A Review and Analysis of the Uncertainty Within Cost Models for Floating Offshore Wind Farms

The development and deployment of offshore wind farms in the last decade have seen a dramatic increase, now totalling 743 GW globally (Global Wind Energy Council, 2022). This rapid increase is expected to further continue now with the potential to explore deeper sites with the adoption of floating offshore platforms. Proof of this growth has recently been seen with an impressive 60% of the 25 GW Scotwind leasing sites planning to install floating platforms in the next ten years (Crown estate, 2022 [1,2]). One main disadvantage of the advancement offshore is uncertainty and the potential increase in costs due to more complex structures and greater distances to shore. The cost increase for floating platforms is expected to be two to three times more expensive than traditional fixed support structures (Eric Paya, 2020). Thus, this work aims to review existing analytical cost models found within the literature to best determine their level of accuracy and compare the assumptions which have been made. Leading on from this review, a collection of all data found in the reviewed literature is presented, which leads to a data analysis that determines the variation across literature and the potential causes. Assessing this literature shows a wide range of model considerations, often leading to assumptions with little or no data to be validated against. Hence, high levels of variation and a lack of consensus on the cheapest floating platform were noted. All aspects of costs related to floating offshore wind systems vary heavily throughout the literature.

Automated summary

The development and deployment of offshore wind farms have significantly increased in the last decade, reaching a total capacity of 743 GW globally. The adoption of floating offshore platforms is expected to further drive this growth. However, uncertainty and cost increase are the main disadvantages of offshore wind, due to complex structures and longer distances to shore. This study reviews existing cost models and compares their accuracy and assumptions.