CCICADA Seminar Series in Homeland Security
Predictive and Prescriptive Analytics for Offshore Wind Energy:
Uncertainty, Operations, and Reliability
Date/Time: Friday, February 23, 2024, 11:45 am to 1:30 pm
Location: 4th Floor Lounge – 401, Computing Research & Education Building (CoRE), Busch Campus, Rutgers, the State University
***Light lunch served at 12 noon, talk at 12:15 pm. Please RSVP to Nicole Clark-Johnson <nicolec@dimacs.rutgers.edu> if you will be attending lunch.***
FEATURED SPEAKER: Ahmed Aziz Ezzat, Renewables & Industrial Analytics (RIA) Research Laboratory, Department of Industrial & Systems Engineering, Rutgers University
Abstract: The rising U.S. offshore wind sector holds great promise—both environmentally and economically—to unlock vast supplies of clean and renewable energy. To harness this valuable resource, Gigawatt (GW)-scale offshore wind (OSW) projects are already under way at several locations off of the U.S. coastline and are set to host 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 assets, which would operate under harsh environmental conditions, in fairly under-explored territories, and at unprecedented scales. In this talk, I will review our research group’s progress in formulating tailored data science (DS) and operations research (OR) solutions aimed at mitigating some of those operational uncertainties. I will primarily focus on DS/OR methods which address two key challenges: (i) Uncertainty and Forecasting: how can we develop DS-based solutions that can make use of the multi-source, multi-resolution data in OSW energy regions to accurately forecast their power output at high spatial and temporal resolutions; and (ii) Reliability and Operations: how can we translate those forecasts into optimal operations and maintenance (O&M) decisions through offshore-tailored optimization models that consider the multi-source uncertainties and complex decision dependencies in the OSW environment. Our models and analyses are tailored and tested using real-world data from the U.S. Mid-Atlantic—where several GW-scale wind farms are planned.
Bio: Dr. Ahmed Aziz Ezzat is an Assistant Professor of Industrial & Systems Engineering at Rutgers University
, where he leads the Renewables & Industrial Analytics (RIA) research group [RIA Research Group]. Before joining Rutgers, Dr. Aziz Ezzat received his Ph.D. from Texas A&M University in 2019, and his B.Sc. degree from Alexandria, Egypt, in 2013, both in Industrial Engineering. His research interests are in the areas of data and decision sciences, data science and operations research, quality and reliability engineering, and their applications to renewable energy and industrial systems. Dr. Aziz Ezzat is the recipient of the A. Walter Tyson Assistant Professorship award from Rutgers University, the 2022 IISE Data Analytics Teaching Award, The 2020 IIF-SAS® research award, the 2020 Rutgers OAT Teaching Award, and the 2014 IISE Sierleja Fellowship. He currently serves as the 2023-2024 president of the IISE Energy Systems Division. His research has been supported by several external and internal grants from the National Science Foundation (NSF), The Department of Energy (DOE) and National Offshore Wind Research and Development Consortium (NOWRDC), The NJ Economic Development Authority, The Rutgers-Provost Chancellor Office, and industry. He is a member of INFORMS, IEEE-PES, and IISE.
***Please RSVP to Nicole Clark-Johnson <nicolec@dimacs.rutgers.edu> if you will be attending lunch.
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