## 6 Conclusions  
In this study, the operating structure of a distribution network comprising a micro energy grid was constructed. Based on the structure, a bi-level optimal dispatching model was established. The micro-energy-grid level used the minimum integrated operating cost of the micro energy grid as the objective function, while the distribution-network level 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 judgment-matrix method was used to transform the multi-objective function of the distribution-network level into a single-objective function. A grey wolf optimization (GWO) algorithm, based on the dynamic adjustment of the proportional weight and convergence factor, was proposed to solve the economical operating model of the distribution network comprising the micro energy grid in this study. The superiority of the proposed algorithm was verified mathematically. The simulation results of the example were as follows:  
(1) The improved grey wolf optimization (GWO) algorithm was used to identify a rational, global optimal solution of the objective function quickly and stably in the distribution network comprising the micro energy grid. In the winter, the fitness value of the objective function with the improved GWO algorithm was reduced by 19.4% relative to that with the particle swarm optimization (PSO) algorithm, by 10.0% relative to that with the invasive weed optimization (IWO) algorithm, by 9.7% relative to that with the GWO algorithm, and by 7.9% relative to that with the grey wolf optimization/evolutionary population dynamics (GWO-EPD) algorithm. In the summer, the fitness value of the objective function with the improved GWO algorithm was reduced by 2.7% relative to that with the PSO algorithm, by 1.8% relative to that with the IWO algorithm, by 1.6% relative to that with the GWO algorithm, and by 1.4% relative to that with the GWO-EPD algorithm. (2) When the improved grey wolf optimization algorithm was adopted to optimize the operation of the distribution network comprising the micro energy grid, the cost of the distribution network, the cost of the micro energy grid, and the total cost of the system decreased considerably. In the winter, the cost of the distribution network, the cost of the micro energy grid, and the total cost of the system with the improved grey wolf optimization (GWO) algorithm were reduced by 62.7%, 40.5%, and 61.1%, respectively, relative to those with the particle swarm optimization (PSO) algorithm, by 44.1%, 21.3%, and 42.1%, respectively, relative to those with the invasive weed optimization (IWO) algorithm, by 43.4%, 18.2%, and 41.3%, respectively, relative to those with the GWO algorithm, and by 43.3%, 13.8%, and 40.9%, respectively, relative to those with the grey wolf optimization/evolutionary population dynamics (GWO-EPD) algorithm. In the summer, the cost of the distribution network, the cost of the micro energy grid, and the total cost of the system with the improved GWO algorithm were reduced by 5.7%, 4.6%, and 5.6%, respectively, relative to those with the PSO algorithm, by 4.0%, 4.0%, and 3.8%, respectively, relative to those with the IWO algorithm, by 3.4%, 3.8%, and 3.4%, respectively, relative to those with the GWO algorithm, and by 3.0%, 3.4%, and 3.1%, respectively, relative to those with the GWO-EPD algorithm. (3) The adjustment pressure of the unit in the distribution network was effectively relieved after the micro energy grid was connected to the distribution network. The micro energy grid played the role of peak load shifting in the distribution network. The network's active power loss was significantly reduced using the proposed method. The network's active power loss decreased by an average of 13.22% in the winter, and 32.4% in the summer. The voltage fluctuations also decreased considerably,and the stability of the system voltage was improved.