Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes
This work addresses data-efficient model learning for grid-edge resource management in active power distribution networks, representing an incremental improvement over existing methods.
The paper tackled the problem of approximating multiphase power flow models in distribution networks by introducing a Gaussian Process-based method that maps net load injections to nodal voltages, achieving a 99.9% reduction in mean absolute error with 85% less training data compared to deep neural network baselines.
Learning-based approaches are increasingly leveraged to manage and coordinate the operation of grid-edge resources in active power distribution networks. Among these, model-based techniques stand out for their superior data efficiency and robustness compared to model-free methods. However, effective model learning requires a learning-based approximator for the underlying power flow model. This study extends existing work by introducing a data-driven power flow method based on Gaussian Processes (GPs) to approximate the multiphase power flow model, by mapping net load injections to nodal voltages. Simulation results using the IEEE 123-bus and 8500-node distribution test feeders demonstrate that the trained GP model can reliably predict the nonlinear power flow solutions with minimal training data. We also conduct a comparative analysis of the training efficiency and testing performance of the proposed GP-based power flow approximator against a deep neural network-based approximator, highlighting the advantages of our data-efficient approach. Results over realistic operating conditions show that despite an 85% reduction in the training sample size (corresponding to a 92.8% improvement in training time), GP models produce a 99.9% relative reduction in mean absolute error compared to the baselines of deep neural networks.