Learning to Pursue AC Optimal Power Flow Solutions with Feasibility Guarantees

TL;DR

This paper introduces a neural network-based approach to solve AC optimal power flow problems, ensuring feasibility and convergence, suitable for real-time and offline applications in distribution systems with renewable energy.

Contribution

It proposes a novel neural network framework that approximates the solution map of convex quadratic programs within a safe gradient flow method for AC OPF.

Findings

Successfully regulates voltages within limits during high renewable generation periods

Ensures practical feasibility of DER setpoints in online and offline modes

Converges to a neighborhood of a local optimizer of the AC OPF

Abstract

This paper focuses on an AC optimal power flow (OPF) problem for distribution feeders equipped with controllable distributed energy resources (DERs). We consider a solution method that is based on a continuous approximation of the projected gradient flow - referred to as the safe gradient flow - that incorporates voltage and current information obtained either through real-time measurements or power flow computations. These two setups enable both online and offline implementations. The safe gradient flow involves the solution of convex quadratic programs (QPs). To enhance computational efficiency, we propose a novel framework that employs a neural network approximation of the optimal solution map of the QP. The resulting method has two key features: (a) it ensures that the DERs' setpoints are practically feasible, even for an online implementation or when an offline algorithm has an early termination; (b) it ensures convergence to a neighborhood of a strict local optimizer of the AC OPF. The proposed method is tested on a 93-node distribution system with realistic loads and renewable generation. The test shows that our method successfully regulates voltages within limits during periods with high renewable generation.