John N. Jiang

Email Dr. Jiang
DEH 443
Laboratory: DEH 418
Lab (405) 325-0181
Office (405) 325-0927

John Jiang, Ph.D. received his doctoral degree from the Department of Electrical and Computer Engineering of the University of Texas at Austin, in a joint program of power system engineering, computational finance and energy economics (graduated with honors from College of Engineering and McCombs School of Business), a master degree in power system engineering from the University of Texas at Austin, a master degree in renewable energies from Institute of Electrical Engineering of Chinese Academy of Sciences (graduated with the CAS President Prize). He received his undergraduate degree in electrical machine and power electronics  from Tsinghua University (graduated with honor).

Jiang joined the University of Oklahoma in 2007 and received 2010 NSF CAREER award. His research interests have been focusing on uncertainty and risk related issues associated with the complex dynamics of energy flow in power systems and grid integration of renewable energy resources. Since 2009, his research group has been dedicated to the development of a novel structure-based analytics, aiming to: 1) re-sculpt the power grid to a  geodesic topology  to reveal critical fractal patterns of complex time domain energy dynamics, 2) use structural information in analysis of dynamical stability of the power grid, 3) provide effective and fast non-electric-circuit-based quantitative methods to address stability problems for integration of renewables.

Working closely with the power industry, a number of structure-based algorithms have been built, which helped solving a number of long-standing challenges known to the power industry, such as fast creation of optimal system restoration plans with detailed instructions, real-time assessment of dynamic operating active power stability limits without time-domain simulations, etc. Some of these algorithms have been translated into various industrial grade solutions and in production for grid operations at various power companies including ERCOT, ISO-New England, Southwest Power Pool (SPP), and the Transmission Owers (TOs).

• Time-space manifestation of energy influx in many-body spin network through,
  • Constructing rudimental models and analytics based on the first principles and the knowledge about the complex energy flows in electric power grids.
  • Exploring dynamical characteristics of energy influx and propagation in a many-body system type network by assessing the information dispersion in a general unitary space unfolded through hybrid geodesic and coordinates transformations.
  • Quantifying the upper-bounds of ultra-fast influx with the standards brought from post-Maxwell's-time advances in physics, including Heisenberg uncertainty principle, special relativity effects, state entanglement and superposition, and applied high-frequency wave propagation theories, etc..
    
• Expansion of the structure-based analytics for fast estimation of energy propagation states in real-time electric energy and information system to,
  • Estimating real-time energy influx and propagation in a less-than-half-cycle time frame period before the first swing being fully developed.
  • Assessing real-time stability limits and security margins using fast network sensing and cyber technology, hybrid transformation tools, graph and physics-based intelligence learning algorithms.
    
• Conversion of structure-analysis-based analytics to practical solutions for mitigation of cascading events in power grids in presence of rich and coupled energy surges induced by both preventive and active resiliency-aimed-switching of  inverter-based resources.