DocumentCode :
2603950
Title :
Risk distribution network planning including distributed generation based on particle swarm optimization algorithm with immunity
Author :
Tang Xiaobo ; Tang Guoqing
Author_Institution :
Southeast Univ., Nanjing, China
fYear :
2009
fDate :
6-7 April 2009
Firstpage :
1
Lastpage :
5
Abstract :
A multi-objective optimal model is proposed that takes into account minimizing the investment cost of DG and the power loss of distribution network, maximizing the static voltage stability margin, and enhancing the reliability of supply. And this model includes an normalized risk area expressing the uncertainty of DG´s output. The immune information processing mechanism of immune system is involved into original particle swarm optimizer. The proposed algorithms have both the properties of the original particle swarm optimization algorithm and the immune mechanism of immune system, can improve the abilities of seeking the global excellent result and evolution speed.
Keywords :
artificial immune systems; cost reduction; distributed power generation; investment; particle swarm optimisation; power distribution economics; power distribution planning; power distribution reliability; power system stability; risk analysis; distributed generation; immune information processing mechanism; investment cost minimization; multiobjective optimal model; normalized risk area; particle swarm optimization algorithm; power loss; risk distribution network planning; static voltage stability margin; supply reliability; Cost function; Distributed control; Immune system; Investments; Particle swarm optimization; Power system modeling; Power system reliability; Stability; Uncertainty; Voltage; Distributed Generation; distribution network planning; immune system; particle swarm; risk evaluation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Sustainable Power Generation and Supply, 2009. SUPERGEN '09. International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4934-7
Type :
conf
DOI :
10.1109/SUPERGEN.2009.5348246
Filename :
5348246
Link To Document :
بازگشت