DocumentCode :
527562
Title :
Intelligent dynamic modeling system for multi-target optimization based on power grid pattern recognition
Author :
Liu Dun Nan ; Li, Chen ; Zheng, Shao Li
Author_Institution :
Sch. of Econ. & Manage., North China Electr. Power Univ., Beijing, China
Volume :
5
fYear :
2010
fDate :
10-12 Aug. 2010
Firstpage :
2769
Lastpage :
2773
Abstract :
Basic: With the proposal of the smart grid strategy, there are higher requirement for the real time dispatching and unit commitment, objectively, the new generation of unit commitment should be dynamic, smart, refining efficiency and operable. The multi-objective unit commitment intelligent optimization system for unit commitment based on the power grid state recognition which this paper established is an improvement of the existing unit commitment optimization pattern. It make use of knowledge base system to it identify and classify the real time operation state of power grid, base on which it determines “the primary optimization objectives” and “the primary constraints”, meanwhile it achieves the simplification and dynamic modeling of the multi-objective problem. It does not only improve the accuracy of the multi-target model and make the model targeted, but also simplify the model and improve the computing efficiency.
Keywords :
optimisation; pattern recognition; power generation dispatch; power generation scheduling; smart power grids; intelligent dynamic modeling system; multiobjective unit commitment intelligent optimization system; multitarget optimization; power grid pattern recognition; power grid state recognition; real time dispatching; smart grid strategy; unit commitment optimization pattern; Biological system modeling; Dispatching; Optimization; Pattern recognition; Power grids; Power system dynamics; Real time systems; Clustering; Decision Tree; Knowledge Base; Multi-objective Optimization; Pattern Recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
Type :
conf
DOI :
10.1109/ICNC.2010.5583214
Filename :
5583214
Link To Document :
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