Multi-stakeholder

multi-level collaborative optimization is imperative for the advancement of integrated energy systems. However

the establishment of multi- stakeholder

multi-level collaborative optimization remains an urgent problem that necessitates immediate attention. Furthermore

the uncertainties in wind and solar power outputs

as well as carbon emission intensities

further exacerbate the difficulty of optimal scheduling. In light of these challenges

this paper proposes a low-carbon optimization strategy for integrated energy systems based on a three-layer master-slave game model that considers carbon source uncertainty. The proposed methodology involves constructing a three-level master-slave game model. The upper-level leader is the distribution system operator

responsible for developing and implementing tariff strategies. The middle-level key leader is the integrated energy operator

tasked with optimizing multi-energy dispatch. The lower-level follower is the user aggregator

responsible for managing demand response resources. Second

a two-stage distributional robust optimization model based on the Wasserstein distance is constructed to address the impact of uncertainty in wind and solar output

as well as carbon emission intensity

on system operation. Subsequently

the three-layer model is transformed into a two-layer model by employing the Karush-Kuhn-Tucker (KKT) condition. The model is then solved through a combination of the dichotomy method and the column and constraint generation algorithm. Finally

simulation results demonstrate that the proposed strategy can achieve a balance of interests among all parties

coordinate the optimization of operations

enhance the system's ability to respond to carbon source uncertainties

and reduce carbon emissions.