The impact of the growing use of machine learning/artificial intelligence in the operation and control of power networks from an operational perspective

Summary

This TB aims to assess the state-of-the-art AI/ML deployments in system operations, the desired capabilities for wider AI/ML deployment, and how system operators may take advantage of these technologies while understanding their impacts, limitations, and risks. It intends to bridge the gap between technological innovation and operational efficiency by highlighting practical applications and presenting possible journeys for deploying this technology.

Additional informations

Authors

Antoine Marot, Convenor (FR), Ricardo Bessa, Secretary (PT)

Mouadh Yagoubi (FR), Sjoerd P.J. Kop (NL), Jochen Cremer (NL), Marija Ilic (US), Amarsagar Reddy Ramapuram (US), Ming Dong (CA), Karin Rodrigues (AU), Guangchao Geng (CN), Milos Subasic (DE), Adrian Kelly (IE), Alberto

Kopiler (BR), Rohit Anand (IN), Teerasak Arunthanakij (TH), Victor Meza (CL), Fabian Heymann (CH), Wolf

Berwouts (BE), Jingyu Wang (CN), Medha Subramanian (IE), Panagiotis Papadopoulos (UK), Koen Vandermot (BE), Samuel Young (UK), Rohit Trivedi (IE), Spyros Chatzivasileiadis (DK), Viktor Eriksson Möllerstedt (SE), Arnaud

Zinflou (CA)

Keywords

Artificial intelligence, AI, Machine learning, System operation, Flexible operation, Renewable energy, Risks and challenges, Implementation, Requirements