On Thursday February 20 there will be a one-day Seminar on Reinforcement Learning & Energy at Leiden University. Please find information about the day and a preliminary programme below.
All contributed talks are scheduled for 25 min slots, with 15 min for the talk and 10 min for discussion.
Coffee and lunch are provided.
Organizers/Contact: Lindsay Spoor & Thomas Moerland
Location: The seminar takes place in the Gorlaeus building, Einsteinweg 55, 2333 CC Leiden: https://maps.app.goo.gl/Uk39ihiXhaq8s1je9.
Room: We are in room BW.0.18. When you enter the front of the building, directly turn into the hall on the left and you’ll find the room there.
Travel:
Public Transport: From train station Leiden Centraal walking takes 25 minutes, or you can take bus (7 minutes) 30, 31, 38 or 183 to bus stop ‘Universiteitsterrein’.
Arriving by car: You can park in the (charged) Ehrenfestgarage across our building: https://maps.app.goo.gl/mjwGYwsMsZVUHmci8.
Registration has closed. (You can email Lindsay Spoor to be placed on the waiting list.)
9.15-9.45 Coffee & Welcome (@Gorlaeus BW.0.18)
9.45-11.00 Contributed talks 1 (3x25 min) (10-15 min talk, 5-10 min discussion)
Ali Rajaei (TUD): Learning power grid topology control
Alessandro Zocca (VU Amsterdam): Power grid topology control using RL
Davide Barbieri (TenneT): Solving real-world challenges within congestion management using Multi-Agent RL
11.00-11.30 Coffee
11.30-13.00 Contributed talks 2 (4x25 min) (10-15 min talk, 5-10 min discussion)
Koen Ponse (LU): Chargax - a modular and fast reinforcement learning environment for electrical vehicle charging scheduling.
Leila Shams Ashkezari (TUD): RL-based Energy Management System for Heavy-Duty Electric Vehicle Charging Stations.
Emre Yilmaz (CWI): Reinforcement Learning for Optimized EV Charging Through Power Setpoint Tracking.
Alessandro Marincioni (LU): A fast reinforcement learning environment for wind farm power optimization with wake transfer and partial observability.
13.00-14.00 Lunch
14.00-15.15 Contributed talks 3 (3x25 min) (10-15 min talk, 5-10 min discussion)
Shuyi Gao (TUD): Symbolic Deep Reinforcement Learning for Energy Storage Systems Optimal Dispatch
Fabio Pavirani (U. Gent): Optimizing the Imbalance Settlement with Reinforcement Learning
Stavros Orfanoudakis (TUD): Improving the Sample Efficiency of Decision Transformers for Multiobjective Optimization
15.15-15.45 Coffee
15.45-16.15 Break-out sessions
16.15-17.00 Panel discussion
17.00-18.30 Drinks (@De Fusie: Gorlaeus ground floor)