Offshore Wind Farms (OWF) are expected to play a crucial role in mitigating climate change and promoting sustainable development, nevertheless, Operation and Maintenance (O&M) costs can reach 25–30 % of the total cost. Efficient O&M strategies reduce maintenance frequency, downtime, and improve overall performance. This paper reviews O&M decision-making for fixed-bottom OWFs according to a three-level decision-making hierarchy, strategic, tactical, and operational, reflecting how decisions vary by scope and time horizon of reference. Strategic decisions are typically focused on the overall maintenance strategy for the wind farm. Tactical decisions are focused on the selection of the fleet and the management of spare parts. Operational decisions are focused on the scheduling of individual maintenance tasks on a daily or weekly basis and the routing of the vessels. Exploring how the different decision-making layers have been addressed in the literature leads to valuable insights into open challenges and paves the way for the development of new decision-making methods. In this paper we highlight the untapped potential of prognostic-driven scheduling, we identify the lack of comprehensive models and methods that encompass all decision-making echelons holistically, and we emphasize the need for the integration of environmental considerations into the decision-making process.

Offshore wind farms are a promising source of renewable energy, but they face significant challenges in terms of operation and maintenance (O&M). Traditional scheduling models often overlook the potential of condition-based maintenance (CBM). Addressing this gap, this paper introduces a novel framework, incorporating principles of Model Predictive Control (MPC), to optimize the O&M scheduling of offshore wind farms using prognostic-driven maintenance. The framework integrates probabilistic remaining useful life (RUL) prognosis in a mixed-integer linear programming (MILP) optimization model with a rolling horizon approach, in alignment with MPC’s predictive and adaptive decision-making approach. The optimization model determines the optimal time to replace each component by minimizing the expected cost over the expected lifetime. This approach seeks to achieve the lowest expense while guaranteeing the highest utilization rate of each component. For the case study presented, the total O&M costs are reduced by up to 15% with respect to corrective maintenance strategies.