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Applying endogenous learning models in energy system optimization

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  • Jabir Ali Ouassou
  • Julian Straus
  • Marte Fodstad
  • Gunhild Reigstad
  • Ove Wolfgang

Abstract

Conventional energy production based on fossil fuels causes emissions which contribute to global warming. Accurate energy system models are required for a cost-optimal transition to a zero-emission energy system, an endeavor that requires an accurate modeling of cost reductions due to technological learning effects. In this review, we summarize common methodologies for modeling technological learning and associated cost reductions. The focus is on learning effects in hydrogen production technologies due to their importance in a low-carbon energy system, as well as the application of endogenous learning in energy system models. Finally, we present an overview of the learning rates of relevant low-carbon technologies required to model future energy systems.

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  • Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying endogenous learning models in energy system optimization," Papers 2106.06373, arXiv.org.
    • Handle: RePEc:arx:papers:2106.06373
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    References listed on IDEAS

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    Cited by:

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    2. Seck, Gondia S. & Hache, Emmanuel & Sabathier, Jerome & Guedes, Fernanda & Reigstad, Gunhild A. & Straus, Julian & Wolfgang, Ove & Ouassou, Jabir A. & Askeland, Magnus & Hjorth, Ida & Skjelbred, Hans , 2022. "Hydrogen and the decarbonization of the energy system in europe in 2050: A detailed model-based analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    3. Heggarty, Thomas & Bourmaud, Jean-Yves & Girard, Robin & Kariniotakis, Georges, 2024. "Assessing the relative impacts of maximum investment rate and temporal detail in capacity expansion models applied to power systems," Energy, Elsevier, vol. 290(C).
    4. Mathias Mier & Jacqueline Adelowo & Valeriya Azarova, 2022. "Endogenous Technological Change in Power Markets," ifo Working Paper Series 373, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    5. Kosugi, Takanobu, 2023. "Learning rate matters: Reexamining optimal power expansion planning with endogenized technological experience curves," Energy, Elsevier, vol. 283(C).

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