Due to lack of operating experience in the field of offshore wind energy and large costs associated with maintaining offshore wind farms, there is a need to develop accurate operation and maintenance models for strategic planning purposes. This paper provides an approach for verifying such simulation models and demonstrates it by describing the verification process for four models. A reference offshore wind farm is defined and simulated using these models to provide test cases and benchmark results for verification for wind farm availability and O&M costs. This paper also identifies key modelling assumptions that impact the results. The calculated availabilities for the four models show good agreement apart from cases where maintenance resources are heavily constrained. There are also larger discrepancies between the cost results. All the differences in the results can be explained by different modelling assumptions. Therefore, the models can be regarded as verified based on the presented approach. INTRODUCTION MotivationOffshore wind energy is a new area for operation and maintenance (O&M) research, and the operational legacy of the industry is only just over a decade. Operation and maintenance cost modelling software tools are being developed to support activities in this field. Because of the novelty of offshore wind energy generation and lack of real data, there are limited options for validation and verification of these models. Verification and validation of a simulation model is essential if the model is to say something useful about the system it is meant to represent. We define verification as ensuring that the simulation model is implemented according to the specifications of the conceptual model of the system; validation is defined as ensuring that this conceptual model is in fact a faithful representation of the real system for the purposes of the model [1]. It may prove difficult for researchers to acquire suitable data to perform model validation. For full operational validation [1], necessary historical data would include repair and logistical costs, statistical information on component reliability and performance indicators such as total operations costs or availability. This type of information is possessed by the farm owner/operator, turbine manufacturer or non-existent for new generation wind turbines. BackgroundSeveral O&M simulation models for offshore wind farms have been developed, of which Hofmann [2] provides a thorough overview. Often, the intended applications of the models differ slightly. For example, one model will focus on assessing heavy-lift vessels, whereas another will be used for maintenance strategy optimisation. [3]. One position is that models are never entirely validated because it is not practicable to assess correspondence between the system and the model for its entire domain of applicability [1]. Even if the system is observable and a comparison of model output and system output is possible, one is often interested in predicting system behaviour under circumst...

Operation and Maintenance (O&M) costs are estimated to account for 14%-30% of total Offshore Wind Farm (OWF) project lifecycle expenditure according to a range of studies. In this respect, identifying factors affecting operational costs and availability are vital for wind farm operators to achieve the most profitable decisions. Many OWFs are built in stages and the important factors may not be consistent for the different phases. To address this issue, three OWF case studies are defined to represent two phases and a complete project. An initial qualitative screening sensitivity analysis was conducted to identify the most important factors of O&M affecting operating cost and availability. The study concluded that the important factors for total O&M cost were access and repair costs along with failure rates for both minor and major repairs. For time-based availability, the important factors identified were those related to the length of time conducting the maintenance tasks, i.e. the operation duration and the working day length. It was found that the two stages had similar results, but these were different compared to the complete project. In this case, the results provide valuable information to OWF operators during the project development and decision making process

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

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