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Study on the Evaluation Index System and Evaluation Method of Voltage Stability of Distribution Network with High DG Penetration

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  • Jingjing Tu

    (School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Zhongdong Yin

    (School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Yonghai Xu

    (School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

With the development of new energy technologies, distributed generation (DG) plays a growing role in power distribution networks. Although it solves the power supply problem in some areas, it also poses a challenge to the traditional power grid. To help with this problem, we establish a static voltage stability analysis model for high penetration DG, taking into account its large capacity, and the high fluctuations of its output. Based on the theoretical analysis and the mathematical derivation, we propose an improved voltage stability evaluation index (IVSE) accounting for DG. Finally, using the typical circuit of the IEEE 33 & 69 node system, we simulate high penetration DG access in different locations and capacities. In the simulations, the traditional power flow equation is modified by a continuous parametric equation accounting for DG. Based on consideration of the system transmission power limit, voltage stability margin and system loss, the value of IVSE is demonstrated, in comparison with the traditional index. Thus, we verify that the IVSE index is easy to understand, simple to calculate, and highly versatile.

Suggested Citation

  • Jingjing Tu & Zhongdong Yin & Yonghai Xu, 2018. "Study on the Evaluation Index System and Evaluation Method of Voltage Stability of Distribution Network with High DG Penetration," Energies, MDPI, vol. 11(1), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:79-:d:124968
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    References listed on IDEAS

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    1. Vasiliki Vita, 2017. "Development of a Decision-Making Algorithm for the Optimum Size and Placement of Distributed Generation Units in Distribution Networks," Energies, MDPI, vol. 10(9), pages 1-13, September.
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    Cited by:

    1. Paschalis A. Gkaidatzis & Aggelos S. Bouhouras & Kallisthenis I. Sgouras & Dimitrios I. Doukas & Georgios C. Christoforidis & Dimitris P. Labridis, 2019. "Efficient RES Penetration under Optimal Distributed Generation Placement Approach," Energies, MDPI, vol. 12(7), pages 1-32, April.
    2. Jingjing Tu & Yonghai Xu & Zhongdong Yin, 2018. "Data-Driven Kernel Extreme Learning Machine Method for the Location and Capacity Planning of Distributed Generation," Energies, MDPI, vol. 12(1), pages 1-21, December.

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