Illya Hicks
Rice University, TX·Mathematics & Statistics
The electric grid was built on the assumption that power flows from a few large plants to many consumers over reliable infrastructure. Climate change, aging utility equipment, safety concerns and the push toward renewable generation all strain that assumption.
- Fields
Mathematics & Statistics
Engineering
Computer Science
- Topics
combinatorial optimization
discrete optimization
microgrids
- Grants
- 1 known grant, through Jun 2027
What they work on
- Develop polyhedral theory — representing discrete choices as geometric shapes amenable to linear methods — to formulate and solve optimization problems for battery storage in microgrids.
- Use branch decompositions, which recursively split a network into loosely connected pieces, to build efficient algorithms for partitioning electrical grids and ensuring observability through sensor placement.
- Design computationally efficient, scalable combinatorial optimization algorithms for power flow problems arising in microgrids, and disseminate them as publications and software.
- Translate the resulting discrete optimization techniques for power systems into a graduate course at the intersection of the two fields.
More about the research
One alternative is the microgrid: a small, self-contained network of batteries, solar panels and sometimes small turbines or engines that can serve a single building, a cluster of buildings, or a small community, connecting to the larger grid or running independently depending on demand and economics. Deciding how to design and operate such systems means solving hard discrete decision problems — which units to install, how to split a network into pieces, where to place sensors — and this project develops the mathematics and algorithms for them.
Why it matters
As utilities add renewable generation and communities consider running their own small power networks, designers need algorithms that can decide quickly how to size storage, divide a grid into manageable sections, and place enough sensors to monitor it. Practical, scalable optimization methods of the kind proposed here could make microgrid design and operation tractable for the smart grid community, and the underlying combinatorial theory would carry over to other network problems.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Amritanshu Pandey
University of Vermont, VT·Engineering
Build a scalable framework that uses equivalent-circuit representations of grid models to solve large nonlinear power grid simulations and optimizations split into bordered-block-diagonal pieces.
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.
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.
The school
Rice University: 24 labs on PI Pages, $278M a year on research, 267 research doctorates a year.
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