Stats, Optimization, and Machine Learning Seminar - Kyri Baker

Kyri Baker, Civil, Environmental, and Architectural Engineering Department, University of Colorado Boulder

Chance Constraints for Smart Buildings and Smarter Grids

Evolving energy systems are introducing heightened levels of stress on the electric power grid. Fluctuating renewable energy sources, dynamic electricity pricing, and new loads such as plug-in electric vehicles are transforming the operation of the grid, from the high-voltage transmission grid down to individual buildings. Grid overvoltages, instabilities, and overloading issues are increasing, but stochastic predictive optimization and control can help alleviate these undesirable conditions.
 
Optimization techniques leveraging chance (probabilistic) constraints will be presented in this talk. Different ways to incorporate chance constraints into optimization problems, including distributionally robust and joint chance constraint reformulations, will be presented. Applications in smart buildings and distribution grids with high integration of solar energy are shown to benefit from chance constrained optimization formulations, reducing grid voltage issues, conserving energy, and allowing buildings and the grid to interact in new ways.
 
Speaker bio:  Prof. Kyri Baker received her Ph.D., M.S., and B.S. in Electrical and Computer Engineering from Carnegie Mellon University in 2014, 2010, and 2009, respectively. She is currently an Assistant Professor in the Civil, Environmental, and Architectural Engineering Department, and the Electrical, Computer, and Energy Engineering Department (by courtesy) at the University of Colorado Boulder. Previously, Dr. Baker was a research engineer at the National Renewable Energy Laboratory. She has worked with academic, industry, and federal organizations to determine how advanced optimization and control techniques can shape the needs of the future smart grid.

Tuesday, September 4, 2018 at 3:30pm to 4:30pm


Engineering Center, ECCR 257 (Newton Lab)
1111 Engineering Drive, Boulder, CO 80309

Event Type

Colloquium/Seminar

Interests

Science & Technology, Research & Innovation

Audience

Faculty, Students, Graduate Students, Postdoc

College, School & Unit

Engineering & Applied Science

Tags

statistics, optimization, machine learning

Group
Applied Mathematics
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