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Robust Optimal Allocation of Decentralized Reactive Power Compensation in Three-Phase Four-Wire Low-Voltage Distribution Networks Considering the Uncertainty of Photovoltaic Generation

Author

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  • Shunjiang Lin

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Sen He

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Haipeng Zhang

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Mingbo Liu

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Zhiqiang Tang

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Hao Jiang

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Yunong Song

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

Abstract

Due to the unbalanced three-phase loads, the single-phase distributed photovoltaic (PV) integration, the long feeders, and the heavy loads in a three-phase four-wire low voltage distribution network (LVDN), the voltage unbalance factor (VUF), the network loss and the voltage deviation are relatively high. Considering the uncertain fluctuation of the PV output and the load power, a robust optimal allocation of decentralized reactive power compensation (RPC) devices model for a three-phase four-wire LVDN is proposed. In this model, the uncertain variables are described as box uncertain sets, the three-phase simultaneous switching capacity and single-phase independent switching capacity of the RPC devices are taken as decision variables, and the objective is to minimize the total power loss of the LVDN under the extreme scenarios of uncertain variables. The bi-level optimization method is used to transform the robust optimization model with uncertain variables into bi-level deterministic optimization models, which could be solved alternately. The nonlinear programming solver IPOPT in the mature commercial software GAMS is adopted to solve the upper and lower deterministic optimization models to obtain a robust optimal allocation scheme of decentralized RPC devices. Finally, the simulation results for an actual LVDN show that the obtained decentralized RPC scheme can ensure that the voltage deviation and the VUF of each bus satisfied the secure operation requirement no matter how the PV output and load power changed within their own uncertain sets, and the network loss could be effectively reduced.

Suggested Citation

  • Shunjiang Lin & Sen He & Haipeng Zhang & Mingbo Liu & Zhiqiang Tang & Hao Jiang & Yunong Song, 2019. "Robust Optimal Allocation of Decentralized Reactive Power Compensation in Three-Phase Four-Wire Low-Voltage Distribution Networks Considering the Uncertainty of Photovoltaic Generation," Energies, MDPI, vol. 12(13), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2479-:d:243528
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    References listed on IDEAS

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    1. Ana Rodríguez & Emilio J. Bueno & Álvar Mayor & Francisco J. Rodríguez & Aurelio García-Cerrada, 2014. "Voltage Support Provided by STATCOM in Unbalanced Power Systems," Energies, MDPI, vol. 7(2), pages 1-24, February.
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    Cited by:

    1. Sander Claeys & Marta Vanin & Frederik Geth & Geert Deconinck, 2021. "Applications of optimization models for electricity distribution networks," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    2. Zbigniew Olczykowski, 2021. "Electric Arc Furnaces as a Cause of Current and Voltage Asymmetry," Energies, MDPI, vol. 14(16), pages 1-18, August.
    3. Saša Vlahinić & Dubravko Franković & Vitomir Komen & Anamarija Antonić, 2019. "Reactive Power Compensation with PV Inverters for System Loss Reduction," Energies, MDPI, vol. 12(21), pages 1-17, October.
    4. Junyong Wu & Chen Shi & Meiyang Shao & Ran An & Xiaowen Zhu & Xing Huang & Rong Cai, 2019. "Reactive Power Optimization of a Distribution System Based on Scene Matching and Deep Belief Network," Energies, MDPI, vol. 12(17), pages 1-24, August.

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