[CCoE Notice] Dissertation Announcement: Mingze Li, "Quantum Assisted Optimization, Machine Learning for Coordinated Power to Hydrogen System"

[Greenwell, Stephen J]

[Dissertation Defense Announcement at the Cullen College of Engineering]
Quantum Assisted Optimization and Machine Learning for Coordinated Power to Hydrogen System
Mingze Li
April 14, 2025, 10 a.m. to 12 p.m. (CST)
Location: T2-204 or Zoom<https://urldefense.com/v3/__https://uh-edu-cougarnet.zoom.us/j/99146061274*success__;Iw!!LkSTlj0I!F0YpWJ0MVuXVBl0tXl2HgmW78QUrpg3XcrHAK9dS1zAyX0CWPwG2FmRsNBK1DizoDjsYNDtDKdoWByirzICLoh6Dxgk$ >
Committee Chair:
Dr. Lei Fan, Ph.D. | Dr. Zhu Han, Ph.D.
Committee Members:
Dr. David Mayerich, Ph.D. | Dr .Miao Pan, Ph.D. | Dr. Xiaodi Wu, Ph.D.
Abstract
The rapid increase in green energy adoption and the global commitment to zero carbon emissions have catalyzed a transformative interest in hydrogen as a sustainable energy carrier. Hydrogen's ability to store excess renewable energy and provide carbon-free energy solutions has led to an explosion in research and development efforts. This growing demand for hydrogen systems drives the need for advanced optimization models that can coordinate energy distribution effectively. Our proposal aims to address this challenge by developing a comprehensive framework that maximizes system profitability while integrating hydrogen storage and distribution. The coordinated operation of energy systems, particularly those involving hydrogen, introduces complexities that require both innovative modeling and computational efficiency to realize the potential of these renewable energy sources.
Traditional optimization methods struggle to handle the complexities of these models due to the nonlinearities and mixed-integer nature inherent in hydrogen and energy system operations. The interplay between hydrogen production, storage, and power distribution adds layers of complexity that cannot be adequately addressed using conventional techniques. Recognizing these challenges, our research focuses on a novel formulation that employs Benders decomposition to manage the intricate structure of the mixed-binary nonlinear optimization problem. By splitting the problem into a pure binary master problem and a linear subproblem, we simplify the computation and enhance the model's solvability. This decomposition approach provides a structured method to optimize energy flows, storage strategies, and resource allocation, paving the way for more efficient and practical energy system designs.
Furthermore, the advancement of quantum annealing technology presents a promising avenue for solving complex pure binary problems. Quantum annealing has the potential to address computational bottlenecks by efficiently finding solutions within polynomial time, making it a powerful tool for the master problem in our Benders decomposition framework. Our proposal leverages this technology to accelerate the solution process, demonstrating the viability of quantum-assisted optimization in real-world energy applications. By integrating quantum and classical computing methods, our approach offers a scalable solution for the coordinated operation of hydrogen and power systems. This research not only advances the field of energy optimization but also contributes to the broader goal of achieving a sustainable and carbon-neutral future through innovative, high-performance computational strategies.
[Engineered For What's Next]


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