DocumentCode :
45328
Title :
State-of-the-Art Techniques and Challenges Ahead for Distributed Generation Planning and Optimization
Author :
Keane, Andrew ; Ochoa, Luis F. ; Borges, Carmen L. T. ; Ault, Graham W. ; Alarcon-Rodriguez, Arturo D. ; Currie, Robert A. F. ; Pilo, F. ; Dent, Chris ; Harrison, G.P.
Volume :
28
Issue :
2
fYear :
2013
fDate :
May-13
Firstpage :
1493
Lastpage :
1502
Abstract :
It is difficult to estimate how much distributed generation (DG) capacity will be connected to distribution systems in the coming years; however, it is certain that increasing penetration levels require robust tools that help assess the capabilities and requirements of the networks in order to produce the best planning and control strategies. The work of this Task Force is focused on the numerous strategies and methods that have been developed in recent years to address DG integration and planning. This paper contains a critical review of the work in this field. Although there have been numerous publications in this area, widespread implementation of the methods has not taken place. The barriers to implementation of the advanced techniques are outlined, highlighting why network operators have been slow to pick up on the research to date. Furthermore, key challenges ahead which remain to be tackled are also described, many of which have come into clear focus with the current drive towards smarter distribution networks.
Keywords :
distributed power generation; power distribution control; power distribution planning; power generation control; power generation planning; DG integration; Task Force; control strategy; distributed generation capacity estimation; distributed generation optimization; distributed generation planning; distribution networks; distribution systems; network operators; penetration levels; robust tool; Biological system modeling; Genetic algorithms; Linear programming; Optimization; Planning; Probabilistic logic; Reliability; AC optimal power flow; active network management; distributed generation; distribution networks; linear programming; multi-objective programming; wind power generation;
fLanguage :
English
Journal_Title :
Power Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8950
Type :
jour
DOI :
10.1109/TPWRS.2012.2214406
Filename :
6307952
Link To Document :
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