A review of learning rates for electricity supply technologies

By Edward S. Rubin, Inês Lima Azevedo, P. Jaramillo, Sonia Yeh. 2015

565 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Pollution, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/9658e45ae4b60af901960f5b2d09e71d82d5e03f

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In Electrical and Electronic Engineering

In Pollution

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

Renewable energy consumption and economic growth nexus: Evidence from a threshold model (2020, background): “Similarly, Rubin et al. (2015) review empirical learning rates that have been reported for a broad spectrum of electric power generation technologies and suggest that learning rates from power plant technologies using fossil fuels were lower in magnitude than those for renewable energy technologies.”
Expert elicitation survey on future wind energy costs (2016, background): “Estimated learning rates span an enormous range, from a 33% cost decline with each doubling of cumulative production (LR = 33%) to a cost increase of 11% for each doubling (LR = -11%) [2, 12-13].”
The Future of Ex-Ante LCA? Lessons Learned and Practical Recommendations (2019, background): “For example, the concept of learning (or experience) curves has been developed in order to link empirically observed cost reductions of a technology to key factors such as the cumulative production volume or the installed capacity [8].”
The experience curve theory and its application in the field of electricity generation technologies – A literature review (2018, background): “A number of reviews of experience curve literature have previously been published, covering both electricity generation technologies in general [4,12,13] and individual technologies, such as wind [14–16] and solar PV [17].”
A review of direct air capture (DAC): scaling up commercial technologies and innovating for the future (2021, background): “Lithium-ion batteries (electronics) 30% [69] Solar PV 23% [68] LED A lamps 18% [71] Natural gas turbines 15% [68] Hydraulic fracturing 13% [76] Onshore wind 12% [68] Nickel-metal hydride HEV batteries 11% [69] Flue gas desulfurization systems 11% [77] PC coal boilers 5.”
Tackling Climate Change with Machine Learning (2019, background): “Nuclear power plants Some argue that nuclear fission reactors (also known as nuclear power plants) are essential to meeting climate change goals [95], but these technologies face significant challenges including public safety, waste disposal, slow technological learning [96, 97], and high costs [95]”
From fossil fuels to renewables: An analysis of long-term scenarios considering technological learning (2019, background): “The latter is measured as the percentage, by which the unit cost declines with each doubling of cumulative production or, alternatively, as the fraction by which the unit price of energy service, such as electricity, declines with each doubling of installed capacity (Sagar and van der Zwaan, 2006).”
Power capacity expansion planning considering endogenous technology cost learning (vol 204, pg 831, 2017) (2017, background): “Differences in local and global technology diffusion across enterprises, industries, and countries has been studied empirically [16, 17] and within energy system models (section 2.”
Market design for a high-renewables European electricity system (2018, background): “8 The learning rate for onshore wind has been estimated at 7% and for offshore wind at 9% (King et al. 2015, Rubin et al. 2015).”
The political logics of clean energy transitions (2018, background): “28 Rubin et al. (2015). obscures how both factors are driven by policy, which itself is driven by politics.”