【目的】 在“双碳”目标稳步推进、分布式能源广泛接入背景下，由于输配电网潮流分布变化，其原有等值模型计算精度和计算效率受到挑战，同时碳排放如何纳入输配运行优化也成为有待探讨的课题。【方法】 为应对以上挑战，首先提出一种基于异构分解算法的输配协同分布式最优潮流模型，然后采用碳流理论计算节点碳排放强度，形成了同时考虑经济性与节点碳排放的多目标最优潮流模型，最后提出了基于满意度函数求解思想的多目标线性加权处理方法。【结果】 提出的基于满意度函数求解思想的线性加权模型能使输配协同潮流求解时间减少超过50%；所引入的节点碳排放强度及多目标优化模型，能够在发电成本可接受的范围内，使得系统碳排放显著降低。【结论】 提出的分布式输配协同最优潮流模型，能够在保障输配电网各自信息安全与计算精度的同时，提升最优潮流计算的收敛性，解决了异构分解算法难以对max-min类目标函数构造增广拉格朗日函数的问题，降低了求解难度，并充分利用配电侧高比例分布式电源实现经济效益和环境效益的均衡。

[Objective] Under the background of the steady progress of the "dual carbon" plan and the widespread access of distributed energy， the calculation accuracy and efficiency of the original equivalent model are challenged due to the changes in the power flow distribution of the transmission and distribution network， and how to incorporate carbon emissions into the optimization of transmission and distribution operation must be discussed. [Methods] To address these challenges， a collaborative distributed optimal power flow model based on a heterogeneous decomposition algorithm was proposed. Carbon flow theory was used to calculate the node carbon emission intensity， and a multi-objective optimal power flow model was developed considering both economic and node carbon emissions. A multi-objective linear weighted processing method based on a satisfaction function was proposed. [Results] A numerical example was provided to verify that the proposed linear weighted model based on the satisfaction function solution concept can reduce the solving time of the collaborative transmission and distribution power flow by more than 50%. The introduced node carbon emission intensity and multi-objective optimization model can significantly reduce the system carbon emissions within an acceptable range of power generation costs. [Conclusions] The proposed distributed transmission and distribution collaborative optimal power flow model not only ensures the information security and calculation accuracy of the transmission and distribution networks， but also improves the convergence of the optimal power flow calculation. This solves the problem that the heterogeneous decomposition algorithm makes it difficult to construct an extended Lagrange function for the max-min class objective function and reduces the difficulty of solving it. It makes use of the distribution side of the high proportion of the distributed power supply to achieve a balance between economic and environmental benefits.