## 6 Discussion  
The modified IEEE 33-bus distribution system as shown in Figure 7 is used in this paper to carry out the analysis. Assume the loads from buses 22 to 32 will participate in demand-side management, which can reduce the load ratio by 10%, and the schedulable time is from 08:00 to 20:00. Two PV cells of 500 kW are installed at buses 9 and 17. Two WTs of 600 kW are installed at buses 4 and 32. Two MTs of 500 kW are installed at buses 8 and 15. Two 500 kW ESSs are installed at 17 and 32, whose SOC is 5%-95% [49]. Assume the on-grid price of WTs is 0.30 CNY/kWh, the on-grid price of PV cells is 0.50 CNY/kWh, and the on-grid price of MTs is 0.40 CNY/kWh. Other specific parameters are shown in Table 11. The time-of-use price and daily forecasting curve of loads, WTs, and PV cells are shown in Figure 8 [50]. Figure 6: Flowchart of multiobjective optimal scheduling of the active distribution network (ADN). Figure 7: Network structure of modified IEEE 33-bus distribution system. WT: wind turbine; MT: microturbine; PV: photovoltaic. To reveal the coordination role of the source network load in the scheduling, the Pareto front solution set is simulated and analyzed according to the scheduling model proposed in this paper. The solution set is shown in Figure 9, which consists of optimal solutions. In practice, the decision-makers can choose the final best solution according to the specific expectations of the distribution network. In this paper, the fuzzy decision method is used to analyze the optimization results. The solution with the largest membership function value is chosen as the final best solution, as shown in Figure 9. This solution contains not only the information of the decision variables including the DG output power, the switch number, and the incentive price for the responsive load but also the information of objective values. The corresponding operating cost is 36811.14 CNY, the renewable energy utilization rate is 0.3909, and the user satisfaction is 0.8917. The active power output plan of the MT and ESSs of the best solution is shown in Figure 10. ESS1 is the energy storage of the PV bus and ESS2 is the energy storage of the WT bus. The MT is used when the load is heavy. On the one hand, the local power supply can reduce the power loss; on the other hand, it can save the electricity purchase cost during the peak period of high electricity price. The charge and discharge scheduling plans of ESSs are those that are charging during the daytime when the renewable DGs output is large, discharging at the peak of high electricity price in the evening, and charging at the low electricity price valley in the early morning. In this way, the ESS can smooth the fluctuation of renewable DG sources, clip peaks and fill valleys, and provide strong support for the economic and safe operation of the ADN. Figure 8: Time-of-use price curve and daily forecasting curve of loads, WTs, and PV cells. Figure 9: Multiobjective optimization results: (**a**) Pareto fronts under source-network–load optimization, and (**b**) normalized membership functions of different Pareto solutions. The daily plan for load reduction and network topology adjustment is shown in Table 16. Load reduction will bring additional demand-side management costs, so the scheduling plan only performs a small amount of load reduction at peak electricity prices. The flexible topology of the ADN is beneficial to reduce network loss, improve voltage quality, and reduce system uncertainty. Figure 11 depicts the voltage uncertainty range of the network at various times. It can be seen from the figure that after adopting the optimized scheduling scheme, the voltage level of all periods is within the acceptable range and the uncertain range is acceptable. Figure 12 shows the probability density curve of power loss under the influence of uncertainty for 24 hours in the ADN. It can be seen that through source-network-load scheduling, power loss is at a lower level in a day and fluctuation is small.