## 5 Simulation Results
### Performance Analysis of the Optimization Algorithm
Figure 5 shows that, compared with the other four algorithms, the ITLCO algorithm results in more minor voltage fluctuations at each bus of the reconfigured UDN incorporating VPP, thereby ensuring the stability of the voltage in the UDN incorporating VPP. Impact of Novel Power System Resources on Efficient Proactive Voltage Control of UDN Incorporating VPPs  
To analyze in detail the influence of new power system resources on the efficient active voltage control of UDN by using VPP, the power changes of energy storage and other important load bus devices in UDN are first presented, as shown in Figures 6 and 7.  
period of the valley price. When participating in adjustment, part of the charging load from 00:00 to 01:00 and 23:00 to 24:00 at buses 8 and 25 is transferred to 01:00-03:00, in which 02:00-03:00 is the system valley filling demand response period, and the load is transferred to this period to obtain higher returns. In addition, 01:00-03:00 is the off-peak load period of the distribution network. In order to take into account the adjustment objective function of peak-valley difference, part of the load is transferred to the 01:00-02:00 period to reduce the peak-valley difference in the distribution network area. As network loss positively correlates with line transmission power, bus 25 at the end of the distribution network has a more noticeable impact on system network loss under the same load variation. As a result, the load during 02:00-03:00 is more significant than that during 00:00-01:00 because it participates in the demand response for valley filling. Calling the charging station at bus 8 from 00:00 to 01:00 increases the system network loss less than calling the charging station at bus 25. Compared with the initial operating state, 5G base stations and IDCs located at buses 11 and 30 have power regulation during the peak clipping response period of 13:00-20:00, and the peak clipping power is maximum during 15:00-19:00. Combined with Figure 4, it can be seen that the system load reaches its peak during this period. Reducing the power of 5G base stations and IDCs can make the distribution network virtual power plant participate in the peak clipping response to obtain benefits and reduce the system peak valley difference while stabilizing the system voltage and reducing the system network loss. Specifically, for the optimization and reconfiguration scheme of UDNs incorporating VPPs that consider novel power system resources such as 5G base stations and IDCs, the voltage distribution is shown in Figure 8 compared to the scheme without considering these resources. The comparison table for benefits, network losses, and peak-valley difference optimization effects is presented in Table 8.  
voltage at all buses. The further a bus is from the main bus, the more significant the increase. For instance, the voltage at bus 18 rises from 0.9096 to 0.9776, an increase of 7.5%. After optimization and reconfiguration, the voltage at all buses exceeds 0.95, meeting the requirement for efficient proactive voltage control in UDNs incorporating VPPs. By comparing the results under different optimization objectives in Table 8, it is found that the optimization and reconfiguration considering novel power system resources such as 5G base stations and IDCs can regulate the network losses and peak-valley difference of UDNs incorporating VPPs while ensuring that the operational benefits are slightly higher than before optimization. This is conducive to improving the operational efficiency of UDNs incorporating VPPs. This variation is presented here to visually observe the total load variation within a day in the UDN. As shown in Figure 9, it represents the total load variation of the UDN. Through the optimization control algorithm, reducing the power consumption during peak load periods can effectively lower the peak-to-valley difference of the system. Meanwhile, adjusting the power at night to participate in the valley-filling response and increase the low-valley load can achieve optimal regulation.