Creating Power Distribution Network Layouts Using Generative Adversarial Networks and Image-Based Representations

• URL: http://arxiv.org/abs/2607.06622v1
• Date: Tue, 07 Jul 2026 10:25:50 GMT
• Title: Creating Power Distribution Network Layouts Using Generative Adversarial Networks and Image-Based Representations
• Abstract summary: This paper proposes a generative framework based on Generative Adversarial Networks (GANs) to create power distribution network layouts.<n>The proposed framework constitutes a data-driven complement to existing synthetic network generation methods.<n>It could be applied to propose distribution network layouts for the electrification of new areas.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Graph Signal Diffusion Models for Wireless Resource Allocation [74.84410305593006]
  We consider constrained ergodic resource optimization in wireless networks with graph-structured interference.<n>We show that time-sharing the generated power allocations achieves near-optimal ergodic sum-rate utility and near-feasible ergodic minimum-rates.
  arXiv  Detail & Related papers  (2026-04-06T21:12:25Z)
• Structural Information-based Hierarchical Diffusion for Offline Reinforcement Learning [13.839214658191038]
  We propose Structural Information-based Hierarchical Diffusion framework for effective and stable offline policy learning.<n>We analyze structural information embedded in offline trajectories to construct the diffusion hierarchy adaptively.<n>We show that SIHD significantly outperforms state-of-the-art baselines in decision-making performance.
  arXiv  Detail & Related papers  (2025-09-26T06:24:06Z)
• Power Grid Control with Graph-Based Distributed Reinforcement Learning [60.49805771047161]
  This work advances a graph-based distributed reinforcement learning framework for real-time, scalable grid management.<n>A Graph Neural Network (GNN) is employed to encode the network's topological information within the single low-level agent's observation.<n>Experiments on the Grid2Op simulation environment show the effectiveness of the approach.
  arXiv  Detail & Related papers  (2025-09-02T22:17:25Z)
• Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion [51.198001060683296]
  Networked urban systems facilitate the flow of people, resources, and services.<n>Current models such as graph neural networks have shown promise but face a trade-off between efficacy and efficiency.<n>This paper addresses this trade-off by drawing inspiration from physical laws to inform essential model designs.
  arXiv  Detail & Related papers  (2025-07-31T01:24:01Z)
• Graph Neural Networks for Electricity Load Forecasting [1.0797981721308225]
  Graph Neural Networks (GNNs) have emerged as a powerful paradigm to model spatial dependencies in load data.<n>This paper introduces a comprehensive framework that integrates graph-based forecasting with attention mechanisms and ensemble aggregation strategies.
  arXiv  Detail & Related papers  (2025-07-04T16:18:18Z)
• Leveraging Graph Neural Networks to Forecast Electricity Consumption [3.157383076370605]
  This research work offers a novel approach that extends beyond the conventional Generalized Additive Model framework. We introduce a range of methods for inferring graphs tailored to consumption forecasting, along with a framework for evaluating the developed models. We conduct experiments on electricity forecasting, in both a synthetic and a real framework considering the French mainland regions.
  arXiv  Detail & Related papers  (2024-08-30T15:54:50Z)
• Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets [74.51865676466056]
  We show how graph convolutional networks and hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning. Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency.
  arXiv  Detail & Related papers  (2022-11-14T01:29:09Z)
• Solving AC Power Flow with Graph Neural Networks under Realistic Constraints [3.114162328765758]
  We propose a graph neural network architecture to solve the AC power flow problem under realistic constraints. In our approach, we demonstrate the development of a framework that uses graph neural networks to learn the physical constraints of the power flow.
  arXiv  Detail & Related papers  (2022-04-14T14:49:34Z)
• Handling Distribution Shifts on Graphs: An Invariance Perspective [78.31180235269035]
  We formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM) EERM resorts to multiple context explorers that are adversarially trained to maximize the variance of risks from multiple virtual environments. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution.
  arXiv  Detail & Related papers  (2022-02-05T02:31:01Z)
• Synthetic Active Distribution System Generation via Unbalanced Graph Generative Adversarial Network [2.2749157557381245]
  An implicit generative model with Wasserstein GAN objectives 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. 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.
  arXiv  Detail & Related papers  (2021-08-02T02:17:01Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.