Plans to pin offshore wind turbines to the seafloor in the Atlantic Ocean has raised questions about potential risks to commercial shipping traffic, as vessels maneuver around the installations.
To address this potential risk, Pacific Northwest National Laboratory (PNNL) scientists have developed an assessment of navigation safety risks, with support from the U.S. Bureau of Ocean Energy Management (BOEM), working closely with BOEM and the US Coast Guard (USCG).
PNNL scientists worked with the very large set of Automated Identification System (AIS) data for shipping in the US. They extracted information for each of the 20,000+ vessels, representing cargo ships, tankers, and tug/towing vessels; the information includes the tonnage, dimensions, horsepower, displacement, and routes travelled by the ship, over a three-year period. PNNL developed new geospatial analytical tools to determine the commonly traveled routes among the 28 major ports on the Atlantic.
PNNL developed a numerical computer model that uses the AIS data and geospatial routing, with the intent of moving ships in a realistic manner for the base case (present conditions with no wind farms) and the future case (with planned wind farms). Each case was also run as scenarios with confounding conditions, such as hurricanes or Nor ‘Easters, with loss of propulsion of one or more ships, etc.
The preliminary results indicate that the presence of wind farms on the Atlantic coast could increase the risk of vessels coming within one half mile of one another (an “encounter”), by approximately 12% and groundings due to avoidance of wind farms could increase by less than 1%. PNNL researchers are continuing to refine the model, to improve the realism of ship routing and encounters, and it is likely that the increased risk of encounters between ships will drop as further refinements are added to the model, included improved route planning, as would be expected by large shipping companies, and more detailed bathymetry of the coastal areas.
These improvements will raise confidence in the predictive capability of the model, and allow it to act as a better tool for evaluating the safety of wind farm installations.