Optimal power flow: A bibliographic survey II Non-deterministic and hybrid methods

Energy Systems

Volumn 3, Issue 3, 2012, Pages 259-289

  Frank, Stephen ^a   Steponavice, Ingrid ^b   Rebennack, Steffen ^a  

^a Department of Geology and Geological Engineering   (United States)

^b NONE   (Finland)

Author keywords

Electric power systems; Global optimization; Heuristics; Hybrid methods; Non deterministic optimization; Optimal power flow; Stochastic search; Survey

Indexed keywords

ACOUSTIC GENERATORS; ELECTRIC LOAD FLOW; ELECTRIC POWER DISTRIBUTION; ELECTRIC POWER GENERATION; ELECTRIC POWER SYSTEMS; ELECTRIC POWER TRANSMISSION; GLOBAL OPTIMIZATION; HEURISTIC METHODS; STOCHASTIC SYSTEMS; SURVEYING; SURVEYS;

DETERMINISTIC OPTIMIZATION; HEURISTICS; HYBRID METHOD; OPTIMAL POWER FLOWS; STOCHASTIC SEARCH;

ELECTRIC POWER SYSTEM CONTROL;

EID: 84870955285     PISSN: 18683967     EISSN: 18683975     Source Type: Journal    
DOI: 10.1007/s12667-012-0057-x     Document Type: Review

References (191)

1. Aarts, E., Korst, J.: Simulated Annealing and Boltzmann Machines. Wiley, New York (1989
2. Abbasy, A., Tabatabaii, I., Hosseini, S.: Optimal reactive power dispatch in electricity markets using a multiagent-based differential evolution algorithm. In: International Conference on Power Engineering, Energy and Electrical Drives. POWERENG 2007, pp. 249-254 (2007
3. Abido, M.: Optimal power flow using tabu search algorithm. Electr. Power Compon. Syst. 30, 469-483 (2002 (Pubitemid 34566882)
4. Abido, M.:Multiobjective optimal power flow using strength Pareto evolutionary algorithm. In: 39th International Universities Power Engineering Conference (uPEC 2004), vol. 1, pp. 457-461 (2004 (Pubitemid 43205931)
5. Abido, M.: Multiobjective particle swarm optimization for optimal power flow problem. In: 12th International Middle-East Power System Conference (MEPCON 2008) (2008
6. Abou El Ela, A., Abido, M., Spea, S.: Optimal power flow using differential evolution algorithm. Electr. Eng. 91, 69-78 (2009
7. Abou El Ela, A., Abido, M., Spea, S.: Optimal power flow using differential evolution algorithm. Electr. Power Syst. Res. 80(7), 878-885 (2010). doi:10.1016/j.epsr.2009.12.018
8. Allaoua, B., Laoufi, A.: Collective intelligence for optimal power flow solution using ant colony optimization. Leonardo Electron. J. Pract. Technol. 13, 88-105 (2008
9. Allaoua, B., Laoufi, A.: Optimal power flow solution using ant manners for electrical network. Adv. Electr. Comput. Eng. 9, 34-40 (2009
10. Alrashidi, M., El-Hawary, M.: Applications of computational intelligence techniques for solving the revived optimal power flow problem. Electr. Power Syst. Res. 79, 694-702 (2009
11. Altun, H., Yalcinoz, T.: Implementing soft computing techniques to solve economic dispatch problem in power systems. Expert Syst. Appl. 35, 1668-1678 (2008
12. Aminudin, N., Rahman, T., Musirin, I.: Optimal power flow for load margin improvement using evolutionary programming. In: The 5th Student Conference on Research and Development (SCOReD 2007), Malaysia (2007
13. Aoki, K., Fan, M., Nishikori, A.: Optimal VAR planning by approximation method for recursive mixed-integer linear programming. IEEE Trans. Power Appar. Syst. 3(4), 1741-1747 (1988
14. Bakare, G., Krost, G., Venayagamoorthy, G., Aliyu, U.: Differential evolution approach for reactive power optimization of Nigerian grid system. In: IEEE Power Engineering Society General Meeting, pp. 1-6 (2007
15. Bakirtzis, A., Biskas, P., Zoumas, C., Petridis, V.: Optimal power flow by enhanced genetic algorithm. IEEE Trans. Power Syst. 17(2), 229-236 (2002 (Pubitemid 34741681)
16. Banu, R., Devaraj, D.: Genetic algorithm approach for optimal power flow with FACTS devices. In: 4th International IEEE Conference Intelligent Systems, vol. 23, pp. 11-16 (2008
17. Basu, M.: Optimal power flow with FACTS devices using differential evolution. Int. J. Electr. Power Energy Syst. 30(2), 150-156 (2008 (Pubitemid 351186750)
18. Biskas, P., Ziogos, N., Tellidou, A., Zoumas, C., Bakirtzis, A., Petridis, V.: Comparison of two metaheuristics with mathematical programming methods for the solution of OPF. IEE Proc., Gener. Transm. Distrib. 153(1), 16-24 (2006). doi:10.1049/ip-gtd:20050047 (Pubitemid 43205671)
19. Bland, J., Dawson, G.: Tabu search and design optimization. Comput. Aided Des. 23(3), 195-201 (1991 (Pubitemid 21650764)
20. Borges, C., Alves, J.: Power system real time operation based on security constrained optimal power flow and distributed processing. In: IEEE Power Tech, Lausanne, pp. 960-965 (2007
21. Bouktir, T., Slimani, L., Belkacemi, M.: A.genetic algorithm for solving the optimal power flow problem. Leonardo J. Sci. 4, 44-58 (2004
22. Cai, H., Chung, C., Wong, K.: Application of differential evolution algorithm for transient stability constrained optimal power flow. IEEE Trans. Power Syst. 23(2), 719-728 (2008 (Pubitemid 351695852)
23. Carpaneto, E., Cavallero, C., Freschi, F., Repetto,M. In: Immune Procedure for Optimal Scheduling of Complex Energy Systems ICARIS 2006. LNCS, pp. 309-320. Springer, Heidelberg (2006
24. Castro, L., Zubben, F.: Artificial immune systems: part I.- basic theory and applications. Tech. rep., FEEC/UNICAMP, Campinas, Brazil (2000
25. Chakraborty, U. (ed.): Advances in Differential Evolution. Springer, Berlin (2008
26. Changa, C.F., Wong, J.J., Chiou, J.P., Sua, C.T.: Robust searching hybrid differential evolution method for optimal reactive power planning in large-scale distribution systems. Electr. Power Syst. Res. 77, 430-437 (2007
27. Chayakulkheeree, K., Ongsakul, W.: Optimal power flow considering non-linear fuzzy network and generator ramprate constrained. Int. Energy J. 8(2), 131-138 (2007
28. Chen, G.: Differential evolution based optimal reactive power flow with simulated annealing updating method. In: International Symposium on Computational Intelligence and Design (2008
29. Chen, L., Suzuki, H., Katou, K.: Mean field theory for optimal power flow. IEEE Trans. Power Syst. 12(4), 1481-1486 (1997 (Pubitemid 127771532)
30. Chibante, R. (ed.): Simulated Annealing Theory with Applications. Sciyo, Rijeka (2010
31. Chiou, J., Wang, F.: A.hybrid method of differential evolution with application to optimal control problems of a bioprocess system. In: Proceeding 1998 IEEE on Evolutionary Computation Conference, vol. 1, pp. 627-632 (1998
32. Chuanwena, J., Bomp, E.: A.hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation. Math. Comput. Simul. 68, 57-65 (2005
33. Clerc, M.: Particle Swarm Optimization. Wiley-ISTE, New York (2006
34. Coath, G., Al-Dabbagh, M., Halgamuge, S.K.: Particle swarm optimisation for reactive power and voltage control with grid-integrated wind farms. In: IEEE Power Engineering Society GeneralMeeting, vol. 1, pp. 303-308 (2004 (Pubitemid 40192012)
35. Coelho, L., Mariani, V.: Improved differential evolution algorithms for handling economic dispatch optimization with generator constraints. Energy Convers. Manag. 48, 1631-1639 (2007 (Pubitemid 46436207)
36. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Actes de la Première Conférence Européenne sur la Vie Artificielle, pp. 134-142. Elsevier, Amsterdam (1991
37. Das, D., Patvardhan, C.: Useful multi-objective hybrid evolutionary approach to optimal power flow. In: IEE Proceedings - Generation, Transmission and Distribution, vol. 150, pp. 275-282 (2003
38. Das, B., Verma, P.: Artificial neural network-based optimal capacitor switching in a distribution system. Electr. Power Syst. Res. 60, 55-62 (2001 (Pubitemid 34086465)
39. de Castro, L., Timmis, J.: Artificial Immune Systems: A.New Computational Intelligence Approach. Springer, Berlin (2002
40. de Mello Honório, L., da Silva, A.M.L., Barbosa, D.A.: A.gradient-based artificial immune system applied to optimal power flow problems. In: De Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) Proceedings of Artificial Immune Systems: 6th International Conference (ICARIS 2007), Santos, Brazil, August 2007 (2007
41. Dorigo, M.: Optimization, learning and natural algorithms. PhD thesis, Politecnico di Milano, Italy (1992
42. Dorigo, M., Stützle, T.: Ant Colony Optimization. Bradford Books. MIT Press, Cambridge (2004
43. Dorigo, M., Birattari,M., Blum, C., Clerc, M., Stützle, T.,Winfield, A. (eds.): Ant Colony Optimization and Swarm Intelligence. Proceedings of 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008. Springer, Berlin (2008
44. Dreyfus, G.: Neural Networks: Methodology and Applications. Springer, Berlin (2005
45. Esmin, A., Lambert-Torres, G.: Loss power minimization using particle swarm optimization. In: International Joint Conference on Neural Networks (IJCNN '06), pp. 1988-1992 (2006 (Pubitemid 351369570)
46. Esmin, A., Lambert-Torres, G., de Souza, A.Z.: A.hybrid particle swarm optimization applied to loss power minimization. IEEE Trans. Power Syst. 20(2), 859-866 (2005 (Pubitemid 40699717)
47. Faigle, U., Kern,W.: Some convergence results for probabilistic tabu search. ORSA J. Comput. 4(1), 32-37 (1992
48. Feoktistov, V.: Differential evolution. In: Search of Solutions, 1st edn. Springer Optimization and Its Applications, vol. 5. Springer, Berlin (2006
49. Floudas, C., Gounaris, C.: A.review of recent advances in global optimization. J. Glob. Optim. 45, 3-38 (2009
50. Fogel, D. (ed.): Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, 3rd edn. Wiley, New York (2006
51. Frank, S., Steponavice, I., Rebennack, S.: Optimal power flow: a bibliographic survey I, formulations and deterministic methods. Energy Syst. (2012). doi:10.1007/s12667-012-0056-y
52. Gaing, Z.L.: Constrained optimal power flow by mixed-integer particle swarm optimization. In: Proceedings of 2005 IEEE Power Engineering Society General Meeting, vol. 1, pp. 243-250 (2005
53. Gaing, Z.L., Chang, R.F.: Security-Constrained optimal power flow by mixed-integer genetic algorithm with arithmetic operators. In: IEEE Power Engineering Society General Meeting (2006
54. Gaing, Z.L., Liu, X.H.: New constriction particle swarm optimization for Security-Constrained optimal power flow solution. In: International Conference on Intelligent Systems Applications to Power Systems (ISAP 2007), pp. 1-6 (2007
55. Gasbaoui, B., Allaoua, B.: Ant colony optimization applied on combinatorial problem for optimal power flow solution. Leonardo J. Sci. 14, 1-17 (2009
56. Glover, F.: Tabu search-part I. ORSA J. Comput. 1, 190-206 (1989
57. Glover, F.: Tabu search: a tutorial. Interfaces 20(4), 74-94 (1990
58. Glover, F.: Tabu search - part II. ORSA J. Comput. 1(2), 4-32 (1990
59. Glover, F., Laguna, M.: Tabu Search. Kluwer Academic, Dordrecht (1997
60. Gomes, B., Saraiva, J., Neves, L.: Impact of load and generation price uncertainties in spot prices. In: IEEE Bucharest Power Tech Conference, Bucharest, Romania (2009
61. Gopalakrishnan, V., Thirunavukkarasu, P., Prasanna, R.: Reactive power planning using hybrid evolutionary programming method. In: IEE Proceedings - Generation, Transmission and Distribution, vol. 150, pp. 275-282 (2003
62. Guan, X., Liu, W., Papalexopoulos, A.: Application of a fuzzy set method in an optimal power flow. Electr. Power Syst. Res. 34, 11-18 (1995
63. Hahn, T., Kim, M.K., Hur, D., Park, J.K., Yoon, Y.: Evaluation of available transfer capability using fuzzy multi-objective contingency- constrained optimal power flow. Electr. Power Syst. Res. 78, 873-882 (2008
64. Hajian-Hoseinabadi, H., Hosseini, S., Hajian, M.: Optimal power flow solution by a modified particle swarm optimization algorithm. In: 43rd International Universities Power Engineering Conference (UPEC 2008), pp. 1-4 (2008
65. Han, F., Lu, Q.S.: An improved chaos optimization algorithm and its application in the economic load dispatch problem. Int. J. Comput. Math. 85(6), 969-982 (2008
66. Han, Z., Jiang, Q., Cao, Y.: Sequential feasible optimal power flow in power systems. Sci. China Ser. E.52(2), 429-435 (2009
67. Haque, M., Kashtiban, A.: Application of neural networks in power systems; a review. In: World Academy of Science, Engineering and Technology, vol. 6, pp. 53-57 (2005 (Pubitemid 43194617)
68. Hartati, R., El-Hawary, M.: Optimal active power flow solutions using a modified Hopfield neural network. In: Canadian Conference on Electrical and Computer Engineering, vol. 1, pp. 189-194 (2001 (Pubitemid 32832919)
69. Haupt, R.: Practical Genetic Algorithms. Wiley/IEEE Press, New York (2004
70. He, S., Wen, J.Y., Prempaint, E., Wu, Q., Fitch, J., Mann, S.: An improved particle swarm optimization for optimal power flow. In: Proceedings of 2004 International Conference on Power System Technology, vol. 2, pp. 1633-1637 (2004 (Pubitemid 40821618)
71. Hsiao, Y.T., Liu, C.C., Chiang, H., Chen, Y.L.: A.new approach for optimal VAR sources planning in large scale electric power systems. IEEE Trans. Power Syst. 8(3), 988-996 (1993
72. Hugang, X., Haozhong, C., Haiyu, L.: Optimal reactive power flow incorporating static voltage stability based on multi-objective adaptive immune algorithm. Energy Convers. Manag. 49, 1175-1181 (2008
73. Ingber, A.L.: Simulated annealing: practice versus theory. Math. Comput. Model. 18(11), 29-57 (1993
74. Jiang, C., Quan, X., Zhang, Y.: A.chaotic optimization method for economical operation of hydro power plants. J. Huazhong Univ. Sci. Technol. 27, 39-40 (1999
75. Jurada, J.: Introduction to Artificial Neural Systems. Jaico Publishing House, Mumbai (1997
76. Kalil, M., Musirin, I., Othman, M.: Ant colony based optimization technique for voltage stability control. In: Proceedings of the 6th WSEAS International Conference on Power Systems (2006
77. Kallrath, J.: Combined strategic design and operative planning in the process industry. Comput. Chem. Eng. 33, 1983-1993 (2009
78. Kallrath, J.: Polylithic modeling and solution approaches using algebraic modeling systems. Optim. Lett. 5(3), 453-466 (2011
79. Kamal, M., Rahman, T., Musirin, I.: Application of improved genetic algorithms for loss minimisation in power system. In: Proceedings National Power and Energy Conference, pp. 258-262 (2004 (Pubitemid 43111369)
80. Karthikeyan, S., Palanisamyl, K., Varghese, L., Raglend, I., Kothari, D.: Comparison of intelligent techniques to solve economic load dispatch problem with line flow constraints. In: IEEE International Advance Computing Conference (IACC) (2009
81. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks IV, pp. 1942-1948 (1995
82. Kephart, J.: A.biologically inspired immune system for computers. In: Proceedings of Artificial Life IV: The Fourth InternationalWorkshop on the Synthesis and Simulation of Living Systems, pp. 130-139. MIT Press, Cambridge (1994
83. Kim, J.Y., Jeong, H.M., Lee, H.S., Park, J.H.: PC cluster based parallel PSO algorithm for optimal power flow. In: International Conference on Intelligent Systems Applications to Power Systems (ISAP 2007), pp. 1-6 (2007
84. Kirpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 220, 671-680 (1983
85. Kulworawanichpong, T., Sujitjorn, S.: Optimal power flow using tabu search. IEEE Power Eng. Rev. 6, 37-40 (2002 (Pubitemid 34808352)
86. Kumar, V., Mohan, M.: Solution to security constrained unit commitment problem using genetic algorithm. Int. J. Electr. Power Energy Syst. 32, 117-125 (2010
87. Kumar, S., Renuga, P.: Reactive power planning using real GA comparison with evolutionary programming. Int. J. Recent Trends Eng. 1(3), 124-148 (2009
88. Kumari, M., Maheswarapu, S.: Enhanced genetic algorithm based computation technique for multiobjective optimal power flow solution. Int. J. Electr. Power Energy Syst. 32, 736-742 (2010
89. Laarhoven, P., Aarts, E.: Simulated Annealing: Theory and Applications. Mathematics and Its Applications, vol. 37. Springer, Berlin (1987
90. Lage, G., de Sousa, V., da Costa, G.: Power flow solution using the penalty/modified barrier method. In: IEEE Bucharest Power Tech Conference, Bucharest, Romania (2009
91. Lai, L., Sinha, N.: Modern heuristic optimization techniques: theory and applications to power systems. In: Genetic Algorithms for Solving Optimal Power Flow Problems, pp. 471-508. Wiley, New York (2008
92. Lai, L., Ma, J., Yokoyama, R., Zhao, M.: Improved genetic algorithms for optimal power flow under both normal and contingent operation states. Int. J. Electr. Power Energy Syst. 19(5), 287-292 (1997 (Pubitemid 127412160)
93. Lampinen, J., Zelinka, I.: Mixed integer-discrete-continuous optimization by differential evolution. In: 5th International Mendel Conference on Soft Computing, Brno, Czech Republic, pp. 77-81 (1999
94. Lee, K., Vlachogiannis, J.: Optimization of power systems based on ant colony system algorithms: an overview. In: Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, pp. 22-35 (2005 (Pubitemid 46343378)
95. Leung, H., Chung, T.: Optimal power flow with a versatile FACTS controller by genetic algorithm approach. International Conference on Advances in Power System Control, Operation and Management, vol. 1, pp. 178-183 (2000
96. Li, B., Jiang, W.: Optimizing complex function by chaos search. Cybern. Syst. 29(4), 409-419 (1998 (Pubitemid 128589685)
97. Li, D., Gao, L., Lu, S., Ma, J., Li, Y.: Adaptive particle swarm optimization algorithm for power system reactive power optimization. In: American Control Conference (ACC '07), pp. 4733-4737 (2007
98. Li, M., Tang, W., Tang, W., Wu, Q., Saunders, J.: Bacterial foraging algorithm with varying population for optimal power flow. In: Applications of Evolutionary Computing. Lectures Notes in Computer Science, pp. 32-41. Springer, Berlin (2007 (Pubitemid 47290057)
99. Liang, C., Chung, C.,Wong, K., Duan, X.: Parallel optimal reactive power flow based on cooperative co-evolutionary differential evolution and power system decomposition. IEEE Trans. Power Syst. 22(1), 249-257 (2007 (Pubitemid 46437734)
100. Liang, C., Chung, C., Wong, K., Duan, X., Tse, C.: Study of differential evolution for optimal reactive power flow. IET Gener. Transm. Distrib. 1(2), 253-260 (2007 (Pubitemid 46582792)
101. Liao, G.C.: Application of an immune algorithm to the short term unit commitment problem in power system operation. IEE Proc., Gener. Transm. Distrib. 153(3), 309-320 (2006
102. Lin, S.Y., Ho, Y.H., Lin, C.H.: An ordinal optimization theory-based algorithm for solving the optimal power flow problem with discrete control variables. IEEE Trans. Power Syst. 19(1), 276-286 (2004
103. Liu, Y., Passino, K.: Biomimicry of social foraging bacteria for distributed optimization: models, principles, and emergent behaviors. J. Optim. Theory Appl. 115(3), 603-628 (2002 (Pubitemid 39583280)
104. Liu, Y., Ma, L., Zhang, J.: GA/SA/TS hybrid algorithms for reactive power optimization. In: Power Engineering Society Summer Meeting, vol. 1, pp. 245-249. IEEE Press, New York (2000
105. Liu, F., Chung, C.,Wong, K., Yan,W., Xu, G.: Hybrid immune genetic method for dynamic reactive power optimization. In: International Conference on Power System Technology (PowerCon 2006), pp. 1-6 (2006
106. Mahdad, B., Bouktir, T., Srairi, K.: Optimal power flow of the Algerian network using genetic algorithm/ fuzzy rules. In: IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, pp. 1-8 (2008
107. Mahdad, B., Srairi, K., Bouktir, T., Benbouzid, M.: Optimal power flow for large-scale power system with shunt FACTS using efficient parallel GA. In: 34th Annual Conference of IEEE Industrial Electronics (IECON 2008) (2008
108. Mahdad, B., Srairi, K., Bouktir, T.: Dynamic strategy based parallel GA coordinated with FACTS devices to enhance the power system security. In: Power & Energy Society General Meeting (PES '09), pp. 1-8. IEEE Press, New York (2009
109. Mahdad, B., Srairi, K., Bouktir, T.: Optimal power flow with environmental constraints of the algerian network using decomposed parallel GA. In: IEEE Bucharest Power Tech Conference, Bucharest, Romania (2009
110. Mahdad, B., Bouktir, T., Srairi, K., Benbouzid, M.E.: Dynamic strategy based fast decomposed GA coordinated with FACTS devices to enhance the optimal power flow. Energy Convers. Manag. 51, 1370-1380 (2010
111. Miranda, V., Saraiva, J.: Fuzzy modelling of power system optimal local flow. IEEE Trans. Power Syst. 7(2), 843-849 (1992
112. Mishra, S.: Bacteria foraging based solution to optimize both real power loss and voltage stability limit. In: IEEE Power Engineering Society General Meeting (2007
113. Mo, N., Zou, Z., Chan, K., Pong, T.: Transient stability constrained optimal power flow using particle swarm optimisation. IET Gener. Transm. Distrib. 1(3), 476-483 (2007 (Pubitemid 46718685)
114. Mori, H., Hayashi, T.: New parallel tabu search for voltage and reactive power control in power systems. In: IEEE International Symposium on Circuit and Systems (ISACAS'98), pp. 431-434 (1998
115. Muthuselvan, N., Somasundaram, P.: Application of tabu search algorithm to security constrained economic dispatch. J. Theor. Appl. Inf. Technol. 5, 602-608 (2009
116. Nakawiro,W., Erlich, I.: A.combined GA-ANN strategy for solving optimal power flow with voltage security constraint. In: Asia-Pacific Power and Energy Engineering Conference (APPEEC) (2009
117. Nguyen, T.: Neural network load-flow. IEE Proc., Gener. Transm. Distrib. 142, 51-58 (1995
118. Nguyen, T.: Neural network optimal-power-flow. In: Proceedings of the 4th International Conference on Advances in Power System Control, Operation and Management, Hong Kong, pp. 266-271 (1997
119. Nualhong, D., Chusanapiputt, S., Phomvuttisarn, S., Jantarang, S.: Reactive tabu search for optimal power flow under constrained emission dispatch. In: IEEE Region 10 Conference 2004 (TENCON), vol. 3, pp. 327-330 (2004
120. Numnonda, T., Annakkage, U.: Optimal power dispatch in multinode electricity market using genetic algorithm. Electr. Power Syst. Res. 49, 211-220 (1999
121. Onate, P., Ramirez, J.: Optimal power flow solution with security constraints by a modified PSO. In: IEEE Power Engineering Society General Meeting, pp. 1-6 (2007
122. Ongsakul, W., Jirapong, P.: Optimal allocation of facts devices to enhance total transfer capability using evolutionary programming. In: IEEE International Symposium (2005
123. Onwubolu, G., Davendra, D. (eds.): Differential Evolution: A.Handbook for Global Permutation- Based Combinatorial Optimization. Studies in Computational Intelligence. Springer, Berlin (2009
124. Panigrahi, B., Pandi, V.: Congestion management using adaptive bacterial foraging algorithm. Energy Convers. Manag. 50, 1202-1209 (2009
125. Passino, K.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52-67 (2002 (Pubitemid 34711477)
126. Passino, K.: Biomimicry for Optimization, Control, and Automation. Springer, Berlin (2005
127. Poli, R., Kennedy, J., Blackwell, T., Freitas, A. (eds.): Particle Swarms: The Second Decade. Hindawi Publishing Corporation, New York (2008
128. Pouya, K., Lesani, H.: An angle-based PSO approach for reactive power management problem. In: Power Systems Conference and Exposition (PSCE '09), pp. 1-6. IEEE/PES, Piscataway (2009
129. Prasanna, T., Somasundaram, P.: OPF with FACTS devices in interconnected power systems using fuzzy stochastic algorithms. Int. J. Power Energy Convers. 1, 279-299 (2009
130. Prasanna, T., Muthuselvan, N., Somasundaram, P.: Security constrained OPF by fuzzy stochastic algorithms in interconnected power systems. J. Electr. Syst. 5(1), P7 (2009
131. Price, K., Storn, R., Lampinen, J.: Differential Evolution: a Practical Approach to Global Optimization. Birkhäuser, Berlin (2005
132. Qiu, Z., Deconinck, G., Belmans, R.: A.literature survey of optimal power flow problems in the electricity market context. In: IEEE/PES Power Systems Conference and Exposition (PSCE '09), Seattle, pp. 1-6 (2009
133. Raju, C., Vaisakh, K., Raju, S.: An IPM-EPSO based hybrid method for security enhancement using SSSC. Int. J. Recent Trends Eng. 2(5), 208-212 (2009
134. Ramech, V., Li, X.: A.fuzzy multiobjective approach to contingency constrained OPF. IEEE Trans. Power Syst. 12(3), 1348-1354 (1997
135. Rashidi, M.A., El-Hawary, M.: Hybrid particle swarm optimization approach for solving the discrete OPF problem considering the valve loading effects. IEEE Trans. Power Syst. 22(4), 2030-2038 (2007 (Pubitemid 350142004)
136. Reeves, C., Rowe, J.: Genetic Algorithms: Principles and Perspectives: A.Guide to GA Theory, 2nd edn. Springer, Berlin (2003
137. Ripley, B.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996
138. Roa-Sepulveda, C., Pavez-Lazo, B.: A.solution to the optimal power flow using simulated annealing. In: IEEE Porto Power Tech Conference, Porto, Portugal (2001
139. Rudolph, G.: Convergence analysis of canonical genetic algorithms. IEEE Trans. Neural Netw. 5, 96-101 (1994
140. Sadati, N., Amraee, T., Ranjbar, A.: A.global particle swarm-based-simulated annealing optimization technique for under-voltage load shedding problem. Appl. Soft Comput. 9, 652-657 (2009
141. Santoso, N., Tan, O.: Neural-net based real-time control of capacitors installed on distribution systems. IEEE Trans. Power Deliv. 5(1), 266-272 (1990 (Pubitemid 20665747)
142. Saraiva, J., Miranda, V.: Evaluation of the performance of a fuzzy optimal power flow algorithm. In: Proceedings of 7th Mediterranean Electrotechnical Conference, vol. 3, pp. 897-900 (1994
143. Sayah, S., Zehar, K.: Modified differential evolution algorithm for optimal power flow with nonsmooth cost functions. Energy Convers. Manag. 49, 3036-3042 (2008
144. Shengsong, L., Min, W., Zhijian, H.: A.hybrid algorithm for optimal power flow using the chaos optimization and the linear interior point algorithm. In: Proceedings International Conference on Power System Technology, vol. 2, pp. 793-797 (2002
145. Shengsong, L., Min, W., Zhijian, H.: Hybrid algorithm of chaos optimisation and SLP for optimal power flow problems with multimodal characteristic. In: IEE Proceedings - Generation, Transmission and Distribution, vol. 150, pp. 543-547 (2003
146. Simon, S., Padhy, N., Anand, R.: An ant colony system approach for unit commitment problem. Int. J. Electr. Power Energy Syst. 28, 315-323 (2006
147. Song, Y., Wang, G., Wang, P., Johns, A.: Environmental/economic dispatch using fuzzy logic controlled genetic algorithms. IEE Proc., Gener. Transm. Distrib. 144(4), 377-382 (1997 (Pubitemid 127758267)
148. Sood, Y., Padhy, N., Gupta, H.: Discussion of optimal power flow by enhanced genetic algorithm. IEEE Trans. Power Syst. 18(3), 1219 (2003
149. Spall, J.: Introduction to Stochastic Search and Optimization. Wiley-Interscience, New York (2003
150. Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Tech. Rep. TR-95-012, ICSI (1995
151. Subbaraj, P., Rajnarayanan, P.: Optimal reactive power dispatch using self-adaptive real coded genetic algorithm. Electr. Power Syst. Res. 79, 374-381 (2009
152. Swapur, K.: Swarm intelligence approach to the solution of optimal power flow. J. Indian Inst. Sci. 86, 439-455 (2006
153. Swarup, K.: Ant colony optimization for economic generator scheduling and load dispatch. In: Proceedings of the 6th WSEAS International Conference on Evolutionary Computing, Portugal, pp. 167-175 (2005
154. Tang, K., Kwong, S.: Genetic Algorithms: Concepts and Designs, 2nd edn. Springer, Berlin (1999
155. Tang, W., Li, M., He, S., Wu, Q., Saunders, J.: Optimal power flow with dynamic loads using bacterial foraging algorithm. In: International Conference on Power System Technology (2006
156. Tang, W., Li, M., Wu, Q., Saunders, J.: Bacterial foraging algorithm for optimal power flow in dynamic environments. IEEE Trans. Circuits Syst. I, Regul. Pap. 55(8), 2433-2443 (2008
157. Tangpatiphan, K., Yokoyama, A.: Optimal power flow with steady-state voltage stability consideration using improved evolutionary programming. In: 2009 IEEE Bucharest Power Tech Conference, Bucharest, Romania (2009
158. Teng, J.H., Liu, Y.H.: A.novel ACS-based optimum switch relocation method. IEEE Trans. Power Syst. 18(1), 113-120 (2003
159. Todorovski, M., Rajicic, D.: An initialization procedure in solving optimal power flow by genetic algorithm. IEEE Trans. Power Syst. 21(2), 480-487 (2006
160. Tripathy, M., Mishra, S.: Bacteria foraging-based solution to optimize both real power loss and voltage stability limit. IEEE Trans. Power Syst. 22(1), 240-248 (2007 (Pubitemid 46437733)
161. Vaisakh, K., Srinivas, L.R.: Differential evolution based OPF with conventional and nonconventional cost characteristics. In: Joint International Conference on Power System Technology and IEEE Power India Conference (POWERCON), pp. 1-9 (2008
162. Varadarajan, M., Swarup, K.: Solving multi-objective optimal power flow using differential evolution. IET Gener. Transm. Distrib. 2(5), 720-730 (2008
163. Venayagamoorthy, G., Harley, R.: Swarm intelligence for transmission system control. In: IEEE Power Engineering Society General Meeting, pp. 1-4 (2007
164. Venkatesh, B., Sadasivam, G., Khan, M.: Optimal reactive power planning against voltage collapse using the successive multiobjective fuzzy LP technique. In: IEE Proceedings on Generation, Transmission and Distribution, vol. 146, pp. 343-348 (1999
165. Vlachogiannis, J., Lee, K.: Reactive power control based on particle swarm multi-objective optimization. In: Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, vol. 1, pp. 303-308 (2005
166. Vlachogiannis, J., Lee, K.: A.comparative study on particle swarm optimization for optimal steadystate performance of power systems. IEEE Trans. Power Syst. 21(4), 1718-1728 (2006 (Pubitemid 46357772)
167. Vlachogiannis, J., Hatziargyriou, N., Lee, K.: Ant colony system-based algorithm for constrained load flow problem. IEEE Trans. Power Syst. 20(3), 1241-1249 (2005 (Pubitemid 41300935)
168. Wang, C.R., Yuan, H.J., Huang, Z.Q., Zhang, J.W., Sun, C.J.: A.modified particle swarm optimization algorithm and its application in optimal power flow problem. In: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 2885-2889 (2005
169. Wong, K., Fung, C.: Simulated annealing based economic dispatch algorithm. In: IEE Proceedings, vol. 140, pp. 509-515 (1993
170. Wong, K., Yuryevich, J.: Optimal power flow method using evolutionary programming. In: Simulated Evolution and Learning, pp. 405-412. Springer, Berlin (1999
171. Wu, Q., Ma, J.: Power system optimal reactive power dispatch using evolutionary programming. IEEE Trans. Power Syst. 10(3), 1243-1249 (1995
172. Xia, X., Elaiw, A.: Optimal dynamic economic dispatch of generation: a review. Electr. Power Syst. Res. 80(8), 975-986 (2010
173. Xiangzheng, X.: Research on reactive power optimizing control based on immune algorithms. In: The Eighth International Conference on Electronic Measurement and Instruments, vol. 3, pp. 898-901 (2007
174. Xu, H., Zhu, Y., Zhang, T.: Application of mutative scale chaos optimization algorithm in power plant units economic dispatch. J. Harbin Inst. Technol. 32, 55-58 (2000
175. Yang, B., Chen, Y., Zhao, Z.: Survey on applications of particle swarm optimization in electric power systems. In: IEEE International Conference on Control and Automation (ICCA), pp. 481-486 (2007
176. Yang, D., Li, G., Cheng, G.: On the efficiency of chaos optimization algorithms for global optimization. Chaos Solitons Fractals 34, 1366-1375 (2007 (Pubitemid 46601952)
177. Yoshida, H., Kawata, K., Fukuyama, Y., Takayama, S., Nakanishi, Y.: A.particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans. Power Syst. 15(4), 1232-1239 (2001
178. Younes, M., Rahli, M., Abdelhakem-Koridak, L.: Optimal power flow based on hybrid genetic algorithm. J. Inf. Sci. Eng. 23, 1801-1816 (2007 (Pubitemid 350150390)
179. Yu, X., Gen, M.: Introduction to Evolutionary Algorithms. Springer, Berlin (2010
180. Yumbla, P., Ramirez, J., Coello, C.: Optimal power flow subject to security constraints solved with a particle swarm optimizer. IEEE Trans. Power Syst. 23(1), 33-40 (2008 (Pubitemid 351292086)
181. Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338-353 (1965
182. Zadeh, L.: Fuzzy Sets, Fuzzy Logic, Fuzzy Systems. Advances in Fuzzy Systems - Applications and Theory, vol. 6. World Scientific, Berlin (1996
183. Zhang,W., Liu, Y.: Reactive power optimization based on PSO in a practical power system. In: IEEE Power Engineering Society General Meeting, vol. 1, pp. 239-243 (2004 (Pubitemid 40192001)
184. Zhang, W., Liu, Y.: Fuzzy logic controlled particle swarm for reactive power optimization considering voltage stability. In: The 7th International Power Engineering Conference (IPEC) (2005
185. Zhang, W., Tolbert, L.: Survey of reactive power planning methods. In: IEEE Power Engineering Society General Meeting, vol. 2, pp. 1430-1440 (2005 (Pubitemid 41497149)
186. Zhang, H., Zhang, L., Meng, F.: Reactive power optimization based on genetic algorithm. In: International Conference on Power System Technology, vol. 2, pp. 1448-1453 (1998
187. Zhao, B., Guo, C., Cao, Y.: Improved particle swam optimization algorithm
188. Zhao, B., Guo, C., Cao, Y.: An improved particle swarm optimization algorithm for optimal reactive power dispatch. In: IEEE Power Engineering Society General Meeting, vol. 1, pp. 272-279 (2005 (Pubitemid 41496931)
189. Zhihuan, L., Yinhong, L., Xianzhong, D.: Improved strength Pareto evolutionary algorithm with local search strategies for optimal reactive power flow. Inf. Technol. J. 9, 749-757 (2010
190. Zhijiang, Y., Zhijian, H., Chuanwen, J.: Economic dispatch and optimal power flow based on chaotic optimization. In: Proceedings of International Conference on Power System Technology (Power- Con), vol. 4, pp. 2313-2317 (2002
191. Zimmerman, R.D., Murillo-Sánchez, C.E., Thomas, R.J.: MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans. Power Syst. 26(1), 12-19 (2011). doi:10.1109/TPWRS.2010.2051168