## 4 Simulation and Results
### Case B  
In this Case, the proposed optimal energy management method is tested on a modified IEEE 10-generator-39-bus system case, as illustrated in Figure 14. Moreover, besides the RES utilization rate, the profit and energy saving performance of the MES are also considered as two objectives of the optimization problem. In Figure 14, the generator on Bus 34 is a wind turbine, and Bus 23, 33, and 36 are respectively connected with an EV charging facility, a CCHP plant, and a P2H/G2P plant. The detailed parameters of these ME applications are the same as in Case A, so are the ME load profile and the forecasted wind power. Figure 13: Performance of the dispatch schemes in Case 1–6. Three scenarios are considered below:  
1. Scenario 1: No ME applications are online;
2. Scenario 2: All the ME applications are online, while without energy management;
3. Scenario 3: All the ME applications are online with the proposed energy management method. Figure 15 shows the performances of dispatch schemes for the three scenarios. In the objective space illustrated in Figure 15, \(x\)-axis represents the profit, \(y\)-axis represents the RES utilization rate, and \(z\)-axis represents the energy saving performance. The dispatch results in Scenario 1 are represented by blue dots, the dispatch results in Scenario 2 are represented by blue dots, and the dispatch results in Scenario 3 are represented by red dots. Each dot represent an energy dispatch scheme for the ME system, and all of which consist of the Pareto front of the proposed optimization method. In the objective space shown in Figure 15, the orange dots are located on the top left of the scattered blue dots. Though the space where the blue dots locate and the space where the orange dots locate overlap a little, the orange dots still outperform most of blue dots. Specifically, profit\({}_{1}\) ranges from 8.7958 \(\times\) 10\({}^{6}\) to 8.7959 \(\times\) 10\({}^{6}\), while Profit\({}_{2}\) ranges from 8.75 \(\times\) 10\({}^{6}\) to 8.83 \(\times\) 10\({}^{6}\); RES\({}_{1}\) ranges from 65.33% to 76.56%, while RES\({}_{2}\) ranges from 35% to 66%; ESP\({}_{1}\) ranges from 2.72% to 2.73%, while ESP  
Figure 14: The diagram of the modified IEEE 39-bus case. Figure 15: Optimization result of Case B.  
ranges from 1.47% to 2.53%. It indicates that though the dispatch results for Scenario 1 and the dispatch results for Scenario 2 share some performance spaces in terms of profit and RES utilization rate, the dispatch schemes for Scenario 1 are still superior than most of the dispatch schemes for Scenario 2. It suggests that without a comprehensive energy management method, ME applications are more likely to harm the operation performance of the MES rather than improve it. The yellow dots concentrate at the top corner. It is apparent that the dispatch results in Scenario 3 are far better than those for both Scenario 1 and Scenario 2 in all the three concerned aspects. Profit\({}_{3}\) ranges from 8.95 \(\times\) 10\({}^{6}\) to 8.98 \(\times\) 10\({}^{6}\); RES\({}_{3}\) ranges from 80.58% to 80.79%; ESP\({}_{3}\) ranges from 2.39% to 2.88%. On average, the profit, RES utilization rate, and energy saving performance increase by 19.91%, 59.77% and 31.75%, respectively. Under the guidance of the proposed energy management method, all the three ME applications could operate in coordinate ways and greatly contribute to the performance improvement of the MES. The number of optimal dispatch results depends on the preset population size. Different results have different advantages, and the MES operators could select their ideal dispatch result according to their preferences. Due to the limitation of paper length, we took one out of the 100 optimized results for Scenario 3 for illustration purposes. The EV charging price is illustrated in Figure 16a; the EDF, input and output of the CCHP plant are illustrated in Figure 16b,c respectively; the input and output of the P2H/G2P plant are illustrate in Figure 16d.