The rising U.S. offshore wind sector has the potential to unlock vast supplies of renewable energy. To harness this valuable resource, Gigawatt (GW)-scale offshore wind projects are already under way at several locations off of the U.S. coastline and are set to host wind turbines that are larger than many of the world’s tallest buildings. Realizing this promise, however, is contingent on innovative solutions to several challenges related to the optimal management of such ultra-scale offshore wind turbines, which are set to operate under harsh environmental and operational conditions, in relatively under-explored territories, and at unprecedented scales. In this talk, I will review our research group’s progress in formulating tailored machine learning (ML) and operations research (OR) solutions aimed at mitigating some of those operational uncertainties and enabling the reliable and cost-effective integration of those soon-to-be-operational wind farms. I will primarily focus on ML/OR methods which address two key challenges: (i) Uncertainty: how can we develop ML-based solutions that can make use of the multi-source, multi-resolution data in offshore wind energy regions to probabilistically and accurately predict the offshore wind power output at high spatial and temporal resolutions; and (ii) quality/reliability: how can we translate those probabilistic forecasts into operations and maintenance (O&M) decisions through offshore-tailored optimization models that consider the multi-source uncertainties and the complex multi-scale decision dependencies in offshore wind farms. Our models and analyses are tailored and tested using real-world data from the NY/NJ Bight—where several GW-scale wind projects are currently under development.

Speaker: Dr Ahmed Aziz Ezzat
Date: 25 October 2023 (Wednesday)
Time: 10:00am – 11:00am
Poster: Click here