Date of Award


Degree Type


Degree Name

Master of Science in Ocean Engineering


Ocean Engineering

First Advisor

Annette Grilli


The exponential growth of the earth's population has lead to the depletion of natural resources in concert with unrepairable environmental damage. One solution for a more sustainable lifestyle is the supply of electricity by renewable energy technology. Offshore wind energy is expected to play a major role in the extension of this sustainable energy supply. However, several challenges lay ahead due to the high expenses of offshore energy. Consequently, the optimization of a wind farm layout for minimizing costs and maximizing revenue gains high importance.

This study determines the sensitivity of a wind farm layout and its revenue to wind time series length, wind direction and wind velocity trend. The sensitivity analysis is conducted at the Renewable Energy Zone (REZ) and the Rhode Island and Massachusetts Area of Mutual Interest (AMI) in Rhode Island, USA. Optimum layouts are found by minimizing an objective function expressed in terms of the Wind Farm Siting Index (WiFSI) with a Genetic Algorithm. The objective function considers wind resource, technological costs as expenses for tower foundation and cable interconnection as well as ecological and fishery cost. Ecological cost is expressed as abundance and sensitivity of species to wind farms. Fishery cost is implemented proportional to fishing activity intensity. The WiFSI is a dynamic tool adjustable by weighting factors to societal or political choices.

Seven simulations are conducted for the REZ and four simulations are researched for the AMI to complete the sensitivity study. All scenarios exclude ecological and fishery constraints from the objective function to focus on effects of a changing wind resource. Simulation 1 is conducted as a base case for the REZ with constant wind resource measured over three years. Simulation 2 to 4 apply wind probability distributions of 1992 to 2012, 1980 to 2012 and 2008 to 2012, respectively. Applying the long wind time series leads to several placement solutions in contrast to one optimum layout for simulation 1. Scenario 5 and 6 apply single-year wind roses of the years 1980 and 2012. Resulting layouts di_er in orientation to the respective dominant wind direction. Simulation 7 implements a positive, linear trend in usable wind velocity. The same layout as for simulation 1 is found but net revenue increases. One base case simulation with constant wind and one simulation with a positive, linear trend in the usable wind resource are operated for the Northern and the Southern part of the AMI. Optimized layouts of the base case and the trend application simulations vary. The change is due to the high number of possible turbine locations with same WiFSI. The implementation of the trend leads to an increase of net revenue.

Wind turbine layout is highly sensitive to a change in wind time series length and wind direction while net revenue is less influenced. In contrast, the sensitivity of the layout to a trend in usable wind velocity is low while the effect on net revenue is significant. Conclusively, long-term wind predictions over the life time of a farm are necessary to determine the optimum layout as well as produced power of a site.