DocumentCode :
1993953
Title :
Mixed AC/DC OPF using differential evolution for global minima identification
Author :
Marten, Anne-Katrin ; Sass, Florian ; Westermann, Dirk
Author_Institution :
Dept. of Power Syst., Tech. Univ. Ilmenau, Ilmenau, Germany
fYear :
2015
fDate :
June 29 2015-July 2 2015
Firstpage :
1
Lastpage :
6
Abstract :
There are a lot of challenges coming up for the transmission system in Europe. This is mainly caused by tremendous increasing infeed from renewable energies in remote areas as the North Sea and increasing cross border energy trades. A suitable solution is a meshed onshore HVDC grid spanning the existing AC transmission grid. This new transmission layer must be actively included in grid´s operation management in order to make use of its whole advantages. A part of it is operation planning for meshed HVDC grids integrated in an AC system. Therefore a mixed AC/DC optimal power flow can be used. As conventional optimization methods for such kind of optimization problems converge in local minima or do not converge at all, this paper proposed application of an artificial intelligence optimization algorithm namely differential evolution.
Keywords :
HVDC power transmission; evolutionary computation; load flow; power grids; power system management; power transmission planning; AC transmission grid; Europe; North Sea; artificial intelligence optimization; differential evolution; global minima identification; grid´s operation management; meshed onshore HVDC grid operation planning; mixed AC-DC OPF; mixed AC-DC optimal power flow; renewable energy; transmission system; Artificial intelligence; Convergence; HVDC transmission; Linear programming; Optimization; Sociology; Statistics; HVDC grid operation; converter schedules; local minima; mixed AC/DC OPF;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
PowerTech, 2015 IEEE Eindhoven
Conference_Location :
Eindhoven
Type :
conf
DOI :
10.1109/PTC.2015.7232830
Filename :
7232830
Link To Document :
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