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A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling

We develop and present a k-nearest neighbor space-time simulator that accounts for the spatiotemporal dependence in high-dimensional hydroclimatic fields (e.g., wind and solar) and can simulate synthetic realizations of arbitrary length. We illustrate how this statistical simulation tool can be used...

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Detalles Bibliográficos
Autores principales: Amonkar, Yash, Farnham, David J., Lall, Upmanu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9058912/
https://www.ncbi.nlm.nih.gov/pubmed/35510191
http://dx.doi.org/10.1016/j.patter.2022.100454
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author Amonkar, Yash
Farnham, David J.
Lall, Upmanu
author_facet Amonkar, Yash
Farnham, David J.
Lall, Upmanu
author_sort Amonkar, Yash
collection PubMed
description We develop and present a k-nearest neighbor space-time simulator that accounts for the spatiotemporal dependence in high-dimensional hydroclimatic fields (e.g., wind and solar) and can simulate synthetic realizations of arbitrary length. We illustrate how this statistical simulation tool can be used in the context of regional power system planning under a scenario of high reliance on wind and solar generation and when long historical records of wind and solar power generation potential are not available. We show how our simulation model can be used to assess the probability distribution of the severity and duration of energy “droughts” at the network scale that need to be managed by long-duration storage or alternate energy sources. We present this estimation of supply-side shortages for the Texas Interconnection.
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spelling pubmed-90589122022-05-03 A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling Amonkar, Yash Farnham, David J. Lall, Upmanu Patterns (N Y) Article We develop and present a k-nearest neighbor space-time simulator that accounts for the spatiotemporal dependence in high-dimensional hydroclimatic fields (e.g., wind and solar) and can simulate synthetic realizations of arbitrary length. We illustrate how this statistical simulation tool can be used in the context of regional power system planning under a scenario of high reliance on wind and solar generation and when long historical records of wind and solar power generation potential are not available. We show how our simulation model can be used to assess the probability distribution of the severity and duration of energy “droughts” at the network scale that need to be managed by long-duration storage or alternate energy sources. We present this estimation of supply-side shortages for the Texas Interconnection. Elsevier 2022-02-28 /pmc/articles/PMC9058912/ /pubmed/35510191 http://dx.doi.org/10.1016/j.patter.2022.100454 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Amonkar, Yash
Farnham, David J.
Lall, Upmanu
A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling
title A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling
title_full A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling
title_fullStr A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling
title_full_unstemmed A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling
title_short A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling
title_sort k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9058912/
https://www.ncbi.nlm.nih.gov/pubmed/35510191
http://dx.doi.org/10.1016/j.patter.2022.100454
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