Interaction Characteristics Modeling of Microgrid Clusters Based on Federated Learning

The interaction characteristics modeling of microgrid clusters is important for its optimal operation. Most of the existing modeling methods are centralized learning and inevitably have the risk of privacy disclosure. In this paper, a modeling approach of microgrid interaction characteristics based on federated learning is proposed. It mainly includes the steps of data sample generation, Bi-LSTM network design and federated learning modeling, and we discuss the detailed technical implementation. It is expected to that the presented approach can provide useful reference material for research on the behaver modeling of microgrid clusters.