Graph Neural Network-based Power Flow Model

TL;DR

This paper introduces a graph neural network model for power flow analysis that offers more accurate and efficient predictions compared to traditional and other neural network models, especially useful for renewable energy integration.

Contribution

The paper presents a novel GNN-based power flow model trained on historical data, improving accuracy and speed over traditional DC models and other neural network approaches.

Findings

GNN model outperforms traditional DC power flow in accuracy.

The GNN approach is more efficient in computation.

Results show significant improvement in line flow predictions.

Abstract

Power flow analysis plays a crucial role in examining the electricity flow within a power system network. By performing power flow calculations, the system's steady-state variables, including voltage magnitude, phase angle at each bus, active/reactive power flow across branches, can be determined. While the widely used DC power flow model offers speed and robustness, it may yield inaccurate line flow results for certain transmission lines. This issue becomes more critical when dealing with renewable energy sources such as wind farms, which are often located far from the main grid. Obtaining precise line flow results for these critical lines is vital for next operations. To address these challenges, data-driven approaches leverage historical grid profiles. In this paper, a graph neural network (GNN) model is trained using historical power system data to predict power flow outcomes. The GNN model enables rapid estimation of line flows. A comprehensive performance analysis is conducted, comparing the proposed GNN-based power flow model with the traditional DC power flow model, as well as deep neural network (DNN) and convolutional neural network (CNN). The results on test systems demonstrate that the proposed GNN-based power flow model provides more accurate solutions with high efficiency comparing to benchmark models.