## 5 Example Analysis
### Analysis of the Simulation Results
#### 5.2.4 Optimal Dispatching Scheme of the Lower Micro-Energy-Grid Level
In the valley period of the electricity price, between 11:00 p.m. and 07:00 a.m., the electricity price was low, and the air-source heat pump satisfied the cooling-load demand, and charged the cooling storage tank. In the flat or peak period of the electricity price, between 07:00 a.m. and 11:00 p.m., the electricity price was high, and the cooling load was mainly satisfied by the lithium-bromide absorption-type refrigerator and the cooling storage tank. If the lithium-bromide absorption-type refrigerator and the cooling storage tank could not satisfy the cooling load, then the surplus cooling-load demand was satisfied by the air-source heat pump. 2.5 Performance Comparison of the Improved GWO Algorithm with the GWO, GWO-EPD, IWO, and PSO Algorithms  
The optimal dispatching schemes of the distribution network comprising the micro energy grid, on a typical day in the winter (a), and in the summer (b), are shown in Figure 14. The objective function of the distribution-network level was normalized using the method presented in Section 3.1.3. In Figure 14, the particle swarm optimization (PSO) algorithm converged early, and often provided a local optimal solution. The optimal ability of the improved grey wolf optimization (GWO) algorithm was stronger than those of the PSO algorithm, invasive weed optimization (IWO) algorithm, grey wolf optimization/evolutionary population dynamics (GWO-EPD), and GWO algorithm. The fitness value of the objective function was significantly reduced by using the improved GWO algorithm. 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 PSO algorithm, by 10.0% relative to that with the IWO algorithm, by 9.7% relative to that with the GWO algorithm, and by 7.9% relative to that with the 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. Figure 13: Electrical load (**a**) and cooling load (**b**) dispatching schemes of the micro energy grid for a typical day in the summer when the distribution network comprising the micro energy grid was in the optimal dispatching state. 2.6. Daily Cost Analysis of the Distribution Network Comprising the Micro Energy Grid Using the Improved GWO Algorithm, the GWO Algorithm, the GWO-EPD Algorithm, the IWO Algorithm, and the PSO Algorithm  
According to Table 6, when the improved grey wolf optimization (GWO) 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 GWO algorithm were reduced by 62.7%, 44.1%, 43.4%, and 43.3%; 40.5%, 21.3%, 18.2%, and 13.8%; and 61.1%, 42.1%, 41.3%, and 40.9% relative to those with the particle swarm optimization (PSO) algorithm, the invasive weed optimization (IWO)  
Figure 14. Comparison of the optimal dispatching schemes of the distribution network comprising the micro energy grid on a typical day in the winter (**a**), and in the summer (**b**). algorithm, the GWO algorithm, and the grey wolf optimization/evolutionary population dynamics (GWO-EPD) algorithm, respectively. 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%, 4.0%, 3.4%, and 3.0%; 4.6%, 4.0%, 3.8%, and 3.4%; and 5.6%, 3.8%, 3.4%, and 3.1% relative to those with the PSO algorithm, the IWO algorithm, the GWO algorithm, and the GWO-EPD algorithm, respectively.