DocumentCode :
3151383
Title :
Using genetic alghoritm for distributed generation allocation to reduce losses and improve voltage profile
Author :
Alinejad-Beromi, Y. ; Sedighizadeh, M. ; Bayat, M.R. ; Khodayar, M.E.
Author_Institution :
Univ. of Semnan, Semnan
fYear :
2007
fDate :
4-6 Sept. 2007
Firstpage :
954
Lastpage :
959
Abstract :
This paper presents a method for the optimal allocation of Distributed generation in distribution systems. In this paper, our aim would be optimal distributed generation allocation for voltage profile improvement and loss reduction in distribution network. Genetic Algorithm (GA) was used as the solving tool, which referring two determined aim; the problem is defined and objective function is introduced. Considering to fitness values sensitivity in genetic algorithm process, there is needed to apply load flow for decision-making. Load flow algorithm is combined appropriately with GA, till access to acceptable results of this operation. We used MATPOWER package for load flow algorithm and composed it with our Genetic Algorithm. The suggested method is programmed under MATLAB software and applied ETAP software for evaluating of results correctness. It was implemented on part of Tehran electricity distributing grid. The resulting operation of this method on some testing system is illuminated improvement of voltage profile and loss reduction indexes.
Keywords :
distributed power generation; load flow; power grids; ETAP software; MATLAB software; MATPOWER package; Tehran electricity distributing grid; genetic algorithm; load flow algorithm; optimal distributed generation allocation; voltage profile improvement; Capacity planning; Costs; Decision making; Distributed control; Genetic algorithms; Load flow; Minimization; Packaging; Technology planning; Voltage; Allocation; Distributed Generation; Genetic Algorithm; Voltage Profile; losses;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Universities Power Engineering Conference, 2007. UPEC 2007. 42nd International
Conference_Location :
Brighton
Print_ISBN :
978-1-905593-36-1
Electronic_ISBN :
978-1-905593-34-7
Type :
conf
DOI :
10.1109/UPEC.2007.4469077
Filename :
4469077
Link To Document :
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