Title :
A Improved Genetic Algorithm for Random-Fuzzy Programming Model in Electricity Market
Author :
Zhu, Quanle ; Ma, Xinshun ; Yuan, Jinsha ; Ding, Qiaolin
Author_Institution :
Dept. of Electr. Eng., North China Electr. Power Univ., Baoding, China
Abstract :
In the sealed-auction-based electricity markets, to fast and accurately solve the random-fuzzy programming problem, which is for building optimal bidding strategies of generation companies and can simultaneously deal with both random variable and fuzzy variable, a improved genetic algorithm (IGA) is proposed based on a genetic algorithm with hybrid Laplace crossover and power mutation (HLCPM). The IGA can fast find the optimal solution in some area and guarantee the enough search area. The performance of IGA is verified by 10 benchmark global optimization test problems and the random-fuzzy programming problem. A very good performance of IGA is shown by the results.
Keywords :
fuzzy systems; genetic algorithms; power markets; electricity market; genetic algorithm; hybrid Laplace crossover and power mutation; random-fuzzy programming model; Automation; Companies; Convergence; Electricity supply industry; Genetic algorithms; Power systems; Programming;
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2011 Asia-Pacific
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6253-7
DOI :
10.1109/APPEEC.2011.5748479
