A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks
Jochen L. Cremer (Austrian Institute of Technology, TU Delft - Electrical Engineering, Mathematics and Computer Science)
Adrian Kelly (EPRI)
Ricardo J. Bessa (Institute for Systems and Computer Engineering, Technology and Science (INESC TEC))
Milos Subasic (Hitachi Energy)
Panagiotis N. Papadopoulos (The University of Manchester)
Samuel Young (Energy Systems Catapult)
Amar Sagar (Arizona State University)
Antoine Marot (Reseau de Transport d'Electricite)
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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.