Optimizing Offshore Wind Farms OeM Logistics in Brazil: A Techno-Economic MILP Model

Resumo

Brazil’s offshore wind potential is largely unexplored and exceeds 1,200 GW (fixed and floating), according to a World Bank Group study, offering vast opportunities for renewable generation and for electrifying offshore oil and gas assets. Recent National Renewable Energy Laboratory (NREL) studies show that operations and maintenance (O&M) costs account for about 18 % of the Levelized Cost of Energy (LCOE) in offshore wind farms, highlighting the importance of logistical cost optimization to achieve techno-economic viability.We propose a mixed-integer linear programming (MILP) model that minimizes the combined fixed and variable costs of O&M while scheduling activities over a multi-period horizon. The MILP optimizes two logistical choices: (i) given the distance from shore, a fixed Service Operation Vessel (SOV) base should be installed offshore or the wind farm should be serviced from an onshore port; (ii) the optimal vessel fleet size and mix - the types and quantities of vessels required to execute a multi-period O&M plan at minimum total cost.Uncertainty in input parameters, such as SOV fixed costs and vessel day rates, is captured by integrating Monte Carlo sampling into the MILP loop. The model is implemented in Python and solved with Gurobi. In a case study with 40 turbines of 15 MW each, the framework produces an optimized task schedule, an expected O&M LCOE of USD 9.94 / MWh with 90 % confidence, and identifies approximately 45 km from shore as the break-even distance at which a fixed offshore base becomes cost-effective.The model's modular structure supports extensions, including stochastic corrective maintenance, crane constraints and delay penalties, providing valuable insights for strategic O&M decision-making in Brazil's offshore wind sector.