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dc.contributor.authorZhang, Jiachen
dc.contributor.authorCrippa, Paola
dc.contributor.authorGenton, Marc G.
dc.contributor.authorCastruccio, Stefano
dc.date.accessioned2020-11-09T13:25:48Z
dc.date.available2020-11-09T13:25:48Z
dc.date.issued2020-11-02
dc.identifier.urihttp://hdl.handle.net/10754/665876
dc.description.abstractFacing 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.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2011.01263
dc.rightsArchived with thanks to arXiv
dc.titleAssessing the Reliability of Wind Power Operations under a Changing Climate with a Non-Gaussian Distributional Adjustment
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Group
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Applied and Computational Mathematics and Statistics, University of Notre Dame (USA).
dc.contributor.institutionDepartment of Civid and Environmental Engineering and Geoscience, University of Notre Dame (USA).
dc.identifier.arxivid2011.01263
kaust.personGenton, Marc G.
refterms.dateFOA2020-11-09T13:26:16Z


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