Typical Scenarios Generation Method Considering System-level Characteristics of Power System
This addresses the need for reliable scenario generation in power-electronics-dominated power systems, though it appears incremental as it builds on existing methods like multidimensional scaling and Mahalanobis distance.
The paper tackles the problem of generating representative power system scenarios that account for renewable energy uncertainties and stability properties, achieving verification of scenario typicality and stability prediction on the CSEE benchmark case.
This paper proposes a method for generating typical scenarios based on system-level macroscopic characteristics of power system and considering its stability properties. First, considering uncertainties such as renewable energy generation in power-electronics-dominated power systems, multidimensional scaling is used to construct an electrical coordinate system. Based on this, system-level characteristics of the distribution of physical quantities, such as power generation and load, are characterized. Furthermore, a method for generating typical scenarios based on the system's system-level characteristics and stability properties is proposed. For the obtained joint probability distribution of system-level characteristics, weighted Mahalanobis distance can be used to predict the stability properties of random scenarios. Finally, the typicality and representativeness of the scenarios generated by the proposed method with respect to stability properties are verified on the CSEE benchmark case, and stability prediction for random scenarios is achieved using a probabilistic testing method.