## 5 Calculus Analysis  
### Simulation System and Parameters  
After establishing the theoretical model, we validated our method by constructing a simulation system. In this paper, the main feeder 4 of IEEE-RBTS BUS6 was taken as an example [22], and the system wiring is shown in Figure 3. This feeder contained 23 load points and 1225 users, totaling 34.26 megawatts. These parameters reflect a medium-sized distribution network and were sufficient to demonstrate the effectiveness of the model in handling complex networks. It also included distributed energy equipment, such as a 6 MW photovoltaic power generation unit and a 6 MW/32 MWh energy storage system. The reliability parameters of components in the system can be found in the reference literature [22]. Different from the traditional wired access network, the access network in this study adopted a wireless 5G communication network and the data transmission backbone network adopted a wired ring network structure, which included a total of five base stations, and the coverage area of each base station is shown in Figure 3.  
Figure 3 shows that the network consisted of multiple nodes, including base stations and load points. The base station represented the key hub of the network communication,  
Figure 3: Information system architecture. while the load point represented the location of power consumption. Each load point was connected to a base station through a wireless 5G network, which was responsible for transmitting and receiving wireless signals. The \"R\" in the figure represents a repeater or router in the network, used to expand the coverage range of wireless signals. The \"S\" in the figure represents different communication paths or channels, which could result in corresponding losses and delays. The advantage of this structure lies in its ability to quickly adapt to environmental changes and support remote monitoring and control. Simultaneously, it can reduce deployment and maintenance costs. The causes of communication delays are complex. On the one hand, they are related to the network load rate. On the other hand, they are related to the number of times information is forwarded. This characteristic can be represented by the probability density function of information element delay. Different information devices have different transmission characteristics; for example, the information transmission of fiber optic lines usually follows an exponential distribution model. The information delay of switches and 5G wireless routers follows a Pareto distribution model with parameters of 67.9 ms and 20. The information transmission delay of IEDs follows a normal distribution model with parameters of 68.35 and 11. These parameters were obtained based on extensive simulation calculations and mathematical analysis in the existing literature. Therefore, the communication node delays followed a Pareto distribution model with parameters 67.9 ms and 20 [23]. The forwarding delay of the IEDs followed a normal distribution N (68.35,11) [24], while the communication line delays were modeled by an exponential distribution [25]. The setting of the upper limit of communication delay usually depends on the size of the information system. Therefore, the communication delay upper limit set in this study was selected from information systems of a similar scale in the published literature. The latency of a distribution information physical system is related to network bandwidth, routers, and network structure. Communication protocols can also affect latency, and this study considered communication protocols under the IEC61850 standard. Considering that the communication system established in this study was relatively small in scale, the maximum system delay was set to 600 ms. This was selected based on the settings of information systems of a similar scale in the published literature [26]. When the access network is a 5G wireless public network, the information processing capacity and time of each node in the network are basically the same.