Learning Optimal Power Flow Value Functions with Input-Convex Neural Networks

TL;DR

This paper introduces a machine learning approach using input-convex neural networks to efficiently approximate solutions for the complex and non-convex Optimal Power Flow problem, enabling faster decision-making in power systems.

Contribution

It proposes a novel method to learn convex approximate solutions for OPF using input-convex neural networks, balancing accuracy and computational speed.

Findings

Achieves faster approximate solutions for OPF problems.

Maintains acceptable accuracy with convex neural network models.

Facilitates integration into real-time power system decision processes.

Abstract

The Optimal Power Flow (OPF) problem is integral to the functioning of power systems, aiming to optimize generation dispatch while adhering to technical and operational constraints. These constraints are far from straightforward; they involve intricate, non-convex considerations related to Alternating Current (AC) power flow, which are essential for the safety and practicality of electrical grids. However, solving the OPF problem for varying conditions within stringent time frames poses practical challenges. To address this, operators resort to model simplifications of varying accuracy. Unfortunately, better approximations (tight convex relaxations) are often computationally intractable. This research explores machine learning (ML) to learn convex approximate solutions for faster analysis in the online setting while still allowing for coupling into other convex dependent decision problems. By trading off a small amount of accuracy for substantial gains in speed, they enable the efficient exploration of vast solution spaces in these complex problems.