MA3a: Physics-Guided Machine Learning for Sustainable Power Systems
Mon, 27 Oct, 08:15 - 09:55 PT (UTC -7)
Location: Scripps
Session Type: Lecture
Track: Networks and Graphs
Mon, 27 Oct, 08:15 - 08:40 PT (UTC -7)

MA3a.1: DecisionISO: An Agile, Physics-Informed Decision-Flow Platform for Real-Time Power-Market Operations
Robert Ferrando, Laurent Pagnier, Michael Chertkov, University of Arizona, United States
Mon, 27 Oct, 08:40 - 09:05 PT (UTC -7)

MA3a.2: Estimating Load Coincidence Factors for Optimal DER Dispatch in Radial Distribution Networks
Yanqing Weng, Andrew Musgrave, Christine Chen, The University of British Columbia, Canada
Mon, 27 Oct, 09:05 - 09:30 PT (UTC -7)

MA3a.3: A Co-Simulation Framework for Distribution System Power Flow with Smart Inverter Dynamics
Daniel Arnold, Lawrence Livermore National Laboratory, United States
Mon, 27 Oct, 09:30 - 09:55 PT (UTC -7)

MA3a.4: Inferring maximum flexibility in large loads for resilient grid operations
Shaohui Liu, Anne Gvozdjak, Deepjyoti Deka, Sungho Shin, MIT, United States