Accessibility and Serviceability Assessment to Inform Offshore Wind Energy Development and Operations off the U.S. East Coast

TL;DR

The paper tackles offshore wind logistics by addressing how met-ocean variability affects O&M costs and vessel dispatch. It develops a TSR-based data-fusion framework that blends observational and numerical met-ocean data to produce high-resolution time series, and introduces serviceability as a route-aware metric that accounts for vessel path and operational profile. The results show substantial spatial and seasonal variability in approachability, accessibility, and serviceability, and reveal that relying solely on numerical models can bias route assessments; data fusion yields more realistic assessments and potential economic benefits for dispatch and planning. The approach provides a practical, route-centric tool for improving site selection, scheduling, and cost-efficiency in future offshore wind development along the U.S. East Coast and beyond.

Abstract

The economic success of offshore wind energy projects relies on accurate projections of the construction, and operations and maintenance (O&M) costs. These projections must consider the logistical complexities introduced by adverse met-ocean conditions that can prohibit access to the offshore assets for sustained periods of time. In response, the goal of this study is two-fold: (1) to provide high-resolution estimates of the accessibility of key offshore wind energy areas in the United States (U.S.) East Coast--a region with significant offshore wind energy potential; and (2) to introduce a new operational metric, called serviceability, as motivated by the need to assess the accessibility of an offshore asset along a vessel travel path, rather than at a specific site, as commonly carried out in the literature. We hypothesize that serviceability is more relevant to offshore operations than accessibility, since it more realistically reflects the success and safety of a vessel operation along its journey from port to site and back. Our analysis reveals high temporal and spatial variations in accessibility and serviceability, even for proximate offshore locations. We also find that solely relying on numerical met-ocean data can introduce considerable bias in estimating accessibility and serviceability, raising the need for a statistical treatment that combines both numerical and observational data sources, such as the one proposed herein. Collectively, our analysis sheds light on the value of high-resolution met-ocean information and models in supporting offshore operations, including but not limited to future offshore wind energy developments.

Figures (13)

• Figure 1: A geographical map showing the locations of the buoys (red stars) on top of the offshore wind regions off the U.S. East Coast (blue polygons), as adapted from the Bureau of Ocean Energy Management (BOEM) as of August, 2025boemmap. There were originally four proximate ASOW buoys, for which the data have been pooled (for increased data coverage) into a central location, depicted as ASOW-pooled. More details about the observational data are in Table \ref{['tab:buoy_data']}.
• Figure 2: Five-year time series of wind speed (left) and significant wave height (right) from the GFS and the GFS-driven Wavewatch III, respectively (cyan lines), and buoy observations (navy cross markers) at three NOAA buoys, 44005, 44014, and 44025, from January 2019 to December 2023. While there are instances where the numerical model is unavailable, coverage is far more complete and consistent relative to observational data.
• Figure 3: Zoomed-in view of numerical model outputs (cyan lines) versus observational data (blue cross markers) for approximately two weeks in March 2022. Despite the overall alignment, noticeable under- and over-estimation biases can be observed especially for severe met-ocean conditions which are the most relevant for the accessibility assessment in offshore operations.
• Figure 4: Left: Toy example of a vessel route, showing the vessel activities from an operational profile, mapped over time. The red text denote port-to-site voyage activities, whereas the green text denotes the return trip activities. Right: Corresponding position matrix, $\mathbf{P}$, constructed from the vessel route and operational profile information. The position matrix $\mathbf{P}$, encoding the expected location of the vessel at each time step, will be a critical input to serviceability calculations.
• Figure 5: Zoomed time series comparison of numerical model (teal lines), true observations (navy markers), and the TSR model fit (red lines) for wind speed (left) and significant wave height (right), across buoys 44005, 44014, and 44025 during March 2022. The fitted TSR model tracks observed values more closely than the raw numerical model output, especially during peaks and sudden transitions, indicating improved predictive skill relative to the base numerical model.