Reinforcement-Learning-based Optimization for Day-ahead Flexibility Extraction in Battery Pools
|G. Chasparis, C. Lettner. Reinforcement-Learning-based Optimization for Day-ahead Flexibility Extraction in Battery Pools. volume 53, number 2, pages 13351-13358, DOI https://doi.org/10.1016/j.ifacol.2020.12.170, 3, 2021.|
We address the problem of trading energy flexibility, derived from pools of residential Photovoltaic and battery-storage systems, to the Day-ahead electricity market. By flexibility, we imply any additional energy that can be stored to or withdrawn from the participating batteries/households at a given time during the next day. The optimization variables include the selection/activation of a subset of participating batteries and the amount of flexibility that should be extracted. Furthermore, the optimization objective corresponds to the expected forecast revenues that can be generated by trading this flexibility to the Day-ahead electricity market. Given the high computationally complexity of a full scale optimization in the case of a large number of participating batteries, we propose a reinforcement-learning-based methodology, which admits linear complexity with the number of participating batteries. The proposed methodology advances prior work with respect to the integration of a large number of batteries. Furthermore, it extends prior work of the authors with respect to providing analytical performance guarantees in comparison with the baseline/nominal operation of the battery. Finally, we compare through simulations the performance of the proposed method with a Linear-Programming-based optimization that provides the exact optimum.