Abstract
Among the challenges generated by the global climate crisis, a significant concern is the constant increase in energy demand. This leads to the need to ensure that any novel energy systems are not only renewable but also reliable in their performance. A viable solution to increase the available renewable energy mix involves tapping into the potential available in ocean waves and harvesting it via so-called wave energy converters (WECs). In this context, a relevant engineering problem relates to finding WEC design solutions that are not only optimal in terms of energy extraction but also exhibit robust behavior in spite of the harsh marine environment. Indeed, the vast majority of design optimization studies available in the state-of-the-art consider only perfect knowledge of nominal (idealized) conditions, neglecting the impact of uncertainties. This study aims to investigate the information that different robustness metrics can provide to designers regarding optimal WEC design solutions under uncertainty. The applied methodology is based on stochastic uncertainty propagation via a Monte Carlo simulation, exploiting a meta-model to reduce the computational burden. The analysis is conducted over a dataset obtained with a genetic algorithm-based optimization process for nominal WEC design. The results reveal a significant deviation in terms of robustness between the nominal Pareto set and those generated by setting different thresholds for robustness metrics, as well as between devices belonging to the same nominal Pareto frontier. This study elucidates the intrinsic need for incorporating robust optimization processes in WEC design.