Reliability assessment of integrated energy systems during wildfire disasters: Application of an iterative algorithm with impact increment state enumeration

Abstract

In recent years, wildfire disasters have become increasingly frequent and severe, presenting significant challenges to power systems. Simultaneously, traditional power systems are evolving into integrated energy systems (IESs). The fluctuation of electric load demand and the uncertainty of renewable generation further complicate the reliability assessment of the IES during wildfire disasters. To address these critical challenges, this study proposes a reliability assessment framework for the IES during wildfire disasters. In this framework, the probability of transmission line failure is modeled by decomposing it into multiple risk factors, and system reliability for a specific failure state is quantified through an optimization model that comprehensively considers both renewable energy utilization and electric load supply. The uncertainty in renewable generation is managed using the third-order polynomial normal transformation (TPNT) and clustering methods. The increasing system scale has also led to challenges in the enumeration calculation of reliability assessment. To address this, we propose an Upper-Lower-Bound Iteration Impact Increment State Enumeration (ULBI-IISE) algorithm, which enhances calculation efficiency by controlling calculation errors. The effectiveness of the proposed method is validated using the IEEE 118-bus system, with further validation conducted using a practical 500 kV system. Lastly, the impact of scenario uncertainty is thoroughly analyzed, emphasizing its critical role in the reliability assessment of the IES.