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Activity Number: 36 - ENVR Student Paper Awards
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics and the Environment
Abstract #311076
Title: Assessing the Risk of Disruption of Wind Turbine Operations in Saudi Arabia Using Bayesian Spatial Extremes
Author(s): Wanfang Chen* and Stefano Castruccio and Marc Genton
Companies: KAUST and University of Notre Dame and KAUST
Keywords: Bayesian hierarchical modeling; Return levels; Spatial extremes; Wind energy; Wind extremes; Wind turbines

Extreme winds can possibly disrupt the wind turbine operations, thus preventing the stable and continuous production of wind energy. In this study, we assess the risk of disruptions of wind turbine operations, based on return levels with a hierarchical spatial extreme modeling approach for wind speeds in Saudi Arabia. Using a unique Weather Research and Forecasting dataset, we provide the first high-resolution risk assessment of wind extremes under spatial dependence and non-stationarity over the country. The computational efficiency is greatly improved by parallel computing on subregions from spatial clustering, and the maps are smoothed by fitting the model to cluster neighbors. Under the Bayesian framework, we measure the uncertainty of return levels from the posterior Markov chain Monto Carlo samples, and produce probability maps of return levels exceeding the cut-out speed of turbines within their lifetime, showing that locations in the South of Saudi Arabia and near the Red Sea and the Persian Gulf are at very high risk of disruption of wind turbine operations. We further identify low risk and high wind locations to launch wind farms for persistently high energy production.

Authors who are presenting talks have a * after their name.

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