The offshore wind industry has historically focused on setting up new projects, with the decommissioning phase receiving little attention. This can cause future problems as decommissioning needs to be planned at the beginning to prevent complications that may arise, as it implies important operations and high costs. There are numerous features that make decommissioning a challenge, such as the marine environment, the technical limitations of vessels and the lack of specific regulations that determine what should be done, increasing the uncertainty of the process. Additionally, the unique characteristics of the sites involve exclusive optimal solutions for each project. This article analyses the main operation parameters that affect the decommissioning process, identifying the benefits and drawbacks of the influencing variables. A model is designed to compare different transportation strategies, searching for cost reduction. A decommissioning methodology is been proposed based on this analysis, taking into consideration the technical aspects of the process, and minimising environmental impacts. The model forecasts that the predicted duration and costs of this process are not being adequately captured in site decommissioning plans

Modern wind turbines vary greatly in their drive train configurations. With the variety of options available, it can be difficult to determine which type is most suitable for on and offshore applications. A large percentage of modern drive trains consist of either doubly fed induction generators with partially rated converters or permanent magnet generators with fully rated converters. These configurations are the focus of this empirical reliability comparison. The turbine population for this analysis contains over 1800 doubly fed induction generators, partially rated converter wind turbines, and 400 permanent magnet generator fully rated converter wind turbines. The turbines analyzed are identical except for their drive train configurations and are modern MW scale turbines making this population the largest and most modern encountered in the literature review. Results of the analysis include overall failure rates, failure rates per operational year, failure rates per failure mode, and failure rates per failure cost category for the two drive train configurations. These results contribute toward deciding on the most suitable turbine type for a particular site, as well as toward cost of energy comparisons for different drive train types. A comparison between failure rates from this analysis and failure rates from similar analyses is also shown in this paper.

Scientific research to characterize the long-term wind energy resource is plentiful. However, if the impact of wind power on the electric power system is the goal of modeling, consideration must be given to diurnal and seasonal effects, as well as the correlation of wind speed between geographical areas. This paper provides such detail by modeling these effects explicitly, enabling accurate evaluations of wind power impact on future power systems to be carried out. This is increasingly important in the context of ambitious wind energy targets driven in the U.K., for example, by the requirement for 20% of Europe's energy to be met from renewable energy sources by 2020. Both univariate and multivariate auto-regressive models are presented here and it is shown how they can be applied to geographically dispersed wind speed data. These models are applied to suitably de-trended data. The accuracy of the models is assessed both by inspection of the residuals and by assessment of the forecasting accuracy of the models. Finally, it is shown how the models can be used to synthesize wind speed and thus wind power time series with the correct seasonal, diurnal, and spatial diversity characteristics.