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    Assessing the Reliability of Wind Power Operations under a Changing Climate with a Non-Gaussian Distributional Adjustment

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    Type
    Preprint
    Authors
    Zhang, Jiachen
    Crippa, Paola
    Genton, Marc G. cc
    Castruccio, Stefano
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Spatio-Temporal Statistics and Data Analysis Group
    Statistics Program
    Date
    2020-11-02
    Permanent link to this record
    http://hdl.handle.net/10754/665876
    
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    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 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 Kullback-Leibler divergence. Future wind abundance will be determined for Saudi Arabia, a country with recently established plan to develop a portfolio of up to 16 GW of wind energy. Further, we estimate the change in profit 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 $273,000 for the wind energy market for the optimal locations for wind farming in the country.
    Publisher
    arXiv
    arXiv
    2011.01263
    Additional Links
    https://arxiv.org/pdf/2011.01263
    Collections
    Preprints; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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