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The effects of mean wind speed uncertainty on project finance debt sizing for offshore wind farms

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  • Mora, Esteve Borràs
  • Spelling, James
  • van der Weijde, Adriaan H.
  • Pavageau, Ellen-Mary

Abstract

Financing costs for offshore projects depend, among many other variables, on the quality of mean wind speed predictions. Financial institutions determine the amount of debt that can be reasonably supported by the project, based on probabilistic cash flow metrics derived from estimated mean wind speeds. Within the offshore wind industry, it is widely believed that longer wind resource campaigns or more precise wind measurement devices that decrease mean wind speed uncertainty lead to lower LCOE values. This paper shows that this is not always true, while a decrease in mean wind speed uncertainty may result in better financing conditions, it typically requires higher development expenditure. We build a theoretical cost modelling framework, which includes detailed project financing constraints, and then apply this to an industrial case study to analyse project financing of different types of offshore wind farms. We show that developers need to find the right balance between a decrease in financing costs and an increase in development expenditure. For projects limited by the maximum gearing or with an unfavourable trade-off between the development expenditure and the increased P90 annual energy production, more precise resource estimation can result in higher LCOE values. This paper suggests a new way of understanding the effects of wind resource assessment campaigns by integrating project finance constraints into cost calculations and highlighting the importance of detailed cost modelling for optimal design of offshore wind farms.

Suggested Citation

  • Mora, Esteve Borràs & Spelling, James & van der Weijde, Adriaan H. & Pavageau, Ellen-Mary, 2019. "The effects of mean wind speed uncertainty on project finance debt sizing for offshore wind farms," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
  • Handle: RePEc:eee:appene:v:252:y:2019:i:c:53
    DOI: 10.1016/j.apenergy.2019.113419
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    References listed on IDEAS

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    1. Ioannou, Anastasia & Angus, Andrew & Brennan, Feargal, 2018. "A lifecycle techno-economic model of offshore wind energy for different entry and exit instances," Applied Energy, Elsevier, vol. 221(C), pages 406-424.
    2. Julia Gottschall & Brian Gribben & Detlef Stein & Ines Würth, 2017. "Floating lidar as an advanced offshore wind speed measurement technique: current technology status and gap analysis in regard to full maturity," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 6(5), September.
    3. Yan, Jie & Zhang, Hao & Liu, Yongqian & Han, Shuang & Li, Li, 2019. "Uncertainty estimation for wind energy conversion by probabilistic wind turbine power curve modelling," Applied Energy, Elsevier, vol. 239(C), pages 1356-1370.
    4. Steffen, Bjarne, 2018. "The importance of project finance for renewable energy projects," Energy Economics, Elsevier, vol. 69(C), pages 280-294.
    5. Loukatou, Angeliki & Howell, Sydney & Johnson, Paul & Duck, Peter, 2018. "Stochastic wind speed modelling for estimation of expected wind power output," Applied Energy, Elsevier, vol. 228(C), pages 1328-1340.
    6. Mytilinou, Varvara & Kolios, Athanasios J., 2019. "Techno-economic optimisation of offshore wind farms based on life cycle cost analysis on the UK," Renewable Energy, Elsevier, vol. 132(C), pages 439-454.
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    Cited by:

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