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Testing the robustness of optimal access vessel fleet selection for operation and maintenance of offshore wind farms


Citation Sperstad, I.B.,Stålhane, M., Dinwoodie, I., Endrerude, O.-E. V., Martin, R. and Warner, E. Testing the robustness of optimal access vessel fleet selection for operation and maintenance of offshore wind farms, Ocean Engineering,145: 334-343, 2017. https://dx.doi.org/10.1016/j.oceaneng.2017.09.009.
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Author(s) Sperstad, I.B.,Stålhane, M., Dinwoodie, I., Endrerude, O.-E. V., Martin, R. and Warner, E.
Project partner(s) SINTEF, Norwegian University of Science and Technology, University of Strathclyde, University of Stavanger, Electricité de France, National Renewable Energy Laboratory
Publisher Ocean Engineering,145: 334-343
DOI https://dx.doi.org/10.1016/j.oceaneng.2017.09.009
Abstract Optimising the operation and maintenance (O&M) and logistics strategy of offshore wind farms implies the decision problem of selecting the vessel fleet for O&M. Different strategic decision support tools can be applied to this problem, but much uncertainty remains regarding both input data and modelling assumptions. This paper aims to investigate and ultimately reduce this uncertainty by comparing four simulation tools, one mathematical optimisation tool and one analytic spreadsheet-based tool applied to select the O&M access vessel fleet that minimizes the total O&M cost of a reference wind farm. The comparison shows that the tools generally agree on the optimal vessel fleet, but only partially agree on the relative ranking of the different vessel fleets in terms of total O&M cost. The robustness of the vessel fleet selection to various input data assumptions was tested, and the ranking was found to be particularly sensitive to the vessels’ limiting significant wave height for turbine access. This is also the parameter with the greatest discrepancy between the tools, implying that accurate quantification and modelling of this parameter is crucial. The ranking is moderately sensitive to turbine failure rates and vessel day rates but less sensitive to electricity price and vessel transit speed

Highlights
  • Six decision support tools are applied to rank vessel fleets for O&M at an offshore wind farm.
  • Their vessel fleet rankings are compared and associated uncertainties in input analyzed.
  • The rankings show sensitivity to which decision support tool is used.
  • The rankings are particularly sensitive to the vessels’ wave height limits.
This work was partly funded via IDCORE, the Industrial Doctorate Centre for Offshore Renewable Energy, which trains research engineers whose work in conjunction with sponsoring companies aims to accelerate the deployment of offshore wind, wave and tidal-current technologies
Associated Project(s) ETI-MA2003: Industrial Doctorate Centre for Offshore Renewable Energy (IDCORE)
Associated Dataset(s) EDC0000134: Tidal Energy Site Environmental Conditions Measured During the ReDAPT Tidal Project 2011-2014
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