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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:VANDERBILT UNIVERSITY, THE
Doing Business As Name:Vanderbilt University
PD/PI:
  • Ahmad Taha
  • (210) 458-4340
  • Ahmad.Taha@utsa.edu
Award Date:09/15/2021
Estimated Total Award Amount: $ 265,000
Funds Obligated to Date: $ 260,291
  • FY 2020=$260,291
Start Date:09/01/2021
End Date:07/31/2023
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Research: Advancing Robust Control and State Estimation of Converter-Based Power Systems
Federal Award ID Number:2151571
DUNS ID:965717143
Parent DUNS ID:004413456
Program:EPCN-Energy-Power-Ctrl-Netwrks
Program Officer:
  • Aranya Chakrabortty
  • (703) 292-8113
  • achakrab@nsf.gov

Awardee Location

Street:Sponsored Programs Administratio
City:Nashville
State:TN
ZIP:37235-0002
County:Nashville
Country:US
Awardee Cong. District:05

Primary Place of Performance

Organization Name:Vanderbilt University
Street:PMB 407749 2301 Vanderbilt Place
City:Nashville
State:TN
ZIP:37235-0002
County:Nashville
Country:US
Cong. District:05

Abstract at Time of Award

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. This project aims at modernizing grid control methods which has traditionally relied on linear systems theory. In particular, the control-theoretic literature addressed a plethora of grid challenges with a focus on linearized, differential equation models whereby algebraic constraints (i.e., power flows) are eliminated. This is in contrast with the more realistic, complex nonlinear differential algebraic equation (NDAE) models. Linearizing grid models around operating points and eliminating algebraic constraints have proven to be a reliable strategy---a trade-off between complexity and tractability. Yet as grids are increasingly pushed to their limits via intermittent renewables, their physical states risk escaping operating regions due to a poor prediction of wind or solar. In lieu of linear differential equation models, control of NDAEs is highly beneficial for grids that are characterized by highly uncertain renewables. This guarantees grid stability for larger operating conditions. Given the limitations of present power system models and the lack of theoretical foundations for control and dynamic state estimation of grid NDAEs, this project will: 1) create a physically representative NDAE model of a power system with a mix of conventional machines and a variety of converter-based technologies; 2) investigate a general theory of dynamic state estimation and robust feedback control algorithms that consider the uncertain nature of power grids modeled via higher-order NDAEs; 3) obtain computationally tractable routines that can be implemented in control centers of power grids. The created theoretical foundations have applications in wide area control, converter-based control, centralized and decentralized and robust dynamic state estimation. This research is critical to guarantee acceptable performance of modern and future power systems and will lead to advancing the state-of-the-art of grid control studies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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