Predictive Analytics for Power Systems Decision Making

Presented by: Rui Yang, Research Engineer, National Renewable Energy Laboratory

April 25th, 2019- 1:00PM EDT 

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Increasing amounts of heterogeneous sensor data and information are becoming available in energy grids from sources such as smart meters, distributed generation, and smart home energy management systems. Being able to collect, curate, and create actionable information with these data will be crucial to power systems operations with the increasing penetrations of distributed energy resources. In this webinar, we will present NREL’s latest work on developing predictive analytics to facilitate the real-time decision making in power systems operations. In this work, a high-precision predictive state estimator is first developed which employs sparse measurement data to provide system-wide awareness in distribution systems, while traditional state estimation techniques have difficulty coping with the low- observability conditions often present on the distribution systems due to the paucity of sensor measurements. Based on the predicted system conditions, grid operators can proactively control all the flexible resources by employing coordinated optimization techniques. The developed technologies allow grid operators to manage power systems with lean reserve margins while maintaining and enhancing grid reliability with high penetrations of renewable energy resources.

WHO SHOULD ATTEND: AnyoneResearchers in smart grid area, utilities, power system operators


ImageRui Yang
Dr. Rui Yang is a research engineer in the Power Systems Engineering Center at the National Renewable Energy Laboratory (NREL). Her areas of expertise include advanced data analytics, machine learning, and optimization in electric power systems. She currently leads multiple efforts at NREL on developing advanced data analytics for energy systems applications. She holds a Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University and a B.E. degree in Electrical Engineering from Tsinghua University.


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To view previous webinars on-demand, visit the IEEE Smart Grid Resource Center