# Reverse and Forward Engineering of Local Voltage Control in Distribution Networks

arXiv: https://arxiv.org/abs/1801.02015 · HTML: https://mlsanity.com/paper/1801.02015/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2018.

## Built on by (most cited first, 3 of 3)

- [Optimal Design of Volt/VAR Control Rules of Inverters using Deep Learning](https://mlsanity.com/paper/2211.09557/) (2022)
- [Anytime-Competitive Reinforcement Learning with Policy Prior](https://mlsanity.com/paper/2311.01568/) (2023)
- [Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks](https://mlsanity.com/paper/2301.01440/) (2023)

## Abstract (arXiv)

The increasing penetration of renewable and distributed energy resources in distribution networks calls for real-time and distributed voltage control. In this paper we investigate local Volt/VAR control with a general class of control functions, and show that the power system dynamics with non-incremental local voltage control can be seen as distributed algorithm for solving a well-defined optimization problem (reverse engineering). The reverse engineering further reveals a fundamental limitation of the non-incremental voltage control: the convergence condition is restrictive and prevents better voltage regulation at equilibrium. This motivates us to design two incremental local voltage control schemes based on the subgradient and pseudo-gradient algorithms respectively for solving the same optimization problem (forward engineering). The new control schemes decouple the dynamical property from the equilibrium property, and have much less restrictive convergence conditions. This work presents another step towards developing a new foundation -- network dynamics as optimization algorithms -- for distributed realtime control and optimization of future power networks.