A modified particle swarm optimization for economicdispatch with nonsmooth cost functions

Abstract This paper presents a new approach to economic dispatch (ED) problems with nonsmooth cost functions using a particle swarm optimization (PSO) technique. The practical ED problems have nonsmooth cost functions with equality and inequality constraints, which makes the problem of finding the global optimum difficult when using any mathematical approaches. In this paper, a modified PSO (MPSO) mechanism is suggested to deal with the equality and inequality constraints in the ED problems. To validate the results obtained by MPSO, Particle Swarm Optimization (PSO) is applied for comparison. Also, the results obtained by MPSO and PSO are compared with the previous approaches reported in the literature. The results show that the MPSO produces optimal or nearly optimal solutions for the study systems.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Chun Che Fung,et al.  Simulated annealing based economic dispatch algorithm , 1993 .

[3]  T. Jayabarathi,et al.  Evolutionary programming‐based economic dispatch for units with multiple fuel options , 2007 .

[4]  Whei-Min Lin,et al.  Nonconvex economic dispatch by integrated artificial intelligence , 2001, PICA 2001. Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications (Cat. No.01CH37195).

[5]  Kwang Y. Lee,et al.  Economic load dispatch for piecewise quadratic cost function using Hopfield neural network , 1993 .

[6]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms for Constrained Parameter Optimization Problems , 1996, Evolutionary Computation.

[7]  June Ho Park,et al.  Adaptive Hopfield neural networks for economic load dispatch , 1998 .

[8]  A. Selvakumar,et al.  A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems , 2007, IEEE Transactions on Power Systems.

[9]  Hong-Tzer Yang,et al.  Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions , 1996 .

[10]  Joong-Rin Shin,et al.  A particle swarm optimization for economic dispatch with nonsmooth cost functions , 2005, IEEE Transactions on Power Systems.

[11]  Whei-Min Lin,et al.  An Improved Tabu Search for Economic Dispatch with Multiple Minima , 2002, IEEE Power Engineering Review.

[12]  G. Sheblé,et al.  Power generation operation and control — 2nd edition , 1996 .

[13]  Malcolm Irving,et al.  Economic dispatch of generators with prohibited operating zones: a genetic algorithm approach , 1996 .

[14]  Zwe-Lee Gaing,et al.  Particle swarm optimization to solving the economic dispatch considering the generator constraints , 2003 .

[15]  P. K. Chattopadhyay,et al.  Evolutionary programming techniques for economic load dispatch , 2003, IEEE Trans. Evol. Comput..

[16]  Jin-Ho Kim,et al.  A Hybrid Particle Swarm Optimization Employing Crossover Operation for Economic Dispatch Problems with Valve-point Effects , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[17]  Chao-Lung Chiang,et al.  Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels , 2005, IEEE Transactions on Power Systems.

[18]  G. Sheblé,et al.  Genetic algorithm solution of economic dispatch with valve point loading , 1993 .

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

[20]  Hong-Chan Chang,et al.  Large-scale economic dispatch by genetic algorithm , 1995 .

[21]  Y. W. Wong,et al.  Genetic and genetic/simulated-annealing approaches to economic dispatch , 1994 .