Abstract: The disclosure provides a method for monitoring a short-term voltage stability of a power system. The method includes: obtaining a topology and time series of a current power system; inputting the topology and the time series of the current power system into a trained spatial-temporal graph network model, the trained spatial-temporal graph network model being obtained by classification learning on a spatial-temporal graph network model based on a simulation sample dataset; outputting a status of the short-term voltage stability of the power system; and sending an alarm signal in response to outputting an unstable status of the short-term voltage stability of the power system.

Abstract: The disclosure provides a method for monitoring a short-term voltage stability of a power system. The method includes: obtaining a topology and time series of a current power system; inputting the topology and the time series of the current power system into a trained spatial-temporal graph network model, the trained spatial-temporal graph network model being obtained by classification learning on a spatial-temporal graph network model based on a simulation sample dataset; outputting a status of the short-term voltage stability of the power system; and sending an alarm signal in response to outputting an unstable status of the short-term voltage stability of the power system.

Abstract: A method for multi-dimensional identification of flexible load demand response effects, including: step 1. determining a target object, a target area and a demand response project that participate in the multi-dimensional identification of flexible load demand response effects; step 2. acquiring flexible load evaluation data of the target area and the target object; step 3. performing data cleaning; step 4. preprocessing the flexible load evaluation data; step 5. constructing four characteristic extraction indicators, a peak load reduction rate, a peak-to-valley difference ratio, a load factor ratio and a response status, inputting a predicted value and an actual collected value of maximum and minimum daily loads before and after the flexible load demand response that are obtained from the prepossessing, to generate a matrix for clustering; step 6. clustering the matrix for clustering generated in step 5; step 7. guiding a more targeted development of demand response projects.