Source-load Extreme Scenarios Recognition Based on GCNN-resnet Network and Multi-level Attention Mechanism

To address the challenges posed by extreme weather events to the safe and stable operation of new power systems, it is imperative to consider extreme scenarios in the production simulation of the power grid. Traditional scenario generation methods cannot directly produce extreme scenarios due to the scarcity of historical extreme samples, necessitating the recognition of scenarios. To address this, the paper proposes an extreme scenario identification method that incorporates the bilateral aspects of source-load dynamics. Wind, photovoltaic (PV), and load sequences are initially reshaped and concatenated along the channel dimension. Subsequently, the temporal features of the scenario and the coupling features between the source-load scenarios are extracted using grouping convolution and deep residual networks. Furthermore, the model integrates a channel attention mechanism and a multi-head attention mechanism to assign greater significance to critical features and categorize the scenarios. Additionally, the issue of imbalanced datasets in the training samples is addressed by employing an improved loss function. Finally, validation is conducted using historical datasets. The results demonstrate the effectiveness of the proposed method in accurately classifying scenarios, particularly in identifying source-load extreme scenarios with risk of power supply guarantee or renewable energy consumption from the historical dataset.