Assessing the risk of disruption of wind turbine operations in Saudi Arabia using Bayesian spatial extremes
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Spatio-Temporal Statistics and Data Analysis Group
Online Publication Date2020-07-22
Print Publication Date2021-06
Embargo End Date2021-07-22
Permanent link to this recordhttp://hdl.handle.net/10754/664449
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AbstractSaudi Arabia has been seeking to reduce its dependence on oil by diversifying its energy portfolio, including the largely underused energy potential from wind. However, 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 non-stationarity over the country. We account for the spatial dependence with a multivariate intrinsic autoregressive prior at the latent Gaussian process level. 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 hierarchical 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 wind speed of wind turbines within their lifetime. The probability maps show 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.
CitationChen, W., Castruccio, S., & Genton, M. G. (2020). Assessing the risk of disruption of wind turbine operations in Saudi Arabia using Bayesian spatial extremes. Extremes. doi:10.1007/s10687-020-00384-1
SponsorsThis publication is based on research supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2018-CRG7-3742 and in part by the Center of Excellence for NEOM Research at KAUST. We are grateful to Professor Georgiy Stenchikov’s group, the Atmospheric and Climate Modeling group at KAUST, for producing and providing the high-resolution WRF dataset. Many thanks also to Professor Daniel Cooley for providing the codes used in the article by Cooley and Sain (2010).