Tesi etd-09042024-205704 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
GALLETTI, ALBERTO
URN
etd-09042024-205704
Titolo
The learning curve for electrolyser and "green" hydrogen in Europe
Dipartimento
INGEGNERIA DELL'ENERGIA, DEI SISTEMI, DEL TERRITORIO E DELLE COSTRUZIONI
Corso di studi
INGEGNERIA ENERGETICA
Relatori
relatore Prof. Desideri, Umberto
correlatore Dott. Melideo, Daniele
supervisore Dott. Pasimeni, Francesco
correlatore Dott. Melideo, Daniele
supervisore Dott. Pasimeni, Francesco
Parole chiave
- electrolysers
- green hydrogen
- learning curve
- learning investment
Data inizio appello
01/10/2024
Consultabilità
Completa
Riassunto
Electrolysers, as one of the key green technologies, could play a crucial role in the decarbonisation of many sectors. This research focuses on analysing and quantifying the technological level of electrolysers and green hydrogen in Europe using the learning curve. The learning curve theory is based on the idea that the more a person or company performs a task, the more efficiently they can complete it in the future, thus reducing costs. Implementing learning curves within energy systems is crucial for forecasting cost trajectories of new technologies and establishing their competitiveness. European energy targets and electrolyser cost projection are used to assess the financial efforts needed. As an outcome policy recommendations are provided to further develop a hydrogen economy.
The results involve different type of learning curves to express each trend. With the ad-hoc dataset developed, it has been possible to evaluate the influence of factors such as technology, size, project type and location, on electrolyser technological progress. The learning rates obtained are 15% for alkaline electrolysers, 27% for proton-exchange membrane electrolysers and 23% for solid oxide electrolysers. Cost reduction from scaling up is more evident in PEM and SOE technologies than in AEL. The analysis predicts that investments of about 50 billion € are needed annually to achieve European capacity targets by 2030.
The results involve different type of learning curves to express each trend. With the ad-hoc dataset developed, it has been possible to evaluate the influence of factors such as technology, size, project type and location, on electrolyser technological progress. The learning rates obtained are 15% for alkaline electrolysers, 27% for proton-exchange membrane electrolysers and 23% for solid oxide electrolysers. Cost reduction from scaling up is more evident in PEM and SOE technologies than in AEL. The analysis predicts that investments of about 50 billion € are needed annually to achieve European capacity targets by 2030.
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