Particle swarm optimization for security constrained economic dispatch

This paper presents an efficient and reliable evolutionary based approach to solve the economic load dispatch (ELD) with security constraints. The proposed approach employ particle swarm optimization (PSO) algorithm for ELD. Incorporation of type 1 PSO as a derivative-free optimization technique in solving ELD with voltages and lineflow constraints significantly relieves the assumptions imposed on the optimized objective function. The proposed approach has been tested on three representative systems, i.e. IEEE 14 bus, IEEE 30 bus and IEEE 57 bus systems respectively. The feasibility of the proposed method is demonstrated and the results are compared with linear programming, quadratic programming and genetic algorithm respectively. The developed algorithms are computationally faster (no. of load flows) than the other methods.

[1]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[2]  J. Nanda,et al.  Application of genetic algorithm to economic load dispatch with Lineflow constraints , 2002 .

[3]  G. C. Contaxis,et al.  Decoupled Optimal Load Flow Using Linear or Quadratic Programming , 1986, IEEE Transactions on Power Systems.

[4]  Y. H. Song,et al.  Advanced engineered-conditioning genetic approach to power economic dispatch , 1997 .

[5]  M. A. Abido,et al.  Optimal power flow using particle swarm optimization , 2002 .

[6]  J. Nanda,et al.  An extremely fast economic load dispatch algorithm through modified coordination equations , 1991 .

[7]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[8]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[9]  H. L. Happ,et al.  OPTIMAL POWER DISPATCH -A COMPREHENSIVE SURVEY , 1977 .

[10]  H.H. Happ,et al.  Optimal power dispatchߞA comprehensive survey , 1977, IEEE Transactions on Power Apparatus and Systems.

[11]  J. Kennedy,et al.  Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[12]  H. M. Merrill,et al.  Some applications of optimization techniques to power systems problems , 1974 .

[13]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[14]  H. Yoshida,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 1999, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[15]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[16]  Lawrence Hasdorff,et al.  Economic Dispatch Using Quadratic Programming , 1973 .

[17]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[18]  J. Nanda,et al.  New optimal power-dispatch algorithm using Fletcher's quadratic programming method , 1989 .

[19]  Yuhui Shi,et al.  Extracting rules from fuzzy neural network by particle swarm optimisation , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).