Published April 1, 2024
| in press
Journal Article
Open
Online Learning for Robust Voltage Control Under Uncertain Grid Topology
Chicago
Abstract
Voltage control generally requires accurate information about the grid’s topology in order to guarantee network stability. However, accurate topology identification is challenging for existing methods, especially as the grid is subject to increasingly frequent reconfiguration due to the adoption of renewable energy. In this work, we combine a nested convex body chasing algorithm with a robust predictive controller to achieve provably finite-time convergence to safe voltage limits in the online setting where there is uncertainty in both the network topology as well as load and generation variations. In an online fashion, our algorithm narrows down the set of possible grid models that are consistent with observations and adjusts reactive power generation accordingly to keep voltages within desired safety limits. Our approach can also incorporate existing partial knowledge of the network to improve voltage control performance. We demonstrate the effectiveness of our approach in a case study on a Southern California Edison 56-bus distribution system. Our experiments show that in practical settings, the controller is indeed able to narrow the set of consistent topologies quickly enough to make control decisions that ensure stability in both linearized and realistic non-linear models of the distribution grid.
Copyright and License
This work is licensed under a Creative Commons Attribution 4.0 License.
Acknowledgement
We thank Dimitar Ho for useful conversations. This work was supported by the Caltech Resnick Sustainability Institute, two Caltech/Amazon AWS AI4Science fellowships, and National Science Foundation grants CNS-2146814, CNS-2106403, CPS-2136197, ECCS-2200692, and NGSDI-2105648.
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Additional details
- ISSN
- 1949-3061
- Resnick Sustainability Institute
- Caltech / Amazon AWS AI4Science Fellowship
- National Science Foundation
- CNS-2106403
- National Science Foundation
- CNS-2146814
- National Science Foundation
- ECCS-2136197
- National Science Foundation
- ECCS-2200692
- National Science Foundation
- CNS-2105648
- Caltech groups
- Resnick Sustainability Institute, AWS Center for Quantum Computing