LNG serves as a pivotal element within integrated energy systems, especially in coastal regions where the implementation of a stable and reliable LNG cold energy power generation system significantly elevates energy efficiency. This system can effectively meet concurrent demands for cold energy utilization and electricity supply while contributing to the mitigation of carbon emissions. However, the inherent complexity of the system coupled with the scarcity of historical operational data for the novel dual-Rankine cycle process renders conventional reliability assessment methodologies inadequate. This study proposes an integrated framework utilizing fuzzy Bayesian methods to address data scarcity during the early stages of equipment deployment. A hierarchical risk factor model, incorporating process decomposition, expert evaluations, and triangular fuzzy numbers, is developed to quantify uncertainties in failure probabilities. The Bayesian network models the causal relationships among equipment failure factors, allowing for the inference of overall system reliability from individual equipment performance. Through a case study of a LNG terminal in Zhoushan, this approach integrates sensitivity analysis with forward-backward reasoning methodologies to rigorously evaluate and quantify system reliability under operational conditions. The results show that under high load conditions within the 1000 h prior to overhaul, following long-term accumulated operation, the probability of complete system shutdown in the power generation system is 3.30%, while the probability of the LNG cold energy power generation system failing to operate fully due to aging-related faults is 8.24%, demonstrating the system’s strong reliability under extreme conditions. Critical risks identified through backward inference include the seawater pump SWP1, with a posterior failure probability of 59.92% during complete shutdown, and the propane-side pump SWP3, with a posterior failure probability of 32.29% when the cold energy power generation system can only operate in a single-cycle mode. This study provides an advanced methodological framework for risk management in newly constructed LNG cold energy power generation systems, playing a crucial role in promoting sustainable, low-carbon technologies in the energy sector.

The migration of offshore wind power into deep, remote waters exposes the techno-economic fragility of the traditional "single source–export" development model. Prohibitive capital costs for long-distance transmission infrastructure, coupled with the finite absorption capacity of onshore grids, severely curb the scalability of standalone bases. This paper systematically reviews the emergence of offshore wind integrated energy systems as a comprehensive solution to these bottlenecks. The analysis highlights that coupling wind with photovoltaics, wave energy, marine biomass, and ocean thermal or salinity-gradient energy harnesses spatiotemporal complementarity to dampen power-output fluctuations and ensure supply stability. Crucially, key pathways for in situ utilization—specifically marine ranching integration, seawater electrolysis for hydrogen and ammonia, the electrification of oil and gas platforms, and multi-energy integrated offshore energy islands are reviewed herein. These mechanisms transform surplus electricity into storable chemical energy or support local loads, effectively coping with electricity consumption fluctuations and relieving the pressure on long-distance transmission corridors. By establishing a holistic development framework spanning power generation, multi-energy complementarity, and diversified utilization, this study explores the synergistic logic required to transition from standalone wind farms to integrated offshore energy hubs. The "source-grid-load" integration framework ultimately provides the theoretical and methodological grounding necessary for the efficient planning and exploitation of future deep-sea wind energy resources.