The EU generally, and the UK, Belgium, Netherlands and Germany specifically, have ambitious plans for the large scale installation of offshore wind-power capacity. However, the cost of energy from offshore wind is much higher than that from land-based generation and a substantial portion of that cost, anything between 15% and 30%, may be due to the cost of O&M alone, largely driven by delays in access and repair caused by adverse weather and sea-state, high vessel costs, higher wage costs, and lost revenue from extended down-time. As part of a condition monitoring project commissioned and funded by the ETI (Energy Technologies Institute), the authors have developed a simple tool to estimate the cost of O&M and associated lost revenue, and also to estimate the potential for condition monitoring to allow operators to reduce those costs and the loss in revenue through better maintenance scheduling. The tool builds on earlier work conducted at Strathclyde and presented at EOW 2009 on estimating offshore access delays and turbine availability using a closed form probabilistic method based on an event tree, but without extensive time-domain or Monte Carlo simulation. It currently uses wind and wave data, reliability data and component cost data mainly available in the public domain. Repairs and replacements of subsystems have been classified into a small range of different repair severities, each having their specific requirements for vessels, plant, personnel and time. Expected delays can be calculated directly for each type of repair and the overall effects are summed. Condition monitoring and other maintenance strategies are assumed to change the allocation of a particular subsystem's faults between repair categories and thereby affect its overall impact on down-time and other costs.Calculations are carried out in a spreadsheet that updates instantly when any parameter is changed. The advantage of the approach developed is that it is possible to explore the impact of changing access thresholds, reliabilities or site parameters quickly and easily without having to run a long series of simulations for each new situation
