Technological Learning in the Transition to a Low-Carbon Energy System: Conceptual Issues, Empirical Findings, and Use, in Energy Modeling

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Technological Learning in the Transition to a Low-Carbon Energy System: Conceptual Issues, Empirical Findings,
and Use, in Energy Modeling
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Contents
	Part I Introduction and methods1
	Part II Case studies63
	Part III Application of experience curves in modeling257
	Part IV Final words307
List of contributors
Part I: Introduction and methods
1 Introduction
	1.1 Introduction
		1.1.1 Background and rationale
		1.1.2 Objectives and structure
2 The experience curve: concept, history, methods, and issues
	2.1 Introduction
	2.2 Learning and experience curves
		2.2.1 The single factor experience curve
		2.2.2 Two and multifactor experience curves
	2.3 Empirical data collection for experience curves
		2.3.1 Cost versus price data
		2.3.2 Functional unit
		2.3.3 Data harmonization
		2.3.4 Common issues with data collection
	2.4 Estimation of experience curve parameters
		2.4.1 Regression method: linear or nonlinear
		2.4.2 Determining fit accuracy and experience curve parameter errors
	2.5 Applications of experience curves
		2.5.1 Direct applications of experience curves for policy makers
		2.5.2 Indirect application of experience curves for policy: energy and integrated modeling
	2.6 Main issues and drawbacks of experience curves
		2.6.1 Nonconstant learning rates and learning rate uncertainty
		2.6.2 Technology systems and components
		2.6.3 No explanation for cost reductions and causality
		2.6.4 Radical innovations
		2.6.5 Technology quality
	References
3 Implementation of experience curves in energy-system models
	3.1 Introduction
	3.2 Energy modeling approaches: bottom-up versus top down
		3.2.1 Integrated assessment models
	3.3 Implementation of experience curves in energy models
		3.3.1 Technical discussion on model implementation of experience curves
	3.4 Practical implications in different types of models
		3.4.1 Endogenous technological learning
		3.4.2 Exogenous technological learning
	3.5 Issues, caveats, and drawbacks of experience-curve implementation in energy models
		3.5.1 Geographical scope of model
		3.5.2 Technological learning in an energy modeling system
		3.5.3 Technical issues
		3.5.4 Technology deployment constrained by modeling scenario and policy targets
		3.5.5 Other issues
	3.6 Concluding remarks
	References
4 Application of experience curves and learning to other fields
	4.1 Introduction
	4.2 Energy experience curves
		4.2.1 Experience curves for energy efficiency in industrial processes
		4.2.2 Experience curves for energy efficiency in energy demand technologies
	4.3 Experience curves for environmental impacts and life cycle assessment
	4.4 Social learning
		4.4.1 Social learning in the transport sector
		4.4.2 Modeling risk premiums and social and technological learning
			4.4.2.1 Model setup
			4.4.2.2 Scenario framework
		4.4.3 Model results
			4.4.3.1 Technological learning scenarios
			4.4.3.2 Social learning and technological learning scenarios
	4.5 Conclusion
	References
Part II: Case studies
5 Photovoltaic solar energy
	5.1 Introduction
	5.2 Methodological issues and data availability
	5.3 Results
		5.3.1 State-of-the-art experience curves for photovoltaic modules, systems, and balance of system components
		5.3.2 Multifactor learning curves for silicon photovoltaic module prices
		5.3.3 Analysis and quantification of observed cost reductions
	5.4 Future outlook for prices of photovoltaic modules, systems, and balance of system components
	5.5 Conclusions and recommendations for science, policy, and business
	References
6 Onshore wind energy
	6.1 Introduction
	6.2 Market development
	6.3 Trends in capital expenditures and levelized cost of energy
	6.4 Experience curves for onshore wind energy
	6.5 Data collection and methodological issues
	6.6 Discussion, conclusion, and future outlook
	References
	Further reading
7 Offshore wind energy
	7.1 Introduction
	7.2 Methodological issues and data availability
	7.3 Results
		7.3.1 Experience curve analyses
		7.3.2 Main drivers behind cost and price changes
		7.3.3 Future outlook
		7.3.4 Conclusions and recommendations for science, policy, and business
	References
8 Grid-scale energy storage
	8.1 Introduction
	8.2 Methodological issues and data availability for technological learning
	8.3 Results
		8.3.1 One-factor learning curves
		8.3.2 Multifactor learning curves
	8.4 Future outlook
	8.5 Conclusions and recommendations for science, policy, and business
	References
	Further reading
9 Electric vehicles
	9.1 Introduction
		9.1.1 Description of technology
		9.1.2 Market development
	9.2 Methodological issues and data availability
		9.2.1 Data collection and methodological issues
	9.3 Results
	9.4 Future outlook
	9.5 Conclusions and recommendations for science, policy, and business
	References
	Further reading
10 Power to gas (H2): alkaline electrolysis
	10.1 Introduction
		10.1.1 Global hydrogen capacity
	10.2 Data availability and methodological issues
	10.3 Results
		10.3.1 Experience curve
		10.3.2 Drivers
	10.4 Future developments
		10.4.1 Current and future markets
		10.4.2 Future drivers in CAPEX reduction
		10.4.3 Future CAPEX of alkaline electrolysis systems
	10.5 Summary and conclusion
	Acknowledgments
	References
	Further reading
11 Heating and cooling in the built environment
	11.1 Introduction
	11.2 Technology description of heat pumps
	11.3 Technology description of condensing boilers
	11.4 Market development of heating technologies in the EU
		11.4.1 Current market developments of gas boilers
		11.4.2 Current market developments of heat pumps
	11.5 Current market developments of gas boilers and heat pumps—a case study for The Netherlands
	11.6 Current market developments of fossil heating systems and heat pumps—a case study for Switzerland
	11.7 Methodological issues and data availability
		11.7.1 Methodological issues for heat pumps
			11.7.1.1 Data availability and data collection
			11.7.1.2 Data collection for heat pumps
		11.7.2 Methodological issues and data collection for gas boilers
	11.8 Techno-economic progress and experience curves
		11.8.1 Decreasing costs (prices) per unit for the case of heat pumps
		11.8.2 Decreasing costs (prices) per unit for the case of heat pump borehole heat exchangers and heat pump modules
			11.8.2.1 Improved energy efficiency in the case of heat pumps
		11.8.3 Decreasing costs (price) per unit in the case of gas boilers
	11.9 Future market trends of heat pumps in The Netherlands and Switzerland
	11.10 Summary and conclusions
	References
	Further reading
12 Concentrating solar power
	12.1 Introduction
	12.2 Methodological issues and data availability
	12.3 Results
		12.3.1 Time trend analyses
		12.3.2 Learning curve analyses
		12.3.3 Main drivers of the price decline
	12.4 Future outlook
	12.5 Conclusions and recommendations for science, policy, and business
	Acknowledgments
	References
	Further reading
13 Light-emitting diode lighting products
	13.1 Light-emitting diode lighting technology in the 2010s
	13.2 Methodological issues and data availability
		13.2.1 Price data for light-emitting diode lighting products
		13.2.2 Approach to inferring cumulative production
		13.2.3 Summary of data and methodological issues
	13.3 Results
		13.3.1 Time-trend analyses
		13.3.2 Learning-curve analyses
		13.3.3 Main drivers of the price decline
	13.4 Future outlook
		13.4.1 Product-level price projections
		13.4.2 Component-based manufacturing cost projections
		13.4.3 Discussion
	13.5 Conclusions and recommendations for science, policy, and business
	Acknowledgments
	References
Part III: Application of experience curves in modeling
14 Experience curves in energy models—lessons learned from the REFLEX project
	14.1 Technological progress and experience curves in energy system modeling
	14.2 Description of applied energy models with implemented experience curves
		14.2.1 Key new and incumbent technologies
			14.2.1.1 Electricity generation technologies
			14.2.1.2 Electricity storages
			14.2.1.3 Energy demand technologies
		14.2.2 Overview of energy markets involved
	14.3 Model results with exogenous technological learning
		14.3.1 Developments of novel technologies and concurrent price reductions
		14.3.2 Effect of experience curve implementation on market developments including uncertainty analysis of experience curve ...
			14.3.2.1 Uncertainty of experience curve parameters in FORECAST
			14.3.2.2 Uncertainty of experience curve parameters in PowerACE
			14.3.2.3 Uncertainty of experience curve parameters in ELTRAMOD
			Sensitivity of −50% of specific investment costs for batteries
			Sensitivity of no technological learning for carbon capture and storage technologies
	14.4 Lessons learned and conclusion
	References
15 Global electric car market deployment considering endogenous battery price development
	15.1 Introduction
		15.1.1 Motivation
		15.1.2 Status quo of electric vehicle market diffusion studies
		15.1.3 System dynamics transport modeling
	15.2 Model description
		15.2.1 ASTRA
			15.2.1.1 Overview
			15.2.1.2 Modular structure of the ASTRA model
			15.2.1.3 Vehicle fleet and technology diffusion in ASTRA
		15.2.2 TE3
			15.2.2.1 Overview
			15.2.2.2 Modular structure of the TE3 model
			15.2.2.3 Vehicle fleet and technology diffusion in TE3
		15.2.3 Comparison of the two models
	15.3 Experience curves and model coupling
		15.3.1 Implementation of experience curves
		15.3.2 Description of the interface and feedback loops between models
	15.4 Scenario and sensitivity analyses
	15.5 Results and discussion
		15.5.1 Results of the Reference scenario
		15.5.2 Results of the sensitivity analyses
		15.5.3 Effects of global learning via model coupling
		15.5.4 Comparison with other studies and limitations
	15.6 Conclusion
	References
Part IV: Final words
16 Synthesis, conclusions, and recommendations
	16.1 Introduction
	16.2 Methodological considerations
		16.2.1 Cost of capacity versus LCOE and other metrics
		16.2.2 Component-based assessments
		16.2.3 Two- and multifactor experience curves
		16.2.4 Environmental experience curves and social learning
		16.2.5 Application in energy and climate models
		16.2.6 Data availability and future data collection
	16.3 Technology outlook till 2030
	16.4 Final conclusions and recommendations
Index
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