Synthetic Active Distribution System Generation via Unbalanced Graph Generative Adversarial Network

TL;DR

This paper introduces UG-GAN, a novel unbalanced graph GAN model that generates realistic synthetic active distribution networks, preserving key features while maintaining data privacy.

Contribution

The paper presents UG-GAN, a new generative model for creating detailed synthetic unbalanced distribution networks using Wasserstein GANs and random walk learning.

Findings

Synthetic networks closely mimic real-world features

Generated data preserves privacy and confidentiality

Method validated on Midwest distribution system

Abstract

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.