Predicting and publishing accurate imbalance prices using Monte Carlo Tree Search

The growing reliance on renewable energy sources, particularly solar and wind, has introduced challenges their uncontrollable production. This complicates maintaining the electrical grid balance, prompting some mission system operators in Western Europe to implement imbalance tariffs that penalize unsustainable deviations. These tariffs create an implicit demand response framework to mitigate grid instability. Yet, challenges limit active participation. In Belgium, for example, imbalance prices are only calculated at of each 15-minute settlement period, creating high risk due to price uncertainty. This risk is further amplified by the inherent volatility of imbalance prices, discouraging participation. Although transmission system ators provide minute-based price predictions, the system imbalance volatility makes accurate price predictions challenging to obtain and requires sophisticated techniques. Moreover, publishing price estimates can participants to adjust their schedules, potentially affecting the system balance and the final price, adding complexity. To address these challenges, we propose a Monte Carlo Tree Search method that publishes imbalance prices while accounting for potential response actions. Our approach models the system dynamics using a neural network forecaster and a cluster of virtual batteries controlled by reinforcement learning Compared to Belgium's current publication method, our technique improves price accuracy by 20.4 ideal conditions and by 12.8 % in more realistic scenarios. This research addresses an unexplored, yet problem, positioning this paper as a pioneering work in analyzing the potential of more advanced imbalance price publishing techniques.