Efficient Probabilistic Assessment of Power System Resilience Using the Polynomial Chaos Expansion Method with Enhanced Stability

TL;DR

This paper introduces an improved Polynomial Chaos Expansion method with a novel experiment design to efficiently and reliably assess power system resilience under extreme weather events, demonstrated on the IEEE 39-bus system.

Contribution

It presents an enhanced PCE approach with a new experiment design to improve repeatability and convergence in probabilistic power system resilience assessment.

Findings

Enhanced PCE shows better repeatability and convergence.

Method efficiently assesses system resilience.

Proposes adaptation measures based on assessment results.

Abstract

Increasing frequency and intensity of extreme weather events motivates the assessment of power system resilience. The random nature of power system failures during these events mandates probabilistic resilience assessment, but state-of-the-art methods are computationally inefficient. In this paper, an enhanced PCE method to quantify power system resilience based on the extended AC Cascading Failure Model (AC-CFM) model is proposed. To address repeatability issues arising from PCE computation with different sample sets, we propose a novel experiment design method. Numerical studies on the IEEE 39-bus system illustrate the improved repeatability and convergence of the method. The enhanced PCE method is then used to efficiently assess the system's resilience and propose adaptation measures.