Delft AI Energy Lab

AI for sustainable, reliable and effective energy systems

Energy systems are the backbone of our modern society. It is of great importance that these systems are sustainable, reliable and effective now and in the future. There is strong expertise in this field on the TU Delft campus. The Delft AI Energy Lab investigates how new AI-based methods can contribute to the management of dynamic energy systems.

Therefore we combine groundbreaking machine learning with the reliable theory of the physical energy system. For example, it is possible with the AI technique 'neural networks' to model differential equations describing dynamics in areas such as fluid dynamics, and for predicting extreme, rare events. Delft AI Energy Lab investigates these promising methods for applicability for monitoring the 'health' of parts of energy systems, and for the early detection of threats.

The Delft AI Energy Lab is part of the TU Delft AI Labs programme.

Advanced AI-based mathematical models together with scalable algorithms offer reliable diagnosis and predictive tools for modern energy systems.

Making optimization algorithms computationally efficient matters in many applications including power systems

Describing experiment methodology.

The team

Directors

PhD candidates

Perine Cunat

PhD researcher

Postdocs

Education

Master projects

Openings​​​​​​​

  • AC Optimal Data Generation (ACODG) for Power System Security (2022/2023)
  • Graph Neural Networks for Security-Constrained Optimal Power Flow (2022/2023)
  • Estimating the Flexibility Evolution in Active Distribution Grids (2022/2023)
  • Neural Ordinary Differential Equations for Power System Dynamics (2022/2023)
  • Optimal PMU Placement for Flexibility and State Estimation (2022/2023)
  • Non-intrusive Load Monitoring of the Electricity Consumption (2022/2023)
  • Coordinated Control of Virtual Power Plants for Frequency Stability (2022/2023)
  • Offering Strategies of Virtual Power Plants in Ancillary Service Markets Based on Stochastic Programming (2022/2023)

Ongoing​​​​​​​

  • Data-Driven Adaptive Dynamic Equivalents of Active Distribution and Transmission Networks, Alex Neagu, Jochen Cremer (2022/2023)
  • End-to-end learning for N-k SC-OPF, Bastien Giraud, Jochen Cremer (2022/2023)
  • Reinforcement learning for transmission network topology control, Geert Jan Meppelink, Jochen Cremer (2022/2023)
  • Market Mechanism Design for Virtual Inertia, Johnny Zheng, Jochen Cremer (2022/2023)

Finished​​​​​​​

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