Background
Restoring power in a 110 kV distribution grid after a blackout involves complex, sequential decisions under strict operational constraints (e.g. voltage, frequency). Traditional rule-based approaches lack flexibility for unexpected scenarios and operational experience.
Challenge
To support operators in real time, reinforcement learning must evaluate safe, interpretable actions within milliseconds. Key requirements include integration with a pre-existing grid restoration simulator (including test grid) built into PowerFactory, as well as strict adherence to stability limits.
Tasks
– Model a reinforcement learning environment for the restoration process
– Incorporate operational constraints
– Implement and train an RL agent (e.g., DQN, PPO)
– Evaluate agent performance (success rate, stability violations)
– Visualize and interpret agent decisions for transparency
– (Optional) Integrate safety mechanisms (shielded learning)
– (Optional) Benchmark inference speed for real-time GridAssist
Requirements
– Basic knowledge in electrical engineering
– Understanding of electrical power systems (PowerFactory)
– Advanced programming skills (Python)
– (basic/advanced) knowledge of reinforcement learning
– Fluent in English
Start: July 15th
End: Jan 15th
Type: Master research project
Language: English
Contact: Changhun Kim(changhun.kim@fau.de), Simon Linnert(simon.linnert@fau.de)
Application:
Please apply by email with the subject line “[RL-Restoration Project Application 2025]”.
Include your CV and transcript of records (grade overview). Applications without these documents will not be considered.
In the body of the email, briefly describe (approx. 100 words) why you are interested in this specific project and how your background prepares you for it.
References:Safe_RL_Power_Grid_restoration
[1] X. Chen, Y. Xu, and P. Zhang, “Deep reinforcement learning for distribution system restoration with DER coordination,” IEEE T. Smart Grid, vol. 13, no. 2, pp. 987-999, 2022.
[2] J. Ding, H. Wang, and S. Low, “Safe policy gradient for microgrid black-start restoration,” in Proc. IEEE PES GM, 2024.
[3] H. Liu et al., “Explainable reinforcement learning: A survey,” ACM Comput. Surveys, vol. 55, no. 7, pp. 1-38, 2023.
[4] R. Li, T. Liu, J. Yu et al., “Graph neural network based voltage-control reinforcement learning for distribution systems,” IEEE T. Smart Grid, vol. 12, no. 6, pp. 5269-5280, 2021.
[5] A. Molina García et al., “Switching impact on MV equipment-a sixyear field study,” CIRED Workshop, 2020.
[6] S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in AISTATS, 2011.
[7] J. Achiam, D. Held, A. Tamar, and P. Abbeel, “Constrained policy optimization,” in Proc. ICML, 2017.
[8] M. Alshiekh, R. Bloem, R. Ehlers et al., “Safe reinforcement learning via shielding,” in AAAI, 2018.
[9] M. Du, N. Liu, Q. Hu et al., “Techniques for interpretable deep learning,” Commun. ACM, vol. 63, no. 1, pp. 68-77, 2020.