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An Efficient New Hybrid ICA-PSO Approach for Solving Large Scale Non-convex Multi Area Economic Dispatch Problems

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Abstract

Multi-area economic dispatch (MAED) is one of the vital problems in economic operation of interconnected power systems. This paper proposes a novel hybrid approach based on combined imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) methods in order to determine the feasible optimal solution of the non-convex economic dispatch (ED) problem considering valve loading effects. In the proposed algorithm we have defined new type of countries in ICA algorithm, namely independent countries. These types of countries improve their position using a PSO based search strategy. The proposed method benefits from the advantage of the both algorithms. The proposed hybrid approach based on ICA-PSO is applied on different test systems and compared with most of the recent methodologies. Also, a large scale multi-area economic dispatch (MAED) problem is solved using the proposed hybrid approach to minimize total fuel cost in all areas while satisfying power balance constraints, generating limits and tie-line capacity constraints. The results show the effectiveness of the proposed approach and prove that ICA-PSO is applicable for solving the power system economic load dispatch problem, especially in large scale power systems.

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References

  1. Subbaraj P, Rengaraj R, Salivahanan S (2011) Enhancement of self-adaptive realcoded genetic algorithm using Taguchi method for economic dispatch problem. Appl Soft Comput 11:83–92

    Google Scholar 

  2. Mohammadi-Ivatloo B, Rabiee A, Soroudi A, Ehsan M (2012) Iteration PSO with time varying acceleration coefficients for solving non-convex economic dispatch problems. Electr Power Energy Syst 42:508–516

    Google Scholar 

  3. Hosseinnezhad V, Babaei E (2013) Economic load dispatch using θ-PSO. Int J Electr Power Energy Syst 49:160–169

    Google Scholar 

  4. Sayah S, Hamouda A (2013) A hybrid differential evolution algorithm based on particle swarm optimization for non-convex economic dispatch problems. Appl Soft Comp 13(4):1608–1619

    Google Scholar 

  5. Wang L, Li L (2013) An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems. Int J Electr Power Energy Syst 44:832–843

    Google Scholar 

  6. Roy P, Roy P, Chakrabarti A (2013) Modified shuffled frog leaping algorithm with genetic algorithm crossover for solving economic load dispatch problem with valve-point effect. Appl Soft Comp 13(11):4244–4252

    Google Scholar 

  7. Niu Q, Zhanga H, Wanga X, Lib K, Irwinb GW (2014) A hybrid harmony search with arithmetic crossover operation for economic dispatch. Elect Power Energy Syst 62:237–257

    Google Scholar 

  8. Mandal B, Roy PK, Mandal S (2014) Economic load dispatch using krill herd algorithm. Elect Power Energy Syst 57:1–10

    Google Scholar 

  9. Morshed MJ, Asgharpourb A (2014) Hybrid imperialist competitive-sequential quadratic programming (HIC-SQP) algorithm for solving economic load dispatch with incorporating stochastic wind power: a comparative study on heuristic optimization techniques. Energy Conv Manag 84:30–40

    Google Scholar 

  10. Hosseinnezhad V, Rafiee M, Ahmadian M, Taghi Ameli M (2014) Species-based quantum particle swarm optimization for economic load dispatch. Elect Power Energy Syst 63:311–322

    Google Scholar 

  11. Ziane I, Benhamida F, Graa A (2017) Simulated annealing algorithm for combined economic and emission power dispatch using max/max price penalty factor. Neural Comput Appl 28(1):197–205

    Google Scholar 

  12. Jap SN, Dhillon JS, Kothari DP (2016) Synergic predator-prey optimization for economic thermal power dispatch problem. Appl Soft Comput 43:298–311

    Google Scholar 

  13. Bhattacharjee K, Bhattacharya A (2014) Oppositional real coded chemical reaction optimization for different economic dispatch problems. Int J Electr Power Energy Syst 55:378–391

    Google Scholar 

  14. Chowdhury BH, Rahman S (1990) A review of recent advances in economic dispatch. IEEE Trans Power Syst 5(4):1248–1259

    MathSciNet  Google Scholar 

  15. Shoults RR, Chang SK, Helmick S, Grady WM (1980) A practical approach to unit commitment, economic dispatch and savings allocation for multiple-area pool operation with import/export constraints. IEEE Trans Power Appar Syst 99(2):625–635

    Google Scholar 

  16. Romano R, Quintana VH, Lopez R, Valadez V (1981) Constrained economic dispatch of multi-area systems using the DantzigeWolfe decomposition principle. IEEE Trans Power Appar Syst 100(4):2127–2137

    Google Scholar 

  17. Doty KW, McEntire PL (1982) An analysis of electric power brokerage systems. IEEE Trans Power Appar Syst 101(2):389–396

    Google Scholar 

  18. Desell AL, McClelland EC, Tammar K, Van Horne PR (1984) Transmission constrained production cost analysis in power system planning. IEEE Trans Power Appar Syst 103(8):2192–2198

    Google Scholar 

  19. Helmick SD, Shoults RR (1985) A practical approach to an interim multi-area economic dispatch using limited computer resources. IEEE Trans Power Appar Syst 104(6):1400–1404

    Google Scholar 

  20. Ouyang Z, Shahidehpour SM (1991) Heuristic multi-area unit commitment with economic dispatch. IEE Proc C 138(3):242–252

    Google Scholar 

  21. Wang C, Shahidehpour SM (1992) A decomposition approach to non-linear multi area generation scheduling with tie-line constraints using expert systems. IEEE Trans Power Syst 7(4):1409–1418

    Google Scholar 

  22. Streiffert D (1995) Multi-area economic dispatch with tie line constraints. IEEE Trans Power Syst 10(4):1946–1951

    Google Scholar 

  23. J. Wernerus, L. Soder (1995) Area price based multi-area economic dispatch with tie line losses and constraints, In: IEEE/KTH Stockholm power tech conference, Sweden, pp. 710–715.

  24. Yalcinoz T, Short MJ (1998) Neural networks approach for solving economic dispatch problem with transmission capacity constraints. IEEE Trans Power Syst 13(2):307–313

    Google Scholar 

  25. Jayabarathi T, Sadasivam G, Ramachandran V (2000) Evolutionary programming based multi-area economic dispatch with tie line constraints. Electr Mach Power Syst 28:1165–1176

    Google Scholar 

  26. Chen CL, Chen N (2001) Direct search method for solving economic dispatch problem considering transmission capacity constraints. IEEE Trans Power Syst Nov 16(4):764–769

    Google Scholar 

  27. Basu M (2014) Teaching learning-based optimization algorithm for multi-area economic dispatch. Energy 68:21–28

    Google Scholar 

  28. Mingfu H et al (2019) Particle swarm optimization with damping factor and cooperative mechanism. Appl Soft Comput 76:45–52

    Google Scholar 

  29. Alawode KO et al (2018) Semidefinite programming solution of economic dispatch problem with non-smooth, non-convex cost functions. Electr Power Syst Res 164:178–187

    Google Scholar 

  30. Elis G et al (2019) Deterministic approach for solving multi-objective non-smooth environmental and economic dispatch problem. Int J Electr Power Energy Syst 104:880–897

    Google Scholar 

  31. Mostafa K et al (2017) Lightning flash algorithm for solving non-convex combined emission economic dispatch with generator constraints. IET Gener Trans Distrib 12(1):104–116

    Google Scholar 

  32. Goudarzi A, Li Y, Xiang Ji (2020) A hybrid non-linear time-varying double-weighted particle swarm optimization for solving non-convex combined environmental economic dispatch problem. Appl Soft Comput 86:105894

    Google Scholar 

  33. Manoharan PS, Kannan PS, Baskar S, Iruthayarajan MW (2009) Evolutionary algorithm solution and KKT based optimality verification to multi-area economic dispatch. Int J Electr Power Energy Syst 31(7–8):365–373

    Google Scholar 

  34. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: proceedings of IEEE congress on evolutionary computation, Singapore, pp 4661–4667.

  35. Hadidi A, Hadidi M, Nazari A (2013) A new design approach for shell-and-tube heat exchangers using imperialist competitive algorithm (ICA) from economic point of view. Energy Conv Manag 67:66–74

    Google Scholar 

  36. Mohammadi-ivatloo B, Rabiee A, Ehsan M (2012) Time-varying acceleration coefficients ipso for solving dynamic economic dispatch with non-smooth cost function. Energy Convers Manag 56:175–183

    Google Scholar 

  37. Meng K, Wang HG, Dong ZY, Wong KP (2010) Quantum-inspired particle swarm optimization for valve-point economic load dispatch. IEEE Trans Power Syst 25(1):215–222

    Google Scholar 

  38. Lu H, Sriyanyong P, Song YH, Dillon T (2010) Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function. Electr Power Energy Syst 32(9):921–935

    Google Scholar 

  39. Mohammadi-Ivatloo B, Rabiee A, Soroudi A (2013) Nonconvex dynamic economic power dispatch problems solution using hybrid immune-genetic algorithm. IEEE Syst J 7:777–785

    Google Scholar 

  40. Kennedy J, Russell E (1995) Particle swarm optimization. In: proceedings of 1995 IEEE international conference on neural networks, pp 1942–1948

  41. Ghodrati AH, Malakooti MV, Soleimani M (2012) A hybrid ICA/PSO algorithm by adding independent countries for large scale global optimization. Intelligent information and database systems. Springer, Berlin Heidelberg, pp 99–108

    Google Scholar 

  42. He S, Wu QH, Saunders JR (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput 13:973–990

    Google Scholar 

  43. Moradi-Dalvand M, Mohammadi-Ivatloo B, Najafi A, Rabiee A (2012) Continuous quick group search optimizer for solving non-convex economic dispatch problems. Electr Power Syst Res 93:93–105

    Google Scholar 

  44. Pothiya S, Ngamroo I, Kongprawechnon W (2008) Application of multiple Tabu search algorithm to solve dynamic economic dispatch considering generator constraints. Energy Convers Manage 49:506–516

    Google Scholar 

  45. Gaing Z (2003) Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans Power Syst 18:1187–1195

    Google Scholar 

  46. Selvakumar A, Thanushkodi K (2007) A new particle swarm optimization solution to non-convex economic dispatch problems. IEEE Trans Power Systems 22:42–51

    Google Scholar 

  47. Panigrahi B, Pandi VR (2008) Bacterial foraging optimization: Nelder-Mead hybrid algorithm for economic load dispatch. IET Gener Transm Distrib 2:556–565

    Google Scholar 

  48. Niknam T, Mojarrad HD, Zeinoddini-Meymand H (2011) A new particle swarm optimization for non-convex economic dispatch. Eur Trans Electr Power 21:656–679

    Google Scholar 

  49. Chaturvedi K, Pandit M, Srivastava L (2008) Self-organizing hierarchical particle swarm optimization for nonconvex economic dispatch. IEEE Trans Power Syst 23:1079–1087

    Google Scholar 

  50. Bhattacharya A, Chattopadhyay PK (2010) Biogeography-based optimization for different economic load dispatch problems. IEEE Trans Power Syst 25:1064–1077

    Google Scholar 

  51. Kuo CC (2008) A novel coding scheme for practical economic dispatch by modified particle swarm approach. IEEE Trans Power Syst 23:1825–1835

    Google Scholar 

  52. Kuo CC (2008) A novel string structure for economic dispatch problems with practical constraints. Energy Convers Manage 49:3571–3577

    Google Scholar 

  53. Venkatakrishnan GR, Rengaraj R, Salivahanan S (2018) Grey wolf optimizer to real power dispatch with non-linear constraints. Comput Model Eng Sci 115(1):25–45

    Google Scholar 

  54. Nomana N, Iba H (2008) Differential evolution for economic load dispatch problems. Electr Power Syst Res 78:1322–1331

    Google Scholar 

  55. dos Santos Coelho L, Mariani VC (2007) Improved differential evolution algorithms for handling economic dispatch optimization with generator constraints. Energy Convers Manag 48:1631–1639

    Google Scholar 

  56. Kumar C, Alwarsamy T (2012) Solution of economic dispatch problem using differential evolution algorithm. Int J Soft Comput Eng 1:236–241

    Google Scholar 

  57. Selvakumar AI, Thanushkodi K (2008) Anti-predatory particle swarm optimization: solution to non-convex economic dispatch problems. Electr Power Compon Syst 78:2–10

    Google Scholar 

  58. Binetti Giulio et al (2013) A distributed auction-based algorithm for the nonconvex economic dispatch problem. IEEE Trans Ind Inform 10(2):1124–1132

    Google Scholar 

  59. Ghasemi Mojtaba et al (2016) Colonial competitive differential evolution: an experimental study for optimal economic load dispatch. Appl Soft Comput 40:342–363

    Google Scholar 

  60. Barisal AK (2013) Dynamic search space squeezing strategy based intelligent algorithm solutions to economic dispatch with multiple fuels. Int J Electr Power Energy Syst 45(1):50–59

    Google Scholar 

  61. Vaisakh K, Srinivasa Reddy A (2013) MSFLA/GHS/SFLA-GHS/SDE algorithms for economic dispatch problem considering multiple fuels and valve point loadings. Appl Soft Comput 13(11):4281–4291

    Google Scholar 

  62. Khamsawang S, Jiriwibhakorn S (2010) DSPSO–TSA for economic dispatch problem with nonsmooth and noncontinuous cost functions. Energy Convers Manage 51:365–375

    Google Scholar 

  63. Ghorbani N, Babaei E (2018) The exchange market algorithm with smart searching for solving economic dispatch problems. Int J Manag Sci Eng Manag 13(3):175–187

    Google Scholar 

  64. Balamurugan R, Subramanian S (2008) Hybrid integer coded differential evolution—dynamic programming approach for economic load dispatch with multiple fuel options. Energy Convers Manage 49:608–614

    Google Scholar 

  65. Wang Y, Li B, Weise T (2010) Estimation of distribution and differential evolution cooperation for large scale economic load dispatch optimization of power systems. Inf Sci 180:2405–2420

    Google Scholar 

  66. Balamurugan R, Subramanian S (2008) An improved dynamic programming approach to economic power dispatch with generator constraints and transmission losses. J Electr Eng Tech 3(3):320–330

    Google Scholar 

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Acknowledgements

The Social science Foundation of Jiangxi Province under Grant (Nos. 19GL44, 18YJ20).

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Correspondence to Jun Chen.

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Chen, J., Imani Marrani, H. An Efficient New Hybrid ICA-PSO Approach for Solving Large Scale Non-convex Multi Area Economic Dispatch Problems. J. Electr. Eng. Technol. 15, 1127–1145 (2020). https://doi.org/10.1007/s42835-020-00416-7

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