Applying Endogenous Learning Models in Energy System Optimization

Jabir Ali Ouassou, Julian Straus, Marte Fodstad, Gunhild Reigstad and Ove Wolfgang
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Jabir Ali Ouassou: SINTEF Energy Research, 7034 Trondheim, Norway
Julian Straus: SINTEF Energy Research, 7034 Trondheim, Norway
Marte Fodstad: SINTEF Energy Research, 7034 Trondheim, Norway
Gunhild Reigstad: SINTEF Energy Research, 7034 Trondheim, Norway
Ove Wolfgang: SINTEF Energy Research, 7034 Trondheim, Norway

Energies, 2021, vol. 14, issue 16, 1-21

Abstract: Conventional energy production based on fossil fuels causes emissions that contribute to global warming. Accurate energy system models are required for a cost-optimal transition to a zero-emission energy system, which is an endeavor that requires a methodical modeling of cost reductions due to technological learning effects. In this review, we summarize common methodologies for modeling technological learning and associated cost reductions via learning curves. This is followed by a literature survey to uncover learning rates for relevant low-carbon technologies required to model future energy systems. The focus is on (i) learning effects in hydrogen production technologies and (ii) the application of endogenous learning in energy system models. Finally, we discuss methodological shortcomings of typical learning curves and possible remedies. One of our main results is an up-to-date overview of learning rates that can be applied in energy system models.

Keywords: learning by doing; learning curve; learning rate; endogenous learning; energy system models; energy system optimization models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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