Optimal Control Strategies for Seasonal Thermal Energy Storage Systems With Market Interaction

Seasonal thermal energy storage systems (STESSs) can shift the delivery of renewable sources and mitigate their uncertainty problems. However, to maximize operational profit STESSs ensure long-term profitability, control strategies that allow them trade on wholesale electricity markets are required. While for have been proposed before, none addressed market interaction trading. In particular, due seasonal nature STESSs, accounting in prices has very challenging. this article, we develop first algorithms when interacting with different markets. As solutions merits, propose based model predictive reinforcement learning. We show is critical since require strategies: MPC better day-ahead flexibility MPC, whereas learning (RL) real-time because fast computation times risk modeling. To study a real-life setup, consider real STESS imbalance The Netherlands Belgium. Based obtained results, that: 1) developed controllers successfully profits trading 2) make important players transition: by optimally controlling reacting imbalances, help reduce grid imbalances.