Sara Eftekharnejad
Syracuse University, NY·Engineering·Early-career
Electric power grids are being asked to run with far more uncertainty than they were designed for. Wind and solar generation rise and fall with the weather, so the supply side is no longer predictable, and increasingly severe storms are knocking out lines and equipment more often.
- Fields
Engineering
Mathematics & Statistics
Computer Science
- Topics
power systems
power grid
uncertainty quantification
- Grants
- 1 known grant, through Feb 2028
What they work on
- Build a scalable framework to quantify interdependent power grid uncertainties from renewable generation and component outages, combining cascading failure modeling with machine learning and statistics for near-real-time analysis.
- Develop hybrid stochastic grid models in which data-driven models and the physical power flow equations inform each other, so that uncertainty estimates remain physically realistic.
- Construct causal models that trace how an individual uncertainty, such as a single outage or a swing in renewable output, propagates into grid operating conditions.
- Integrate the research into education and outreach, training undergraduate and high school students and partnering with industry experts, public libraries, and local schools to broaden participation in engineering.
More about the research
These two sources of uncertainty are not independent: the same weather that changes renewable output also causes outages. Operators need tools that can represent both together, fast enough to be useful while the grid is running, so that reliability can be maintained as more renewable generation is connected.
Why it matters
If grid operators can quantify how renewable variability and weather-driven outages interact in near real time, they can run the system more efficiently and reliably and remove a practical barrier to connecting more wind and solar generation. That matters for utilities and for communities exposed to severe weather and to outages during it.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Anirudh Subramanyam
Pennsylvania State University, PA·Engineering
Develop optimization methods for AC Optimal Power Flow that ensure grid safety limits hold with high probability when wind, solar and load forecast errors are non-Gaussian, using Gaussian mixture models.
Yang Weng
Arizona State University Campus Immersion, AZ·Engineering·Early-career
Develop reduced-order models of data-center power electronics showing how converters, control loops and energy storage shape the load and impedance seen by the electric grid.
Wencong Su
University of Michigan-Dearborn, MI·Engineering
Build a fundamental understanding of the practical challenges facing low-inertia power grids with high penetration of grid-forming inverter-based energy resources, which set grid voltage and frequency themselves.
The school
Syracuse University: 13 labs on PI Pages, $199M a year on research, 168 research doctorates a year.
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