# A Fully Distributed Reactive Power Optimization and Control Method for Active Distribution Networks

arXiv: https://arxiv.org/abs/1407.5032 · HTML: https://mlsanity.com/paper/1407.5032/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2014.

## Built on by (most cited first, 4 of 4)

- [Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks](https://mlsanity.com/paper/2110.14300/) (2021)
- [Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage Control](https://mlsanity.com/paper/2405.08443/) (2024)
- [Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Information](https://mlsanity.com/paper/2401.15848/) (2024)
- [A Human-AI Teaming Framework for Deep Reinforcement Learning-Based Voltage Regulation in Distribution Networks](https://mlsanity.com/paper/2607.19667/) (2026)

## Abstract (arXiv)

This paper proposes a fully distributed reactive power optimization algorithm that can obtain the global optimum of non-convex problems for distribution networks without a central coordinator. Second-order cone (SOC) relaxation is used to achieve exact convexification. A fully distributed algorithm is then formulated corresponding to the given division of areas based on an alternating direction method of multipliers (ADMM) algorithm, which is greatly simplified by exploiting the structure of active distribution networks (ADNs). The problem is solved for each area with very little interchange of boundary information between neighboring areas. The standard ADMM algorithm is extended using a varying penalty parameter to improve convergence. The validity of the method is demonstrated via numerical simulations on an IEEE 33-node distribution network, a PG&E 69-node distribution system, and an extended 137-node system.