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Power System Resilience to Extreme Weather: Fragility Modelling, Probabilistic Impact Assessment, and Adaptation Measures

Lookup NU author(s): Cassie Pickering, Dr Sean Wilkinson, Professor Richard Dawson

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Historical electrical disturbances highlight the impact of extreme weather on power system resilience. Even though the occurrence of such events is rare, the severity of their potential impact calls for (i) developing suitable resilience assessment techniques to capture their impacts and (ii) assessing relevant strategies to mitigate them. In this paper, a fragility model of the transmission system is built for mapping the real-time impact of severe weather, with focus on wind events, on components’ failure probabilities. A probabilistic multi-temporal and multi-regional resilience assessment model, based on optimal power flow and sequential Monte Carlo simulation and coupled with the component fragility model, is then introduced, allowing the assessment of the spatiotemporal impact of a weather front moving across a transmission network. Different risk-based resilience enhancement (or “adaptation”) measures are evaluated, which are driven by the resilience achievement worth (RAW) index of the individual transmission components. The methodology is demonstrated using a reduced version of the Great Britain’s system. The results demonstrate how, by using a mix of infrastructure and operational indices, it is possible to effectively quantify system resilience to extreme weather, identify and prioritize critical network sections, and assess the technical benefits of different adaptation measures to enhance resilience.


Publication metadata

Author(s): Panteli M, Pickering C, Wilkinson S, Dawson R, Mancarella P

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Power Systems

Year: 2017

Volume: 32

Issue: 5

Pages: 3747-3757

Print publication date: 01/09/2017

Online publication date: 29/12/2016

Acceptance date: 26/06/2016

Date deposited: 31/10/2016

ISSN (print): 0885-8950

ISSN (electronic): 1558-0679

Publisher: IEEE

URL: http://dx.doi.org/10.1109/TPWRS.2016.2641463

DOI: 10.1109/TPWRS.2016.2641463

Data Source Location: http://dx.doi.org/10.17634/102759-2


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