LAPSE:2023.32563v1
Published Article
LAPSE:2023.32563v1
Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization
April 20, 2023
Widespread interest in environmental issues is growing. Many studies have examined the effect of distributed generation (DG) from renewable energy resources on the electric power grid. For example, various studies efficiently connect growing DG to the current electric power grid. Accordingly, the objective of this study is to present an algorithm that determines DG location and capacity. For this purpose, this study combines particle swarm optimization (PSO) and the Volt/Var control (VVC) of DG while regulating the voltage magnitude within the allowable variation (e.g., ±5%). For practical optimization, the PSO algorithm is enhanced by applying load profile data (e.g., 24-h data). The objective function (OF) in the proposed PSO method considers voltage variations, line losses, and economic aspects of deploying large-capacity DG (e.g., installation costs) to transmission networks. The case studies validate the proposed method (i.e., optimal allocation of DG with the capability of VVC with PSO) by applying the proposed OF to the PSO that finds the optimal DG capacity and location in various scenarios (e.g., the IEEE 14- and 30-bus test feeders). This study then uses VVC to compare the voltage profile, loss, and installation cost improved by DG to a grid without DG.
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Keywords
distributed generation, Particle Swarm Optimization, Volt/Var control
Subject
Suggested Citation
Lee D, Son S, Kim I. Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization. (2023). LAPSE:2023.32563v1
Author Affiliations
Lee D: Electrical Engineering, Inha University, Incheon 22212, Korea
Son S: Electrical Engineering, Inha University, Incheon 22212, Korea
Kim I: Electrical Engineering, Inha University, Incheon 22212, Korea
Son S: Electrical Engineering, Inha University, Incheon 22212, Korea
Kim I: Electrical Engineering, Inha University, Incheon 22212, Korea
Journal Name
Energies
Volume
14
Issue
11
First Page
3112
Year
2021
Publication Date
2021-05-26
Published Version
ISSN
1996-1073
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Original Submission
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PII: en14113112, Publication Type: Journal Article
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LAPSE:2023.32563v1
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doi:10.3390/en14113112
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[v1] (Original Submission)
Apr 20, 2023
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Apr 20, 2023
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