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Vulnerability in dynamically driven oscillatory networks and power grids.

Xiaozhu Zhang1, Cheng Ma2, Marc Timme1

  • 1Chair for Network Dynamics, Institute for Theoretical Physics and Center for Advancing Electronics Dresden (cfaed), Cluster of Excellence Physics of Life, Technical University of Dresden, 01062 Dresden, Germany.

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Summary
This summary is machine-generated.

We introduce a new Dynamic Vulnerability Index (DVI) to identify vulnerable nodes in driven networks. This index predicts nodes most susceptible to external signals, crucial for power grid stability.

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Area of Science:

  • Network science
  • Complex systems dynamics
  • Systems engineering

Background:

  • Network vulnerability is typically assessed by structural properties, overlooking dynamic responses.
  • Driven systems exhibit complex, heterogeneous dynamic patterns influenced by driving frequency, topology, and driven nodes.
  • Identifying critical nodes susceptible to dynamic driving is a significant challenge.

Purpose of the Study:

  • To develop a computationally efficient metric for quantifying node vulnerability in driven networks.
  • To identify nodes most susceptible to dynamic driving signals based on their response amplitude.
  • To provide a tool for predicting system-wide vulnerability in complex networks.

Main Methods:

  • Linear response theory is applied to derive the Dynamic Vulnerability Index (DVI).
  • The DVI quantifies the amplitude response of individual nodes to dynamic driving signals with specified power spectra.
  • The method is designed to be generic and computationally accessible.

Main Results:

  • The proposed Dynamic Vulnerability Index (DVI) effectively identifies nodes with the largest amplitude responses.
  • The DVI enables robust predictions of node susceptibility to external dynamic inputs.
  • The index's efficacy is demonstrated in the context of driven oscillator networks and AC power grid models.

Conclusions:

  • The Dynamic Vulnerability Index (DVI) offers a powerful new approach to assessing network vulnerability.
  • This index is crucial for enhancing the stability and resilience of dynamically driven systems.
  • Applications include identifying vulnerable nodes in power grids facing fluctuating renewable energy inputs and consumer demand.