Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow

TL;DR

This paper presents an interpretable learning approach for AC optimal power flow that balances transparency and accuracy, outperforming neural networks especially with limited data.

Contribution

It introduces a novel, interpretable learning method that bridges traditional and black-box approaches for AC-OPF, enhancing transparency without sacrificing accuracy.

Findings

Achieves accuracy comparable to neural networks

Performs well with limited training data

Provides transparent and interpretable solutions

Abstract

The AC optimal power flow (AC-OPF) problem is essential for power system operations, but its non-convex nature makes it challenging to solve. A widely used simplification is the linearized DC optimal power flow (DC-OPF) problem, which can be solved to global optimality, but whose optimal solution is always infeasible in the original AC-OPF problem. Recently, neural networks (NN) have been introduced for solving the AC-OPF problem at significantly faster computation times. However, these methods necessitate extensive datasets, are difficult to train, and are often viewed as black boxes, leading to resistance from operators who prefer more transparent and interpretable solutions. In this paper, we introduce a novel learning-based approach that merges simplicity and interpretability, providing a bridge between traditional approximation methods and black-box learning techniques. Our approach not only provides transparency for operators but also achieves competitive accuracy. Numerical results across various power networks demonstrate that our method provides accuracy comparable to, and often surpassing, that of neural networks, particularly when training datasets are limited.