December 2021 Assessing the reliability of wind power operations under a changing climate with a non-Gaussian bias correction
Jiachen Zhang, Paola Crippa, Marc G. Genton, Stefano Castruccio
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Ann. Appl. Stat. 15(4): 1831-1849 (December 2021). DOI: 10.1214/21-AOAS1460


Facing increasing societal and economic pressure, many countries have established strategies to develop renewable energy portfolios whose penetration in the market can alleviate the dependence on fossil fuels. In the case of wind, there is a fundamental question related to the resilience and hence profitability of future wind farms to a changing climate, given that current wind turbines have lifespans of up to 30 years. In this work we develop a new non-Gaussian method to adjust assimilated observational data to simulations and to estimate future wind, predicated on a trans-Gaussian transformation and a clusterwise minimization of the Kullback–Leibler divergence. Future winds abundance will be determined for Saudi Arabia, a country with a recently established plan to develop a portfolio of up to 16 GW of wind energy. Further, we estimate the change in profits over future decades using additional high-resolution simulations, an improved method for vertical wind extrapolation and power curves from a collection of popular wind turbines. We find an overall increase in daily profit of $272,000 for the wind energy market for the optimal locations for wind farming in the country.


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Jiachen Zhang. Paola Crippa. Marc G. Genton. Stefano Castruccio. "Assessing the reliability of wind power operations under a changing climate with a non-Gaussian bias correction." Ann. Appl. Stat. 15 (4) 1831 - 1849, December 2021.


Received: 1 October 2020; Revised: 1 March 2021; Published: December 2021
First available in Project Euclid: 21 December 2021

MathSciNet: MR4355078
zbMATH: 1498.62299
Digital Object Identifier: 10.1214/21-AOAS1460

Keywords: bias correction , Kullback–Leibler divergence , non-Gaussian process , nonstationary model , spatiotemporal model , wind energy

Rights: Copyright © 2021 Institute of Mathematical Statistics


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Vol.15 • No. 4 • December 2021
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