Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels

This paper presents an improved genetic algorithm with multiplier updating (IGA/spl I.bar/MU) to solve power economic dispatch (PED) problems of units with valve-point effects and multiple fuels. The proposed IGA/spl I.bar/MU integrates the improved genetic algorithm (IGA) and the multiplier updating (MU). The IGA equipped with an improved evolutionary direction operator and a migration operation can efficiently search and actively explore solutions, and the MU is employed to handle the equality and inequality constraints of the PED problem. Few PED problem-related studies have seldom addressed both valve-point loadings and change fuels. To show the advantages of the proposed algorithm, which was applied to test PED problems with one example considering valve-point effects, one example considering multiple fuels, and one example addressing both valve-point effects and multiple fuels. Additionally, the proposed algorithm was compared with previous methods and the conventional genetic algorithm (CGA) with the MU (CGA/spl I.bar/MU), revealing that the proposed IGA/spl I.bar/MU is more effective than previous approaches, and applies the realistic PED problem more efficiently than does the CGA/spl I.bar/MU. Especially, the proposed algorithm is highly promising for the large-scale system of the actual PED operation.

[1]  M. J. D. Powell,et al.  Algorithms for nonlinear constraints that use lagrangian functions , 1978, Math. Program..

[2]  G. L. Viviani,et al.  Hierarchical Economic Dispatch for Piecewise Quadratic Cost Functions , 1984, IEEE Transactions on Power Apparatus and Systems.

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  Z.-X. Liang,et al.  A zoom feature for a dynamic programming solution to economic dispatch including transmission losses , 1992 .

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

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

[7]  Osamu Inoue,et al.  New evolutionary direction operator for genetic algorithms , 1995 .

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

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

[10]  Ji-Pyng Chiou,et al.  A hybrid method of differential evolution with application to optimal control problems of a bioprocess system , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

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

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

[13]  P. Attaviriyanupap,et al.  A Hybrid EP and SQP for Dynamic Economic Dispatch with Nonsmooth Fuel Cost Function , 2002, IEEE Power Engineering Review.

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

[15]  Ching-Tzong Su,et al.  An incorporated algorithm for combined heat and power economic dispatch , 2004 .

[16]  A. Ebenezer Jeyakumar,et al.  Hybrid PSO–SQP for economic dispatch with valve-point effect , 2004 .

[17]  Chao-Lung Chiang,et al.  Optimal Position/Speed Control of Induction Motor Using Improved Genetic Algorithm and Fuzzy Phase Plane Controller , 2004, Control. Intell. Syst..

[18]  Ching-Tzong Su,et al.  Nonconvex Power Economic Dispatch by Improved Genetic Algorithm with Multiplier Updating Method , 2004 .

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