This paper reports on ongoing research to advance probabilistic risk assessment (PRA) methods for integrated energy systems (IES). This research focuses on multi-network energy system configurations (e.g., those that couple electricity, natural gas/hydrogen, and heat networks) while integrating multiple generation sources, including nuclear, fossil-fuel, and renewable energy sources. This paper summarizes the key findings of the literature review to assess the state of the art in IES risk analysis. The findings indicated several key research gaps, including: (i) incorporation of the effect of operational history on component hazard rate functions; (ii) explicit consideration of energy network dynamics, especially concerning the transient stability of electrical networks; and (iii) treatment of time-dependent correlations among uncertain input parameters. To address these gaps, this research develops a probabilistic simulation framework for multi-network IES analysis. At the time of this abstract submission (February 2026), the authors’ work is developing a probabilistic natural gas network simulation, which is one of the main modules of the IES risk analysis framework. A natural gas network model is developed based on physical governing equations (mass balance and the Weymouth formula) and operational constraints (pipeline pressure and flow rate). Uncertainty propagation is performed using Monte Carlo simulation, where the gas flow demands and failure events of pipelines and compressor stations are treated as random variables. The gas supply is optimized, and the energy not supplied (ENS) is calculated. The system outcome is categorized into three consequences: (i) full delivery, where the gas flow demands are met; (ii) partial delivery, where the gas flow demands are partially met; and (iii) infeasibility, where the system fails, and the gas supply is disrupted. The simulation results revealed that the ENS consistently remained zero when all pipelines succeeded, indicating a direct correlation between pipeline failure and the partial delivery and infeasibility scenarios. Ongoing work by the authors focuses on adding the coupling between the gas network and electric power network models to capture cascading events driven by their dynamic feedback.