# Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks

arXiv: https://arxiv.org/abs/2301.01440 · HTML: https://mlsanity.com/paper/2301.01440/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 4 Jan 2023.

## Builds on (most cited first, 6 of 6)

- [Fast Local Voltage Control under Limited Reactive Power: Optimality and Stability Analysis](https://mlsanity.com/paper/1510.07013/) (2015)
- [Real-Time Local Volt/VAR Control Under External Disturbances with High PV Penetration](https://mlsanity.com/paper/1710.02551/) (2017)
- [Voltage Regulation Algorithms for Multiphase Power Distribution Grids](https://mlsanity.com/paper/1508.06594/) (2015)
- [Reverse and Forward Engineering of Local Voltage Control in Distribution Networks](https://mlsanity.com/paper/1801.02015/) (2018)
- [Optimal Design of Volt/VAR Control Rules for Inverter-Interfaced Distributed Energy Resources](https://mlsanity.com/paper/2210.12805/) (2022)
- [Optimal Design of Volt/VAR Control Rules of Inverters using Deep Learning](https://mlsanity.com/paper/2211.09557/) (2022)

## Abstract (arXiv)

Volt/VAR control rules facilitate the autonomous operation of distributed energy resources (DER) to regulate voltage in power distribution grids. According to non-incremental control rules, such as the one mandated by the IEEE Standard 1547, the reactive power setpoint of each DER is computed as a piecewise-linear curve of the local voltage. However, the slopes of such curves are upper-bounded to ensure stability. On the other hand, incremental rules add a memory term into the setpoint update, rendering them universally stable. They can thus attain enhanced steady-state voltage profiles. Optimal rule design (ORD) for incremental rules can be formulated as a bilevel program. We put forth a scalable solution by reformulating ORD as training a deep neural network (DNN). This DNN emulates the Volt/VAR dynamics for incremental rules derived as iterations of proximal gradient descent (PGD). Analytical findings and numerical tests corroborate that the proposed ORD solution can be neatly adapted to single/multi-phase feeders.