Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow

TL;DR

This paper introduces physics-informed neural networks with worst-case guarantees for DC optimal power flow, reducing violations and improving reliability in power system optimization tasks.

Contribution

It is the first to apply physics-informed neural networks with worst-case guarantees to the DC optimal power flow problem, enhancing safety and trustworthiness.

Findings

Reduced worst-case violations compared to conventional neural networks

Demonstrated guarantees on maximum constraint violations and sub-optimality

Applicable to PGLib-OPF networks with promising results

Abstract

Physics-informed neural networks exploit the existing models of the underlying physical systems to generate higher accuracy results with fewer data. Such approaches can help drastically reduce the computation time and generate a good estimate of computationally intensive processes in power systems, such as dynamic security assessment or optimal power flow. Combined with the extraction of worst-case guarantees for the neural network performance, such neural networks can be applied in safety-critical applications in power systems and build a high level of trust among power system operators. This paper takes the first step and applies, for the first time to our knowledge, Physics-Informed Neural Networks with Worst-Case Guarantees for the DC Optimal Power Flow problem. We look for guarantees related to (i) maximum constraint violations, (ii) maximum distance between predicted and optimal decision variables, and (iii) maximum sub-optimality in the entire input domain. In a range of PGLib-OPF networks, we demonstrate how physics-informed neural networks can be supplied with worst-case guarantees and how they can lead to reduced worst-case violations compared with conventional neural networks.