A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks

TL;DR

This paper presents a strategic roadmap for integrating machine learning into electrical network control and planning, based on expert surveys and analysis of the current research environment, aiming to accelerate AI-driven innovations.

Contribution

It develops a comprehensive innovation roadmap to align research efforts with practical goals for AI advancements in electrical networks.

Findings

R&D environment needs adaptation for faster AI development

High testing quality and safety must be maintained

Roadmap benefits operators, academics, and labs

Abstract

Advanced control, operation, and planning tools of electrical networks with ML are not straightforward. 110 experts were surveyed to show where and how ML algorithms could advance. This paper assesses this survey and research environment. Then, it develops an innovation roadmap that helps align our research community with a goal-oriented realisation of the opportunities that AI upholds. This paper finds that the R&D environment of system operators (and the surrounding research ecosystem) needs adaptation to enable faster developments with AI while maintaining high testing quality and safety. This roadmap serves system operators, academics, and labs advancing next-generation electrical network tools.