Typical Scenarios Generation Method Considering System-level Characteristics of Power System

TL;DR

This paper introduces a novel method for generating typical power system scenarios that incorporate system-level characteristics and stability properties, enhancing stability prediction accuracy.

Contribution

It presents a new scenario generation approach based on system-level features and stability considerations, utilizing multidimensional scaling and weighted Mahalanobis distance.

Findings

Scenarios generated reflect system stability properties accurately.

The method effectively predicts stability of random scenarios.

Validation on CSEE benchmark case confirms the approach's effectiveness.

Abstract

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.