Arbeitspapier

Endogenous technological change in power markets

Decarbonization requires the transformation of power markets towards renewable energies and investment costs are decisive for the deployed technologies. Exogenous cost assumptions cannot fully reflect the underlying dynamics of technological change. We implement divergent learning-by-doing specifications in a multi-region power market model by means of mixed-integer programming to approximate non-linear investment costs. We consider European learning, regional learning, and three different ways to depreciate experience stocks within the European learning metric: perfect recall, continuous forgetting, and lifetime forgetting. Learning generally yields earlier investments. European learning fosters the deployment of solar PV and wind onshore, whereas regional learning leads to more wind offshore deployment in regions with high wind offshore quality. Perfect recall fosters solar PV and wind onshore expansion, whereas lifetime forgetting fosters wind offshore usage. Results for continuous forgetting are in between those of perfect recall and lifetime forgetting. Generally, learning leads to the earlier deployment of learning technologies but regional patterns are different across learning specifications and also deviate significantly from this general pattern of preponing investments.

Sprache
Englisch

Erschienen in
Series: ifo Working Paper ; No. 373

Klassifikation
Wirtschaft
Optimization Techniques; Programming Models; Dynamic Analysis
Taxation and Subsidies: Efficiency; Optimal Taxation
Taxation and Subsidies: Externalities; Redistributive Effects; Environmental Taxes and Subsidies
Project Evaluation; Social Discount Rate
Electric Utilities
Thema
endogenous technological change
learning-by-doing
forgetting
renewable energies
power market model
decarbonization

Ereignis
Geistige Schöpfung
(wer)
Mier, Mathias
Adelowo, Jacqueline
Azarova, Valeriya
Ereignis
Veröffentlichung
(wer)
ifo Institute - Leibniz Institute for Economic Research at the University of Munich
(wo)
Munich
(wann)
2022

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Mier, Mathias
  • Adelowo, Jacqueline
  • Azarova, Valeriya
  • ifo Institute - Leibniz Institute for Economic Research at the University of Munich

Entstanden

  • 2022

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