Introducing global learning in regional energy system models. (November 2021)

• Record Type: Journal Article
• Authors: Straus, Julian; Ouassou, Jabir Ali; Wolfgang, Ove; Reigstad, Gunhild Allard
• Is Part Of: Energy strategy reviews. Volume 38(2021)
• Publication Date: 2021-11
• Subjects: Learning-by-doing -- Energy system modelling -- Future cost predictions; Energy policy -- Periodicals; 333.7905
• DOI: 10.1016/j.esr.2021.100763
• Languages: English
• ISSNs: 2211-467X

Abstract

Abstract: 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. Highlights: Methodology for inclusion of global learning effects in regional energy system models. Methodology can be used in both MILP models and models with investment packages. Two approaches presented for accounting for global capacity investments. Important when technologies experience both global and exclusive regional learning.