DocumentCode :
1545454
Title :
Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow
Author :
Siano, Pierluigi ; Cecati, Carlo ; Yu, Hao ; Kolbusz, Janusz
Author_Institution :
Dept. of Ind. Eng., Univ. of Salerno, Fisciano, Italy
Volume :
8
Issue :
4
fYear :
2012
Firstpage :
944
Lastpage :
952
Abstract :
This paper proposes an Energy Management System for the optimal operation of Smart Grids and Microgrids, using Fully Connected Neuron Networks combined with Optimal Power Flow. An adaptive training algorithm based on Genetic Algorithms, Fuzzy Clustering and Neuron-by-Neuron Algorithms is used for generating new clusters and new neural networks. The proposed approach, integrating Demand Side Management and Active Management Schemes, allows significant enhancements in energy saving, customers´ active participation in the open market and exploitation of renewable energy resources. The effectiveness of the proposed Energy Management System and adaptive training algorithm is verified on a 23-bus 11 kV microgrid.
Keywords :
demand side management; distributed power generation; energy management systems; genetic algorithms; load flow; neural nets; power engineering computing; smart power grids; FCN networks; active management schemes; adaptive training algorithm; demand side management; energy management system; fully connected neuron networks; fuzzy clustering; genetic algorithms; microgrids; neural networks; neuron-by-neuron algorithms; optimal power flow; real time operation; renewable energy resources; smart grids; voltage 11 kV; Algorithm design and analysis; Energy management; Generators; Genetic algorithms; Reactive power; Real time systems; Active management; demand side management; energy management systems; fuzzy clustering; genetic algorithms; neural networks; real time power market; smart grid;
fLanguage :
English
Journal_Title :
Industrial Informatics, IEEE Transactions on
Publisher :
ieee
ISSN :
1551-3203
Type :
jour
DOI :
10.1109/TII.2012.2205391
Filename :
6221999
Link To Document :
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