Title :
An improved genetic algorithm for dynamic reactive power optimization in electricity market
Author :
Shu, Jun ; Zhang, Lizi ; Liu, Yi ; Xianchao Huang
Author_Institution :
Dept. of Electr. Eng., North China Electr. Power Univ., Beijing, China
Abstract :
A mathematical model considering reactive power cost is proposed for dynamic reactive power optimization in electricity market. To solve this complicated problem, this paper presents a mixed optimization strategy that sufficiently combines the advantages of immune theory and genetic algorithm (GA). By simulating homeostatic mechanism of antibody in immune system, the density of individuals is restrained and promoted automatically. Further more, in order to obtain the heuristic GA, variable region and stable region of antibody are studied in this paper, and an expert knowledge based on effective variety of load is proposed for gene recombination of individuals. The proposed model and algorithm are applied to IEEE30 system, and the numerical results verify the correctness and validity of them.
Keywords :
costing; expert systems; genetic algorithms; power markets; power system analysis computing; reactive power; GA; IEEE30 system; antibody; electricity market; expert knowledge; gene recombination; genetic algorithm; heuristic GA; homeostatic mechanism; immune theory; mathematical model; optimization; reactive power cost; Cost function; Electricity supply industry; Genetics; Immune system; Mathematical model; Nonlinear dynamical systems; Power generation; Power system dynamics; Power system security; Reactive power;
Conference_Titel :
Power System Technology, 2004. PowerCon 2004. 2004 International Conference on
Print_ISBN :
0-7803-8610-8
DOI :
10.1109/ICPST.2004.1460241
