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The Optimal Allocation of Distributed Generators Considering Fault Current and Levelized Cost of Energy Using the Particle Swarm Optimization Method

Author

Listed:
  • Beopsoo Kim

    (Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea)

  • Nikita Rusetskii

    (School of Information Technology and Data Science, Irkutsk National Research Technical University, 664074 Irkutsk, Russia)

  • Haesung Jo

    (Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea)

  • Insu Kim

    (Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea)

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.

Suggested Citation

  • Beopsoo Kim & Nikita Rusetskii & Haesung Jo & Insu Kim, 2021. "The Optimal Allocation of Distributed Generators Considering Fault Current and Levelized Cost of Energy Using the Particle Swarm Optimization Method," Energies, MDPI, vol. 14(2), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:418-:d:479819
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    References listed on IDEAS

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    1. Viral, Rajkumar & Khatod, D.K., 2012. "Optimal planning of distributed generation systems in distribution system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 5146-5165.
    2. Mishra, Sudhanshu, 2006. "Some new test functions for global optimization and performance of repulsive particle swarm method," MPRA Paper 2718, University Library of Munich, Germany.
    3. Abdmouleh, Zeineb & Gastli, Adel & Ben-Brahim, Lazhar & Haouari, Mohamed & Al-Emadi, Nasser Ahmed, 2017. "Review of optimization techniques applied for the integration of distributed generation from renewable energy sources," Renewable Energy, Elsevier, vol. 113(C), pages 266-280.
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    Cited by:

    1. 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.
    2. Yadav, Monika & Pal, Nitai & Saini, Devender Kumar, 2021. "Resilient electrical distribution grid planning against seismic waves using distributed energy resources and sectionalizers: An Indian's urban grid case study," Renewable Energy, Elsevier, vol. 178(C), pages 241-259.
    3. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.

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