A power-flow emulator approach for resilience assessment of repairable power grids subject to weather-induced failures and data deficiency
Rocchetta, Roberto and Zio, Enrico and Patelli, Edoardo (2018) A power-flow emulator approach for resilience assessment of repairable power grids subject to weather-induced failures and data deficiency. Applied Energy, 210. pp. 339-350. ISSN 0306-2619
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Abstract
A generalised uncertainty quantification framework for resilience assessment of weather-coupled, repairable power grids is presented. The framework can be used to efficiently quantify both epistemic and aleatory uncertainty affecting grid-related and weather-related factors. The power grid simulator has been specifically designed to model interactions between severe weather conditions and grid dynamic states and behaviours, such as weather-induced failures or delays in components replacements. A resilience index is computed by adopting a novel algorithm which exploits a vectorised emulator of the power-flow solver to reduce the computational efforts. The resilience stochastic modelling framework is embedded into a non-intrusive generalised stochastic framework, which enables the analyst to quantify the effect of parameters imprecision. A modified version of the IEEE 24 nodes reliability test system has been used as representative case study. The surrogate-based model and the Power-Flow-based model are compared, and the results show similar accuracy but enhanced efficiency of the former. Global sensitivity of the resilience index to increasing imprecision in parameters of the probabilistic model has been analysed. The relevance of specific weather/grid uncertain factors is highlighted by global sensitivity analysis and the importance of dealing with imprecision in the information clearly emerges.
ORCID iDs
Rocchetta, Roberto, Zio, Enrico and Patelli, Edoardo
ORCID: https://orcid.org/0000-0002-5007-7247;
Item type: Article ID code: 72118 Dates: DateEvent15 January 2018Published9 November 2017Published Online31 October 2017AcceptedKeywords: artificial neural network, credal sets, global sensitivity, load curtailing, power grids, resilience, severe weather, Electrical engineering. Electronics Nuclear engineering, Building and Construction, Energy(all), Mechanical Engineering, Management, Monitoring, Policy and Law Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Civil and Environmental Engineering Depositing user: Pure Administrator Date deposited: 21 Apr 2020 11:55 Last modified: 19 Mar 2021 03:03 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/72118
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