In this seminar, Soroush Karimi will give a talk about a Data-driven Trading Algorithm for Short-Term Electricity Market.
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.
