Least cost generation expansion planning based on an improved genetic algorithm

Author

Park, Jong-Bae ; Park, Young-Moon ; Won, Jong-Ryul ; Lee, Kwang Y.

Author_Institution

Dept. of Electr. Eng., Anyang Univ., South Korea

Volume

2

fYear

1999

fDate

1999

Firstpage

1043

Abstract

An improved genetic algorithm (IGA) is applied to a least-cost generation expansion planning (GEP) problem, which is a highly constrained nonlinear dynamic optimization problem that can only be fully solved by complete enumeration, and is computationally impossible for a real-world GEP problem. An improved genetic algorithm incorporates a stochastic crossover technique and an artificial initial population scheme in order to provide a faster search mechanism and the robustness of the performance. The IGA approach is applied to two test systems, one over a 14-year planning period, and the other over a 24-year planning period. Simulation results showed better solutions than the conventional simple genetic algorithm approaches

Keywords

genetic algorithms; power generation economics; power generation planning; stochastic processes; artificial initial population scheme; constrained nonlinear dynamic optimization problem; global optimisation; improved genetic algorithm; least cost generation expansion planning; performance robustness; search mechanism; stochastic crossover technique; Capacity planning; Constraint optimization; Demand forecasting; Genetic algorithms; Mathematical programming; Meeting planning; Power generation; Power system planning; Robustness; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering Society Summer Meeting, 1999. IEEE

Conference_Location

Edmonton, Alta.

Print_ISBN

0-7803-5569-5

Type

conf

DOI

10.1109/PESS.1999.787459

Filename

787459