Wind turbine cost reduction: A detailed bottom-up analysis of innovation drivers

Elia, Alessia; Taylor, M. Scott; Gallachóir, Brian Ó; Rogan, Fionn

doi:10.1016/j.enpol.2020.111912

Cited by 60 publications

(23 citation statements)

References 67 publications

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“…65 This method has been applied in various manufacturing industries. [65][66][67] Regarding energy technologies other than batteries, it has been used to project costs for fuel cells and electrolysers, 68,69 renewable energy technologies [70][71][72] and integrated energy systems. 73,74 In order to derive cost projections, the product is first separated into its individual components, required resources and processes are assigned, and cumulative cost are calculated.…”

Section: Authors and Yearmentioning

confidence: 99%

Battery cost forecasting: a review of methods and results with an outlook to 2050

Mauler

Duffner

Zeier

et al. 2021

Energy Environ. Sci.

244

120

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Section: Authors and Yearmentioning

confidence: 99%

Battery cost forecasting: a review of methods and results with an outlook to 2050

Mauler

Duffner

Zeier

et al. 2021

Energy Environ. Sci.

244

120

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“…As discussed in the Background section, many learning effects contribute to an aggregated single factor learning rate (SFLR). Other studies [4,41,66] have attempted to attribute cost reductions in more mature RET sectors to discrete learning effects. However, given the immaturity of the wave energy sector, the relative importance of these different sources of incremental cost reduction is unclear.…”

Section: Faster Learningmentioning

confidence: 99%

Implementing Radical Innovation in Renewable Energy Experience Curves

Kerr

Noble

Hodges³

et al. 2021

Energies

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Cost reductions in nascent forms of Renewable Energy Technology (RET) are essential for them to contribute to the energy mix. Policy intervention can facilitate this cost reduction; however, this may require a significant investment from the public sector. These cost reductions fall into two broad categories: (1) incremental cost reductions through continual improvements to existing technologies, and (2) radical innovation where technologies that significantly differ from the incumbents are developed. This study presents a modelling methodology to integrate radical innovation in RET experience curve and learning investment analysis, using wave energy as an example nascent RET. This aims to quantify the potential effects of radical innovation on the learning investment, allowing the value of successful innovation to be better analysed. The study highlights the value offered by radical innovations in long-term deployment scenarios for wave energy. This suggests that high-risk R&D efforts in nascent RET sectors, even with low success rates, could still present significant expected value in offsetting future revenue support.

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“…For example, a study of data on PV modules found that while improvements in cell efficiency were most important in the 1980s and 1990s, increases in manufacturing plant size have been the dominant driver of cost reductions in the 2001–2012 period (Kavlak et al, 2018). In an assessment of the drivers of cost components in onshore wind energy systems, cost reductions in labor and materials were the dominant cost reduction drivers to wind turbine costs between 2005 and 2017 (Elia et al, 2020). These studies have provided a clearer picture of what is actually happening with observed cost reductions, moving beyond the somewhat black‐box aspect of learning curves.…”

Section: Assessing Learning In Low Carbon Technologiesmentioning

confidence: 99%

Assessing learning in low carbon technologies: Toward a more comprehensive approach

Lewis

Nemet

2021

WIREs Climate Change

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With decades of experience developing and deploying low carbon technologies around the world, much has been learned. We assess six categories that represent the diversity of methodological approaches that have been used to study low carbon learning: (1) learning curves; (2) expert elicitations; (3) patent analysis; (4) engineering-based decomposition; (5) policy intervention studies; and (6) case studies. Based on a review of low carbon learning studies in these six areas, we summarize what we know about low carbon learning, and what we have yet to fully understand, including the methodological strengths and limitations of key studies conducted to date. We find that a more comprehensive understanding of low carbon learning is necessary and timely given the massive scale and short time horizon of the low carbon transition, and that there are real benefits to employing a comprehensive approach using multiple methods. We find a need for better data sets, and for studies of a more diverse set of technologies, as well as of interactions among technologies. In addition, studies should be more explicit about local context, with a particular need for additional focus on emerging and developing countries. We identify key topics that warrant further research, including technology specific learning methods; spatial distinctions and the local and global linkages that influence learning; and an expanded study of the cultural, social, environmental, and political factors that influence learning. Finally, we recommend more nuance in the design of policies directed at accelerating low carbon learning. This article is categorized under:The Carbon Economy and Climate Mitigation > Future of Global Energy K E Y W O R D S climate change, innovation, learning, low carbon technologies, methods 1 | INTRODUCTION Climate change necessitates the transition to a low carbon economy, presenting both challenges and opportunities. One main challenge is the scale of energy investment needed (estimated at between 1.6 and 3.8 trillion USD annually between 2016 and 2050) (Climate Policy Initiative, 2019; Intergovernmental Panel on Climate Change [IPCC], 2018), along with the need to overcome technological lock in and path dependency, all in a relatively short time horizon given looming climate disasters. However, many opportunities lie in the commercialization of low carbon energy

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Wind turbine cost reduction: A detailed bottom-up analysis of innovation drivers

Cited by 60 publications

References 67 publications

Battery cost forecasting: a review of methods and results with an outlook to 2050

Battery cost forecasting: a review of methods and results with an outlook to 2050

Implementing Radical Innovation in Renewable Energy Experience Curves

Assessing learning in low carbon technologies: Toward a more comprehensive approach

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