Chapter 6 - Onshore wind energy

Publication date

2020

Authors

Junginger, H.M.ORCID 0000-0002-5010-2051ISNI 0000000389848632
Hittinger, Eric
Williams, Eric
Wiser, Ryan

Editors

Junginger, Martin
Louwen, Atse

Advisors

Supervisors

Document Type

Part of book
Open Access logo

License

taverne

Abstract

Onshore wind has shown cost reductions for more than three decades, but the importance of underlying factors has varied over time. While in the past the cost of capacity was mainly used to measure cost reductions, this chapter analyzes the reduction of the levelized cost of electricity (LCOE). Next to lower upfront capital expenditures (Capex), the capacity factor has also increased significantly, going hand in hand with higher hub heights and larger rotor diameters. While Capex and LCOE have also temporarily increased between 2005 and 2011, the overall learning rate for LCOE for data between 1990 and 2017 is 11.4%. Combining this learning rate with anticipated growth in global onshore wind deployment yields a projected LCOE of 3.7$ cents/kWh by 2030, a reduction of approximately 25% from 2018 levels, making it highly competitive with expected prices of new coal and natural gas generation. A recent expert elicitation study of future costs of wind power yielded a range of implicit future learning rates between 14% and 18%. The 11.4% learning rate estimated here yields less bullish prospects for cost reductions; this distinction can be partly explained by the fact that there are a few factors still excluded or partially excluded from the learning-rate analysis (specifically, improvements in project lifetime, operational expenditures, and financing costs).

Keywords

Capacity factor, Capital expenditures, Europe, Onshore wind energy, Usa, Taverne, General Energy

Citation

Junginger, M, Hittinger, E, Williams, E & Wiser, R 2020, Chapter 6 - Onshore wind energy. in M Junginger & A Louwen (eds), Technological Learning in the Transition to a Low-Carbon Energy System : Conceptual Issues, Empirical Findings, and Use in Energy Modeling. Academic Press, pp. 87-102. https://doi.org/10.1016/B978-0-12-818762-3.00006-6