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Fast dynamic identification algorithm for key nodes in distribution networks with large-scale DG and EV integration

Author

Listed:
  • Liu, Haiquan
  • Zhou, Suyang
  • Gu, Wei
  • Zhuang, Wennan
  • Zhou, Aihua
  • Peng, Lin
  • Liu, Meizhao

Abstract

The increasing penetration of distributed generators and electric vehicles (EV) in distribution networks has significantly impacted their safe and stable operation. To enhance the reliability of distribution networks and improve their emergency response capabilities, this paper proposes a fast dynamic identification algorithm (FDIA) for key nodes in distribution networks. Firstly, we characterized the dynamic characteristics of conventional loads and the spatiotemporal transfer features of EV loads in the distribution network. We proposed a method to represent the vulnerability of network nodes and defined a network fault linkage matrix that reflects the dynamic impact on node voltage. Subsequently, we utilized hidden fault nodes to characterize the uncertainties in the actual operation of the power grid, forming a derivative network and its correlation matrix. Based on an improved PageRank algorithm, and in conjunction with the derivative network correlation matrix, we designed an FDIA for key nodes. Finally, we applied the FDIA to the modified IEEE 33-bus system and a real 186-bus distribution network system in a region of China for validation. The results indicate that the overall identification time for FDIA is only 17.94 s, and the identification accuracy reaches 95 %, confirming the effectiveness and feasibility of FDIA. This study provides a new perspective for the dynamic identification of critical nodes in distribution networks under new power systems.

Suggested Citation

  • Liu, Haiquan & Zhou, Suyang & Gu, Wei & Zhuang, Wennan & Zhou, Aihua & Peng, Lin & Liu, Meizhao, 2025. "Fast dynamic identification algorithm for key nodes in distribution networks with large-scale DG and EV integration," Applied Energy, Elsevier, vol. 388(C).
    • Handle: RePEc:eee:appene:v:388:y:2025:i:c:s0306261925003381
    DOI: 10.1016/j.apenergy.2025.125608
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