Cost Dynamics of Clean Energy Technologies

49 Pages Posted: 28 Jul 2021

See all articles by Gunther Glenk

Gunther Glenk

University of Mannheim - Mannheim Institute for Sustainable Energy Studies

Rebecca Meier

University of Mannheim

Stefan Reichelstein

Stanford University - Graduate School of Business

Multiple version iconThere are 3 versions of this paper

Date Written: 2021

Abstract

The pace of the global decarbonization process is widely believed to hinge on the rate of cost improvements for clean energy technologies, in particular renewable power and energy storage. This paper adopts the classical learning-by-doing framework of Wright (1936), which predicts that cost will fall as a function of the cumulative volume of past deployments. We first examine the learning curves for solar photovoltaic modules, wind turbines and electrolyzers. These estimates then become the basis for estimating the dynamics of the life-cycle cost of generating the corresponding clean energy, i.e., electricity from solar and wind power as well as hydrogen. Our calculations point to significant and sustained learning curves, which, in some contexts, predict a much more rapid cost decline than suggested by the traditional 80% learning curve. Finally, we argue that the observed learning curves for individual clean energy technologies reinforce each other in advancing the transition to a decarbonized energy economy.

Keywords: learning-by-doing, renewable energy, energy storage, electrolysis, levelized cost of energy

JEL Classification: M1, O33, Q41, Q42, Q48, Q54, Q55

Suggested Citation

Glenk, Gunther and Meier, Rebecca and Reichelstein, Stefan, Cost Dynamics of Clean Energy Technologies (2021). ZEW - Centre for European Economic Research Discussion Paper No. 21-054, Available at SSRN: https://ssrn.com/abstract=3889531 or http://dx.doi.org/10.2139/ssrn.3889531

Gunther Glenk (Contact Author)

University of Mannheim - Mannheim Institute for Sustainable Energy Studies ( email )

Mannheim, 68131
Germany

Rebecca Meier

University of Mannheim ( email )

L 7, 3-5
Mannheim, 68161
Germany

Stefan Reichelstein

Stanford University - Graduate School of Business ( email )

Stanford, CA 94305
United States

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