针对多园区电热系统在可再生能源大规模接入场景下存在的调度灵活性不足与运行成本攀升问题，提出一种基于边缘计算的梯级双时间尺度分布式电热系统最优调度模型。首先，构建包含物理设备层、边缘计算层与云层的三层协同架构，通过边缘计算实现园区间数据快速处理与分布式决策；然后，在改进目标级联法中引入双时间尺度策略，下层以5 min为尺度优化电能交互，上层以1 h为尺度协调热能交互，并采用增广拉格朗日法实现多时间尺度问题的解耦与滚动优化；最后，设计基于能源贡献度的收益再分配机制，通过非对称映射函数量化各园区在电热交易及可再生能源消纳中的贡献，确保利益公平分配。算例分析表明，所提模型较传统方法综合运行成本降低34.46%，可再生能源消纳率显著提升，且算法在8次迭代内收敛，验证了边缘计算与双时间尺度策略的结合可有效应对能流时空特性差异，为多能源耦合系统的协同优化提供理论支撑与实践参考。

Aiming at the challenges of insufficient scheduling flexibility and rising operational costs in multi-park electric-thermal systems under large-scale renewable energy integration，this paper proposes an optimal scheduling model for tiered dual-time-scale distributed electric-thermal system based on edge computing. Firstly，a three-tier collaborative architecture comprising a physical equipment layer，edge computing layer，and cloud layer is constructed. Edge computing facilitates rapid data processing and distributed decision-making among parks. Secondly，the improved analytical target cascading method is employed with a dual-time-scale strategy： the lower layer optimizes electrical energy interactions at a 5 min granularity，while the upper layer coordinates thermal energy interactions at a 1 h granularity. The augmented Lagrangian method is integrated to decouple and iteratively solve multi-time-scale optimization problems. Finally，a benefit redistribution mechanism based on energy contribution degrees is designed，utilizing an asymmetric mapping function to quantify each park’s contributions to electric-thermal exchanges and renewable energy consumption，ensuring equitable profit distribution. Case studies demonstrate that the proposed model reduces comprehensive operational costs by 34.46% compared to conventional methods，significantly improves renewable energy consumption rates，and achieves convergence within eight iterations. The findings confirm that the integration of edge computing and dual-time-scale strategies effectively addresses spatiotemporal disparities in energy flows，providing theoretical and practical insights for coordinated optimization in multi-energy-coupled systems.