Clustering of Operating Scenarios for New Energy Power Systems Based on the Variables Affecting Static Voltage Stability

With the large-scale integration of renewable energy units into the power grid, a series of voltage stability issues, such as reduced voltage stability margin, have emerged. To address the frequent occurrence of voltage instability phenomena, a method for typical operation scenario clustering based on sensitivity index of minimum modulus eigenvalue is proposed. Firstly, based on eigenvalue structure analysis, the sensitivity of minimum modulus eigenvalue to various parameters in power system is calculated. Secondly, taking the sensitivity of minimum modulus eigenvalue as data object, the K-means algorithm is applied to classify typical operating scenarios. The key variables affecting system voltage stability of these typical scenarios are extracted based on the magnitude of sensitivity index. Finally, simulations on an improved IEEE 39-bus system validate that the proposed scenario clustering method accurately identifies the key variables influencing static voltage stability in each type of typical scenario. This provides a theoretical basis for power grid dispatchers to monitor and improve system voltage stability levels.