Abstract
In this research, a hierarchical met-ocean data selection model is proposed to reduce the computational cost in stochastic simulation of operation and maintenance (O&M) and enable rapid evaluation of offshore renewable energy systems. The proposed model identifies the most representative data for each calendar month from the long-term historical met-ocean data in two steps, namely the preselection and the refined selection. The preselection incorporates three distinct metrics to evaluate the characteristics of statistical distributions, including the Jensen−Shannon divergence, the encapsulation of extreme met-ocean conditions, as well as the overall vessel accessibility. For the refined selection, a component of temporal synchrony is devised to emulate dynamic changes of met-ocean conditions. As such, a met-ocean reference year comprising twelve representative historical months is subsequently produced and deployed as the input for O&M stochastic simulation. While this research focuses on the development of a generalised methodology for selecting representative met-ocean data, the proposed statistical method is validated empirically using a case study inspired by real-life floating offshore wind installations in Scotland, e.g., Hywind and Kincardine projects. According to the O&M simulation results with five capacity scenarios, the proposed data selection model reduces the computational cost by up to 97.65% while emulating the original results with minor deviations, i.e., within ±5%. The simulation speed is therefore 43 times quicker. Overall, the proposed met-ocean data selection model attains an excellent trade off between computational efficiency and accuracy in O&M stochastic simulation.