Lei Wu
Stevens Institute of Technology, NJ·Engineering
Local power grids are changing fast as rooftop solar and other distributed clean energy sources come online. Operators of these distribution networks must know the system's current condition (the "state", such as voltages across the network) and how it is wired at any moment (its "topology").
- Fields
Engineering
Mathematics & Statistics
Computer Science
- Topics
power systems
smart grid
electricity distribution systems
- Grants
- 1 known grant, through Apr 2027
What they work on
- Design likelihood functions and prior distributions for Bayesian models that capture realistic measurement errors and physical characteristics of smart electricity distribution systems.
- Develop methods for non-Gaussian priors, improper priors and non-Gaussian likelihoods, along with fast variational expectation-maximization algorithms to estimate parameters in hierarchical Bayesian models.
- Create efficient distributed algorithms for state estimation and topology identification that exploit distribution-grid structure, and prove their convergence using optimization theory and statistics.
More about the research
Unlike high-voltage transmission grids, distribution grids have few real-time sensors, wiring that changes often, and many mixed sources of uncertainty. As a result, tools built for transmission grids often fail here.
Why it matters
Utilities running grids with growing amounts of solar and other distributed energy need reliable views of system conditions to operate securely and at low cost. Estimates that come with trustworthy uncertainty, despite sparse sensors and frequently changing wiring, could support safer and more economical operation of smart distribution grids during the clean-energy transition.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Anil Pahwa
Kansas State University, KS·Engineering
Develop optimal polling strategies that choose which smart meters to read, and when, so a limited subset of meters gives a full picture of the low-voltage distribution system.
Mingxi Liu
University of Utah, UT·Engineering·Early-career
Develop optimization, machine learning and statistical tools for a grid-edge resource control framework that scales with device population and network size and works across different device types and grid services.
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
Stevens Institute of Technology: 4 labs on PI Pages, $71M a year on research, 72 research doctorates a year.
Reach out
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