Title :
DEPSO and Bacterial Foraging Pptimization based Dynamic Economic Dispatch with non-smooth fuel cost functions
Author :
Vaisakh, K. ; Praveena, P. ; Rao, S. Rama Mohana
Author_Institution :
Dept. of Electr. Eng., AU Coll. of Eng., Visakhapatnam, India
Abstract :
Dynamic economic dispatch (DED) problem 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 Particle Swarm Optimization (DEPSO) and BFOA for solving the DED problem of generating units considering valve-point effects. The proposed hybrid algorithm has been extensively compared with the classical approach. The new method is shown to be statistically significantly better on two test systems consisting of five and ten generating units. The results obtained through the proposed method are compared with those reported in the literature.
Keywords :
biology computing; microorganisms; particle swarm optimisation; DEPSO; Escherichia coli bacteria; bacterial foraging optimization algorithm; differential evolution particle swarm optimization; dynamic economic dispatch; nonsmooth fuel cost functions; optimization problem; social foraging behavior; total fuel cost; Constraint optimization; Cost function; Distributed control; Fuel economy; Hybrid power systems; Microorganisms; Particle swarm optimization; Power generation; Power generation economics; System testing; Bacterial Foraging; Dynamic economic dispatc; non-smooth fuel cost function; particle swarm optimization;
Conference_Titel :
Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on
Conference_Location :
Coimbatore
Print_ISBN :
978-1-4244-5053-4
DOI :
10.1109/NABIC.2009.5393632