Probabilistic Risk Assessment for Maintenance Decision-Making in Complex Energy Systems

Maintenance decision-making of complex energy systems is complicated by the aging of the assets, system interdependency, uncertainty of operations, and high consequences of failures. The conventional deterministic and reliability-based solutions cannot be usually sufficient to such high risk and interconnected infrastructures. Probabilistic Risk Assessment (PRA) has become a prominent analytical tool that combines failure probability, consequence severity, and uncertainty to aid in making risk-informed maintenance decisions. This review offers an analytical synthesis of the maintenance decision-making frameworks based on PRA in nuclear and power generation and transmission systems and renewable energy systems or hybrid systems. The literature review approach was chosen as narrative and the studies were identified in major databases (Scopus, Web of Science, IEEE Xplore, and ScienceDirect) that included articles published in 2018-2026, on the basis of their relevance to the topic of PRA-driven maintenance under uncertainty. The review looks at major frameworks, such as risk-based maintenance, condition-based PRA, Bayesian and dynamic models, decision-theoretic approaches, and predictive maintenance strategies. The results emphasize trade-offs among trade-offs between static and adaptive models, component-level and system-level methods, and note the major constraints of data, computational complexity, human decision biases, and the ability to implement in real-time. On the whole, the review suggests a holistic view and pinpoints problematic research gaps that can be addressed to design more resilient, scalable, and risk-aware maintenance approaches in the contemporary energy systems.