The impact of the growing use of in the operation and control of power networks from an operational perspective

As the complexities of operating modern power systems increase, many stakeholders throughout the energy value chain are turning to machine learning (ML) and artificial intelligence (AI) to manage these challenges. This shift is driven by systems’ digitalisation and larger computational resources’ availability.
For instance, transmission and distribution system operators can leverage these technologies to enhance load and generation forecasting, plan maintenance actions, and provide decision-making support tools for human operators in control rooms. Nevertheless, in financial markets, where the adoption of AI/ML has become widespread, the World Economic Forum (WEF) has raised concerns about the over-reliance on technology and the diminishing role of human judgment. Reducing human oversight in decision-making processes can lead to a
diminished understanding of the operational impact of AI-driven outcomes. Consequently, all stakeholders must comprehend the maturity and adoption levels of these technologies, their requirements, potential growth areas, and the associated risks and impacts.
CIGRÉ Study Committee C2, therefore, established the Working Group (WG) C2.42, “The impact of the growing use of Machine Learning/Artificial Intelligence in the operation and control of Power Networks from an Operational Perspective”, in early 2022. The work done by the WG was concluded by mid-2024
in a technical brochure (TB) with seven chapters and one appendix to inform system operator professionals about how advances in AI/ML can influence and enhance critical improvements in power systems operations, handling, for instance, operational challenges like data overload, new operational
scenarios, and increased cognitive load of operators and excessive manual processes.