Predictive operation of low voltage grids with an autoregressive graph neural network for enhanced resilience

The rising share of flexibilities combined with the recent development of machine learning techniques has the potential to create a sustainable and efficient low voltage grid infrastructure. The new operational challenges, as the increasing uncertainty of the production of renewable energy sources, have to be met by effective measures. To prevent or mitigate disturbances such as the overloading of the operating equipment or excessive voltage deviations, a predictive operation based on an autoregressive model using graph neural networks is presented in this paper. The autoregressive approach enables predictive grid operation over multiple time steps that influence each other sequentially. This approach was demonstrated on a real existing grid with high shares of renewable generation and flexible loads. The results presented in this study show a good performance on a large simulated data set applied on a real-world grid topology. The proposed method can prevent violations of the operational constraints of low voltage grids in real time by regulating controllable generation of PV systems and adjusting transformer tap changers, thereby protecting lines and substations in the grid from overload at an early stage.