Genetic algorithm of Chu and Beasley for static and multistage transmission expansion planning

Author

Silva, Isaac J. ; Rider, Marcos J. ; Romero, Rubén ; Murari, Carlos A.

Author_Institution

Dept. of Electr. Energy Syst., State Univ. of Campinas

fYear

0

fDate

0-0 0

Abstract

In this paper the genetic algorithm of Chu and Beasley (GACB) is applied to solve the static and multistage transmission expansion planning problem. The characteristics of the GACB, and some modifications that were done, to efficiently solve the problem described above are also presented. Results using some known systems show that the GACB is very efficient. To validate the GACB, we compare the results achieved using it with the results using other meta-heuristics like tabu-search, simulated annealing, extended genetic algorithm and hybrid algorithms

Keywords

genetic algorithms; power transmission planning; search problems; simulated annealing; genetic algorithm of Chu and Beasley; hybrid algorithms; multistage transmission expansion planning; simulated annealing; tabu-search; Circuits; Genetic algorithms; Heuristic algorithms; Load flow; Mathematical model; Power system planning; Power system simulation; Power transmission lines; Simulated annealing; Transformers; Transmission expansion planning; combinatorial optimization; genetic algorithm of Chu and Beasley; meta-heuristics;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering Society General Meeting, 2006. IEEE

Conference_Location

Montreal, Que.

Print_ISBN

1-4244-0493-2

Type

conf

DOI

10.1109/PES.2006.1709172

Filename

1709172