Multi-source Sensing Information-driven Flexibility and Resilience Optimization of Regional Integrated Energy Systems with Power-thermal-cold Interconnections under a Single Large Load Scenario

multi-objective optimization, single large load Currently, regional integrated energy systems are crucial for achieving low-carbon transformation and enhancing resilience in energy systems.In this paper, we address the challenges presented by high proportions of renewable energy integration and single large load scenarios, including temporal mismatches in multi-energy complementarity, insufficient coordination in multi-time-scale scheduling, and the complex nature of multi-objective optimization problems.First, a multi-time-scale collaborative scheduling framework for powerthermal-cold multi-energy flexible interaction under a single large load is proposed.This framework introduces a dynamic prioritization mechanism for power-thermal-cold multi-energy flows, which considers load peak-to-valley differences and energy transmission delays.Second, a collaborative scheduling strategy that integrates day-ahead forecasting, intraday rolling adjustments, and real-time feedback corrections is proposed, which is driven by multi-source sensing information.The pervasive sensor data serves as the foundation for the accurate dayahead forecasting of renewable energy and loads, provides the basis for intraday rolling adjustments to correct forecast deviations, and enables real-time feedback control to mitigate the impact of sudden disturbances, particularly from the single large load.This closed-loop, datadriven scheduling process is central to enhancing system flexibility and resilience.Furthermore, the multi-objective optimization algorithm is improved by incorporating elite cooperation and crowded distance sorting to improve its search capabilities and convergence performance with respect to the complex "economic-low carbon-high load" Pareto frontier.Simulation results indicate that the proposed optimization strategy leads to a 9.1% reduction in operating costs for the regional integrated energy system, a 9.9% decrease in carbon emissions, and a significant 23.9% reduction in gas costs.These findings effectively validate the superiority of the proposed method in improving system economy, environmental performance, and operational resilience,