TY - JOUR
T1 - Wind turbine cost reduction
T2 - A detailed bottom-up analysis of innovation drivers
AU - Elia, A.
AU - Taylor, M.
AU - Ó Gallachóir, B.
AU - Rogan, F.
N1 - Publisher Copyright:
© 2020 The Authors
PY - 2020/12
Y1 - 2020/12
N2 - Wind energy technologies have seen a rapid decline in costs in the last two decades, but the drivers for these cost reductions are poorly understood. This paper addresses this knowledge gap by quantitatively investigating the drivers behind the cost reductions of onshore wind turbines between 2005 and 2017. Starting from a bottom-up cost model, the paper advances the methodology by identifying the techno-economic variables responsible for cost reductions of individual components (in $/kW) and linking them to drivers, specifically: learning by-deployment, learning-by-researching, supply-chain dynamics, and market dynamics. The analysis finds that changes in materials (copper, fiberglass, and iron), labour (employee productivity), legal and financial costs contributed over 30% to the cost reduction of wind turbine prices over the period 2005–2017. Moreover, learning-by-deployment was the most important innovation driver, being responsible for half of the cost reduction. The findings point to the importance of policies tailored to technology's stage of development. For onshore wind energy, which entered a mature phase in the period covered by this analysis, policy support for the needs of a growing industry such as stable support schemes together with appropriate regulatory and investment environments were more important than direct policy support for R&D which played a more important role in earlier periods.
AB - Wind energy technologies have seen a rapid decline in costs in the last two decades, but the drivers for these cost reductions are poorly understood. This paper addresses this knowledge gap by quantitatively investigating the drivers behind the cost reductions of onshore wind turbines between 2005 and 2017. Starting from a bottom-up cost model, the paper advances the methodology by identifying the techno-economic variables responsible for cost reductions of individual components (in $/kW) and linking them to drivers, specifically: learning by-deployment, learning-by-researching, supply-chain dynamics, and market dynamics. The analysis finds that changes in materials (copper, fiberglass, and iron), labour (employee productivity), legal and financial costs contributed over 30% to the cost reduction of wind turbine prices over the period 2005–2017. Moreover, learning-by-deployment was the most important innovation driver, being responsible for half of the cost reduction. The findings point to the importance of policies tailored to technology's stage of development. For onshore wind energy, which entered a mature phase in the period covered by this analysis, policy support for the needs of a growing industry such as stable support schemes together with appropriate regulatory and investment environments were more important than direct policy support for R&D which played a more important role in earlier periods.
KW - Bottom-up cost model
KW - Innovation drivers
KW - Onshore wind technology
KW - Technology innovation
KW - Technology learning
UR - https://www.scopus.com/pages/publications/85092690001
U2 - 10.1016/j.enpol.2020.111912
DO - 10.1016/j.enpol.2020.111912
M3 - Article
AN - SCOPUS:85092690001
SN - 0301-4215
VL - 147
JO - Energy Policy
JF - Energy Policy
M1 - 111912
ER -