A Reference Modelling Approach for Cost Optimal Maintenance for Offshore Wind Farms

This paper presents a novel reference model designed to optimize the integration of preventive and predictive maintenance strategies for offshore wind farms (OWFs), enhancing operational decision-making. The model’s flexible and declarative architecture facilitates the incorporation of new constraints while maintaining computational efficiency, distinguishing it from existing methodologies. Unlike previous research that did not explore the intricate cost dynamics between predictive and preventive maintenance, our approach explicitly addresses the balance between maintenance expenses and wind turbine (WT) downtime costs. We quantify the impacts of these maintenance strategies on key operational metrics, including the Levelized Cost of Energy (LCOE). Using a constraint programming framework, the model enables rapid prototyping of alternative maintenance scenarios, incorporating real-time data on maintenance history, costs, and resource availability. This approach supports the scheduling of service logistics, including the optimization of vessel fleets and service teams. Simulations are used to evaluate the model’s effectiveness in real-world scenarios, such as handling the maintenance of up to 11 wind turbines per business day using no more than four service teams and four vessels, achieving a reduction in overall maintenance costs in simulated case of up to 32% compared to a solution that aims to prevent all downtime events. The prototype implementation as a task-oriented Decision Support System (DSS) further shows its potential in minimizing downtime and optimizing logistics, providing a robust tool for OWF operators.