Technological Learning in the Transition to a Low-Carbon Energy System: Conceptual Issues, Empirical Findings, and Use, in Energy Modeling. Questo articolo non è disponibile.
Lingua: inglese
Editore: Elsevier Science Publishing Co Inc, 2019
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Codice articolo B9780128187623
- Titolo
- Technological Learning in the Transition to a Low-Carbon Energy System: Conceptual Issues, Empirical Findings, and Use, in Energy Modeling
- Autore
- Martin Junginger
- Editore
- Elsevier Science Publishing Co Inc
- Anno di pubblicazione
- 2019
- Condizione
- New
- Rilegatura
- Paperback / softback
- Lingua
- inglese
- ISBN 10
- 012818762X
- ISBN 13
- 9780128187623
- Peso dell'articolo
- 641 grammi
Technological Learning in the Transition to a Low-Carbon Energy System: Conceptual Issues, Empirical Findings, and Use in Energy Modeling quantifies key trends and drivers of energy technologies deployed in the energy transition. It uses the experience curve tool to show how future cost reductions and cumulative deployment of these technologies may shape the future mix of the electricity, heat and transport sectors. The book explores experience curves in detail, including possible pitfalls, and demonstrates how to quantify the 'quality' of experience curves. It discusses how this tool is implemented in models and addresses methodological challenges and solutions.
For each technology, current market trends, past cost reductions and underlying drivers, available experience curves, and future prospects are considered. Electricity, heat and transport sector models are explored in-depth to show how the future deployment of these technologies-and their associated costs-determine whether ambitious decarbonization climate targets can be reached - and at what costs. The book also addresses lessons and recommendations for policymakers, industry and academics, including key technologies requiring further policy support, and what scientific knowledge gaps remain for future research.
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Informazioni sull’autore
Dr. Atse Louwen is a senior researcher at the Institute for Renewable Energy at Eurac Research in Bolzano, Italy. His current work focuses on analysis of PV system performance and reliability using large datasets, machine learning and PV performance and irradiance modelling. Before his current position, he worked as a postdoctoral researcher at Utrecht University’s Copernicus Institute of Sustainable Development. In his position as a postdoc, Atse was a work package leader in the EU H2020 project REFLEX, where he studied experience curves for a large variety of energy technologies, and was responsible for coordinating data collection in a European consortium of private and public research institutes. His wider work includes lifecycle assessment and techno-economic assessment of PV and other renewable energy technologies. He obtained his PhD at Utrecht University in January 2017 for his research on photovoltaic assessment. His PhD research involved the environmental and economic assessment of existing and prospective silicon heterojunction photovoltaic cells and modules, and performance analyses of a variety of commercial and prototype PV modules.
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