Differential Evolution and Bacterial Foraging Optimization Based Dynamic Economic Dispatch with Non-smooth Fuel Cost Functions

The Dynamic economic dispatch (DED) is an optimization problem with an objective to determine the optimal combination of power outputs for all generating units over a certain period of time in order to minimize the total fuel cost while satisfying dynamic operational constraints and load demand in each interval. Recently social foraging behavior of Escherichia coli bacteria has been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA) is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real-world optimization problems. This article comes up with a hybrid approach involving Differential Evolution (DE) and BFOA algorithm for solving the DED problem of generating units considering valve-point effects. The proposed hybrid algorithm has been extensively compared with the classical approach and those reported in the literature. The new method is shown to be statistically significantly better on two test systems consisting of five and ten generating units.

[1]  G. P. Granelli,et al.  Fast and efficient gradient projection algorithm for dynamic generation dispatching , 1989 .

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  A. Ebenezer Jeyakumar,et al.  Deterministically guided PSO for dynamic dispatch considering valve-point effect , 2005 .

[4]  Furong Li,et al.  Hybrid genetic approaches to ramping rate constrained dynamic economic dispatch , 1997 .

[5]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[6]  Dong Hwa Kim,et al.  A hybrid genetic algorithm and bacterial foraging approach for global optimization , 2007, Inf. Sci..

[7]  Dong Hwa Kim,et al.  Bacteria Foraging Based Neural Network Fuzzy Learning , 2005, IICAI.

[8]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[9]  Sukumar Mishra,et al.  A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation , 2005, IEEE Transactions on Evolutionary Computation.

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

[11]  Mirjana Cangalovic,et al.  Solving spread spectrum radar polyphase code design problem by tabu search and variable neighbourhood search , 2003, Eur. J. Oper. Res..

[12]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

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

[14]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[15]  T.A.A. Victoire,et al.  Reserve constrained dynamic dispatch of units with valve-point effects , 2005, IEEE Transactions on Power Systems.

[16]  Sukumar Mishra,et al.  Transmission Loss Reduction Based on FACTS and Bacteria Foraging Algorithm , 2006, PPSN.

[17]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[18]  Hoay Beng Gooi,et al.  Dynamic Economic Dispatch: Feasible and Optimal Solutions , 2001 .