DocumentCode
1996330
Title
A gene complementary genetic algorithm for unit commitment
Author
Maojun, Li ; Tiaosheng, Tong
Author_Institution
Hunan Univ., Changsha, China
Volume
1
fYear
2001
fDate
2001
Firstpage
648
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/ICEMS.2001.970758
Filename
970758
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