NDLI: Disco planner flexible DG allocation in MV distribution networks using multi-objective optimization procedures

Content Provider	IEEE Xplore Digital Library
Author	Moeini, A. Yassami, H. Owlady, M. Sadeghi, M.H.
Copyright Year	2010
Description	Author affiliation: Sama Organization (Affiliated with Islamic Azad University)-Neishaboor Branch, Iran (Moeini, A.; Yassami, H.; Owlady, M.; Sadeghi, M.H.)
Abstract	The most common way of multi-objective optimizations is assigning a weight to each objective and construct a single fitness function. In this method finding of the appropriate weights is the most important problem. The second method, which is not very well-known for power system researchers, is to optimize the objectives independently. Many optimum answers are achieved instead of a single one. These optimum results are known as Pareto front. The Strength Pareto Evolutionary Algorithm (SPEA) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are two successful approaches, which obtain the Pareto front. Nowadays, Distributed Generations (DGs) are widely used in the power systems to improve the overall conditions of the network. One of the most important issues about a DG is its optimum size and place allocation. In this paper, the two aforementioned algorithms are employed to find the optimum sizes and places of DG installation while DG cost and system total power loss are optimization objectives. These procedures give several results, which all of them are optimal and designer could select one of them according to his interest the results are compared with that of GA that optimizes weighted sum of objectives.
Starting Page	240
Ending Page	245
File Size	280640
Page Count	6
File Format	PDF
ISBN	9781424470198
ISSN	18420133
e-ISBN	9781424470204
DOI	10.1109/OPTIM.2010.5510369
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-20
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Substations Optimization methods Evolutionary computation Power system economics Cost function Economic forecasting Power systems Sorting Genetic algorithms Capacity planning
Content Type	Text
Resource Type	Article

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