A novel GRU-TCN network based Interactive Behavior Learning of multi-energy Microgrid under incomplete information

Abstract

The interactive operation of multi-energy Microgrid (MEMG) in energy market is facing more and more incomplete information decision-making requirements due to the enhanced privacy of users, which brings great challenges to the participation flexibility of MEMG in demand response programs. Meanwhile, considering the uncertainty of the external MEMG unit combination, the randomness of the environmental meteorology, the interactive characteristics of the external MEMGs are complex and time-varying. To solve these problems, a novel GRU-TCN network based Interactive Behavior Learning method for MEMG interactive operation is proposed. The proposed Interactive Behavior Learning framework needs merely externally available input information and historical interaction power data to efficiently predict the interaction power, which protects the data privacy of users. Besides, the GRU-TCN network also leverages the strengths of Recurrent Neural Network and Convolutional Neural Networks while incorporating the self-attention mechanism. This combination enables the GRU-TCN network to effectively capture the relationship between interactive power and the available resources data beyond the MEMG. The example analysis and test are carried out on a typical MEMG system, and the case study turns out that the proposed GRU-TCN network has the capability to capture coupling mechanisms even in the presence of incomplete information and present more accurate prediction of multi-energy interactive data.