A Framework for Incorporation of Infeed Uncertainty in Power System Risk-Based Security Assessment

Martijn de Jong, Georgios Papaefthymiou, Peter Palensky

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In this paper, a risk-based security assessment methodology is presented, which allows the assessment of operational security of a power system’s future state under uncertainty deriving from varying topology scenarios (contingencies) and forecast errors (loads and renewable infeeds). The methodology models input uncertaintywith a copula function-based Monte–Carlo (MC) framework. Furthermore, it provides the highest level of accuracy on initiating causes of failures through an AC power flow (AC PF) framework. Finally, it achieves speed in solution by the combination of twomeasures of risk. A fast screening tool, based on severity functions, allows us to quickly screen the system for the most severe states. A detailed analysis tool, based on an AC optimal power flow (AC OPF) framework and the notion of lost load, provides additional valuable information, including remedial actions to steer away from severe system states. This paper presents results from the application of the methodology proving the necessity of such a framework. It is shown that not taking into account stochastic
dependence through a proper MC setup seriously underestimates system risk and that an AC framework is needed, as voltage deviations are shown to often be initiators of system collapse.
Original languageEnglish
Pages (from-to)613-621
Number of pages9
JournalIEEE Transactions on Power Systems
Issue number1
Publication statusPublished - 2018

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • AC OPF
  • Monte-Carlo simulation
  • RBSA
  • copula theory
  • correlation
  • severity functions
  • stochastic dependence


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