DocumentCode :
2057740
Title :
Optimum capacity allocation of DG units based on unbalanced three-phase optimal power flow
Author :
Anwar, A. ; Pota, H.R.
Author_Institution :
Sch. of Eng. & Inf. Technol. (SEIT), Univ. of New South Wales at ADFA, Canberra, ACT, Australia
fYear :
2012
fDate :
22-26 July 2012
Firstpage :
1
Lastpage :
8
Abstract :
In this paper, a methodology for determining optimum generation capacity of multiple distributed generation (DG) units is presented. The proposed method is based on unbalanced three-phase optimal power flow (TOPF) using particle swarm intelligence. To solve the optimum generation capacity problem, a co-simulation platform has been used under the MATLAB and OpenDSS environment. An adaptive weight particle swarm optimization algorithm has been developed in MATLAB and the unbalanced three-phase distribution load flow (DLF) has been performed using Electric Power Research Institute´s (EPRI) open source tool OpenDSS. The analysis is carried out on IEEE 123 node distribution test feeder for three different DG technologies. The results obtained from the proposed method have been compared with the results obtained from a `brute-force search´ method. This analysis shows that the proposed method finds out the optimum solution successfully while computational complexity and time is reduced extensively. Using multiple DG units with optimum generation capacity, power loss of the network is reduced significantly while voltage profile remains within stability margin.
Keywords :
computational complexity; distributed power generation; load flow; mathematics computing; particle swarm optimisation; power distribution; search problems; DG units; EPRI open source tool; IEEE 123 node distribution test feeder; Matlab; OpenDSS environment; adaptive weight particle swarm optimization algorithm; brute-force search method; computational complexity; distributed generation units; electric power research institute open source tool; optimum capacity allocation; optimum generation capacity; optimum generation capacity problem; particle swarm intelligence; stability margin; unbalanced TOPF; unbalanced three-phase DLF; unbalanced three-phase distribution load flow; unbalanced three-phase optimal power flow; voltage profile; Algorithm design and analysis; Equations; Linear programming; Mathematical model; Particle swarm optimization; Power systems; Resource management; DG capacity allocation; Planning for smart grid; smart grid co-simulation platform; swarm intelligence; unbalanced TOPF;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting, 2012 IEEE
Conference_Location :
San Diego, CA
ISSN :
1944-9925
Print_ISBN :
978-1-4673-2727-5
Electronic_ISBN :
1944-9925
Type :
conf
DOI :
10.1109/PESGM.2012.6345265
Filename :
6345265
Link To Document :
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