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SESSION V-05: Advanced Control Applications
Done
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SESSION V-22: Smart Systems and Technologies
Done
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SESSION V-27: Special Session on"Shaping the Future Through Serious Games, Gamification, and AI-Driven Innovation Across Domains"
Done
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SESSION V-24: Optimal Control Applications
Done
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SESSION P-02: Artificial Intelligence with Applications
Done
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SESSION P-09: Special Session on"Nonlinear Control Strategies for Robotic Systems"
Done
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SESSION P-13: Discrete Event Systems
Done
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SESSION P-39: Supply Chain and Operational Research Applications
Done
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SESSION P-49: Signal Processing
Done
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SESSION V-08: Graphs and Networks
Done
“Stability-Constrained Voltage Control in Distribution Grids”
Prof. Jorge Cortes
University of California, San Diego, USA
Chair: Carla Seatzu, University of Cagliari, Italy Room: Ballroom
Abstract
Motivated by the deployment of distributed energy resources (DERs) in power distribution grids, this talk presents a data-driven framework to design Volt/Var controllers capable of steering a power distribution network towards efficient network configurations. Our approach employs machine learning techniques to learn local surrogates that map voltages and reactive powers to ideal reactive power setpoints approximating solutions to the optimal reactive power flow problem. Equipped with these, we propose control update schemes and identify conditions on the surrogates and control parameters that guarantee that the resulting reactive power point globally asymptotically converges. For decentralized control, where controllers only have access to local measurements, we show the key role played by the monotonicity of the surrogates in ensuring asymptotically stability. We extend this idea to the case where the controllers can take advantage of an arbitrary communication infrastructure on top of the physical network. This allows the controllers to incorporate information beyond their local bus, covering the decentralized control case as a special case, and leading to less conservative constraints on the controller design. We train neural networks so that, by design, they meet the conditions on surrogates and illustrate the performance of the resulting control scheme. Our simulations show that the proposed framework guarantees voltage stability while significantly reducing the operation cost, and highlight the role of communication in improving control performance.
Biography of Prof. Jorge Cortes
Jorge Cortes is a Professor and Cymer Corporation Endowed Chair in High Performance Dynamic Systems Modeling and Control in the Department of Mechanical and Aerospace Engineering, University of California, San Diego.He is the author of "Geometric, Control and Numerical Aspects of Nonholonomic Systems" (New York: Springer-Verlag, 2002) and co-author of "Distributed Control of Robotic Networks" (Princeton: Princeton University Press, 2009).He is a Fellow of IEEE, SIAM, and IFAC.He has co-authored papers that have won the 2008 and the 2021 IEEE Control Systems Outstanding Paper Award, the 2009 SIAM Review SIGEST selection from the SIAM Journal on Control and Optimization, the 2012 O. Hugo Schuck Best Paper Award in the Theory category, and the 2019 and 2023 IEEE Transactions on Control of Network Systems Outstanding Paper Award.At the IEEE Control Systems Society, he has been a Distinguished Lecturer (2010-2014), an elected member (2018-2020) of the Board of Governors, and Director of Operations (2019-2022) of its Executive Committee.His research interests include distributed control and optimization, network science and complex systems, resource-aware control and coordination, distributed decision making and autonomy, network neuroscience, and multi-agent coordination in robotic, power, and transportation networks.