LAPSE:2023.29094
Published Article
LAPSE:2023.29094
The Optimal Allocation of Distributed Generators Considering Fault Current and Levelized Cost of Energy Using the Particle Swarm Optimization Method
April 13, 2023
Abstract
The power requirements of grids have risen as artificial intelligence and electric vehicle technologies have been used. Thus, the installation of distributed generators (DGs) has become an essential factor to streamline power grids. The objective of this study is to optimize the capacity and location of DGs. For this purpose, an objective function was defined, which takes into account the fault current and the levelized cost of energy, and a modified particle swarm optimization method was applied. Then, we analyzed a case of a single line-to-ground fault with a test feeder (i.e., the IEEE 30 bus system) with no DGs connected, as well as a case where the DGs are optimally connected. The effect of the optimally allocated DGs on the system was analyzed. We discuss an optimal layout method that takes the economic efficiency of the DG installation into account.
Keywords
distributed generator (DG), fault analysis, levelized cost of energy, particle swarm optimization (PSO), single line-to-ground fault
Suggested Citation
Kim B, Rusetskii N, Jo H, Kim I. The Optimal Allocation of Distributed Generators Considering Fault Current and Levelized Cost of Energy Using the Particle Swarm Optimization Method. (2023). LAPSE:2023.29094
Author Affiliations
Kim B: Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea [ORCID]
Rusetskii N: School of Information Technology and Data Science, Irkutsk National Research Technical University, 664074 Irkutsk, Russia [ORCID]
Jo H: Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea [ORCID]
Kim I: Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea [ORCID]
Journal Name
Energies
Volume
14
Issue
2
Article Number
en14020418
Year
2021
Publication Date
2021-01-13
ISSN
1996-1073
Version Comments
Original Submission
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PII: en14020418, Publication Type: Journal Article
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LAPSE:2023.29094
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https://doi.org/10.3390/en14020418
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