Near-Optimal Replacement Policies for Offshore Wind Farms

We consider the problem of optimally maintaining an offshore wind farm wherein wind turbines progressively degrade over time because of normal usage and exposure to a randomly varying environment. The turbines exhibit both economic and stochastic dependence due to shared maintenance setup costs and their common environment. However, even modestly sized farms can be difficult to analyze because of dimensionality considerations. Our aim is to identify near-optimal replacement policies that minimize the total expected discounted setup, replacement, and lost production costs over an infinite time horizon. The problem is formulated using a Markov decision process model that becomes intractable as the number of turbines increases. Therefore, we introduce a novel value function decomposition within the approximate linear programming (ALP) framework that exploits the unique features of the problem. Using a column generation algorithm to solve the dual of ALP (DALP) formulation, we establish lower and upper bounds for the value function, enabling us to quantify the optimality gap, which is under 1% for homogeneous farms and between 4% and 6% for heterogeneous farms. Furthermore, we bound the optimality gap between the exact value function and the approximate one using the optimal solutions of the dual of the linear programming formulation and the DALP formulation. Finally, we provide conditions under which an optimal policy can be retrieved from the approximate value function. Our results offer invaluable guidance to wind farm operators on managing the high costs of offshore wind farm maintenance. Because a significant portion of wind energy costs are linked to the replacement of major components, near-optimal replacement policies may help reduce these costs and make wind energy more cost-effective. Through sensitivity analyses, we explain the impact of three key factors—the environment, shared setup costs, and the number of turbines—on the characteristics of these policies. Additionally, we provide insights into the structure of the near-optimal policies and explain nonintuitive behaviors within the context of offshore wind turbine farms.

Supplemental Material: The electronic companion is available at https://doi.org/10.1287/serv.2025.0001.