A power-flow emulator approach for resilience assessment of repairable power grids subject to weather-induced failures and data deficiency

• Published in: Applied energy Vol. 210; pp. 339 - 350
• Main Authors: Rocchetta, Roberto, Zio, Enrico, Patelli, Edoardo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.01.2018; Elsevier
• Subjects: algorithms; Artificial neural network; case studies; Credal sets; Engineering Sciences; Global sensitivity; Load curtailing; Power grids; probabilistic models; Resilience; Severe weather; stochastic processes; uncertainty; weather; Severe weather; Global sensitivity; Load curtailing; Power grids; Artificial neural network; Credal sets; Resilience
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2017.10.126

•Weather extreme conditions affect power grid failures/repairs and data is scarce.•A stochastic resilience framework is proposed for assess the weather-grid interaction.•A power-flow emulator is constructed to reduce the computational cost.•Imprecise probabilistic methodology is used to tackle lack of data issues.•Most relevant weather-grid factors are identified by the global sensitivity analysis. 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.