Sixth Workshop on Autonomous Energy Systems

The Sixth Workshop on Autonomous Energy Systems was held Sept. 6–8, 2023.

The Workshop on Autonomous Energy Systems was the sixth in a series of free workshops focused on basic research in optimization theory, control theory, big data analytics, and complex system theory. One of the goals of this workshop was to identify research directions for achieving 100% clean electricity by 2035. The workshop also aimed to bridge gaps between academic and industry energy systems communities, featuring talks for academia, national laboratories, and industry leaders to identify and build fruitful collaborations that address challenges in the adoption of resilient and efficient autonomous energy grids.

Wednesday, Sept. 6, 2023

Microgrid Program R&D Within the U.S. Department of Energy—Dan Ton, U.S. Department of Energy (DOE) Office of Electricity

Industry Progression Towards Adaptive Networked Microgrids—John J. O’Donnell Jr., DTE Energy

Challenges and Opportunities in Exploiting Flexible Resources in Electric Power Systems—Dennice Gayme, Johns Hopkins University

Interoperability of Grid-Forming Controls for Inverter-Based Resources—Sairaj Dhople, University of Minnesota

Control and Coordination of Virtual Power Plants Via Reset Controllers—Jorge Poveda, UC San Diego

Dynamic Shaping of Grid Response With Inverters—Bala Kameshwar Poolla, NREL

Electric Demand Management Without Price Elasticity Models—Emiliano Dall’anese, CU Boulder

Physics Informed and Data Driven Approaches to Managing Energy Systems at Scales Under
Uncertainty—Michael Chertkov, University of Arizona

Fuel Reliability, Energy Equity, and Decarbonization by Optimized Management for Grid Automation
and Security—Anatoly Zlotnik, Los Alamos National Laboratory

Modeling and Analysis of Dynamic Interactions Between Converter-Dominated Transmission and
Distribution Systems—Dominic Groß, University of Wisconsin

Thursday, Sept. 7, 2023

A Hierarchical Control Strategy for Demand Response Application: Testbed-Based Validation—Shakil Hossan, Eaton

Beyond PMU: AI-powered Monitoring, Control, and Data Streaming—Lang Tong, Cornell University

Bayesian Hankel Matrix Completion for Synchrophasor Data Recovery—Ming Yi, Columbia University

Physics-Informed Closed-Form Twining of Power Flow—Parikshit Pareek, Los Alamos National Laboratory

Neuromancer: A Differentiable Programming Library for Modeling and Control of Dynamical Systems With Constraints—Jan Drgona, Pacific Northwest National Laboratory

Learning Coherent Clusters in Weakly Connected Power Networks—Enrique Mallada, Johns Hopkins University

Learning-Augmented Algorithms for Sustainable Systems—Adam Wierman, Caltech

Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation—Jorge Cortes, UC San Diego

Sorta Solving the OPF by Not Solving the OPF: DAE Control Theory and the Price of Realtime Regulation—Ahmad Taha, Vanderbilt University

Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach With Safe Gradient Flow—Yuanyuan Shi, UC San Diego

System-Constrained Multi-Agent RL in Control of Energy Resources—Ahmed S. Zamzam, NREL

Friday, Sept. 8, 2023

Follow the Money, and How Does Storage Actually Make It?—Paul Reed, Doral Renewables

How Can Consumers Most Effectively Reduce the Carbon Footprint of Their Electricity Consumption?—Line A Roald, University of Wisconsin

Facilitating Energy Storage Integration With Model-Based Machine Learning—Bolun Xu, Columbia University

Energy Justice for Detroit—Johanna Mathieu, University of Michigan

A Consensus-Based Multi-Agent Reinforcement Learning Framework for Peer-to-Peer Energy Trading With Voltage Control—Andrew Liu, Purdue University

System Optimization With Human in the Loop—Andrey Bernstein, NREL