Bing Yan
Rochester Institute of Technology, NY·Engineering·Early-career
Wind and solar power rise and fall with the weather, so a low-carbon power grid needs places to store energy and release it later. Large batteries and other energy storage resources (ESRs) can do this, but they are hard for grid operators to schedule.
- Fields
Engineering
Mathematics & Statistics
Computer Science
- Topics
power systems
energy storage
battery storage
- Grants
- 1 known grant, through Jun 2029
What they work on
- Build market participation models for energy storage resources that range from self-scheduling to full operator management, accounting for changing state-of-charge limits and uncertain renewable output.
- Develop a deep-learning approach that tightens grid scheduling formulations toward their convex hull, the tightest simple description of valid solutions, so that problems with storage solve faster.
- Apply Ordinal Optimization, which ranks options to find good-enough answers, to sharply reduce the complexity of grid operation problems involving very large numbers of storage units.
- Address mismatches between day-ahead scheduling and real-time dispatch caused by storage features such as two-way charging and time-linked state of charge.
More about the research
A storage device can both charge and discharge. What it can do in one hour also depends on how full it was in earlier hours. These features make grid planning problems slow to solve, especially when thousands of storage units take part.
Why it matters
Grid operators that manage regional electricity markets could use these models and solvers to schedule large fleets of batteries and other storage more efficiently alongside wind and solar power. Because the tools are designed to plug into operators' existing software, they may help lower operating costs and emissions, supporting a cleaner and more reliable power supply.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
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.
Subhonmesh Bose
University of Illinois Urbana-Champaign, IL·Engineering·Early-career
Determine whether agents can learn each other's preferences through repeated interactions in incentive design problems, using tools from game theory, control theory and machine learning.
Jan Kleissl
University of California-San Diego, CA·Engineering
Build DERConnect, a remote-access testbed that integrates solar panels, battery storage, electric vehicles, wind and solar emulators, and controllable building lighting and HVAC (heating, ventilation and air conditioning) systems.
The school
Rochester Institute of Technology: 9 labs on PI Pages, $78M a year on research, 65 research doctorates a year.
Reach out
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