Site selection for offshore renewable energy platforms: a multi-criteria decision-making approach

This thesis presents an innovative methodology for site selection of offshore renewable systems, addressing the growing global energy demand and the need for sustainable solutions to climate change. Recognizing waves as a significant, yet underutilized renewable energy source, this research focuses on optimizing Wave Energy Converter (WEC) deployment amidst the uncertainties and costs of offshore environments. The study involves a comprehensive evaluation of potential sites, considering key factors like power generation capacity, mooring system fatigue life, and tether response to extreme loads.

Initial wave data analysis for various locations is followed by numerical simulations of a point absorber WEC under different conditions. A Bayesian Network (BN) model is then employed to integrate uncertainties in multi-criteria decision-making, enhancing the robustness of site selection. This approach facilitates the calculation of utility values for various site and installation options, leading to the identification of the optimal decision alternative based on maximum expected utility.

This work provides a detailed framework for stakeholders in the renewable energy sector, aiding in the assessment of both profitability and survivability of WECs in chosen locations. It significantly contributes to minimizing economic and performance risks associated with WEC installations, promoting efficient and sustainable energy production from ocean resources.