Seventh Workshop on Autonomous Energy Systems
The Seventh Workshop on Autonomous Energy Systems was held Sept. 3–6, 2024.
The Workshop on Autonomous Energy Systems was the seventh in a series of free workshops focused on basic research in optimization theory, control theory, big data analytics, and complex system theory. This workshop aimed to identify research directions for achieving 100% clean electricity by 2035, provide tools to design planning and operation frameworks accounting for the complexity of modern energy systems, and bridge gaps between academic and industrial energy systems communities. This workshop held a special emphasis on incentive-based operation and AI-based modeling and control.
Tuesday, Sept. 3, 2024
Risk Management and Clean Energy Transition—Yury Dvorkin, Johns Hopkins University
Resilience and Distributed Decision-Making in a Renewable-Rich Power Grid—Anuradha M Annaswamy, Massachusetts Institute of Technology
Energy Storage Market Power and Strategic Withholding—James Anderson, Columbia University
Decentralized Integration of Solar and Storage into Wholesale Energy Markets via Mean-Field Games—Andrew L Liu, Purdue University
Feedback Optimization of Energy Prices for Real-Time Demand-Response—Guido Cavraro, National Renewable Energy Laboratory
Online Learning for Residential Demand Response via Advanced Multi-Armed Bandits—Xin Chen, Texas A&M University
Decentralized Adaptive Under-Frequency Load Shedding in Power Systems With Socio-Technical Criticality Functions—Jorge Poveda, University of California San Diego
Physics-Informed Artificial Intelligence Simulator for Power System Applications—Rahul Nellikkath, Technical University of Denmark
Wednesday, Sept. 4, 2024
Networked Microgrids as Quasi-Autonomous Energy Systems in an Evolving Grid—Emeka Obikwelu, U.S. Department of Energy Office of Electricity
End-to-End Microgrid Protection Using a Distributed Data-Driven Method—Jing Wang, National Renewable Energy Laboratory
A Co-Simulation Platform for Modeling and Testing Dynamic Boundary Fractal—Christabella Annalicia, Lawrence Livermore National Laboratory
Non-Cooperative Games to Control Learned Inverter Dynamics of Distributed Energy Resources—Patricia Hidalgo-Gonzalez, University of California San Diego
Dynamic Networked Microgrids for Large-Scale Distributed Energy Resource Integration—Andrey Bernstein, National Renewable Energy Laboratory
Incentive-Based DSO Participation in Grid Voltage Support—Saverio Bolognani, ETH Zurich
Distributed Optimization for Infeasible Combinded T&D Networks—Amritanshu Pandey, University of Vermont
Generative AI Approaches for the Design and Transition of Modern Energy Systems—Alexandre Cortiella, National Renewable Energy Laboratory
Thursday, Sept. 5, 2024
Revolutionizing Grid Dynamics: Distributed AI for Electric Vehicles—YC Zhang, Utilidata, Inc.
Learning-Enhanced Design and Optimization of Microgrids Under Uncertainty—Harsha Nagarajan, Los Alamos National Laboratory
Learning to Optimize via Implicit Networks—Samy Wu Fung, Colorado School of Mines
Neuromancer: Scientific Machine Learning Library for Modeling, Optimization, and Control—Jan Drgona, Pacific Northwest National Laboratory
Modelling the Impact of Singapore’s Largest Distributed Energy Resources—John Zhen Fu Pang, A*STAR's Institute of High Performance Computing
Characterizing Atmospheric Effects in Offshore Wind Integration—Dennice Gayme, Johns Hopkins University
Probabilistic Forecasting via Generative AI—Lang Tong, Cornell University
Understanding Impactful Extremes for Controlled Resilience—Michael (Misha) Chertkov, University of Arizona
Friday, Sept. 6, 2024
Optimal Power Flow Sensitivities: Analysis and Applications—Manish K Singh, University of Wisconsin-Madison
GPU-Accelerated Nonlinear Programming—Mihai Anitescu, Argonne National Laboratory
Optimizing Power Distribution Grids on a Data Budget—Vassilis Kekatos, Purdue University
Optimal Multi-Microgrid Service Guarantees With Diminishing Power Transfer Rates over Increasing Horizons—Murti Salapaka, University of Minnesota
Data-driven Modeling of Linearizable Power Flow—Hao Zhu, The University of Texas at Austin
Data-Driven Optimization Using Limited Data—Deep Deka, Massachusetts Institute of Technology Energy Initiative
Learning to Optimize Meets Neural-ODE: Real-Time Stability-Constrained AC Optimal Power Flow—Kyri A Baker, University of Colorado Boulder
Previous Workshops
Sixth workshop on Autonomous Energy Systems (2023)
Fifth Autonomous Energy Systems Workshop (2022)
Fourth Resilient Autonomous Energy Systems Workshop (2021)
Third Autonomous Energy Systems Workshop (2020)
Second Autonomous Energy Systems Workshop (2019)
First Autonomous Energy Grids Workshop (2017)
Contact
Any questions can be directed to Michelle Patton.
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