Dr. Ahmad F. Taha, Assistant Professor in the Department of Electrical and Computer Engineering, has recently received a $265,000 National Science Foundation (NSF) Grant Award for his research in “Collaborative Research: Advancing Robust Control and State Estimation of Converter-Based Power Systems” on May 5th, 2020.
Future power grids, the nation’s most critical infrastructure, will be extremely difficult to manage due to large-scale integration of renewable energy resources. The strategy proposed in this project is aided by new grid technologies (converter-based assets in wind/solar farms and high-frequency sensing devices) that are developed and deployed to allow new real-time control-theoretic algorithms to be implemented with little overhead—while guaranteeing grid stability and resilience. The literature in this area had addressed various scientific research questions, but mostly adopted simplified models that cannot adequately capture the real-time operation of future grids. This project addresses this science gap by developing a new set of real-time algorithms, leading to a more robust operation of future power grids characterized with high penetration of renewable energy resources. These control algorithms can be implemented by grid operators throughout the nation. The project will also include: a) hosting an outreach workshop on renewable energy systems for a low-income, minority-majority, and female-only high school in San Antonio; b) organizing a technical industry workshop that showcases the created algorithms in the state of Iowa; c) disseminating the created scientific methods within the curricula at the University of Texas at San Antonio and Iowa State University.
As we move to 100% renewables and ditch fossil fuels, we need new control theory and applied algorithms to manage the second-to-second, real-time operation of power networks. Current theory and practice, based on linear systems theory, simply won’t be good enough. Don’t like the fact that almost all control studies in power networks linearize the nonlinear dynamics, eliminate algebraic equations, and often assume simplistic models? This project will change that, and we promise that you’ll never see a linearization every again.
Read more about the grant here.