Technological Learning in the Transition to a Low-Carbon Energy System

Prof. Dr. Martin Junginger leads the biobased economy research cluster of Utrecht University’s Energy & Resources group of the Copernicus Institute of Sustainable Development. Martin’s work encompasses analysis of (bio)energy systems, including technology assessment and experience curve analyses of more than a dozen technologies. His wider work includes research on biomass potentials and resource assessments in both developed and developing countries, related sustainability assessment of biomass production for energy and materials (including GHG emissions and other environmental impacts), international bioenergy trade and policy evaluation. He (co-) published over 90 titles in peer-reviewed scientific journals. He is the editor of several books on technological learning in the energy sector, international bioenergy trade and mobilisation of biomass from boreal and temperate forests, and bioenergy section editor of the journal Energies. 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.

• Part I1. Introduction2. The Experience Curve: Concept, History, Methods and Issues3. Implementation of Experience Curves in Energy energy-system models4. Application of experience curves and learning to other fieldsPart II Case Studies5. Photovoltaic solar energy6. Onshore wind energy7. Offshore wind energy8. Grid-scale energy storage9. Electric Vehicles10. Power to gas (H2): alkaline electrolysis11. Heating and cooling in the built environment12. Concentrating solar power13. Light-emitting diode lighting productsPart III Application of Experience Curves in Modeling14. Experience Curves in Energy Models by Lessons Learned from the REFLEX project15. Global electric car market deployment considering endogenous battery price developmentPart IV Final words16. Synthesis, conclusions, and recommendations