## 5 Conclusions  
At present, the traditional energy development model of high consumption and high pollution needs to be abandoned. Seeking a multi-energy complementary comprehensive energy system that can not only meet the changing energy supply structure and user demand, but also promote the efficient utilization of resources in the energy system and low-carbon environmental protection, has become the main direction of future energy development. Consequently, our research on MCS life cycle multi-objective optimization methods was carried out. Based on the bi-level multi-objective optimization theory, a bi-level multi-objective programming operation collaborative optimization model of MCSs was constructed. The upper level takes the minimum life cycle cost, the minimum life cycle carbon emissions, and the maximum primary energy utilization as the optimization objectives. The lower level takes the minimum total operating cost of the whole life cycle as the optimization objective, considering carbon transaction costs and the introduction of high renewable energy penetration as the constraints. At the same time, based on previous research, NSGA-III was improved to enhance the global search ability and convergence speed of the algorithm. According to the case analysis, the constructed bi-level multi-objective collaborative optimization model was solved using the improved NSGA-III. By comparison with the results of a single-level programming multi-objective optimization model, the following conclusions could be drawn:  
* The MCS can determine the rated capacity of a set of systems after obtaining the optimal solution via bi-level optimization or single-level optimization. Overall, the boiler capacity is moderate. The solar panels are mainly photovoltaic power generation panels, and solar energy is very limited. Combined with the energy storage capacity, the MCS makes full use of energy storage devices for energy storage and conversion by relying on renewable energy for power generation;
* Compared with the optimization results of the single-level model, the bi-level optimization considers the constraint conditions of maximum permeability of renewable energy and the objective requirement of minimum carbon transaction cost, reducing the boiler capacity and increasing the flat-plate solar collector capacity. At the same time, the decrease in heat exchanger capacity causes the energy consumption in the system life cycle to decrease to a certain extent;
* By comparison with the single-level multi-objective optimization model, the bi-level multi-objective collaborative optimization model proposed in this study can better realize the comprehensive optimization of system planning and operation. Consideringthe impact of renewable energy penetration constraints and carbon transaction costs, the system's integrated costs and carbon emissions are better optimized;
* The iteration time of the bi-level multi-objective collaborative optimization model is longer than that of the single-level multi-objective optimization model because of its relatively complex structure. Multiple independent experiments are needed in order to obtain more reliable results in practical engineering optimization; thus, the application of this method has large time limit. In practical engineering applications, it is necessary to consider which optimization scheme should be adopted according to the complexity of the project;
* The optimization results of the two optimization schemes of the case were compared with the cost values before optimization. It can be seen that the investment cost after optimization is higher than that before optimization because of the increase in renewable energy equipment investment during optimization. However, due to the investment of renewable energy, the annual carbon emissions of purchased electricity and natural gas are greatly reduced after optimization.