Economic Load Dispatch Using Genetic algorithms


This paper presents the solution of economic load dispatch (ELD) with Lineflow constraints through the application of genetic algorithm (GA). Two representative systems, i.e. IEEE 14 bus [Calculations and programs for power system networks (1986)] and IEEE 30 bus [Calculations and programs for power system networks (1986)] systems have been considered for the investigations. The ELD results with GA have been compared with those obtained through Classical Technique [IEE Proc C 139(4) (1992)], Linear Programming [IEEE Trans Power Syst, 1986] and Quadratic Programming [IEE Proc C 136(3) (1989)] techniques.

INTRODUCTION: Genetic Algorithms (GA‟s) are based on analogy , and are adaptive heuristic search
algorithm based on , evolutionary ideas of natural selection and genetics. As such , they GA‟s represent an
intelligent exploitation of the random search used , to solve search and optimization problems. Although
randomized, GA‟s are by no means random, instead they are exploit historical information to direct the search
in to the region of better performance with in the search space. The basic techniques of the GA are designed
to simulate processes in natural systems necessary for evolution, especially those follow the principles first laid
down by Charles Darwin of , “Survival Of The Fittest”. Since in nature, competition among individuals for
scanty resources , results in the fittest individuals dominating over the weaker ones.
Genetic Algorithms are better than conventional algorithms in that they are more robust. They do not break easily ,
even if the inputs are changed slightly , or in the presence of reasonable noise. Also, in searching a large
state-space, multi-modal state-space, or n-dimensional surface, a genetic algorithm may offer significant benefits
over more typical search of optimization techniques such as linear programming, heuristic, depth-first, breathfirst,
and praxis.
GA‟s are based on an analogy , with the genetic structure and behavior of chromosomes with in a
population of individuals using the following foundations:
 Those individuals most successful in each ‘competition’ will produce more offspring than those individuals
that perform poorly.
 Genes from „good‟ individuals propagate throughout the population , so that two good parents will
sometimes produce offspring that are better than either parent.
 Thus , each successive generation will become more suited to their envir

The general GA is as follows:
STEP1: CREATE A RANDOM INITIAL STATE: An initial population is created from a random selection of
solutions .this is unlike the situation for symbolic AIsystem, where the initial state in a problem isalready given.
STEP2: EVALUATE FITNESS: A value for fitness is assigned to each solution depending on how close it actually is
solving the problem. These solutions are not to be confused with answers of the problem; think of them as possible
characteristics that the system would imply in order to reach the answer .
STEP3: REPRODUE (AND CHILDREN MUTATE): Those chromosomes with a higher fitness value are more likely
to reproduce offspring .The offspring is a product of the father and mother, whose composition consists of a
combination of genes from the two This process is known as crossing over .
STEP 4: NEXT GENERATION :If the new generation contains solution that produces an output that is a close
enough or equal to the desired answer then the problem has been solved . if this is not the case ,then the new
generation will go through the same process as their parents did. This will continue until a solution is reached.