Growth in the penetration of renewable energy sources makes electricity supply more uncertain and leads to an increase in the grid system imbalance and imbalance (real-time) price volatility. In some European countries, large-scale batteries can be used to support transmission system operators (TSOs) in maintaining grid stability and earn profit, a practice called implicit balancing. Model predictive control (MPC) strategies to exploit these implicit balancing strategies capture arbitrage opportunities but fail to accurately model imbalance markets and face high computational costs. Model-free reinforcement learning (RL) methods are fast to execute but require data-intensive training and usually rely on real-time and historical data for decision making. In this talk, I will present our proposed MPC-guided distributional RL method that combines the complementary strengths of both MPC and RL. The proposed method can effectively incorporate forecasts into the decision making process (as in MPC), while maintaining the fast inference capability of RL.
Soroush Karimi: A Trading Algorithm for the Electricity Market 12 March 2026 16:00 - 17:00
About Soroush Karimi: A Trading Algorithm for the Electricity Market
Starting date
- 12 March 2026
Time
- 16:00 - 17:00
Location
- VU Main Building
Address
- De Boelelaan 1105
- 1081 HV Amsterdam
Organised by
- Operations Analytics
Language
- English
Soroush Karimi
Soroush Karimi is currently pursuing a Ph.D. in Computer Science at Ghent university. He received both his B.Sc. and M.Sc. degrees in Electrical Engineering from Amirkabir University of Technology, Tehran, Iran. His research interests include applications of machine learning and deep learning in electricity markets, electric vehicle smart charging, and data-driven demand response algorithms.
Interested in attending the seminar or in giving a talk?
Please send an email to Tim Oosterwijk