## 1 Introduction
proposed a bi-level optimization scheduling strategy to reduce the coordination control and interaction difficulty between the lower plants and the upper multi-energy operators in an industrial park; this strategy adopts multi-energy operators and user-side resources to achieve optimal control and dispatch, so as to maximize the benefits under the constraint of peak transfer [11]. [PERSON] et al. used a two-stage programming model: the first stage used a multi-objective genetic algorithm to optimize the system structure; in the second stage, the mixed-integer programming method was used to solve the optimal operation problem of the system [12]. [PERSON] et al. constructed a two-stage model based on an artificial immune system and mixed-integer linear programming to obtain a variety of different planning schemes to solve large-scale functional network structure design problems [13]; the structural design problem was involved with the upper level, while the operational operation problem was concerned with the lower level; then, the model was applied to the multi-energy structural design of a four-person residential complex, and the validity of the model was verified by comparing the calculation results with the commercial MILP solver. In particular, the limitation of the planning capacity of MCSs in the design and construction stage is key to determining whether an MCS is low-carbon in the later stage. However, the builders or investors of the system pay more attention to the economic cost input of the whole system. Therefore, the overall optimization research on MCSs in terms of life cycle planning and operation coordination should be treated as essential. In view of this, this paper studies the optimization of MCS life cycle planning and operational coordination. The innovations of this paper are as follows:
* To optimize the overall structure of MCS system, the coordination between planning and operation during the system life cycle is studied through a bi-level optimization model of MCS planning and operation constructed using the bi-level optimization theory. The MCS life cycle multi-objective programming optimization model is set as the upper model, while the system life cycle operation optimization model is set as the lower model;
* To optimize the impact on the environment during the operation of the system, and improve the comprehensive energy utilization efficiency of the system, the carbon transaction cost is included in the objective function of the operation optimization model. The high permeability of renewable energy is taken as the constraint condition of the operational optimization. Thus, the comprehensive collaborative optimization of the MCS under the regulation of social mechanisms and considering the high permeability of renewable energy could be achieved;
* To improve the global search ability and convergence speed of the algorithm in the optimization calculation, the non-dominated sorting genetic algorithm III (NSGA-III) is appropriately improved based on the previous research of the authors;
* Because the limitation of system planning capacity plays a decisive role in the subsequent low-carbon operation of the system, the effectiveness of the constructed bi-level optimization model is verified by comparison with the single-level planning optimization. This research provides theoretical guidance and technical reference for the planning, design, and operational optimization of MCSs, which has important social significance and engineering application value.