Wildfires increasingly threaten the reliable operation of electric power systems due to climate-driven factors and expanding infrastructure. However, existing research remains fragmented, limiting the development of integrated resilience strategies. The objective of this study is to systematically review the literature on power system resilience under wildfire events, focusing on modeling approaches, operational strategies, and learning-based methods. This review was conducted in accordance with PRISMA 2020 guidelines. A structured search was performed in the Scopus database (May 2025; updated January 2026). Studies published between 2016 and 2025 were screened in two stages using predefined eligibility criteria. Studies addressing power system operation under wildfire disturbances with optimization or learning-based methods were included, whereas purely ecological studies were excluded. Thirty studies were included. Data extraction and qualitative thematic synthesis were conducted across four analytical layers. Risk of bias was not formally assessed, and no meta-analysis was performed. Results show increasing research activity and a shift toward stochastic and data-driven methods. Optimization remains dominant, while reinforcement learning is emerging. Hybrid approaches that integrate optimization and learning-based methods are emerging as particularly promising solutions. However, the evidence is limited by methodological heterogeneity and lack of standardized validation.
Navarro-Zeballos et al. (2026) studied this question.