Incorporating stochastic operation and maintenance models into the techno-economic analysis of floating offshore wind farms

Floating offshore wind is rapidly gaining traction in deep water locations. As with all new technologies, to gain the confidence of developers and investors, the technical and economic feasibility of this technology must be proven and robust cost estimates are necessary. In this paper, the authors present a methodology to calculate the capital and operational indicators of a floating wind farm over its project lifetime. A set of computational models is used to reduce the uncertainties in the estimation of the technical and economical parameters. In particular, the effect of using detailed operation and maintenance models and strategies allows a better estimation of operational cost. The paper highlights the requirements and specific adjustments considered for floating offshore wind technology. The methodology is demonstrated for two case studies inspired by real floating wind installations in the United Kingdom, namely the Hywind and Kincardine projects. The related input data, gathered from publicly available sources, constitute a reference database for future studies in the floating offshore wind sector. Results are presented for the two case studies. These show that availability and energy production are in line with typical values for offshore wind projects, and highlight the substantial contribution of operational expenses to the cost of energy. Results are also compared against previous estimations for floating offshore wind projects, showing satisfactory agreement for the overall project costs but an underestimation of operation and maintenance costs in previous studies. This highlights the importance of using detailed operation and maintenance models to adequately capture operational expenses.

Automated summary

This paper presents a methodology to calculate the capital and operational indicators of a floating wind farm, reducing uncertainties in the estimation of technical and economic parameters and emphasizing the importance of using detailed operation and maintenance models.