  Despite the promising outlook, the numerous economic and environmental
benefits of offshore wind energy are still compromised by its high operations
and maintenance (O&M) expenditures. On one hand, offshore-specific challenges
such as site remoteness, harsh weather, transportation requirements, and
production losses, significantly inflate the O&M costs relative to land-based
wind farms. On the other hand, the uncertainties in weather conditions, asset
degradation, and electricity prices largely constrain the farm operator's
ability to identify the time windows at which maintenance is possible, let
alone optimal. In response, we propose STOCHOS, short for the stochastic
holistic opportunistic scheduler--a maintenance scheduling approach tailored to
address the unique challenges and uncertainties in offshore wind farms. Given
probabilistic forecasts of key environmental and operational parameters,
STOCHOS optimally schedules the offshore maintenance tasks by harnessing the
opportunities that arise due to favorable weather conditions, on-site
maintenance resources, and maximal operating revenues. STOCHOS is formulated as
a two-stage stochastic mixed integer linear program, which we solve using a
scenario-based rolling horizon algorithm that aligns with the industrial
practice. Tested on real-world data from the U.S. North Atlantic where several
offshore wind farms are in-development, STOCHOS demonstrates considerable
improvements relative to prevalent maintenance benchmarks, across various O&M
metrics, including total cost, downtime, resource utilization, and maintenance
interruptions.
