Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization
Donghyeon Lee,
Seungwan Son and
Insu Kim
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Donghyeon Lee: Electrical Engineering, Inha University, Incheon 22212, Korea
Seungwan Son: Electrical Engineering, Inha University, Incheon 22212, Korea
Insu Kim: Electrical Engineering, Inha University, Incheon 22212, Korea
Energies, 2021, vol. 14, issue 11, 1-19
Abstract:
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.
Keywords: distributed generation; Volt/Var control; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:11:p:3112-:d:562925
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