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Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling

Background: Energy system optimisation models (ESOMs) are commonly used to support long-term planning at national, regional, or continental scales. The importance of recognising uncertainty in energy system modelling is regularly commented on but there is little practical guidance on how to best inc...

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Autores principales: Usher, William, Barnes, Trevor, Moksnes, Nandi, Niet, Taco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10445882/
https://www.ncbi.nlm.nih.gov/pubmed/37645505
http://dx.doi.org/10.12688/openreseurope.15461.1
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author Usher, William
Barnes, Trevor
Moksnes, Nandi
Niet, Taco
author_facet Usher, William
Barnes, Trevor
Moksnes, Nandi
Niet, Taco
author_sort Usher, William
collection PubMed
description Background: Energy system optimisation models (ESOMs) are commonly used to support long-term planning at national, regional, or continental scales. The importance of recognising uncertainty in energy system modelling is regularly commented on but there is little practical guidance on how to best incorporate existing techniques, such as global sensitivity analysis, despite some good applications in the literature. Methods: In this paper, we provide comprehensive guidelines for conducting a global sensitivity analysis of an ESOM, aiming to remove barriers to adopting this approach. With a pedagogical intent, we begin by exploring why you should conduct a global sensitivity analysis. We then describe how to implement a global sensitivity analysis using the Morris method in an ESOM using a sequence of simple illustrative models built using the Open Source energy Modelling System (OSeMOSYS) framework, followed by a realistic example. Results: Results show that the global sensitivity analysis identifies influential parameters that drive results in the simple and realistic models, and identifies uninfluential parameters which can be ignored or fixed. We show that global sensitivity analysis can be applied to ESOMs with relative ease using freely available open-source tools. The results replicate the findings of best-practice studies from the field demonstrating the importance of including all parameters in the analysis and avoiding a narrow focus on particular parameters such as technology costs. Conclusions: The results highlight the benefits of performing a global sensitivity analysis for the design of energy system optimisation scenarios. We discuss how the results can be interpreted and used to enhance the transparency and rigour of energy system modelling studies.
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spelling pubmed-104458822023-08-29 Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling Usher, William Barnes, Trevor Moksnes, Nandi Niet, Taco Open Res Eur Research Article Background: Energy system optimisation models (ESOMs) are commonly used to support long-term planning at national, regional, or continental scales. The importance of recognising uncertainty in energy system modelling is regularly commented on but there is little practical guidance on how to best incorporate existing techniques, such as global sensitivity analysis, despite some good applications in the literature. Methods: In this paper, we provide comprehensive guidelines for conducting a global sensitivity analysis of an ESOM, aiming to remove barriers to adopting this approach. With a pedagogical intent, we begin by exploring why you should conduct a global sensitivity analysis. We then describe how to implement a global sensitivity analysis using the Morris method in an ESOM using a sequence of simple illustrative models built using the Open Source energy Modelling System (OSeMOSYS) framework, followed by a realistic example. Results: Results show that the global sensitivity analysis identifies influential parameters that drive results in the simple and realistic models, and identifies uninfluential parameters which can be ignored or fixed. We show that global sensitivity analysis can be applied to ESOMs with relative ease using freely available open-source tools. The results replicate the findings of best-practice studies from the field demonstrating the importance of including all parameters in the analysis and avoiding a narrow focus on particular parameters such as technology costs. Conclusions: The results highlight the benefits of performing a global sensitivity analysis for the design of energy system optimisation scenarios. We discuss how the results can be interpreted and used to enhance the transparency and rigour of energy system modelling studies. F1000 Research Limited 2023-02-13 /pmc/articles/PMC10445882/ /pubmed/37645505 http://dx.doi.org/10.12688/openreseurope.15461.1 Text en Copyright: © 2023 Usher W et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Usher, William
Barnes, Trevor
Moksnes, Nandi
Niet, Taco
Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling
title Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling
title_full Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling
title_fullStr Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling
title_full_unstemmed Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling
title_short Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling
title_sort global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10445882/
https://www.ncbi.nlm.nih.gov/pubmed/37645505
http://dx.doi.org/10.12688/openreseurope.15461.1
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