PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis
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3770854.3780235.pdf
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Author(s)
He, Xinyu
Xiao, Chenhan
Li, Haoran
Qiu, Ruizhong
Xu, Zhe
Weng, Yang
He, Jingrui
Tong, Hanghang
Date Issued
April 20, 2026
Publisher
Association for Computing Machinery
Citation
Xinyu He, Chenhan Xiao, Haoran Li, Ruizhong Qiu, Zhe Xu, Yang Weng, Jingrui He, and Hanghang Tong. 2026. PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1 (KDD '26). Association for Computing Machinery, New York, NY, USA, 428–439.
Version
Final published version
Abstract
Modern power systems are becoming increasingly dynamic, with changing topologies and time-varying loads driven by renewable energy variability, electric vehicle adoption, and active grid reconfiguration. Despite these changes, publicly available test cases remain scarce, due to security concerns and the significant effort required to anonymize real systems. Such limitations call for generative tools that can jointly synthesize grid structure and nodal dynamics. However, modeling the joint distribution of network topology, branch attributes, bus properties, and dynamic load profiles remains a major challenge, while preserving physical feasibility and avoiding prohibitive computational costs. We present PowerGrow, a co-generative framework that significantly reduces computational overhead while maintaining operational validity. The core idea is dependence decomposition: the complex joint distribution is factorized into a chain of conditional distributions over feasible grid topologies, time-series bus loads, and other system attributes, leveraging their mutual dependencies. By constraining the generation process at each stage, we implement a hierarchical graph beta-diffusion process for structural synthesis, paired with a temporal autoencoder that embeds time-series data into a compact latent space, improving both training stability and sample fidelity. Experiments across benchmark settings show that PowerGrow not only outperforms prior diffusion models in fidelity and diversity but also achieves a 98.9% power flow convergence rate and improved N-1 contingency resilience. This demonstrates its ability to generate operationally valid and realistic power grid scenarios.
Description
KDD ’26, Jeju Island, Republic of Korea
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3770854.3780235
