Active Distribution System Coordinated Control Method via Artificial Intelligence

TL;DR

This paper proposes an AI-based control method for active distribution systems using neural networks with self-attention, aiming to improve system reliability and coordination amidst variable renewable resources.

Contribution

It introduces a novel AI approach employing neural networks with self-attention mechanisms for optimizing active distribution system control.

Findings

Preliminary results show promising potential of the AI method.

The approach addresses the complexity of coordinating numerous small resources.

AI methods can offer practical solutions over classical optimization.

Abstract

The increasing deployment of end use power resources in distribution systems created active distribution systems. Uncontrolled active distribution systems exhibit wide variations of voltage and loading throughout the day as some of these resources operate under max power tracking control of highly variable wind and solar irradiation while others exhibit random variations and/or dependency on weather conditions. It is necessary to control the system to provide power reliably and securely under normal voltages and frequency. Classical optimization approaches to control the system towards this goal suffer from the dimensionality of the problem and the need for a global optimization approach to coordinate a huge number of small resources. Artificial Intelligence (AI) methods offer an alternative that can provide a practical approach to this problem. We suggest that neural networks with self-attention mechanisms have the potential to aid in the optimization of the system. In this paper, we present this approach and provide promising preliminary results.