasegawa . A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function. IEEE Power Eng. Rev. , 4 , 77 - 77
    28. 28)
      • S. Koziel , Z. Michalewicz . Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evol. Comput. , 1 , 19 - 44
    29. 29)
      • A.J. Wood , B.F. Wollenberg . (1996) Power generation operation and control.
    30. 30)
    31. 31)
    32. 32)
      • Kennedy, J., Eberhart, R.: `Particle swarm optimization', Proc. IEEE 1995 Int. Conf. on Neural Networks, 1995, 4, p. 1942–1948.
    33. 33)
    34. 34)
      • J.C. Smith , M.R. Milligan , E.A. DeMeo , B. Parsons . Utility wind integration and operating impact state of the art. IEEE Trans. Power Syst. , 3 , 900 - 908
    35. 35)
    36. 36)
      • El-Gallad, A.I., El-Hawary, M.E., Sallam, A.A., Kalas, A.: `Swarm intelligence for hybrid cost dispatch problem', Canadian Conf. on Electrical and Computer Engineering, 2001, 2, p. 753–757.
    37. 37)
    38. 38)
      • Tang, W.J., Li, M.S., He, S., Wu, Q.H., Saunders, J.R.: `Optimal power flow with dynamic loads using bacterial foraging algorithm', Int. Conf. on Power System Technology (PowerCon 2006), 2006, p. 1–5.
    39. 39)
    40. 40)
    41. 41)
    42. 42)
      • Shi, Y., Eberhart, R.: `A modified particle swarm optimizer', Proc. IEEE Int. Conf. on Evolutionary Computation, 1998 (IEEE World Congress on Computational Intelligence), 1998, p. 69–73.
    43. 43)
      • Naka, S., Genji, T., Yura, T., Fukuyama, Y.: `Practical distribution state estimation using hybrid particle swarm optimization', 2001 IEEE Power Eng. Society Winter Meeting, 2001, 2, p. 815–820.
    44. 44)
    45. 45)
    46. 46)
      • J.C. Smith . Winds of change: issues in utility wind integration. IEEE Power Energy Mag. , 6 , 20 - 25
    47. 47)
    48. 48)
    49. 49)
    50. 50)
    51. 51)
      • Peram, T., Veeramachanenim, K., Mohan, C.K.: `Fitness-distance-ratio based particle swarm optimization', Proc. 2003 IEEE Swarm Intelligence Symp. (SIS'03), 2003, p. 174–181.
    52. 52)
      • R. Fletcher . (1987) Practical methods of optimization.
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