# Data-Driven Network Latency Processing for Auxiliary Services in Virtual Power Plant
## 6 Discussions and Conclusions  
In this paper, we propose a new industrial digitalization landing paradigm that combines the best effort transmission model of a network with practical industrial application. The control strategy of industrial applications is updated in real time by estimating and predicting the network state to meet the needs of industrial applications. First, we have summarized the communication requirements of a VPP participating in auxiliary services, and proposed a general VPP network architecture, which divides the network into local networks for a flexible and cheap access to massive DERs and backhaul networks to ensure the effectiveness and security of DERs' monitoring and scheduling. Then, aiming at the contradiction between the inability of a backhaul network to guarantee transmission effectiveness across the public network and the strict requirements of a VPP to participate in  
Figure 11: The negative report rate of prediction.  
Figure 10: The MSE of prediction.  
auxiliary service, we have proposed a method to obtain a link delay by using a delay probe. Based on the analysis and processing of the realistic data, the network latency is divided into a normal and warning range, which can effectively guide the DERs of the VPP to participate in the interaction of auxiliary services. The proposed network latency processing method for predicting network latency has a low overall negative report rate, which is 1.4% for network latency warning, which can strongly support the VPP to participate in auxiliary service operation. Obviously, the network latency obtained by our prediction method can improve the robustness of industrial control strategies in a targeted manner.  
In the future, we will carry out further research on the accuracy of latency data acquisition, the accuracy of network latency prediction in a large-scale time interval. For the accuracy of link delay acquisition, network node cross-domain time synchronization and cross-domain network latency measurement are important directions, and introducing a satellite into a network node time synchronization system should be an effective solution. For the accuracy and long-term performance of network latency prediction, it will become one of our key directions to consider the introduction of an artificial intelligence processing method to capture cyclical fluctuations of network latency and network operator policies. In addition, a solution for the network latency tensor prediction of multinode networks will be considered in our future research. This method can reveal the intrinsic properties of network latency when crossing the public network to a certain extent.  
Conceptualization, C.L. and J.T.; methodology, C.L. and J.T.; software, J.T.; validation, J.T. and Y.L.; formal analysis, C.L. and W.P.; investigation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, J.T. and X.W.; visualization, X.W.; supervision, W.P.; project administration, C.L.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fund for Young Scholars of State Grid Smart Grid Research Institute Co., Ltd., named Study on Distributed Resource Virtual Trajectory Inference and Value Attenuation Evaluation in fast time-varying channels.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.