Robust online voltage control with an unknown grid topology

Abstract

Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability. However, accurate topology identification is a challenging problem for existing methods, especially as the grid is subject to increasingly frequent reconfiguration due to the adoption of renewable energy. Further, running existing control mechanisms with incorrect network information may lead to unstable control. 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 the network topology is initially unknown. Specifically, the online controller does not know the true network topology and line parameters, but instead must learn them over time by narrowing down the set of network topologies and line parameters that are consistent with its observations and adjusting reactive power generation accordingly to keep voltages within desired safety limits. We demonstrate the effectiveness of the approach using a case study, which shows 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. CCS CONCEPTS• Hardware → Smart grid; Power networks; • Theory of computation → Online learning algorithms; • Computer systems organization → Sensor networks; • Computing methodologies → Control methods.

Online Learning for Robust Voltage Control Under Uncertain Grid Topology

“…This paper supersedes the results of the preliminary version of this work [44] in the following aspects:…”

“…Proof. The proof is similar to [44,Lemma 2], except that learning η adds an additional dimension to the parameter space. That is, there exists a norm-preserving linear bijection between (S n ×R, • ,δ ) and Euclidean space (R m , • 2 ).…”

“…1) We improve the analysis of [44], which assumes no uncertainty in the maximum load/generation variability, to both reduce the mistake bound by a factor of 2 and also improve empirical voltage control performance. 2) We extend our approach to handle uncertainty in the maximum variability of load and generation entities in the grid, and we show that in the limiting case of 0 uncertainty, our result coincides with the improved analysis mentioned in (1).…”

“…An unknown η indicates uncertainty in the maximum variability of the exogenous power injections. Unlike [44] which assumes a fixed η, our inclusion of both an unknown η and a known upper-bound η allows more flexibility in our algorithmic design and the incorporation of prior knowledge.…”

Stability Constrained Reinforcement Learning for Real-Time Voltage Control in Distribution Systems

“…Further, with the introduction of neural networks to RL, deep reinforcement learning (DRL) has great expressive power and has shown impressive results in learning nonlinear controllers with good transient performance. As a result, there has been tremendous interest in using RL for voltage control [10]- [23] and optimize the transient performance.…”