Anirudh Subramanyam
Pennsylvania State University, PA·Engineering
Wind and solar power are key to cutting carbon emissions, but their output rises and falls in ways that are hard to predict. Grid operators must plan each day's power flows without knowing exactly how much renewable energy will arrive.
- Fields
Engineering
Mathematics & Statistics
Computer Science
- Topics
power grid
electric power systems
AC optimal power flow
- Grants
- 1 known grant, through Nov 2027
What they work on
- 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.
- Design modular algorithms so that existing chance-constrained methods proven for Gaussian errors can be reused with minor changes for nonlinear, nonconvex problems under Gaussian mixture uncertainty.
- Create non-Gaussian ambiguity sets that account for misspecified mixture model parameters, and validate the resulting robust grid operating points on real utility data.
More about the research
If they misjudge that uncertainty, the result can be outages and an unstable grid. This project develops mathematical models, theory and algorithms that let operators plan for this uncertainty more realistically.
Why it matters
Power grid operators and the companies that build their planning software need tools that handle the real, irregular uncertainty of wind and solar forecasts. More reliable grid planning methods could reduce the risk of service loss and grid instability as more renewable energy comes online, which supports decarbonization goals.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Sara Eftekharnejad
Syracuse University, NY·Engineering·Early-career
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.
Lei Fan
University of Houston, TX·Engineering·Early-career
Develop mixed-integer linear and convex optimization models that capture how data center computing load can shift across time and location, embedded within security-constrained unit commitment for scheduling generators under network limits.
Lei Wu
Stevens Institute of Technology, NJ·Engineering
Design likelihood functions and prior distributions for Bayesian models that capture realistic measurement errors and physical characteristics of smart electricity distribution systems.
The school
Pennsylvania State University: 60 labs on PI Pages, $1.3B a year on research, 743 research doctorates a year.
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