Presentations

Abstract

Operations and maintenance (O&M) is a major cost driver in offshore wind, and improving logistics efficiency is essential for reducing levelized cost of electricity (LCOE). The current O&M practices may be outdated and miss opportunities for smarter approach of planning. While logistics modelling approaches such as discrete-event simulation (DES), mixed-integer programming (MIP), and Markov decision processes (MDP), effectively capture failures and weather effects, they generally treat maintenance tasks independently, overlooking coordination opportunities. Opportunistic maintenance, where additional tasks are carried out when an opportunity arises, offers a promising alternative and has been reviewed comprehensively, but realistic treatment of offshore accessibility remains scarce. This study presents a novel decision-support framework that operationalizes opportunistic maintenance within the TNO UWiSE discrete-event simulation model, which has been widely applied in offshore logistics research. The framework enables grouping of multiple tasks within the same vessel trip, subject to extended weather windows, and integrates an optimization algorithm to determine the most effective initiation week for scheduled campaigns. Historical metocean datasets are used to capture interannual variability, ensuring that offshore accessibility constraints are represented realistically. The approach is demonstrated on a blade repair campaign at the Offshore Wind Farm Egmond aan Zee (OWEZ), using real maintenance data to validate assumptions. This is, to our knowledge, the first study to implement opportunistic maintenance in a logistics-integrated, weather-aware framework, verified with real-world operational data. The work provides new insights into how adaptive, data-driven decision-support tools can enhance the cost-effectiveness and reliability of offshore wind O&M.