Decentralized Dynamic Optimization for Power Network Voltage Control

TL;DR

This paper analyzes a decentralized voltage control method for power networks, demonstrating its effectiveness under asynchronous updates and time-varying conditions through theoretical bounds and numerical validation.

Contribution

It provides a comprehensive analysis of a GP-based decentralized voltage control under dynamic and asynchronous scenarios, extending to general constrained optimization problems.

Findings

Improved convergence conditions for asynchronous voltage control.

Error bounds for tracking optimal solutions in dynamic settings.

Validation through numerical tests on realistic power networks.

Abstract

Voltage control in power distribution networks has been greatly challenged by the increasing penetration of volatile and intermittent devices. These devices can also provide limited reactive power resources that can be used to regulate the network-wide voltage. A decentralized voltage control strategy can be designed by minimizing a quadratic voltage mismatch error objective using gradient-projection (GP) updates. Coupled with the power network flow, the local voltage can provide the instantaneous gradient information. This paper aims to analyze the performance of this decentralized GP-based voltage control design under two dynamic scenarios: i) the nodes perform the decentralized update in an asynchronous fashion, and ii) the network operating condition is time-varying. For the asynchronous voltage control, we improve the existing convergence condition by recognizing that the voltage based gradient is always up-to-date. By modeling the network dynamics using an autoregressive process and considering time-varying resource constraints, we provide an error bound in tracking the instantaneous optimal solution to the quadratic error objective. This result can be extended to more general \textit{constrained dynamic optimization} problems with smooth strongly convex objective functions under stochastic processes that have bounded iterative changes. Extensive numerical tests have been performed to demonstrate and validate our analytical results for realistic power networks.