Title :
A gene complementary genetic algorithm for unit commitment
Author :
Maojun, Li ; Tiaosheng, Tong
Author_Institution :
Hunan Univ., Changsha, China
Abstract :
This paper presents a modified genetic algorithm solution to the unit commitment problem (UCP), and constructs three kinds of genetic operators. To enhance convergence rate of the algorithm and prevent converging at a local optimal solution, a gene complementary technology is proposed and is applied to the modified genetic algorithm, which is called a gene complementary genetic algorithm (GCGA). Simulation results show that GCGA is a very efficient algorithm for solution to UCP
Keywords :
genetic algorithms; power generation scheduling; convergence rate enhancement; gene complementary technology; genetic algorithm; genetic operators; local optimal solution; unit commitment problem; Cost function; Demand forecasting; Electronics packaging; Genetic algorithms; Lagrangian functions; Neural networks; Power generation; Power system simulation; Production; Simulated annealing;
Conference_Titel :
Electrical Machines and Systems, 2001. ICEMS 2001. Proceedings of the Fifth International Conference on
Conference_Location :
Shenyang
Print_ISBN :
7-5062-5115-9
DOI :
10.1109/ICEMS.2001.970758
