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Research on the Weak-Bus Voltage Support Effect of Energy Storage Type Intelligent Soft Open Point

Author

Listed:
  • Tao Yan

    (National Key Laboratory of Renewable Energy Grid Connection, China Electric Power Research Institute Co., Ltd., Beijing 100192, China)

  • Teng Li

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Zerong Liang

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

Abstract

With the integration of a high proportion of distributed generators (DGs), the imbalance between source and load power intensifies, causing the distribution grid to become ‘weak’ during certain periods, which can easily lead to voltage over-limit for the distribution grid. Due to the coupling of active and reactive power in weak power grids for voltage regulation, the effectiveness of single reactive voltage regulation for voltage over-limit is not satisfactory. This paper uses an Energy Storage type Intelligent Soft Open Point (E-SOP) with grid-forming controlled energy storage to simultaneously adjust the active and reactive power between different grid clusters to suppress voltage over-limit for a weak grid. Firstly, based on the system architecture and bus load characteristics, the distribution grid is divided into clusters, and the cluster Thevenin equivalent model is established. Then, different voltage support schemes are compared and analyzed through the cluster active power-voltage (P-V) curve. Through simulation verification of the IEEE 33-bus system containing DGs, it is concluded that the scheme of installing E-SOP in the distribution grid containing DGs enhances the active power transmission capability of the grid and provides better voltage support compared to traditional reactive power compensation and system capacity expansion schemes.

Suggested Citation

  • Tao Yan & Teng Li & Zerong Liang, 2024. "Research on the Weak-Bus Voltage Support Effect of Energy Storage Type Intelligent Soft Open Point," Energies, MDPI, vol. 17(23), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5815-:d:1525841
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    References listed on IDEAS

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    1. Monica Borunda & Adrián Ramírez & Raul Garduno & Gerardo Ruíz & Sergio Hernandez & O. A. Jaramillo, 2022. "Photovoltaic Power Generation Forecasting for Regional Assessment Using Machine Learning," Energies, MDPI, vol. 15(23), pages 1-25, November.
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