Introducing global learning in regional energy system models

Energy system models are increasingly used to identify climate change mitigation measures. Crucially, such models require future cost estimates, which depend on both technological advancement and investments. In global models, which encompass the whole world, this can be implemented via learning cur...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Julian Straus, Jabir Ali Ouassou, Ove Wolfgang, Gunhild Allard Reigstad
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Learning-by-doing Energy system modelling Future cost predictions Energy industries. Energy policy. Fuel trade HD9502-9502.5
Acceso en línea:	https://doaj.org/article/78d06ec883ad4e5b9ca7a0f1dc03f9d3
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:78d06ec883ad4e5b9ca7a0f1dc03f9d3
record_format	dspace
spelling	oai:doaj.org-article:78d06ec883ad4e5b9ca7a0f1dc03f9d32021-11-22T04:24:40ZIntroducing global learning in regional energy system models2211-467X10.1016/j.esr.2021.100763https://doaj.org/article/78d06ec883ad4e5b9ca7a0f1dc03f9d32021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2211467X21001474https://doaj.org/toc/2211-467XEnergy system models are increasingly used to identify climate change mitigation measures. Crucially, such models require future cost estimates, which depend on both technological advancement and investments. In global models, which encompass the whole world, this can be implemented via learning curves. In regional models, which typically span a country or continent, it can however be challenging to reconcile global cost reductions with local investments. We propose a new approach to account for global cost developments in endogenous regional energy system models. Moreover, we show how this approach can be implemented using either a MILP formulation or discretized investment packages. Finally, we demonstrate and compare the proposed approach of implementing cost reductions to exclusively exogenous and endogenous approaches in a simple case study.Julian StrausJabir Ali OuassouOve WolfgangGunhild Allard ReigstadElsevierarticleLearning-by-doingEnergy system modellingFuture cost predictionsEnergy industries. Energy policy. Fuel tradeHD9502-9502.5ENEnergy Strategy Reviews, Vol 38, Iss , Pp 100763- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Learning-by-doing Energy system modelling Future cost predictions Energy industries. Energy policy. Fuel trade HD9502-9502.5
spellingShingle	Learning-by-doing Energy system modelling Future cost predictions Energy industries. Energy policy. Fuel trade HD9502-9502.5 Julian Straus Jabir Ali Ouassou Ove Wolfgang Gunhild Allard Reigstad Introducing global learning in regional energy system models
description	Energy system models are increasingly used to identify climate change mitigation measures. Crucially, such models require future cost estimates, which depend on both technological advancement and investments. In global models, which encompass the whole world, this can be implemented via learning curves. In regional models, which typically span a country or continent, it can however be challenging to reconcile global cost reductions with local investments. We propose a new approach to account for global cost developments in endogenous regional energy system models. Moreover, we show how this approach can be implemented using either a MILP formulation or discretized investment packages. Finally, we demonstrate and compare the proposed approach of implementing cost reductions to exclusively exogenous and endogenous approaches in a simple case study.
format	article
author	Julian Straus Jabir Ali Ouassou Ove Wolfgang Gunhild Allard Reigstad
author_facet	Julian Straus Jabir Ali Ouassou Ove Wolfgang Gunhild Allard Reigstad
author_sort	Julian Straus
title	Introducing global learning in regional energy system models
title_short	Introducing global learning in regional energy system models
title_full	Introducing global learning in regional energy system models
title_fullStr	Introducing global learning in regional energy system models
title_full_unstemmed	Introducing global learning in regional energy system models
title_sort	introducing global learning in regional energy system models
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/78d06ec883ad4e5b9ca7a0f1dc03f9d3
work_keys_str_mv	AT julianstraus introducinggloballearninginregionalenergysystemmodels AT jabiraliouassou introducinggloballearninginregionalenergysystemmodels AT ovewolfgang introducinggloballearninginregionalenergysystemmodels AT gunhildallardreigstad introducinggloballearninginregionalenergysystemmodels
_version_	1718418229715533824

Introducing global learning in regional energy system models

Ejemplares similares