# Synthetic Active Distribution System Generation via Unbalanced Graph Generative Adversarial Network

arXiv: https://arxiv.org/abs/2108.00599 · HTML: https://mlsanity.com/paper/2108.00599/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [NetGAN: Generating Graphs via Random Walks](https://mlsanity.com/paper/1803.00816/) (2018)
- [Model-Free Renewable Scenario Generation Using Generative Adversarial Networks](https://mlsanity.com/paper/1707.09676/) (2017)
- [A Time-Series Distribution Test System Based on Real Utility Data](https://mlsanity.com/paper/1906.04078/) (2019)
- [A Framework for Generating Synthetic Distribution Feeders using OpenStreetMap](https://mlsanity.com/paper/1910.07673/) (2019)
- [Generation of Synthetic Spatially Embedded Power Grid Networks](https://mlsanity.com/paper/1508.04447/) (2015)
- [Outage Detection in Partially Observable Distribution Systems using Smart Meters and Generative Adversarial Networks](https://mlsanity.com/paper/1912.04992/) (2019)
- [Distribution Grid Modeling Using Smart Meter Data](https://mlsanity.com/paper/2103.00660/) (2021)

## Built on by (most cited first, 2 of 2)

- [PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis](https://mlsanity.com/paper/2509.12212/) (2025)
- [Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution](https://mlsanity.com/paper/2608.03878/) (2026)

## Abstract (arXiv)

Real active distribution networks with associated smart meter (SM) data are critical for power researchers. However, it is practically difficult for researchers to obtain such comprehensive datasets from utilities due to privacy concerns. To bridge this gap, an implicit generative model with Wasserstein GAN objectives, namely unbalanced graph generative adversarial network (UG-GAN), is designed to generate synthetic three-phase unbalanced active distribution system connectivity. The basic idea is to learn the distribution of random walks both over a real-world system and across each phase of line segments, capturing the underlying local properties of an individual real-world distribution network and generating specific synthetic networks accordingly. Then, to create a comprehensive synthetic test case, a network correction and extension process is proposed to obtain time-series nodal demands and standard distribution grid components with realistic parameters, including distributed energy resources (DERs) and capacity banks. A Midwest distribution system with 1-year SM data has been utilized to validate the performance of our method. Case studies with several power applications demonstrate that synthetic active networks generated by the proposed framework can mimic almost all features of real-world networks while avoiding the disclosure of confidential information.