## 6 Conclusions  
This paper proposes a low-carbon collaborative optimization operation scheduling model for a two-level dynamic community integrated energy system. The ES is used as the upper layer, and new energy CCHP operators and load aggregators are used as the lower layer to solve their equilibrium interaction strategies to achieve multi-agent distributed collaborative optimization operation. A multi-agent stage feedback carbon trading model is proposed to calculate carbon trading costs at stages. At the same time, feedback factors are introduced to conduct cost feedback based on the actual carbon emissions of the system, so as to reduce carbon emissions indirectly. Finally, the validity of the model is verified by a practical example. The following conclusions are obtained:  
1. The proposed low-carbon collaborative optimization operation scheduling model of the two-layer dynamic community integrated energy system can ensure the optimal interests of each differentiated subject and reduce the carbon emissions of the system. The NSGA-QP distributed method is used to solve the problem, which reduces the difficulty of solving, improves the efficiency of solving, and avoids falling into the local optimal solution.
2. The simulation results show that compared with the traditional scheduling method, the ES revenue, CCHP revenue, and LA consumer surplus in the scheduling results of the proposed method increased by 23.77%, 8.62%, and 12.79%, respectively, and the carbon emission decreased 16.34%. Therefore, the proposed two-layer dynamic low-carbon collaborative optimization scheduling model can reduce the system's carbon emissions and ensure its economical operation.  
Figure 6: Optimization scheduling results of new energy CCHP system. (**a**) Power equipment optimization scheduling results. (**b**) Heat equipment optimization scheduling results.  
3. Using the price signal to guide the new energy CCHP output and adjust the LA's energy consumption plan can improve the energy supply side income, reduce the energy cost under the premise of ensuring the satisfaction of energy consumption, stabilize the load fluctuation, and make the energy supply lower carbon economy and more reasonable.  
The model proposed in this paper describes the interaction behavior among agents from the vertical dimension but does not consider the modeling of the horizontal dimension. In future research, various coupling relationships among agents, such as master-slave relationships, non-cooperative games, etc., will be considered. The model proposed in this paper will be extended to adapt to the CIES with more complex participants and more intensive information interactions.  
[PERSON] and [PERSON] contributed to the conception and design of this study. [PERSON] and [PERSON] wrote the draft of this manuscript. [PERSON] analyzed the computing results. Moreover, all authors contributed to the revision of this manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (Project Number: 2022 YFE0208800).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare that this study received funding from the National Natural Science Foundation of China. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.