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Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization

Author

Listed:
  • 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)

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.

Suggested Citation

  • Donghyeon Lee & Seungwan Son & Insu Kim, 2021. "Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization," Energies, MDPI, vol. 14(11), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3112-:d:562925
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    References listed on IDEAS

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    1. Ana Cabrera-Tobar & Eduard Bullich-Massagué & Mònica Aragüés-Peñalba & Oriol Gomis-Bellmunt, 2019. "Active and Reactive Power Control of a PV Generator for Grid Code Compliance," Energies, MDPI, vol. 12(20), pages 1-25, October.
    2. Kim, Insu, 2018. "Optimal capacity of storage systems and photovoltaic systems able to control reactive power using the sensitivity analysis method," Energy, Elsevier, vol. 150(C), pages 642-652.
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    Cited by:

    1. Mengjun Liao & Lin Zhu & Yonghao Hu & Yang Liu & Yue Wu & Leke Chen, 2023. "Dynamic Equivalent Modeling of a Large Renewable Power Plant Using a Data-Driven Degree of Similarity Method," Energies, MDPI, vol. 16(19), pages 1-20, October.
    2. Jaemin Park & Haesung Jo & Insu Kim, 2021. "The Selection of the Most Cost-Efficient Distributed Generation Type for a Combined Cooling Heat and Power System Used for Metropolitan Residential Customers," Energies, MDPI, vol. 14(18), pages 1-25, September.
    3. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.
    4. Qianlong Zhu & Jun Tao & Tianbai Deng & Mingxing Zhu, 2022. "A General Equivalent Modeling Method for DFIG Wind Farms Based on Data-Driven Modeling," Energies, MDPI, vol. 15(19), pages 1-14, September.

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