## 1 Introduction
For the solution of a dispatching model, the interior point method and other non-intelligent algorithms may not be able to achieve an optimal solution when a large amount of computation is required. The particle swarm optimization (PSO) algorithm converged early, and often provided a local optimal solution [29]. Some references proposed the grey wolf optimization (GWO) algorithm [30; 31; 32], which was reported to show a better convergence rate and optimization ability when compared with the PSO algorithm. For example, [PERSON] [31] proposed a method to solve the cogeneration dispatching model using the GWO algorithm, and achieved economical operation of the cogeneration system. [PERSON] [32] proposed an innovative tuning approach for fuzzy control systems, with reducedparametric sensitivity using the GWO algorithm. However, some deficiencies existed in the convergence speed and global optimization ability of the above algorithms. In summary, the current research had the following problems: (1) In the distribution network comprising the microgrid, only a simple electrical-load demand existed, and the demands of heating and cooling loads were not considered. (2) Recent studies of the interaction between the microgrid and the distribution network considered only the economic performance of both sides, and did not consider that the output power of the microgrid would affect the operation of the distribution network. In fact, the output power of the microgrid will affect the stability and security of the operation of the distribution network, and the dispatching demand of the distribution network for the microgrid will also affect the economy of the microgrid; therefore, optimal operation of both sides involves mutual restraint and mutual influence. To address the above problems, the contributions of this paper are presented as follows:  
(1) The power-supply structure of a micro energy grid comprising cooling, heating, electricity, and gas grids was established. The energies of cooling, heating, electricity, and gas transformed and supported each other. Then, the operation structure of a distribution network comprising the micro energy grid was established. At the same time, the demands of the heating, cooling, and electricity loads were comprehensively considered. (2) Considering the interaction between the micro energy grid and the distribution network, a bi-level optimal model for their integrated operation was proposed. The upper model used the minima of three objectives, including the integrated operating cost of the distribution network, the network's active power loss, and the standard deviation of the voltage deviation in the distribution network. The lower model used the minimum integrated operating cost for the micro energy grid as the objective function. (3) A grey wolf optimization algorithm based on the dynamic adjustment of the proportional weight and convergence factor was proposed to solve the model, which overcame the problem of the local optimum and slow convergence rate associated with the traditional particle swarm algorithm, and the grey wolf algorithm. Then, the superiority of the proposed algorithm was verified mathematically by six test functions. (4) China's Gansu region is rich in wind and solar energy resources. To fully exploit these resources, a demonstration project of the micro energy grid in an agricultural park is being established. The safe, stable, and economical operation of the distribution network and the micro energy grid will be addressed in the future. Therefore, the model and algorithm were analyzed in an actual 10-kV distribution network in a region of Gansu Province, China. The organization of the remainder of this paper is as follows: Section 2 establishes the operation structure of the distribution network comprising the micro energy grid, according to the problems discussed. Section 3 establishes a bi-level optimal model for the integrated operation of the micro energy grid and the distribution network.