Stochastic optimal power flow using a paired-bacteria optimizer

IEEE PES General Meeting, PES 2010

Volumn , Issue , 2010, Pages

  Li, M S ^a   Ji, T Y ^a   Wu, Q H ^a   Xue, Y S ^b  

^a UNIVERSITY OF LIVERPOOL   (United Kingdom)

^b Static Grid Electric Power Research Institute/NARI   (China)

Author keywords

Bacterial behavior; Expensive optimization; Modeling; Stochastic optimal power flow

Indexed keywords

BACTERIAL BEHAVIOR; COMPUTATIONAL TIME; EFFECTIVE TOOL; EXPENSIVE OPTIMIZATION; FORAGING MODEL; FUEL COST; HIGH-DIMENSIONAL; MODELING; MULTIMODAL BENCHMARK; OBJECTIVE FUNCTIONS; OPTIMAL POWER FLOWS; OPTIMIZATION PROBLEMS; OPTIMIZERS; POWER SYSTEM PLANNING; POWER SYSTEMS; REDUCTION PROBLEM; RELIABLE PERFORMANCE; STOCHASTIC OPTIMAL POWER FLOWS;

ACOUSTIC GENERATORS; BACTERIA; BACTERIOLOGY; COST REDUCTION; ELECTRIC LOAD FLOW; ELECTRIC POTENTIAL; EVOLUTIONARY ALGORITHMS; STOCHASTIC SYSTEMS; VOLTAGE REGULATORS;

OPTIMIZATION;

EID: 78649566486     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/PES.2010.5589620     Document Type: Conference Paper

References (24)

1. J. Carpentier. Contribution to the econimoc dispatch problem, volume 8. Bull. Soc. Franc. Elect, 1962.
2. Y. Taiyou and R. H. Lasseter. Stochastic optimal power flow: formulation and solution. IEEE Power Engineering Society Summer Meeting, 1:237-242, 2000.
3. W. J. Tang, M. S. Li, Q. H. Wu, and J. R. Saunders. Bacterial foraging algorithm for optimal power flow in dynamic environments. IEEE transactions on circuits and systems . I , Regular papers, 55(8):2433-2442, 2008.
4. A. G. Bakirtzis, P. N. Biskas, C. E. Zoumas, and V. Petridis. Optimal power flow by enhanced genetic algorithm. IEEE Transactions on Power Systems, 17(2):229-236, MAY 2002. (Pubitemid 34741681)
5. M. Clerc and J. Kennedy. Bacterial foraging algorithm with varying population for optimal power flow. Lecture Notes in Computer Science, 4448:58-73, 2007.
6. Z. Ji, J. R. Zhou, H. L. Liao, and Q. H. Wu. Requantization codebook design using particle-pair optimiser. IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence), pages 1851-1855, 2008.
7. H. A. Abbass, A. M. Bagirov, and J. Zhang. The discrete gradient evolutionary strategy method for global optimisation. Congress on Evolutionary Computation, 1:8-12, 2003.
8. M. Bundzel and P. Sincak. Combining gradient and evolutionary approaches to the artificial neural networks training according to principles of support vector machines. IEEE International Joint Conference on Neural Networks, pages 2068-2074, 2006.
9. J. Hewlett, B. Wilamowski, and G. Dundar. Merge of evolutionary computation with gradient based method for optimisation problems. IEEE International Symposium on Industrial Electronics, pages 3304-3309, 2007.
10. M. Manic and B. Wilamowski. Random weights search in compressed neural networks using over-determined pseudo-inverse. IEEE Interantional Symposium on Industrial Electronics, pages 678-683, 2003.
11. J. Y. Wen, Q. H. Wu, L. Jiang, and S. J. Cheng. Pseudo-gradient based evolutionary programming. Electronics Letters, 39:631-632, 2003.
12. T. J. Ypma. Historical development of the newton-raphson method. SIAM Review, 37:531-551, 1995.
13. Z. Yang, K. Tang, and X. Yao. Large scale evolutionary optimization using cooperative coevolution. Information Sciences, 178(15):2985-2999, 2008.
14. X. Yao, Y. Liu, and G. Lin. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3:82-102, July 1999.
15. K. Chellapilla. Combining mutation operators in evolutionary programming. IEEE Trans. on Evolutionary Computation, 2:91-96, 1998.
16. D. B. Fogel. System Identification Through Simulated Evolution: A machine Learning Approach to modelling. Ginn Press, 160 Gould Street, Needham Heights, MA 01294, 1991.
17. J. Holland. Adaptation in nature and artificial systems. The University of Michigan Press, Ann Arbor, Michigan, 1975.
18. J. Kennedy and R.C. Eberhart. Particle swarm optimization. volume 4, pages 1942-1948, Perth, Australia, 1995.
19. S. He, Q. H. Wu, and J. R. Saunders. A group search optimiser for neural network training. volume 3948, pages 1324-1330, Glasgow, UK, May 8-11 2006.
20. M. Clerc and J. Kennedy. The particle swarm: Explosion, stability, and convergence in a multi-dimensional complex space. IEEE Transaction on Evolutionary Computation, 6(1):58-73, February 2002.
21. S. He, Q. H. Wu, and J. R. Saunders. Group search optimiser - an optimisation algorithm inspired by animal searching behavior. accepted by IEEE Transactions on Evolutionary Computation, 2008.
22. S. He, Q. H. Wu, J. Y. Wen, J. R. Saunders, and R. C. Paton. A particle swarm optimizer with passive congregation. BioSystems, 78:135-147, 2004. (Pubitemid 39536138)
23. W. J. Tang, M.S. Li, S. He, W. H. Wu, and J. R. Saunders. Optimal power flow with dynamic loads using bacterial foraging algorithm. in Proceeding of 2006 International Conference on Power Systems Technology, pages 22-26, October 2006.
24. O. Alsac and B. Scott. Optimal load flow with steady state security. IEEE Trans. on Power Appara. Syst., PAS-93:745-751, May-June 1974.