Title :
Optimal Power Flow by Enhanced Genetic Algorithm
Author :
Bakirtzis, Anastasios G. ; Biskas, P. N. ; Zoumas, C. E. ; Petridis, V.
Author_Institution :
Aristotle University, Greece
Abstract :
This paper presents an enhanced genetic algorithm for the solution of the optimal power flow with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of functional operating constraints, such as branch flow limits, load bus voltage magnitude limits, and generator reactive capabilities are included as penalties in the genetic algorithm fitness function. Advanced and problem-specific operators are introduced in order to enhance the algorithm´s efficiency and accuracy. Numerical results on two test systems are presented and compared with results of other approaches.
Keywords :
Costs; Data analysis; Dictionaries; Genetic algorithms; Information analysis; Load flow; Power generation; Power system analysis computing; Power system dynamics; Protocols; Optimal power flow; genetic algorithms;
Journal_Title :
Power Engineering Review, IEEE
DOI :
10.1109/MPER.2002.4311997