Title :
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
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;
Conference_Titel :
Power Engineering Society Summer Meeting, 1999. IEEE
Conference_Location :
Edmonton, Alta.
Print_ISBN :
0-7803-5569-5
DOI :
10.1109/PESS.1999.787459
