Title :
Refined genetic algorithm-economic dispatch example
Author :
Sheble, Gerald B. ; Brittig, Kristin
Author_Institution :
Iowa State Univ., Ames, IA, USA
fDate :
2/1/1995 12:00:00 AM
Abstract :
A genetic-based algorithm is used to solve a power system economic dispatch (ED) problem. The algorithm utilizes payoff information of perspective solutions to evaluate optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are eliminated. Using an economic dispatch problem as a basis for comparison, several different techniques which enhance program efficiency and accuracy, such as mutation prediction, elitism, interval approximation and penalty factors, are explored. Two unique genetic algorithms are also compared. The results are verified for a sample problem using a classical technique
Keywords :
approximation theory; digital simulation; economics; genetic algorithms; load dispatching; power system analysis computing; accuracy; computer simulation; elitism; interval approximation; mutation prediction; optimality; payoff information; penalty factors; perspective solutions; power system economic dispatch; program efficiency; refined genetic algorithm; Biological cells; Economic forecasting; Encoding; Genetic algorithms; Genetic mutations; Power generation economics; Power system economics; Power systems; Senior members; Student members;
Journal_Title :
Power Systems, IEEE Transactions on