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.

Projects

Publications

Code & Datasets (Github)

The team

Directors

Jochen Cremer

Faculty of EEMCS
Lab director

Peyman Mohajerin Esfahani

Faculty of ME
Lab director

PhD candidates

Olayiwola Arowolo

PhD candidate

Mohammad Boveiri

PhD candidate

Demetris Chrysostomou

PhD candidate

Mert Karaçelebi

PhD candidate

Shabnam Khodakaramzadeh

PhD candidate

Basel Morsy

PhD candidate

Ali Rajaei

PhD candidate

Haiwei Xie

PhD candidate

Viktor Zobernig

PhD candidate

Perine Cunat

PhD researcher

Runyao Yu

PhD researcher

Luca Hofstadler

PhD researcher

Vacancies

We have currently several PhD positions available within the Delft AI Energy Lab

Postdocs

Jochen Stiasny

Courses

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

Neural Ordinary Differential Equations for Frequency Dynamics, Nila Krishnakumar, Jochen Cremer (2022/2023)
Offering Strategies of Virtual Power Plants in Ancillary Service Markets Based on Stochastic Programming, Torben Zeller, Jochen Cremer (2022/2023)
Energy Community Management with Reinforcement Learning, Catarina Santos Neves, Jochen Cremer (2022/2023)
Multivariable Anomaly Detection Framework for Multi-sensor Network, Jingru Feng, Peyman Mohajerin Esfahani (2022/2023)
Transferring Domain Knowledge to Data-driven Controller, Lex Razoux Schultz, Peyman Mohajerin Esfahani (2022/2023)
Multi-Modal End-to-End Learning for Real-Time Monitoring of Sustainable Energy Systems, Rushil Vohra, Jochen Cremer (2022/2023)
Deep Statistical Solver for Distribution System State Estimation, Benjamin Habib, Jochen Cremer (2022/2023)
Meter placement for state estimation in distribution networks, Sattama Datta, Jochen Cremer (2022/2023)
End-to-End Learning for Sustainable Energy Scheduling, Dariush Wahdany, Jochen Cremer (2021/2022)
Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response, Jasper van Tilburg, Jochen Cremer (2021/2022)
On the Road from Active Inference to Regret Minimization, Jesse Coehoorn, Peyman Mohajerin Esfahani (2021/2022)
Conjugate Dynamic Programming, Charalampos Rodopoulos, Peyman Mohajerin Esfahani (2021/2022)
Tractable Algorithms for Large Scale Mixed Integer Quadratic Programming: A Principal Component Analysis Approach, Robbie Vreugdenhil, Peyman Mohajerin Esfahani (2021/2022)
Learning Parametric Mixed Integer Quadratic Programming via Inverse Optimization, Milan Dankovic, Peyman Mohajerin Esfahani (2021/2022)
On Complexity of Data-driven Controls in Stochastic Environments, Ioannis Dimanidis, Peyman Mohajerin Esfahani (2021/2022)

Partners

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