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Activity Number: 435 - Volume, Velocity, and Variety in Environmental Statistics: New Perspectives and Methods
Type: Topic-Contributed
Date/Time: Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #317470
Title: ssessing the Reliability of Wind Power Operations under a Changing Climate with a Non-Gaussian Bias Correction
Author(s): Jiachen Zhang* and Stefano Castruccio and Marc Genton and Paola Crippa
Companies: University of Notre Dame and University of Notre Dame and King Abdullah University of Science and Technology and University of Notre Dame
Keywords: Bias correction; Kullback-Leibler divergence; Non-Gaussian proces; Nonstationary model; Spatio-temporal model; Wind energy
Abstract:

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 thirty 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 cluster-wise minimization of the KullbackÔÇôLeibler 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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