Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic efficiency, and environmental impact. Rather than installing additional, costly sensing and communication infrastructure to obtain additional real-time information, it may be possible to use existing sensing capabilities and leverage knowledge about the system to reduce the need for new infrastructure. In this talk I will describe our efforts to disaggregate a distribution feeder’s demand measurements into: 1) the demand of a population of air conditioners, and 2) the demand of the remaining loads connected to the feeder. We employ two online learning algorithms Dynamic Mirror Descent and Dynamic Fixed Share, which use real-time distribution feeder measurements as well as dynamical system models generated from historical building- and device-level data. We have developed several implementations of the algorithms and conducted case studies using real demand data from households and commercial buildings to investigate the effectiveness of the algorithms. We have also explored connections between these online learning algorithms and Kalman filtering. Collaborators: Gregory Ledva, Laura Balzano, and Zhe Du.
Johanna Mathieu is an assistant professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. She received her Ph.D. in mechanical engineering from the University of California, Berkeley in 2012 and was a postdoctoral researcher in the Power Systems Laboratory at ETH Zurich, Switzerland before starting at UM in January 2014. Her research focuses on ways to reduce the environmental impact, cost, and inefficiency of electric power systems via new operational and control strategies. She is particularly interested in developing new methods to actively engage distributed flexible resources such as energy storage, electric loads, and distributed renewable resources in power system operation.