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.
- Provides a comprehensive overview of trends and drivers for major energy technologies expected to play a role in the energy transition
- Delivers data on cost trends, helping readers gain insights on how competitive energy technologies may become, and why
- Reviews the use of learning curves in environmental impacts for lifecycle assessments and energy modeling
- Features social learning for cost modeling and technology diffusion, including where consumer preferences play a major role
Early career researchers studying energy technologies. Energy system modelers. Energy economists. Utilities, technology manufacturers, grid operators. Policy makers involved in energy policy
2. The Experience Curve: Concept, History, Methods and Issues
3. Implementation of Experience Curves in Energy and Climate Models
4. Novel approaches in applications of experience curves and technological learning
Part II Case Studies
5. Photovoltaic Modules, Balance-of-System Components and Systems
6. Onshore wind
7. Offshore wind
8. Technological learning for grid-scale energy storage
9. Battery Electric Vehicles
10. Hydrogen Electrolysis
11. Residential Heating
12. Concentrated solar power
13. Experience Curves for LED lighting
Part III Application of Experience Curves in Modeling
14. Experience Curves in Energy Models – Lessons Learned in REFLEX
15. Global EV market development dependent on battery prices and learning curves simulated by model coupling
Part IV Final words
- No. of pages:
- © Academic Press 2020
- 1st February 2020
- Academic Press
- Paperback ISBN:
Prof.Dr. Martin Junginger leads the biobased economy research cluster of Utrecht University’s Energy & Resources division. He is also the leader of the IEA Bioenergy Task 40 on Sustainable International Bioenergy Trade, currently including 9 countries. Martin’s work encompasses various types of analysis of (bio)energy systems, including technology assessment and experience curve analyses of more than a dozen technologies. His wider work includes 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), and policy evaluation. He (co-) published over 70 titles in peer-reviewed scientific journals, and numerous project reports, book chapters and conference proceedings. He is the editor of three books on technological learning in the energy sector, international bioenergy trade and mobilisation of biomass from boreal and temperate forests.
Professor of Energy and Resources, Utrecht University, Utrecht, The Netherlands
Dr. Atse Louwen is a postdoctoral researcher at Utrecht University’s Copernicus Institute of Sustainable Development. He obtained his PhD in January 2017 for his research on photovoltaic assessment. His PhD research involves 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. After his PhD, the scope of research has broadened to include analysis of technological learning in the energy sector. Atse is a work package leader within the EU H2020 project REFLEX, for which he coordinated and performed data collection on a large variety of energy technologies to devise experience curves.
Researcher, Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands