High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow

TL;DR

This paper introduces OPF-DNN, a deep learning model integrated with Lagrangian duality, to efficiently approximate large-scale AC Optimal Power Flow solutions with high accuracy and speed, suitable for real-time power system applications.

Contribution

The paper presents a novel deep neural network approach combined with Lagrangian duality to accurately approximate large-scale AC-OPF solutions efficiently.

Findings

OPF-DNN achieves solutions within 0.01% of optimal cost.

The model produces solutions in milliseconds.

High fidelity constraint satisfaction in large systems.

Abstract

The AC Optimal Power Flow (AC-OPF) is a key building block in many power system applications. It determines generator setpoints at minimal cost that meet the power demands while satisfying the underlying physical and operational constraints. It is non-convex and NP-hard, and computationally challenging for large-scale power systems. Motivated by the increased stochasticity in generation schedules and increasing penetration of renewable sources, this paper explores a deep learning approach to deliver highly efficient and accurate approximations to the AC-OPF. In particular, the paper proposes an integration of deep neural networks and Lagrangian duality to capture the physical and operational constraints. The resulting model, called OPF-DNN, is evaluated on real case studies from the French transmission system, with up to 3,400 buses and 4,500 lines. Computational results show that OPF-DNN produces highly accurate AC-OPF approximations whose costs are within 0.01% of optimality. OPF-DNN generates, in milliseconds, solutions that capture the problem constraints with high fidelity.