Fifth Workshop on Autonomous Energy Systems
The Fifth Workshop on Autonomous Energy Systems was held July 13–15, 2022.
The Workshop on Autonomous Energy Systems was the fifth in a series of free workshops focused on novel solutions for practical problems in energy systems monitoring, control, and optimization. The workshop aimed to bridge gaps between academic and industry energy systems communities and build fruitful collaborations that address challenges in the adoption of resilient and efficient energy systems.
Access presentations from the workshop using the links below.
Wednesday, July 13, 2022
Economics of Grid-Edge Cyber Resiliency – Yury Dvorkin, New York University
Grid Edge Intelligence Powered by GPU – Yingchen Zhang, Utilidata
Scalable Solutions for Grid-Edge Integration for Resilience – Anamika Dubey, Washington State University
Toward Future Electricity Markets With Massive DER Penetration and Optimal Transmission-Distribution Coordination – Meg Wu, Arizona State University
Thursday, July 14, 2022
Autonomous Energy Systems at NREL – Benjamin Kroposki, National Renewable Energy Laboratory
Data-Centric Approach To Capture Non-Polynomial Nonlinear Dynamics – Marcos Netto, National Renewable Energy Laboratory
Data-Driven Algorithms for Energy Justice in Detroit – Johanna Mathieu, University of Michigan
Differentiable Programming for Modeling and Control of Energy Systems – Jan Drgona, Pacific Northwest National Laboratory
Federated Architecture for Secure and Transactive Distributed Energy Resource Management Solutions (FAST-DERMS) – Yashen Lin, National Renewable Energy Laboratory
Learning-Based Analysis and Control of Safety-Critical Systems – Enrique Mallada, Johns Hopkins University
Solar Energy Technologies: System Integration RDD&D – Yi Yang, Department of Energy Solar Energy Technologies Office
Toward the Optimization of Integrated Transmission-Distribution Networks via the Rapid Prototyping of OPF Formulations with PowerModelsITD.jl – Juan Ospina, Los Alamos National Laboratory
Zero-Trust Applications for the Grid – David Lawrence, Duke Energy
Friday, July 15, 2022
Accelerated Methods for Solving Optimal Power Flow Problems – Priyank Srivastava, Massachusetts Institute of Technology
Dynamic Virtual Power Plant Control – Florian Dorfler, ETH Zurich
Inter-Area Oscillations: Monitoring and Optimization Solutions – Vassilis Kekatos, Virginia Tech University
Localization and Approximation Based Methods for Distributed Control and Optimization – James Anderson, Columbia University
Machine Learning Solutions for Monitoring U.S. Transmission Grid With Large-Scale Real-World PMU Data – Nanpeng Yu, University of California, Riverside
Physics-Aware and Risk-Aware Machine Learning for Power System Operations – Hao Zhu, University of Texas at Austin
Robust Data-Driven Control With Noisy Data – Chin-Yao Chang, National Renewable Energy Laboratory
Safe and Efficient Control Using Neural Networks: An Interior Point Approach – Baosen Zhang, University of Washington
Toward Model Reduction for Power System Transient With Physics-Informed Neural PDE – Misha Chertkov, University of Arizona
Toward Distributed Intelligence and Control for Emerging Energy Systems – Soumya Kundu, Pacific Northwest National Laboratory
Share