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Electrical and topological drivers of the cascading failure dynamics in power transmission networks

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  • Azzolin, Alberto
  • Dueñas-Osorio, Leonardo
  • Cadini, Francesco
  • Zio, Enrico

Abstract

To systematically study key factors affecting cascading failures in power systems, this paper advances algorithms for generating synthetic power grids with realistic topological and electrical features, while computationally quantifying how such factors influence system performance probabilistically. Key parameters affecting line outages and power losses during cascading failures include line redundancy, load/generator layout and re-dispatch strategies. Our study combines a synthetic power grid generator with a direct current (DC) cascading failure simulator. The impact of each of the factors and their interactions unravel useful insights for interventions aimed at reducing the probabilities of large blackouts on existing and future power systems. Moreover, conclusions drawn from a spectrum of different power grid topologies and electrical configurations offer more generality than typically attained when studying specific test cases. Line redundancy and distributed generation appear as the most efficacious parameters for reducing the probabilities of large power losses and multiple line overloads, although the effect decays with network density. Also, re-dispatch strategies are critical on the distribution of the cascading failure size in terms of line failures. These and related results provide the basis for probabilistic analyses and future design of evolving power transmission systems under uncertainty.

Suggested Citation

  • Azzolin, Alberto & Dueñas-Osorio, Leonardo & Cadini, Francesco & Zio, Enrico, 2018. "Electrical and topological drivers of the cascading failure dynamics in power transmission networks," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 196-206.
  • Handle: RePEc:eee:reensy:v:175:y:2018:i:c:p:196-206
    DOI: 10.1016/j.ress.2018.03.011
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    21. Zhao, Jianyu & Yu, Lean & Xi, Xi & Li, Shengliang, 2023. "Knowledge percolation threshold and optimization strategies of the combinatorial network for complex innovation in the digital economy," Omega, Elsevier, vol. 120(C).
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