SOC consistency control of distributed energy storage clusters based on federated learning
基于联邦学习的分布式储能集群SOC一致性控制

摘要

针对分布式储能系统在电网中的广泛应用，提出了一种基于联邦学习的SOC一致性控制方法。通过联邦学习框架，各分布式储能单元可以在不共享数据的前提下，实现高效的状态监测与协同控制。该方法通过联合训练多个储能单元的电池状态，实现SOC一致性的精确控制，降低了由于单个储能单元电量不均衡带来的系统损耗与性能下降。本文通过对比实验验证了该方法在电池管理系统中的有效性和鲁棒性，具有较强的可扩展性和适应性，适合未来智能电网中对分布式储能系统的控制需求。

Abstract

Aiming at the wide application of distributed energy storage systems in power grids, this paper proposes a SOC consistency control method based on federated learning. Through the federated learning framework, each distributed energy storage unit can achieve efficient state monitoring and collaborative control without sharing data. This method realizes precise control of SOC consistency by jointly training the battery states of multiple energy storage units, reducing system losses and performance degradation caused by unbalanced power levels of individual energy storage units. Comparative experiments in this paper verify the effectiveness and robustness of the proposed method in battery management systems. It has strong scalability and adaptability, and is suitable for the control requirements of distributed energy storage systems in future smart grids.