Random Forest and Order Parameters: A Combined Framework for Scenario Recognition for Power Systems with Renewable Penetration

With the popularization of microgrid construction and the connection of renewable energy sources to the power system, the problem of source and load uncertainty faced by the coordinated operation of multi-microgrid is becoming increasingly prominent, and the accuracy of typical scenario predictions is low. In order to improve the accuracy of scenario prediction under source and load uncertainty, this paper proposes a typical scenario identification model based on random forests and order parameters. Firstly, a method for ordinal parameter identification and quantification is provided for the coordinated operating mode of multi-microgrids, taking into account source-load uncertainty. Secondly, the dynamic change characteristics of the order parameters of the daily load curve, wind and solar curve, and load curve of typical scenarios are statistically analyzed to identify the key order parameters that have the most significant impact on the uncertainty of the load. Then, the order parameters and seasonal distribution are used as features to train a random forest classification model to achieve efficient scenario prediction. Finally, the simulation of actual data from a provincial distribution network shows that the proposed method can accurately classify typical scenarios with an accuracy rate of 92.7%. Additionally, sensitivity analysis is conducted to assess how changes in uncertainty levels affect the importance of each order parameter, allowing for adaptive uncertainty mitigation strategies.