Accelerating Power Flow Calculations in LV Networks Using Physics-Informed Graph Neural Networks

Precise calculations of network states and parameters are crucial for the modeling, monitoring, and operation of the distribution grid. AC power flow calculations are the preferred method for determining grid operating limits and assessing system hosting capacity. Traditional numerical power flow solvers are computationally intensive for large networks due to their iterative nature. In this context, we propose a physics-informed graph neural network technique as a fast alternative to the iterative power flow calculation methods. Our approach leverages the inherent physical knowledge of power systems through the integration of different physical constraints inside the model architecture and loss function. By incorporating physical laws such as Kirchhoff's law and active power balance as prior knowledge, the proposed method maintains high accuracy while significantly reducing computational time. The results show that the proposed model achieves better performance and higher generalizability than off-the-shelf data-driven approaches.