Assessment of power grid vulnerabilities accounting for stochastic loads and model imprecision

Roberto Rocchetta, Edoardo Patelli

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Vulnerability and robustness are major concerns for future power grids. Malicious attacks and extreme weather conditions have the potential to trigger multiple components outages, cascading failures and large blackouts. Robust contingency identification procedures are necessary to improve power grids resilience and identify critical scenarios. This paper proposes a framework for advanced uncertainty quantification and vulnerability assessment of power grids. The framework allows critical failure scenarios to be identified and overcomes the limitations of current approaches by explicitly considering aleatory and epistemic sources of uncertainty modelled using probability boxes. The different effects of stochastic fluctuation of the power demand, imprecision in power grid parameters and uncertainty in the selection of the vulnerability model have been quantified. Spectral graph metrics for vulnerability are computed using different weights and are compared to power-flow-based cascading indices in ranking N-1 line failures and random N-k lines attacks. A rank correlation test is proposed for further comparison of the vulnerability metrics. The IEEE 24 nodes reliability test power network is selected as a representative case study and a detailed discussion of the results and findings is presented.
LanguageEnglish
Pages219-232
Number of pages14
JournalElectrical Power and Energy Systems
Volume98
Early online date14 Dec 2017
DOIs
Publication statusPublished - 30 Jun 2018

Fingerprint

Imprecision
Vulnerability
Grid
Outages
Attack
Cascading Failure
Metric Graphs
Uncertainty
Spearman's coefficient
Uncertainty Quantification
Scenarios
Power Flow
Model
Line
Resilience
Trigger
Weather
Ranking
Extremes
Fluctuations

Keywords

  • vulnerability assessment
  • contingency ranking
  • power grid
  • uncertainty
  • overload cascading failures
  • spectral graph metrics

Cite this

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abstract = "Vulnerability and robustness are major concerns for future power grids. Malicious attacks and extreme weather conditions have the potential to trigger multiple components outages, cascading failures and large blackouts. Robust contingency identification procedures are necessary to improve power grids resilience and identify critical scenarios. This paper proposes a framework for advanced uncertainty quantification and vulnerability assessment of power grids. The framework allows critical failure scenarios to be identified and overcomes the limitations of current approaches by explicitly considering aleatory and epistemic sources of uncertainty modelled using probability boxes. The different effects of stochastic fluctuation of the power demand, imprecision in power grid parameters and uncertainty in the selection of the vulnerability model have been quantified. Spectral graph metrics for vulnerability are computed using different weights and are compared to power-flow-based cascading indices in ranking N-1 line failures and random N-k lines attacks. A rank correlation test is proposed for further comparison of the vulnerability metrics. The IEEE 24 nodes reliability test power network is selected as a representative case study and a detailed discussion of the results and findings is presented.",
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Assessment of power grid vulnerabilities accounting for stochastic loads and model imprecision. / Rocchetta, Roberto; Patelli, Edoardo.

In: Electrical Power and Energy Systems, Vol. 98, 30.06.2018, p. 219-232.

Research output: Contribution to journalArticle

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AB - Vulnerability and robustness are major concerns for future power grids. Malicious attacks and extreme weather conditions have the potential to trigger multiple components outages, cascading failures and large blackouts. Robust contingency identification procedures are necessary to improve power grids resilience and identify critical scenarios. This paper proposes a framework for advanced uncertainty quantification and vulnerability assessment of power grids. The framework allows critical failure scenarios to be identified and overcomes the limitations of current approaches by explicitly considering aleatory and epistemic sources of uncertainty modelled using probability boxes. The different effects of stochastic fluctuation of the power demand, imprecision in power grid parameters and uncertainty in the selection of the vulnerability model have been quantified. Spectral graph metrics for vulnerability are computed using different weights and are compared to power-flow-based cascading indices in ranking N-1 line failures and random N-k lines attacks. A rank correlation test is proposed for further comparison of the vulnerability metrics. The IEEE 24 nodes reliability test power network is selected as a representative case study and a detailed discussion of the results and findings is presented.

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