FEN invites to AMLD EPFL session
We invite you to the track titled "AI for Energy Utilities" in AMLD - Applied Machine Learning Days EPFL @ 14:00 on Tuesday, March 26th, co-organized by Dr. Braulio Barahona (HSLU-DEEP) and Dr. Adamantios Marinakis (FEN) as part of the AISOP Project, funded through ERA-Net Smart Energy Systems.
The track will be held in the "Garden 4B". The program is as follows:
14:00 - 14:05 Introduction to AI for Energy Utilities, Braulio Barahona
14:05 - 14:20 Where AI could help to keep operating the transmission grid in a safe and efficient way, Matthias Bucher, Swissgrid AG
14:20 - 14:35 AI for energy trading, Stefanos Delikaraoglou, Axpo Group
14:35 - 14:50 Data-driven generation of synthetic load curves for grid planning, Arthur Cherubini, Romande Energie
14:50 - 15:05 Enhancing LLM performance with Retrieval-Augmented-Generation, Max Zurkinden, SwissLLM
15:05 - 15:25 Panel discussion, moderated by Adamantios Marinakis
Two pillars of the transition to net-zero energy systems are the proliferation of distributed energy generation and storage resources, and the electrification of the heating and mobility sectors. This results in an increased loading of the electricity distribution networks, progressively approaching the capacity for which they were designed for. Distribution system operators (DSO) face a continuously changing situation, with more and more installations of customer-side components such as rooftop PV, batteries, heat pumps, and EV charging stations, and new and more volatile power flow patterns. More sophisticated practices and advanced tools for distribution grid monitoring, operational planning, and grid management are required for high levels of reliability to be maintained in an efficient manner. On the other hand, the new components and the increasing number of new sensors offer a significant source of controllability, flexibility, and grid observability for the DSOs to manage their active distribution networks (ADN). Advances in artificial intelligence (AI) and big data methods offer DSOs with the tools to leverage heterogenous data and technical knowledge for situational awareness and decision support in a cost-effective manner.
This track aims at bringing together energy utilities and AI experts to identify how AI can bring value. The track will first elaborate on how AI methods can be integrated into the workflows of the DSOs to extract knowledge from the abundant sources of heterogeneous data, such as measurements from smart sensors, grid monitoring and topology information, but also data sources such as weather or traffic. Following, the track will elaborate on how DSOs can exploit AI to intelligently utilize the flexibility that can be offered by the active generation and consumption, for example by means of dynamic prices or appropriate dimensioned control schemes.