This presentation explores challenges and advancements in optimizing power systems operations through Grid Mind, an innovative data-driven framework designed to enhance the integration of renewable energy sources. Employing advanced learning algorithms, this framework innovates in strategic resource allocation and control, thereby improving efficiency and reliability in power systems with a high penetration of renewables. The transformative potential of this AI-assisted technology is showcased through real-world applications, demonstrating its effectiveness in addressing the complexities of modern power systems. Moreover, the talk addresses critical safety considerations and practical deployment issues, underscoring the need for robust, secure, and adaptable solutions. The capabilities of Grid Mind as a distributed, learning-based system optimized for edge devices are discussed, showcasing a significant advancement towards sustainable, safe, and efficient power system operations in an era dominated by renewable energy.
Di Shi is an Associate Professor with the Klipsch School of Electrical and Computer Engineering at New Mexico State University. Before academia, he founded a tech startup focusing on energy systems AI, commercialized two technologies, and founded and led the AI & System Analytics group at GEIRINA. He has also held research positions at NEC Labs, EPRI, and Arizona State University. He leads two IEEE task forces/working groups on IoT and machine learning for power systems, holds 27 patents, and serves as associate editor for several IEEE and IET transactions. He led a team to the championship of 2019 L2RPN power system AI competition. He earned his Ph.D. and M.S. degrees from Arizona State University and a B.S. from Xi’an Jiaotong University (in China).