🤖 AI Summary
This study addresses the scarcity of realistic distribution network datasets—a key bottleneck in benchmarking planning and operational tools under high penetration of distributed energy resources. To overcome this, the authors propose a generative adversarial network (GAN)-based framework for synthesizing distribution grid layouts, uniquely integrating rasterized image representations with GANs to enable both unconditional generation and geographically conditioned synthesis incorporating street maps and customer distribution. Through GIS preprocessing, image-based topological encoding, and a multi-resolution training strategy, the method successfully reproduces realistic topologies aligned with geographic structures across low-, medium-, and high-voltage scenarios. The approach offers data-driven layout recommendations for electrifying new areas while also highlighting persistent challenges in training stability and modeling electrical constraints.
📝 Abstract
Utilities increasingly rely on planning and operational tools to cope with the increased penetrations of distributed energy resources, yet the lack of realistic, openly available datasets remains a major barrier for benchmarking and comparison. Traditional test feeders, and recently proposed large-scale synthetic networks alleviate this issue but are typically based on heuristic rules and do not learn directly from data. This paper proposes a generative framework based on Generative Adversarial Networks (GANs) to create power distribution network layouts using image-based representations. The model is trained on rasterised views of distribution systems and can operate in two modes: an unconditional configuration that learns layout patterns from the training dataset, and conditional configurations that incorporate geographical context such as street maps and the spatial distribution of consumers. The methodology includes dataset preparation from Geographic Information System (GIS) sources, GAN architecture design, and the analysis of training stability and image resolution. Results from three representative cases show that the proposed approach can reproduce the topologies of low (LV), medium (MV) and high voltage (HV) feeders and align generated layouts with underlying geographical structures. At the same time, the study reveals limitations related to training stability, resolution-dependent artefacts and limits, and the absence of explicit electrical constraints. The proposed framework constitutes a data-driven complement to existing synthetic network generation methods, and could be applied to propose distribution network layouts for the electrification of new areas. This would require future extensions towards power flow, electrically validated models.