Machine Learning for the Grid
Deepjyoti Deka, Scott Backhaus, Michael Chertkov, Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray, Krishnamurthy Dvijotham · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2016
Changes in the modern Grid: • Penetration of Renewables • Storage devices • Loads becomes active (not controlled) Challenges • Strong fluctuations/uncertainty • Needs real-time observability, control • Millions of devices, many entities Data Analytics can improve resiliency in the Dynamic Grid New (available) Solutions • Hardware: Smart meters, PMUs, micro-PMUs • Software/New algorithms: Machine Learning, IoT Vision: Design Algorithms for smart meter data to learn and control (state of the grid) Features: • Build upon Physics of Power flow & the network/graph features.• Scalable and computationally tractable • Address desired (spatio-temporal) sparsity