Towards an AI assistant for power grid operators
Not on the map. Why?
Abstract (arXiv)
Power grids are becoming more complex to operate in the digital age given the current energy transition to cope with climate change. As a result, real-time decision-making is getting more challenging as the human operator has to deal with more information, more uncertainty, more applications, and more coordination. While supervision has been primarily used to help them make decisions over the last decades, it cannot reasonably scale up anymore. There is a great need for rethinking the human-machine interface under more unified and interactive frameworks. Taking advantage of the latest developments in Human-Machine Interface and Artificial Intelligence, we expose our vision of a new assistant framework relying on an hypervision interface and greater bidirectional interaction. We review the known principles of decision-making driving our assistant design alongside with its supporting assistance functions. We finally share some guidelines to make progress towards the development of such an assistant.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 0; 2026: 1.
6 papers.
- 2022Power Grid Congestion Management via Topology Optimization with AlphaZerocited by 20
- 2023Hierarchical Reinforcement Learning for Power Network Topology Controlcited by 11
- 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
- 2023Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Controlcited by 1
- 2024Robustness Analysis of AI Models in Critical Energy Systemscited by 1
- 2026A Governance-Aware Large Language Model Orchestrated Agentic Digital Twin for Transmission System Operator Control Room Decision Supportcited by 0
- 2026A Governance-Aware Large Language Model Orchestrated Agentic Digital Twin for Transmission System Operator Control Room Decision Support18 Sep 2026
- 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Survey5 Jul 2024
- 2024Robustness Analysis of AI Models in Critical Energy Systems20 Jun 2024
- 2023Hierarchical Reinforcement Learning for Power Network Topology Control3 Nov 2023
- 2023Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control15 Feb 2023
- 2022Power Grid Congestion Management via Topology Optimization with AlphaZero10 Nov 2022