Convergence of AI, Physics, Computing, and Control for Energy Transformation by Dr. Qiuhua Huang

#power #system #grid #computation

Abstract: Energy transformation from decarbonized power systems, distributed energy resources, to building and transportation electrification is vital to meet clean energy goals in all counties and tackle climate changes. It also poses significant challenges in managing, optimizing and controlling the resulting complex energy systems. In the era of unprecedented technological advancement, the synergy between artificial intelligence (AI), physics, computing, and control systems presents unparalleled opportunities for addressing these challenges and revolutionizing the energy landscape. This talk explores the convergence of these disciplines and its profound implications for energy transformation. An integrated framework based on the idea of Convergence of AI, Physics, Computing, and Control is developed. At the heart of this convergence lies the fusion of AI with principles of physics, e.g., leveraging AI algorithms to model complex physical phenomena and exploiting physics to enhance the efficiency and robustness of AI algorithms. Moreover, computing technologies serve as the backbone of this interdisciplinary synergy, facilitating data analytics and decision-making in energy operations. In tandem with AI and computing, control theory plays a pivotal role in orchestrating the dynamics of energy transformation. This presentation delves into implementation of the convergence framework in two case studies, i.e., intelligent emergency control decision support in power system control rooms and intelligent energy management of a community. Finally, this talk also discusses the potential of this convergency framework, when combined with new hardware and software platform, for transforming the grid operation and control from the control rooms to the grid edge.

Dr. Qiuhua Huang is an Associate Professor in the Electrical Engineering Department at Colorado School of Mines (Mines). Before joining Mines, he was a Principal Power System Engineer at Utilidata Inc and a Staff Power System Research Engineer at Pacific Northwest National Laboratory. He received his Ph.D. degree in electrical engineering from Arizona State University in 2016. He is the recipient of the 2019 IEEE Power and Energy Society (PES) Prize Paper Award, 2018 R&D 100 Award and a few best conference paper awards in IEEE PES General Meeting. His research interests include power system modeling, simulation and control, fusion and application of AI/machine learning and advanced computing technologies for digitizing and transforming power and energy systems.