NDLI: Optimal DG placement and capacity allocation using intelligent algorithms

Content Provider	IEEE Xplore Digital Library
Author	Alinezhad, P. Bakhoda, O.Z. Menhaj, M.B.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran (Bakhoda, O.Z.; Menhaj, M.B.) \|\| Dept. of Electr. Eng., Shahab-Danesh Univ., Oom, Iran (Alinezhad, P.)
Abstract	Now a Day, using distributed generators (DGs) as power generator resources in Distribution Networks, is growing increasingly. DG installation will cause many advantages such as power loss reduction, increasing reliability, improving voltage profile, power quality and etc. Therefore, proposing an optimal pattern for installing DGs, attracts lots of attention these days. This paper proposes an optimal DGs' placement for Sirou-Feeder in Qom's distribution substation. The problem is considered from the viewpoint of supplier company which itself is both investor and installer of DGs and consequently, both economical and marketing constraints should be considered beside technical constraints. A multi-objective cost function will be defined to reach all objectives, means optimal placement and power allocation of DGs, loss reduction, voltage profile improvement, reliability increase and decreasing DGs' installation costs. Three intelligent algorithms, PSO, GA and GSA, are examined here. The simulation results indicate efficiency of applied methods in solution finding and dominancy of GSA over PSO and GA in optimal placement.
Starting Page	1
Ending Page	8
File Size	356900
Page Count	8
File Format	PDF
e-ISBN	9781467385459
DOI	10.1109/CFIS.2015.7391657
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-09
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithm (GA) Gravitational search algorithm (GSA) Particle swarm optimization (PSO) Optimal placement Companies Linear programming Distributed generation resources Reliability Resource management Particle swarm optimization Genetic algorithms Investment
Content Type	Text
Resource Type	Article

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