NDLI: Analysis of economic-emission aspects of distribution generation in a radial distribution system using genetic algorithm

Content Provider	IEEE Xplore Digital Library
Author	Hota, S. Kumar, G.V.N.
Copyright Year	2015
Description	Author affiliation: Dept. of EEE, GITAM Univ., Visakhapatnam, India (Hota, S.; Kumar, G.V.N.)
Abstract	Distribution generation (DG) are connected to meet the extra requirement of power in distribution lines without much interference of electric grid. Optimal placement and sizing of Distributed Generation (DG) is a challenging problem for power system engineers. Renewable DGs are emission free but some green house gas emissions like NOx (oxides of nitrogen), CO (carbon monoxide), HC (hydrocarbons), SOx (oxides of sulfur), CHO (aldehydes), PM10 are present from nonrenewable DGs. Combined economic emission (CEE) problem is to find the optimal economic outputs to meet the required load demand at minimum operating cost with minimum emission simultaneously. This paper deals with economic and emissions aspects of a optimally placed and sized DG in a radial distribution network using genetic algorithm (GA) to improve the voltage profile at each bus, to minimize the total real power loss and the overall cost of Distributed Generation along with that reduction in emissions coming from nonrenewable DGs. The proposed GA algorithm, based on the concepts of natural genetics and natural selection is used to determine optimal sizes and locations of a clean DG. MATLAB Simulation is carried out on IEEE-33 bus distribution systems and the results are presented and analyzed. Test results indicate that better improvement in voltage profile, reduction in cost of DG, power loss and penalty factor for clean DG.
Starting Page	1
Ending Page	5
File Size	558475
Page Count	5
File Format	PDF
ISBN	9781479976768
e-ISBN	9781479976782
DOI	10.1109/EESCO.2015.7253769
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-24
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gases Economic indicators Power loss reduction Distribution generation Genetic algorithm Carbon dioxide Genetics Voltage profile improvement Engines Turbines Methanol Emissions
Content Type	Text
Resource Type	Article

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