KCLNet: Physics-Informed Power Flow Prediction via Constraints Projections

TL;DR

KCLNet is a physics-informed graph neural network that enforces Kirchhoff's Current Law through constraints projections, providing fast, accurate, and physically consistent power flow predictions for modern power grids.

Contribution

It introduces a novel GNN model that incorporates physical constraints directly, ensuring physically plausible predictions without sacrificing computational efficiency.

Findings

Achieves zero KCL violations in predictions.

Maintains high accuracy comparable to traditional methods.

Ensures physically consistent power flow forecasts.

Abstract

In the modern context of power systems, rapid, scalable, and physically plausible power flow predictions are essential for ensuring the grid's safe and efficient operation. While traditional numerical methods have proven robust, they require extensive computation to maintain physical fidelity under dynamic or contingency conditions. In contrast, recent advancements in artificial intelligence (AI) have significantly improved computational speed; however, they often fail to enforce fundamental physical laws during real-world contingencies, resulting in physically implausible predictions. In this work, we introduce KCLNet, a physics-informed graph neural network that incorporates Kirchhoff's Current Law as a hard constraint via hyperplane projections. KCLNet attains competitive prediction accuracy while ensuring zero KCL violations, thereby delivering reliable and physically consistent power flow predictions critical to secure the operation of modern smart grids.