Research on Interactive Control and Revenue Maximization of Distributed Battery Energy Storage Based on Machine Learning

This paper discusses the application necessity and modeling feasibility of machine learning in the interactive control and dynamic game of distributed energy storage systems in distribution networks. By implementing multi-dimensional deep optimization of the network structure, training mechanism and experience replay strategy of the deep Q-learning algorithm, and introducing the sum-tree data structure to achieve efficient access and replay training of transformed data, the convergence speed, decision accuracy and control stability of the algorithm have been significantly improved, enabling it to meet the requirements of online real-time control. The research results show that compared with traditional methods and other learning algorithms, the proposed method demonstrates obvious application advantages. Meanwhile, this algorithm has a good adaptability to the discretization differences of the action space and can effectively suppress the convergence risk that may be caused by the significant expansion of the action space. In addition, combined with the capacity attenuation law of distributed energy storage, its operational characteristic model was constructed; Based on the life loss characteristics of distributed energy storage, the costs and economic benefits of its participation in grid operation were analyzed, the corresponding economic assessment model was established, and the controllable potential of distributed energy storage was quantitatively calculated.