Dynamic Equivalence Modeling for Microgrid Cluster by Using Physical-Data-Driven Method

In practical application of microgrid cluster, the lack of full detailed information cause the failure of dynamic modeling. Although some data-driven black-box modeling method can tackle this problem, insufficient usage of prior known physical information may reduce the modeling accuracy. To tackle this challenge, a hybrid physical-data-driven method is proposed for the dynamic behavior modeling of microgrid cluster. Motivated by the equivalence of recurrent neural network (RNN) and differential equations, the differential-algebraic equations (DAEs) of unknown part are represented by gate recurrent unit (GRU) based neural network. The DAEs of prior known physical stage are embedded into the proposed neural network, which avoid unnecessary model training of prior known section and improving the modeling efficiency. At first, the basic idea of RNN based dynamic modeling is explained. Then, the modeling guidelines including data preparation and parameter design are suggested. Finally, the effectiveness of the proposed method is confirmed by a test system formed by three microgrids under grid fault and operating point changing conditions.