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Applying Endogenous Learning Models in Energy System Optimization

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying Endogenous Learning Models in Energy System Optimization," Energies, MDPI, vol. 14(16), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4819-:d:610233
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. 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.

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