Title :
Unit Commitment Optimal Research Based on the Improved Genetic Algorithm
Author :
Ma, Rui ; Huang, Yingmin ; Li, Manhui
Author_Institution :
Changsha Univ. of Sci. & Technol., Changsha, China
Abstract :
This paper proposes the unit commitment problems which can be solved by the improved Genetic Algorithm (GA). In this improved GA, the variables of the unit commitment problems are charged into the Genetic operators by binary coding. The best individual reserve is to be proposed and can be used in the genetic operating. This paper utilizes C++ language compilation to compare the two different 6-unit commitments. One is based on GA with the best individual reserve and the other is base on traditional GA. The results show that the proposed GA with the best individual reserve is an effective method to solve the unit commitment problems and also has important meaning in electricity grid operation, energy conservation and emission reduction.
Keywords :
C++ language; energy conservation; genetic algorithms; power engineering computing; power generation scheduling; power grids; C++ language compilation; GA; binary coding; electricity grid operation; emission reduction; energy conservation; genetic algorithm; genetic operators; unit commitment optimal research; Biological cells; Dynamic programming; Economics; Gallium; Genetic algorithms; Genetics; Power generation; Best Individual Reserve; BinaryCoding; Genetic Algorithm; Unit Commitment;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on
Conference_Location :
Shenzhen, Guangdong
Print_ISBN :
978-1-61284-289-9
DOI :
10.1109/ICICTA.2011.83
