A high-resolution bilevel skew- t stochastic generator for assessing Saudi Arabia's wind energy resources

Felipe Tagle, Marc G. Genton, Andrew Yip, Suleiman Mostamandi, Georgiy L. Stenchikov, Stefano Castruccio

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Saudi Arabia has recently established its renewable energy targets as part of its “Vision 2030” proposal, which represents a roadmap for reducing the country's dependence on oil over the next decade. This study provides a foundational assessment of the wind resource in Saudi Arabia that serves as a guide for the development of the outlined wind energy component. The assessment is based on a new high-resolution weather simulation of the region generated with the Weather Research and Forecasting (WRF) model. Furthermore, we propose a spatiotemporal stochastic generator of daily wind speeds that assists in characterizing the uncertainty of the energy estimates. The stochastic generator considers a vector autoregressive structure in time, with innovations from a novel biresolution model based on a skew-t distribution with a low-dimensional latent structure. Estimation of the spatial model parameters is performed using a Monte Carlo expectation-maximization (EM) algorithm, which achieves inference over approximately 184 million points and enables to capture the spatial patterns of the higher order moments that typically characterize high-resolution wind fields. Our results identify regions along the western mountain ranges and central escarpments that are suitable for the deployment of wind energy infrastructure. According to the assessment, between 30 and 70% of the national electricity demand could be met by wind energy.
Original languageEnglish (US)
JournalEnvironmetrics
DOIs
StatePublished - May 4 2020

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