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Innovative Optimization and Control Methods for Highly Distributed Autonomous Systems

The Innovative Optimization and Control Methods for Highly Distributed Autonomous Systems workshop was held April 11–12, 2019.

The workshop brought together experts in the field of distributed optimization and control to exchange ideas on state-of-the-art control and optimization strategies as well as get feedback on work being done at NREL. It featured 12 leading experts in the field of optimization and control and was attended by 43 external collaborators from national labs and universities.  Topics ranged from revolutionary new data-driven control designs to scalable distributed optimization algorithms.

Access presentations from the workshop using the links below.

Distributed Control of Residential and Commercial HVAC Loads for Virtual Energy Storage
Prabir Barooah, University of Florida

Asynchronous and Distributed Tracking of Time-Varying Fixed Points
Andrey Bernstein, NREL

Toward Robustness Guarantees for Feedback-Based Optimization
Marcello Colombino, NREL

Distributed Solvers for Online, Data-Driven Network Optimization
Jorge Cortes, University of California San Diego

Data-Enabled Predictive Control: In the Shallows of the DeePC
Florian Dorfler, ETH

Online Scalable Learning Adaptive to Unknown Dynamics and Graphs
Georgios Giannakis, University of Minnesota

Distributed Reinforcement Learning with ADMM-RL
Peter Graf and Dave Biagioni, NREL

The Proximal Augmented Lagrangian Method for Non-Smooth Composite Optimization
Mihailo Jovanovic, University of Southern California

Distributed Optimization for Wind
Jennifer King, NREL

Kalman Filter and Its Modern Extensions: An Interacting Particle Perspective
Prashant Mehta, University of Illinois

On Active Constraints in Optimal Power Flow: Learning Optimal Solutions and Identifying Important Constraints
Line Roald, University of Madison Wisconsin

Optimal Steady-State Control (with Application to Secondary Frequency Control of Power Systems)
John W. Simpson-Porco, University of Waterloo

Dynamical Systems with Multiplicative Noise: Control, Sparse Design, and Learning
Tyler Summers, University of Texas-Dallas

Data-Driven Recovery of Frequency Response from Ambient Synchrophasor Data
Hao Zhu, University of Texas at Austin