Online learning for robust voltage control under uncertain grid topology

TL;DR

This paper presents an online voltage control method that uses a nested convex body chasing algorithm combined with robust predictive control to ensure network stability despite uncertain grid topology and load variations.

Contribution

It introduces a novel online approach that narrows down grid models and adjusts reactive power in real-time, improving voltage stability under topology uncertainty.

Findings

Controller quickly narrows down possible topologies

Ensures voltage safety in linearized and non-linear models

Effective in a real distribution system case study

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.