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Script for resilience analysis in energy systems: Python programming code and partial associated data of four cogeneration plants

This article presents a script developed to evaluate resilience in energy systems. The files corresponding to the system description, simulation and metrics calculation are included in the dataset, as well as partial raw and processed data from the associated paper [1]. The model was developed focus...

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Detalles Bibliográficos
Autores principales: da Silva, Fellipe Sartori, Matelli, José Alexandre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050704/
https://www.ncbi.nlm.nih.gov/pubmed/33889691
http://dx.doi.org/10.1016/j.dib.2021.106986
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author da Silva, Fellipe Sartori
Matelli, José Alexandre
author_facet da Silva, Fellipe Sartori
Matelli, José Alexandre
author_sort da Silva, Fellipe Sartori
collection PubMed
description This article presents a script developed to evaluate resilience in energy systems. The files corresponding to the system description, simulation and metrics calculation are included in the dataset, as well as partial raw and processed data from the associated paper [1]. The model was developed focusing on covering all cogeneration and power plants, being the user responsible for describing the system, simulating and processing the data in the files here available. In the present work, the steps for the simulation are presented in detail, which contributes to other researchers that are interested in either adopting resilience as one of the possible system analyses or understanding the processes of metrics calculation of the associated paper.
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spelling pubmed-80507042021-04-21 Script for resilience analysis in energy systems: Python programming code and partial associated data of four cogeneration plants da Silva, Fellipe Sartori Matelli, José Alexandre Data Brief Data Article This article presents a script developed to evaluate resilience in energy systems. The files corresponding to the system description, simulation and metrics calculation are included in the dataset, as well as partial raw and processed data from the associated paper [1]. The model was developed focusing on covering all cogeneration and power plants, being the user responsible for describing the system, simulating and processing the data in the files here available. In the present work, the steps for the simulation are presented in detail, which contributes to other researchers that are interested in either adopting resilience as one of the possible system analyses or understanding the processes of metrics calculation of the associated paper. Elsevier 2021-03-23 /pmc/articles/PMC8050704/ /pubmed/33889691 http://dx.doi.org/10.1016/j.dib.2021.106986 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
da Silva, Fellipe Sartori
Matelli, José Alexandre
Script for resilience analysis in energy systems: Python programming code and partial associated data of four cogeneration plants
title Script for resilience analysis in energy systems: Python programming code and partial associated data of four cogeneration plants
title_full Script for resilience analysis in energy systems: Python programming code and partial associated data of four cogeneration plants
title_fullStr Script for resilience analysis in energy systems: Python programming code and partial associated data of four cogeneration plants
title_full_unstemmed Script for resilience analysis in energy systems: Python programming code and partial associated data of four cogeneration plants
title_short Script for resilience analysis in energy systems: Python programming code and partial associated data of four cogeneration plants
title_sort script for resilience analysis in energy systems: python programming code and partial associated data of four cogeneration plants
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050704/
https://www.ncbi.nlm.nih.gov/pubmed/33889691
http://dx.doi.org/10.1016/j.dib.2021.106986
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