On the definition of a risk index based on long-term metocean data to assist in the design of Marine Renewable Energy systems. (15th December 2021) - Penalba, Markel; Aizpurua, Jose Ignacio; Martinez-Perurena, Ander
Abstract: Marine Renewable Energy (MRE) systems are designed to maximise energy generation and ensure survivability. The traditional design process is based on pure environmental conditions, tends to be too conservative and limits the decision-making options. This paper presents a preliminary study on a novel risk-index combining the probabilistic occurrence matrix of sea-states with a consequence matrix. The stochastic direct sampling method is used for the quantification of occurrence matrices and consequences are estimated for fatigue effects and extreme loads. The paper shows a comparison of three design points (DPs) with increasing conservatism selected using metocean data for the period 1990–2000: high- and medium-risk DPs based on the novel risk index, and a low-risk DP obtained from a traditional PCA-based environmental contour. These DPs are compared to metocean data collected via in-situ measurements for the period 2000–2020, where the designed MRE system is supposed to operate. Results show that the low-risk DP overestimates the design H s by 50%, while the high-risk DP underestimates it by 20%. The former would result in significant over-costs, while the later would very likely lead to catastrophic damages. The design H s suggested by the medium-risk DP matches with the maximum H s measured between 2000–2020, showing its suitability. Highlights: A novel risk-index is presented that combines future probabilistic resource estimations with environmental loading consequences. Future metocean probabilistic occurrence matrices are computed via direct sampling. Consequence criticality is quantified including fatigue effects and extreme loads. Low-, medium- and high-risk design points are evaluated using real buoy measurements for the analysed time period. Design points based on pure environmental contours are demonstrated to be excessively conservative (50% overestimation).