Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks

TL;DR

This paper introduces a physics-informed machine learning approach using attention neural networks to solve optimal power flow problems efficiently in highly renewable power systems, enabling real-time operation and improved resilience.

Contribution

The work presents a novel attention neural network model trained with imitation learning to directly predict power dispatch, bypassing traditional iterative OPF methods in renewable-rich power systems.

Findings

Outperforms existing data-driven OPF methods in accuracy and speed.

Enables real-time power system optimization with high renewable integration.

Validated on European power system data.

Abstract

The Optimal Power Flow (OPF) problem is pivotal for power system operations, guiding generator output and power distribution to meet demand at minimized costs, while adhering to physical and engineering constraints. The integration of renewable energy sources, like wind and solar, however, poses challenges due to their inherent variability. This variability, driven largely by changing weather conditions, demands frequent recalibrations of power settings, thus necessitating recurrent OPF resolutions. This task is daunting using traditional numerical methods, particularly for extensive power systems. In this work, we present a cutting-edge, physics-informed machine learning methodology, trained using imitation learning and historical European weather datasets. Our approach directly correlates electricity demand and weather patterns with power dispatch and generation, circumventing the iterative requirements of traditional OPF solvers. This offers a more expedient solution apt for real-time applications. Rigorous evaluations on aggregated European power systems validate our method's superiority over existing data-driven techniques in OPF solving. By presenting a quick, robust, and efficient solution, this research sets a new standard in real-time OPF resolution, paving the way for more resilient power systems in the era of renewable energy.