# A Review of Edge Computing Technology and Its Applications in Power Systems
## 4 Review of Edge Computing in Power Systems
### State Estimation  
In modern power systems, state estimation (SE) is a necessary approach to monitor power grids. SE is a method to process the imperfect measurements and find the actual values of the unknown variables. It is a digital scheme that can estimate the true state of the power system. Modern power systems perform many functions, such as generating and transmitting electricity across multiple geographic areas. Computer-based management systems have emerged for controlling and managing such complex systems. Power systems typically are divided into several areas. Different areas are interconnected with each other. All areas need to keep the frequency the same under steady-state conditions ([PERSON] et al., 2019). However, power systems constantly suffer from disturbances covering a wide range of conditions. Therefore, real-time monitoring and control are essential functions in power systems. SE can provide real-time data for many of the central control and dispatch functions in a power system ([PERSON] et al., 2019).  
In power systems, there are always some imperfect measurements that are considered unknown variables. SE can estimate the true values of these unknown variables by using statistical criteria. With the development of the smart grid, the volume of the measuring data is increasing. In this situation, real-time control requires a wider bandwidth and more computing resources. To solve this issue, the state estimation can be executed at the edge nodes in the local area. Then, the estimated results can be updated in the control center. Edge computing is an ideal architecture to achieve this goal.  
[PERSON] et al. (2019) proposed a cloud-edge-based architecture for SE in modern smart grids. This solution combines cloud resources and edge computing advantages. Meanwhile, virtualization techniques are used to split the processing of measurement data from the physical devices. The traditional evaluation of the state of the power grid is performed at a slow rate and is suitable for steady-state operating conditions. The increasing scale of the power grid challenges power management. Therefore, the proposed system includes sensing devices such as PMUs that can dynamically monitor and control the system and properly handle anomalous states. This effective wide-area measurement system is based on PMUs and is bandwidth-consuming. To improve accuracy and reduce delay, edge computing is introduced. The edge devices in the network are considered micro-cloudservers to host the advanced processing functions and application modules. The results show that this cloud-edge-based architecture can ensure the accuracy and latency of the estimation. The bandwidth is greatly reduced even when the PMUs execute full operating estimation. In another work, [PERSON] et al. [113] proposed a distributed hierarchy-based framework to ensure the SE data is not attacked and manipulated. Typically, the data for SE are collected from the PMUs and transmitted to a fixed center for future processing. The proposed framework guarantees that both data aggregation and SE are executed on random devices. The simulations on the Institute of Electrical and Electronics Engineers (IEEE) 5-bus system and IEEE 118-bus system show that the framework can observably protect the SE data.