MA3a.4

Inferring Maximum Flexibility in Large Loads for Resilient Grid Operations

Shaohui Liu, Anne Gvozdjak, Deepjyoti Deka, Sungho Shin, Massachusetts Institute of Technology, United States
Session:
MA3a: Physics-Guided Machine Learning for Sustainable Power Systems Lecture
Track:
Networks and Graphs
Location:
Scripps
Presentation Time:
Mon, 27 Oct, 09:30 - 09:55 PT (UTC -7)
Presentation
Discussion
Resources
No resources available.
Session MA3a
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
MA3a.2: Estimating Load Coincidence Factors for Optimal DER Dispatch in Radial Distribution Networks
Yanqing Weng, Andrew Musgrave, Christine Chen, University of British Columbia, Canada
MA3a.3: A Co-Simulation Framework for Distribution System Power Flow with Smart Inverter Dynamics
Daniel Arnold, Lawrence Livermore National Laboratory, United States
MA3a.4: Inferring Maximum Flexibility in Large Loads for Resilient Grid Operations
Shaohui Liu, Anne Gvozdjak, Deepjyoti Deka, Sungho Shin, Massachusetts Institute of Technology, United States
Contacts