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An Optimal Phase Arrangement of Distribution Transformers under Risk Assessment

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  • Chia-Sheng Tu

    (College of Intelligence Robot, Fuzhou Polytechnic, Fuzhou 350108, China)

  • Chung-Yuen Yang

    (Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung 833, Taiwan)

  • Ming-Tang Tsai

    (Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung 833, Taiwan)

Abstract

This paper presents a phase arrangement procedure for distribution transformers to improve system unbalance and voltage profile of distribution systems, while considering the location and uncertainties of the wind turbine (WT) and photovoltaics (PV). Based on historical data, the Monte Carlo method is used to calculate the power generation value-at-risk (VAR) of WTs/PVs installed under a given level of confidence. The main target of this paper is to reduce the line loss and unbalance factor during 24-hour intervals. Assessing the various confidence levels of risk, a feasible particle swarm optimization (FPSO) is proposed to solve the optimal location of WTs/PVs installed and transformer load arrangement. A three-phase power flow with equivalent current injection (ECI) is analyzed to demonstrate the operating efficiency of the FPSO in a Taipower feeder. Simulation results will support the planner in the proper location of WTs/PVs installed to reduce system losses and maintain the voltage profile. They can also provide more risk information for handing uncertainties when the renewable energy is connected to the distribution system.

Suggested Citation

  • Chia-Sheng Tu & Chung-Yuen Yang & Ming-Tang Tsai, 2020. "An Optimal Phase Arrangement of Distribution Transformers under Risk Assessment," Energies, MDPI, vol. 13(21), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5852-:d:442275
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    References listed on IDEAS

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    2. Pérez Odeh, Rodrigo & Watts, David & Flores, Yarela, 2018. "Planning in a changing environment: Applications of portfolio optimisation to deal with risk in the electricity sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3808-3823.
    3. Alberto Reyes & L. Enrique Sucar & Pablo H. Ibargüengoytia & Eduardo F. Morales, 2020. "Planning Under Uncertainty Applications in Power Plants Using Factored Markov Decision Processes," Energies, MDPI, vol. 13(9), pages 1-17, May.
    4. Mahmud, Khizir & Khan, Behram & Ravishankar, Jayashri & Ahmadi, Abdollah & Siano, Pierluigi, 2020. "An internet of energy framework with distributed energy resources, prosumers and small-scale virtual power plants: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    5. Chang, Ching-Ter, 2015. "Multi-choice goal programming model for the optimal location of renewable energy facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 379-389.
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