Purpose Life cycle cost (LCC) considerations are of increasing importance to offshore wind farm operators and their insurers to undertake long-term profitable investments and to make electricity generation more price-competitive. This paper presents a cost breakdown structure (CBS) and develops a whole life cost (WLC) analysis framework for offshore wind farms throughout their life span (∼25 years). Methods A combined multivariate regression/neural network approach is developed to identify key cost drivers and evaluate all the costs associated with five phases of offshore wind projects, namely pre-development and consenting (P&C), production and acquisition (P&A), installation and commissioning (I&C), operation and maintenance (O&M) and decommissioning and disposal (D&D). Several critical factors such as geographical location and meteorological conditions, rated power and capacity factor of wind turbines, reliability of subsystems and availability and accessibility of transportation means are taken into account in cost calculations. The O&M costs (including the cost of renewal and replacement, cost of lost production, cost of skilled maintenance labour and logistics cost) are assessed using the data available in failure databases (e.g. fault logs and O&M reports) and the data supplied by inspection agencies. A net present value (NPV) approach is used to quantify the current value of future cash flows, and then, a bottom-up estimate of the overall cost is obtained. Results and discussion The proposed model is tested on an offshore 500-MW baseline wind farm project, and the results are compared to experimental ones reported in the literature. Our results indicate that the capital cost of wind turbines and their installation costs account for the largest proportion of WLC, followed by the O&M costs. A sensitivity analysis is also conducted to identify those factors having the greatest impact on levelized cost of energy (LCOE). Conclusions The installed capacity of a wind farm, distance from shore and fault detection capability of the condition monitoring system are identified as parameters with significant influence on LCOE. Since the service lifetime of a wind farm is relatively long, a small change in interest rate leads to a large variation in the project's total cost. The presented models not only assist stakeholders in evaluating the performance of ongoing projects but also help the wind farm developers reduce their costs in the medium-long term. Keywords Capital expenditure (CAPEX). Levelized cost of energy (LCOE). Multivariate regression. Offshore wind farm. Operating expenditure (OPEX). Whole life cost (WLC)

Wind power, especially offshore, is considered one of the most promising sources of 'clean' energy towards meeting the EU and UK targets for 2020 and 2050.Deployment of wind turbines in constantly increasing water depths has raised the issue of the appropriate selection of the most suitable support structures' options.Based on experience and technology from the offshore oil and gas industry, several different configurations have been proposed for different operational conditions. This paper presents a methodology for the systematic assessment of the selection of the most preferable, among the different configurations, support structures for offshore wind turbines, taking into consideration several attributes through the widely used multi-criteria decision making method TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) for the benchmarking of those candidate options. An application comparing a monopile, a tripod and a jacket, for a reference 5.5 MW wind turbine and a reference depth of 40 m, considering multiple engineering, economical and environmental attributes, will illustrate the effectiveness of the proposed methodology.

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