Abstract

AbstractTechnological learning encompasses a variety of mechanisms by which technologies improve and decrease in costs. Experience curves are commonly used to analyze and explicitly quantify technological learning. This chapter presents the history and basic methodology of experience curves, and discusses the implementation of experience curves in energy system and sectoral energy models. Several key results of the REFLEX project with respect to state-of-the-art experience curves, and the implementation of experience curves in the REFLEX Energy Modeling System are highlighted. Finally, a set of key lessons learned in the REFLEX project are presented, discussing both methodological issues of experience curves as well as key issues with regard to the implementation of experience curves in different types of energy system and sectoral energy models.

Highlights

  • Within the REFLEX project, a large effort was made to include the effects of technological learning in the different energy and transport models that are used within the project

  • In order to derive the experience curve, it is necessary that the cost data is expressed in real terms, e.g., that is corrected for inflation using for instance a GDP-deflator (Junginger et al 2010; Louwen and Subtil Lacerda 2020), as without this correction, the derived experience curve parameters would otherwise be affected by the rise in prices resulting from inflation

  • Multi-factor experience curves can be a valuable extension of the single-factor experience curves adopted in the REFLEX project, giving the possibility to include the effects of additional parameters such as R&D activities, commodity prices, and market dynamics

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Summary

History and Concept

Within the REFLEX project, a large effort was made to include the effects of technological learning in the different energy and transport models that are used within the project. Technological learning is considered as a term that encompasses a variety of mechanisms by which technologies can improve, in relation to production costs, efficiency, quality, etc. It includes mechanisms like learning-by-doing, learning-by-searching (R&D), and upscaling. The experience curve describes an empirical relationship between cumulative production of a technology and its unit costs. It was developed in the form considered

Louwen Institute of Renewable Energy, Eurac Research, Bolzano, Italy
Key Applications of Experience Curves
Key Issues and Drawbacks of Experience Curves
Experience Curve Parameter Uncertainty
System Boundaries and Functional Units
Explanatory Value of Single-Factor Experience Curves
Data Collection and Derivation of Experience Curves
Functional Unit and System Boundaries
Correction for Currency and Inflation
Deriving Experience Curve Parameters
Experience Curves in Energy System Models
Model Implementation of Experience Curves
Issues with Implementation of Experience Curves in Energy Models
Description of Energy Models with Implemented Experience Curves
State-of-the-Art Experience Curves and Modeling Results
Overview of State-of-the-Art Experience Curves
Deployments and Cost Developments of Relevant Technologies
Lessons Learned
Methodological Issues
Model Implementation Issues
Findings
Conclusions

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