Synthetic power grids enable secure, real-world energy system simulations and are crucial for algorithm testing, resilience assessment, and policy formulation. We propose a novel method for the generation of synthetic transmission power grids using Exponential Random Graph (ERG) models. Our two main contributions are: (1) the formulation of an ERG model tailored specifically for capturing the topological nuances of power grids, and (2) a general procedure for estimating the parameters of such a model conditioned on working with connected graphs. From a modeling perspective, we identify the edge counts per bus type and $k$-triangles as crucial topological characteristics for synthetic power grid generation. From a technical perspective, we develop a rigorous methodology to estimate the parameters of an ERG constrained to the space of connected graphs. The proposed model is flexible, easy to implement, and successfully captures the desired topological properties of power grids.

翻译：合成电力网络能够支持安全、真实的能源系统仿真，对算法测试、韧性评估和政策制定至关重要。本文提出一种基于指数随机图（ERG）模型生成合成输电网络的新方法。主要贡献包括：（1）构建了专门捕捉电力网络拓扑细微特征的ERG模型；（2）提出在连通图约束条件下估计该模型参数的通用流程。在建模层面，我们确定每类母线边数和k-三角形是合成电力网络生成的关键拓扑特征；在技术层面，我们开发了严格的方法论用于估计限定在连通图空间的ERG参数。所提模型灵活易用，成功捕捉了电力网络期望的拓扑特性。